
@article{honkela_model-based_2010,
	title = {Model-based method for transcription factor target identification with limited data},
	volume = {107},
	issn = {0027-8424},
	doi = {10.1073/pnas.0914285107},
	abstract = {We present a computational method for identifying potential targets of a transcription factor ({TF}) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the {TF} is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation ({ChIP}-chip) and loss-of-function mutant expression data for two {TFs}, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the {ChIP}-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with {TF} expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.},
	pages = {7793--7798},
	number = {17},
	journaltitle = {Proceedings of the National Academy of Sciences of the United States of America},
	shortjournal = {Proc Natl Acad Sci U S A},
	author = {Honkela, Antti and Girardot, Charles and Gustafson, E. Hilary and Liu, Ya-Hsin and Furlong, Eileen E. M. and Lawrence, Neil D. and Rattray, Magnus},
	date = {2010-04-27},
	pmid = {20385836},
	pmcid = {2867914},
	file = {Honkela-Model-based method for transcription factor target identification with limited data.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/I8SHM6XX/Honkela-Model-based method for transcription factor target identification with limited data.pdf:application/pdf;PubMed Central Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2GDCSKNX/Honkela et al. - 2010 - Model-based method for transcription factor target.pdf:application/pdf}
}

@article{aijo_learning_2009,
	title = {Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics},
	volume = {25},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/25/22/2937},
	doi = {10.1093/bioinformatics/btp511},
	abstract = {Motivation: Regulation of gene expression is fundamental to the operation of a cell. Revealing the structure and dynamics of a gene regulatory network ({GRN}) is of great interest and represents a considerably challenging computational problem. The {GRN} estimation problem is complicated by the fact that the number of gene expression measurements is typically extremely small when compared with the dimension of the biological system. Further, because the gene regulation process is intrinsically complex, commonly used parametric models can provide too simple description of the underlying phenomena and, thus, can be unreliable. In this article, we propose a novel methodology for the inference of {GRNs} from time-series and steady-state gene expression measurements. The presented framework is based on the use of Bayesian analysis with ordinary differential equations ({ODEs}) and non-parametric Gaussian process modeling for the transcriptional-level regulation.
Results: The performance of the proposed structure inference method is evaluated using a recently published in vivo dataset. By comparing the obtained results with those of existing {ODE}- and Bayesian-based inference methods we demonstrate that the proposed method provides more accurate network structure learning. The predictive capabilities of the method are examined by splitting the dataset into a training set and a test set and by predicting the test set based on the training set.
Availability: A {MATLAB} implementation of the method will be available from http://www.cs.tut.fi/{\textasciitilde}aijo2/gp upon publication
Contact: harri.lahdesmaki@tut.fi
Supplementary information: Supplementary data are available at Bioinformatics online.},
	pages = {2937--2944},
	number = {22},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Äijö, Tarmo and Lähdesmäki, Harri},
	urldate = {2014-11-11},
	date = {2009-11-15},
	langid = {english},
	pmid = {19706742},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VSSDAT2S/Äijö and Lähdesmäki - 2009 - Learning gene regulatory networks from gene expres.pdf:application/pdf;pdf file:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZZFNZEJR/Äijö and Lähdesmäki - 2009 - Learning gene regulatory networks from gene expres.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/IABR9S7K/2937.html:text/html}
}

@article{buganim_single-cell_2012,
	title = {Single-Cell Expression Analyses during Cellular Reprogramming Reveal an Early Stochastic and a Late Hierarchic Phase},
	volume = {150},
	issn = {00928674},
	url = {http://libsta28.lib.cam.ac.uk:2053/science/article/pii/S0092867412010215},
	doi = {10.1016/j.cell.2012.08.023},
	pages = {1209--1222},
	number = {6},
	journaltitle = {Cell},
	author = {Buganim, Yosef and Faddah, Dina A. and Cheng, Albert W. and Itskovich, Elena and Markoulaki, Styliani and Ganz, Kibibi and Klemm, Sandy L. and van Oudenaarden, Alexander and Jaenisch, Rudolf},
	urldate = {2012-10-18},
	date = {2012-09},
	file = {1-s2.0-S0092867412010215-main.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/STF3PSA6/1-s2.0-S0092867412010215-main.pdf:application/pdf;Cell - Single-Cell Expression Analyses during Cellular Reprogramming Reveal an Early Stochastic and a Late Hierarchic Phase:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7BSPTF22/S0092867412010215.html:text/html;NIHMS404284-supplement-01.docx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/QK2M9K4P/NIHMS404284-supplement-01.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/zip;NIHMS404284-supplement-02.docx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WDNC29RT/NIHMS404284-supplement-02.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/zip;ScienceDirect.com - Cell - Single-Cell Expression Analyses during Cellular Reprogramming Reveal an Early Stochastic and a Late Hierarchic Phase:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9X4F4R8E/S0092867412010215.html:text/html;science.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JV37UTHM/science.pdf:application/pdf}
}

@article{kirk_bayesian_2012,
	title = {Bayesian correlated clustering to integrate multiple datasets},
	volume = {28},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/28/24/3290},
	doi = {10.1093/bioinformatics/bts595},
	abstract = {Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as {MDI} (Multiple Dataset Integration). {MDI} can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation ({DMA}) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.
Results: Using a set of six artificially constructed time series datasets, we show that {MDI} is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that {MDI}’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that {MDI} is competitive, while also providing information that would be difficult or impossible to extract using other methods.
Availability: A Matlab implementation of {MDI} is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/.
Contact: D.L.Wild@warwick.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.},
	pages = {3290--3297},
	number = {24},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Kirk, Paul and Griffin, Jim E. and Savage, Richard S. and Ghahramani, Zoubin and Wild, David L.},
	urldate = {2012-12-07},
	date = {2012-01-12},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XXWTKGNC/Kirk et al. - 2012 - Bayesian correlated clustering to integrate multip.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/E7U7B7KJ/3290.html:text/html}
}

@article{shalek_single-cell_2013,
	title = {Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells},
	volume = {advance online publication},
	issn = {1476-4687},
	url = {http://libsta28.lib.cam.ac.uk:2090/nature/journal/vaop/ncurrent/full/nature12172.html},
	doi = {10.1038/nature12172},
	abstract = {Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected {RNAs} or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell {RNA} sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells ({BMDCs}) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger {RNA} abundance and splicing patterns, which we validate by {RNA}-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of {BMDCs}; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.},
	journaltitle = {Nature},
	shortjournal = {Nature},
	author = {Shalek, Alex K. and Satija, Rahul and Adiconis, Xian and Gertner, Rona S. and Gaublomme, Jellert T. and Raychowdhury, Raktima and Schwartz, Schragi and Yosef, Nir and Malboeuf, Christine and Lu, Diana and Trombetta, John T. and Gennert, Dave and Gnirke, Andreas and Goren, Alon and Hacohen, Nir and Levin, Joshua Z. and Park, Hongkun and Regev, Aviv},
	urldate = {2013-07-03},
	date = {2013-05-19},
	langid = {english},
	keywords = {Gene Expression Profiling, {RNA} sequencing, Single-cell imaging, transcriptomics},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PC7B6M2D/Shalek et al. - 2013 - Single-cell transcriptomics reveals bimodality in .pdf:application/pdf;nature12172-s1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RSP63SPM/nature12172-s1.pdf:application/pdf;nature12172-s1.zip:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/85RJE7XA/nature12172-s1.zip:application/zip;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TGUXEISD/nature12172.html:text/html}
}

@book{rasmussen_gaussian_2006,
	location = {Cambridge, Mass.},
	title = {Gaussian processes for machine learning},
	isbn = {026218253X 9780262182539},
	abstract = {"Gaussian processes ({GPs}) provide a principled, practical, probabilistic approach to learning in kernel machines. {GPs} have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of {GPs} in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics."--Jacket.},
	publisher = {{MIT} Press},
	author = {Rasmussen, Carl Edward and Williams, Christopher K. I},
	date = {2006},
	keywords = {Data processing, Gaussian processes, machine learning, Mathematical models},
	file = {Rasmussen-GPs-for-ML.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TV8N3JJ3/Rasmussen-GPs-for-ML.pdf:application/pdf}
}

@article{shalek_single-cell_2014,
	title = {Single-cell {RNA}-seq reveals dynamic paracrine control of cellular variation},
	volume = {510},
	rights = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {0028-0836},
	url = {http://www.nature.com/nature/journal/v510/n7505/full/nature13437.html},
	doi = {10.1038/nature13437},
	abstract = {High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell {RNA}-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger {RNA} and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.},
	pages = {363--369},
	number = {7505},
	journaltitle = {Nature},
	shortjournal = {Nature},
	author = {Shalek, Alex K. and Satija, Rahul and Shuga, Joe and Trombetta, John J. and Gennert, Dave and Lu, Diana and Chen, Peilin and Gertner, Rona S. and Gaublomme, Jellert T. and Yosef, Nir and Schwartz, Schraga and Fowler, Brian and Weaver, Suzanne and Wang, Jing and Wang, Xiaohui and Ding, Ruihua and Raychowdhury, Raktima and Friedman, Nir and Hacohen, Nir and Park, Hongkun and May, Andrew P. and Regev, Aviv},
	urldate = {2014-06-18},
	date = {2014-06-19},
	langid = {english},
	keywords = {biotechnology, Engineering, Gene expression analysis, Gene regulation in immune cells},
	file = {nature13437.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KV63G92A/nature13437.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/43723QT2/nature13437.html:text/html;Supplementary Information. Supplementary Notes, Supplementary Methods and Supplementary References.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GSQVHSF5/Shalek et al. - 2014 - Single-cell RNA-seq reveals dynamic paracrine cont.pdf:application/pdf;Supplementary Table 1. This table contains a summary of single cell RNA-Seq experiments.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TQ54UFWA/Shalek et al. - 2014 - Single-cell RNA-seq reveals dynamic paracrine cont.xls:application/vnd.ms-excel;Supplementary Table 2. This table contains sequencing metrics for single cell and population RNA-seq libraries.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/965987GX/Shalek et al. - 2014 - Single-cell RNA-seq reveals dynamic paracrine cont.xls:application/vnd.ms-excel;Supplementary Table 3. This table shows cluster assignments and GO enrichments for the 813 genes that were induced by at least two-fold (compared to unstimulated cells) at a population level at any time point during the LPS time course.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UHB4GIAP/Shalek et al. - 2014 - Single-cell RNA-seq reveals dynamic paracrine cont.xls:application/vnd.ms-excel;Supplementary Table 4. This table contains ingenuity analysis of the genes differentially regulated between a wildtype 4 hour LPS stimulation and either the "on-chip" stimulation, an Ifnar knockout, or a Stat1 knockout.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/F7GXNXZW/Shalek et al. - 2014 - Single-cell RNA-seq reveals dynamic paracrine cont.xls:application/vnd.ms-excel}
}

@article{brennecke_accounting_2013,
	title = {Accounting for technical noise in single-cell {RNA}-seq experiments},
	volume = {10},
	issn = {1548-7091},
	url = {http://libsta28.lib.cam.ac.uk:2072/nmeth/journal/v10/n11/full/nmeth.2645.html},
	doi = {10.1038/nmeth.2645},
	abstract = {Single-cell {RNA}-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.},
	pages = {1093--1095},
	number = {11},
	journaltitle = {Nature Methods},
	shortjournal = {Nat Meth},
	author = {Brennecke, Philip and Anders, Simon and Kim, Jong Kyoung and Kołodziejczyk, Aleksandra A. and Zhang, Xiuwei and Proserpio, Valentina and Baying, Bianka and Benes, Vladimir and Teichmann, Sarah A. and Marioni, John C. and Heisler, Marcus G.},
	urldate = {2014-08-15},
	date = {2013-11},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PD77AFD6/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZMPBZBSG/nmeth.2645.html:text/html;Supplementary Software. Accounting for technical noise in single-cell RNA-seq experiments – Supplement II. This file contains the R code used to perform the analysis described in the manuscript.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SB66QGK4/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.pdf:application/pdf;Supplementary Table 1. Estimated amounts of total RNA in single plant cells. Content of total RNA from single cells was estimated based on the 50 pg HeLa total RNA spike-in. The calculation is based on the assumption that the fraction of polyadenylated RNA is comparable between HeLa and A. thaliana input material. For detailed description refer to the Methods section.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JD86ZZSP/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 2. List of highly variable genes in GL2 cells. The columns give the gene ID, the gene name, the normalized average read count, the cell with the strongest expression, and, for each cell, the log2 ratio of the cell's expression to the average.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V9FMDGD3/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 3. List of highly variable genes in QC cells. Columns are the same as in Supplementary Table 2.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3KZACJ7Z/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 4. List of GO categories that are significantly enriched for highly variable genes (Online Methods).:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4IXMUMPX/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 5. Read counts for the 91 mouse immune cells spiked with ERCC spike-ins. Each column corresponds to a cell, each row to gene or an ERCC spike-in molecule. Mouse gene names have been replaces by randomized identifiers (column 1). The second column contains the transcript lengths used for the analysis in Supplementary Note 5. The transcript lengths are computed from Ensembl annotation by taking the union of all exons within a gene, where the exons annotated as “retained introns” and “nonsense mediated decay” are excluded.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JXSGBJE6/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 6. Full list of barcoded Illumina PE adapters used for multiplexing of cDNA libraries. The set of adapters used depends on the degree of multiplexing applied to the samples. 4-plex is the lower limit for multiplexing.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3QZJPEPG/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 7. List of qPCR primers used:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/83IKKU9N/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 8. Read counts for A. thaliana experiments. Raw number of reads mapped to each gene for all samples.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/MEDBVT8J/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Table 9. Transcript lengths for the human genome used for the analysis described in Supplementary Note 5, computed as described above, in the legend for Supplementary Table 5.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/25NUX6W2/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.xls:application/vnd.ms-excel;Supplementary Text and Figures. Supplementary Figures 1–14 and Supplementary Notes 1–9:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/52U2GDXD/Brennecke et al. - 2013 - Accounting for technical noise in single-cell RNA-.pdf:application/pdf}
}

@article{pollen_low-coverage_2014,
	title = {Low-coverage single-cell {mRNA} sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex},
	issn = {1087-0156, 1546-1696},
	url = {http://www.nature.com/doifinder/10.1038/nbt.2967},
	doi = {10.1038/nbt.2967},
	journaltitle = {Nature Biotechnology},
	author = {Pollen, Alex A and Nowakowski, Tomasz J and Shuga, Joe and Wang, Xiaohui and Leyrat, Anne A and Lui, Jan H and Li, Nianzhen and Szpankowski, Lukasz and Fowler, Brian and Chen, Peilin and Ramalingam, Naveen and Sun, Gang and Thu, Myo and Norris, Michael and Lebofsky, Ronald and Toppani, Dominique and Kemp, Darnell W and Wong, Michael and Clerkson, Barry and Jones, Brittnee N and Wu, Shiquan and Knutsson, Lawrence and Alvarado, Beatriz and Wang, Jing and Weaver, Lesley S and May, Andrew P and Jones, Robert C and Unger, Marc A and Kriegstein, Arnold R and West, Jay A A},
	urldate = {2014-08-18},
	date = {2014-08-03},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/C5GBMKUD/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.pdf:application/pdf;Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex_Pollen_West_NatureBiotech_Aug2014.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/NFMJJ2I3/Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex_Pollen_West_NatureBiotech_Aug2014.pdf:application/pdf;nbt.2967-S1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/78GKSIX7/nbt.2967-S1.pdf:application/pdf;nbt.2967-S2.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9KUWCMMA/nbt.2967-S2.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.2967-S3.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SD62KGQ8/nbt.2967-S3.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.2967-S4.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/QFNPW86T/nbt.2967-S4.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.2967-S5.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/26JCCJME/nbt.2967-S5.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.2967-S6.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DX5CTI47/nbt.2967-S6.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SF9CNC4G/nbt.2967.html:text/html;Supplementary Table 1. Overview of cell types studied, sequencing depth, and alignment rates:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9PU7R43V/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.xls:application/vnd.ms-excel;Supplementary Table 2. Single cell capture efficiency data:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WGTVGBT5/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.xls:application/vnd.ms-excel;Supplementary Table 3. Biological properties of PCA gene clusters:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3WGPQ7RV/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.xls:application/vnd.ms-excel;Supplementary Table 4. Comparison of the top 500 PCA genes identified in low- and high-coverage mRNA Seq data across 301 cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/HUH6EGH4/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.xls:application/vnd.ms-excel;Supplementary Table 5. Top 500 PCA genes explaining variation across 65 neural cells:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XBV3ZJ7K/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.xls:application/vnd.ms-excel;Supplementary Text and Figures. Supplementary Figures 1–15:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/QSWV7A8H/Pollen et al. - 2014 - Low-coverage single-cell mRNA sequencing reveals c.pdf:application/pdf}
}

@article{treutlein_reconstructing_2014,
	title = {Reconstructing lineage hierarchies of the distal lung epithelium using single-cell {RNA}-seq},
	volume = {509},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/doifinder/10.1038/nature13173},
	doi = {10.1038/nature13173},
	pages = {371--375},
	number = {7500},
	journaltitle = {Nature},
	author = {Treutlein, Barbara and Brownfield, Doug G. and Wu, Angela R. and Neff, Norma F. and Mantalas, Gary L. and Espinoza, F. Hernan and Desai, Tushar J. and Krasnow, Mark A. and Quake, Stephen R.},
	urldate = {2014-08-18},
	date = {2014-04-13},
	file = {Reconstructing lineage hierarchies of the distal lung_SQuake_Nature2014.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/B3BXF79Z/Reconstructing lineage hierarchies of the distal lung_SQuake_Nature2014.pdf:application/pdf}
}

@article{mcdavid_data_2013,
	title = {Data exploration, quality control and testing in single-cell {qPCR}-based gene expression experiments},
	volume = {29},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/29/4/461},
	doi = {10.1093/bioinformatics/bts714},
	abstract = {Motivation: Cell populations are never truly homogeneous; individual cells exist in biochemical states that define functional differences between them. New technology based on microfluidic arrays combined with multiplexed quantitative polymerase chain reactions now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However, few analytic tools have been developed specifically for the statistical and analytical challenges of single-cell quantitative polymerase chain reactions data.
Results: We present a statistical framework for the exploration, quality control and analysis of single-cell gene expression data from microfluidic arrays. We assess accuracy and within-sample heterogeneity of single-cell expression and develop quality control criteria to filter unreliable cell measurements. We propose a statistical model accounting for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, we derive a combined likelihood ratio test for differential expression that incorporates both the discrete and continuous components. Using an experiment that examines treatment-specific changes in expression, we show that this combined test is more powerful than either the continuous or dichotomous component in isolation, or a t-test on the zero-inflated data. Although developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events.
Availability: All results presented here were obtained using the {SingleCellAssay} R package available on {GitHub} (http://github.com/{RGLab}/{SingleCellAssay}).
Contact: rgottard@fhcrc.org
Supplementary information: Supplementary data are available at Bioinformatics online.},
	pages = {461--467},
	number = {4},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {{McDavid}, Andrew and Finak, Greg and Chattopadyay, Pratip K. and Dominguez, Maria and Lamoreaux, Laurie and Ma, Steven S. and Roederer, Mario and Gottardo, Raphael},
	urldate = {2014-08-20},
	date = {2013-02-15},
	langid = {english},
	pmid = {23267174},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZXD3X8VS/McDavid et al. - 2013 - Data exploration, quality control and testing in s.pdf:application/pdf;pdf file:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/HG2XQ7MI/McDavid et al. - 2013 - Data exploration, quality control and testing in s.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/P87FD5WM/461.html:text/html}
}

@article{trapnell_dynamics_2014,
	title = {The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells},
	volume = {32},
	rights = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1087-0156},
	url = {http://www.nature.com/nbt/journal/v32/n4/full/nbt.2859.html},
	doi = {10.1038/nbt.2859},
	abstract = {Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell {RNA}-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.},
	pages = {381--386},
	number = {4},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotech},
	author = {Trapnell, Cole and Cacchiarelli, Davide and Grimsby, Jonna and Pokharel, Prapti and Li, Shuqiang and Morse, Michael and Lennon, Niall J. and Livak, Kenneth J. and Mikkelsen, Tarjei S. and Rinn, John L.},
	urldate = {2014-09-04},
	date = {2014-04},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/669GRJGT/Trapnell et al. - 2014 - The dynamics and regulators of cell fate decisions.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CWIS7WXG/nbt.2859.html:text/html;Supplementary Text and Figures. Supplementary Figures 1–13 and Supplementary Tables 1 and 2:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2JDRRU4M/Trapnell et al. - 2014 - The dynamics and regulators of cell fate decisions.pdf:application/pdf}
}

@article{chung_single_2014,
	title = {Single Cell Analysis Reveals the Stochastic Phase of Reprogramming to Pluripotency Is an Ordered Probabilistic Process},
	volume = {9},
	url = {http://dx.doi.org/10.1371/journal.pone.0095304},
	doi = {10.1371/journal.pone.0095304},
	abstract = {Despite years of research, the reprogramming of human somatic cells to pluripotency remains a slow, inefficient process, and a detailed mechanistic understanding of reprogramming remains elusive. Current models suggest reprogramming to pluripotency occurs in two-phases: a prolonged stochastic phase followed by a rapid deterministic phase. In this paradigm, the early stochastic phase is marked by the random and gradual expression of pluripotency genes and is thought to be a major rate-limiting step in the successful generation of induced Pluripotent Stem Cells ({iPSCs}). Recent evidence suggests that the epigenetic landscape of the somatic cell is gradually reset during a period known as the stochastic phase, but it is known neither how this occurs nor what rate-limiting steps control progress through the stochastic phase. A precise understanding of gene expression dynamics in the stochastic phase is required in order to answer these questions. Moreover, a precise model of this complex process will enable the measurement and mechanistic dissection of treatments that enhance the rate or efficiency of reprogramming to pluripotency. Here we use single-cell transcript profiling, {FACS} and mathematical modeling to show that the stochastic phase is an ordered probabilistic process with independent gene-specific dynamics. We also show that partially reprogrammed cells infected with {OSKM} follow two trajectories: a productive trajectory toward increasingly {ESC}-like expression profiles or an alternative trajectory leading away from both the fibroblast and {ESC} state. These two pathways are distinguished by the coordinated expression of a small group of chromatin modifiers in the productive trajectory, supporting the notion that chromatin remodeling is essential for successful reprogramming. These are the first results to show that the stochastic phase of reprogramming in human fibroblasts is an ordered, probabilistic process with gene-specific dynamics and to provide a precise mathematical framework describing the dynamics of pluripotency gene expression during reprogramming by {OSKM}.},
	pages = {e95304},
	number = {4},
	journaltitle = {{PLoS} {ONE}},
	shortjournal = {{PLoS} {ONE}},
	author = {Chung, Kyung-Min and Kolling {IV}, Frederick W. and Gajdosik, Matthew D. and Burger, Steven and Russell, Alexander C. and Nelson, Craig E.},
	urldate = {2014-09-04},
	date = {2014-04-17},
	file = {PLoS Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/99WH7XRK/Chung et al. - 2014 - Single Cell Analysis Reveals the Stochastic Phase .pdf:application/pdf;PLoS Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2GJNXJIQ/infodoi10.1371journal.pone.html:text/html}
}

@misc{stan_development_team_stan_2014,
	title = {Stan Modeling Language (2.4.0)},
	author = {{Stan Development Team}},
	date = {2014},
	file = {stan-reference-2.4.0.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6C57V7IG/stan-reference-2.4.0.pdf:application/pdf}
}

@article{mcdavid_modeling_2014,
	title = {Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells},
	volume = {10},
	issn = {1553-734X},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102402/},
	doi = {10.1371/journal.pcbi.1003696},
	abstract = {Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5\%–17\% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome., Recent technological advances have enabled the measurement of gene expression in individual cells, revealing that there is substantial variability in expression, even within a homogeneous cell population. In this paper, we develop new analytical methods that account for the intrinsic, stochastic nature of single cell expression in order to characterize the effect of cell cycle on gene expression at the single-cell level. Applying these methods to populations of asynchronously cycling cells, we are able to identify large numbers of genes with cell cycle-associated expression patterns. By measuring and adjusting for cellular-level factors, we are able to derive estimates of co-expressing gene networks that more closely reflect cellular-level processes as opposed to sample-level processes. We find that cell cycle phase only accounts for a modest amount of the overall variability of gene expression within an individual cell. The analytical methods demonstrated in this paper are universally applicable to single cell expression data and represent a promising tool to the scientific community.},
	number = {7},
	journaltitle = {{PLoS} Computational Biology},
	shortjournal = {{PLoS} Comput Biol},
	author = {{McDavid}, Andrew and Dennis, Lucas and Danaher, Patrick and Finak, Greg and Krouse, Michael and Wang, Alice and Webster, Philippa and Beechem, Joseph and Gottardo, Raphael},
	urldate = {2014-09-29},
	date = {2014-07-17},
	pmid = {25032992},
	pmcid = {PMC4102402},
	file = {journal.pcbi.1003696.s008.csv:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XG4EFUIA/journal.pcbi.1003696.s008.csv:text/csv;journal.pcbi.1003696.s009.zip:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CFMTK6EK/journal.pcbi.1003696.s009.zip:application/zip;PubMed Central Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6J92TJVJ/McDavid et al. - 2014 - Modeling Bi-modality Improves Characterization of .pdf:application/pdf}
}

@article{islam_characterization_2011,
	title = {Characterization of the single-cell transcriptional landscape by highly multiplex {RNA}-seq},
	volume = {21},
	issn = {1088-9051, 1549-5469},
	url = {http://genome.cshlp.org/content/21/7/1160},
	doi = {10.1101/gr.110882.110},
	abstract = {Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell {RNA}-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves—all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.},
	pages = {1160--1167},
	number = {7},
	journaltitle = {Genome Research},
	shortjournal = {Genome Res.},
	author = {Islam, Saiful and Kjällquist, Una and Moliner, Annalena and Zajac, Pawel and Fan, Jian-Bing and Lönnerberg, Peter and Linnarsson, Sten},
	urldate = {2014-09-29},
	date = {2011-01-07},
	langid = {english},
	pmid = {21543516},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/M9DW9TJF/Islam et al. - 2011 - Characterization of the single-cell transcriptiona.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RNZCATEN/1160.html:text/html;Supp Information.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RV34U3CR/Islam et al. - 2011 - Characterization of the single-cell transcriptiona.pdf:application/pdf}
}

@article{buettner_novel_2012,
	title = {A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst},
	volume = {28},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/28/18/i626},
	doi = {10.1093/bioinformatics/bts385},
	abstract = {Motivation: Single-cell experiments of cells from the early mouse embryo yield gene expression data for different developmental stages from zygote to blastocyst. To better understand cell fate decisions during differentiation, it is desirable to analyse the high-dimensional gene expression data and assess differences in gene expression patterns between different developmental stages as well as within developmental stages. Conventional methods include univariate analyses of distributions of genes at different stages or multivariate linear methods such as principal component analysis ({PCA}). However, these approaches often fail to resolve important differences as each lineage has a unique gene expression pattern which changes gradually over time yielding different gene expressions both between different developmental stages as well as heterogeneous distributions at a specific stage. Furthermore, to date, no approach taking the temporal structure of the data into account has been presented.
Results: We present a novel framework based on Gaussian process latent variable models ({GPLVMs}) to analyse single-cell {qPCR} expression data of 48 genes from mouse zygote to blastocyst as presented by (Guo et al., 2010). We extend {GPLVMs} by introducing gene relevance maps and gradient plots to provide interpretability as in the linear case. Furthermore, we take the temporal group structure of the data into account and introduce a new factor in the {GPLVM} likelihood which ensures that small distances are preserved for cells from the same developmental stage. Using our novel framework, it is possible to resolve differences in gene expressions for all developmental stages. Furthermore, a new subpopulation of cells within the 16-cell stage is identified which is significantly more trophectoderm-like than the rest of the population. The trophectoderm-like subpopulation was characterized by considerable differences in the expression of Id2, Gata4 and, to a smaller extent, Klf4 and Hand1. The relevance of Id2 as early markers for {TE} cells is consistent with previously published results.
Availability: The mappings were implemented based on Prof. Neil Lawrence's {FGPLVM} toolbox1; extensions for relevance analysis and including the structure of the data can be obtained from one of the authors' homepage.2
Contact: f.buettner@helmholtz-muenchen.de},
	pages = {i626--i632},
	number = {18},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Buettner, Florian and Theis, Fabian J.},
	urldate = {2014-10-02},
	date = {2012-09-15},
	langid = {english},
	pmid = {22962491},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TTZ24DDA/Buettner and Theis - 2012 - A novel approach for resolving differences in sing.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZNKJFGD9/i626.html:text/html}
}

@article{buettner_probabilistic_2014,
	title = {Probabilistic {PCA} of censored data: accounting for uncertainties in the visualization of high-throughput single-cell {qPCR} data},
	volume = {30},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/30/13/1867},
	doi = {10.1093/bioinformatics/btu134},
	shorttitle = {Probabilistic {PCA} of censored data},
	abstract = {Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction ({qPCR}) is a promising technique allowing for new insights in complex cellular processes. However, the {PCR} reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell {qPCR} data. Principal component analysis ({PCA}) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a {PCA} of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell {qPCR} data.
Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical {qPCR} datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic {PCA}. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard {PCA}.
Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/{SheffieldML}/{GPmat}); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm.
Contact: fbuettner.phys@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.},
	pages = {1867--1875},
	number = {13},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Buettner, Florian and Moignard, Victoria and Göttgens, Berthold and Theis, Fabian J.},
	urldate = {2014-10-02},
	date = {2014-01-07},
	langid = {english},
	pmid = {24618470},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZSBKNUUK/Buettner et al. - 2014 - Probabilistic PCA of censored data accounting for.pdf:application/pdf;pdf file:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JF4XIWAA/Buettner et al. - 2014 - Probabilistic PCA of censored data accounting for.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/Q84SG3XC/1867.html:text/html}
}

@article{guo_resolution_2010,
	title = {Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst},
	volume = {18},
	issn = {1534-5807},
	url = {http://www.sciencedirect.com/science/article/pii/S1534580710001103},
	doi = {10.1016/j.devcel.2010.02.012},
	abstract = {Summary
Three distinct cell types are present within the 64-cell stage mouse blastocyst. We have investigated cellular development up to this stage using single-cell expression analysis of more than 500 cells. The 48 genes analyzed were selected in part based on a whole-embryo analysis of more than 800 transcription factors. We show that in the morula, blastomeres coexpress transcription factors specific to different lineages, but by the 64-cell stage three cell types can be clearly distinguished according to their quantitative expression profiles. We identify Id2 and Sox2 as the earliest markers of outer and inner cells, respectively. This is followed by an inverse correlation in expression for the receptor-ligand pair Fgfr2/Fgf4 in the early inner cell mass. Position and signaling events appear to precede the maturation of the transcriptional program. These results illustrate the power of single-cell expression analysis to provide insight into developmental mechanisms. The technique should be widely applicable to other biological systems.},
	pages = {675--685},
	number = {4},
	journaltitle = {Developmental Cell},
	shortjournal = {Developmental Cell},
	author = {Guo, Guoji and Huss, Mikael and Tong, Guo Qing and Wang, Chaoyang and Li Sun, Li and Clarke, Neil D. and Robson, Paul},
	urldate = {2014-10-02},
	date = {2010-04-20},
	keywords = {{DEVBIO}},
	file = {Document S1. Four Figures, Table S3, Table S6, Supplemental Experimental Procedures, and Supplemental References.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/B7DINEPU/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/C78K4I3P/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/B24RIHHF/S1534580710001103.html:text/html;Table S1. Threshold cycle (Ct) values for the 802 transcription factors profiled in pools of embryo materials (whole embryos or dissected ICMs) using Taqman low density arrays (Applied Biosystems).:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TTRM3AHI/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.xls:application/vnd.ms-excel;Table S2. Detailed information on the 48 genes analyzed at the single cell level.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SKAVRGTP/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.xls:application/vnd.ms-excel;Table S4. Ct values generated by the Biomark realtime PCR system software for each of the 48 genes in all 442 individual cells collected from unlabeled embryos.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/H3QCIIN2/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.xls:application/vnd.ms-excel;Table S5. Ct values generated by the Biomark realtime PCR system software for each of the 48 genes in all 134 individual cells from the 16-cell to 32-cell transition after cell membrane labeling with fluorescent dye (PKH26).:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/EVVGPQZB/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.xls:application/vnd.ms-excel;Table S7. Ct values generated by the Biomark realtime PCR system software for the blastocyst RNA titration series to determine the background for each individual gene.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/STKXPUDB/Guo et al. - 2010 - Resolution of Cell Fate Decisions Revealed by Sing.xls:application/vnd.ms-excel}
}

@article{tipping_probabilistic_1999,
	title = {Probabilistic Principal Component Analysis},
	volume = {61},
	rights = {1999 Royal Statistical Society},
	issn = {1467-9868},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/1467-9868.00196/abstract},
	doi = {10.1111/1467-9868.00196},
	abstract = {Principal component analysis ({PCA}) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood estimation of parameters in a latent variable model that is closely related to factor analysis. We consider the properties of the associated likelihood function, giving an {EM} algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach to {PCA}.},
	pages = {611--622},
	number = {3},
	journaltitle = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	author = {Tipping, Michael E. and Bishop, Christopher M.},
	urldate = {2014-10-02},
	date = {1999-01-01},
	langid = {english},
	keywords = {Density estimation, {EM} algorithm, Gaussian mixtures, Maximum likelihood, Principal component analysis, Probability model},
	file = {bishop-ppca-jrss.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/I8DMFNSJ/bishop-ppca-jrss.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/87V2PI3B/abstract.html:text/html}
}

@article{lawrence_probabilistic_2005,
	title = {Probabilistic non-linear principal component analysis with Gaussian process latent variable models},
	volume = {6},
	url = {http://dl.acm.org/citation.cfm?id=1194904},
	pages = {1783--1816},
	journaltitle = {The Journal of Machine Learning Research},
	author = {Lawrence, Neil},
	urldate = {2014-10-03},
	date = {2005},
	file = {lawrence05a.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CU6FTUVT/lawrence05a.pdf:application/pdf}
}

@article{anders_differential_2010,
	title = {Differential expression analysis for sequence count data},
	volume = {11},
	rights = {2010 Anders et al},
	issn = {1465-6906},
	url = {http://genomebiology.com/2010/11/10/R106/abstract},
	doi = {10.1186/gb-2010-11-10-r106},
	abstract = {High-throughput sequencing assays such as {RNA}-Seq, {ChIP}-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, {DESeq}, as an R/Bioconductor package.
{PMID}: 20979621},
	pages = {R106},
	number = {10},
	journaltitle = {Genome Biology},
	author = {Anders, Simon and Huber, Wolfgang},
	urldate = {2014-10-20},
	date = {2010-10-27},
	langid = {english},
	pmid = {20979621},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UF6TPQF9/Anders and Huber - 2010 - Differential expression analysis for sequence coun.pdf:application/pdf;gb-2010-11-10-r106-s1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/93NB24J8/gb-2010-11-10-r106-s1.pdf:application/pdf;gb-2010-11-10-r106-s2.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DI5BHJAC/gb-2010-11-10-r106-s2.pdf:application/pdf;gb-2010-11-10-r106-s3.tgz:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SK2TDSXX/gb-2010-11-10-r106-s3.tgz:application/x-compressed-tar;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VQSBQHQ9/r106.html:text/html}
}

@article{hoffman_no-u-turn_2014,
	title = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
	volume = {15},
	pages = {1593--1623},
	issue = {Apr},
	journaltitle = {Journal of Machine Learning Research},
	author = {Hoffman, Matthew D. and Gelman, Andrew},
	date = {2014}
}

@article{etzrodt_quantitative_2014,
	title = {Quantitative Single-Cell Approaches to Stem Cell Research},
	volume = {15},
	issn = {1934-5909},
	url = {http://www.cell.com/article/S1934590914004664/abstract},
	doi = {10.1016/j.stem.2014.10.015},
	abstract = {Understanding the molecular control of cell fates is central to stem cell research. Such insight requires quantification of molecular and cellular behavior at the single-cell level. Recent advances now permit high-throughput molecular readouts from single cells as well as continuous, noninvasive observation of cell behavior over time. Here, we review current state-of-the-art approaches used to query stem cell fate at the single-cell level, including advances in lineage tracing, time-lapse imaging, and molecular profiling. We also offer our perspective on the advantages and drawbacks of available approaches, key technical limitations, considerations for data interpretation, and future innovation.},
	pages = {546--558},
	number = {5},
	journaltitle = {Cell Stem Cell},
	author = {Etzrodt, Martin and Endele, Max and Schroeder, Timm},
	urldate = {2014-11-08},
	date = {2014-11-06},
	file = {1-s2.0-S1934590914004664-main.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RBAEF2H9/1-s2.0-S1934590914004664-main.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CXSJ59WB/S1934-5909(14)00466-4.html:text/html}
}

@article{bendall_single-cell_2014,
	title = {Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development},
	volume = {157},
	issn = {00928674},
	url = {http://libsta28.lib.cam.ac.uk:2088/science/article/pii/S0092867414004711?np=y},
	doi = {10.1016/j.cell.2014.04.005},
	pages = {714--725},
	number = {3},
	journaltitle = {Cell},
	author = {Bendall, Sean C. and Davis, Kara L. and Amir, El-ad David and Tadmor, Michelle D. and Simonds, Erin F. and Chen, Tiffany J. and Shenfeld, Daniel K. and Nolan, Garry P. and Pe’er, Dana},
	urldate = {2014-11-11},
	date = {2014-04},
	langid = {english},
	file = {1-s2.0-S0092867414004711-main.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GVHKUCMG/1-s2.0-S0092867414004711-main.pdf:application/pdf;Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RC7SCVGE/S0092867414004711.html:text/html}
}

@article{kouno_temporal_2013,
	title = {Temporal dynamics and transcriptional control using single-cell gene expression analysis},
	volume = {14},
	rights = {2013 Kouno et al.; licensee {BioMed} Central Ltd.},
	issn = {1465-6906},
	url = {http://genomebiology.com/2013/14/10/R118/abstract},
	doi = {10.1186/gb-2013-14-10-r118},
	abstract = {Changes in environmental conditions lead to expression variation that manifest at the level of gene regulatory networks. Despite a strong understanding of the role noise plays in synthetic biological systems, it remains unclear how propagation of expression heterogeneity in an endogenous regulatory network is distributed and utilized by cells transitioning through a key developmental event.
{PMID}: 24156252},
	pages = {R118},
	number = {10},
	journaltitle = {Genome Biology},
	author = {Kouno, Tsukasa and Hoon, Michiel de and Mar, Jessica C. and Tomaru, Yasuhiro and Kawano, Mitsuoki and Carninci, Piero and Suzuki, Harukazu and Hayashizaki, Yoshihide and Shin, Jay W.},
	urldate = {2015-01-09},
	date = {2013-10-24},
	langid = {english},
	pmid = {24156252},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KM3NDJNX/Kouno et al. - 2013 - Temporal dynamics and transcriptional control usin.pdf:application/pdf;gb-2013-14-10-r118-s1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/5CQKK8XK/gb-2013-14-10-r118-s1.pdf:application/pdf;gb-2013-14-10-r118-s2.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PNK89DKW/gb-2013-14-10-r118-s2.pdf:application/pdf;gb-2013-14-10-r118-s3.csv:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/X9WVAF56/gb-2013-14-10-r118-s3.csv:text/csv;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4MK3RFKZ/R118.html:text/html}
}

@inproceedings{bar-joseph_new_2002,
	title = {A new approach to analyzing gene expression time series data},
	url = {http://dl.acm.org/citation.cfm?id=565202},
	pages = {39--48},
	booktitle = {Proceedings of the sixth annual international conference on Computational biology},
	publisher = {{ACM}},
	author = {Bar-Joseph, Ziv and Gerber, Georg and Gifford, David K. and Jaakkola, Tommi S. and Simon, Itamar},
	urldate = {2015-01-13},
	date = {2002},
	file = {BarGerGifJaa-recomb02.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/IS6Z39KD/BarGerGifJaa-recomb02.pdf:application/pdf}
}

@article{aijo_methods_2014,
	title = {Methods for time series analysis of {RNA}-seq data with application to human Th17 cell differentiation},
	volume = {30},
	issn = {1367-4803},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4058923/},
	doi = {10.1093/bioinformatics/btu274},
	abstract = {Motivation: Gene expression profiling using {RNA}-seq is a powerful technique for screening {RNA} species’ landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of {RNA}-seq data, proper tools to anal.yze {RNA}-seq time-course have not been proposed., Results: In this study, we use {RNA}-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, {DyNB}, for analyzing time-course {RNA}-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an {MCMC} sampling method to make inference of differential expression dynamics between conditions. {DyNB} identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use {qRT}-{PCR} to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected., Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/, Contact:
tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fi, Supplementary information:
Supplementary data are available at Bioinformatics online.},
	pages = {i113--i120},
	number = {12},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Äijö, Tarmo and Butty, Vincent and Chen, Zhi and Salo, Verna and Tripathi, Subhash and Burge, Christopher B. and Lahesmaa, Riitta and Lähdesmäki, Harri},
	urldate = {2015-01-13},
	date = {2014-06-15},
	pmid = {24931974},
	pmcid = {PMC4058923},
	file = {Lahdesmaki_123_sup_1.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/G95J4FKB/Lahdesmaki_123_sup_1.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Lahdesmaki_123_sup_2.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZXH5MNCQ/Lahdesmaki_123_sup_2.pdf:application/pdf;PubMed Central Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/MCF3KKPZ/Äijö et al. - 2014 - Methods for time series analysis of RNA-seq data w.pdf:application/pdf}
}

@article{stegle_robust_2010,
	title = {A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series},
	volume = {17},
	issn = {1066-5277},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198888/},
	doi = {10.1089/cmb.2009.0175},
	abstract = {Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.},
	pages = {355--367},
	number = {3},
	journaltitle = {Journal of Computational Biology},
	shortjournal = {J Comput Biol},
	author = {Stegle, Oliver and Denby, Katherine J. and Cooke, Emma J. and Wild, David L. and Ghahramani, Zoubin and Borgwardt, Karsten M.},
	urldate = {2015-01-13},
	date = {2010-03},
	pmid = {20377450},
	pmcid = {PMC3198888},
	file = {PubMed Central Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/NIE223EC/Stegle et al. - 2010 - A Robust Bayesian Two-Sample Test for Detecting In.pdf:application/pdf}
}

@thesis{philip_john_law_quantitative_2013,
	title = {Quantitative analysis of time-series microarray data, with application to investigating resp onses to environmental stresses in Arabidopsis},
	url = {http://wrap.warwick.ac.uk/60420/1/WRAP_THESIS_Law_2013.pdf},
	institution = {Warwick},
	type = {phdthesis},
	author = {{Philip John Law}},
	urldate = {2015-01-13},
	date = {2013-11},
	file = {WRAP_THESIS_Law_2013.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TJ6UGSSE/WRAP_THESIS_Law_2013.pdf:application/pdf}
}

@article{windram_arabidopsis_2012,
	title = {Arabidopsis Defense against Botrytis cinerea: Chronology and Regulation Deciphered by High-Resolution Temporal Transcriptomic Analysis},
	volume = {24},
	issn = {1040-4651, 1532-298X},
	url = {http://www.plantcell.org/content/24/9/3530},
	doi = {10.1105/tpc.112.102046},
	shorttitle = {Arabidopsis Defense against Botrytis cinerea},
	abstract = {Transcriptional reprogramming forms a major part of a plant’s response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for {TGA}3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.},
	pages = {3530--3557},
	number = {9},
	journaltitle = {The Plant Cell Online},
	shortjournal = {Plant Cell},
	author = {Windram, Oliver and Madhou, Priyadharshini and {McHattie}, Stuart and Hill, Claire and Hickman, Richard and Cooke, Emma and Jenkins, Dafyd J. and Penfold, Christopher A. and Baxter, Laura and Breeze, Emily and Kiddle, Steven J. and Rhodes, Johanna and Atwell, Susanna and Kliebenstein, Daniel J. and Kim, Youn-sung and Stegle, Oliver and Borgwardt, Karsten and Zhang, Cunjin and Tabrett, Alex and Legaie, Roxane and Moore, Jonathan and Finkenstadt, Bärbel and Wild, David L. and Mead, Andrew and Rand, David and Beynon, Jim and Ott, Sascha and Buchanan-Wollaston, Vicky and Denby, Katherine J.},
	urldate = {2015-01-13},
	date = {2012-09-01},
	langid = {english},
	pmid = {23023172},
	file = {Analysis of transcription factor binding sites in network gene loci. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UDSIHT7S/nbt.3154_SF10.html:text/html;Comparison of multivariate analysis techniques. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PKAS5B3R/nbt.3154_SF7.html:text/html;Development is asynchronous. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZF46GJBA/nbt.3154_SF2.html:text/html;Diffusion plots. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GN3HJ564/nbt.3154_SF5.html:text/html;Diffusion plots highlighting each stage. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/79DDCX2S/nbt.3154_SF6.html:text/html;Flk1+Runx1+ cells are found in all anatomical stages. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XUC4DKNE/nbt.3154_SF3.html:text/html;Fluorescence activated cell sorting of cells with blood potential. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TKED77N6/nbt.3154_SF1.html:text/html;Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/8KBFRG67/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.pdf:application/pdf;nbt.3154-S1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/8XCIB7VM/nbt.3154-S1.pdf:application/pdf;nbt.3154-S1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BBN9K79E/nbt.3154-S1.pdf:application/pdf;nbt.3154-S2.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V73DRBIB/nbt.3154-S2.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.3154-S3.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WGNFMS3P/nbt.3154-S3.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.3154-S4.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/Z6Q44NHV/nbt.3154-S4.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.3154-S5.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/IZN47R7X/nbt.3154-S5.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.3154-S6.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/Q7V2ISV4/nbt.3154-S6.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;nbt.3154-S7.xlsx:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WJT7SZRP/nbt.3154-S7.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Partial correlation analysis. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TUPC84N6/nbt.3154_SF12.html:text/html;Populations mask variation and asynchrony. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CKBES59F/nbt.3154_SF9.html:text/html;RNA sequencing of populations of 50 cells. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/G6PDRPPD/nbt.3154_SF4.html:text/html;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RJIPTG3A/3530.html:text/html;Some states occur in multiple cells. \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/EN8JAWDH/nbt.3154_SF8.html:text/html;Supplemental Dataset 1:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/X4BB936Z/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 2:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RPMFXNJ5/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 3:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UXPR33KR/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 4:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZBM9CWDD/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 5:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DZF2BIIG/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 6:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/NM94MM4H/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 7:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WQMPI462/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 8:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RV6BQ55F/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 9:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CEIQ6B5F/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 10:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/J539GQ7B/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 11:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3E76XQVZ/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Dataset 12:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PCPEEEP7/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.xls:application/vnd.ms-excel;Supplemental Figures and Tables:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/U9EV3CDZ/Windram et al. - 2012 - Arabidopsis Defense against Botrytis cinerea Chro.pdf:application/pdf;The Erg+85 enhancer is active during haematopoietic development \: Decoding the regulatory network of early blood development from single-cell gene expression measurements \: Nature Biotechnology \: Nature Publishing Group:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/62W5MD9V/nbt.3154_SF11.html:text/html}
}

@book{stan_development_team_rstan:_2014,
	title = {{RStan}: the R interface to Stan, Version 2.4.0},
	url = {http://mc-stan.org/rstan.html},
	author = {{Stan Development Team}},
	date = {2014}
}

@article{santos_cyclebase_2015,
	title = {Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes},
	volume = {43},
	issn = {0305-1048, 1362-4962},
	url = {http://nar.oxfordjournals.org/content/43/D1/D1140},
	doi = {10.1093/nar/gku1092},
	shorttitle = {Cyclebase 3.0},
	abstract = {The eukaryotic cell division cycle is a highly regulated process that consists of a complex series of events and involves thousands of proteins. Researchers have studied the regulation of the cell cycle in several organisms, employing a wide range of high-throughput technologies, such as microarray-based {mRNA} expression profiling and quantitative proteomics. Due to its complexity, the cell cycle can also fail or otherwise change in many different ways if important genes are knocked out, which has been studied in several microscopy-based knockdown screens. The data from these many large-scale efforts are not easily accessed, analyzed and combined due to their inherent heterogeneity. To address this, we have created Cyclebase—available at http://www.cyclebase.org—an online database that allows users to easily visualize and download results from genome-wide cell-cycle-related experiments. In Cyclebase version 3.0, we have updated the content of the database to reflect changes to genome annotation, added new {mRNA} and protein expression data, and integrated cell-cycle phenotype information from high-content screens and model-organism databases. The new version of Cyclebase also features a new web interface, designed around an overview figure that summarizes all the cell-cycle-related data for a gene.},
	pages = {D1140--D1144},
	issue = {D1},
	journaltitle = {Nucleic Acids Research},
	shortjournal = {Nucl. Acids Res.},
	author = {Santos, Alberto and Wernersson, Rasmus and Jensen, Lars Juhl},
	urldate = {2015-01-26},
	date = {2015-01-28},
	langid = {english},
	pmid = {25378319},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/E29WPXZR/Santos et al. - 2015 - Cyclebase 3.0 a multi-organism database on cell-c.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/I7FSGWP4/D1140.html:text/html}
}

@article{geiss_direct_2008,
	title = {Direct multiplexed measurement of gene expression with color-coded probe pairs},
	volume = {26},
	issn = {1087-0156},
	url = {http://www.nature.com/doifinder/10.1038/nbt1385},
	doi = {10.1038/nbt1385},
	pages = {317--325},
	number = {3},
	journaltitle = {Nature Biotechnology},
	author = {Geiss, Gary K and Bumgarner, Roger E and Birditt, Brian and Dahl, Timothy and Dowidar, Naeem and Dunaway, Dwayne L and Fell, H Perry and Ferree, Sean and George, Renee D and Grogan, Tammy and James, Jeffrey J and Maysuria, Malini and Mitton, Jeffrey D and Oliveri, Paola and Osborn, Jennifer L and Peng, Tao and Ratcliffe, Amber L and Webster, Philippa J and Davidson, Eric H and Hood, Leroy},
	urldate = {2015-01-30},
	date = {2008-03},
	file = {nbt1385.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3WCWKZR7/nbt1385.pdf:application/pdf}
}

@article{buettner_computational_2015,
	title = {Computational analysis of cell-to-cell heterogeneity in single-cell {RNA}-sequencing data reveals hidden subpopulations of cells},
	volume = {advance online publication},
	issn = {1087-0156},
	url = {http://libsta28.lib.cam.ac.uk:2076/nbt/journal/vaop/ncurrent/full/nbt.3102.html},
	doi = {10.1038/nbt.3102},
	abstract = {Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model ({scLVM}) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotech},
	author = {Buettner, Florian and Natarajan, Kedar N. and Casale, F. Paolo and Proserpio, Valentina and Scialdone, Antonio and Theis, Fabian J. and Teichmann, Sarah A. and Marioni, John C. and Stegle, Oliver},
	urldate = {2015-02-02},
	date = {2015-01-19},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PHQA7WUC/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2VQ7PXUA/nbt.3102.html:text/html;Supplementary Data 1. Corrected and uncorrected expression values for T-cell data.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GKHKG5HU/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Data 2. Corrected and uncorrected expression values for the newly generated mouse ESC data.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/95WFBAWB/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Table 1. List of genes annotated from cell cycle, either from GO or from CycleBase.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RSIQ4HUT/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Table 2. List of contribution of all variance components for the T-cell data.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KM9ZMSZH/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Table 4. List of significantly differentially expressed genes between identified cell sub clusters.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/263G7AZ9/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Table 5. Manually curated list of 122 Th2 signature genes.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3BDMHIXM/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Table 7. List of genes with more than 5% of the variance explained by interaction between cell cycle and differentiation.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/42EX482S/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.xls:application/vnd.ms-excel;Supplementary Text and Figures. Supplementary Figures 1–22, Supplementary Tables 3, 6, 8–9 and Supplementary Notes:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2UHWE9RQ/Buettner et al. - 2015 - Computational analysis of cell-to-cell heterogenei.pdf:application/pdf}
}

@article{fusi_joint_2012,
	title = {Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies},
	volume = {8},
	url = {http://dx.doi.org/10.1371/journal.pcbi.1002330},
	doi = {10.1371/journal.pcbi.1002330},
	abstract = {Author Summary The computational analysis of genetical genomics studies is challenged by confounding variation that is unrelated to the genetic factors of interest. Several approaches to account for these confounding factors have been proposed, greatly increasing the sensitivity in recovering direct genetic (cis) associations between variable genetic loci and the expression levels of individual genes. Crucially, these existing techniques largely rely on the true association signals being orthogonal to the confounding variation. Here, we show that when studying indirect (trans) genetic effects, for example from master regulators, their association signals can overlap with confounding factors estimated using existing methods. This technical overlap can lead to overcorrection, erroneously explaining away true associations as confounders. To address these shortcomings, we propose {PANAMA}, a model that jointly learns hidden factors while accounting for the effect of selected genetic regulators. In applications to several studies, {PANAMA} is more accurate than existing methods in recovering the hidden confounding factors. As a result, we find an increase in the statistical power for direct (cis) and indirect (trans) associations. Most strikingly on yeast, {PANAMA} not only finds additional associations but also identifies master regulators that can be better reproduced between independent studies.},
	pages = {e1002330},
	number = {1},
	journaltitle = {{PLoS} Comput Biol},
	shortjournal = {{PLoS} Comput Biol},
	author = {Fusi, Nicoló and Stegle, Oliver and Lawrence, Neil D.},
	urldate = {2015-02-03},
	date = {2012-01-05},
	file = {PLoS Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7NZFBQDK/Fusi et al. - 2012 - Joint Modelling of Confounding Factors and Promine.pdf:application/pdf}
}

@article{cantone_yeast_2009,
	title = {A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches},
	volume = {137},
	issn = {0092-8674},
	url = {http://www.cell.com/article/S0092867409001561/abstract},
	doi = {10.1016/j.cell.2009.01.055},
	abstract = {Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows “de novo” construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, {IRMA}, for in vivo “benchmarking” of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.},
	pages = {172--181},
	number = {1},
	journaltitle = {Cell},
	author = {Cantone, Irene and Marucci, Lucia and Iorio, Francesco and Ricci, Maria Aurelia and Belcastro, Vincenzo and Bansal, Mukesh and Santini, Stefania and di Bernardo, Mario and di Bernardo, Diego and Cosma, Maria Pia},
	urldate = {2015-02-09},
	date = {2009-04-03},
	pmid = {19327819},
	keywords = {{SYSBIO}},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SMBPGDPW/Cantone et al. - 2009 - A Yeast Synthetic Network for In Vivo Assessment o.pdf:application/pdf;mmc1.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/AX55AV8T/mmc1.pdf:application/pdf;mmc2.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/92HAUBH4/mmc2.xls:application/vnd.ms-excel;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6AEXC8R9/S0092-8674(09)00156-1.html:text/html}
}

@article{tang_tracing_2010,
	title = {Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell {RNA}-Seq Analysis},
	volume = {6},
	issn = {1934-5909},
	url = {http://www.cell.com/article/S1934590910001141/abstract},
	doi = {10.1016/j.stem.2010.03.015},
	abstract = {During the transition from the inner cell mass ({ICM}) cells of blastocysts to pluripotent embryonic stem cells ({ESCs}) in vitro, a normal developmental program is replaced in cells that acquire a capacity for infinite self-renewal and pluripotency. We explored the underlying mechanism of this switch by using {RNA}-Seq transcriptome analysis at the resolution of single cells. We detected significant molecular transitions and major changes in transcript variants, which include genes for general metabolism. Furthermore, the expression of repressive epigenetic regulators increased with a concomitant decrease in gene activators that might be necessary to sustain the inherent plasticity of {ESCs}. Furthermore, we detected changes in {microRNAs} ({miRNAs}), with one set that targets early differentiation genes while another set targets pluripotency genes to maintain the unique {ESC} epigenotype. Such genetic and epigenetic events may contribute to a switch from a normal developmental program in adult cells during the formation of diseased tissues, including cancers.},
	pages = {468--478},
	number = {5},
	journaltitle = {Cell Stem Cell},
	author = {Tang, Fuchou and Barbacioru, Catalin and Bao, Siqin and Lee, Caroline and Nordman, Ellen and Wang, Xiaohui and Lao, Kaiqin and Surani, M. Azim},
	urldate = {2015-02-09},
	date = {2010-05-07},
	pmid = {20452321},
	keywords = {{RNA}, {STEMCELL}},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/FE2SAF2K/Tang et al. - 2010 - Tracing the Derivation of Embryonic Stem Cells fro.pdf:application/pdf;mmc2.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PVSMBT4M/mmc2.xls:application/vnd.ms-excel;mmc3.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZQXFBWAX/mmc3.xls:application/vnd.ms-excel;mmc4.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/P7BEGI8Q/mmc4.xls:application/vnd.ms-excel;mmc5.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/U39F9JGR/mmc5.xls:application/vnd.ms-excel;mmc6.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CR9X5RHM/mmc6.xls:application/vnd.ms-excel;mmc7.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/749627G8/mmc7.xls:application/vnd.ms-excel;mmc8.xls:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/M42DZ5HP/mmc8.xls:application/vnd.ms-excel;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VPMJD3SJ/S1934-5909(10)00114-1.html:text/html}
}

@article{brooks_general_1998,
	title = {General methods for monitoring convergence of iterative simulations},
	volume = {7},
	url = {http://www.tandfonline.com/doi/abs/10.1080/10618600.1998.10474787},
	pages = {434--455},
	number = {4},
	journaltitle = {Journal of computational and graphical statistics},
	author = {Brooks, Stephen P. and Gelman, Andrew},
	urldate = {2015-02-18},
	date = {1998},
	file = {BrooksGelman.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JUV347DP/BrooksGelman.pdf:application/pdf}
}

@article{vavoulis_dgeclust:_2015,
	title = {{DGEclust}: differential expression analysis of clustered count data},
	volume = {16},
	rights = {http://creativecommons.org/licenses/by/2.0/},
	issn = {1465-6906},
	url = {http://genomebiology.com/2015/16/1/39/abstract},
	doi = {10.1186/s13059-015-0604-6},
	shorttitle = {{DGEclust}},
	abstract = {We present a statistical methodology, {DGEclust}, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supported by the data and uncertainty in parameter estimation. {DGEclust} successfully identifies differentially expressed genes under a number of different scenarios, maintaining a low error rate and an excellent control of its {FDR} with reasonable computational requirements. It is formulated to perform particularly well on low–replicated data and be applicable to multi–group data. {DGEclust} is available at http://dvav.github.io/dgeclust/.},
	pages = {39},
	number = {1},
	journaltitle = {Genome Biology},
	author = {Vavoulis, Dimitrios V. and Francescatto, Margherita and Heutink, Peter and Gough, Julian},
	urldate = {2015-02-20},
	date = {2015-02-20},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/W3S4QR22/Vavoulis et al. - 2015 - DGEclust differential expression analysis of clus.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZFGGFP2U/abstract.html:text/html}
}

@inproceedings{liang_permutation-augmented_2007,
	title = {A permutation-augmented sampler for {DP} mixture models},
	url = {http://dl.acm.org/citation.cfm?id=1273565},
	pages = {545--552},
	booktitle = {Proceedings of the 24th international conference on Machine learning},
	publisher = {{ACM}},
	author = {Liang, Percy and Jordan, Michael I. and Taskar, Ben},
	urldate = {2015-02-23},
	date = {2007},
	file = {permutation-dp-icml2007.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KQ9AENPN/permutation-dp-icml2007.pdf:application/pdf}
}

@article{ryall_nad+-dependent_2015,
	title = {The {NAD}+-Dependent {SIRT}1 Deacetylase Translates a Metabolic Switch into Regulatory Epigenetics in Skeletal Muscle Stem Cells},
	volume = {16},
	issn = {1934-5909},
	url = {http://www.sciencedirect.com/science/article/pii/S1934590914005621},
	doi = {10.1016/j.stem.2014.12.004},
	abstract = {Summary
Stem cells undergo a shift in metabolic substrate utilization during specification and/or differentiation, a process that has been termed metabolic reprogramming. Here, we report that during the transition from quiescence to proliferation, skeletal muscle stem cells experience a metabolic switch from fatty acid oxidation to glycolysis. This reprogramming of cellular metabolism decreases intracellular {NAD}+ levels and the activity of the histone deacetylase {SIRT}1, leading to elevated H4K16 acetylation and activation of muscle gene transcription. Selective genetic ablation of the {SIRT}1 deacetylase domain in skeletal muscle results in increased H4K16 acetylation and deregulated activation of the myogenic program in {SCs}. Moreover, mice with muscle-specific inactivation of the {SIRT}1 deacetylase domain display reduced myofiber size, impaired muscle regeneration, and derepression of muscle developmental genes. Overall, these findings reveal how metabolic cues can be mechanistically translated into epigenetic modifications that regulate skeletal muscle stem cell biology.},
	pages = {171--183},
	number = {2},
	journaltitle = {Cell Stem Cell},
	shortjournal = {Cell Stem Cell},
	author = {Ryall, James G. and Dell’Orso, Stefania and Derfoul, Assia and Juan, Aster and Zare, Hossein and Feng, Xuesong and Clermont, Daphney and Koulnis, Miroslav and Gutierrez-Cruz, Gustavo and Fulco, Marcella and Sartorelli, Vittorio},
	urldate = {2015-02-25},
	date = {2015-02-05},
	file = {Document S1. Supplemental Experimental Procedures and Figures S1–S7.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V67AXZDM/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/THX92MVA/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KJCI69GH/S1934590914005621.html:text/html;Table S1. RNAseq FPKM, Fold-Change, and Gene Ontology, Related to Figure 1.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/D8DNNR6S/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Table S2. RNAseq FPKM, Fold-Change, and Gene Ontology, Related to Figure 5.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZMK6EWED/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Table S3. ChIP-seq and Gene Ontology Results for H4K16ac, Related to Figure 6.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/K5NCRQ5S/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Table S4. ChIP-seq Coordinates for SIRT1 from Freshly Isolated and Cultured SCs, Related to Figure 6.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7IDKXTRN/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Table S5. Microarray and Gene Ontology Results for RNA Isolated from the Gastrocnemius Muscles of Adult WT and Sirt1mKO Mice, Related to Figure 7.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CGRJ5FUZ/Ryall et al. - 2015 - The NAD+-Dependent SIRT1 Deacetylase Translates a .xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet}
}

@article{chen_single-cell_2014,
	title = {Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btu777},
	doi = {10.1093/bioinformatics/btu777},
	journaltitle = {Bioinformatics},
	author = {Chen, H. and Guo, J. and Mishra, S. K. and Robson, P. and Niranjan, M. and Zheng, J.},
	urldate = {2015-04-02},
	date = {2014-11-20},
	langid = {english},
	file = {1060.full.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V58DFW8J/1060.full.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/8MR9WTIM/1060.html:text/html}
}

@article{yosef_dynamic_2013,
	title = {Dynamic regulatory network controlling {TH}17 cell differentiation},
	volume = {496},
	issn = {0028-0836},
	url = {http://libsta28.lib.cam.ac.uk:2056/nature/journal/v496/n7446/full/nature11981.html},
	doi = {10.1038/nature11981},
	abstract = {Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based perturbation tools to systematically derive and experimentally validate a model of the dynamic regulatory network that controls the differentiation of mouse {TH}17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The {TH}17 transcriptional network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, the coupled action of which may be essential for maintaining the balance between {TH}17 and other {CD}4+ T-cell subsets. Our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles; it also highlights novel drug targets for controlling {TH}17 cell differentiation.},
	pages = {461--468},
	number = {7446},
	journaltitle = {Nature},
	shortjournal = {Nature},
	author = {Yosef, Nir and Shalek, Alex K. and Gaublomme, Jellert T. and Jin, Hulin and Lee, Youjin and Awasthi, Amit and Wu, Chuan and Karwacz, Katarzyna and Xiao, Sheng and Jorgolli, Marsela and Gennert, David and Satija, Rahul and Shakya, Arvind and Lu, Diana Y. and Trombetta, John J. and Pillai, Meenu R. and Ratcliffe, Peter J. and Coleman, Mathew L. and Bix, Mark and Tantin, Dean and Park, Hongkun and Kuchroo, Vijay K. and Regev, Aviv},
	urldate = {2015-05-19},
	date = {2013-04-25},
	langid = {english},
	keywords = {immunology, Nanoscience and technology, systems biology},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TD5EJ4Z6/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ETV579GF/nature11981.html:text/html;Supplementary Information. Supplementary Notes, Supplementary Methods, Supplementary Figures 1-12, legends for Supplementary Tables 1-8 (see separate excel files) and Supplementary References.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/5T55TMVA/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.pdf:application/pdf;Supplementary Table 1. A list of microarray probesets that were differentially expressed in the TGF-β1+Il6 microarray data - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PFKZIPFU/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 2. The functional enrichments for expression clusters -see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/R44994KS/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 3. The regulatory interactions in the three canonical temporal networks (Early, Intermediate, and Late) - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XCPBRXX7/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 4. A table of ranked regulators of Th17 differentiation - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UVEXDXA9/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 5. The results of Nanostring nCounter and Fluidigm analysis - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ET2IV574/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 6. The Primers for Nanostring STA and qRT-PCR/Fluidigm and siRNA sequences - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CMUPC745/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 7. The RNA-seq data analysis - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/MZB4WBS7/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel;Supplementary Table 8. The Tsc22d3 ChIP-seq data analysis - see Supplementary Information file for full legend.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/NQUUVBDZ/Yosef et al. - 2013 - Dynamic regulatory network controlling TH17 cell d.xls:application/vnd.ms-excel}
}

@article{deng_single-cell_2014,
	title = {Single-Cell {RNA}-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells},
	volume = {343},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/content/343/6167/193},
	doi = {10.1126/science.1245316},
	abstract = {Expression from both alleles is generally observed in analyses of diploid cell populations, but studies addressing allelic expression patterns genome-wide in single cells are lacking. Here, we present global analyses of allelic expression across individual cells of mouse preimplantation embryos of mixed background ({CAST}/{EiJ} × C57BL/6J). We discovered abundant (12 to 24\%) monoallelic expression of autosomal genes and that expression of the two alleles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal X chromosome. We conclude that independent and stochastic allelic transcription generates abundant random monoallelic expression in the mammalian cell.
Expressing One Allele at a Time
Although genetic traits are often dominant or recessive, the impact of the same heterozygous genotype can vary quite a bit between individuals. Deng et al. (p. 193), analyzed global gene expression in hundreds of individual mouse cells and found that a substantial fraction of the genes only expressed one of the alleles, chosen randomly, at any given point in time. Such stochasticity in transcription increases the heterogeneity among cells and likely contributes to the phenotypic variance among individuals of identical genotype.},
	pages = {193--196},
	number = {6167},
	journaltitle = {Science},
	shortjournal = {Science},
	author = {Deng, Qiaolin and Ramsköld, Daniel and Reinius, Björn and Sandberg, Rickard},
	urldate = {2014-01-15},
	date = {2014-10-01},
	langid = {english},
	pmid = {24408435},
	file = {193.full.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JBX59E7X/193.full.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/G3TU33MS/193.html:text/html}
}

@article{morris_mathematical_2014,
	title = {Mathematical approaches to modeling development and reprogramming},
	volume = {111},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/111/14/5076},
	doi = {10.1073/pnas.1317150111},
	abstract = {Induced pluripotent stem cells ({iPSCs}) are created by the reprogramming of somatic cells via overexpression of certain transcription factors, such as the originally described Yamanaka factors: Oct4, Sox2, Klf4, and c-Myc ({OSKM}). Here we discuss recent advancements in {iPSC} reprogramming and introduce mathematical approaches to help map the landscape between cell states during reprogramming. Our modelization indicates that {OSKM} expression diminishes and/or changes potential barriers between cell states and that epigenetic remodeling facilitate these transitions. From a practical perspective, the modeling approaches outlined here allow us to predict the time necessary to create a given number of {iPSC} colonies or the number of reprogrammed cells generated in a given time. Additional investigations will help to further refine modeling strategies, rendering them applicable toward the study of the development and stability of cancer cells or even other reprogramming processes such as lineage conversion. Ultimately, a quantitative understanding of cell state transitions might facilitate the establishment of regenerative medicine strategies and enhance the translation of reprogramming technologies into the clinic.},
	pages = {5076--5082},
	number = {14},
	journaltitle = {Proceedings of the National Academy of Sciences},
	shortjournal = {{PNAS}},
	author = {Morris, Rob and Sancho-Martinez, Ignacio and Sharpee, Tatyana O. and Belmonte, Juan Carlos Izpisua},
	urldate = {2014-04-10},
	date = {2014-08-04},
	langid = {english},
	pmid = {24706886},
	keywords = {dedifferentiation, elite model, stochastic model, Waddington landscape},
	file = {5076.full.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BHBRMRZU/5076.full.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CE4JSRAV/5076.html:text/html;Supporting Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/D9PKXTQP/Morris et al. - 2014 - Mathematical approaches to modeling development an.pdf:application/pdf}
}

@article{macaulay_single_2014,
	title = {Single Cell Genomics: Advances and Future Perspectives},
	volume = {10},
	url = {http://dx.doi.org/10.1371/journal.pgen.1004126},
	doi = {10.1371/journal.pgen.1004126},
	shorttitle = {Single Cell Genomics},
	abstract = {Advances in whole-genome and whole-transcriptome amplification have permitted the sequencing of the minute amounts of {DNA} and {RNA} present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Single-cell approaches stand poised to revolutionise our capacity to understand the scale of genomic, epigenomic, and transcriptomic diversity that occurs during the lifetime of an individual organism. Here, we review the major technological and biological breakthroughs achieved, describe the remaining challenges to overcome, and provide a glimpse into the promise of recent and future developments.},
	pages = {e1004126},
	number = {1},
	journaltitle = {{PLoS} Genet},
	shortjournal = {{PLoS} Genet},
	author = {Macaulay, Iain C. and Voet, Thierry},
	urldate = {2014-09-29},
	date = {2014-01-30},
	file = {PLoS Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4R5RB6XX/Macaulay and Voet - 2014 - Single Cell Genomics Advances and Future Perspect.pdf:application/pdf;PLoS Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/8EFMW28B/infodoi10.1371journal.pgen.html:text/html}
}

@article{mahata_single-cell_2014,
	title = {Single-Cell {RNA} Sequencing Reveals T Helper Cells Synthesizing Steroids De Novo to Contribute to Immune Homeostasis},
	volume = {7},
	issn = {2211-1247},
	url = {http://www.cell.com/article/S2211124714002988/abstract},
	doi = {10.1016/j.celrep.2014.04.011},
	pages = {1130--1142},
	number = {4},
	journaltitle = {Cell Reports},
	author = {Mahata, Bidesh and Zhang, Xiuwei and Kolodziejczyk, Aleksandra A. and Proserpio, Valentina and Haim-Vilmovsky, Liora and Taylor, Angela E. and Hebenstreit, Daniel and Dingler, Felix A. and Moignard, Victoria and Göttgens, Berthold and Arlt, Wiebke and {McKenzie}, Andrew N. J. and Teichmann, Sarah A.},
	urldate = {2014-09-29},
	date = {2014-05-22},
	pmid = {24813893},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SPUMSHF4/Mahata et al. - 2014 - Single-Cell RNA Sequencing Reveals T Helper Cells .pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JNKWQJX7/S2211-1247(14)00298-8.html:text/html}
}

@article{xue_genetic_2013,
	title = {Genetic programs in human and mouse early embryos revealed by single-cell {RNA} sequencing},
	volume = {500},
	rights = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {0028-0836},
	url = {http://www.nature.com/nature/journal/v500/n7464/abs/nature12364.html},
	doi = {10.1038/nature12364},
	abstract = {Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell {RNA} sequencing. Based on single-nucleotide variants in human blastomere messenger {RNAs} and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53\%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.},
	pages = {593--597},
	number = {7464},
	journaltitle = {Nature},
	shortjournal = {Nature},
	author = {Xue, Zhigang and Huang, Kevin and Cai, Chaochao and Cai, Lingbo and Jiang, Chun-yan and Feng, Yun and Liu, Zhenshan and Zeng, Qiao and Cheng, Liming and Sun, Yi E. and Liu, Jia-yin and Horvath, Steve and Fan, Guoping},
	urldate = {2014-09-29},
	date = {2013-08-29},
	langid = {english},
	keywords = {developmental biology, Gene expression analysis},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/M9XNPEM2/Xue et al. - 2013 - Genetic programs in human and mouse early embryos .pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/U26B362M/nature12364.html:text/html;Supplementary Information. Supplementary Text, Supplementary Figures 1-9 and Supplementary References.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TNSGGS9U/Xue et al. - 2013 - Genetic programs in human and mouse early embryos .pdf:application/pdf;Supplementary Table 1. Single-cell RNA-Seq statistics.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KW4NSJEW/Xue et al. - 2013 - Genetic programs in human and mouse early embryos .xls:application/vnd.ms-excel;Supplementary Table 2. Inferred Haplotype blocks as described in the methods.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6MNQP3Q9/Xue et al. - 2013 - Genetic programs in human and mouse early embryos .xls:application/vnd.ms-excel;Supplementary Table 3. A summary of hub gene data sets.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9FHKC5UE/Xue et al. - 2013 - Genetic programs in human and mouse early embryos .xls:application/vnd.ms-excel}
}

@article{yan_single-cell_2013,
	title = {Single-cell {RNA}-Seq profiling of human preimplantation embryos and embryonic stem cells},
	volume = {20},
	rights = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1545-9993},
	url = {http://www.nature.com/nsmb/journal/v20/n9/full/nsmb.2660.html},
	doi = {10.1038/nsmb.2660},
	abstract = {Measuring gene expression in individual cells is crucial for understanding the gene regulatory network controlling human embryonic development. Here we apply single-cell {RNA} sequencing ({RNA}-Seq) analysis to 124 individual cells from human preimplantation embryos and human embryonic stem cells ({hESCs}) at different passages. The number of maternally expressed genes detected in our data set is 22,687, including 8,701 long noncoding {RNAs} ({lncRNAs}), which represents a significant increase from 9,735 maternal genes detected previously by {cDNA} microarray. We discovered 2,733 novel {lncRNAs}, many of which are expressed in specific developmental stages. To address the long-standing question whether gene expression signatures of human epiblast ({EPI}) and in vitro {hESCs} are the same, we found that {EPI} cells and primary {hESC} outgrowth have dramatically different transcriptomes, with 1,498 genes showing differential expression between them. This work provides a comprehensive framework of the transcriptome landscapes of human early embryos and {hESCs}.},
	pages = {1131--1139},
	number = {9},
	journaltitle = {Nature Structural \& Molecular Biology},
	shortjournal = {Nat Struct Mol Biol},
	author = {Yan, Liying and Yang, Mingyu and Guo, Hongshan and Yang, Lu and Wu, Jun and Li, Rong and Liu, Ping and Lian, Ying and Zheng, Xiaoying and Yan, Jie and Huang, Jin and Li, Ming and Wu, Xinglong and Wen, Lu and Lao, Kaiqin and Li, Ruiqiang and Qiao, Jie and Tang, Fuchou},
	urldate = {2014-09-29},
	date = {2013-09},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZREHEXZE/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.pdf:application/pdf;Huang.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6DCPVZAM/Huang.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BG7N3ZD7/nsmb.2660.html:text/html;Supplementary Table 1. Expression (RPKM) of known RefSeq genes in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3M273EPW/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 2. Expression (RPKM) of known RefSeq transcripts in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/E5I6TWG8/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 3. GO enrichment analysis for EPI-specific genes compared to all the other cells after the 8-cell stage (including 8-cell–stage embryos).:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6CN4Q7XP/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 4. Number of exon-exon junction reads that are unique to transcript isoforms of known RefSeq genes in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/79M6WTTB/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 5. Expression (counts) of known lncRNA genes in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/HKBZH8GD/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 6. Expression (counts) of novel transcripts in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GAFF4FZ4/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 7. Expression (RPKM) of the differentially expressed genes among the epiblast (EPI), primitive endoderm (PE) and trophectoderm (TE) lineages of late blastocysts.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/56M8SPT9/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 8. Expression (RPKM) of the differentially expressed genes between the epiblast (EPI) cells of late blastocysts and passage #0 hESCs.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/Z2IBJB7E/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 9. The rRNA contamination in the single cell RNA-Seq data for 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WK5RJ3HV/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 10. Summary of the sequencing exercise, quality control (Q20 and Q30 percentage of the sequencing reads) and mapped rates of RNA-Seq data to RefSeq, Ensemble, known lncRNAs, and genome of 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/R42JH878/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Table 11. The distribution of all mapped reads in different features (exon, intron and intergenic) of the human genome in 124 single cells from mature oocytes, preimplantation embryos and embryonic stem cells.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/QRCHHSJT/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.xls:application/vnd.ms-excel;Supplementary Text and Figures. Supplementary Figures 1–7 and Supplementary Note:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/C53K39PE/Yan et al. - 2013 - Single-cell RNA-Seq profiling of human preimplanta.pdf:application/pdf}
}

@article{moignard_characterization_2013,
	title = {Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis},
	volume = {15},
	issn = {1465-7392},
	url = {http://libsta28.lib.cam.ac.uk:2094/ncb/journal/v15/n4/abs/ncb2709.html?mkt_tok=3RkMMJWWfF9wsRow5%2FmYJoDpwmWGd5mht7VzDtPj1OY6hBsvKriJK1TtuMFUGpsqOO%2BSFAsUGpds},
	doi = {10.1038/ncb2709},
	abstract = {Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.
View full text},
	pages = {363--372},
	number = {4},
	journaltitle = {Nature Cell Biology},
	shortjournal = {Nat Cell Biol},
	author = {Moignard, Victoria and Macaulay, Iain C. and Swiers, Gemma and Buettner, Florian and Schütte, Judith and Calero-Nieto, Fernando J. and Kinston, Sarah and Joshi, Anagha and Hannah, Rebecca and Theis, Fabian J. and Jacobsen, Sten Eirik and de Bruijn, Marella F. and Göttgens, Berthold},
	urldate = {2014-10-02},
	date = {2013-04},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4HD9ZRKA/Moignard et al. - 2013 - Characterization of transcriptional networks in bl.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9PSZIEIA/ncb2709.html:text/html;Supplementary Information. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/I3HNPF8T/Moignard et al. - 2013 - Characterization of transcriptional networks in bl.pdf:application/pdf;Supplementary Table 1. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/F46D9DPT/Moignard et al. - 2013 - Characterization of transcriptional networks in bl.xls:application/vnd.ms-excel;Supplementary Table 2. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/96WWB8HK/Moignard et al. - 2013 - Characterization of transcriptional networks in bl.xls:application/vnd.ms-excel;Supplementary Table 3. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/T972ZRE6/Moignard et al. - 2013 - Characterization of transcriptional networks in bl.xls:application/vnd.ms-excel}
}

@article{marco_bifurcation_2014,
	title = {Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape},
	volume = {111},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/111/52/E5643},
	doi = {10.1073/pnas.1408993111},
	abstract = {We present single-cell clustering using bifurcation analysis ({SCUBA}), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. {SCUBA} draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying {SCUBA} to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between {RT}-{PCR} and {RNA} sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between {SCUBA} predictions and the experimental data. We further extended the utility of {SCUBA} by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, {SCUBA} provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.},
	pages = {E5643--E5650},
	number = {52},
	journaltitle = {Proceedings of the National Academy of Sciences},
	shortjournal = {{PNAS}},
	author = {Marco, Eugenio and Karp, Robert L. and Guo, Guoji and Robson, Paul and Hart, Adam H. and Trippa, Lorenzo and Yuan, Guo-Cheng},
	urldate = {2015-01-05},
	date = {2014-12-30},
	langid = {english},
	pmid = {25512504},
	keywords = {bifurcation, differentiation, Gene expression, single cell},
	file = {Dataset_S01:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4UFVZQ8C/Marco et al. - 2014 - Bifurcation analysis of single-cell gene expressio.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ECI7ITRD/Marco et al. - 2014 - Bifurcation analysis of single-cell gene expressio.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/HX3FJMWS/E5643.html:text/html;Supporting Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/T6P7IHX5/Marco et al. - 2014 - Bifurcation analysis of single-cell gene expressio.pdf:application/pdf}
}

@article{moignard_decoding_2015,
	title = {Decoding the regulatory network of early blood development from single-cell gene expression measurements},
	volume = {33},
	issn = {1087-0156},
	url = {http://libsta28.lib.cam.ac.uk:2583/nbt/journal/v33/n3/full/nbt.3154.html},
	doi = {10.1038/nbt.3154},
	abstract = {Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.},
	pages = {269--276},
	number = {3},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotech},
	author = {Moignard, Victoria and Woodhouse, Steven and Haghverdi, Laleh and Lilly, Andrew J. and Tanaka, Yosuke and Wilkinson, Adam C. and Buettner, Florian and Macaulay, Iain C. and Jawaid, Wajid and Diamanti, Evangelia and Nishikawa, Shin-Ichi and Piterman, Nir and Kouskoff, Valerie and Theis, Fabian J. and Fisher, Jasmin and Göttgens, Berthold},
	urldate = {2015-03-07},
	date = {2015-03},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VZ2DD7U9/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4QSJ2B4G/nbt.3154.html:text/html;Supplementary Table 5. Summary of in silico perturbations:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/TMWTHVU5/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Table 7. Raw Ct values and normalised dCt values for the 3,934 cells presented in this paper:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/79FMBNEP/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Table 8. FPKM values for RNAseq of 50 cell populations:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/N4F34XQQ/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Table 9. Initial and final states used in network synthesis:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VMMWTKCD/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Table 10. Binary gene expression data:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/975TTDRV/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Table 11. List of cells with equivalent binary expression states:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CZEZMNHB/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.xls:application/vnd.ms-excel;Supplementary Text and Figures. Supplementary Figures 1–12, Supplementary Tables 1–4, 6 and Supplementary Note:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RFKCTDZ5/Moignard et al. - 2015 - Decoding the regulatory network of early blood dev.pdf:application/pdf}
}

@article{bin_oct4_2014,
	title = {Oct4 is required for lineage priming in the developing inner cell mass of the mouse blastocyst},
	volume = {141},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/141/5/1001},
	doi = {10.1242/dev.096875},
	abstract = {The transcription factor Oct4 is required in vitro for establishment and maintenance of embryonic stem cells and for reprogramming somatic cells to pluripotency. In vivo, it prevents the ectopic differentiation of early embryos into trophoblast. Here, we further explore the role of Oct4 in blastocyst formation and specification of epiblast versus primitive endoderm lineages using conditional genetic deletion. Experiments involving mouse embryos deficient for both maternal and zygotic Oct4 suggest that it is dispensable for zygote formation, early cleavage and activation of Nanog expression. Nanog protein is significantly elevated in the presumptive inner cell mass of Oct4 null embryos, suggesting an unexpected role for Oct4 in attenuating the level of Nanog, which might be significant for priming differentiation during epiblast maturation. Induced deletion of Oct4 during the morula to blastocyst transition disrupts the ability of inner cell mass cells to adopt lineage-specific identity and acquire the molecular profile characteristic of either epiblast or primitive endoderm. Sox17, a marker of primitive endoderm, is not detected following prolonged culture of such embryos, but can be rescued by provision of exogenous {FGF}4. Interestingly, functional primitive endoderm can be rescued in Oct4-deficient embryos in embryonic stem cell complementation assays, but only if the host embryos are at the pre-blastocyst stage. We conclude that cell fate decisions within the inner cell mass are dependent upon Oct4 and that Oct4 is not cell-autonomously required for the differentiation of primitive endoderm derivatives, as long as an appropriate developmental environment is established.},
	pages = {1001--1010},
	number = {5},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Bin, Gloryn Chia Le and Muñoz-Descalzo, Silvia and Kurowski, Agata and Leitch, Harry and Lou, Xinghua and Mansfield, William and Etienne-Dumeau, Charles and Grabole, Nils and Mulas, Carla and Niwa, Hitoshi and Hadjantonakis, Anna-Katerina and Nichols, Jennifer},
	urldate = {2015-04-30},
	date = {2014-01-03},
	langid = {english},
	pmid = {24504341},
	keywords = {Blastocyst, Chimaera, Nanog, Oct4 (Pou5f1), Primitive endoderm, Sox17},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BZUD9TH9/Bin et al. - 2014 - Oct4 is required for lineage priming in the develo.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/J562ETMF/1001.html:text/html;Supplementary Material PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CQX8A999/Bin et al. - 2014 - Oct4 is required for lineage priming in the develo.pdf:application/pdf}
}

@article{ohnishi_cell--cell_2014,
	title = {Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages},
	volume = {16},
	rights = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1465-7392},
	url = {http://www.nature.com/ncb/journal/v16/n1/full/ncb2881.html},
	doi = {10.1038/ncb2881},
	abstract = {It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast ({EPI}) versus primitive endoderm ({PrE}) lineage segregation within the inner cell mass ({ICM}) of the mouse blastocyst, we characterized the gene expression profiles of individual {ICM} cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of {PrE} and {EPI} was halted, indicating that Fgf4 drives, and is required for, {ICM} lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies {ICM} lineage segregation by antagonistically separating equivalent cells.},
	pages = {27--37},
	number = {1},
	journaltitle = {Nature Cell Biology},
	shortjournal = {Nat Cell Biol},
	author = {Ohnishi, Yusuke and Huber, Wolfgang and Tsumura, Akiko and Kang, Minjung and Xenopoulos, Panagiotis and Kurimoto, Kazuki and Oleś, Andrzej K. and Araúzo-Bravo, Marcos J. and Saitou, Mitinori and Hadjantonakis, Anna-Katerina and Hiiragi, Takashi},
	urldate = {2015-04-30},
	date = {2014-01},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4P4XAP7P/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by si.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UBWGMSJB/ncb2881.html:text/html;Supplementary Information. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/C9396RJ2/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by si.pdf:application/pdf;Supplementary Table 1. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/MQVXWXKP/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by si.xls:application/vnd.ms-excel;Supplementary Table 2. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6HQHVNTA/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by si.xls:application/vnd.ms-excel;Supplementary Table 3. Supplementary Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/WE3M29AX/Ohnishi et al. - 2014 - Cell-to-cell expression variability followed by si.xls:application/vnd.ms-excel}
}

@article{wicklow_hippo_2014,
	title = {{HIPPO} Pathway Members Restrict {SOX}2 to the Inner Cell Mass Where It Promotes {ICM} Fates in the Mouse Blastocyst},
	volume = {10},
	url = {http://dx.doi.org/10.1371/journal.pgen.1004618},
	doi = {10.1371/journal.pgen.1004618},
	abstract = {Author Summary Pluripotent stem cells can give rise to any cell type in the body, making them an attractive tool for regenerative medicine. Pluripotent stem cells can be derived from the mammalian embryo at the blastocyst stage or they can be created from mature adult cells by reprogramming. During reprogramming, {SOX}2 helps establish pluripotency, but it is not clear how {SOX}2 establishes pluripotency in the blastocyst. We evaluated where {SOX}2 is present, how {SOX}2 is regulated, and where {SOX}2 is active during blastocyst formation. Our data show that the roles and the regulation of {SOX}2 are unique compared to other pluripotency/reprogramming factors, such as {OCT}4 and {NANOG}. {SOX}2 marks pluripotent cells earlier than do other factors, but does not regulate pluripotency until several days later. Rather, the earlier role of {SOX}2 is to help establish the yolk sac lineage, which is essential for gestation.},
	pages = {e1004618},
	number = {10},
	journaltitle = {{PLoS} Genet},
	shortjournal = {{PLoS} Genet},
	author = {Wicklow, Eryn and Blij, Stephanie and Frum, Tristan and Hirate, Yoshikazu and Lang, Richard A. and Sasaki, Hiroshi and Ralston, Amy},
	urldate = {2015-04-30},
	date = {2014-10-23},
	file = {PLoS Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/AN85RKMV/Wicklow et al. - 2014 - HIPPO Pathway Members Restrict SOX2 to the Inner C.pdf:application/pdf}
}

@article{kanter_single_2015,
	title = {Single cell transcriptomics: methods and applications},
	volume = {5},
	url = {http://journal.frontiersin.org/article/10.3389/fonc.2015.00053/abstract},
	doi = {10.3389/fonc.2015.00053},
	shorttitle = {Single cell transcriptomics},
	abstract = {Traditionally, gene expression measurements were performed on “bulk” samples containing populations of thousands of cells. Recent advances in genomic technology have made it possible to measure gene expression in hundreds of individual cells at a time. As a result, cellular properties that were previously masked in “bulk” measurements can now be observed directly. In this review, we will survey emerging technologies for single cell transcriptomics, and describe how they are used to study complex disease such as cancer, as well as other biological phenomena such as tissue regeneration, embryonic development, and immune response.},
	pages = {53},
	journaltitle = {Molecular and Cellular Oncology},
	shortjournal = {Front. Oncol.},
	author = {Kanter, Itamar and Kalisky, Tomer},
	urldate = {2015-04-30},
	date = {2015},
	keywords = {{FISH}, Gene expression, {qPCR}, {RNA} sequencing, single cell},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4IK9M7MX/Kanter and Kalisky - 2015 - Single cell transcriptomics methods and applicati.pdf:application/pdf}
}

@article{teles_single-cell_2014,
	title = {Single-cell {PCR} profiling of gene expression in hematopoiesis},
	volume = {1185},
	issn = {1940-6029},
	doi = {10.1007/978-1-4939-1133-2_3},
	abstract = {Single-cell analysis of gene expression offers the possibility of exploring cellular and molecular heterogeneity in stem and developmental cell systems, including cancer, to infer routes of cellular specification and their respective gene regulatory modules. {PCR}-based technologies, although limited to the analysis of a predefined set of genes, afford a cost-effective balance of throughput and biological information and have become a method of choice in stem cell laboratories. Here we describe an experimental and analytical protocol based on the Fluidigm microfluidics platform for the simultaneous expression analysis of 48 or 96 genes in multiples of 48 or 96 cells. We detail wet laboratory procedures and describe clustering, principal component analysis, correlation, and classification tools for the inference of cellular pathways and gene networks.},
	pages = {21--42},
	journaltitle = {Methods in Molecular Biology (Clifton, N.J.)},
	shortjournal = {Methods Mol. Biol.},
	author = {Teles, José and Enver, Tariq and Pina, Cristina},
	date = {2014},
	pmid = {25062620},
	keywords = {Animals, Cell Death, Cell Line, Cell Separation, Cluster Analysis, {DNA} Primers, Gene Expression Profiling, Hematopoiesis, Humans, Mice, Polymerase Chain Reaction, Principal component analysis, Quality Control, Reverse Transcription, Single-Cell Analysis}
}

@article{rayon_notch_2014,
	title = {Notch and Hippo Converge on Cdx2 to Specify the Trophectoderm Lineage in the Mouse Blastocyst},
	volume = {30},
	issn = {1534-5807},
	url = {http://www.sciencedirect.com/science/article/pii/S1534580714004080},
	doi = {10.1016/j.devcel.2014.06.019},
	abstract = {Summary
The first lineage choice in mammalian embryogenesis is that between the trophectoderm, which gives rise to the trophoblast of the placenta, and the inner cell mass, from which is derived the embryo proper and the yolk sac. The establishment of these lineages is preceded by the inside-versus-outside positioning of cells in the early embryo and stochastic expression of key transcription factors, which is then resolved into lineage-restricted expression. The regulatory inputs that drive this restriction and how they relate to cell position are largely unknown. Here, we show an unsuspected role of Notch signaling in regulating trophectoderm-specific expression of Cdx2 in cooperation with {TEAD}4. Notch activity is restricted to outer cells and is able to influence positional allocation of blastomeres, mediating preferential localization to the trophectoderm. Our results show that multiple signaling inputs at preimplantation stages specify the first embryonic lineages.},
	pages = {410--422},
	number = {4},
	journaltitle = {Developmental Cell},
	shortjournal = {Developmental Cell},
	author = {Rayon, Teresa and Menchero, Sergio and Nieto, Andres and Xenopoulos, Panagiotis and Crespo, Miguel and Cockburn, Katie and Cañon, Susana and Sasaki, Hiroshi and Hadjantonakis, Anna-Katerina and de la Pompa, Jose Luis and Rossant, Janet and Manzanares, Miguel},
	urldate = {2015-04-30},
	date = {2014-08-25},
	file = {Document S1. Figures S1–S6.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9JFZK4XB/Rayon et al. - 2014 - Notch and Hippo Converge on Cdx2 to Specify the Tr.pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CGACE543/Rayon et al. - 2014 - Notch and Hippo Converge on Cdx2 to Specify the Tr.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/34BMK8KB/S1534580714004080.html:text/html;Table S1. Data Used for Quantitative Analysis.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GNN5IDJ5/Rayon et al. - 2014 - Notch and Hippo Converge on Cdx2 to Specify the Tr.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet}
}

@article{huang_time-variant_2014,
	title = {Time-variant clustering model for understanding cell fate decisions},
	volume = {111},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1407388111},
	doi = {10.1073/pnas.1407388111},
	pages = {E4797--E4806},
	number = {44},
	journaltitle = {Proceedings of the National Academy of Sciences},
	author = {Huang, W. and Cao, X. and Biase, F. H. and Yu, P. and Zhong, S.},
	urldate = {2015-04-30},
	date = {2014-11-04},
	langid = {english},
	file = {E4797.full.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RJE3GHH2/E4797.full.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ASNXIQPT/E4797.html:text/html}
}

@article{schrode_gata6_2014,
	title = {{GATA}6 Levels Modulate Primitive Endoderm Cell Fate Choice and Timing in the Mouse Blastocyst},
	volume = {29},
	issn = {1534-5807},
	url = {http://www.sciencedirect.com/science/article/pii/S1534580714002317},
	doi = {10.1016/j.devcel.2014.04.011},
	abstract = {Summary
Cells of the inner cell mass ({ICM}) of the mouse blastocyst differentiate into the pluripotent epiblast or the primitive endoderm ({PrE}), marked by the transcription factors {NANOG} and {GATA}6, respectively. To investigate the mechanistic regulation of this process, we applied an unbiased, quantitative, single-cell-resolution image analysis pipeline to analyze embryos lacking or exhibiting reduced levels of {GATA}6. We find that Gata6 mutants exhibit a complete absence of {PrE} and demonstrate that {GATA}6 levels regulate the timing and speed of lineage commitment within the {ICM}. Furthermore, we show that {GATA}6 is necessary for {PrE} specification by {FGF} signaling and propose a model where interactions between {NANOG}, {GATA}6, and the {FGF}/{ERK} pathway determine {ICM} cell fate. This study provides a framework for quantitative analyses of mammalian embryos and establishes {GATA}6 as a nodal point in the gene regulatory network driving {ICM} lineage specification.},
	pages = {454--467},
	number = {4},
	journaltitle = {Developmental Cell},
	shortjournal = {Developmental Cell},
	author = {Schrode, Nadine and Saiz, Néstor and Di Talia, Stefano and Hadjantonakis, Anna-Katerina},
	urldate = {2015-04-30},
	date = {2014-05-27},
	file = {Document S1. Supplemental Experimental Procedures, Figures S1–S4, and Table S1.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RNJRBQIV/Schrode et al. - 2014 - GATA6 Levels Modulate Primitive Endoderm Cell Fate.pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/UJQ5HSKX/Schrode et al. - 2014 - GATA6 Levels Modulate Primitive Endoderm Cell Fate.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XZGWW2TZ/S1534580714002317.html:text/html;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KT7UADH6/S1534-5807(14)00231-7.html:text/html}
}

@article{park_identifying_2015,
	title = {Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability},
	volume = {117},
	issn = {0079-6107},
	url = {http://www.sciencedirect.com/science/article/pii/S0079610714001837},
	doi = {10.1016/j.pbiomolbio.2014.11.004},
	series = {Multi-scale Systems Biology},
	abstract = {Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.},
	pages = {87--98},
	number = {1},
	journaltitle = {Progress in Biophysics and Molecular Biology},
	shortjournal = {Progress in Biophysics and Molecular Biology},
	author = {Park, James and Ogunnaike, Babatunde and Schwaber, James and Vadigepalli, Rajanikanth},
	urldate = {2015-04-30},
	date = {2015-01},
	keywords = {Fuzzy logic, high-throughput, Single-cell, Systems identification},
	file = {ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RSR5X9E8/Park et al. - 2015 - Identifying functional gene regulatory network phe.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/A46D9MJI/S0079610714001837.html:text/html}
}

@article{krupa_allocation_2014,
	title = {Allocation of inner cells to epiblast vs primitive endoderm in the mouse embryo is biased but not determined by the round of asymmetric divisions (8→16- and 16→32-cells)},
	volume = {385},
	issn = {0012-1606},
	url = {http://www.sciencedirect.com/science/article/pii/S0012160613004818},
	doi = {10.1016/j.ydbio.2013.09.008},
	abstract = {The epiblast ({EPI}) and the primitive endoderm ({PE}), which constitute foundations for the future embryo body and yolk sac, build respectively deep and surface layers of the inner cell mass ({ICM}) of the blastocyst. Before reaching their target localization within the {ICM}, the {PE} and {EPI} precursor cells, which display distinct lineage-specific markers, are intermingled randomly. Since the {ICM} cells are produced in two successive rounds of asymmetric divisions at the 8→16 (primary inner cells) and 16→32 cell stage (secondary inner cells) it has been suggested that the fate of inner cells (decision to become {EPI} or {PE}) may depend on the time of their origin. Our method of dual labeling of embryos allowed us to distinguish between primary and secondary inner cells contributing ultimately to {ICM}. Our results show that the presence of two generations of inner cells in the 32-cell stage embryo is the source of heterogeneity within the {ICM}. We found some bias concerning the level of Fgf4 and Fgfr2 expression between primary and secondary inner cells, resulting from the distinct number of cells expressing these genes. Analysis of experimental aggregates constructed using different ratios of inner cells surrounded by outer cells revealed that the fate of cells does not depend exclusively on the timing of their generation, but also on the number of cells generated in each wave of asymmetric division. Taking together, the observed regulatory mechanism adjusting the proportion of outer to inner cells within the embryo may be mediated by {FGF} signaling.},
	pages = {136--148},
	number = {1},
	journaltitle = {Developmental Biology},
	shortjournal = {Developmental Biology},
	author = {Krupa, Magdalena and Mazur, Ewa and Szczepańska, Katarzyna and Filimonow, Katarzyna and Maleszewski, Marek and Suwińska, Aneta},
	urldate = {2015-04-30},
	date = {2014-01-01},
	keywords = {Asymmetric division, Epiblast, Fgf/{MAP} kinase pathway, Mouse blastocyst, Primitive endoderm},
	file = {ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GSSSJ5SK/Krupa et al. - 2014 - Allocation of inner cells to epiblast vs primitive.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6H8JWNIZ/S0012160613004818.html:text/html}
}

@article{huang_naive_2014,
	title = {The Naive State of Human Pluripotent Stem Cells: A Synthesis of Stem Cell and Preimplantation Embryo Transcriptome Analyses},
	volume = {15},
	issn = {1934-5909},
	url = {http://www.sciencedirect.com/science/article/pii/S1934590914004056},
	doi = {10.1016/j.stem.2014.09.014},
	shorttitle = {The Naive State of Human Pluripotent Stem Cells},
	abstract = {Here we use a systems biology approach to comprehensively assess the conservation of gene networks in naive pluripotent stem cells ({PSCs}) with preimplantation embryos. While gene networks in murine naive and primed pluripotent states are reproducible across data sets, different sources of human stem cells display high degrees of variation, partly reflecting disparities in culture conditions. Finally, naive gene networks between human and mouse {PSCs} are not well conserved and better resemble their respective blastocysts.},
	pages = {410--415},
	number = {4},
	journaltitle = {Cell Stem Cell},
	shortjournal = {Cell Stem Cell},
	author = {Huang, Kevin and Maruyama, Toru and Fan, Guoping},
	urldate = {2015-04-30},
	date = {2014-10-02},
	file = {Document S1. Supplemental Experimental Procedures.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/SBXPS33Q/Huang et al. - 2014 - The Naive State of Human Pluripotent Stem Cells A.pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/EP9PRVI9/Huang et al. - 2014 - The Naive State of Human Pluripotent Stem Cells A.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CKCIX9M3/S1934590914004056.html:text/html;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/APHQMDEX/S1934-5909(14)00405-6.html:text/html;Table S1. WGCNA of Mouse and Human Naive and Primed Datasets.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/EBV5ETBG/Huang et al. - 2014 - The Naive State of Human Pluripotent Stem Cells A.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet}
}

@article{robson_deciphering_2014,
	title = {Deciphering Developmental Processes from Single-Cell Transcriptomes},
	volume = {29},
	issn = {1534-5807},
	url = {http://www.sciencedirect.com/science/article/pii/S1534580714002792},
	doi = {10.1016/j.devcel.2014.04.032},
	abstract = {Alveoli facilitate air-blood interchange in the lungs from birth to the last breath. Reporting in Nature, Treutlein et al. (2014) apply single-cell {RNA} sequencing to the developing mouse lung to address some of the unanswered questions surrounding the embryonic origins of alveoli.},
	pages = {260--261},
	number = {3},
	journaltitle = {Developmental Cell},
	shortjournal = {Developmental Cell},
	author = {Robson, Paul},
	urldate = {2015-04-30},
	date = {2014-05-12},
	file = {ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GVPM65RU/Robson - 2014 - Deciphering Developmental Processes from Single-Ce.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7BKIDDH3/S1534580714002792.html:text/html}
}

@article{lanner_lineage_2014,
	title = {Lineage specification in the early mouse embryo},
	volume = {321},
	issn = {0014-4827},
	url = {http://www.sciencedirect.com/science/article/pii/S0014482713005211},
	doi = {10.1016/j.yexcr.2013.12.004},
	series = {Developmental Biology},
	abstract = {Before the mammalian embryo is ready to implant in the uterine wall, the single cell zygote must divide and differentiate into three distinct tissues; trophectoderm (prospective placenta), primitive endoderm (prospective yolk sac), and pluripotent epiblast cells which will form the embryo proper. In this review I will discuss our current understanding of how positional information, cell polarization, signaling pathways, and transcription factor networks converge to drive and regulate the progressive segregation of the first three cell types in the mouse embryo.},
	pages = {32--39},
	number = {1},
	journaltitle = {Experimental Cell Research},
	shortjournal = {Experimental Cell Research},
	author = {Lanner, Fredrik},
	urldate = {2015-04-30},
	date = {2014-02-01},
	keywords = {Lineage specification, Mouse development, Preimplantation development},
	file = {ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/FESZ4INS/Lanner - 2014 - Lineage specification in the early mouse embryo.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/F8P6RVJG/S0014482713005211.html:text/html}
}

@article{li_identification_2015,
	title = {Identification of cardiovascular lineage descendants at single-cell resolution},
	volume = {142},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/142/5/846},
	doi = {10.1242/dev.116897},
	abstract = {The transcriptional profiles of cardiac cells derived from murine embryos and from mouse embryonic stem cells ({mESCs}) have primarily been studied within a cell population. However, the characterization of gene expression in these cells at a single-cell level might demonstrate unique variations that cannot be appreciated within a cell pool. In this study, we aimed to establish a single-cell quantitative {PCR} platform and perform side-by-side comparison between cardiac progenitor cells ({CPCs}) and cardiomyocytes ({CMs}) derived from {mESCs} and mouse embryos. We first generated a reference map for cardiovascular single cells through quantifying lineage-defining genes for {CPCs}, {CMs}, smooth muscle cells ({SMCs}), endothelial cells ({EDCs}), fibroblasts and {mESCs}. This panel was then applied against single embryonic day 10.5 heart cells to demonstrate its ability to identify each endocardial cell and chamber-specific {CM}. In addition, we compared the gene expression profile of embryo- and {mESC}-derived {CPCs} and {CMs} at different developmental stages and showed that {mESC}-derived {CMs} are phenotypically similar to embryo-derived {CMs} up to the neonatal stage. Furthermore, we showed that single-cell expression assays coupled with time-lapse microscopy can resolve the identity and the lineage relationships between progenies of single cultured {CPCs}. With this approach, we found that {mESC}-derived Nkx2-5+ {CPCs} preferentially become {SMCs} or {CMs}, whereas single embryo-derived Nkx2-5+ {CPCs} represent two phenotypically distinct subpopulations that can become either {EDCs} or {CMs}. These results demonstrate that multiplex gene expression analysis in single cells is a powerful tool for examining the unique behaviors of individual embryo- or {mESC}-derived cardiac cells.},
	pages = {846--857},
	number = {5},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Li, Guang and Plonowska, Karolina and Kuppusamy, Rajarajan and Sturzu, Anthony and Wu, Sean M.},
	urldate = {2015-04-30},
	date = {2015-01-03},
	langid = {english},
	pmid = {25633351},
	keywords = {Cardiac progenitor cells, Cardiogenesis, differentiation, embryonic stem cell, Nkx2-5},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JKR2EST4/Li et al. - 2015 - Identification of cardiovascular lineage descendan.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DBRTXA9E/846.html:text/html;Supplementary Material:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7IF86ZQ8/Li et al. - 2015 - Identification of cardiovascular lineage descendan.pdf:application/pdf}
}

@article{hoppe_single-cell_2014,
	title = {Single-cell technologies sharpen up mammalian stem cell research},
	volume = {16},
	rights = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1465-7392},
	url = {http://www.nature.com/ncb/journal/v16/n10/full/ncb3042.html},
	doi = {10.1038/ncb3042},
	abstract = {Analysis of the mechanisms underlying cell fates requires the molecular quantification of cellular features. Classical techniques use population average readouts at single time points. However, these approaches mask cellular heterogeneity and dynamics and are limited for studying rare and heterogeneous cell populations like stem cells. Techniques for single-cell analyses, ideally allowing non-invasive quantification of molecular dynamics and cellular behaviour over time, are required for studying stem cells. Here, we review the development and application of these techniques to stem cell research.},
	pages = {919--927},
	number = {10},
	journaltitle = {Nature Cell Biology},
	shortjournal = {Nat Cell Biol},
	author = {Hoppe, Philipp S. and Coutu, Daniel L. and Schroeder, Timm},
	urldate = {2015-04-30},
	date = {2014-10},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2CMMWFPZ/Hoppe et al. - 2014 - Single-cell technologies sharpen up mammalian stem.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3CF4GRZS/ncb3042.html:text/html}
}

@article{wilkinson_single-cell_2014,
	title = {Single-cell analyses of regulatory network perturbations using enhancer-targeting {TALEs} suggest novel roles for {PU}.1 during haematopoietic specification},
	volume = {141},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/141/20/4018},
	doi = {10.1242/dev.115709},
	abstract = {Transcription factors ({TFs}) act within wider regulatory networks to control cell identity and fate. Numerous {TFs}, including Scl (Tal1) and {PU}.1 (Spi1), are known regulators of developmental and adult haematopoiesis, but how they act within wider {TF} networks is still poorly understood. Transcription activator-like effectors ({TALEs}) are a novel class of genetic tool based on the modular {DNA}-binding domains of Xanthomonas {TAL} proteins, which enable {DNA} sequence-specific targeting and the manipulation of endogenous gene expression. Here, we report {TALEs} engineered to target the {PU}.1-14kb and Scl+40kb transcriptional enhancers as efficient new tools to perturb the expression of these key haematopoietic {TFs}. We confirmed the efficiency of these {TALEs} at the single-cell level using high-throughput {RT}-{qPCR}, which also allowed us to assess the consequences of both {PU}.1 activation and repression on wider {TF} networks during developmental haematopoiesis. Combined with comprehensive cellular assays, these experiments uncovered novel roles for {PU}.1 during early haematopoietic specification. Finally, transgenic mouse studies confirmed that the {PU}.1-14kb element is active at sites of definitive haematopoiesis in vivo and {PU}.1 is detectable in haemogenic endothelium and early committing blood cells. We therefore establish {TALEs} as powerful new tools to study the functionality of transcriptional networks that control developmental processes such as early haematopoiesis.},
	pages = {4018--4030},
	number = {20},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Wilkinson, Adam C. and Kawata, Viviane K. S. and Schütte, Judith and Gao, Xuefei and Antoniou, Stella and Baumann, Claudia and Woodhouse, Steven and Hannah, Rebecca and Tanaka, Yosuke and Swiers, Gemma and Moignard, Victoria and Fisher, Jasmin and Hidetoshi, Shimauchi and Tijssen, Marloes R. and Bruijn, Marella F. T. R. de and Liu, Pentao and Göttgens, Berthold},
	urldate = {2015-04-30},
	date = {2014-10-15},
	langid = {english},
	pmid = {25252941},
	keywords = {Haematopoiesis, {PU}.1, Regulatory networks, Transcription activator-like effectors},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/6JRG5WN9/Wilkinson et al. - 2014 - Single-cell analyses of regulatory network perturb.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V3BDK7FD/4018.html:text/html;Supplementary Material:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BRKS8WFM/Wilkinson et al. - 2014 - Single-cell analyses of regulatory network perturb.pdf:application/pdf}
}

@article{iqbal_rna-seq_2014,
	title = {{RNA}-Seq Transcriptome Profiling of Equine Inner Cell Mass and Trophectoderm},
	volume = {90},
	issn = {0006-3363, 1529-7268},
	url = {http://www.biolreprod.org/content/90/3/61},
	doi = {10.1095/biolreprod.113.113928},
	abstract = {Formation of the inner cell mass ({ICM}) and trophectoderm ({TE}) marks the first differentiation event in mammalian development. These two cell types have completely divergent fates for the remainder of the developmental process. The molecular mechanisms that regulate {ICM} and {TE} formation are poorly characterized in horses. The objective of this study was to establish the transcriptome profiles of {ICM} and {TE} cells from horse blastocysts using {RNA} sequencing ({RNA}-seq). A total of 12 270 genes were found to be expressed in either lineage. Global analysis of the transcriptome profiles by unsupervised clustering indicated that {ICM} and {TE} samples presented different gene expression patterns. Statistical analysis indicated that 1662 genes were differentially expressed (adjusted P {\textless} 0.05 and fold change {\textgreater} 2) between {ICM} and {TE}. Genes known to be specific to the {ICM} and {TE} were expressed primarily in their respective tissue. Transcript abundance for genes related to biological processes important for horse blastocyst formation and function is presented and discussed. Collectively, our data and analysis serve as a valuable resource for gene discovery and unraveling the fundamental mechanisms of early horse development.},
	pages = {61},
	number = {3},
	journaltitle = {Biology of Reproduction},
	shortjournal = {Biol Reprod},
	author = {Iqbal, Khursheed and Chitwood, James L. and Meyers-Brown, Geraldine A. and Roser, Janet F. and Ross, Pablo J.},
	urldate = {2015-04-30},
	date = {2014-01-03},
	langid = {english},
	pmid = {24478389},
	keywords = {Blastocyst, embryonic capsule, horse, inner cell mass, pluripotency, {RNA}-seq, transcriptome, trophectoderm},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZU3RXIAS/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;List of genes overexpressed in ICM and TE.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/65NMP483/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;Lists of genes and expression levels for selected blastocyst formation related genes.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/GVVGWACQ/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;Lists of genes and expression levels for selected capsule biosynthesis related genes.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/33E6DKX5/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;Lists of genes and expression levels for selected epigenetic modifier related genes.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XXT4D4PE/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;Lists of genes and expression levels for selected signaling pathway related genes.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4V3ZR7PP/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/236FXEDT/61.html:text/html;Summary of sequencing output and read mapping.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4TFI7SSB/Iqbal et al. - 2014 - RNA-Seq Transcriptome Profiling of Equine Inner Ce.pdf:application/pdf}
}

@article{torres-padilla_transcription_2014,
	title = {Transcription factor heterogeneity in pluripotent stem cells: a stochastic advantage},
	volume = {141},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/141/11/2173},
	doi = {10.1242/dev.102624},
	shorttitle = {Transcription factor heterogeneity in pluripotent stem cells},
	abstract = {When pluripotent cells are exposed to a uniform culture environment they routinely display heterogeneous gene expression. Aspects of this heterogeneity, such as Nanog expression, are linked to differences in the propensity of individual cells to either self-renew or commit towards differentiation. Recent findings have provided new insight into the underlying causes of this heterogeneity, which we summarise here using Nanog, a key regulator of pluripotency, as a model gene. We discuss the role of transcription factor heterogeneity in facilitating the intrinsically dynamic and stochastic nature of the pluripotency network, which in turn provides a potential benefit to a population of cells that needs to balance cell fate decisions.},
	pages = {2173--2181},
	number = {11},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Torres-Padilla, Maria-Elena and Chambers, Ian},
	urldate = {2015-04-30},
	date = {2014-01-06},
	langid = {english},
	pmid = {24866112},
	keywords = {Embryonic stem cell ({ESC}), Epiblast, Heterogeneity, Monoallelic, pluripotency, Transcription},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/S5MPG5NB/Torres-Padilla and Chambers - 2014 - Transcription factor heterogeneity in pluripotent .pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/J7EW4HDA/2173.html:text/html}
}

@article{bessonnard_gata6_2014,
	title = {Gata6, Nanog and Erk signaling control cell fate in the inner cell mass through a tristable regulatory network},
	volume = {141},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/141/19/3637},
	doi = {10.1242/dev.109678},
	abstract = {During blastocyst formation, inner cell mass ({ICM}) cells differentiate into either epiblast (Epi) or primitive endoderm ({PrE}) cells, labeled by Nanog and Gata6, respectively, and organized in a salt-and-pepper pattern. Previous work in the mouse has shown that, in absence of Nanog, all {ICM} cells adopt a {PrE} identity. Moreover, the activation or the blockade of the Fgf/{RTK} pathway biases cell fate specification towards either {PrE} or Epi, respectively. We show that, in absence of Gata6, all {ICM} cells adopt an Epi identity. Furthermore, the analysis of Gata6+/− embryos reveals a dose-sensitive phenotype, with fewer {PrE}-specified cells. These results and previous findings have enabled the development of a mathematical model for the dynamics of the regulatory network that controls {ICM} differentiation into Epi or {PrE} cells. The model describes the temporal dynamics of Erk signaling and of the concentrations of Nanog, Gata6, secreted Fgf4 and Fgf receptor 2. The model is able to recapitulate most of the cell behaviors observed in different experimental conditions and provides a unifying mechanism for the dynamics of these developmental transitions. The mechanism relies on the co-existence between three stable steady states (tristability), which correspond to {ICM}, Epi and {PrE} cells, respectively. Altogether, modeling and experimental results uncover novel features of {ICM} cell fate specification such as the role of the initial induction of a subset of cells into Epi in the initiation of the salt-and-pepper pattern, or the precocious Epi specification in Gata6+/− embryos.},
	pages = {3637--3648},
	number = {19},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Bessonnard, Sylvain and Mot, Laurane De and Gonze, Didier and Barriol, Manon and Dennis, Cynthia and Goldbeter, Albert and Dupont, Geneviève and Chazaud, Claire},
	urldate = {2015-04-30},
	date = {2014-01-10},
	langid = {english},
	pmid = {25209243},
	keywords = {bifurcation, Cell lineage specification, Epiblast, Gata6 mutants, mathematical model, Mouse, Multistability, Preimplantation, Primitive endoderm},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/7I4NKFVC/Bessonnard et al. - 2014 - Gata6, Nanog and Erk signaling control cell fate i.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/E5CIAHCC/3637.html:text/html;Supplementary Material:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BIQDSA43/Bessonnard et al. - 2014 - Gata6, Nanog and Erk signaling control cell fate i.pdf:application/pdf}
}

@article{kalkan_mapping_2014,
	title = {Mapping the route from naive pluripotency to lineage specification},
	volume = {369},
	rights = {. © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.},
	issn = {0962-8436, 1471-2970},
	url = {http://rstb.royalsocietypublishing.org/content/369/1657/20130540},
	doi = {10.1098/rstb.2013.0540},
	abstract = {In the mouse blastocyst, epiblast cells are newly formed shortly before implantation. They possess a unique developmental plasticity, termed naive pluripotency. For development to proceed, this naive state must be subsumed by multi-lineage differentiation within 72 h following implantation. In vitro differentiation of naive embryonic stem cells ({ESCs}) cultured in controlled conditions provides a tractable system to dissect and understand the process of exit from naive pluripotency and entry into lineage specification. Exploitation of this system in recent large-scale {RNAi} and mutagenesis screens has uncovered multiple new factors and modules that drive or facilitate progression out of the naive state. Notably, these studies show that the transcription factor network that governs the naive state is rapidly dismantled prior to upregulation of lineage specification markers, creating an intermediate state that we term formative pluripotency. Here, we summarize these findings and propose a road map for state transitions in {ESC} differentiation that reflects the orderly dynamics of epiblast progression in the embryo.},
	pages = {20130540},
	number = {1657},
	journaltitle = {Philosophical Transactions of the Royal Society of London B: Biological Sciences},
	author = {Kalkan, Tüzer and Smith, Austin},
	urldate = {2015-04-30},
	date = {2014-12-05},
	langid = {english},
	pmid = {25349449},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/JWN53IF3/Kalkan and Smith - 2014 - Mapping the route from naive pluripotency to linea.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/E4D4GAE6/20130540.html:text/html}
}

@article{padovan-merhar_using_2013,
	title = {Using variability in gene expression as a tool for studying gene regulation},
	volume = {5},
	rights = {Copyright © 2013 Wiley Periodicals, Inc.},
	issn = {1939-005X},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/wsbm.1243/abstract},
	doi = {10.1002/wsbm.1243},
	abstract = {With the advent of quantitative tools for measuring gene expression in single cells, researchers have made the discovery that in many contexts, messenger {RNA} and protein levels can vary widely from cell to cell, often because of inherently stochastic events associated with gene expression. The study of this cellular individuality has become a field of study in its own right, characterized by a blend of technological development, theoretical analysis, and, more recently, applications to biological phenomena. In this review, we focus on the use of the variability inherent to gene expression as a tool to understand gene regulation. We discuss the use of variability as a natural systems-level perturbation, its use in quantitatively characterizing the biological processes underlying transcription, and its application to the discovery of new gene regulatory interactions. We believe that use of variability can provide new biological insights into different aspects of transcriptional control and can provide a powerful complementary approach to that of existing techniques. {WIREs} Syst Biol Med 2013, 5:751–759. doi: 10.1002/wsbm.1243 For further resources related to this article, please visit the {WIREs} website. Conflict of interest: {AR} receives royalties from patents related to {RNA} {FISH} methods described in the article, as well as consulting income.},
	pages = {751--759},
	number = {6},
	journaltitle = {Wiley Interdisciplinary Reviews: Systems Biology and Medicine},
	shortjournal = {{WIREs} Syst Biol Med},
	author = {Padovan-Merhar, Olivia and Raj, Arjun},
	urldate = {2015-04-30},
	date = {2013-11-01},
	langid = {english},
	file = {Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VQBG7NHP/abstract\;jsessionid=3772CA1E1E219B2026509A92E2937BDB.html:text/html}
}

@article{morris_differential_2013,
	title = {The differential response to Fgf signalling in cells internalized at different times influences lineage segregation in preimplantation mouse embryos},
	volume = {3},
	rights = {. © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.},
	issn = {2046-2441},
	url = {http://rsob.royalsocietypublishing.org/content/3/11/130104},
	doi = {10.1098/rsob.130104},
	abstract = {Lineage specification in the preimplantation mouse embryo is a regulative process. Thus, it has been difficult to ascertain whether segregation of the inner-cell-mass ({ICM}) into precursors of the pluripotent epiblast ({EPI}) and the differentiating primitive endoderm ({PE}) is random or influenced by developmental history. Here, our results lead to a unifying model for cell fate specification in which the time of internalization and the relative contribution of {ICM} cells generated by two waves of asymmetric divisions influence cell fate. We show that cells generated in the second wave express higher levels of Fgfr2 than those generated in the first, leading to {ICM} cells with varying Fgfr2 expression. To test whether such heterogeneity is enough to bias cell fate, we upregulate Fgfr2 and show it directs cells towards {PE}. Our results suggest that the strength of this bias is influenced by the number of cells generated in the first wave and, mostly likely, by the level of Fgf signalling in the {ICM}. Differences in the developmental potential of eight-cell- and 16-cell-stage outside blastomeres placed in the inside of chimaeric embryos further support this conclusion. These results unite previous findings demonstrating the importance of developmental history and Fgf signalling in determining cell fate.},
	pages = {130104},
	number = {11},
	journaltitle = {Open Biology},
	author = {Morris, Samantha A. and Graham, Sarah J. L. and Jedrusik, Agnieszka and Zernicka-Goetz, Magdalena},
	urldate = {2015-04-30},
	date = {2013-11-01},
	langid = {english},
	pmid = {24258274},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/B54J88HT/Morris et al. - 2013 - The differential response to Fgf signalling in cel.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KXPD65VM/130104.html:text/html}
}

@article{krawchuk_fgf4_2013,
	title = {{FGF}4 is a limiting factor controlling the proportions of primitive endoderm and epiblast in the {ICM} of the mouse blastocyst},
	volume = {384},
	issn = {0012-1606},
	url = {http://www.sciencedirect.com/science/article/pii/S0012160613005083},
	doi = {10.1016/j.ydbio.2013.09.023},
	abstract = {The primitive endoderm ({PE}) and epiblast ({EPI}) are two lineages derived from the inner cell mass ({ICM}) of the E3.5 blastocyst. Although it has been shown that {FGF} signaling is necessary and sufficient for {PE} specification in the {ICM}, it is unknown what mechanisms control the {PE}/{EPI} proportion in the embryo. Because modulation of {FGF} signaling alone is sufficient to convert all {ICM} cells to either {PE} or {EPI}, a model has been proposed in which the amount of {FGF} in the embryo controls the {PE}/{EPI} proportion. To test this model, we reduced the amount of {FGF}4, the major {FGF} in the preimplantation embryo, using various genotypes of Fgf4 mutants. We observed a maternal contribution of Fgf4 in {PE} specification, but it was dispensable for development. In addition, upon treatment of Fgf4 mutant embryos with exogenous {FGF}4, we observed a progressive increase of {PE} proportions in an {FGF}4 dose dependent manner, regardless of embryo genotype. We conclude that the amount of {FGF}4 is limited and regulates {PE}/{EPI} proportions in the mouse embryo.},
	pages = {65--71},
	number = {1},
	journaltitle = {Developmental Biology},
	shortjournal = {Developmental Biology},
	author = {Krawchuk, Dayana and Honma-Yamanaka, Nobuko and Anani, Shihadeh and Yamanaka, Yojiro},
	urldate = {2015-04-30},
	date = {2013-12-01},
	keywords = {inner cell mass, Nanog, Salt and pepper, Sox17},
	file = {ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9T2PUA5A/Krawchuk et al. - 2013 - FGF4 is a limiting factor controlling the proporti.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/54JR2EXW/S0012160613005083.html:text/html}
}

@article{liu_single-cell_2014,
	title = {Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos},
	volume = {71},
	issn = {1420-682X, 1420-9071},
	url = {http://link.springer.com/article/10.1007/s00018-014-1601-8},
	doi = {10.1007/s00018-014-1601-8},
	abstract = {Cellular heterogeneity within a cell population is a common phenomenon in multicellular organisms, tissues, cultured cells, and even {FACS}-sorted subpopulations. Important information may be masked if the cells are studied as a mass. Transcriptome profiling is a parameter that has been intensively studied, and relatively easier to address than protein composition. To understand the basis and importance of heterogeneity and stochastic aspects of the cell function and its mechanisms, it is essential to examine transcriptomes of a panel of single cells. High-throughput technologies, starting from microarrays and now {RNA}-seq, provide a full view of the expression of transcriptomes but are limited by the amount of {RNA} for analysis. Recently, several new approaches for amplification and sequencing the transcriptome of single cells or a limited low number of cells have been developed and applied. In this review, we summarize these major strategies, such as {PCR}-based methods, {IVT}-based methods, phi29-{DNA} polymerase-based methods, and several other methods, including their principles, characteristics, advantages, and limitations, with representative applications in cancer stem cells, early development, and embryonic stem cells. The prospects for development of future technology and application of transcriptome analysis in a single cell are also discussed.},
	pages = {2707--2715},
	number = {14},
	journaltitle = {Cellular and Molecular Life Sciences},
	shortjournal = {Cell. Mol. Life Sci.},
	author = {Liu, Na and Liu, Lin and Pan, Xinghua},
	urldate = {2015-04-30},
	date = {2014-03-21},
	langid = {english},
	keywords = {Biochemistry, general, Biomedicine general, Cancer stem cells, Cell biology, Embryonic stem cells, Life Sciences, general, {RNA}-seq, Single cells, transcriptome},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/2RUNRE7J/Liu et al. - 2014 - Single-cell analysis of the transcriptome and its .pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/N8VEPCSF/10.html:text/html}
}

@article{graham_bmp_2014,
	title = {{BMP} signalling regulates the pre-implantation development of extra-embryonic cell lineages in the mouse embryo},
	volume = {5},
	rights = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	url = {http://www.nature.com/ncomms/2014/141216/ncomms6667/full/ncomms6667.html},
	doi = {10.1038/ncomms6667},
	abstract = {Pre-implantation development requires the specification and organization of embryonic and extra-embryonic lineages. The separation of these lineages takes place when asymmetric divisions generate inside and outside cells that differ in polarity, position and fate. Here we assess the global transcriptional identities of these precursor cells to gain insight into the molecular mechanisms regulating lineage segregation. Unexpectedly, this reveals that complementary components of the bone morphogenetic protein ({BMP}) signalling pathway are already differentially expressed after the first wave of asymmetric divisions. We investigate the role of {BMP} signalling by expressing dominant negative forms of ​Smad4 and ​Bmpr2, by downregulating the pathway using {RNA} interference against {BMP} ligands and by applying three different {BMP} inhibitors at distinct stages. This reveals that {BMP} signalling regulates the correct development of both extra-embryonic lineages, primitive endoderm and trophectoderm, but not the embryonic lineage, before implantation. Together, these findings indicate multiple roles of {BMP} signalling in the early mouse embryo.},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	author = {Graham, Sarah J. L. and Wicher, Krzysztof B. and Jedrusik, Agnieszka and Guo, Guoji and Herath, Wishva and Robson, Paul and Zernicka-Goetz, Magdalena},
	urldate = {2015-04-30},
	date = {2014-12-16},
	langid = {english},
	keywords = {Biological Sciences, developmental biology},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/4VCWCK3B/Graham et al. - 2014 - BMP signalling regulates the pre-implantation deve.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/3WK46HDK/ncomms6667.html:text/html;Supplementary Dataset 1. Exon list for mapped sequence reads with less than two-fold difference:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DFZE6AUT/Graham et al. - 2014 - BMP signalling regulates the pre-implantation deve.xls:application/vnd.ms-excel;Supplementary Dataset 2. Exon list for mapped sequence reads with more than two-fold difference:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VF8NB8FQ/Graham et al. - 2014 - BMP signalling regulates the pre-implantation deve.xls:application/vnd.ms-excel;Supplementary Figures and Table. Supplementary Figures 1-4 and Supplementary Tables 1.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KRFFIMBR/Graham et al. - 2014 - BMP signalling regulates the pre-implantation deve.pdf:application/pdf}
}

@article{nishioka_hippo_2009,
	title = {The Hippo Signaling Pathway Components Lats and Yap Pattern Tead4 Activity to Distinguish Mouse Trophectoderm from Inner Cell Mass},
	volume = {16},
	issn = {1534-5807},
	url = {http://www.sciencedirect.com/science/article/pii/S153458070900077X},
	doi = {10.1016/j.devcel.2009.02.003},
	abstract = {Summary
Outside cells of the preimplantation mouse embryo form the trophectoderm ({TE}), a process requiring the transcription factor Tead4. Here, we show that transcriptionally active Tead4 can induce Cdx2 and other trophoblast genes in parallel in embryonic stem cells. In embryos, the Tead4 coactivator protein Yap localizes to nuclei of outside cells, and modulation of Tead4 or Yap activity leads to changes in Cdx2 expression. In inside cells, Yap is phosphorylated and cytoplasmic, and this involves the Hippo signaling pathway component Lats. We propose that active Tead4 promotes {TE} development in outside cells, whereas Tead4 activity is suppressed in inside cells by cell contact- and Lats-mediated inhibition of nuclear Yap localization. Thus, differential signaling between inside and outside cell populations leads to changes in cell fate specification during {TE} formation.},
	pages = {398--410},
	number = {3},
	journaltitle = {Developmental Cell},
	shortjournal = {Developmental Cell},
	author = {Nishioka, Noriyuki and Inoue, Ken-ichi and Adachi, Kenjiro and Kiyonari, Hiroshi and Ota, Mitsunori and Ralston, Amy and Yabuta, Norikazu and Hirahara, Shino and Stephenson, Robert O. and Ogonuki, Narumi and Makita, Ryosuke and Kurihara, Hiroki and Morin-Kensicki, Elizabeth M. and Nojima, Hiroshi and Rossant, Janet and Nakao, Kazuki and Niwa, Hitoshi and Sasaki, Hiroshi},
	urldate = {2015-05-14},
	date = {2009-03-17},
	keywords = {{DEVBIO}, {STEMCELL}},
	file = {Document S1. Four Figures, One Table, Supplemental Experimental Procedures, and Supplemental References.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RH22XIV6/Nishioka et al. - 2009 - The Hippo Signaling Pathway Components Lats and Ya.pdf:application/pdf;ScienceDirect Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/CQM9UF36/Nishioka et al. - 2009 - The Hippo Signaling Pathway Components Lats and Ya.pdf:application/pdf;ScienceDirect Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/65AAB6NT/S153458070900077X.html:text/html}
}

@article{oron_cell_2012,
	title = {Cell fate regulation in early mammalian development},
	volume = {9},
	issn = {1478-3975},
	url = {http://iopscience.iop.org/1478-3975/9/4/045002},
	doi = {10.1088/1478-3975/9/4/045002},
	abstract = {Preimplantation development in mammals encompasses a period from fertilization to implantation and results in formation of a blastocyst composed of three distinct cell lineages: epiblast, trophectoderm and primitive endoderm. The epiblast gives rise to the organism, while the trophectoderm and the primitive endoderm contribute to extraembryonic tissues that support embryo development after implantation. In many vertebrates, such as frog or fish, maternally supplied lineage determinants are partitioned within the egg. Cell cleavage that follows fertilization results in polarization of these factors between the individual blastomeres, which become restricted in their developmental fate. In contrast, the mouse oocyte and zygote lack clear polarity and, until the eight-cell stage, individual blastomeres retain the potential to form all lineages. How are cell lineages specified in the absence of a maternally supplied blueprint? This is a fundamental question in the field of developmental biology. The answer to this question lies in understanding the cell–cell interactions and gene networks involved in embryonic development prior to implantation and using this knowledge to create testable models of the developmental processes that govern cell fates. We provide an overview of classic and contemporary models of early lineage development in the mouse and discuss the emerging body of work that highlights similarities and differences between blastocyst development in the mouse and other mammalian species.},
	pages = {045002},
	number = {4},
	journaltitle = {Physical Biology},
	shortjournal = {Phys. Biol.},
	author = {Oron, Efrat and Ivanova, Natalia},
	urldate = {2015-05-14},
	date = {2012-08-01},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/MX5XT5PR/Oron and Ivanova - 2012 - Cell fate regulation in early mammalian developmen.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BRZPI6Z4/045002.html:text/html}
}

@article{wennekamp_self-organization_2013,
	title = {A self-organization framework for symmetry breaking in the mammalian embryo},
	volume = {14},
	rights = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1471-0072},
	url = {http://www.nature.com/nrm/journal/v14/n7/full/nrm3602.html},
	doi = {10.1038/nrm3602},
	abstract = {The mechanisms underlying the appearance of asymmetry between cells in the early embryo and consequently the specification of distinct cell lineages during mammalian development remain elusive. Recent experimental advances have revealed unexpected dynamics of and new complexity in this process. These findings can be integrated in a new unified framework that regards the early mammalian embryo as a self-organizing system.},
	pages = {452--459},
	number = {7},
	journaltitle = {Nature Reviews Molecular Cell Biology},
	shortjournal = {Nat Rev Mol Cell Biol},
	author = {Wennekamp, Sebastian and Mesecke, Sven and Nédélec, François and Hiiragi, Takashi},
	urldate = {2015-05-14},
	date = {2013-07},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ZW4BFD3V/Wennekamp et al. - 2013 - A self-organization framework for symmetry breakin.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/KH6GHK4A/nrm3602.html:text/html;Supplementary information S1 (box) | A mathematical description of the self-organization theory.:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BU2K6RRU/Wennekamp et al. - 2013 - A self-organization framework for symmetry breakin.pdf:application/pdf}
}

@article{kaneko_tead4_2013,
	title = {{TEAD}4 establishes the energy homeostasis essential for blastocoel formation},
	volume = {140},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/140/17/3680},
	doi = {10.1242/dev.093799},
	abstract = {It has been suggested that during mouse preimplantation development, the zygotically expressed transcription factor {TEAD}4 is essential for specification of the trophectoderm lineage required for producing a blastocyst. Here we show that blastocysts can form without {TEAD}4 but that {TEAD}4 is required to prevent oxidative stress when blastocoel formation is accompanied by increased oxidative phosphorylation that leads to the production of reactive oxygen species ({ROS}). Both two-cell and eight-cell Tead4-/- embryos developed into blastocysts when cultured under conditions that alleviate oxidative stress, and Tead4-/- blastocysts that formed under these conditions expressed trophectoderm-associated genes. Therefore, {TEAD}4 is not required for specification of the trophectoderm lineage. Once the trophectoderm was specified, Tead4 was not essential for either proliferation or differentiation of trophoblast cells in culture. However, ablation of Tead4 in trophoblast cells resulted in reduced mitochondrial membrane potential. Moreover, Tead4 suppressed {ROS} in embryos and embryonic fibroblasts. Finally, ectopically expressed {TEAD}4 protein could localize to the mitochondria as well as to the nucleus, a property not shared by other members of the {TEAD} family. These results reveal that {TEAD}4 plays a crucial role in maintaining energy homeostasis during preimplantation development.},
	pages = {3680--3690},
	number = {17},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Kaneko, Kotaro J. and {DePamphilis}, Melvin L.},
	urldate = {2015-05-14},
	date = {2013-01-09},
	langid = {english},
	pmid = {23903192},
	keywords = {Anti-oxidant, Blastocoel, Blastocyst, Mouse, Oxidative stress, Tead4, trophectoderm},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/AGCSMGSZ/Kaneko and DePamphilis - 2013 - TEAD4 establishes the energy homeostasis essential.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/XX8DC2F3/3680.html:text/html;Supplementary Material PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/RIWRSSRD/Kaneko and DePamphilis - 2013 - TEAD4 establishes the energy homeostasis essential.pdf:application/pdf}
}

@article{qiu_extracting_2011,
	title = {Extracting a cellular hierarchy from high-dimensional cytometry data with {SPADE}},
	volume = {29},
	rights = {© 2011 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1087-0156},
	url = {http://www.nature.com/nbt/journal/v29/n10/full/nbt.1991.html},
	doi = {10.1038/nbt.1991},
	abstract = {The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events ({SPADE}). We applied {SPADE} to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, {SPADE} organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that {SPADE} is robust to measurement noise and to the choice of cellular markers. {SPADE} facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.},
	pages = {886--891},
	number = {10},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotech},
	author = {Qiu, Peng and Simonds, Erin F. and Bendall, Sean C. and Gibbs Jr, Kenneth D. and Bruggner, Robert V. and Linderman, Michael D. and Sachs, Karen and Nolan, Garry P. and Plevritis, Sylvia K.},
	urldate = {2015-05-14},
	date = {2011-10},
	langid = {english},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/VIPPC3R5/Qiu et al. - 2011 - Extracting a cellular hierarchy from high-dimensio.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/DESG9BNH/nbt.1991.html:text/html;Supplementary Text and Figures. Supplementary Sections 1–8:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/25IPPUQZ/Qiu et al. - 2011 - Extracting a cellular hierarchy from high-dimensio.pdf:application/pdf}
}

@article{morris_origin_2010,
	title = {Origin and formation of the first two distinct cell types of the inner cell mass in the mouse embryo},
	volume = {107},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/107/14/6364},
	doi = {10.1073/pnas.0915063107},
	abstract = {A crucial question in mammalian development is how cells of the early embryo differentiate into distinct cell types. The first decision is taken when cells undertake waves of asymmetric division that generate one daughter on the inside and one on the outside of the embryo. After this division, some cells on the inside remain pluripotent and give rise to the epiblast, and hence the future body, whereas others develop into the primitive endoderm, an extraembryonic tissue. How the fate of these inside cells is decided is unknown: Is the process random, or is it related to their developmental origins? To address this question, we traced all cells by live-cell imaging in intact, unmanipulated embryos until the epiblast and primitive endoderm became distinct. This analysis revealed that inner cell mass ({ICM}) cells have unrestricted developmental potential. However, cells internalized by the first wave of asymmetric divisions are biased toward forming pluripotent epiblast, whereas cells internalized in the next two waves of divisions are strongly biased toward forming primitive endoderm. Moreover, we show that cells internalized by the second wave up-regulate expression of Gata6 and Sox17, and changing the expression of these genes determines whether the cells become primitive endoderm. Finally, with our ability to determine the origin of cells, we find that inside cells that are mispositioned when they are born can sort into the correct layer. In conclusion, we propose a model in which the timing of cell internalization, cell position, and cell sorting combine to determine distinct lineages of the preimplantation mouse embryo.},
	pages = {6364--6369},
	number = {14},
	journaltitle = {Proceedings of the National Academy of Sciences},
	shortjournal = {{PNAS}},
	author = {Morris, Samantha A. and Teo, Roy T. Y. and Li, Huiliang and Robson, Paul and Glover, David M. and Zernicka-Goetz, Magdalena},
	urldate = {2015-05-15},
	date = {2010-06-04},
	langid = {english},
	pmid = {20308546},
	keywords = {Cell fate, Epiblast, Gata6, Primitive endoderm, Sox17},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/ADBI92XB/Morris et al. - 2010 - Origin and formation of the first two distinct cel.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/BDWNJRI7/6364.html:text/html;Supporting Information:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/M6DDRCMP/Morris et al. - 2010 - Origin and formation of the first two distinct cel.pdf:application/pdf}
}

@article{tang_tracing_2010-1,
	title = {Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell {RNA}-Seq Analysis},
	volume = {6},
	issn = {1934-5909},
	url = {http://www.cell.com/article/S1934590910001141/abstract},
	doi = {10.1016/j.stem.2010.03.015},
	abstract = {During the transition from the inner cell mass ({ICM}) cells of blastocysts to pluripotent embryonic stem cells ({ESCs}) in vitro, a normal developmental program is replaced in cells that acquire a capacity for infinite self-renewal and pluripotency. We explored the underlying mechanism of this switch by using {RNA}-Seq transcriptome analysis at the resolution of single cells. We detected significant molecular transitions and major changes in transcript variants, which include genes for general metabolism. Furthermore, the expression of repressive epigenetic regulators increased with a concomitant decrease in gene activators that might be necessary to sustain the inherent plasticity of {ESCs}. Furthermore, we detected changes in {microRNAs} ({miRNAs}), with one set that targets early differentiation genes while another set targets pluripotency genes to maintain the unique {ESC} epigenotype. Such genetic and epigenetic events may contribute to a switch from a normal developmental program in adult cells during the formation of diseased tissues, including cancers.},
	pages = {468--478},
	number = {5},
	journaltitle = {Cell Stem Cell},
	author = {Tang, Fuchou and Barbacioru, Catalin and Bao, Siqin and Lee, Caroline and Nordman, Ellen and Wang, Xiaohui and Lao, Kaiqin and Surani, M. Azim},
	urldate = {2015-05-15},
	date = {2010-05-07},
	pmid = {20452321},
	keywords = {{RNA}, {STEMCELL}},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/PBQJIRMP/Tang et al. - 2010 - Tracing the Derivation of Embryonic Stem Cells fro.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/736FA3UF/S1934-5909(10)00114-1.html:text/html}
}

@article{tang_deterministic_2011,
	title = {Deterministic and Stochastic Allele Specific Gene Expression in Single Mouse Blastomeres},
	volume = {6},
	url = {http://dx.doi.org/10.1371/journal.pone.0021208},
	doi = {10.1371/journal.pone.0021208},
	abstract = {Stochastic and deterministic allele specific gene expression ({ASE}) might influence single cell phenotype, but the extent and nature of the phenomenon at the onset of early mouse development is unknown. Here we performed single cell {RNA}-Seq analysis of single blastomeres of mouse embryos, which revealed significant changes in the transcriptome. Importantly, over half of the transcripts with detectable genetic polymorphisms exhibit {ASE}, most notably, individual blastomeres from the same two-cell embryo show similar pattern of {ASE}. However, about 6\% of them exhibit stochastic expression, indicated by altered expression ratio between the two alleles. Thus, we demonstrate that {ASE} is both deterministic and stochastic in early blastomeres. Furthermore, we also found that 1,718 genes express two isoforms with different lengths of 3′{UTRs}, with the shorter one on average 5–6 times more abundant in early blastomeres compared to the transcripts in epiblast cells, suggesting that {microRNA} mediated regulation of gene expression acquires increasing importance as development progresses.},
	pages = {e21208},
	number = {6},
	journaltitle = {{PLoS} {ONE}},
	shortjournal = {{PLoS} {ONE}},
	author = {Tang, Fuchou and Barbacioru, Catalin and Nordman, Ellen and Bao, Siqin and Lee, Caroline and Wang, Xiaohui and Tuch, Brian B. and Heard, Edith and Lao, Kaiqin and Surani, M. Azim},
	urldate = {2015-05-15},
	date = {2011-06-23},
	file = {PLoS Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/S9DQCVBE/Tang et al. - 2011 - Deterministic and Stochastic Allele Specific Gene .pdf:application/pdf}
}

@article{yamanaka_fgf_2010,
	title = {{FGF} signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst},
	volume = {137},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/137/5/715},
	doi = {10.1242/dev.043471},
	abstract = {Primitive endoderm ({PE}) and epiblast ({EPI}) are two lineages derived from the inner cell mass ({ICM}) of the E3.5 blastocyst. Recent studies showed that {EPI} and {PE} progenitors expressing the lineage-specific transcriptional factors Nanog and Gata6, respectively, arise progressively as the {ICM} develops. Subsequent sorting of the two progenitors during blastocyst maturation results in the ormation of morphologically distinct {EPI} and {PE} layers at E4.5. It is, however, unknown how the initial differences between the two populations become established in the E3.5 blastocyst. Because the {ICM} cells are derived from two distinct rounds of polarized cell divisions during cleavage, a possible role for cell lineage history in promoting {EPI} versus {PE} fate has been proposed. We followed cell lineage from the eight-cell stage by live cell tracing and could find no clear linkage between developmental history of individual {ICM} cells and later cell fate. However, modulating {FGF} signaling levels by inhibition of the receptor/{MAP} kinase pathway or by addition of exogenous {FGF} shifted the fate of {ICM} cells to become either {EPI} or {PE}, respectively. Nanog- or Gata6-expressing progenitors could still be shifted towards the alternative fate by modulating {FGF} signaling during blastocyst maturation, suggesting that the {ICM} progenitors are not fully committed to their final fate at the time that initial segregation of gene expression occurs. In conclusion, we propose a model in which stochastic and progressive specification of {EPI} and {PE} lineages occurs during maturation of the blastocyst in an {FGF}/{MAP} kinase signal-dependent manner.},
	pages = {715--724},
	number = {5},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Yamanaka, Yojiro and Lanner, Fredrik and Rossant, Janet},
	urldate = {2015-05-15},
	date = {2010-01-03},
	langid = {english},
	pmid = {20147376},
	keywords = {Asymmetric division, Epiblast, Gata, {ICM}, Mouse, Nanog, Primitive endoderm},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/AZMZT6XI/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/83WRVT8N/715.html:text/html;Supplemental Figure S1:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/HMBSM7BC/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Supplemental Figure S2:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/Q9468XHW/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Supplemental Figure S3:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/V6E9IW4J/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Supplemental Figure S4:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/662FC7UD/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Supplemental Figure S5:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/STPHJZJC/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf;Supplemental Figure S6:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/X9B4NVEG/Yamanaka et al. - 2010 - FGF signal-dependent segregation of primitive endo.pdf:application/pdf}
}

@article{bischoff_formation_2008,
	title = {Formation of the embryonic-abembryonic axis of the mouse blastocyst: relationships between orientation of early cleavage divisions and pattern of symmetric/asymmetric divisions},
	volume = {135},
	issn = {0950-1991, 1477-9129},
	url = {http://dev.biologists.org/content/135/5/953},
	doi = {10.1242/dev.014316},
	shorttitle = {Formation of the embryonic-abembryonic axis of the mouse blastocyst},
	abstract = {Setting aside pluripotent cells that give rise to the future body is a central cell fate decision in mammalian development. It requires that some blastomeres divide asymmetrically to direct cells to the inside of the embryo. Despite its importance, it is unknown whether the decision to divide symmetrically versus asymmetrically shows any spatial or temporal pattern, whether it is lineage-dependent or occurs at random, or whether it influences the orientation of the embryonic-abembryonic axis. To address these questions, we developed time-lapse microscopy to enable a complete 3D analysis of the origins, fates and divisions of all cells from the 2- to 32-cell blastocyst stage. This showed how in the majority of embryos, individual blastomeres give rise to distinct blastocyst regions. Tracking the division orientation of all cells revealed a spatial and temporal relationship between symmetric and asymmetric divisions and how this contributes to the generation of inside and outside cells and thus embryo patterning. We found that the blastocyst cavity, defining the abembryonic pole, forms where symmetric divisions predominate. Tracking cell ancestry indicated that the pattern of symmetric/asymmetric divisions of a blastomere can be influenced by its origin in relation to the animal-vegetal axis of the zygote. Thus, it appears that the orientation of the embryonic-abembryonic axis is anticipated by earlier cell division patterns. Together, our results suggest that two steps influence the allocation of cells to the blastocyst. The first step, involving orientation of 2- to 4-cell divisions along the animal-vegetal axis, can affect the second step, the establishment of inside and outside cell populations by asymmetric 8- to 32-cell divisions.},
	pages = {953--962},
	number = {5},
	journaltitle = {Development},
	shortjournal = {Development},
	author = {Bischoff, Marcus and Parfitt, David-Emlyn and Zernicka-Goetz, Magdalena},
	urldate = {2015-05-15},
	date = {2008-01-03},
	langid = {english},
	pmid = {18234722},
	keywords = {Blastocyst, Mouse embryos, pluripotency},
	file = {Full Text PDF:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/9TVDBT2Q/Bischoff et al. - 2008 - Formation of the embryonic-abembryonic axis of the.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/P4655F9P/953.html:text/html;Supplemental Figures S1-S11:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/F4FP6SQH/Bischoff et al. - 2008 - Formation of the embryonic-abembryonic axis of the.pdf:application/pdf;Supplemental Table S1:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/X9AH66TX/Bischoff et al. - 2008 - Formation of the embryonic-abembryonic axis of the.pdf:application/pdf}
}

@article{kurotaki_blastocyst_2007,
	title = {Blastocyst Axis Is Specified Independently of Early Cell Lineage But Aligns with the {ZP} Shape},
	volume = {316},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/content/316/5825/719},
	doi = {10.1126/science.1138591},
	abstract = {The mechanisms controlling the establishment of the embryonic-abembryonic (E-Ab) axis of the mammalian blastocyst are controversial. We used in vitro time-lapse imaging and in vivo lineage labeling to provide evidence that the E-Ab axis of the mouse blastocyst is generated independently of early cell lineage. Rather, both the boundary between two-cell blastomeres and the E-Ab axis of the blastocyst align relative to the ellipsoidal shape of the zona pellucida ({ZP}), an extraembryonic structure. Lack of correlation between cell lineage and the E-Ab axis can be explained by the rotation of the embryo within the {ZP}.},
	pages = {719--723},
	number = {5825},
	journaltitle = {Science},
	shortjournal = {Science},
	author = {Kurotaki, Yoko and Hatta, Kohei and Nakao, Kazuki and Nabeshima, Yo-ichi and Fujimori, Toshihiko},
	urldate = {2015-05-15},
	date = {2007-04-05},
	langid = {english},
	pmid = {17446354},
	file = {719.full.pdf:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/X5ZPXKDE/719.full.pdf:application/pdf;Snapshot:/home/john/.mozilla/firefox/d39h8gpz.default/zotero/storage/NZ8FIB4H/719.html:text/html}
}