Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages

By Yusuke Ohnishi, Wolfgang Huber, A. Tsumura, Minjung Kang, Panagiotis Xenopoulos, K. Kurimoto, Andrzej K. Oles, M. Araúzo-Bravo, M. Saitou, A. Hadjantonakis and 1 more. Nature Cell Biology, 2013

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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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3ea70155b49210d1e323398b6ed67a94bde58c45

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Citations in the corpus

Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (2015, unknown): “Previous studies have indicated that ES cells are heterogeneous in gene expression (Guo et al. 2010; Hayashi et al. 2008; MacArthur et al. 2012; Martinez Arias and Brickman, 2011; Ohnishi et al. 2014; Singer et al. 2014; Torres-Padilla and Chambers, 2014; Yan et al. 2013).”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “…is then amplified, using PCR 26,28,29,32 or in vitro transcription 31 , to obtain enough material for hybridization to a gene expression microarray 15 or, more recently, to be profiled using NGS 26 ; in the latter case, the technique is referred to as single-cell RNA-seq (scRNA-seq). scRNA-seq…”
Orchestrating high-throughput genomic analysis with Bioconductor (2015, unknown): “, the data and vignette of the package Hiiragi2013 reproduce a recently reported transcriptome analysis of single cells in mouse embryos [6]).”
Single-cell RNA-seq: advances and future challenges (2014, unknown): “Similar to unrelated findings in mouse blastocysts (143), heterogeneity in the transcriptomes of individual cells seems to be a prerequisite for them to segregate into different lineages.”