scholarly journals Newly developed single-cell computational approach elucidates the stabilization of Oct4 expression in the E3.25 mouse preimplantation embryo

2018 ◽  
Author(s):  
Daniela Gerovska ◽  
Marcos J. Arauzo-Bravo

AbstractThe time of onset of the second cell fate decision in the mouse preimplantation embryo is still unknown. Ohnishi et al. (2014) looked for cell heterogeneity in the ICM at E3.25 that could indicate the time preceding the apparent segregation of PE and EPI at E3.5, but were not able to detect an early splitting transcriptomics event using state-of-the-art clustering techniques. We developed a new clustering algorithm, hierarchical optimal k-means (HOkM), and identified from single cell (sc) transcriptomics microarray data two groups of ICM cells during the 32 to 64 mouse embryo transition: from embryos with less than 34 cells, and more than 33 cells, corresponding to two developmental sub-stages. The genes defining these sub-stages indicate that the development of the embryo to 34 cells triggers a dramatic event as a result of which Bsg is high expressed, the canonical Wnt pathway is activated, Oct4 is stabilized to high expression and the chromatin remodeling program is initialized to establish a very early narve pluripotent state from the preceding totipotency. We characterized our HOkM partition comparing with independent scRNA-seq datasets. It was staggering to discover that from the 3.4360×1010 possible bi-partitions of the E3.25 data of Ohnishi et al. (2014), our HOkM discovered one partition that shares the biological features of the early and late 32 ICM cells of Posfai et al. (2017). We propose that the stabilization of Oct4 expression is a non-cell autonomous process that requires a minimal number of four inner cell contacts acquired during the transition from a homogeneous outer-cell environment to a heterogeneous inner/outer cell environment formed by the niche of a kernel of at least six inner cells covered by trophectoderm.


Open Biology ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. 170210 ◽  
Author(s):  
Aleksandar I. Mihajlović ◽  
Alexander W. Bruce

During the first cell-fate decision of mouse preimplantation embryo development, a population of outer-residing polar cells is segregated from a second population of inner apolar cells to form two distinct cell lineages: the trophectoderm and the inner cell mass (ICM), respectively. Historically, two models have been proposed to explain how the initial differences between these two cell populations originate and ultimately define them as the two stated early blastocyst stage cell lineages. The ‘positional’ model proposes that cells acquire distinct fates based on differences in their relative position within the developing embryo, while the ‘polarity’ model proposes that the differences driving the lineage segregation arise as a consequence of the differential inheritance of factors, which exhibit polarized subcellular localizations, upon asymmetric cell divisions. Although these two models have traditionally been considered separately, a growing body of evidence, collected over recent years, suggests the existence of a large degree of compatibility. Accordingly, the main aim of this review is to summarize the major historical and more contemporarily identified events that define the first cell-fate decision and to place them in the context of both the originally proposed positional and polarity models, thus highlighting their functional complementarity in describing distinct aspects of the developmental programme underpinning the first cell-fate decision in mouse embryogenesis.



2020 ◽  
Vol 2 (12) ◽  
pp. 1382-1390
Author(s):  
Masayuki Tsukasaki ◽  
Nam Cong-Nhat Huynh ◽  
Kazuo Okamoto ◽  
Ryunosuke Muro ◽  
Asuka Terashima ◽  
...  


2019 ◽  
Vol 38 (8) ◽  
Author(s):  
Xin‐Xin Yu ◽  
Wei‐Lin Qiu ◽  
Liu Yang ◽  
Yu Zhang ◽  
Mao‐Yang He ◽  
...  


2017 ◽  
Author(s):  
Britta Werthmann ◽  
Wolfgang Marwan

AbstractThe developmental switch to sporulation inPhysarum polycephalumis a phytochrome-mediated far-red light-induced cell fate decision that synchronously encompasses the entire multinucleate plasmodial cell and is associated with extensive reprogramming of the transcriptome. By repeatedly taking samples of single cells after delivery of a light stimulus pulse, we analysed differential gene expression in two mutant strains and in a heterokaryon of the two strains all of which display a different propensity for making the cell fate decision. Multidimensional scaling of the gene expression data revealed individually different single cell trajectories eventually leading to sporulation. Characterization of the trajectories as walks through states of gene expression discretized by hierarchical clustering allowed the reconstruction of Petri nets that model and predict the observed behavior. Structural analyses of the Petri nets indicated stimulus- and genotype-dependence of both, single cell trajectories and of the quasipotential landscape through which these trajectories are taken. The Petri net-based approach to the analysis and decomposition of complex cellular responses and of complex mutant phenotypes may provide a scaffold for the data-driven reconstruction of causal molecular mechanisms that shape the topology of the quasipotential landscape.



2019 ◽  
Author(s):  
Shila Ghazanfar ◽  
Yingxin Lin ◽  
Xianbin Su ◽  
David M. Lin ◽  
Ellis Patrick ◽  
...  

ABSTRACTSingle-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along ‘pseudotime’ enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT – single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.



2020 ◽  
Vol 3 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Sagar ◽  
Dominic Grün

Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during development, regeneration, homeostasis, and disease is a central goal of modern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality reduction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single-cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.



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