Dataset Information

Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

ABSTRACT: The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

SUBMITTER: Poirion OB

PROVIDER: S-EPMC5030210 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

Poirion Olivier B OB Zhu Xun X Ching Travers T Garmire Lana L

Frontiers in genetics 20160921

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical ...[more]

PMID: 27708664

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Project description:Our computational analysis focuses on the 32- to 64-cell mouse embryo transition, Embryonic day (E3.25), whose study in literature is concentrated mainly on the search for an early onset of the second cell-fate decision, the specification of the inner cell mass (ICM) to primitive endoderm (PE) and epiblast (EPI). We analysed single-cell (sc) microarray transcriptomics data from E3.25 using Hierarchical Optimal k-Means (HOkM) clustering, and identified two groups of ICM cells: a group of cells from embryos with less than 34 cells (E3.25-LNCs), and another group of cells from embryos with more than 33 cells (E3.25-HNCs), corresponding to two developmental stages. Although we found massive underlying heterogeneity in the ICM cells at E3.25-HNC with over 3,800 genes with transcriptomics bifurcation, many of which are PE and EPI markers, we showed that the E3.25-HNCs are neither PE nor EPI. Importantly, analysing the differently expressed genes between the E3.25-LNCs and E3.25-HNCs, we uncovered a non-autonomous mechanism, based on a minimal number of four inner-cell contacts in the ICM, which activates Oct4 in the preimplantation embryo. Oct4 is highly expressed but unstable at E3.25-LNC, and stabilizes at high level at E3.25-HNC, with Bsg highly expressed, and the chromatin remodelling program initialised to establish an early naïve pluripotent state. Our results indicate that the pluripotent state we found to exist in the ICM at E3.25-HNC is the in vivo counterpart of a new, very early pluripotent state. We compared the transcriptomics profile of this in vivo E3.25-HNC pluripotent state, together with the profiles of E3.25-LNC, E3.5 EPI and E4.5 EPI cells, with the profiles of all embryonic stem cells (ESCs) available in the GEO database from the same platform (over 600 microarrays). The shortest distance between the set of inner cells (E3.25, E3.5 and E4.5) and the ESCs is between the E3.25-HNC cells and 2i + LIF ESCs; thus, the developmental transition from 33 to 34 cells decreases dramatically the distance with the naïve ground state of the 2i + LIF ESCs. We validated the E3.25 events through analysis of scRNA-seq data from early and late 32-cell ICM cells.

Dataset Information

Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

Publications

Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

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