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Cellular diversity and lineage trajectory: insights from mouse single cell transcriptomes.

Patrick P L Tam1,2, Joshua W K Ho3

  • 1Embryology Unit, Children's Medical Research Institute, The University of Sydney, Sydney, NSW 2145, Australia devtam@cmri.usyd.edu.au.

Development (Cambridge, England)
|January 26, 2020
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Summary

Single cell RNA-sequencing (scRNA-seq) enables detailed study of mouse embryonic development, revealing cell lineages and molecular changes. Multi-omic and computational advances enhance understanding of embryogenesis, cell identity, and differentiation.

Keywords:
BioinformaticsCell lineagesDevelopmental trajectoryEmbryo cell atlasSingle cell analytics

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Area of Science:

  • Developmental Biology
  • Genomics
  • Computational Biology

Background:

  • Single cell RNA-sequencing (scRNA-seq) technology has advanced, enabling large-scale atlases of developing mouse embryos.
  • These atlases are crucial for dissecting developmental cell lineages and molecular alterations during embryogenesis.

Purpose of the Study:

  • To discuss recent single cell experimental and computational methods for studying mouse embryogenesis.
  • To highlight key insights gained from applying these methods to understand embryonic development.
  • To identify challenges in data analysis and interpretation for developmental biology.

Main Methods:

  • Generation of large single cell atlases using scRNA-seq.
  • Integration of multi-omic data, including chromatin landscape, epigenome, proteome, metabolome, and spatial organization.
  • Application of advanced computational techniques for inferring developmental lineages.

Main Results:

  • scRNA-seq atlases provide detailed insights into developmental cell lineages and molecular changes.
  • Multi-omic data integration offers a comprehensive view of mouse embryogenesis.
  • Computational methods facilitate lineage inference without genetic markers.

Conclusions:

  • Single cell technologies and computational approaches are revolutionizing the study of mouse embryonic development.
  • These methods enhance our understanding of cell identity, diversity, and lineage differentiation.
  • Addressing analytical and interpretational challenges is key to expanding knowledge from traditional studies.