Author
Listed:
- Michelle M. Chan
(University of California, San Francisco
University of California, San Francisco)
- Zachary D. Smith
(Broad Institute of MIT and Harvard
Harvard University
Harvard University)
- Stefanie Grosswendt
(Max Planck Institute for Molecular Genetics)
- Helene Kretzmer
(Max Planck Institute for Molecular Genetics)
- Thomas M. Norman
(University of California, San Francisco
University of California, San Francisco)
- Britt Adamson
(University of California, San Francisco
University of California, San Francisco
Princeton University)
- Marco Jost
(University of California, San Francisco
University of California, San Francisco
University of California, San Francisco)
- Jeffrey J. Quinn
(University of California, San Francisco
University of California, San Francisco)
- Dian Yang
(University of California, San Francisco
University of California, San Francisco)
- Matthew G. Jones
(University of California, San Francisco
University of California, San Francisco
University of California, San Francisco)
- Alex Khodaverdian
(University of California, Berkeley
University of California, Berkeley)
- Nir Yosef
(University of California, Berkeley
University of California, Berkeley
Chan Zuckerberg Biohub
MIT and Harvard University)
- Alexander Meissner
(Broad Institute of MIT and Harvard
Harvard University
Max Planck Institute for Molecular Genetics)
- Jonathan S. Weissman
(University of California, San Francisco
University of California, San Francisco)
Abstract
Ontogeny describes the emergence of complex multicellular organisms from single totipotent cells. This field is particularly challenging in mammals, owing to the indeterminate relationship between self-renewal and differentiation, variation in progenitor field sizes, and internal gestation in these animals. Here we present a flexible, high-information, multi-channel molecular recorder with a single-cell readout and apply it as an evolving lineage tracer to assemble mouse cell-fate maps from fertilization through gastrulation. By combining lineage information with single-cell RNA sequencing profiles, we recapitulate canonical developmental relationships between different tissue types and reveal the nearly complete transcriptional convergence of endodermal cells of extra-embryonic and embryonic origins. Finally, we apply our cell-fate maps to estimate the number of embryonic progenitor cells and their degree of asymmetric partitioning during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems, which will facilitate the construction of a quantitative framework for understanding developmental processes.
Suggested Citation
Michelle M. Chan & Zachary D. Smith & Stefanie Grosswendt & Helene Kretzmer & Thomas M. Norman & Britt Adamson & Marco Jost & Jeffrey J. Quinn & Dian Yang & Matthew G. Jones & Alex Khodaverdian & Nir , 2019.
"Molecular recording of mammalian embryogenesis,"
Nature, Nature, vol. 570(7759), pages 77-82, June.
Handle:
RePEc:nat:nature:v:570:y:2019:i:7759:d:10.1038_s41586-019-1184-5
DOI: 10.1038/s41586-019-1184-5
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Citations
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Cited by:
- Xinhai Pan & Hechen Li & Pranav Putta & Xiuwei Zhang, 2023.
"LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data,"
Nature Communications, Nature, vol. 14(1), pages 1-15, December.
- Zhiqian Li & Lang You & Anita Hermann & Ethan Bier, 2024.
"Developmental progression of DNA double-strand break repair deciphered by a single-allele resolution mutation classifier,"
Nature Communications, Nature, vol. 15(1), pages 1-19, December.
- Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023.
"BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity,"
Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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