Lineage tracing on transcriptional landscapes links state to fate during differentiation

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<jats:title>Mapping cell fate during hematopoiesis</jats:title> <jats:p> Biologists have long attempted to understand how stem and progenitor cells in regenerating and embryonic tissues differentiate into mature cell types. Through the use of recent technical advances to sequence the genes expressed in thousands of individual cells, differentiation mechanisms are being revealed. Weinreb <jats:italic>et al.</jats:italic> extended these methods to track clones of cells (cell families) across time. Their approach reveals differences in cellular gene expression as cells progress through hematopoiesis, which is the process of blood production. Using machine learning, they tested how well gene expression measurements account for the choices that cells make. This work reveals that a considerable gap still exists in understanding differentiation mechanisms, and future methods are needed to fully understand—and ultimately control—cell differentiation. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aaw3381">eaaw3381</jats:related-article> </jats:p>

Journal

  • Science

    Science 367 (6479), eaaw3381-, 2020-02-14

    American Association for the Advancement of Science (AAAS)

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