Toward a Choate View of Fate
Author(s) -
Andrew J. Rennekamp
Publication year - 2018
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2018.06.024
Subject(s) - biology
We begin life as a single totipotent cell that divides into millions of progeny cells. As our cells divide, daughter cells become morphologically and functionally different from their grandmothers, mothers, sisters, and aunts. This process of specialization forms the basis of our development into complex multicellular organisms. A cell’s functional identity and fate are thought to be controlled by the RNAs it expresses, and cellular identity and fate can be altered by changing its RNA compilation, a process called reprogramming. If a cell becomes aberrantly programmed during development, an entire linage can be disrupted with devastating consequences. Thus, an important objective in biology is to completely understand cell-type lineages and the transcriptional states underlying cell fates so we can recognize and correct lineage problems when they happen and artificially reprogram cell fates when desired. But the task of mapping every cell lineage in a multicellular organism has been piecemeal and challenging. In a collection of recent papers, researchers now report some exciting progress using single-cell RNA sequencing (scRNA-seq) to efficiently determine the complete transcriptomes of tens of thousands of individual cells from either flatworms, frogs, or fish at different time points (Briggs et al., 2018; Farrell et al., 2018; Fincher et al., 2018; Plass et al., 2018; Wagner et al., 2018). Then, using computational algorithms, they order every cell based on both transcriptome similarity and chronology and generate unprecedented whole-organism charts of ordered cell lineages as branching trees. Impressive in both their resolution and scale, these studies can deconstruct the composition and histories of complex tissues in an unbiased way without the need for
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