期刊
NATURE
卷 564, 期 7735, 页码 219-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41586-018-0744-4
关键词
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资金
- National Institutes of Health (NIH) [R01-GM126112, R21-HG009750, P30-DK052574]
- Silicon Valley Community Foundation
- Chan Zuckerberg Initiative [HCA-A-1704-01646, HCA2-A-1708-02799]
- Children's Discovery Institute of Washington University
- St. Louis Children's Hospital [MI-II-2016-544]
- Vallee Scholar Award
- Japan Society for the Promotion of Science Postdoctoral Fellowship [NIH-T32HG000045-18, NIH-5T32GM007200-42, NIH-5T32GM007067-44]
Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present 'CellTagging', a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a 'dead-end' state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.
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