Single-cell analysis of clonal dynamics in direct lineage reprogramming: A combinatorial indexing method for lineage tracing
bioRxiv, ISSN: 2692-8205
2017
- 5Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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- Citations5
- Citation Indexes5
- CrossRef5
Article Description
Single-cell technologies are offering unprecedented insight into complex biology, revealing the behavior of rare cell populations that are typically masked in bulk population analyses. The application of these methodologies to cell fate reprogramming holds particular promise as the manipulation of cell identity is typically inefficient, generating heterogeneous cell populations. One current limitation of single-cell approaches is that lineage relationships are lost as a result of cell processing, restricting interpretations of the data collected. Here, we present a single-cell resolution lineage-tracing approach based on the combinatorial indexing of cells, ‘CellTagging’. Application of this method, in concert with high-throughput single-cell RNA-sequencing, reveals the transcriptional dynamics of direct reprogramming from fibroblasts to induced endoderm progenitors. These analyses demonstrate that while many cells initiate reprogramming, complete silencing of fibroblast identity and transition to a progenitor-like state represents a rare event. Clonal analyses uncover a remarkable degree of heterogeneity arising from individual cells. Overall, very few cells fully reprogram to generate expanded populations with a low degree of clonal diversity. Extended culture of these engineered cells reveals an instability of the reprogrammed state and reversion to a fibroblast-like phenotype. Together, these results demonstrate the utility of our lineage-tracing approach to reveal dynamics of lineage reprogramming, and will be of broad utility in many cell biological applications.
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