Dynamics of Cell Fate Decisions during Chemically Induced Multi‐Lineage Trans‐Differentiation at Single‐Cell Level
Source abstract
Abstract
Abstract Cell trans‐differentiation offers a powerful means to manipulate cell identities. By exposing cells to a combination of small molecules (SMs), cell trans‐differentiation can be induced in a simple and cost‐effective manner. However, a comprehensive atlas detailing chemical‐induced cell trans‐differentiation across multiple cell fates has yet to be established. In this study, the underlying mechanisms of trans‐differentiation is investigated and constructed an in‐depth single‐cell atlas of this process. The time‐course trajectory is demonstrated for trans‐differentiation of mouse embryonic fibroblasts (MEFs) into multiple cell lineages including epithelial, neural, extraembryonic endoderm like (XEN‐like) cells, and endothelial cells, when induced by SMs cocktail 6TCF (E616452, tranylcypromine, CHIR99021, and forskolin). These trans‐differentiated cells closely resemble various somatic cell types in the fetus. It is found that trans‐differentiation is marked by dynamic shifts in entropy and the cell cycle during cell fate transitions. A common intermediate feature is revealed characterized by high ribosomal gene expression. This study combines high‐resolution landscape with comparative analyses of trans‐differentiation dynamics, providing new insights into the complex mechanisms driving cell fate determination in vitro. Future study shall explore the applicability of the model in human cell trans‐differentiation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Single-cell and spatial transcriptomics · Gene Regulatory Network Analysis · Pluripotent Stem Cells Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine Zhejiang University School of Medicine Hangzhou Zhejiang 310000 China
Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China
School of Medicine Tsinghua University Beijing 100191 China
Zhejiang Key Laboratory of Multi‐omics Precision Diagnosis and Treatment of Liver Diseases Hangzhou Zhejiang 310000 China
References (59)
Show source-provided references
1. 10.1038/nbt.194610.1038/nbt.1946 ↗
2. 10.1038/nrm.2016.810.1038/nrm.2016.8 ↗
3. 10.1016/j.cell.2019.01.00610.1016/j.cell.2019.01.006 ↗
4. 10.1039/D0MD00221F10.1039/D0MD00221F ↗
5. 10.1126/science.123927810.1126/science.1239278 ↗
6. 10.1016/j.cell.2015.11.01710.1016/j.cell.2015.11.017 ↗
7. 10.1016/j.stem.2018.05.02510.1016/j.stem.2018.05.025 ↗
8. 10.1038/cr.2017.7810.1038/cr.2017.78 ↗
9. 10.1038/s41580-021-00335-z10.1038/s41580-021-00335-z ↗
10. 10.1038/nature1832310.1038/nature18323 ↗
11. 10.1038/nature2445410.1038/nature24454 ↗
12. 10.1038/s41586-022-04593-510.1038/s41586-022-04593-5 ↗
13. 10.1126/sciadv.aba208410.1126/sciadv.aba2084 ↗
14. 10.1093/bioinformatics/btaa98710.1093/bioinformatics/btaa987 ↗
15. 10.1038/s41588-022-01118-810.1038/s41588-022-01118-8 ↗
16. 10.1038/s41401-019-0264-210.1038/s41401-019-0264-2 ↗
17. 10.1016/j.chembiol.2006.05.00410.1016/j.chembiol.2006.05.004 ↗
18. 10.1038/s41598-017-15454-x10.1038/s41598-017-15454-x ↗
19. 10.3390/cells1118277110.3390/cells11182771 ↗
20. 10.1002/j.1460-2075.1996.tb00407.x10.1002/j.1460-2075.1996.tb00407.x ↗
21. 10.1038/s41467-021-24130-810.1038/s41467-021-24130-8 ↗
22. 10.1016/j.gep.2023.11930510.1016/j.gep.2023.119305 ↗
23. 10.1242/dev.15771910.1242/dev.157719 ↗
24. 10.1038/s41467-021-27865-610.1038/s41467-021-27865-6 ↗
25. 10.1038/s41586-019-0969-x10.1038/s41586-019-0969-x ↗
26. 10.1038/s41588-022-01018-x10.1038/s41588-022-01018-x ↗
27. 10.1038/s41592-021-01346-610.1038/s41592-021-01346-6 ↗
28. 10.1016/j.celrep.2020.10763110.1016/j.celrep.2020.107631 ↗
29. 10.1002/path.297710.1002/path.2977 ↗
30. 10.1038/s42003-020-01346-w10.1038/s42003-020-01346-w ↗
31. 10.1038/s41556-023-01193-x10.1038/s41556-023-01193-x ↗
32. De Donno C. · bioRxiv. · 2022
33. 10.1146/annurev-biodatasci-111419-09175010.1146/annurev-biodatasci-111419-091750 ↗
34. 10.1016/j.devcel.2021.04.01610.1016/j.devcel.2021.04.016 ↗
35. 10.1126/science.aaq068110.1126/science.aaq0681 ↗
36. 10.1007/s13238-020-00763-110.1007/s13238-020-00763-1 ↗
37. 10.1038/s41467-018-03282-010.1038/s41467-018-03282-0 ↗
38. 10.1016/j.cell.2004.12.01210.1016/j.cell.2004.12.012 ↗
39. 10.1016/j.euroneuro.2017.05.00710.1016/j.euroneuro.2017.05.007 ↗
40. 10.1038/s41422-023-00794-310.1038/s41422-023-00794-3 ↗
41. Takayama Y. · PLoS One. · 2020 · 15
42. 10.4324/978131576547110.4324/9781315765471 ↗
43. 10.1016/j.cell.2018.05.01210.1016/j.cell.2018.05.012 ↗
44. 10.1186/s13059-018-1426-010.1186/s13059-018-1426-0 ↗
45. 10.1038/s41586-020-2157-410.1038/s41586-020-2157-4 ↗
46. 10.1186/s13059-017-1382-010.1186/s13059-017-1382-0 ↗
47. 10.1038/s41467-021-25785-z10.1038/s41467-021-25785-z ↗
48. 10.1016/j.cell.2019.05.03110.1016/j.cell.2019.05.031 ↗
49. 10.1038/nmeth.440210.1038/nmeth.4402 ↗
50. 10.1093/nar/gkw25710.1093/nar/gkw257 ↗
51. 10.1093/bioinformatics/btp32410.1093/bioinformatics/btp324 ↗
52. 10.1016/j.xinn.2021.10014110.1016/j.xinn.2021.100141 ↗
53. 10.1038/s41587-021-01001-710.1038/s41587-021-01001-7 ↗
54. Donno C. D. · bioRxiv. · 2022
55. 10.1126/science.aax024910.1126/science.aax0249 ↗
56. 10.1186/1471-2105-14-710.1186/1471-2105-14-7 ↗
57. 10.1038/nbt.285910.1038/nbt.2859 ↗
58. 10.1038/s41588-021-00790-610.1038/s41588-021-00790-6 ↗
59. 10.1016/j.devcel.2019.12.00410.1016/j.devcel.2019.12.004 ↗