Title |
Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development
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Published in |
BMC Genomics, December 2017
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DOI | 10.1186/s12864-017-4342-x |
Pubmed ID | |
Authors |
Xianbin Su, Yi Shi, Xin Zou, Zhao-Ning Lu, Gangcai Xie, Jean Y. H. Yang, Chong-Chao Wu, Xiao-Fang Cui, Kun-Yan He, Qing Luo, Yu-Lan Qu, Na Wang, Lan Wang, Ze-Guang Han |
Abstract |
The differentiation and maturation trajectories of fetal liver stem/progenitor cells (LSPCs) are not fully understood at single-cell resolution, and a priori knowledge of limited biomarkers could restrict trajectory tracking. We employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells randomly selected from seven stages between embryonic day 11.5 and postnatal day 2.5 during mouse liver development, and also 52 Epcam-positive cholangiocytes from postnatal day 3.25 mouse livers. LSPCs in developing mouse livers were identified via marker-free transcriptomic profiling. Single-cell resolution dynamic developmental trajectories of LSPCs exhibited contiguous but discrete genetic control through transcription factors and signaling pathways. The gene expression profiles of cholangiocytes were more close to that of embryonic day 11.5 rather than other later staged LSPCs, cuing the fate decision stage of LSPCs. Our marker-free approach also allows systematic assessment and prediction of isolation biomarkers for LSPCs. Our data provide not only a valuable resource but also novel insights into the fate decision and transcriptional control of self-renewal, differentiation and maturation of LSPCs. |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 125 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 26 | 21% |
Researcher | 23 | 18% |
Student > Bachelor | 14 | 11% |
Student > Master | 8 | 6% |
Student > Doctoral Student | 7 | 6% |
Other | 19 | 15% |
Unknown | 28 | 22% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 35 | 28% |
Agricultural and Biological Sciences | 15 | 12% |
Medicine and Dentistry | 15 | 12% |
Engineering | 7 | 6% |
Computer Science | 5 | 4% |
Other | 15 | 12% |
Unknown | 33 | 26% |