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Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development

Overview of attention for article published in BMC Genomics, December 2017
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Title
Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development
Published in
BMC Genomics, December 2017
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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 125 100%

Demographic breakdown

Readers by professional status Count As %
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 %
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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 December 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from BMC Genomics
#9,845
of 11,249 outputs
Outputs of similar age
#385,339
of 445,848 outputs
Outputs of similar age from BMC Genomics
#188
of 212 outputs
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