小柯机器人

科学家绘制出单细胞分辨率下变化的小鼠胚胎转录组
2020-08-01 23:24
来源：小柯机器人

美国加州理工学院Barbara J. Wold、Brian A. Williams等研究人员合作绘制出单细胞分辨率下变化的小鼠胚胎转录组。该项研究成果发表在2020年7月29日出版的《自然》杂志上。

通过对17个组织和器官进行了采样，研究人员系统地定量了从胚胎发育的第10.5天到出生的小鼠polyA-RNA。最终的发育转录组由动态细胞分化、体轴和细胞增殖基因集整体构成，这些特征进一步由其启动子的转录因子基序代码表征。研究人员使用单细胞RNA序列（逆转录成cDNA的RNA序列）分解了组织水平的转录组，并发现神经发生和造血在基因和细胞水平上均占主导地位，共同占差异基因表达的三分之一，并超过40％的已知细胞类型。
 
通过整合启动子序列基序与伴侣ENCODE表观基因组图谱，研究人员在神经元表达簇中发现了一个重要启动子的去阻抑机制。通过集中于发育中的肢体，单细胞RNA数据确定了25种候选细胞类型，其中包括祖细胞和分化状态以及可计算的谱系关系。通过使用单细胞RNA-seq分解源自整个组织表观基因组染色质数据的整合顺式元件（IDEAS）模型，研究人员提取了细胞类型的转录因子网络和候选增强子元件的互补集。这些ENCODE参考数据、计算出的网络成分以及IDEAS染色质划分是匹配的表观基因组学发育矩阵的辅助资源，可供研究人员进一步挖掘和整合。
 
据悉，在哺乳动物胚胎发生过程中，差异基因的表达逐渐建立了每个组织和器官系统的特性和复杂性。
 
附：英文原文

Title: The changing mouse embryo transcriptome at whole tissue and single-cell resolution

Author: Peng He, Brian A. Williams, Diane Trout, Georgi K. Marinov, Henry Amrhein, Libera Berghella, Say-Tar Goh, Ingrid Plajzer-Frick, Veena Afzal, Len A. Pennacchio, Diane E. Dickel, Axel Visel, Bing Ren, Ross C. Hardison, Yu Zhang, Barbara J. Wold

Issue&Volume: 2020-07-29

Abstract: During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate.

DOI: 10.1038/s41586-020-2536-x

Source: https://www.nature.com/articles/s41586-020-2536-x

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html


本期文章：《自然》：Online/在线发表

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