Modeling population size independent tissue epigenomes by ChIL-seq with single thin sections.
Modeling population size independent tissue epigenomes by ChIL-seq with single thin sections.
复制标题
用单个薄切片对种群大小独立组织的表观基因组进行建模。
DOI：
10.15252/msb.202110323
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发表时间：
2021-11
影响因子：
9.9
通讯作者：
Ohkawa Y
中科院分区：
文献类型：
--
作者：
Maehara K;Tomimatsu K;Harada A;Tanaka K;Sato S;Fukuoka M;Okada S;Handa T;Kurumizaka H;Saitoh N;Kimura H;Ohkawa Y
全基因组技术的最新进展使得即使是单个细胞的小细胞数量也能够进行分析。然而，从大型器官和组织中获得具有细胞类型分辨率的组织表观基因组仍然具有挑战性，特别是当可用材料有限时。在这里，我们提出了一种基于ChIL的方法，用于使用高深度表观基因组数据在组织水平上分析不同的细胞动力学。“用于组织的ChIL”允许分析单个组织切片，并且可以基于靶表观基因组状态、组织形态和细胞数量的分布，从几种组织类型可再现地产生表观基因组谱。所提出的方法使独立的评价细胞群的变化和基因激活的细胞再生骨骼肌组织，使用RNA聚合酶II基因位点分布的统计模型。因此，使用ChIL进行的综合分析可以阐明组织的体内细胞类型动力学。“ChIL for tissues”可从单个薄组织切片中获得高灵敏度的表观基因组信息。RNApolII分布的统计模型与ChIL相结合，以分析组织中的细胞类型特异性转录状态。
Recent advances in genome‐wide technologies have enabled analyses using small cell numbers of even single cells. However, obtaining tissue epigenomes with cell‐type resolution from large organs and tissues still remains challenging, especially when the available material is limited. Here, we present a ChIL‐based approach for analyzing the diverse cellular dynamics at the tissue level using high‐depth epigenomic data. “ChIL for tissues” allows the analysis of a single tissue section and can reproducibly generate epigenomic profiles from several tissue types, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation in cells from regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analyses performed using ChIL can elucidate in vivo cell‐type dynamics of tissues. “ChIL for tissues” allows obtaining epigenome information from a single‐thin tissue section with high sensitivity. A statistical model of RNApolII distribution is combined with ChIL to analyze cell‐type‐specific transcriptional states in a tissue.
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影响因子：
48
作者：
Cao Z;Chen C;He B;Tan K;Lu C
通讯作者：
Lu C
影响因子：
5.7
作者：
Amatori, Stefano;Persico, Giuseppe;Fanelli, Mirco
通讯作者：
Fanelli, Mirco
DOI：
10.1073/pnas.1007647107
发表时间：
2010-12-14
影响因子：
11.1
作者：
Fanelli, Mirco;Amatori, Stefano;Minucci, Saverio
通讯作者：
Minucci, Saverio
影响因子：
46.9
作者：
Bartosovic M;Kabbe M;Castelo-Branco G
通讯作者：
Castelo-Branco G
影响因子：
64.8
作者：
Chen, Xi;Iliopoulos, Dimitrios;Zhang, Qing;Tang, Qianzi;Greenblatt, Matthew B.;Hatziapostolou, Maria;Lim, Elgene;Tam, Wai Leong;Ni, Min;Chen, Yiwen;Mai, Junhua;Shen, Haifa;Hu, Dorothy Z.;Adoro, Stanley;Hu, Bella;Song, Minkyung;Tan, Chen;Landis, Melissa D.;Ferrari, Mauro;Shin, Sandra J.;Brown, Myles;Chang, Jenny C.;Liu, X. Shirley;Glimcher, Laurie H.
通讯作者：
Glimcher, Laurie H.