Roy Sushmita, Sridharan Rupa
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin 53715, USA.
Wisconsin Institute for Discovery, Madison, Wisconsin 53715, USA.
Genome Res. 2017 Jul;27(7):1250-1262. doi: 10.1101/gr.215004.116. Epub 2017 Apr 19.
Changes in chromatin state play important roles in cell fate transitions. Current computational approaches to analyze chromatin modifications across multiple cell types do not model how the cell types are related on a lineage or over time To overcome this limitation, we developed a method called Chromatin Module INference on Trees (CMINT), a probabilistic clustering approach to systematically capture chromatin state dynamics across multiple cell types. Compared to existing approaches, CMINT can handle complex lineage topologies, capture higher quality clusters, and reliably detect chromatin transitions between cell types. We applied CMINT to gain novel insights in two complex processes: reprogramming to induced pluripotent stem cells (iPSCs) and hematopoiesis. In reprogramming, chromatin changes could occur without large gene expression changes, different combinations of activating marks were associated with specific reprogramming factors, there was an order of acquisition of chromatin marks at pluripotency loci, and multivalent states (comprising previously undetermined combinations of activating and repressive histone modifications) were enriched for CTCF. In the hematopoietic system, we defined critical decision points in the lineage tree, identified regulatory elements that were enriched in cell-type-specific regions, and found that the underlying chromatin state was achieved by specific erasure of preexisting chromatin marks in the precursor cell or by de novo assembly. Our method provides a systematic approach to model the dynamics of chromatin state to provide novel insights into the relationships among cell types in diverse cell-fate specification processes.
染色质状态的变化在细胞命运转变中起着重要作用。当前用于分析多种细胞类型中染色质修饰的计算方法并未对细胞类型在谱系上或随时间的关联方式进行建模。为克服这一局限性，我们开发了一种名为“基于树的染色质模块推断”（CMINT）的方法，这是一种概率聚类方法，用于系统地捕捉多种细胞类型中的染色质状态动态。与现有方法相比，CMINT能够处理复杂的谱系拓扑结构，捕获更高质量的聚类，并可靠地检测细胞类型之间的染色质转变。我们应用CMINT在两个复杂过程中获得了新的见解：重编程为诱导多能干细胞（iPSC）和造血过程。在重编程过程中，染色质变化可能在基因表达没有大幅变化的情况下发生，激活标记的不同组合与特定的重编程因子相关，多能性位点的染色质标记获取存在一定顺序，并且多价状态（包括先前未确定的激活和抑制组蛋白修饰组合）在CTCF中富集。在造血系统中，我们定义了谱系树中的关键决策点，识别了在细胞类型特异性区域富集的调控元件，并发现潜在的染色质状态是通过在前体细胞中特异性擦除预先存在的染色质标记或通过从头组装实现的。我们的方法提供了一种系统的方法来模拟染色质状态的动态，从而为不同细胞命运决定过程中细胞类型之间的关系提供新的见解。
