Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
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DOI：
10.1038/s41467-019-11052-9
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发表时间：
2019-07-31
影响因子：
16.6
通讯作者：
Halperin, Eran
中科院分区：
文献类型：
--
作者：
Rahmani, Elior;Schweiger, Regev;Halperin, Eran
细胞分选和单细胞技术的高成本和技术局限性目前限制了大规模、细胞类型特异性DNA甲基化数据的收集。这反过来又阻碍了我们解决与群体内变异有关的关键生物学问题的能力，例如在细胞类型特异性分辨率下识别疾病相关基因。在这里，我们在数学上和经验上表明，个体的细胞类型特异性甲基化水平可以从其组织水平的批量数据中学习，在概念上模拟了个体已经用单细胞分辨率进行了分析，然后信号分别聚集在每个细胞群中的情况。提供了这种前所未有的方式来执行强大的大规模表观遗传学研究与细胞类型特异性分辨率，我们重新审视以前的研究与组织水平的批量甲基化，并揭示了新的关联与血液中的白细胞成分和类风湿性关节炎。对于后者，我们进一步显示了与从分类的白细胞亚型收集的验证数据的一致性。
High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types.