Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology.

Nature Communications
Elior RahmaniEran Halperin

Abstract

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.

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Citations

Dec 4, 2019·Journal of Experimental Botany·Sharmila SinghAnanda K Sarkar
Aug 23, 2020·Genes·Roberto SpreaficoAmalio Telenti
Aug 8, 2020·Nature Reviews. Rheumatology·Esteban BallestarQianjin Lu
Aug 28, 2020·Current Atherosclerosis Reports·Carmen de la RochaGertrud Lund
Jul 8, 2020·Briefings in Bioinformatics·Xiangyu LuoZhonghua Liu
Mar 24, 2021·BMC Bioinformatics·Fumihiko Takeuchi, Norihiro Kato
Oct 16, 2021·Briefings in Bioinformatics·Maria K Jaakkola, Laura L Elo
Nov 4, 2021·Molecular Systems Biology·Kazumitsu MaeharaYasuyuki Ohkawa
Jun 26, 2021·The European Journal of Neuroscience·Jacqueline S WomersleySîan M J Hemmings

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Datasets Mentioned

BETA
GSE131989

Software Mentioned

GLINT
ReFACTor
R
TCA
HIRE
R package
Tensor Composition Analysis ( TCA
CellDMC
missMethyl
Matlab

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