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

Elior Rahmani,Eleazar Eskin,Brooke Rhead,Saharon Rosset,Lisa F Barcellos,Regev Schweiger,Lindsey A Criswell,Eran Halperin,Sriram Sankararaman

doi:10.1038/s41467-019-11052-9

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.

Highlights

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
Cell-type-specific studies are typically drastically restricted in their sample sizes owing to high costs and technical limitations imposed by both cell sorting and single-cell approaches
We demonstrate the utility of Tensor Composition Analysis (TCA) by applying it to data from previously published epigenome-wide association studies (EWAS)

Summary

Introduction

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. We evaluated the performance of TCA in detecting celltype-specific associations by simulating whole-blood methylation and corresponding phenotypes with cell-type-specific effects. We compared the performance of TCA with a standard regression analysis of the bulk levels and with the method CellDMC, an interaction-based test that was recently evaluated in the context of detecting cell-type-specific associations with methylation[23].

Results

Conclusion