Dataset Information

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

ABSTRACT:

Background

Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).

Results

The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R???0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (? 96-100%) and chromatin mark QTL (?87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.

Conclusions

Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).

SUBMITTER: Aguirre-Gamboa R

PROVIDER: S-EPMC7291428 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

Aguirre-Gamboa Raúl R de Klein Niek N di Tommaso Jennifer J Claringbould Annique A van der Wijst Monique Gp MG de Vries Dylan D Brugge Harm H Oelen Roy R Võsa Urmo U Zorro Maria M MM Chu Xiaojin X Bakker Olivier B OB Borek Zuzanna Z Ricaño-Ponce Isis I Deelen Patrick P Xu Cheng-Jiang CJ Swertz Morris M Jonkers Iris I Withoff Sebo S Joosten Irma I Sanna Serena S Kumar Vinod V Koenen Hans J P M HJPM Joosten Leo A B LAB Netea Mihai G MG Wijmenga Cisca C Franke Lude L Li Yang Y

BMC bioinformatics 20200612 1

<h4>Background</h4>Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and ...[more]

PMID: 32532224

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Project description:BackgroundTo facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells.MethodsOur deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT.ResultsIn silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT.ConclusionsWe developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Project description:While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell-type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.

Dataset Information

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

Background

Results

Conclusions

Publications

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

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