Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution
- Justyna A. Resztak1,8,
- Julong Wei1,8,
- Samuele Zilioli2,3,
- Edward Sendler1,
- Adnan Alazizi1,
- Henriette E. Mair-Meijers1,
- Peijun Wu4,
- Xiaoquan Wen4,
- Richard B. Slatcher5,
- Xiang Zhou4,
- Francesca Luca1,6,7 and
- Roger Pique-Regi1,6
- 1Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- 2Department of Psychology, Wayne State University, Detroit, Michigan 48201, USA;
- 3Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan 48201, USA;
- 4Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 5Department of Psychology, University of Georgia, Athens, Georgia 30602, USA;
- 6Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA;
- 7Department of Biology, University of Rome “Tor Vergata,” 00133 Rome, Italy
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↵8 These authors contributed equally to this work.
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.276765.122.
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Freely available online through the Genome Research Open Access option.
- Received March 17, 2022.
- Accepted June 8, 2023.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
