Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets

Wei Feng Ma; Chani J Hodonsky; Adam W Turner; Doris Wong; Yipei Song; Jose Verdezoto Mosquera; Alexandra V Ligay; Lotte Slenders; Christina Gancayco; Huize Pan; Nelson B Barrientos; David Mai; Gabriel F Alencar; Katherine Owsiany; Gary K Owens; Muredach P Reilly; Mingyao Li; Gerard Pasterkamp; Michal Mokry; Sander W van der Laan; Bohdan B Khomtchouk; Clint L Miller

doi:10.1016/j.atherosclerosis.2021.11.025

Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets

Atherosclerosis. 2022 Jan:340:12-22. doi: 10.1016/j.atherosclerosis.2021.11.025. Epub 2021 Nov 26.

Authors

Wei Feng Ma¹, Chani J Hodonsky², Adam W Turner², Doris Wong³, Yipei Song⁴, Jose Verdezoto Mosquera³, Alexandra V Ligay⁵, Lotte Slenders⁶, Christina Gancayco⁷, Huize Pan⁸, Nelson B Barrientos², David Mai², Gabriel F Alencar⁹, Katherine Owsiany¹⁰, Gary K Owens¹¹, Muredach P Reilly⁸, Mingyao Li¹², Gerard Pasterkamp⁶, Michal Mokry¹³, Sander W van der Laan⁶, Bohdan B Khomtchouk¹⁴, Clint L Miller¹⁵

Affiliations

¹ Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA.
² Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA.
³ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA , 22908, USA.
⁴ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Computer Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
⁵ Master of Science in Biomedical Informatics (MScBMI) Program, University of Chicago, Chicago, IL, 60637, USA.
⁶ Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands.
⁷ Research Computing, University of Virginia, Charlottesville, VA, 22908, USA.
⁸ Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, 10032, USA.
⁹ Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA.
¹⁰ Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA.
¹¹ Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA; Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22908, USA.
¹² Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
¹³ Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands; Department of Experimental Cardiology, University Medical Center Utrecht, 3584, CX, Utrecht, the Netherlands.
¹⁴ Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL , 60637, USA. Electronic address: bohdan@uchicago.edu.
¹⁵ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA , 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA. Electronic address: clintm@virginia.edu.

PMID: 34871816
PMCID: PMC8919504
DOI: 10.1016/j.atherosclerosis.2021.11.025

Abstract

Background and aims: The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets.

Methods: Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets.

Results: We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge.

Conclusions: This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.

Keywords: Atherosclerosis; Cardioinformatics; Drug Discovery; Pipeline; PlaqView; Precision Medicine; Web Application; scRNA.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Coronary Artery Disease* / drug therapy
Coronary Artery Disease* / genetics
Gene Expression Profiling
Humans
Pharmaceutical Preparations*
RNA-Seq
Sequence Analysis, RNA
Single-Cell Analysis
Software
Workflow

Substances

Pharmaceutical Preparations

Abstract

Publication types

MeSH terms

Substances

Grants and funding