Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets
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, repr...
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| Published in: | Atherosclerosis Vol. 340; pp. 12 - 22 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-01-2022
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| Abstract | 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.
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.
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.
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.
[Display omitted]
•Automated cell annotation and pseudotemporal analyses reveal expression changes in de-differentiated smooth muscle cells (SMC).•Ligand-receptor profiling reveals potential drug targets that modulate SMC and fibroblast signaling during atherosclerosis.•Reproducible workflow allows for detailed evaluation of scRNA-seq datasets with minimal coding and eliminates potential bias.•PlaqView web application allows for interactive analysis of scRNA-seq datasets and facilitates data sharing and reanalysis. |
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| AbstractList | 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.
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.
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.
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. 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. 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. 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. 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. [Display omitted] •Automated cell annotation and pseudotemporal analyses reveal expression changes in de-differentiated smooth muscle cells (SMC).•Ligand-receptor profiling reveals potential drug targets that modulate SMC and fibroblast signaling during atherosclerosis.•Reproducible workflow allows for detailed evaluation of scRNA-seq datasets with minimal coding and eliminates potential bias.•PlaqView web application allows for interactive analysis of scRNA-seq datasets and facilitates data sharing and reanalysis. |
| Author | Mosquera, Jose Verdezoto Miller, Clint L. Pan, Huize Khomtchouk, Bohdan B. Turner, Adam W. Gancayco, Christina Pasterkamp, Gerard Hodonsky, Chani J. Slenders, Lotte van der Laan, Sander W. Mai, David Reilly, Muredach P. Ma, Wei Feng Owens, Gary K. Song, Yipei Barrientos, Nelson B. Alencar, Gabriel F. Li, Mingyao Mokry, Michal Wong, Doris Ligay, Alexandra V. Owsiany, Katherine |
| AuthorAffiliation | 12 Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA 9 Research Computing, University of Virginia, Charlottesville, VA 22908, USA 13 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA 3 Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA 4 Department of Computer Engineering, University of Virginia, Charlottesville, VA 22908, USA 14 Department of Experimental Cardiology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands 5 Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA 7 Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA 8 Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical genetics, University Medi |
| AuthorAffiliation_xml | – name: 3 Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA – name: 5 Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA – name: 12 Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA – name: 14 Department of Experimental Cardiology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands – name: 1 Medical Scientist Training Program, University of Virginia, Charlottesville, VA 22908, USA – name: 4 Department of Computer Engineering, University of Virginia, Charlottesville, VA 22908, USA – name: 8 Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical genetics, University Medical Center Utrecht, Utrecht University, 3584 CX, Utrecht, The Netherlands – name: 13 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA – name: 6 Master of Science in Biomedical Informatics (MScBMI) Program, University of Chicago, Chicago, IL 60637, USA – name: 11 Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22908, USA – name: 7 Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA – name: 10 Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, Irving Institute for Clinical and Translational Research, Columbia University, New York, NY 10032 – name: 2 Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA – name: 9 Research Computing, University of Virginia, Charlottesville, VA 22908, USA |
| Author_xml | – sequence: 1 givenname: Wei Feng orcidid: 0000-0002-2177-1387 surname: Ma fullname: Ma, Wei Feng organization: Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 2 givenname: Chani J. orcidid: 0000-0001-8566-5877 surname: Hodonsky fullname: Hodonsky, Chani J. organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 3 givenname: Adam W. surname: Turner fullname: Turner, Adam W. organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 4 givenname: Doris orcidid: 0000-0002-1094-8677 surname: Wong fullname: Wong, Doris organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 5 givenname: Yipei surname: Song fullname: Song, Yipei organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 6 givenname: Jose Verdezoto orcidid: 0000-0002-2816-2250 surname: Mosquera fullname: Mosquera, Jose Verdezoto organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 7 givenname: Alexandra V. orcidid: 0000-0003-4851-779X surname: Ligay fullname: Ligay, Alexandra V. organization: Master of Science in Biomedical Informatics (MScBMI) Program, University of Chicago, Chicago, IL, 60637, USA – sequence: 8 givenname: Lotte surname: Slenders fullname: Slenders, Lotte organization: Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands – sequence: 9 givenname: Christina orcidid: 0000-0002-2845-2565 surname: Gancayco fullname: Gancayco, Christina organization: Research Computing, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 10 givenname: Huize surname: Pan fullname: Pan, Huize organization: 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 – sequence: 11 givenname: Nelson B. orcidid: 0000-0001-8971-3784 surname: Barrientos fullname: Barrientos, Nelson B. organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 12 givenname: David surname: Mai fullname: Mai, David organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 13 givenname: Gabriel F. surname: Alencar fullname: Alencar, Gabriel F. organization: Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 14 givenname: Katherine orcidid: 0000-0001-7917-3411 surname: Owsiany fullname: Owsiany, Katherine organization: Medical Scientist Training Program, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 15 givenname: Gary K. orcidid: 0000-0002-7119-9657 surname: Owens fullname: Owens, Gary K. organization: Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 16 givenname: Muredach P. surname: Reilly fullname: Reilly, Muredach P. organization: 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 – sequence: 17 givenname: Mingyao surname: Li fullname: Li, Mingyao organization: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA – sequence: 18 givenname: Gerard surname: Pasterkamp fullname: Pasterkamp, Gerard organization: Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands – sequence: 19 givenname: Michal orcidid: 0000-0002-5298-4852 surname: Mokry fullname: Mokry, Michal organization: Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands – sequence: 20 givenname: Sander W. surname: van der Laan fullname: van der Laan, Sander W. organization: Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, the Netherlands – sequence: 21 givenname: Bohdan B. surname: Khomtchouk fullname: Khomtchouk, Bohdan B. email: bohdan@uchicago.edu organization: Department of Medicine, Section of Computational Biomedicine and Biomedical Data Science, Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL , 60637, USA – sequence: 22 givenname: Clint L. surname: Miller fullname: Miller, Clint L. email: clintm@virginia.edu organization: Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34871816$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2021 The Authors Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved. |
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| Keywords | Atherosclerosis scRNA PlaqView Web Application Cardioinformatics Drug Discovery Pipeline Precision Medicine |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved. |
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| Notes | Authors’ contributions WFM designed and performed the statistical analysis. CJH, AWT, AVL, BBK, DW and YS refined the methodology and edited the manuscript. JVM, DM, and GFA helped with scripting. AVL, BBK, LS, HZP, NBB, and KO helped with data acquisition and refinement of PlaqView, and edited the manuscript. GKO, MPR, MYL, BBK, GP, MM, and SWvL provided critical feedback on the manuscript. MYL provided statistical review and edited the manuscript. CG provided deployment and application scripting support, and edited the manuscript. CLM and SWvL conceived the project. CLM and BBK refined the project and edited the manuscript. |
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| PublicationTitle | Atherosclerosis |
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| Title | Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets |
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