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Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Chen, Sisi and Rivaud, Paul and Park, Jong H. and Tsou, Tiffany and Charles, Emeric and Haliburton, John R. and Pichiorri, Flavia and Thomson, Matt (2020) Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign. Proceedings of the National Academy of Sciences of the United States of America, 117 (46). pp. 28784-28794. ISSN 0027-8424. https://resolver.caltech.edu/CaltechAUTHORS:20180927-114224716

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Abstract

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. We apply PopAlign to analyze the impact of 42 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1073/pnas.2005990117DOIArticle
https://www.pnas.org/content/suppl/2020/10/30/2005990117.DCSupplementalPublisherSupporting Information
https://doi.org/10.1101/421354DOIDiscussion Paper
https://doi.org/10.6084/m9.figshare.11837097DOIData
https://github.com/thomsonlab/popalignRelated ItemCode
ORCID:
AuthorORCID
Chen, Sisi0000-0001-9448-9713
Rivaud, Paul0000-0001-8637-3331
Tsou, Tiffany0000-0002-5651-2879
Alternate Title:Dissecting heterogeneous cell-populations across signaling and disease conditions with PopAlign
Additional Information:© 2020 National Academy of Sciences. Published under the PNAS license. Edited by Jonathan S. Weissman, University of California, San Francisco, CA, and approved September 25, 2020 (received for review March 31, 2020). PNAS first published October 30, 2020. We thank Justin Bois, Eric Chow, Allan-Hermann Pool, Jase Gehring, Tami Khazaei, and members of the M.T. laboratory for helpful feedback and discussions; Chris McGinnis and David Patterson for experimental guidance; and Inna-Marie Strazhnik for figure editing and illustrations. This work was performed at the Beckman Institute Single-Cell Profiling and Engineering Center. M.T. was supported by the Shurl and Kay Curci Foundation and the Heritage Medical Research Institute. Data Availability: Single-cell gene-expression data have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.11837097) (48). The software package, implemented in Python 3, can be found at GitHub, https://github.com/thomsonlab/popalign. Author contributions: S.C., P.R., F.P., and M.T. designed research; S.C., P.R., J.H.P., T.T., E.C., J.R.H., and M.T. performed research; S.C., P.R., and M.T. contributed new reagents/analytic tools; S.C., P.R., and M.T. analyzed data; S.C. and M.T. wrote the paper; and F.P. provided clinical guidance and context for data interpretation. Competing interest statement: S.C., M.T., and P.R. have filed a US and Patent Cooperation Treaty patent for the PopAlign computational framework. This article is a PNAS Direct Submission. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2005990117/-/DCSupplemental.
Group:Heritage Medical Research Institute
Funders:
Funding AgencyGrant Number
Shurl and Kay Curci Foundation12540322
Heritage Medical Research InstituteHMRI-15-09-01
Subject Keywords:single-cell genomics; probabilistic models; single cell mRNA-seq
Issue or Number:46
Record Number:CaltechAUTHORS:20180927-114224716
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180927-114224716
Official Citation:Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign Sisi Chen, Paul Rivaud, Jong H. Park, Tiffany Tsou, Emeric Charles, John R. Haliburton, Flavia Pichiorri, Matt Thomson. Proceedings of the National Academy of Sciences Nov 2020, 117 (46) 28784-28794; DOI: 10.1073/pnas.2005990117
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90008
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:27 Sep 2018 21:04
Last Modified:18 Nov 2020 20:11

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