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Single Cell Analytics Identify Novel Subpopulation of Cells with Enhanced Wound Healing
Sacha ML Khong, PhD1, Dominik Duscher, MD2, Nina Kosaric, AB1, Michael Januszyk, MD1, Ming H. Li, AB3, Alexander Y. Li, MS1, Peter A. Than, MD1, Geoffrey C. Gurtner, MD1.
1Stanford, Stanford, CA, USA, 2Johannes Kepler University, Linz, Austria, 3Emory University, Atlanta, GA, USA.

Purpose: Mesenchymal stem cells (MSCs) hold great promise in regenerative medicine. These cells are thought to function through a combination of: 1) secretion of progenitor-recruiting trophic factors, 2) modulation of the local immune response, 3) enhancement of angiogenesis and 4) improvement of extracellular matrix production. These effects represent a comprehensive collection of the various therapeutic approaches to wound healing. A caveat in their safe clinical use is the incomplete understanding of their mechanism of action, made difficult by cellular heterogeneity. We aim to utilize bioinformatics approaches to characterize functionally distinct subsets of human bone marrow derived MSCs (BMMSC), and determine the mechanism that underlies their efficacy.
Methods: Passage three human BMMSCs were subjected to high-throughput single cell multiplex qPCR, looking at 96 manually curated genes pertaining to wound healing. Advanced mathematical modeling was applied to this multidimensional data to unravel heterogeneity and decipher cell surface markers that will enable functional testing. Elucidated subpopulations were subjected to fluorescence activated cell sorting (FACs) for functional testing in diabetic (DB/DB mice).
Results: Single cell analysis revealed two transcriptionally distinct subpopulations with different protein signatures (Subpopulation 1: vasculogenic vs. Subpopulation 2: immunomodulatory and remodeling). Functional tests of these subpopulations in diabetic mice revealed accelerated wound closure by Subpopulation 2 (DB/DB: Unsorted: 26.5 ± 0.95, Subpopulation 1: 25.5 ± 0.73; p=0.4; Subpopulation 2: 20.1 ± 1.12 days, p=0.0008).
Conclusion: We demonstrate the utility of bioinformatics in unraveling BMMSC heterogeneity to identify a subset of BMMSCs that heal diabetic wounds 6 days faster than heterogeneous BMMSCs.


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