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Disease-relevant single cell photonic signatures identify S100β stem cells and their myogenic progeny in vascular lesions

Molony, Claire, King, Damien orcid logoORCID: 0000-0001-7720-064X, Di Luca, Mariana, Kitching, Michael orcid logoORCID: 0000-0002-9884-0790, Olayinka, Abidemi, Hakimjavadi, Roya, Julius, Lourdes A.N., Fitzpatrick, Emma, Gusti, Yusof, Burtenshaw, Denise, Healy, Killian, Finlay, Emma K. orcid logoORCID: 0000-0003-0621-2744, Kernan, David, Llobera, Andreu orcid logoORCID: 0000-0002-2941-478X, Liu, Weimin, Morrow, David, Redmond, Eileen M. orcid logoORCID: 0000-0001-8642-4418, Ducrée, Jens and Cahill, Paul A. orcid logoORCID: 0000-0002-5385-6502 (2021) Disease-relevant single cell photonic signatures identify S100β stem cells and their myogenic progeny in vascular lesions. Stem Cell Reviews and Reports, 17 . pp. 1713-1740. ISSN 2629-3269

Abstract
A hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk(Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF- β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+ vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Photonics; Autofluorescence imaging; S100β vascular stem cells; Carotid artery ligation; Lineage tracing; arteriosclerosis; Smooth muscle differentiation; Multivariate analysis; Supervised machine learning
Subjects:Engineering > Biomedical engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Biotechnology
Publisher:Springer
Official URL:https://doi.org/10.1007/s12015-021-10125-x
Copyright Information:© 2021 The Authors. Open Access (CC-BY 4.0)
Funders:Science Foundation Ireland grant SFI-11/PI/1128, Health Research Board (HRB) of Ireland grant HRA-POR-2015-1315, European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB) to PAC, and NIH R21AA023213 and RO1AA024082 to EMR, Irish Research Council (IRC) GOIPG/2014/43 (M.DiL), Fraunhofer-Gesellschaft under the SFI Strategic Partnership Programme [Grant Number 16/SPP/3321] Science Foundation Ireland [Grant Number: 10/CE/B1821]; the ERDF; the LiPhos project funded by the European Commission [Grant Number: 317916]
ID Code:27804
Deposited On:28 Sep 2022 13:59 by Thomas Murtagh . Last Modified 18 Jan 2023 12:45
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