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

• Published in: bioRxiv
• Main Authors: Molony, Claire, King, Damien, Mariana Di Luca, Kitching, Michael, Abidemi Olayinka, Hakimjavadi, Roya, Lourdes An Julius, Fitzpatrick, Emma, Yusof Gusti, Burtenshaw, Denise, Healy, Killian, Finlay, Emma K, Kernan, David, Llobera, Andreu, Liu, Weimin, Morrow, David, Redmond, Eileen M, Ducrée, Jens, Cahill, Paul A
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 25.01.2021; Cold Spring Harbor Laboratory
• Edition: 1.5
• Subjects: Aorta; Arteriosclerosis; Cell Biology; Cell differentiation; Discriminant analysis; Learning algorithms; Lesions; Lymphocytes B; Macrophages; Mesenchyme; Neural networks; Smooth muscle; Stem cells; Transforming growth factor-b1; Vascular diseases; supervised machine learning; S100β vascular stem cells; lineage tracing, arteriosclerosis; smooth muscle differentiation; autofluorescence imaging; photonics; multivariate analysis; carotid artery ligation
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2020.05.13.093518

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 Jaggedl, 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. Competing Interest Statement The authors have declared no competing interest. Footnotes * The manuscript has been edited for final publication