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The accumulation of vascular smooth muscle (SMC)-like cells and stem cell-derived myogenic and osteogenic progeny contributes significantly to arteriosclerotic disease. This study established whether label-free vibrational spectroscopy can discriminate de-differentiated 'synthetic' SMCs from undifferentiated stem cells and their myogenic and osteogenic progeny in vitro, compared with conventional immunocytochemical and genetic analyses. TGF-β1- and Jagged1-induced myogenic differentiation of CD44 mesenchymal stem cells was confirmed in vitro by immunocytochemical analysis of specific SMC differentiation marker expression (α-actin, calponin and myosin heavy chain 11), an epigenetic histone mark (H3K4me2) at the myosin heavy chain 11 locus, promoter transactivation and mRNA transcript levels. Osteogenic differentiation was confirmed by alizarin red staining of calcium deposition. Fourier Transform Infrared (FTIR) maps facilitated initial screening and discrimination while Raman spectroscopy of individual cell nuclei revealed specific spectral signatures of each cell type in vitro, using Principal Components Analysis (PCA). PCA fed Linear Discriminant Analysis (LDA) enabled quantification of this discrimination and the sensitivity and specificity value was determined for all cell populations based on a leave-one-out cross validation method and revealed that de-differentiated SMCs and stem-cell derived myogenic progeny in culture shared the greatest similarity. FTIR and Raman spectroscopy discriminated undifferentiated stem cells from both their myogenic and osteogenic progeny. The ability to detect stem cell-derived myogenic progeny using label-free platforms in situ may facilitate interrogation of these important phenotypes during vascular disease progression.

A hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening. While medial SMCs contribute, the participation of hedgehog-responsive resident vascular stem cells (vSCs) to lesion formation remains unclear. Using transgenic eGFP mice and genetic lineage tracing of S100β vSCs in vivo, we identified S100β/Sca1 cells derived from a S100β non-SMC parent population within lesions that co-localise with smooth muscle α-actin (SMA) cells following iatrogenic flow restriction, an effect attenuated following hedgehog inhibition with the smoothened inhibitor, cyclopamine. In vitro, S100β/Sca1 cells isolated from atheroprone regions of the mouse aorta expressed hedgehog signalling components, acquired the di-methylation of histone 3 lysine 4 (H3K4me2) stable SMC epigenetic mark at the Myh11 locus and underwent myogenic differentiation in response to recombinant sonic hedgehog (SHh). Both S100β and PTCH1 cells were present in human vessels while S100β cells were enriched in arteriosclerotic lesions. Recombinant SHh promoted myogenic differentiation of human induced pluripotent stem cell-derived S100β neuroectoderm progenitors in vitro. We conclude that hedgehog-responsive S100β vSCs contribute to lesion formation and support targeting hedgehog signalling to treat subclinical arteriosclerosis.

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. Graphical abstract

Background: A hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening, lipid retention and plaque formation, yet their origin remains controversial. The ability to discriminate heterogeneous populations of cells in the context of various disease processes is of potential clinical and diagnostic value. Methods:The feasibility of multivariate analysis of single cell photonics as a discriminator of cell phenotype was assessed using label-free optical multi-parameter interrogation of single cells on a novel Lab-on-a-Disk (LoaD) platform before myogenic differentiation of a series of multipotent stem cells in vitro, and the cellular heterogeneity within vascular lesions in vivo, was determined using supervised machine learning and validated by genetic lineage tracing in vivo.Findings: Single cell photonics were of sufficient coverage, specificity, and quality to discriminate various disparate cell phenotypes in vitro and normal medial SMCs from SMClike cells following injury ex vivo, in addition to distinguishing myogenic differentiation of a series of multipotent stem cells in vitro. Supervised machine learning of these photonic datasets supported genetic lineage tracing analysis and identified the presence of S100β + stem-derived myogenic progeny within vascular lesions, in part, due to upregulation of Coll 3A1 and elastin.Interpretation: Disease-relevant photonic signatures reflect cell type and differentiation state and may have important predictive value as a phenotypic discriminator for vascular disease. Research in contextEvidence before this study: Cardiovascular disease (CVD), the leading cause of death and disability world-wide is characterized by pathological structural changes to the blood vessel wall. Pathologic observations in humans vessels confirm that early 'transitional' lesions enriched with SMC-like cells are routinely present in atherosclerotic-prone regions of arteries during pathologic intimal thickening, prior to lipid retention, and the appearance of a atherosclerotic plaque. Lineage tracing and single cell RNA sequence analysis (scRNA-seq) analysis has provided compelling evidence for the involvement of differentiated medial SMC and adventitial/medial progenitors derived from SMCs in progressing intimal thickening.Despite these insights, the putative role of resident vascular stem cells that do not originate from medial SMCs in promoting intimal thickening remains controversial. Light as a diagnostic and prognostic tool has several advantages including high sensitivity, non-destructive measurement, small or even non-invasive analysis and low limits of detection for early detection of disease phenotypes. In combination with microfluidics, photonics enables real time measurement of single cells in very small sample volumes. To accompany these photonic platforms, deep learning, a subset of supervised machine learning based primarily on artificial neural network geometries, has rapidly grown as a predictive method t...