bioRxivImmunologyPreprint
Utilizing combined spatial transcriptomics to elucidate localized immune responses within human coronary arteries throughout the progression of atherosclerosis
Joana CamposiD, Jack L. McMurrayiD, Michelangelo CertoiD, Ketaki Hardikar, Chris Morse, C. Russell Corfield, Melanie Weigand, Desley NeiliD, Pasquale MaffiaiD, Claudio MauroiD
Abstract
Atherosclerosis is a complex inflammatory disease characterized by the accumulation of lipids and immune cells in the arterial wall, leading to the narrowing and stiffening of blood vessels. The involvement of both innate and adaptive immunity in the pathogenesis of human atherosclerosis is increasingly recognised. However, the spatial organization and specific roles of immune cells during the various stages of disease progression remain poorly understood, underscoring the necessity for additional research to elucidate their functions throughout the disease course. A better understanding of the immune responses contribution to atherosclerosis progression could unveil novel therapeutic targets to mitigate plaque development and rupture, ultimately reducing the burden of cardiovascular events. In this study, we utilised NanoString GeoMx(R) and CosMx technologies to analyse serial sections of human coronary arteries from patients with varying degrees of atherosclerotic lesion severity. Our work consists of a series of investigations, and integrated findings from both the GeoMx(R) and CosMx datasets, including pathway analyses, cell typing, and neighbourhood analysis. This workflow underscores the power of combining these spatial transcriptomics platforms to elucidate biological processes at the single-cell level, hence unbiasedly providing molecules and pathways of relevance to aid in the understanding of disease pathogenesis and assessing the opportunity of novel therapies.
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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