Multiparametric identification of putative senescent cells in skeletal muscle via mass cytometry

• Published in: Cytometry. Part A 2024-08, Vol.105 (8), p.580-594
• Main Authors: Li, Yijia, Baig, Nameera, Roncancio, Daniel, Elbein, Kris, Lowe, Dawn, Kyba, Michael, Arriaga, Edgar A.
• Format: Article
• Language: English
• Subjects: aging; Animals; Autophagy; Biomarkers - metabolism; Cell cycle; Cells (biology); Cellular Senescence - physiology; Clustering; Cyclin-dependent kinase inhibitor p21; Cyclin-Dependent Kinase Inhibitor p21 - metabolism; Data analysis; erythro‐myeloid progenitors; Female; fibro/adipogenic progenitors; Flow cytometry; Flow Cytometry - methods; Glyceraldehyde-3-phosphate dehydrogenase; Heterogeneity; LRRK2 protein; Macrophages; Male; mass cytometry; Mice; Mice, Inbred C57BL; Muscle, Skeletal - cytology; Muscle, Skeletal - metabolism; Muscles; Musculoskeletal system; outlier detection; Outliers (statistics); Phenotypes; Progenitor cells; Proteins; Satellite cells; Satellite observation; Senescence; Single-Cell Analysis - methods; Skeletal muscle
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.24853

Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI‐67. The characterization of different senescence‐associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single‐cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry‐based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well‐established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy‐related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence‐related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL‐6. Overall, the analysis framework prioritizes multiple senescence‐associated proteins to characterize putative senescent cells sourced from tissue made of different cell types. Identifying putative senescent cells in mouse skeletal muscle using CyTOF2 analysis and clustering. Clustering of centroids and using an outlier method, multiple senescence‐associated markers can be compared using Boolean logic. The figure was created with BioRender.com.