Osteochondroprogenitor cells and neutrophils expressing p21 and senescence markers modulate fracture repair
Description
In the present study we leverage the power of mass cytometry by time-of-flight (CyTOF) to define, at the single-cell level, mesenchymal bone and marrow senescent cells in mice during fracture. Three main CyTOF experiments were performed: 1) TIMECOURSE: Assessment of senescent cell burden in skeletal and immune cell populations during a 28-day timecourse of fracture healing. 2) P21-ATTAC: Identification of cell populations targeted through p21+ cell clearance in p21-ATTAC mice. 3) OCH-STEM: In-depth assessment of osteochondroprogenitor (OCH) cells through expanded panel, including skeletal stem cell (SSC) markers.
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Cleanup of cell debris–including removal of beads, dead cells, and doublets–was performed using Cytobank software. Visual representation of single-cell data was achieved using viSNE mapping (5,000 iterations, 100 perplexity, 0.5 theta), which is based on the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. FlowSOM clustering was performed within Cytobank (hierarchical consensus, 10 iterations) and cluster labels were assigned using established literature on skeletal cell types, with relative marker intensities per cluster visualized by heatmap. FCS files were exported, concatenated in R, then re-uploaded for visualization of merged populations. Quantified values were exported to Graphpad Prism 8 to construct plots and perform statistical analyses. CITRUS analyses were performed in Cytobank using Significance Analysis of Microarrays (SAM) correlative association model. Nearest Shrunken Centroid (PAMR) and L1-Penalized Regression (LASSO via GLMNET) predictive association models were run simultaneously to analyze model error rates to confirm validity of the statistical model. For CITRUS assessment of median expression changes, cells were clustered by identification markers and statistics channels included all functional markers; for assessment of abundances, all markers were used for clustering. All CITRUS analyses used the following settings: 2,000 events samples per file, 2% minimum cluster size, 5 cross validation folds, and 5% false discovery rate (FDR). See associated preprint (doi: https://doi.org/10.1101/2024.02.01.578420) for additional details.