![]() The CODEX protocol and subsequent data processing resolved specific signals of 34 distinct cell subsets, allowing Yu Xin Wang et al. to monitor dynamic changes in cell-cell numbers and organization at sites of injury following NTX. The analysis revealed a constant influx of myogenic, immune, vascular and fibrogenic cell subsets during muscle regeneration. ![]() HFcluster was developed by the authors and relies on learning antibody staining patterns of different cell types to identify cells sharing similar patterns within a dataset. Cell-type clustering and annotation was done using the HFcluster pipeline. developed FiberNet which relies on a CNN to characterize muscle fiber states and ECM features. To overcome these limitations, Xin Wang et al. The inherent multinucleated nature of myofibers and extracellular matrix (ECM) structures made these features difficult to characterize using CellSeg. In doing so, they were able to quantify the intensity of each antibody in the nuclear and perinuclear compartments. After using CRISP, the authors utilized the deep learning module CellSeg which relies on a convolutional neural network (CNN) to segment nuclei present in an image. developed an imaging processing pipeline termed CRISP that registered, stitched and cleared each image. Images generated by CODEX were analyzed to create a single-cell spatial atlas during skeletal muscle regeneration. Afterwards, CODEX was performed on tissue sections over a 10-day period to generate single-cell multiplexed imaging data at day 1, 3, 6 and 10 after injury. Intramuscular injections of notexin (NTX) in the tibialis anterior (TA) of young mice was used to induce various muscle injuries similar to those encountered in sports or trauma. monocytes, dendritic cells (DCs), B cells, T cells and macrophages), vascular, fibrogenic and motor neuron cell subsets and cell states. started off by constructing a CODEX antibody panel using previously characterized cell-type specific markers representative of myogenic, immune (i.e. The authors created a single-cell spatial atlas of cell subsets involved in skeletal muscle regeneration using Co-Detection by Indexing (CODEX) technology which allows for a highly multiplexed analysis of over 40 protein markers to be visualized simultaneously in one tissue section. In doing so, they uncovered intercellular crosstalk events characterizing various cellular neighborhoods and gained insight into how these interactions change throughout the regenerative response and when confronted with macrophage depletion.Ĭellular heterogeneity during muscle repair used multiplex imaging to profile the spatial distribution of 34 cell types at single-cell resolution during muscle regeneration. However, current technologies (i.e., single cell RNA-sequencing, flow cytometry, etc.) limit our understanding of the stem cell niche because we are unable to accurately assess cell-cell interactions within their spatial context. Multiple studies suggest that the skeletal muscle stem cell niche is composed of a dynamic and highly regulated interplay of various cell types responsible for maintaining MuSC quiescence and the regenerative response. During aging, these coordinated events are deregulated which in turn disrupts the signals necessary for MuSCs activation and subsequent muscle repair. The activation of MuSCs and, thus, the generation of myogenic progenitors that will fuse to form myofibers depends on various cellular interactions orchestrated by the surrounding niche. With age, there is a decline in the regenerative capacity of skeletal muscle. The regenerative capacity of skeletal muscle depends on muscle stem cells (MuSCs) that remain dormant until an injury is detected.
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