A single cell spatial temporal atlas of skeletal muscle reveals cellular neighborhoods that orchestrate regeneration and become disrupted in aging
Wang, Y. X.; Holbrook, C. A.; Hamilton, J. N.; Garoussian, J.; Afshar, M.; Su, S.; Schurch, C. M.; Lee, M. Y.; Goltsev, Y.; Kundaje, A.; Nolan, G. P.; Blau, H. M.
10.1101/2022.06.10.494732 bioRxivShow abstract
Our mobility requires muscle regeneration throughout life. Yet our knowledge of the interplay of cell types required to rebuild injured muscle is lacking, because most single cell assays require tissue dissociation. Here we use multiplexed spatial proteomics and neural network analyses to resolve a single cell spatiotemporal atlas of 34 cell types during muscle regeneration and aging. This atlas maps interactions of immune, fibrogenic, vascular, nerve, and myogenic cells at sites of injury in relation to tissue architecture and extracellular matrix. Spatial pseudotime mapping reveals sequential cellular neighborhoods that mediate repair and a nodal role for immune cells. We confirm this role by macrophage depletion, which triggers formation of aberrant neighborhoods that obstruct repair. In aging, immune dysregulation is chronic, cellular neighborhoods are disrupted, and an autoimmune response is evident at sites of denervation. Our findings highlight the spatial cellular ecosystem that orchestrates muscle regeneration, and is altered in aging. HighlightsO_LISingle cell resolution spatial atlas resolves a cellular ecosystem of 34 cell types in multicellular neighborhoods that mediate efficient skeletal muscle repair C_LIO_LIHighly multiplexed spatial proteomics, neural network and machine learning uncovers temporal dynamics in the spatial crosstalk between immune, fibrogenic, vascular, nerve, and muscle stem cells and myofibers during regeneration C_LIO_LISpatial pseudotime mapping reveals coherent formation of multicellular neighborhoods during efficacious repair and the nodal role of immune cells in coordinating muscle repair C_LIO_LIIn aged muscle, cellular neighborhoods are disrupted by a chronically inflamed state and autoimmunity C_LI
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adrenergic signaling coordinates distant and local responses to amputation in axolotl 95%
- Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches 94%
- Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma 94%
Similar papers in this journal
- Single-cell analysis of the muscle stem cell hierarchy identifies heterotypic communication signals involved in skeletal muscle regeneration 98%
- Heterogeneity of satellite cells implicates DELTA1/ NOTCH2 signaling in self-renewal 96%
- The immune landscape of murine skeletal muscle regeneration and aging 96%
Similar papers in this journal
- Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers 96%
- Single-nuclei sequencing of skeletal muscle reveals subsynaptic-specific transcripts involved in neuromuscular junction maintenance 96%
- In vivo self-renewal and expansion of quiescent stem cells from a non-human primate 96%
Similar papers in this journal
- Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp 95%
- RNA-Binding Proteins Direct Myogenic Cell Fate Decisions 95%
- Single Cell Deconstruction of Muscle Stem Cell Heterogeneity During Aging Reveals Sensitivity to the Neuromuscular Junction 95%
Similar papers in this journal
- Muscle-secreted neurturin couples myofiber oxidative metabolism and slow motor neuron identity. 96%
- Fasting Induces a Highly Resilient Deep Quiescent State in Muscle Stem Cells via Ketone Body Signaling 96%
- Human skeletal muscle CD90+ fibro-adipogenic progenitors are associated with muscle degeneration in type 2 diabetes patients 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.