A single cell spatial temporal atlas of skeletal muscle reveals cellular neighborhoods that orchestrate regeneration and become disrupted in aging
Bibliographic record
Abstract
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. Highlights Single cell resolution spatial atlas resolves a cellular ecosystem of 34 cell types in multicellular neighborhoods that mediate efficient skeletal muscle repair Highly 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 Spatial pseudotime mapping reveals coherent formation of multicellular neighborhoods during efficacious repair and the nodal role of immune cells in coordinating muscle repair In aged muscle, cellular neighborhoods are disrupted by a chronically inflamed state and autoimmunity
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".