Integrating <i>in silico</i> predictions with an engineered tissue assay identifies Perlecan as an age-perturbed re-quiescence cue for muscle stem cells
Bibliographic record
Abstract
Abstract Skeletal muscle regeneration is mediated by resident muscle stem cells that produce progeny to repair or recreate muscle. Critical to this function is the ability to transition between states of proliferation and quiescence. This balancing act is disrupted with age, leading to eroded regenerative capacity. Notwistanding, mechanisms by which the regenerating niche directs MuSCs return to the dormant state are largely unknown. Since single-cell RNAseq methods exclude the analysis of multinucleated cells, we generated single-nuclei RNAseq datasets of regenerating muscle to capture the full breadth of myogenic progression. With this, we uncovered new transition states between differentiating myocytes and syncytial multinucleated cells. Using cell communication inference tools, we highlighted receptor-ligand interactions between MuSCs and fusing nuclei. We leveraged a bespoke biomimetic 3D niche that induces MuSC quiescence, to filter the predicted interactions using a Cas9-based functional genomics approach. We found the proteoglycan Perlecan (Hspg2) promotes MuSC re-quiescence. Hspg2 silencing in vivo , during muscle regeneration, induced an aging-like phenotype and perturbed MuSC re-quiescence. Notably, the temporal profile and overall levels of Perlecan were altered with age. Exogenous supplementation with Endorepellin (truncated Perlecan) rescued MuSC decline following aged muscle regeneration, offering a new therapeutic target. Thus, coupling in silico predictions with a 3D in vitro assay followed by in vivo investigations revealed a previously inaccessible window of biology; that spatiotemporal coordination of MuSC re-quiescence is directed by fusing myonuclei.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".