Optimizing a Novel Fate-Mapping Strategy to Assess Tissue Resident Macrophages in Muscle Regeneration
Bibliographic record
Abstract
Tissue resident macrophages (TRMs) are heterogenous innate immune cells involved in tissue homeostasis and repair, yet their role in muscle regeneration is currently unknown. TRMs differ from classical monocyte-derived macrophages (MDMs) in both ontogeny and function. Our inability to differentiate TRMs from MDMs has limited the development of subset specific immunotherapies. The novel Cx3cr1CreER/+:RosaTd/+ fate-mapping mouse model employs tamoxifen inducible labeling of MDMs and TRMs with a TdTomato reporter (Td) that persists only in self-renewing TRMs, while MDMs are replaced by Td- monocyte progenitors over a 4-week period. This strategy has been successfully used in adult mice heart and brain tissue. However, in the skeletal muscle, expression level of the Cx3cr1 reporter gene is unknown. We assessed muscle TRM Cx3cr1 expression in neonate (P3), young (P21), and adult (10wk) mice to optimize a pulse/chase strategy to label muscle TRMs with the Td reporter. Like heart and brain tissue, we found muscle TRMs express high levels of Cx3cr1 throughout development and adulthood suggesting viability of this strategy. We performed a single injection of tamoxifen in adult Cx3cr1CreER/+:RosaTd/+ mice and showed ~20% induction in blood monocytes and muscle TRMs. Although Td induction was poor, levels were similar between the blood and muscle, suggesting accuracy of the model. The next step is to optimize tamoxifen administration to achieve >90% labelling in blood and muscle. To assess spatial localization of TRMs with muscle resident stem cells (satellite cells) we optimized immunofluorescence staining on tissue sections. We found a statistically significant increase in colocalization of macrophages with satellite cells in healthy and injured gastrocnemius tissue, consistent with their predicted role in satellite cell proliferation. Once complete, our fate-mapping approach will allow for the labelling of muscle TRM subsets and reveal their role in the regeneration process.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".