Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions
Bibliographic record
Abstract
Abstract Fibroblast dysregulation contributes to pathological fibrosis and aberrant repair. Emerging evidence suggest that fibroblasts accumulate in lesions following central nervous system injury, but whether and how they influence oligodendrocyte repair responses, including in aging, is uncertain. Here we report that fibroblasts accumulate in the parenchyma of spinal cord white matter lesions of 6–10 week old young mice after lysolecithin-induced demyelination. This was first observed through immunofluorescence microscopy that employed several markers attributed to fibroblasts, including platelet-derived growth factor-β, collagen type 1α1, α-smooth muscle actin, periostin and fibronectin; and by the use of platelet-derived growth factor-β TdTomato reporter transgenic mice. Spatial transcriptomics and single-nucleus RNA sequencing of lysolecithin lesions established the presence of fibroblasts in lysolecithin lesions and delineated them from closely related pericytes. CellChat ligand – receptor analyses highlight fibroblasts in the lysolecithin environment as a major source of input of signals for microglia/macrophages and oligodendrocyte precursor cells, with numerous reciprocal interactions. The infiltration of fibroblasts was promoted by microglia/macrophages, as anticipated by their temporal representation in lysolecithin lesions, and by tissue culture experiments where the migration of fibroblasts was enhanced by macrophages. Particularly relevant to regenerative events that occur spontaneously after lysolecithin demyelination, the areas of fibroblast accumulation were devoid of oligodendrocyte precursor cells. In tissue culture, oligodendrocyte precursor cells were excluded from fibroblast domains. Moreover, fibroblast accumulation after lysolecithin injury was enhanced with increasing age, a known detriment to the capacity to remyelinate after injury, and exclusion of oligodendrocyte precursor cells from fibroblast areas of 48–52 week mice exceed that occurring in younger 6–10 weeks animals. Finally, by mining a publicly available single-nucleus RNA database of multiple sclerosis, we found fibroblasts in the edge of chronic active and chronic inactive lesions and in lesion core, and fewer in periplaque or normal white matter. There were several communication networks between fibroblasts, microglia/macrophages and oligodendrocyte precursor cells in these MS lesions. Our collective results demonstrate a role of fibroblasts in demyelination-associated neuropathology, which is exacerbated by aging, and highlight the importance of regulating fibroblasts to promote effective CNS repair.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".