Tissue imaging reveals disruption of epithelial mitochondrial networks and loss of mitochondria-associated cytochrome-C in inflamed human and murine colon
Bibliographic record
Abstract
Mitochondrial dysfunction as defined by transcriptomic and proteomic analysis of biopsies or ultra-structure in transmission electron microscopy occurs in inflammatory bowel disease (IBD); however, mitochondrial dynamics in IBD have received minimal attention, with most investigations relying on cell-based in vitro models. We build on these studies by adapting the epithelial cell immunofluorescence workflow to imaging mitochondrial networks in normal and inflamed colonic tissue (i.e., murine di-nitrobenzene sulphonic acid (DNBS)-induced colitis, human ulcerative colitis). Using antibodies directed to TOMM20 (translocase of outer mitochondrial membrane 20) and cytochrome-C, we have translated the cell-based protocol for high-fidelity imaging to examine epithelial mitochondria networks in intact intestine. In epithelia of non-inflamed small or large intestinal tissue, the mitochondrial networks were dense and compact. This pattern was more pronounced in the basal region of the cell compared to that between the nucleus and apical surface facing the gut lumen. In comparison, mitochondrial networks in inflamed tissue displayed substantial loss of TOMM20+ staining. The remaining networks were less dense and fragmented, and contained isolated spherical mitochondrial fragments. The degree of mitochondrial network fragmentation mirrored the severity of inflammation, as assessed by blinded semi-quantitative scoring. As an indication of poor cell ‘health’ or viability, cytosolic cytochrome-C was observed in enterocytes with highly fragmented mitochondria. Thus, high-resolution and detailed visualization of mitochondrial networks in tissue is a feasible and valuable approach to assess disease, suited to characterizing mitochondrial abnormalities in tissue. We speculate that drugs that maintain a functional remodelling mitochondrial network and limit excess fragmentation could be a valuable addition to current therapies for IBD.
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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 | 0.000 | 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 | 0.000 |
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 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".