A pipeline for multidimensional confocal analysis of mitochondrial morphology, function, and dynamics in pancreatic β-cells
Bibliographic record
Abstract
Live-cell imaging of mitochondrial function and dynamics can provide vital insights into both physiology and pathophysiology, including of metabolic diseases like type 2 diabetes. However, without super-resolution microscopy and commercial analysis software, it is challenging to accurately extract features from dense multilayered mitochondrial networks, such as those in insulin-secreting pancreatic β-cells. Motivated by this, we developed a comprehensive pipeline and associated ImageJ plugin that enables 2D/3D quantification of mitochondrial network morphology and dynamics in mouse β-cells and by extension other similarly challenging cell types. The approach is based on standard confocal microscopy and shareware, making it widely accessible. The pipeline was validated using mitochondrial photolabeling and unsupervised cluster analysis and is capable of morphological and functional analyses on a per-organelle basis, including in 4D ( xyzt). Overall, this tool offers a powerful framework for multiplexed analysis of mitochondrial state/function and provides a valuable resource to accelerate mitochondrial research in health and disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".