A novel algorithm identifies stress-induced alterations in mitochondrial connectivity and inner membrane structure from confocal images
Bibliographic record
Abstract
Mitochondria exist as a highly interconnected network that is exquisitely sensitive to variations in nutrient availability, as well as a large array of cellular stresses. Changes in length and connectivity of this network, as well as alterations in the mitochondrial inner membrane (cristae), regulate cell fate by controlling metabolism, proliferation, differentiation, and cell death. Given the key roles of mitochondrial dynamics, the process by which mitochondria constantly fuse and fragment, the measure of mitochondrial length and connectivity provides crucial information on the health and activity of various cell populations. However, despite the importance of accurately measuring mitochondrial networks, the tools required to rapidly and accurately provide this information are lacking. Here, we developed a novel probabilistic approach to automatically measure mitochondrial length distribution and connectivity from confocal images. This method accurately identified mitochondrial changes caused by starvation or the inhibition of mitochondrial function. In addition, we successfully used the algorithm to measure changes in mitochondrial inner membrane/matrix occurring in response to Complex III inhibitors. As cristae rearrangements play a critical role in metabolic regulation and cell survival, this provides a rapid method to screen for proteins or compounds affecting this process. The algorithm will thus provide a robust tool to dissect the molecular mechanisms underlying the key roles of mitochondria in the regulation of cell fate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".