Comparative Analysis of SPLICS and MCS-DETECT for Detecting Mitochondria-ER Contact Sites (MERCs)
Bibliographic record
Abstract
Detection of mitochondria-ER contacts (MERCs) from diffraction limited confocal images commonly uses fluorescence colocalization analysis of mitochondria and endoplasmic reticulum (ER) as well as split fluorescent probes, such as the split-GFP-based contact site sensor (SPLICS). However, inter-organelle distances (∼10–60 nm) for MERCs are lower than the 200–250 nm diffraction limited resolution obtained by standard confocal microscopy. Super-resolution microscopy of 3D volume analysis provides a two-fold resolution improvement (∼120 nm XY; 250 nm Z), which remains unable to resolve MERCs. MCS-DETECT, a membrane contact site (MCS) detection algorithm faithfully detects elongated ribosome-studded riboMERCs when applied to 3D STED super-resolution image volumes. Here, we expressed the SPLICS L reporter in HeLa cells co-transfected with the ER reporter RFP-KDEL and label fixed cells with antibodies to RFP and the mitochondrial protein TOM20. MCS-DETECT analysis of 3D STED volumes was compared to contacts determined by co-occurrence colocalization analysis of mitochondria and ER or the SPLICS L probe. Percent mitochondria coverage by MCS-DETECT derived contacts was significantly smaller than those obtained for colocalization analysis or SPLICS L , and more closely matched contact site metrics obtained by 3D electron microscopy. Further, STED analysis localized a subset of the SPLICS L label to mitochondria with some SPLICS L puncta observed to be completely enveloped by mitochondria in 3D views. These data suggest that MCS-DETECT reports on a limited set of MERCs that more closely corresponds to those observed by EM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".