Mitochondria Endoplasmic Reticulum Contact Sites (MERCs): Proximity Ligation Assay as a Tool to Study Organelle Interaction
Bibliographic record
Abstract
Organelles cooperate with each other to regulate vital cellular homoeostatic functions. This occurs through the formation of close connections through membrane contact sites. Mitochondria-Endoplasmic-Reticulum (ER) contact sites (MERCS) are one of such contact sites that regulate numerous biological processes by controlling calcium and metabolic homeostasis. However, the extent to which contact sites shape cellular biology and the underlying mechanisms remain to be fully elucidated. A number of biochemical and imaging approaches have been established to address these questions, resulting in the identification of a number of molecular tethers between mitochondria and the ER. Among these techniques, fluorescence-based imaging is widely used, including analysing signal overlap between two organelles and more selective techniques such as in-situ proximity ligation assay (PLA). While these two techniques allow the detection of endogenous proteins, preventing some problems associated with techniques relying on overexpression (FRET, split fluorescence probes), they come with their own issues. In addition, proper image analysis is required to minimise potential artefacts associated with these methods. In this review, we discuss the protocols and outline the limitations of fluorescence-based approaches used to assess MERCs using endogenous proteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".