Proximity Ligation Assay (PLA) for Fillets of<i>Drosophila</i>Larvae
Bibliographic record
Abstract
The ability to detect protein–protein interactions is critical for understanding the mechanisms underlying protein and cell function. Current methods to assay protein–protein interactions, such as co-immunoprecipitation (Co-IP) and fluorescence resonance energy transfer (FRET), have limitations; for example, Co-IP is an in vitro technique and may not reflect the situation in vivo, and FRET typically suffers from low signal-to-noise ratio. The proximity ligation assay (PLA) is an in situ method for inferring protein–protein interaction with a high signal-to-noise ratio. The PLA technique can indicate that two different proteins are closely associated by the ability of two secondary antibody oligonucleotide probes to hybridize when they are close to each other. This interaction generates a signal with rolling-circle amplification using fluorescent nucleotides. Although a positive result does not indicate that two proteins directly interact, it implies a potential in vivo interaction that can then be verified in vitro. PLA uses primary antibodies against the two proteins (or epitopes) of interest, one raised in mouse and the other raised in rabbit. When these antibodies bind to proteins that lie within 40 nm of each other in the tissue, complementary oligonucleotides conjugated individually to mouse and rabbit secondary antibodies can anneal to form a template for rolling-circle amplification. Using fluorescently labeled nucleotides, rolling circle amplification generates a strong fluorescent signal in areas of the tissue where the two proteins are found together that is detected using conventional fluorescence microscopy. This protocol describes how to perform PLA in vivo on the central nervous system and peripheral nervous system of third-instar larvae of the fruit fly Drosophila melanogaster .
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".