Lysosome Targeting RedGreen-assay: Selective Autophagy Sensing Assay for Mammalian Cells
Bibliographic record
Abstract
The process of autophagy is an essential cellular mechanism, required to maintain general cell health through the removal of dysfunctional organelles, such as the ER, peroxisomes and mitochondria, as well as protein aggregates, and bacteria. Autophagy is an extremely dynamic process, and tools are constantly being developed to study the various steps of this process. This protocol details a method to study the end steps of autophagy-lysosomal fusion and the formation of the autolysosome. Many techniques have been used to study the various steps of the autophagy process. Here we describe the RedGreen-assay (RG-assay), an immunofluorescence-based technique used to visualize the targeting of substrates to the autolysosome in live cells. This technique takes advantage of the low lysosomal pH and over-expression of a tandem GFP-mCherry tagged protein targeted to an organelle of interest. While in the neutral cytosol or autophagosome, both GFP and RFP will fluoresce. However, within the autolysosome, the GFP signal is quenched due to the low pH environment and the RFP emission signal will predominate. This technique is readily quantifiable and amenable to high throughput experiments. Additionally, by tagging the GFP-RFP tandem fluorescent protein with organelle specific targeting sequences, it can be used to measure a wide range of substrates of autophagy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".