Monitoring Autophagic Flux by Using Lysosomal Inhibitors and Western Blotting of Endogenous MAP1LC3B
Bibliographic record
Abstract
Assays that monitor autophagic flux, or degradative completion of autophagy, are crucial for the assessment of the dynamic autophagy process in a variety of systems. Such assays help to distinguish between an increase in autophagosomes resulting from induced autophagic activity versus an increase in autophagosomes due to reduced lysosomal turnover. The majority of flux assays use autophagy protein MAP1LC3B (microtubule-associated proteins 1A/1B light chain 3B, here referred to as LC3B) as a marker for autophagy, and most are based on the use of reporters. Here, we describe a method, suitable for monitoring flux in primary cells and/or when reporters are not available or desirable, that uses lysosomal inhibitors and the analysis of endogenous LC3B-II (the lipidated form of LC3B that is associated with autophagosomes) by western blotting. A common application of this method, detailed here, is to test whether a treatment of interest (e.g., chemotherapy drug) induces autophagic flux in the cells of interest. If it is found that there is no difference in LC3B-II levels between treatment with lysosomal inhibitor alone versus drug plus lysosomal inhibitor, then this suggests that the drug is not inducing autophagic flux. Elevated levels of LC3B-II in treatments with drug plus lysosomal inhibitor, compared with drug treatment alone and inhibitor treatment alone, indicate that the drug is probably leading to an increase in autophagic flux.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".