Evolution of tools and methods for monitoring autophagic flux in mammalian cells
Bibliographic record
Abstract
Autophagy is an evolutionarily conserved lysosome-mediated degradation and recycling process, which functions in cellular homeostasis and stress adaptation. The process is highly dynamic and involves autophagosome synthesis, cargo recognition and transport, autophagosome-lysosome fusion, and cargo degradation. The multistep nature of autophagy makes it challenging to quantify, and it is important to consider not only the number of autophagosomes within a cell but also the autophagic degradative activity. The rate at which cargos are recognized, segregated, and degraded through the autophagy pathway is defined as autophagic flux. In practice, methods to measure autophagic flux typically evaluate the lysosome-mediated cargo degradation step by leveraging known autophagy markers such as MAP1LC3B (microtubule-associated proteins 1A/1B light chain 3 beta) or lysosome-dependent fluorescent agents. In this review, we summarize the tools and methods used in mammalian cultured cells pertaining to these two approaches, and highlight innovations that have led to their evolution in recent years. We also discuss the potential limitations of these approaches and recommend using a combination of strategies and multiple different autophagy markers to reliably evaluate autophagic flux in mammalian cells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".