A non‐fluorescent immunohistochemistry method for measuring autophagy flux using <scp>MAP1LC3</scp>/<scp>LC3</scp> and <scp>SQSTM1</scp> as core markers
Bibliographic record
Abstract
Macroautophagy/autophagy is a crucial cellular process for degrading and recycling damaged proteins and organelles, playing a significant role in diseases such as cancer and neurodegeneration. Evaluating autophagy flux, which tracks autophagosome formation, maturation, and degradation, is essential for understanding disease mechanisms. Current fluorescence-based methods are resource-intensive, requiring advanced equipment and expertise, limiting their use in clinical laboratories. Here, we introduce a non-fluorescent immunohistochemistry (IHC) method using MAP1LC3/LC3 and SQSTM1 as core markers for autophagy flux assessment. LC3 levels reflect autophagosome formation, whereas SQSTM1 degradation and a decrease in the number of its puncta indicate active flux (i.e., lysosomal turnover). We optimized chromogenic detection using diaminobenzidine (DAB) staining and developed a scoring system based on puncta number and the percentage of stained cells. This accessible, cost-effective method enables reliable autophagy quantification using a standard light microscope, bridging the gap between experimental research and clinical diagnostics. Our protocol allows accurate autophagy evaluation in fixed tissues, offering practical applications in biomedical research and clinical pathology assessment.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".