Single-cell analysis of autophagy activity in normal and de novo transformed human mammary cells
Bibliographic record
Abstract
Assessment of autophagy activity has historically been limited to investigations of fixed tissue or bulk cell populations. To address questions of heterogeneity and relate measurements to functional properties of viable cells isolated from primary tissue, we created a lentiviral (RFP-GFP-MAP1LC3B) vector that allows the autophagosome and autolysosome content of transduced cells to be monitored at the single-cell level. Use of this strategy to analyze purified subsets of normal human mammary cells showed that both the luminal progenitor-containing (LP) subset and the basal cells (BCs) display highly variable but overall similar autophagic flux activity despite differences suggested by measurements of the proteins responsible (i.e., LC3B, ATG7 and BECLIN1) in bulk lysates. Autophagosome content was also highly variable in the clonogenic cells within both the LPs and BCs, but the proliferative response of the BCs was more sensitive to autophagy inhibition. In addition, use of this vector showed cells with the lowest autophagosome content elicited the fastest tumor growth in 2 different models of human mammary tumorigenesis. These results illustrate the utility of this vector to define differences in the autophagy properties of individual cells in primary tissue and couple these with their responses to proliferative and oncogenic stimuli.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".