New Method for Quantitation of Lipid Droplet Volume From Light Microscopic Images With an Application to Determination of PAT Protein Density on the Droplet Surface
Bibliographic record
Abstract
Determination of lipid droplet (LD) volume has depended on direct measurement of the diameter of individual LDs, which is not possible when LDs are small or closely apposed. To overcome this problem, we describe a new method in which a volume-fluorescence relationship is determined from automated analysis of calibration samples containing well-resolved LDs. This relationship is then used to estimate total cellular droplet volume in experimental samples, where the LDs need not be individually resolved, or to determine the volumes of individual LDs. We describe quantitatively the effects of various factors, including image noise, LD crowding, and variation in LD composition on the accuracy of this method. We then demonstrate this method by utilizing it to address a scientifically interesting question, to determine the density of green fluorescent protein (GFP)-tagged Perilipin-Adipocyte-Tail (PAT) proteins on the LD surface. We find that PAT proteins cover only a minority of the LD surface, consistent with models in which they primarily serve as scaffolds for binding of regulatory proteins and enzymes, but inconsistent with models in which their major function is to sterically block access to the droplet surface.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".