Flow cytometry‐assisted purification and proteomic analysis of the corticotropes dense‐core secretory granules
Bibliographic record
Abstract
The field of organellar proteomics has emerged as an attempt to minimize the complexity of the proteomics data obtained from whole cell and tissue extracts while maximizing the resolution on the protein composition of a single subcellular compartment. Standard methods involve lengthy density-based gradient and/or immunoaffinity purification steps followed by extraction, 1-DE or 2-DE, gel staining, in-gel tryptic digestion, and protein identification by MS. In this paper, we present an alternate approach to purify subcellular organelles containing a fluorescent reporter molecule. The gel-free procedure involves fluorescence-assisted sorting of the secretory granules followed by gentle extraction in a buffer compatible with tryptic digestion and MS. Once the subcellular organelle labeled, this procedure can be done in a single day, requires no major modification to any instrumentation and can be readily adapted to the study of other organelles. When applied to corticotrope secretory granules, it led to a much enriched granular fraction from which numerous proteins could be identified through MS.
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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 | 0.000 | 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".