Antibody titrations are critical for microflow cytometric analysis of extracellular vesicles
Bibliographic record
Abstract
Optimization of flow cytometry assays for extracellular vesicles (EVs) often fail to include appropriate reagent titrations - the most critically antibody titration is either not performed or is incomplete. Using nonoptimal antibody concentration is one of the main sources of error leading to a lack of reproducible data. Antibody titration for the analysis of antigens on the surface of EVs is challenging for a variety of technical reasons. Using platelets as surrogates for cells and platelet-derived particles as surrogates for EV populations, we demonstrate our process for antibody titration, highlighting some of the key analysis parameters that may confound and surprise new researchers moving into the field of EV research. Additional care must be exercised to ensure instrument and reagent controls are utilized appropriately. Complete graphical analysis of positive and negative signal intensities, concentration, and separation or stain index data is highly beneficial when paired with visual analysis of the cytometry data. Using analytical flow cytometry procedures optimized for cells for EV analysis can lead to misleading and nonreproducible results.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".