Detection and Quantification of Microparticles from Different Cellular Lineages Using Flow Cytometry. Evaluation of the Impact of Secreted Phospholipase A2 on Microparticle Assessment
Bibliographic record
Abstract
Microparticles, also called microvesicles, are submicron extracellular vesicles produced by plasma membrane budding and shedding recognized as key actors in numerous physio(patho)logical processes. Since they can be released by virtually any cell lineages and are retrieved in biological fluids, microparticles appear as potent biomarkers. However, the small dimensions of microparticles and soluble factors present in body fluids can considerably impede their quantification. Here, flow cytometry with improved methodology for microparticle resolution was used to detect microparticles of human and mouse species generated from platelets, red blood cells, endothelial cells, apoptotic thymocytes and cells from the male reproductive tract. A family of soluble proteins, the secreted phospholipases A2 (sPLA2), comprises enzymes concomitantly expressed with microparticles in biological fluids and that catalyze the hydrolysis of membrane phospholipids. As sPLA2 can hydrolyze phosphatidylserine, a phospholipid frequently used to assess microparticles, and might even clear microparticles, we further considered the impact of relevant sPLA2 enzymes, sPLA2 group IIA, V and X, on microparticle quantification. We observed that if enriched in fluids, certain sPLA2 enzymes impair the quantification of microparticles depending on the species studied, the source of microparticles and the means of detection employed (surface phosphatidylserine or protein antigen detection). This study provides analytical considerations for appropriate interpretation of microparticle cytofluorometric measurements in biological samples containing sPLA2 enzymes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".