Analytical Considerations in Nanoscale Flow Cytometry of Extracellular Vesicles to Achieve Data Linearity
Bibliographic record
Abstract
BACKGROUND: Platelet microparticles (PMPs) and their abundance in the blood are a prognostic biomarker in thrombotic disorders and cancer. Nanoscale flow cytometry (nFC) is ideal for high-throughput analysis of PMPs but these clinical assays have not been developed previously. OBJECTIVE: This article demonstrates that nFC is a suitable technology to enumerate PMPs present in plasma samples in a clinical setting. MATERIALS AND METHODS: nFC was performed using the Apogee A50-Micro instrument. Instrument settings and acquisition parameters were developed with the use of fluorescent beads and plasma samples. Sample preparation and handling was also optimized. RESULTS: nFC allows for linear detection of particles between approximately 200 and 1,000 nm based on calibration beads and was dependent on dilution factor and flow rate. Linearity in event analysis as samples became more diluted was lost when events approximately 100 nm were gated while linearity was maintained despite dilution of sample in events larger than 200 nm in diameter. Higher flow rates lead to an under-estimation of events analysed per microlitre of analyte and this was more pronounced when plasma samples were not diluted more than 1/20×. CONCLUSION: nFC offers multi-parametric analysis of PMPs when optimal calibration of acquisition and sample processing settings is performed. Analysis of plasmas from metastatic prostate cancer patients and leukaemia patients revealed that PMP levels were larger than 100 nm and were equally abundant in patients that responded to or failed androgen deprivation therapy or between patients representing different stages of leukaemia.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".