Fluorescence and Light Scatter Calibration Allow Comparisons of Small Particle Data in Standard Units across Different Flow Cytometry Platforms and Detector Settings
Bibliographic record
Abstract
Flow cytometers have been utilized for the analysis of submicron-sized particles since the late 1970s. Initially, virus analyses preceded extracellular vesicle (EV), which began in the 1990s. Despite decades of documented use, the lack of standardization in data reporting has resulted in a growing body of literature that cannot be easily interpreted, validated, or reproduced. This has made it difficult for objective assessments of both assays and instruments, in-turn leading to significant hindrances in scientific progress, specifically in the study of EVs, where the phenotypic analysis of these submicron-sized vesicles is becoming common-place in every biomedical field. Methods for fluorescence and light scatter standardization are well established and the reagents to perform these analyses are commercially available. However, fluorescence and light scatter calibration are not widely adopted by the small particle community as methods to standardize flow cytometry (FCM) data. In this proof-of-concept study carried out as a resource for use at the CYTO2019 workshop, we demonstrate for the first-time simultaneous fluorescence and light scatter calibration of small particle data to show the ease and feasibility of this method for standardized FCM data reporting. This data was acquired using standard configuration commercial flow cytometers, with commercially available materials, published methods, and freely available software tools. We show that application of light scatter, fluorescence, and concentration calibration can result in highly concordant data between FCM platforms independent of instrument collection angle, gain/voltage settings, and flow rate; thus, providing a means of cross comparison in standard units. © 2020 International Society for Advancement of Cytometry.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".