A Novel Semiconductor-Based Flow Cytometer with Enhanced Light-Scatter Sensitivity for the Analysis of Biological Nanoparticles
Bibliographic record
Abstract
The CytoFLEX is a novel semiconductor-based flow cytometer that utilizes avalanche photodiodes, wavelength-division multiplexing, enhanced optics, and diode lasers to maximize light capture and minimize optical and electronic noise. Due to an increasing interest in the use of extracellular vesicles (EVs) as disease biomarkers, and the growing desire to use flow cytometry for the analyses of biological nanoparticles, we assessed the light-scatter sensitivity of the CytoFLEX for small-particle detection. We found that the CytoFLEX can fully resolve 70 nm polystyrene and 98.6 nm silica beads by violet side scatter (VSSC). We further analyzed the detection limit for biological nanoparticles, including viruses and EVs, and show that the CytoFLEX can detect viruses down to 81 nm and EVs at least as small as 65 nm. Moreover, we could immunophenotype EV surface antigens, including directly in blood and plasma, demonstrating the double labeling of platelet EVs with CD61 and CD9, as well as triple labeling with CD81 for an EV subpopulation in one donor. In order to assess the refractive indices (RIs) of the viruses and EVs, we devised a new method to inversely calculate the RIs using the intensity vs. size data together with Mie-theory scatter efficiencies scaled to reference-particle measurements. Each of the viruses tested had an equivalent RI, approximately 1.47 at 405 nm, which suggests that flow cytometry can be more broadly used to easily determine virus sizes. We also found that the RIs of EVs increase as the particle diameters decrease below 150 nm, increasing from 1.37 for 200 nm EVs up to 1.61 for 65 nm EVs, expanding the lower range of EVs that can be detected by light scatter. Overall, we demonstrate that the CytoFLEX has an unprecedented level of sensitivity compared to conventional flow cytometers. Accordingly, the CytoFLEX can be of great benefit to virology and EV research, and will help to expand the use of flow cytometry for minimally invasive liquid biopsies by allowing for the direct analysis of antigen expression on biological nanoparticles within patient samples, including blood, plasma, urine and bronchoalveolar lavages.
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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 | 0.001 | 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".