From cellular waste to biomarkers; insights into past, present, and future methods to detect immune cell-derived extracellular vesicles using flow cytometry
Bibliographic record
Abstract
Extracellular vesicles (EVs) often possess both ubiquitous and unique tetraspanin molecules that can help elucidate their cell-of-origin. Furthermore, the presence and/or absence of specific tetraspanins can be used to phenotype and identify specific subpopulations of EVs. In the context of immune-related disorders (i.e. multiple sclerosis, rheumatoid arthritis, and various cancers), specific immune cell-derived EVs are now being investigated in the context of biomarker exploration, identifying novel disease mechanisms, and monitoring therapeutic responses in patients. Flow cytometry (FCM) is a technique that uses the light scattering properties of cells and/or subcellular particles (e.g. EVs), while combining fluorescent signals that can detect the presence, absence, or abundance of surface and/or intracellular molecules. To date, however, using FCM to accurately quantify EV populations has been challenging due to their relatively small size and weak light scattering and fluorescence properties compared to intact cells. Historically, the application of calibration beads of known sizes, refractory indices, a violet-side scatter, and standardized methodologies have made positive contributions towards the accurate detection and quantification of EVs while also permitting exploration into their biological properties. This review provides a summary and perspective of current FCM methodologies that are used to assess immune cell-derived EVs within biological fluids and cell supernatants. While acknowledging past and current limitations, as well as the recent successes, improvements, and efficiencies of assays used in EV-related research, the field will inevitably continue to advance through the implementation of standards and guidelines to enhance discovery and reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".