Immunoaffinity based methods are superior to kits for purification of prostate derived extracellular vesicles from plasma samples
Bibliographic record
Abstract
BACKGROUND: The ability to isolate extracellular vesicles (EVs) such as exosomes or microparticles is an important method that is currently not standardized. While commercially available kits offer purification of EVs from biofluids, such purified EV samples will also contain non-EV entities such as soluble protein and nucleic acids that could confound subsequent experimentation. Ideally, only EVs would be isolated and no soluble protein would be present in the final EV preparation. METHODS: We compared commercially available EV isolation kits with immunoaffinity purification techniques and evaluated our final EV preparations using atomic force microscopy (AFM) and nanoscale flow cytometry (NFC). AFM is the only modality capable of detecting distinguishing soluble protein from EVs which is important for downstream proteomics approaches. NFC is the only technique capable of quantitating the proportion of target EVs to non-target EVs in the final EV preparation. RESULTS: To determine enrichment of prostate derived EVs relative to non-target MPs, anti-PSMA (Prostate Specific Membrane Antigen) antibodies were used in NFC. Antibody-based immunoaffinity purification generated the highest quality of prostate derived EV preparations due to the lack of protein and RNA present in the samples. All kits produced poor purity EV preparations that failed to deplete the sample of plasma protein. CONCLUSIONS: While attractive due to their ease of use, EV purification kits do not provide substantial improvements in isolation of EVs from biofluids such as plasma. Immunoaffinity approaches are more efficient and economical and will also eliminate a significant portion of plasma proteins which is necessary for downstream approaches.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".