Abstract 3624: A low-cost kit for gentle, effective and timely extracellular vesicle (GET EVs) isolation: Accelerating development of RNA-based liquid biopsies for neuroendocrine neoplasms
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) are cell fragments released by all cells, making them promising platforms for cancer biomarker development. Effective isolation of ultra-pure EVs for downstream analyses is key for EV-based liquid biopsy development. However, current EV isolation methods such as ultracentrifugation (UC) and other commercialized EV isolation kits are lackluster because of damage to EVs, length of time required, and low EV recovery rates. In response, we developed an “GET EV” kit which is based on the principles of lipid energy states, also known as the aqueous two-phase system (ATPS) to isolate EVs from any media. Nanoscale flow cytometry (nFC) analysis revealed that ATPS has greater EV enrichment capability (21.4 × vs. 10.9 × times fold enrichment) and higher EV recovery efficiency (97.6% vs. 69.3% recovery) than UC. Other EV isolation kits were tested and were inferior to ATPS in terms of EV recovery and EV-RNA recovery. EV subpopulations as determined by EV biomarkers (CD9, CD63, CD81, etc.) were confirmed by nFC. “Omics” studies (small RNA sequencing and proteomics) also confirmed the presence of expected EV markers. We developed an RNA-based liquid biopsy for Neuroendocrine Neoplasms (NENs) by quantitating the presence of EVs containing miR-375, an established biomarker for NENs. Using the GET EVs kit, we isolated EVs from healthy volunteer and patient plasma samples of NEN patients. RT-qPCR demonstrated enriched expression of miR-375 in EVs isolated from Lung and GI NEN patient plasma samples. Our study establishes the use of GET EV kits to efficiently, rapidly, and gently isolate EVs from small plasma samples (250 uL). This is a low-cost innovation that will finally enable the development of other RNA-based Liquid Biopsies for other disease sites with significant diagnostic and prognostic implications. Citation Format: Boyang Su, Morteza Jeyhani, Xiaojing Yang, Jina Nanayakkara, Reese Wunsche, Neil Renwich, Scott Tsai, Hon Leong. A low-cost kit for gentle, effective and timely extracellular vesicle (GET EVs) isolation: Accelerating development of RNA-based liquid biopsies for neuroendocrine neoplasms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3624.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".