Proteome profiling of extracellular vesicles captured with the affinity peptide Vn96: comparison of Laemmli and TRIzol© protein‐extraction methods
Bibliographic record
Abstract
Sample amount is often a limiting factor for multi-parametric analyses that encompass at least three areas of '-omics' research: genomics, transcriptomics and proteomics. Limited sample amounts are also an important consideration when these multi-parametric analyses are performed on extracellular vesicles (EVs), as the amount of EVs (and EV cargo) that can be isolated is often very low. It is well understood that a monophasic solution of phenol and guanidine isothiocyanate (i.e. TRIzol©) can simultaneously isolate DNA, RNA and proteins from biological samples; however, it is most commonly used for the extraction of RNA. Validation of this reagent for the isolation of multiple classes of biological molecules from EVs would provide a widely applicable method for performing multi-parametric analyses of EV material. In this report, we describe a comparison of proteins identified from EVs processed with either TRIzol© or the conventional Laemmli buffer protein-extraction reagents. EVs were isolated from 3 mL of cell-culture supernatant derived from MCF-10A, MCF-7 and MDA-MB-231 cells using the Vn96 EV capture technology. For the TRIzol© extraction protocol, proteins were precipitated with acetone from the organic phase and then re-solubilized in a mixture of 8M urea, 0.2% SDS and 1 M Tris-HCl pH 6.8, followed by dilution in 5× loading buffer prior to fractionation with 1D SDS-PAGE. NanoLC-MS/MS of the trypsin-digested proteins was used to generate proteomic profiles from EV protein samples extracted with each method. Of the identified proteins, 57.7%, 69.2% and 57.0% were common to both extraction methods for EVs from MCF-10A, MCF-7 and MDA-MB-231, respectively. Our results suggest that TRIzol© extraction of proteins from EVs has significant equivalence to the traditional Laemmli method. The advantage of using TRIzol
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".