P05.04.B ENRICHMENT AND ANALYTICAL METHODS IN CSF PROTEOMICS: EACH NAIL NEEDS A DIFFERENT HAMMER
Bibliographic record
Abstract
Abstract BACKGROUND While proteomic analysis of CSF is emerging as a promising field in neuro-oncology, numerous enrichment and analytical approaches are possible and one size does not fit all. Leveraging the high volume of CSF available through our multi-agent intraventricular therapy program for CNS tumors, we report a systematic technical comparison of various proteomic analytic methods. MATERIAL AND METHODS Samples: Cohort 1: 5 patients (3 CNS lymphoma, 2 CNS leukemia); Cohort 2: 15 CNS lymphoma; Cohort 3: 19 CNS lymphoma.EV isolation: Volume optimization (1.5-9mL ranges) for EV isolation was conducted on Cohort 1. Nanoparticle tracking was used to measure concentration and size distribution of EV fractions. Reproducibility of the fractionation process was evaluated in pooled samples from Cohort 2 (6mL replicates). Evaluation of enrichment methods: Cohort 3 samples were used for evaluation of peptide detection profiles across MStern (50μL), Seer (250μL), N-glycoproteomics (N-Gp, 200μL), P150 and P20 (both EV isolation techniques, 6mL). Bias toward specific biophysical properties based on method was evaluated. RESULTS EV isolation: Based on EV-specific peptides detected, 6mL was established as the lowest feasible input volume. The P20 fractions showed consistently higher protein detection and particle concentration than P150. Evaluation of enrichment methods: Quantitative analysis revealed substantial variation in detection capacity; Seer had the greatest average peptide identification (17,000), followed by P20 (9000), MStern (5,500), P150 (5,000) and N-Gp (1,000). Seer had the greatest number of unique peptides. Principle component analysis showed assay led to greater variance than patient differences. Seer had highest reproducibility, followed by MStern & P20 > P150 > N-Gp. Biophysical properties were similar with all methods except N-Gp, which showed slight preferential enrichment of acidic glycopeptides and preponderance for larger, hydrophilic peptides. Cellular compartment enrichment was complementary: P20 (mitochondrial), N-GP (lysosomal), and Seer (nuclear). Pathway and functional enrichment patterns were complementary across methods as well. CONCLUSION In CSF proteomics, one size does not fit all, and each enrichment method yields complementary information. EV analysis can be conducted on as little as 6mL of CSF. While Seer demonstrates greatest yield and reproducibility, cost is very high. MStern may represent a balance of moderate peptide detection, limited input volume and cost, and high throughput for discovery proteomics. Future optimization will evaluate different mass spectrometry scan modes, such as data intendent acquisition (DIA-MS) to further enhance throughput and quantitative precision.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.037 |
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".