Comparison of the MHC I Immunopeptidome Repertoire of B‐Cell Lymphoblasts Using Two Isolation Methods
Bibliographic record
Abstract
Significant technological advances in both affinity chromatography and mass spectrometry have facilitated the identification of peptides associated with the major histocompatibility complex class I (MHC I) molecules, and enabled a greater understanding of the dynamic nature of the immunopeptidome of normal and neoplastic cells. While the isolation of MHC I-associated peptides (MIPs) typically used mild acid elution (MAE) or immunoprecipitation (IP), limited information currently exists regarding their respective analytical merits. Here, a comparison of these approaches for the isolation of two different B-cell lymphoblast cell models is presented, and it is reported on the recovery, reproducibility, scalability, and complementarity of identification from each method. Both approaches yielded reproducible datasets for peptide extracts obtained from 2 to 100 million cells, with 2016 to 5093 MIPs, respectively. The IP typically provides up to 6.4-fold increase in MIPs compared to the MAE. The comprehensiveness of these immunopeptidome analyses is extended using personalized genomic database of B-cell lymphoblasts, and it is discovered that 0.4% of their respective MIP repertoire harbored nonsynonymous single nucleotide variations (also known as minor histocompatibility antigens, MiHAs).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".