Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
Bibliographic record
Abstract
Immunopeptidomics is an emerging field that fuels and guides the development of vaccines and immunotherapies. More specifically, it refers to the science of investigating the composition of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS) technology platforms. Among all the steps in an MS-based immunopeptidomics workflow, sample preparation is critically important for capturing high-quality data of therapeutic relevance. Here, step-by-step instructions are described to isolate MHC class I and II-associated peptides by immunoaffinity purification from quality control samples, from mouse (EL4 and A20), and human (JY) cell lines more specifically. The various reagents and specific antibodies are thoroughly described to isolate MHC-associated peptides from these cell lines, including the steps to verify the beads-binding efficiency of the antibody and the elution efficiency of the MHC-peptide complexes from the beads. The protocol can be used to establish and standardize an immunopeptidomics workflow, as well as to benchmark new protocols. Moreover, the protocol represents a great starting point for any non-experts in addition to foster the intra- and inter-laboratory reproducibility of the sample preparation procedure in immunopeptidomics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".