HLA Ligand Atlas DIA: extending the benign immunopeptidomics resource with increased sensitivity through data-independent acquisition mass spectrometry
Bibliographic record
Abstract
The human leukocyte antigen (HLA)-presented peptide repertoire, termed immunopeptidome, plays a crucial role for T-cell mediated immune reactions. Previously, the human immunopeptidome of non-malignant tissues has been mapped in a large-scale study, the HLA Ligand Atlas, via high-resolution data-dependent acquisition (DDA) mass spectrometry. This publicly available and user-friendly web interface (https://hla-ligand-atlas.org) is frequently used as a benign tissue reference in antigen discovery, especially for immunotherapy of cancer. Here, we extend the HLA Ligand Atlas resource with paired data-independent acquisition (DIA) runs for all tissue-subject combinations. This novel dataset comprises 946 DIA HLA class I and II immunopeptidomic runs from 242 non-malignant human samples across 18 subjects and 29 distinct tissues. Together with the published DDA runs, this extends the range and depth of analyses performed on the HLA Ligand Atlas dataset. In a concise analysis, we showcase advantages of DIA over DDA concerning spectral sampling and sensitivity. These findings are attributed to the increased dynamic range in DIA, enabling the identification of peptide transitions with low signal intensities. Moreover, we demonstrate the superior sensitivity by applying an HLA-A*02:01 allotype-specific spectral library search to identify and quantify HLA-presented peptides. We encourage reanalysis of the provided DDA and DIA data in combination as a reference for future research concerning human immunology.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".