SDRF-Proteomics

1. Status of this Template

This document provides a template for annotating immunopeptidomics experiments in SDRF-Proteomics format. This template defines additional columns that can be combined with any core template (human, vertebrates, etc.) to capture immunopeptidomics-specific metadata.

Type: Specialized Template (used in combination with core templates)

Status: Draft - This template is under active development and may change significantly.

Version: 1.0.0-dev - 2026-01

2. Abstract

Immunopeptidomics focuses on the identification of peptides presented by major histocompatibility complex (MHC) molecules on the cell surface. These MHC-bound peptides are essential for understanding T-cell immune responses, antigen presentation, tumor-associated antigens for cancer immunotherapy, and vaccine development.

Immunopeptidomics experiments have unique characteristics: specialized MHC enrichment methods, naturally processed short peptides (8-15 aa for MHC-I, 12-30 aa for MHC-II), MHC polymorphism requiring typing information, and no enzymatic digestion. This template defines the additional metadata requirements for annotating immunopeptidomics datasets.

3. Connections to Other Omics Fields

Immunopeptidomics data is often integrated with:

  • Genomics/Exome sequencing: For neoantigen identification in cancer research

  • Transcriptomics: To correlate peptide presentation with gene expression

  • HLA typing databases: IMGT/HLA database for allele information

  • Immunological databases: IEDB (Immune Epitope Database) for epitope information

When available, linking SDRF files to related genomic or transcriptomic data through BioSamples accession numbers is highly RECOMMENDED.

4. Additional Ontologies

In addition to the ontologies supported by the core SDRF-Proteomics specification, immunopeptidomics templates utilize:

  • IMGT/HLA nomenclature: For HLA allele naming (https://www.ebi.ac.uk/ipd/imgt/hla/)

  • MHC Restriction Ontology (MRO): For MHC molecule classification

  • Vaccine Ontology (VO): For immunization-related terms when applicable

5. Checklist

This section defines the metadata columns required and recommended for immunopeptidomics experiments.

5.1. Required Columns

The following columns are REQUIRED for immunopeptidomics experiments in addition to the core SDRF-Proteomics requirements:

Column Name Description Cardinality Ontology/CV Example Values

characteristics[immunopeptidome enrichment method]

Method used to enrich for MHC-bound peptides from cell surface

1

EFO, PRIDE CV

immunoaffinity purification, immunoaffinity purification (iodoacetamide), mild acid elution, anti-pan MHC class I antibody (W6/32)

characteristics[MHC class]

Class of MHC molecules being studied

1

Controlled vocabulary

MHC class I, MHC class II, MHC class I and II, non-canonical

5.2. Recommended Columns

The following columns are RECOMMENDED for immunopeptidomics experiments:

Column Name Description Cardinality Ontology/CV Example Values

characteristics[MHC typing]

MHC allelic composition of the sample following appropriate nomenclature (IMGT/HLA for human)

0..*

IMGT/HLA nomenclature for human, appropriate nomenclature for other species

HLA-A*02:01, HLA-B*07:02, HLA-C*07:02, H-2Kb, H-2Db

characteristics[MHC typing method]

Method used to determine MHC alleles

0..1

Free text or controlled vocabulary

sequence-based typing, PCR-SSO, NGS-based typing, inferred from mass spectrometry

comment[cell number]

Number of cells used for immunopeptidome extraction

0..1

Numeric value with unit

1e8 cells, 5e7 cells, 2e9 cells

comment[tissue mass]

Mass of tissue used for immunopeptidome extraction when applicable

0..1

Numeric value with unit

500 mg, 1 g

comment[elution conditions]

Conditions used for peptide elution from MHC molecules

0..1

Free text description

0.1% TFA at room temperature, 10% acetic acid, mild acid elution pH 2.5

comment[antibody used]

Specific antibody clone used for immunoaffinity purification

0..1

Free text or MRO (MHC Restriction Ontology) (https://www.ebi.ac.uk/ols4/ontologies/mro)

W6/32 (pan-HLA class I), L243 (HLA-DR), BB7.2 (HLA-A2)

comment[MHC binding prediction]

Whether MHC binding predictions were performed and with which tool

0..1

Free text description

NetMHCpan 4.1, MHCflurry 2.0, not performed

5.3. Sample Preparation Context

Immunopeptidomics samples often do NOT use enzymatic digestion. When no enzyme is used:

  • Set comment[cleavage agent details] to not applicable

  • This indicates peptides were naturally processed

For samples that include an additional enzymatic digestion step (e.g., for database construction):

  • Annotate the enzyme used following standard SDRF-Proteomics conventions

6. MHC Typing Annotation

MHC typing information is critical for immunopeptidomics data interpretation. When annotating MHC types:

6.1. Nomenclature

Follow the appropriate MHC nomenclature standard for your organism:

For Human (HLA):

  • Format: HLA-Gene*Allele_group:Protein

  • Example: HLA-A*02:01

For multiple alleles, use multiple characteristics[MHC typing] columns or separate alleles with semicolons:

source name

characteristics[MHC typing]

characteristics[MHC typing]

characteristics[MHC typing]

sample_001

HLA-A*02:01

HLA-A*03:01

HLA-B*07:02

Or alternatively:

source name

characteristics[MHC typing]

sample_001

HLA-A*02:01;HLA-A*03:01;HLA-B*07:02;HLA-B*44:02;HLA-C*07:02;HLA-C*05:01

6.2. Resolution Levels

For human HLA types, different resolution levels can be reported:

  • Two-field (protein level): HLA-A*02:01 - RECOMMENDED minimum

  • Three-field (synonymous variants): HLA-A*02:01:01

  • Four-field (intronic variants): HLA-A*02:01:01:01

Use the highest resolution available from your typing method.

6.3. Unknown MHC Types

When MHC typing is not available:

  • Use not available if typing was not performed

  • Use not determined if typing was attempted but failed

7. Example SDRF File

A minimal example for an immunopeptidomics experiment:

source name characteristics[cell line] characteristics[MHC class] characteristics[MHC typing] ... comment[cell number] comment[antibody used] comment[cleavage agent details] comment[data file] factor value[cell line]
JY_cells_rep1 JY MHC class I HLA-A*02:01;HLA-B*07:02;HLA-C*07:02 ... 1e8 cells W6/32 not applicable JY_rep1.raw JY
JY_cells_rep2 JY MHC class I HLA-A*02:01;HLA-B*07:02;HLA-C*07:02 ... 1e8 cells W6/32 not applicable JY_rep2.raw JY
Sample metadata Data file metadata
Note
 The …​ column indicates omitted columns (organism, disease, immunopeptidome enrichment method, biological replicate, assay name, technology type, fraction identifier, label, instrument).

8. Best Practices for Immunopeptidomics Annotation

  1. Always specify MHC class: Indicate whether the experiment targets MHC class I, class II, or both.

  2. Provide MHC typing when available: MHC information is essential for interpreting binding motifs and comparing datasets.

  3. Document enrichment protocol details: Include antibody clone names, elution conditions, and any modifications to standard protocols.

  4. Report cell or tissue quantities: This information is crucial for assessing sensitivity and comparing yields across experiments.

  5. Link to patient/sample metadata: Use characteristics[individual] and characteristics[biosample accession number] when working with clinical samples.

  6. Note when enzyme is not used: Set cleavage agent to "not applicable" for naturally processed immunopeptides.

  7. Consider factor values: Use factor value columns to indicate the experimental variables under study (e.g., disease state, treatment, cell type).

9. Annotated Project Examples

Examples of immunopeptidomics datasets annotated with SDRF-Proteomics:

10. Template File

The immunopeptidomics SDRF template file is available in this directory:

11. Validation

Immunopeptidomics SDRF files should be validated using the sdrf-pipelines tool with the immunopeptidomics template:

pip install sdrf-pipelines
parse_sdrf validate-sdrf --sdrf_file your_file.sdrf.tsv --template immunopeptidomics

12. Authors and Maintainers

This template was developed by the SDRF-Proteomics community with contributions from immunopeptidomics researchers:

For questions or suggestions, please open an issue on the GitHub repository.

13. References

  • Bassani-Sternberg M, et al. (2015) Mass spectrometry-based antigen discovery for cancer immunotherapy. Current Opinion in Immunology.

  • Purcell AW, et al. (2019) Mass spectrometry-based identification of MHC-bound peptides for immunopeptidomics. Nature Protocols.

  • IEDB: Immune Epitope Database (https://www.iedb.org/)

  • IMGT/HLA Database (https://www.ebi.ac.uk/ipd/imgt/hla/)