Exploring the power of the timsTOF Ultra to investigate the HLA immunopeptidome with high sensitivity and accuracy, allowing for smaller sample sizes while maintaining a robust immunopeptidome

Exploring the power of the timsTOF Ultra to investigate the HLA immunopeptidome with high sensitivity and accuracy

Significance of the topic:
The analysis of HLA-bound peptides (immunopeptidomics) is critical for vaccine development, cancer immunotherapy and biomarker discovery. Achieving high sensitivity in peptide identification enables work with limited sample amounts such as small biopsies or rare cell populations.

Study objectives and overview:
This study evaluates an optimized workflow using the timsTOF Ultra mass spectrometer to profile the HLA class I immunopeptidome from human B-lymphocyte (IM9) extracts. The goals are to demonstrate high sensitivity, maintain accurate peptide identification with reduced sample input, and compare data-dependent (dda-PASEF) versus data-independent (dia-PASEF) acquisition modes.

Methodology and workflow:

• Cell input range: 2×10^4 to 1×10^7 IM9 cells.
• Immunoaffinity enrichment: Automatable HLA isolation followed by selective peptide elution.
• Peptide standards: Heavy isotope-labeled peptides spiked prior to LC–MS.
• LC–MS methods: dda-PASEF ramps of 100 ms (cycle time 1.18 s) at 137, 90, 60 min gradients; dia-PASEF ramps of 70 ms (cycle time 0.92 s) with comparable gradients.
• Data processing: PEAKS 11 for dda-PASEF; DIA-NN and Spectronaut for dia-PASEF; HLA binding prediction via PIG DIco 3.8.

Instrumentation:

• timsTOF Ultra equipped with high-pressure ion transfer optics, large bore capillary, and Trapped Ion Mobility (TIMS) source.
• XR tims cartridge and multipole ion transmission system.
• Nano-LC front end interfaced to mass spectrometer.

Key results and discussion:

• Ultra-sensitive detection of >9 000 peptides from 5×10^5 cells using 60 min dda-PASEF (72 % HLA-associated).
• Dia-PASEF doubled peptide identifications to >21 000 from 5×10^5 cells with 30 min gradient, halving analysis time.
• Linear increase in peptide counts from 2×10^4 to 1×10^7 cells, demonstrating scalability.
• HLA binding predictions showed high numbers of strong binders for alleles A*02:01, A*02:05, B*49:01, C*01:03 across gradients.
• Estimated peptide copy numbers per cell confirmed robust immunopeptidome coverage even at low cell inputs.

Benefits and practical applications:
High sensitivity enables immunopeptidome profiling from limited clinical specimens. The automation of enrichment and rapid gradients supports high throughput in translational research, vaccine target discovery and neoantigen mapping.

Future trends and possibilities:
Advancements may include real-time spectrum interpretation, deeper coverage via optimized window schemes, integration with AI-driven peptide scoring, and broader application to MHC class II and other post-translationally modified peptides.

Conclusion:
The timsTOF Ultra with optimized PASEF workflows delivers unprecedented sensitivity and throughput in immunopeptidomics. Both dda and dia acquisition modes yield rich peptide datasets from small samples, facilitating downstream immunological applications and accelerating biomarker discovery.