Introduction

Immunopeptidomics is the gold standard method to profile MHC-bound peptides. These MHC peptides are monitored by immune cells and may elicit a response against potential pathogenic or malignant anomalies. This has an important application for cancer treatment as these peptides can be tumour-specific, allowing hijacking the immune system against cancer cells. However, immunopeptidomics methods require high input material and large amounts of anti-MHC antibody, and they are low-throughput, resulting in a bottleneck for patient cohort studies. To solve this, we aim to optimise a high-throughput method capable of processing up to 96 well samples (one plate) in one week from samples within the range of 50 to 5 million cells. This would result in an 8-fold increase in throughput and a 100-fold decrease in sample and antibody requirements.

Methods

To select the workflow providing the highest coverage and reproducibility, we will benchmark three plate-based methods: (1) An adaptation of our in house protocol (Tenzer lab HI-TRON Mainz), (2) a flow-through method (Bassani-Steinberg lab, LICR), and (3) a workflow using magnetic beads (Mann lab, MPIB). First, we focused on transferring our protocol (1) to a plate format. After immunoprecipitation, peptides were ultra-filtered and the eluates were desalted before LC-MS analysis in an Evosep One coupled to TimsTOF Ultra using Thunder-DDA-PASEF. Data was analysed using Peaks X Pro boosted by MS2Rescore, and MHC binding was predicted by NetMHCpan via MhcVizPipe.

Results

To transfer our protocol (1) to a plate format, we scaled-down the input material from 100 to 50, 10, and 5 million JY cells. On average, 84.5% of peptides had the expected size of HLA class I ligands (8-13 amino acids), and 88% of these were predicted MHC binders. From 50 million cell input and injection of 5 million cell equivalents we identified 3050 HLA binding peptides. Furthermore, from 5 million cell input and injection of 0.25 million cell equivalents we were able to identify 545 HLA binding peptides.

Conclusion

The initial in-plate method provided promising results, indicating that it can be further optimised. After optimization, we will benchmark it to other methods previously described to select the optimal workflow. Altogether, this optimisation will enable us to process larger numbers of samples therefore profiling the immunopeptidome of patient cohorts which could result in the detection of cancer-specific neo-epitopes from patient plasma or tumour samples.