Philipp Mertins | Search PubMed

PubMed2026

Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.

The isolation of major histocompatibility complex (MHC) ligands and subsequent analysis by mass spectrometry is considered the gold standard for defining targets for T cell-based immunotherapies. However, as many targets of high tumor specificity are only presented at low abundance on the cell surface of tumor cells, the efficient isolation of these peptides is crucial for their successful detection. Here, we demonstrate how optimizing the MHC ligand isolation strategy, based on both the presenting MHC alleles and the individual peptide level, enhances the identification of specific MHC ligands. This ideally acknowledges not only the hydrophobicity but also the post-translational modifications of the respective MHC ligands. To further improve the identification and characterization of MHC ligands, we developed an MHC class I ligand prediction algorithm (ARDisplay-I) that outperforms current state-of-the-art tools when benchmarked against competitors such as netMHCpan 4.1, MixMHCpred, or MHCflurry. Implementing these strategies can augment the development of T cell receptor-based therapies by improving the identification of novel immunotherapy targets and enriching the resources available in the computational immunology field through a superior MHC presentation prediction algorithm.

Ligands↗

PubMed2024

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Isotope Labeling↗