Patrick Willems

Data-independent acquisition (DIA)-based proteomics is increasingly becoming a standard procedure for comprehensive protein analysis in plant systems. This high-throughput approach is now extensively employed to characterize protein dynamics across various plant physiological processes including growth, development, and stress responses. Particularly in plant science, this approach facilitates the discovery of novel disease resistance proteins and regulatory pathways involved in plant defense mechanisms. Continuing advancements in DIA proteomics methodologies bolster its sensitivity, reproducibility, and throughput, thus broadening its utility in botanical research. This review offers a comprehensive overview of the myriad applications of DIA proteomics in elucidating biological processes and molecular mechanisms with a primary focus on plants. Ultimately, it underscores the pivotal role of DIA proteomics in advancing our understanding of plant biological systems, while also discussing future directions and challenges in the field.

The oxidative pentose phosphate pathway (OPPP) is a source of cellular NADPH, generated through the sequential activities of glucose-6-phosphate dehydrogenase (G6PDH) and 6-phosphogluconate dehydrogenase (6PGDH). Using the catalase-deficient cat2 background as a model for H ₂ O ₂ -triggered salicylic acid (SA) signaling we identified the cytosolic G6PDH isoform G6PD5 as a key determinant of redox homeostasis and SA-dependent defense activation (Trémulot et al., companion manuscript). However, the mechanisms underlying this function remain enigmatic. In this work, genetic and transcriptomic analyses show that the role of G6PD5 cannot be explained solely by altered NADPH generation for either NADPH oxidases or the ascorbate-glutathione pathway, suggesting other possible links. To identify such links, a forward genetic screen was employed. We searched for mutations that modulate the suppressed lesion phenotype in cat2 g6pd5 in a photorespiration-dependent manner. This screen identified a mutation in PGD2 , encoding the cytosolic 6PGDH. Strikingly, functional analyses of mutants and overexpression lines revealed that PGD2 exerts effects opposite to those of G6PD5 in SA signaling. Our observations uncover an unexpected antagonism between the two cytosolic NADPH-producing steps within the OPPP. Pharmacological analyses support a signaling role for the metabolic intermediate 6-phosphogluconolactone in linking the OPPP to SA signaling. These findings indicate that the OPPP is not solely a source of reducing power during oxidative stress but also acts as a signaling module in which metabolic intermediates contribute to the control of stress-induced immune responses.

Mass spectrometry (MS)-based immunopeptidomics is a powerful approach for untargeted discovery of peptides presented on major histocompatibility complex (MHC) molecules, which can guide the selection of vaccine antigens and immunotherapy targets. First-generation immunopeptidomics workflows require processing of hundreds of millions of cells using lengthy, manual procedures. More recent approaches focus on increasing either sensitivity or throughput, but rarely combine both aspects. Here, we describe a semi-automated immunopeptidomics platform that combines high sensitivity with high throughput by implementing highly optimized conditions for immunoprecipitation, elution and purification of MHC class I and II peptides on a 96-well positive-pressure device. Upon analysis of 25% of the eluate from 16 million cells, our workflow identified over 13,500 MHC I and 6,000 MHC II peptides on a timsTOF SCP mass spectrometer, operating in DDA-PASEF mode. Exploring the sensitivity limits of our platform, we identified over 1,000 MHC I peptides from as few as 20,000 JY cells. Validating the platform’s performance for quantitative biological discovery, we report the identification of known and novel bacterial immunopeptides from U937 macrophages infected with Listeria monocytogenes or Bacillus Calmette-Guérin (BCG). Together, our optimized immunopeptidomics platform enables robust immunopeptide detection from lower-input samples in a high-throughput fashion, enabling its use for biological applications where sample amounts are limiting.

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with nonenzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in the identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

Moyamoya disease (MMD) is a rare cerebrovascular disorder characterized by progressive stenosis of large intracranial arteries and formation of fragile collateral vessels that can be triggered by a broad range of genetic and immune factors. Central to MMD pathology is excessive proliferation of vascular smooth muscle cells (VSMCs) in the intima of affected arteries associated with contractile-to-synthetic phenotypic switching, but the underlying molecular mechanisms remain unclear. To identify dysregulated pathways we studied a cohort of 12 patients with well-documented MMD, including one post-mortem autopsy case, using a multi-omics approach combining whole exome sequencing with plasma proteomics. In addition, we conducted an in-depth spatial proteomics analysis of an occluded artery retrieved post-mortem from one idiopathic patient, combining targeted antibody-based imaging with laser capture microdissection coupled to mass spectrometry. Genetic predispositions for MMD was found in 8 out of 12 patients (67%), including three patients with variants in the major susceptibility gene RNF213 and five with varying underlying genetic conditions (trisomy 21, pathogenic variants in ACTA2 , SAMHD1 , NFIA ). Artery spatial proteomics revealed phenotypic switching of vascular smooth muscle cells associated with infiltration of these cells in the intima, including loss of contractile and gain of synthetic marker proteins. Most notably, increased expression of cellular fibronectin in the occluded lesion was associated with increased levels in patients’ plasma, providing a rational for cellular fibronectin as potential tissue leakage biomarker for moyamoya disease. Finally, infiltration of macrophages and antigen-presenting cells in the intima pointed to a role for inflammatory signals in disease progression. Together, our data provide an unprecedented spatial view on protein changes in an occluded moyamoya artery, revealing intimal infiltration of synthetic vascular smooth muscle cells and antigen-presenting immune cells as key pathological findings, opening novel avenues for future diagnosis and research.

In this issue of JEM, Leddy et al. (https://doi.org/10.1084/jem.20250444) present PathMHC, a computationally guided mass spectrometry approach that boosts the detection of pathogen immunopeptides presented on infected cells.

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules that can guide vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes . We show that DIA spectrum-centric workflows that search pseudo-MS/MS spectra complement DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged DIA-NN for generating and searching proteome-wide predicted HLA class I peptide spectral libraries, scoring approximately 150 million immunopeptide peptide precursors. This approach outperformed other spectrum-based methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to identify low-abundant immunopeptides.

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted discovery of bacterial antigens that can serve as vaccine candidates. However, reliable identification of bacterial epitopes is challenged by their extremely low abundance. Here, we describe an optimized bioinformatic framework to enhance the confident identification of bacterial immunopeptides. Immunopeptidomics data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage and MSFragger, followed by data-driven rescoring with MS²Rescore. Compared with individual search engine results, this integrated workflow boosted immunopeptide identification by an average of 27% and led to the high-confidence detection of 18 additional bacterial peptides (+27%) matching 15 different Listeria proteins (+36%). Despite the strong agreement between the search engines, a small number of spectra (<1%) had ambiguous matches to multiple peptides and were excluded to ensure high-confidence identifications. Finally, we demonstrate our workflow with sensitive timsTOF SCP data acquisition and find that rescoring, now with inclusion of ion mobility features, identifies 76% more peptides compared to Q Exactive HF acquisition. Together, our results demonstrate how integration of multiple search engine results along with data-driven rescoring maximizes immunopeptide identification, boosting the detection of high-confidence bacterial epitopes for vaccine development.

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted discovery of bacterial antigens that can serve as vaccine candidates. However, reliable identification of bacterial epitopes is challenged by their extreme low abundance. Here, we describe an optimized bioinformatical framework to enhance the confident identification of bacterial immunopeptides. Immunopeptidomics data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage and MSFragger, followed by data-driven rescoring with MS ² Rescore. Compared to individual search engine results, this integrated workflow boosted immunopeptide identification by an average of 27% and led to the high-confidence detection of 18 additional bacterial peptides (+27%) matching 15 different Listeria proteins (+36%). Despite the strong agreement between the search engines, a small number of spectra (< 1%) had ambiguous matches to multiple peptides and were excluded to ensure high-confident identifications. Finally, we demonstrate our workflow with sensitive timsTOF SCP data acquisition and find that rescoring, now with inclusion of ion mobility features, identifies 76% more peptides compared to Q Exactive HF acquisition. Together, our results demonstrate how integration of multiple search engine results along with data-driven rescoring maximizes immunopeptide identification, boosting the detection of high-confidence bacterial epitopes for vaccine development.