The MacCoss Lab used Claude to build and validate an open-source DIA search tool – an experiment that could change how scientists and software developers work together

A tenfold expansion mass spectrometry imaging (10X ExMSI) method was developed, achieving submicrometer lateral resolution (down to ∼500 nm) and enabling the visualization of intricate subcellular structures in tissues and cells while maintaining broad lipid coverage. ABSTRACT Achieving  submicrometer lateral resolution in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is essential for molecular characterization at the single-cell and subcellular levels but is fundamentally limited by optical focusing and matrix crystallization. Here, we present tenfold expansion mass spectrometry imaging (10X ExMSI), an expansion MALDI-MSI workflow that reaches an effective lateral resolution of ∼500 nm on standard commercial instruments using a 5 µm acquisition step size without pixel oversampling. The SDS-free digestion protocol minimizes lipid leaching and supports broad detection of major lipid classes, including glycerophospholipids and sphingolipids, although a reduction in primary amine-containing lipids is observed due to cross-linking. Using 10X ExMSI, we resolve subcellular structures in mouse brain tissues, such as dendritic arborizations, that are challenging to access with existing MALDI-MSI implementations. We further demonstrate expansion-based MSI on cultured A549 cells, achieving subcellular-level lipid mapping. The method is fully compatible with standard MALDI-MSI systems, providing an accessible and scalable route to high-resolution, label-free molecular imaging. These capabilities open new opportunities for studying cellular architecture and molecular heterogeneity in biological and biomedical research.

Koenig et al. present a phosphoproteome atlas of 33 human cell lines covering >200,000 phosphosites, advancing cellular kinase activity scoring and drug sensitivity prediction, providing a resource for cell signaling insights and precision oncology.

This Comment discusses depth, robustness and bias in plasma proteomics, concluding that increasing the depth of coverage does not necessarily translate to quantitative robustness.

Targeted multiplexed proteomic technologies enable quantification of target proteins without prior assay development. Here, the authors describe improvements enabling quantification of site-specific modifications, including post-translational modifications and covalent compound-protein interactions.

Detailed guide describing how to accurately detect deamidation in bottom-up proteomics datasets.

Serum proteomics is increasingly used for minimally invasive biomarker discovery and disease phenotyping, and the choice of serum preprocessing workflow can shape proteome depth, quantitative characteristics, and downstream biological readouts. However, disease-oriented comparisons within a single cohort remain limited. Here, we compared four serum preprocessing workflows - Top14 depletion (TOP14D), tomato lectin affinity purification (TomAP), and two nanoparticle-based enrichment workflows (NPA and NPB) - using serum from six patients with systemic juvenile idiopathic arthritis (sJIA) and six age- and sex-matched healthy controls, and analyzed them using unified data-independent acquisition mass spectrometry (DIA-MS) and a statistical pipeline. We evaluated proteome depth, missingness, quantitative characteristics, group separation, differential abundance signatures, pathway enrichment, curated sJIA-related gene set coverage, pre-ranked gene set enrichment analysis (GSEA) results, and detection of inflammasome/interferon-related proteins. TomAP yielded the greatest proteome depth (7612 proteins), followed by NPB (6735 proteins) and NPA (6602 proteins), whereas TOP14D yielded the smallest protein set (3303 proteins). Principal component analysis (PCA) showed a separation between the sJIA and control groups for all workflows. Differentially expressed proteins (DEPs) showed limited overlap, with only 75 DEPs common to all four workflows. Functional enrichment patterns were workflow-dependent; TOP14D and TomAP mainly captured neutrophil/myeloid and inflammatory processes, whereas NPA and NPB captured RNA processing- and translation-related signals. TomAP showed relatively broad coverage and positive enrichment of curated sJIA-related gene sets associated with inflammation, innate immunity, and macrophage activation syndrome (MAS). Inflammasome/interferon-related proteins, including NLRC4, PYCARD, GSDMD, MEFV, IL-18, OAS3, and MYD88, showed workflow-dependent detectability and differential abundance. These findings support a disease-oriented benchmark for fit-for-purpose workflow selection according to the disease axis and analytical objective rather than proteome depth alone.

PROTEOMICS, EarlyView.

Happy to share my first preprint which closes another chapter of my PhD thesis. This work demonstrates that FFPE tissues retain biologically meaningful phosphorylation signalling, creating a robust fr...

Purpose: To characterize tear proteome changes in male non-obese diabetic (NOD) mice with Sjogren's disease (SjD)-like autoimmune dacryoadenitis and determine whether identified tear proteins are associated with lacrimal gland (LG) pathogenesis. Methods: Tears were collected from 14-week-old male NOD mice and age-matched male BALB/c controls and analyzed by tandem mass tag (TMT)-based liquid chromatography tandem mass spectrometry (LC-MS/MS). Differentially expressed proteins (DEPs) were defined using adjusted P < 0.05 and absolute fold change > 1.5. Functional enrichment analysis was performed using Enrichr. Selected upregulated DEPs were further examined in tear and LG samples from independent mouse cohorts using Western blotting, immunofluorescence, and RT-qPCR. Results: A total of 142 proteins were quantified across all tear samples. Hierarchical clustering and principal component analysis (PCA) separated NOD from BALB/c tear proteomes. A total of 41 proteins were differentially expressed in NOD mouse tears (33 increased, 8 decreased). Increased tear proteins were enriched in immune and inflammatory responses, secretory compartments, RAGE receptor binding, glutathione metabolism, oxidative stress, and redox regulation. S100A8/A9, GSTO1-1, Gal-3, and pIgR/secretory component (SC) were increased in both NOD mouse tears and LG. In NOD LG, increased S100A8/A9 was accompanied by elevated RAGE, whereas increased GSTO1-1 was associated with increased NLRP3, cleaved caspase-1, cleaved gasdermin, cleaved IL-1{beta}, and increased Il1b, Il18, and Il18r gene expression. Conclusions: Male NOD mouse tears contain disease-related proteins reflecting pathological inflammatory and epithelial changes in the LG including increased RAGE signaling, NLRP3 inflammasome activation, and altered epithelial transcytosis.

Publication date: 15 October 2026 Source: Journal of Proteomics, Volume 331 Author(s): Xiyu Wu, Wanmeng Li, Jie Zhang, Rui Wang, Xia Li, Manjiangcuo Wang

Untargeted liquid chromatography- tandem mass spectrometry (LC - MS/MS) - based metabolomics is an important technology for unbiased discovery of small molecules in biomedical (e.g., drug discovery to diagnostics), animal, plant, environmental, and microbial research. Over the past decade, ion mobility has added an additional dimension to the triplet of MS1, MS2, and retention time, helping resolve co-eluting or isomeric features in an LC- MS/MS that aid in compound identification. Here, we focused on evaluating the current trapped ion mobility spectrometry (TIMS) - amenable feature-finding tools (MZmine 4.9, MS-DIAL 5.5, and MetaboScape 2025 14.0.3) for pre-processing of metabolomics-scale data generated using a popular ion mobility mass spectrometry (IM- MS) technique, TIMS. We leveraged ten public and three benchmark TIMS datasets to evaluate these tools for their strengths and weaknesses. Our results show that MZmine consistently identified the highest number of features and confidently annotated features; however, this performance was accompanied by an increased number of false positives, due to peak splitting, as well as reduced accuracy in collision cross section (CCS) measurements. In contrast, MetaboScape achieved the highest fraction of high-quality MS2 spectra, reflecting a more conservative feature detection strategy. MS-DIAL demonstrated balanced performance, identifying features that other tools missed. Finally, we publicly release the ground-truth datasets and code to support future developments in improving IMS data analysis.

Single-cell proteomics (SCP) has emerged as a transformative approach for characterizing cellular heterogeneity at the protein level. Recent advances …

Publication date: July 2026 Source: TrAC Trends in Analytical Chemistry, Volume 200 Author(s): Xinying Zhu, Jia Yi, Liang Qiao

PROTEOMICS, EarlyView.

Accurate control of false discovery rates (FDR) and false localization rates (FLR) is central to quantitative proteomics and phosphoproteomics, yet rigorous validation is limited by the absence of hig...

ABSTRACT Solvent mismatch between the two dimensions is one of the main limitations in two-dimensional liquid chromatography (2D-LC) and presents a significant challenge for method development. Although 2D-LC provides a powerful means to increase peak capacity for oligonucleotides analysis compared to conventional one-dimensional LC, solvent incompatibility remains a major obstacle that discourages broader development of such methods. In this work, we investigate a technical solution that can be easily implemented and that eliminates solvent mismatch effects during 2D-LC analysis of oligonucleotides. This approach is based on the total breakthrough behavior of oligonucleotides, which is a phenomenon that allows the injection of large volumes into the second dimension (2D) without peak distortion. In this work, we showed that under appropriate conditions, oligonucleotides exhibit total breakthrough behavior in HILIC. This behavior in HILIC is particularly advantageous, as the IP-RPLC × HILIC configuration offers improved mass spectrometry (MS) compatibility compared to IP-RPLC or HILIC × IP-RPLC. Assuming an IP-RPLC × HILIC configuration, we systematically investigated the composition of first-dimension (1D) fractions and the 2D-HILIC parameters influencing total breakthrough to identify the key factors promoting this behavior. Our results offer clear guidance for implementing successful IP-RPLC × HILIC conditions that avoid mismatch effects for oligonucleotides analysis while maintaining a high injection volume in the second dimension. This work demonstrates the potential of the total breakthrough strategy for implementing 2D-LC methods with HILIC as the second dimension.

PROTEOMICS, EarlyView.

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-Guerin (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.

Advances in mass spectrometry have enabled increasingly fast data-independent acquisition (DIA) experiments, producing datasets whose scale and complexity challenge existing analysis tools. Those same advances have also led to the use of narrow isolation windows, which alter MS2 spectra via fragment isotope effects and give rise to systematic deviations from spectral libraries. Here we introduce Pioneer and Altimeter, open-source tools for fast DIA analysis with explicit modeling of isolation-window effects. Altimeter predicts deisotoped fragment intensity as a continuous function of collision energy, allowing a single spectral library to be reused across datasets. Pioneer re-isotopes predicted spectra per scan and combines an intensity-aware fragment index, spectral deconvolution, and dual-window quantification for fast, spectrum-centric DIA analysis. Across instruments, experimental designs, and sample inputs, Pioneer enables high-confidence identification and precise quantification at scale, completing analyses 2-6x faster and maintaining conservative false-discovery rate control.

Protein-protein interactions (PPIs) are dynamic and critical to adaptive homeostasis. While there have been massive efforts to catalogue proteome-wide PPIs, global quantification of changes remains a ...