Advanced Proteomics Techniques and Applications

The paper benchmarks peptide spectral library search methods using a new dataset, finding poor recall (~70%) and varying performance across scoring functions and preprocessing scenarios.

msmu provides a provenance-aware framework for mass spectrometry-based proteomics analysis, enabling reproducible and interoperable data processing.

This paper develops a rapid phosphopeptide enrichment method (Ti-PAN) for nanoscale samples, achieving high sensitivity and recovery, and identifies key kinases in oocyte maturation.

The paper introduces QuantSelect, a deep learning method for fragment ion selection in DIA proteomics, improving quantification accuracy and differential expression sensitivity.

DelPi is a peptide identification framework that learns peptide-signal correspondence from raw spectra, improving interpretation across different acquisition methods while controlling false discovery.

This paper introduces a method to transport urine samples over long distances without cold chain by heating, showing it preserves protein and bacterial content similarly to frozen storage.

This paper introduces AbTags, a method to enhance detection of low-abundant proteins in single-cell proteomics by mass spectrometry through antibody-based signal amplification.

ProteoBench provides a centralized platform for comparing proteomics data analysis workflows, enabling benchmarking and community collaboration.

This paper optimizes liquid chromatography conditions for single-cell proteomics across platforms, demonstrating its effectiveness even on non-specialized instruments and showing its ability to detect biological responses to oxidative stress.

This paper introduces multi-tiered Dicers for microdissection, achieving better protein preservation and higher peptide/protein yield compared to LCM at 10-20 micron resolution.

The paper presents a harmonized framework for analyzing large-scale plasma proteomics data across neurodegenerative diseases, identifying disease-specific circulating signatures and biomarkers.

Meta-PepView evaluates and visualizes metaproteomics data through a web-based platform, offering modular design and open-source access.

The paper presents a comprehensive human phosphoproteome atlas with over 200,000 phosphosites, improving phosphoproteomics speed and accuracy, and identifies kinase activity patterns linked to drug sensitivity.

The paper benchmarks proteomics accuracy using a large-scale dataset, identifying systematic errors and providing a resource for method optimization and software evaluation.

This study evaluates two mass spectrometry-based phenotypic screening approaches for identifying anti-inflammatory compounds in macrophages and compares them to cytokine profiling.

The paper introduces a method combining deuterated water labeling and DIA-MS/MS to quantify protein turnover rates, validated in a murine model.

SPRINT-MS identifies protein-protein interactions using pooled IP-MS and sparse signal recovery, achieving high throughput and efficiency comparable to individual IP-MS experiments.

This study uses amphipols to improve protein detection in exhaled breath condensate but finds high variability and missing data hinder reliable biomarker discovery.

The paper develops a workflow using long-read RNA-seq and MS data to construct haplotype-resolved proteomes, enabling detection of variant and allele-specific protein isoforms.

The paper develops a method to identify and validate kinase-substrate relationships using phosphoproteomics data and applies it to estimate kinase activity in cancer biopsies.

The paper identifies cold-sensitive protein conformational changes across the proteome using mass spectrometry in E. coli.

This paper introduces a single-aliquot, enrichment-free workflow for high-throughput plasma proteome and N-glycoproteome profiling, achieving deep coverage and reproducibility in a clinical setting.

This paper develops a high-sensitivity single-cell proteomics platform and applies it to analyze small immune cells, achieving deep proteome coverage and identifying key markers in PBMCs.

This study systematically maps metal-binding sites in E. coli using Metal-LiP, identifies 424 candidate metal-binding proteins, and suggests functional roles for metal binding.

This paper identifies matrix and immune context markers that improve early multi-cancer detection by analyzing proteomic data and protein-protein interactions across multiple cancers.

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

This paper introduces a high-throughput spatial proteomics workflow for FFPE samples, enabling efficient processing and analysis of subcellular regions with high resolution.

The paper introduces an integrated workflow for high-throughput single-cell proteomics, achieving high throughput and protein identification using SPRINT and dual-spray LC-MS.

This study benchmarks enrichment and depletion methods for plasma proteomics across different plasma types and evaluates their correlation with clinical assays.

This paper develops a proteomic workflow using data-independent acquisitions to analyze APOE3-derived neurons and microglia, identifying cell type-specific markers and pathways.