Advanced Proteomics Techniques and Applications

This paper introduces Peptergents, a detergent-free method for native membrane proteomics that preserves protein structure and function, enabling better analysis of membrane proteins and their interactions.

GenomeProt integrates genomics, transcriptomics, and proteomics to characterise proteoforms, discovering novel proteins and variant-containing peptides in human brain and melanoma samples.

The paper introduces RyboCypher, a platform that maps the dark transcriptome to unannotated peptides, identifying cryptic proteins in cancer cell lines, tumors, and healthy tissues.

This study quantitatively profiles JMJD6-catalyzed lysine hydroxylation, revealing a graded, residue-dependent response to oxygen availability in lysine-rich regions.

maniFasta and AllOralsDB provide a tool and resource for creating standardized, comprehensive protein reference databases for metaproteomics.

The paper develops a low-input spatial proteomics workflow using standard lab equipment, achieving deep proteome coverage of the choroid plexus across age and sex in mice.

The paper develops a workflow to detect and classify alternative microproteins in colorectal cancer cells, identifying biases in extraction methods and validating a subset experimentally.

This paper demonstrates that a targeted plasma proteome measurement can detect multiple diseases with high accuracy, though validation remains needed for clinical use.

The paper compares AUC and SpC quantification methods in metaproteomics, finding AUC outperforms SpC in accuracy and dynamic range, but MBR harms AUC results.

The paper generates an organ-resolved phosphoproteome map from FFPE rat tissues, showing it preserves signaling biology and enables kinase activity inference.

The paper develops a pipeline for protein-level cell annotation in human PBMCs using mass spectrometry-based single-cell proteomics and validates it on unsorted samples.

Spectronaut-nf is a Nextflow pipeline that enables parallel processing of DIA proteomics data on HPC systems, reducing analysis time compared to single workstation execution.

This paper introduces a novel serum peptidomics workflow that improves peptide yield, throughput, and recovery, identifying subtype-specific peptide biomarkers for stroke subtypes.

The paper develops a targeted proteomics method to quantify retinoid metabolism and signaling proteins across species and tissues, revealing tissue-specific networks and developmental changes in the heart.

STRIDE improves phosphopeptide quantification by reducing false negatives and enhancing consistent peak assignment across DIA-MS runs.

This study uses targeted plasma proteomics to identify a biomarker axis distinguishing autoimmune thyroid disease from growth-hormone deficiency, but not within-class severity.

The paper investigates the differential stability of cardiac protein isoforms using proteogenomics and heavy water-based turnover analysis in mouse hearts, revealing widespread stability differences.

This study evaluates how detergent selection and timing affect thermal stability proteomics results, finding that DDM improves detection of some drug-target interactions but may obscure others.

This paper demonstrates a method to enrich lysosomes from low cell numbers by combining plasma membrane destabilization with immunoprecipitation, improving proteome characterization from half a million cells.

This paper introduces a plate-based ISD-SPE workflow for integrated proteome and secretome analysis of TLR signaling in macrophages, showing improved reproducibility and scalability for drug discovery.

The paper identifies age-dependent structural changes in the human plasma proteome using Covalent Protein Painting and MRM-LC-MS/MS, revealing patterns that may serve as structural biomarkers.

This paper reports the deepest human axonal proteome to date using microfluidic separation and DIA-MS on iPSC-derived neurons, identifying axon-enriched proteins and disease-related pathways.

SoftHybrid improves single-cell proteomics data imputation by jointly modeling missingness and protein abundance, outperforming existing methods at low input and matching them at the mini-bulk level.

The paper evaluates non-linear models for peptide-spectrum match rescoring in proteomics, finding that complex models increase PSMs but introduce biased FDR estimates.

The paper develops a computationally efficient, library-free DIA workflow for metaproteomics, improving throughput and reducing reliance on spectral libraries.

This study shows sonication causes selective fragmentation of intrinsically disordered proteins in proteomics, leading to structural bias and potential misinterpretation of protein size and stability.

Radiant DIA is a fast and cost-effective search engine for proteomics that maintains accuracy and sensitivity, enabling efficient analysis of large-scale datasets.

This paper develops an EDTA-enhanced deep proteomic method for DBS samples, achieving over 7,000 protein identifications and enabling high-throughput analysis in mouse disease models.

The paper identifies phosphorylation sites that regulate protein interactions using pan-cancer data and AlphaFold predictions, revealing 6,160 associations between phosphosites and interacting protein abundance.

This study establishes quality control standards for single-cell proteomics through inter-laboratory benchmarking, identifying software and technical factors affecting data reliability.