Advanced Proteomics Techniques and Applications

This paper introduces PCA-N, a simplified perchloric acid workflow for plasma proteomics that enables high-throughput, cost-effective analysis of large population samples.

The paper evaluates protein quantitation in DIA proteomics using controlled experiments on two mass spectrometry platforms, identifying over 10,000 proteins and highlighting the impact of post-processing on accuracy and false quantitative rates.

The paper develops and evaluates a multiplexed PRM-MS assay for quantifying plasma proteins in an IBD cohort, demonstrating high quantifiability and consistent data acquisition.

The paper develops a mass-spectrometry-based method to absolutely quantify fluorescent protein fusions and estimate their mature chromophore fraction, validated through a case study on expression kinetics.

This paper introduces a framework for interpreting single-cell proteomics data from patch-clamped neurons in acute brain slices, showing how retrieval quality affects proteomic outcomes.

This study evaluates bead-based and conventional plasma proteomics workflows, finding bead methods enhance low-abundance protein detection but are prone to contamination bias, while perchloric acid-based methods resist contamination better.

The paper introduces an FDR control procedure for de novo peptide sequencing and demonstrates its validity and utility.

MSstats+ improves differential protein analysis in proteomic experiments by incorporating longitudinal quality metrics to reduce false positives from poor-quality measurements.

GoDig-LiF enables targeted proteomics without data libraries by using predicted spectra and retention times, applied to quantify mutated proteins in cancer cell lines.

GoDig 2.0 improves targeted proteomics by increasing multiplexing, efficiency, and target coverage, enabling analysis of post-translational modifications and compound-protein interactions.

This study develops and validates an LC-MS/MS method for absolute quantitation of RBP4 and TTR in serum and plasma, showing good linearity, precision, and correlation with ELISA.

This paper introduces a SILAC-TMT-RTS method to study proteome changes in T cell-cancer cell co-cultures, revealing mechanisms of differential sensitivity to T cell killing.

The paper introduces pepR-MS and SALT to map protein-RNA interfaces at single-amino acid and nucleotide resolution, identifying over 34,000 crosslinks in breast cancer cells.

The paper profiles proteome, phosphoproteome, and glycoproteome of 54 cancer cell lines using MS and RPPA, revealing tissue-specific patterns and drug targets.

QuickProt provides Python-based Colab notebooks for analyzing DIA and PRM proteomics data, enabling quality control, visualization, and interpretation without coding expertise.

SLB-msSIM is a single-cell proteomic method that enhances sensitivity and robustness for analyzing cellular heterogeneity and EMT trajectories in pancreatic cancer cells.

The paper refutes the claim of histone H3 tyrosine sulfation by showing that published mass spectrometry data do not match known sulfotransferase fragmentation patterns.

The paper uses Deep Visual Proteomics to show that human colon organoids switch to an in vivo-like phenotype after xenotransplantation.

The paper analyzes proteome and phospho-proteome changes during Xenopus embryonic development, identifying key transitions in protein expression and phosphorylation patterns.

This paper develops a sensitive LC-MS method to quantify circulating proinsulin proteoforms and C-peptide, showing improved separation of control and T1D groups compared to ELISA.

This paper introduces an improved chemoproteomics method for mapping kinase PPI networks, showing it identifies more interactions and predicts drug targets in neuroblastoma models.

The paper develops a high-sensitivity protein extraction method for micropatterned hiPSC-CMs and shows they exhibit more mature molecular signatures compared to traditional cultures.

The paper introduces an unbiased proteomic platform for ATE1-based arginylation profiling, identifying 235 unique arginylation sites in human proteomes.

The paper introduces ProteomeGenerator3, an end-to-end proteogenomics method that detects non-canonical proteoforms in cancer using long-read transcriptomics and multi-dimensional proteomics.

The paper shows that stochastic resonance enhances detection of low-level peptide signals in single-cell proteomics using TMT and a carrier proteome.

The paper evaluates nonporous C-18 stationary phases in proteomics workflows, finding they provide comparable or superior performance to traditional porous particles in separation and sensitivity.

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

This study evaluates whether chromatographic performance affects proteome coverage under high-speed data acquisition, finding that proteome depth is now more influenced by mass spectrometry than chromatography.

PELSA-Decipher processes and interprets PELSA data to extract ligand-protein interaction details like binding proteins, sites, and affinities.