Advanced Proteomics Techniques and Applications

The paper introduces a method to impute missing proteomics data using regularized regression and biological covariation, achieving high accuracy across multiple measurement modalities.

The paper develops an ultra-high-throughput LC-MS method for TPD compound screening, demonstrating high proteome coverage and quantitation accuracy.

The paper creates a spatially-resolved human brain proteome atlas, revealing protein diversity and functional specialization, and identifies therapeutic targets in epilepsy.

BiomarkerML automates ML/DL analysis of proteomic data for biomarker discovery, using SHAP to identify candidate biomarkers and avoiding over-fitting through cross-validation.

This paper introduces PhenoSCoP, a framework that uses single-cell proteomics and microscopy to identify drug-resistant proteotypes in cancer cells and their progression in patient tissues.

The paper compares proteomic complexity across human neural models, finding increased molecular complexity in tissue-like models that better reflect in vivo brain biology.

This paper introduces UB-PIM, a method for protein identification by mass spectrometry using Core Unique Peptides (CrUPs) to improve specificity and reduce ambiguity in peptide-to-protein mapping.

This paper introduces a proteogenomic framework combining genetic perturbations and proteomics to map phospho-signaling pathways in primary human T cells, validated with inflammatory stimuli.

This paper introduces a one-pot LC/MS-based method to quantitatively map protease substrate specificity using a large peptide library.

PLMNovo improves de novo peptide sequencing by aligning spectrum and peptide embeddings using pre-trained protein language models.

This study presents an optimized single-cell proteomics workflow using automated platforms and MS systems to improve reproducibility and identifications in human primary cells.

The paper presents a proteomics and glycoproteomics dataset of 15 human body fluids and provides protocols for their analysis, with results available in an online repository.

This paper uses single-cell proteomics by mass spectrometry (SCP-MS) to analyze mouse kidney cells, revealing previously undetected protein-level heterogeneity, including a proliferative subset of distal convoluted tubule cells.

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 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.

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 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 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.

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.

The paper introduces a rapid CE-MS platform with AI-based processing to profile single human neutrophils, revealing proteomic heterogeneity across immune functions.

The paper presents a metaproteomic dataset to evaluate the performance of ddaPASEF and diaPASEF in terms of detection, coverage, and strain-level resolution.

DIANNeR improves detection of GR interactomes in normal tissues by integrating DIA, ion mobility, and library-free analysis, revealing context-specific networks and previously undetected interactions.

This study uses nanoparticle-enhanced plasma proteomics to identify low-abundant proteins in atherosclerosis-prone mice, revealing disease-related changes in inflammation, lipid metabolism, and potential biomarkers.