Advanced Proteomics Techniques and Applications

This paper introduces CASA, a targeted mass spectrometry method for protein quantification, and demonstrates its application in determining stoichiometries of kinetochore components in budding yeast.

ThunderBolt provides an interactive web platform for analyzing large proteomics datasets through guided exploration, preprocessing, and statistical analysis.

This study introduces PulsPhos, a method to quantify site-specific phosphorylation lifetimes in living cells and applies it to pharmacological perturbations.

MAGMa is a tool for differential expression analysis in mass-spectrometry proteomic data, showing improved sensitivity and specificity in detecting true signals.

LooMS improves peptide identification in DIA datasets by using an unbiased sample generation strategy and integrating results with DiaNN to reduce false discovery rates.

This study compares urine proteomes of methamphetamine users, withdrawers, and healthy individuals, identifying differentially expressed proteins linked to addiction and neurotoxicity.

The paper introduces an improved method for collecting and analyzing murine cerebrospinal fluid, enabling quantitative proteomics and revealing proteome changes in Alzheimer's disease models.

The paper identifies regulators of nucleocytoplasmic protein transport in macrophages using single-nucleus proteomics and validates findings through knockdown experiments.

This study compares protein extraction methods and data analysis strategies for soil metaproteomics, finding that combining proteomic and genomic data improves protein identifications.

The paper introduces a framework to deconvolve LiP-MS signals and infer protein structural changes using data from three species.

The paper compares mitochondrial interactomes of podocytes and tubules in mouse kidney using quantitative cross-linking mass spectrometry, revealing distinct pathways and enzyme activities.

IsoBayes infers protein isoform presence and abundance using Bayesian methods integrating proteomics and transcriptomics data, showing good sensitivity and specificity in simulations and real datasets.

This paper introduces a transformer-based model called Cascadia for de novo sequencing of data-independent acquisition mass spectrometry data, achieving state-of-the-art performance.

Koina provides an open-source, accessible ML/DL prediction service for proteomics, improving integration with existing tools like FragPipe.

TIMS2Rescore is a data-driven rescoring pipeline optimized for DDA-PASEF data that improves protein identification in proteomics analyses.

The paper benchmarks dia-PASEF with timsTOF HT for high-throughput proteome analysis in a multi-center study, showing consistent protein and peptide identification across laboratories.

AlphaDIA enables end-to-end transfer learning for feature-free proteomics, improving identification and quantification of post-translational modifications.

LiF-MS+ improves peptide-protein interaction mapping by replacing the mass tag with a positively charged compound, enhancing detection of binding sites.

The paper reanalyzes human tissue proteomics data using pangenome references to identify novel peptides and variants, demonstrating the effectiveness of proteogenomics approaches with pangenome data.

π-PrimeNovo is a non-autoregressive deep learning model for de novo peptide sequencing that achieves higher accuracy and 69x faster performance than existing methods.

MultiStageSearch improves viral strain identification by combining proteomic and genomic databases, outperforming existing methods without relying on NCBI taxonomy.

The paper presents a method for isolating serum, cerebrospinal fluid, and hippocampal neuron proteins from rats and analyzing them using mass spectrometry-based shotgun proteomics.

The paper presents open-source software libraries for processing mzQC data in Python, R, and Java, enabling QC analysis and integration in mass spectrometry workflows.

This study develops a low-input proteomics workflow to analyze rare hematopoietic stem and progenitor cells, identifying 7,000 proteins and a novel marker for long-term hematopoietic stem cells.

The paper investigates how impurities in isobaric labeling reagents can cause contamination in single-cell proteomics, leading to inaccurate quantification when carrier channels are overused.

This study identifies dysregulated membrane proteins in liver disease using peptidisc-based proteomics in mice, highlighting lipid metabolism and xenobiotic processing pathways.

The paper describes a 2D chromatography system for proteomics that improves coverage and intensity with nanogram-scale samples, showing benefits for phosphoproteomics and pull-down experiments.

This study reanalyzes Orbitrap Astral DIA data using DirectMS1 to show it detects additional proteins and reveals new biological insights in disease-related samples.

This study introduces SeaPIC, an XL-MS tool for identifying post-translational modifications in protein-protein interactions and structures, successfully detecting previously undiscovered PTMs.

This study finds the serum proteome is remarkably similar between type 2 diabetics and controls, with major differences being glycations of abundant proteins and associations between BMI and a ternary complex.