Advanced Proteomics Techniques and Applications

This study uses single-cell proteomics to identify proteins distinguishing two adjacent cell types in Arabidopsis root cells.

PEPerMINT imputes missing peptide abundance values in proteomics using graph neural networks, outperforming existing methods across multiple datasets and missingness scenarios.

The paper shows that in-gel protein digestion with acidic methanol selectively methylates glutamic acid residues, not other methylable residues like aspartic acid.

The paper uses single-tissue proteomics in C. elegans to identify proteins in intestinal lysosome-related organelles, revealing novel components and interorgan protein transport.

This study develops a 3-compartment model to simultaneously analyze protein dynamics in blood plasma and cerebrospinal fluid, including a hidden CNS compartment, and applies it to 69 proteins from a single individual.

TSWIFT enables iterative staining of mounted FFPE brain sections for high-dimensional proteome analysis.

MSModDetector identifies PTM patterns in individual ion mass spectrometry data by detecting mass shifts and using linear programming to infer modifications.

The paper investigates structural changes in the saliva proteome associated with oral cancer aggressiveness, identifying 51 proteins linked to clinical features and post-translational modifications.

The paper introduces a clinical metaproteomics workflow that enables analysis of host-microbe dynamics in disease by integrating mass spectrometry and bioinformatics tools.

The paper presents an automated workflow for proteome and phosphoproteome sample preparation, enabling high-throughput clinical proteomics analysis of metastatic melanoma patients' plasma.

The paper trains a deep learning model to predict fragment ion intensities, improving immunopeptide identification rates using timsTOF instruments.

The paper identifies optimal workflow combinations for proteomics differential expression analysis using frequent pattern mining and ensemble inference, improving accuracy across multiple quantification platforms.

The paper reanalyzes public pig proteomics data to map baseline protein expression across 14 organs and compares it with human orthologs.

This paper introduces AI-TMT, a method enhancing single-cell proteomics sensitivity by improving reporter ion generation, resulting in higher signal and faster analysis.

This study tracks longitudinal changes in the plasma proteome and complexome of kidney failure patients before and after transplantation, revealing acute phase protein dynamics and macromolecular complex formation during inflammation.

This study systematically identifies structure-specific protein-protein interactions using LiP-MS, demonstrating its application to alpha-synuclein in Parkinson's disease.

This paper introduces a workflow for improved XL-MS data analysis using PASEF, integrating MeroX and Skyline for better identification of cross-linked products.

CLIPPER 2.0 is a computational tool for peptide-level annotation and analysis of positional proteomics data, demonstrating its use on GluC and MMP9 cleavage in HeLa lysates.

The paper optimizes Spectronaut search parameters to reduce false positives in DIA proteomics with minimal loss of true positives in heterogeneous samples.

The paper investigates FDR control in proteomics MS/MS database searches, finding no strong evidence of issues in open modification or narrow window approaches after accounting for chimeric spectra and candidate differences.

MassDash provides a web-based tool for visualizing and validating DIA mass spectrometry data across multiple software tools.

The paper quantifies protein covariation in single human cells during EMT, revealing dynamic protein-protein correlations that reflect biological regulation masked by cluster-average dynamics.

The paper introduces CONGA, a method that combines narrow and open modification searches to improve detection of post-translational modifications while maintaining statistical power.

The paper introduces a dual-column electrospray source for nano-flow proteomics to improve throughput while maintaining sensitivity in low-input samples.

This study introduces a multi-omics method to distinguish target-specific and off-target effects of antibiotics using proteome solubility in Helicobacter pylori.

This paper introduces CLASP, a cross-link assisted spatial proteomics method, to map sub-organelle proteomes and membrane protein topology in human mitochondria.

The paper introduces GoDigViewer to optimize GoDig assays by implementing chromatographic priming runs, significantly improving peptide-level quantification success rates in targeted pathway proteomics.

TIMAHAC enables high-throughput simultaneous analysis of plant phosphoproteomics and N-glycoproteomics, identifying 1,954 N-glycopeptides and 11,255 phosphopeptides in Arabidopsis under ABA treatment.

DROPPS enables high-fidelity proteomic profiling of rare cell populations with low input, revealing CD36 as a marker of progenitor capacity in mammary basal cells.

APIR aggregates database search algorithms for proteomics to increase peptide identification while controlling FDR, outperforming individual methods on standard and real data.