Advanced Proteomics Techniques and Applications

Solid-Phase Extraction Capture (SPEC) in nanoliter volumes for fast, robust and ultra-sensitive proteomics

The paper introduces SPEC, a proteomics sample preparation method that improves proteome depth and reproducibility from limited samples.

msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery

DIAVariant identifies low abundance peptide variants from DIA-MS data without relying on reference databases, improving variant discovery while maintaining specificity.

Informed Data-Independent Acquisition Enables Targeted Quantification of Key Regulatory Proteins in Cell Fate Decisions at Single-Cell Resolution

The paper introduces iDIA, a method that enhances sensitivity for quantifying regulatory proteins in single cells, revealing lineage priming and cell-cycle coordination in hematopoietic stem and progenitor cells.

Streamlined isolation of the n-terminome via phosphonate tagging and coordination-based depletion

This paper introduces a method to isolate N-termini for proteome analysis, demonstrating its application in identifying cleaved proteins during apoptosis.

TopLib: Building and searching top-down mass spectral libraries for proteoform identification

TopLib is a software package for building and searching top-down mass spectral libraries, offering faster and more reproducible proteoform identification than conventional methods.

nanoPhos enables ultra-sensitive and cell-type resolved spatial phosphoproteomics

nanoPhos improves phosphoproteomics sensitivity and enables cell-type resolved spatial analysis of mouse brain tissue.

Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage

The paper develops a deep learning model that integrates multiple fragmentation techniques into standard LC-MS workflows, improving proteome coverage and identification rates.

Improved quantitation in data-independent acquisition proteomics via retention time boundary imputation

This paper introduces a method to improve quantitation in DIA proteomics by imputing peptide retention time boundaries, leading to more accurate quantifications and better estimation of radiation exposure.

Development of an Adaptive, Economical, and Easy-to-Use SP3-TMT Automated Sample Preparation Workflow for Quantitative Proteomics

The paper develops an automated SP3-TMT sample preparation workflow for proteomics, showing it matches manual methods in performance while improving throughput, cost, and error reduction.

hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments

Machine learning integration of over 25,000 mass spectrometry experiments yields 15,326 human protein complexes covering 13,769 proteins, plus 511 AlphaFold-derived mutually exclusive subunit pairs.

Automated SP3 Workflow Enables Robust Sample Preparation on Proteonano Ultraplex Proteomics Platform

The paper evaluates three proteomics sample preparation methods, finding that automated SP3 (autoSP3) provides high consistency and scalability for proteomics workflows.

Deep Coverage and Extended Sequence Reads Obtained with a Single Archaeal Protease Expedite de novo Protein Sequencing by Mass Spectrometry

This paper introduces HTA-proteases for de novo antibody sequencing, achieving higher peptide read coverage and accuracy compared to trypsin or chymotrypsin.

Spike-in enhanced phosphoproteomics uncovers synergistic signaling responses to MEK inhibition in colon cancer cells

The paper introduces SPIED-DIA, a phosphoproteomics method, to identify synergistic signaling responses in colon cancer cells to MEK inhibition and growth factor stimulation.

Analysis of Post-Translational Modifications in the Urinary Proteome of Stroke-Prone Spontaneously Hypertensive Rats (SHRSP)

This study analyzes PTMs in the urinary proteome of SHRSP rats across different ages to identify stage-specific and shared PTM alterations linked to hypertension progression.

Deconvolution of multiplexed peptidoform mass spectra enables high-resolution profiling of complex protein modification patterns

The paper introduces PTMdecoder, a tool for deconvoluting multiplexed mass spectra to identify and quantify isobaric modification peptidoforms with high sensitivity and accuracy.

A flexible end-to-end automated sample preparation workflow enables reproducible large-scale bottom-up proteomics

This paper develops an automated sample preparation platform for bottom-up proteomics, demonstrating improved reproducibility, throughput, and suitability for drug development.

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

This paper evaluates kinase activity inference methods using phosphoproteomics data and benchmarking approaches, finding that curated kinase-substrate libraries significantly impact results.

Expanding the Landscape of Aging via Orbitrap Astral Mass Spectrometry and Tandem Mass Tag (TMT) Integration

This study uses Orbitrap Astral mass spectrometry with TMT to analyze proteomic changes in mouse brain and kidney tissues across age and sex, revealing tissue-specific and non-linear age-related protein dynamics.

JMod: Joint modeling of mass spectra for empowering multiplexed DIA proteomics

JMod improves sequence identification and quantification in multiplexed DIA proteomics by jointly modeling overlapping mass spectra, enabling higher throughput with maintained accuracy.

Optimization of Bio-Orthogonal Non-Canonical Amino acid Tagging (BONCAT) for effective low-disruption labelling of Arabidopsis proteins in vivo

This paper optimizes BONCAT for low-disruption labeling of Arabidopsis proteins, showing improved growth and effective enrichment under stress conditions.

timePlex: Increasing mass spectrometry throughput using time-encoded sample multiplexing

timePlex increases LC-MS throughput by multiplexing samples in the time domain, achieving combinatorial scaling and enabling high-throughput proteomics.

PSMtags improve peptide sequencing and throughput in sensitive proteomics

PSMtags enhance peptide detection and sequence identification in proteomics, enabling 9-plexDIA and significantly increasing sample throughput.

SPROUTS_DB: an implemented database of contaminants for extracellular vesicle proteomics studies

SPROUTS_DB catalogs serum-derived contaminants in extracellular vesicle proteomics to improve data reliability by distinguishing contaminants from true proteins.

Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body

The paper identifies over 12,000 blood proteins using proteomic strategies and develops a database to catalog their detectability and abundance across different conditions.

Improving proteomic dynamic range with Multiple Accumulation Precursor Mass Spectrometry (MAP-MS)

This study introduces MAP-MS, a method to improve proteomic dynamic range using Orbitrap mass spectrometers without hardware or software changes, achieving nearly 2x dynamic range with minimal impact.

A Technical Evaluation of Plasma Proteomics Technologies

This paper evaluates six plasma proteomic technologies, finding that Seer Proteograph XT provides the highest proteomic depth and quantification, while Olink Explore HT shows comparable LOD but worse LOQ.

R2HaPpY: Rapid-robust phosphotyrosine peptide enrichment using HaloTag-Src SH2 pY superbinder

The paper introduces a rapid and cost-effective method for phosphotyrosine peptide enrichment, enabling sensitive detection of phosphotyrosine sites in biological samples.

ProteoPlotter: an executable proteomics visualization tool compatible with Perseus

ProteoPlotter is a visualization tool for proteomics data that complements Perseus, offering enhanced features for data analysis and interpretation.

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Pfly predicts peptide detectability in mass spectrometry using a deep learning model trained on synthetic and biological data, offering customizable classification for different experimental conditions.