AIMC Topic: Proteomics

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Application of MALDI-TOF MS-based peptidome profiling for the identification of Bacillus cereus, Staphylococcus aureus, and Escherichia coli in single and mixed inoculum.

Food chemistry
The detection of mixed-species bacterial samples plays a vital role in ensuring food safety, yet research in this area remains notably limited. This study investigates the integration of MALDI-TOF MS-derived peptidome profiles with artificial intelli...

Deep learning-derived optimal annotation strategies to power the systematic mapping of peptide space.

Food chemistry
Rapid and reliable peptide identification techniques are essential for proteomics. High-resolution tandem mass spectrometry acquires a large amount of data through data-dependent acquisition (DDA) and data-independent acquisition (DIA), but tradition...

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

Alzheimer's research & therapy
BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, necessitating further investigation into reliable universal biomarkers or combinations.

Glycoproteomics Analysis to Identify Potential Biomarkers and Investigate CD14 Monocyte Activation Mechanisms via Exosomal Proteins in Cerebral Infarction Using a Novel Glycogen-Functionalized Nanoprobe.

Analytical chemistry
Cerebral infarction (CI) is a leading cause of disability and mortality, with activated plasma monocytes and altered protein glycosylation identified as critical contributors to its pathology. However, the mechanisms linking peripheral monocyte activ...

Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.

Nature communications
Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using magnetic beads called Mag-Net. Proteomic interrogation of this plasma EV fraction enables the detect...

Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank.

Scientific reports
Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we develo...

Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.

Nature communications
The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achieve an ultradeep and precision analysis of the N-glycoproteome of mouse tissues by integrating multi...

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from...

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Genome research
Recent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins ...

Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach.

PloS one
Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for better diagnosis and follow-up. An interesting question is whether biomarker patterns could contribut...