Mass Spectrometry Techniques and Applications

The paper develops deep learning models to predict FAIMS ion mobility of peptides from sequence, achieving high F2 score on E. coli peptides.

This paper introduces a correlative imaging method using nano-DESI MSI to map lipids, metabolites, peptides, and proteins in single tissue sections, revealing molecular gradients in mouse pancreatic tissue.

This paper improves crosslinking mass spectrometry by using diazirine-based photo-crosslinking to better study dynamic protein regions and their conformational changes.

msiFlow automates and streamlines multimodal mass spectrometry imaging and immunofluorescence data analysis to enhance reproducibility and scalability in biological research.

The paper introduces a new spatial multiomics method combining mRNA detection with lipid analysis using MALDI ISH MSI to link gene expression with metabolic profiles in mouse brain tissue.

This paper introduces a MALDI MSI method to spatially profile metals in tissues, applying it to study embryogenesis, genetic disorders, and chemotherapy delivery.

The paper introduces HDX FineMapping, a method that achieves full sequence coverage and epitope characterization for AI-designed therapeutics without requiring mutations or crystallization.

This study uses top-down mass spectrometry to determine the detailed structure of B. fragilis lipooligosaccharide, revealing its poly-galactose-rhamnose-KDO-lipid A architecture and modifications.

This paper introduces a multimodal imaging workflow combining MALDI-MSI and IF microscopy to enhance spatial single-cell metabolomics, validated on mouse spleen sections.

This paper introduces a method using gradient open port probe electrospray ionization mass spectrometry to distinguish specific from non-specific protein-metabolite interactions by analyzing charge state distributions under varying methanol concentrations.

The paper defines lipid profiles for five brain cell types and maps their spatial distribution using mass spectrometry imaging.

The paper evaluates spatial metabolomics protocols and technologies, showing extensive metabolite coverage and comparable results across imaging MS methods.

The paper evaluates a workflow for spatially co-registering molecular and phenotypical data from human kidney tissue using DESI-MSI under varying conditions.

The paper uses MALDI-IM-MS imaging to map sterol and lipid distributions in Dhcr7-KO and WT mouse brains, identifying differentially expressed lipids linked to SLOS pathophysiology.

This study introduces a workflow using isotopic labeling to identify and remove matrix adducts in MALDI-MSI, improving metabolite annotation and tissue morphology interpretation.

The paper introduces an aggregated molecular phenotype (AMP) scoring system to visualize and classify molecular changes in mass spectrometry imaging data with high accuracy.

The paper characterizes an Omnitrap-Orbitrap mass spectrometer with multiple ion activation methods, showing its versatility in peptide and protein analysis.

The paper uses a machine learning-based high-throughput approach to analyze charge state distributions of peptides, revealing two distinct charging regimes with different sequence determinants.

KineticMSI quantifies spatial isotopic incorporation in kMSI data across replicated experiments, revealing metabolic changes in HD mouse hippocampus.

The paper compares LSTM, CNN, and transformer models for predicting retention time and collisional cross section in mass spectrometry data, finding LSTM and CNN perform similarly while the transformer underperforms.

This paper introduces moleculaR, a computational framework for probabilistic mapping and statistical testing of metabolites in mass spectrometry imaging data.

This paper introduces a modified mass spectrometry system for depositing gas-phase protein complexes onto EM grids, enabling 3D structure determination of non-covalent protein complexes like GroEL.

The paper introduces Madi, a workflow combining MALDI-MSI and machine learning to analyze FFPE tissues, revealing metabolic patterns and pathological features in human tissues.

MS2DeepScore uses a Siamese neural network to predict structural similarity from mass fragmentation spectra, outperforming classical methods and enabling chemically meaningful spectral embeddings.

The paper introduces a linear deconvolution method using optimal transport to improve accuracy in mass spectrometry by reducing sensitivity to measurement inaccuracies.

The paper investigates how cocaine and methylphenidate affect brain phospholipids in Drosophila using mass spectrometry imaging, showing persistent changes and partial reversal with methylphenidate.

The paper describes a workflow for creating MS/MS reference spectra in the GNPS infrastructure, achieving 99.2% accurate annotation of chemical standards.

The paper develops a mass spectrometry imaging method to map sterol distributions in mouse brain tissue, revealing spatial cholesterol metabolism patterns in wild-type and cholesterol 24S-hydroxylase knock-out mice.

ColocAI quantifies co-localization between mass spectrometry images using AI, outperforming existing methods and providing insights into 10,273 molecules from public datasets.

The paper develops machine and deep learning methods to identify off-sample ions in mass spectrometry images, achieving high F1 scores.