Mass Spectrometry Techniques and Applications

Spatial Multiomics Lipids and Gene expression using MALDI In Situ Hybridization Mass Spectrometry Imaging

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.

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

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

A Fast and Low-Cost Approach for Binding Mode Validation of AI-Designed Therapeutics

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

Cell lipotypes localization in brain by mass spectrometry imaging

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

Large-Scale Evaluation of Spatial Metabolomics Protocols and Technologies

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

Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry

This paper introduces extended similarity indices to improve the interpretation of imaging mass spectrometry data by complementing PCA and enabling efficient analysis of large datasets.

Cardinal v3 - a versatile open source software for mass spectrometry imaging analysis

Cardinal v3 provides a versatile open-source tool for reproducible mass spectrometry imaging analysis, supporting advanced data processing and statistical methods for large-scale experiments.

Spatial Metabolomics co-registered with Multiplex Phenotyping for the Evaluation of Human Kidney Tissue

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

Matrix selection for the visualization of small molecules and lipids in brain tumors using untargeted MALDI-TOF mass spectrometry imaging

The paper compares matrix, deposition technique, and polarity settings in MALDI-TOF mass spectrometry imaging for brain tumors, finding recrystallized cyano-4-hydroxycinnamic acid in positive polarity provides best performance.

MALDI-IM-MS Imaging of Brain Sterols and Lipids in a Mouse Model of Smith-Lemli-Opitz Syndrome

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.

Peptide collision cross sections of 22 post-translational modifications

The paper investigates how 22 post-translational modifications affect peptide collision cross sections using ion mobility spectrometry, finding sequence- and modification-specific effects.

Charge-State-Dependent Collision-Induced Dissociation Behaviors of RNA Oligonucleotides via High-Resolution Mass Spectrometry

This study characterizes the fragmentation behaviors of RNA oligonucleotides via CID-MS, revealing charge-state-dependent patterns and nucleobase loss mechanisms.

Discovering Matrix Adducts for Enhanced Metabolite Profiling with Stable Isotope-Labeled MALDI-MSI

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

Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data

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

3DMolMS: Prediction of Tandem Mass Spectra from Three Dimensional Molecular Conformations

3DMolMS predicts tandem mass spectra from 3D molecular conformations, achieving high cosine similarity with experimental spectra and enabling generalized predictions across instruments.

Characterisation of an Omnitrap-Orbitrap platform equipped with IRMPD, UVPD and ExD for the analysis of peptides and proteins

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

SmartGate is a spatial metabolomics tool for resolving tissue structures

SmartGate improves spatial segmentation and biomarker identification in IMS data through iterative peak picking and graph attention auto-encoder modeling.

A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry

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.

Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth

The paper evaluates pixel-cell deconvolution methods in spatial single-cell metabolomics using fluorescein as ground truth, showing that weighted average outperforms linear inverse modeling and demonstrating the impact of ion suppression.

KineticMSI, an R-based framework for relative quantification of spatial isotopic incorporation in mass spectrometry imaging experiments

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

TIMSCONVERT: A workflow to convert trapped ion mobility data to open data formats

TIMSCONVERT converts trapped ion mobility data to open formats for compatibility with downstream analysis tools.

Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry

This paper develops an empirical Bayes functional model for HDX-MS data to improve statistical power and reduce false positives by incorporating a physical model of hydrogen deuterium exchange.

MSNovelist: De novo structure generation from mass spectra

MSNovelist generates small molecule structures de novo from mass spectra, outperforming database-based methods for novel compounds.

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

The paper creates a large-scale CCS database for drugs and metabolites and develops a machine learning model to predict CCS values for different molecular forms.

Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions.

The paper introduces a post-acquisition method to correct mass drift in DESI-MSI data, reducing mass errors by up to 60 ppm and improving molecular annotation and spatial distribution quality.

Comparing Machine Learning Architectures for the Prediction of Peptide Collisional Cross Section

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.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

massNet uses deep learning to classify spatial mass spectrometry data without preprocessing, achieving higher accuracy and faster speed than classical methods for tumor delineation in mouse brain tissue.

Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow

This paper demonstrates that Trapped Ion Mobility Spectrometry (TIMS) reduces cofragmentation rates in mass spectrometry-based proteomics, increasing peptide identification rates.

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

MassGenie uses a transformer-based deep learning model to predict small molecule structures from mass spectra, achieving accurate results without explicit fragmentation rules.

Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

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