Theodore Alexandrov

This study identifies a MEF2C transcription factor network that regulates endothelial cell proliferation in diabetic kidney disease and shows that SGLT2i treatment can reverse its dysregulation.

The paper identifies lipid cargoes for half of analyzed human LTPs, revealing selectivity for lipid head groups and acyl chains and new lipid-organelle relationships.

HT SpaceM is a high-throughput single-cell metabolomics method that detects small molecules with high reproducibility and identifies cell-type markers and metabolic pathways in diverse cell lines.

The paper investigates how the branched-chain amino acid (BCAA) pathway is regulated during the diauxic shift in yeast and its impact on carbon metabolism.

The paper introduces S2IsoMEr and METASPACE tools to handle molecular ambiguity in spatial and single-cell metabolomics through bootstrapping for enrichment analysis.

METASPACE-ML improves metabolite annotation in imaging mass spectrometry by using machine learning, achieving higher precision and throughput than a rule-based method.

The paper introduces ms1-id, a tool for structural annotation of MS1 data in LC-MS and MS imaging, enabling level 2/3 annotation and uncovering previously overlooked metabolites.

This paper introduces 13C-SpaceM, a method for spatial single-cell isotope tracing of de novo fatty acid synthesis in cancer cells, revealing metabolic heterogeneity in liver and glioma tissues.

This study shows that fly populations rapidly adapt to increased bicoid dosage by normalizing gene expression and improving survival through metabolic and developmental changes.

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

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

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.

The paper identifies lipid metabolism as a key factor in developmental pausing, showing that enhancing fatty acid oxidation extends embryo survival during mTOR inhibition.

The paper constructs a reference tissue atlas of the human kidney using multi-omics data to identify cell types, pathways, and genes across nephron segments and interstitium.

This study shows that H3K4me1 hypomethylation in Drosophila leads to context-dependent changes in morphology, metabolism, and behavior, suggesting it acts as a phenotypic capacitor.

The paper quantifies the early dynamics of bcd mRNA and Staufen protein in Drosophila embryos, revealing coordinated redistribution patterns and key developmental stages.

This study uses single-cell RNA-seq data to analyze how SARS-CoV-2 infection alters intestinal epithelial-immune cell interactions and identifies potential drug targets and repurposing strategies for COVID-19.

The paper uses single-cell RNA-Seq and imaging to study how intestinal cell types respond to viral infection, finding that each lineage has unique antiviral responses.

SpaceM integrates MALDI-imaging mass spectrometry with pre- and post-MALDI microscopy to measure >100 metabolites in >10000 individual cultured cells alongside fluorescence, morphology and spatial context.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

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 generates 3D maps of microbiomes and metabolomes in cystic fibrosis lungs, revealing personalized and regiospecific chemical environments and microbial interactions.

An open-source cloud engine annotates imaging mass spectrometry data at controlled false discovery rate; publicly shared results reached over 3200 datasets and 6 million metabolite images by January 2019.

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

Curatr is a web application that enables the manual curation of mass spectral libraries from LC-MS data, supporting export to standard formats and public access.