Nathalie Y.R. Agar

The paper introduces INSIGHT, a platform for phosphoproteomic analysis of rare cell subpopulations, and applies it to study signaling changes in disseminated glioblastoma cells in vivo.

This study identifies hypertranscription as a vulnerability in H3K27M-driven diffuse midline gliomas, showing ATR inhibition effectively targets this feature in preclinical models.

The paper develops a platform to trace 30 nitrogen isotope-labeled metabolites, revealing cell state-specific pyrimidine synthesis pathway preferences in primitive and differentiated cells.

This study develops a nanoparticle system to deliver Panobinostat intrathecally for treating metastatic medulloblastoma, showing improved drug distribution and survival benefits in mice.

This paper develops a multimodal data fusion approach to enhance spatial and spectral resolution in mass spectrometry imaging using machine learning and physical constraints.

This study identifies a pathogenic subpopulation of glioma-associated macrophages (mGAMs) linked to glioma progression through GRN analysis and functional validation.

This paper develops a chemical barcoding platform using halocodes to enable high-sensitivity detection and pooled screening of nanoparticles, demonstrating its utility in quantifying delivery and spatial mapping.

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

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.

MIAAIM integrates multimodal imaging data across tissue types to map tissue states and link cellular phenotypes with molecular microenvironments.

The paper introduces msiPL, a deep learning model for analyzing MSI data, enabling efficient non-linear visualization and identification of biologically relevant patterns.