Garry P. Nolan

The paper benchmarks Quantum Barcoding against mass cytometry in human and rhesus macaque blood, integrating data to reveal conserved and divergent signaling responses across species.

This study uses single-cell RNA sequencing and spatial profiling to show fibroblasts influence neutrophil inflammation in UC, with 5β1 targeting reducing inflammatory responses.

This paper introduces 3D-Omics-Flow, a method that reconstructs 3D molecular volumes from 2D spatial proteomics sections by repairing damaged sections and interpolating between them.

The paper identifies IL13-driven immune-epithelial remodeling in the nasal epithelium that restricts respiratory virus infection by reshaping the mucus barrier.

The paper uses multiplex imaging to track the development of T cell-APC triad neighborhoods during immune checkpoint therapy, showing their role in spatially organizing tumor immunity.

This study uses spatial multi-omics to compare EBV-positive and EBV-negative cHL, identifying distinct TME features and immunological signatures linked to EBV.

The paper constructs an integrated atlas of tumor-infiltrating dendritic cells across species and immunotherapy conditions, revealing conserved activation states and spatial reorganization in response to therapy.

SPACEc provides a Python workflow for analyzing multiplexed imaging data with interactive tools for image processing, segmentation, and spatial analysis.

Fluid-Squid enables customizable, cost-effective multiplexed imaging of cells and tissues through automated fluidics and barcoding strategies.

This study maps the spatiotemporal changes in the bone marrow microenvironment during CML progression using single-cell and 3D imaging, revealing immune evasion and niche disruption.

This study identifies immune cell changes and gene expression patterns in Barrett's esophagus progression to cancer using spatial and multi-omic analyses.

The paper identifies spatial immune patterns in Merkel cell carcinoma linked to viral status and patient outcomes.

MONTAGE identifies spatially resolved functional enrichment gradients in tumor microenvironments by analyzing spatial communities and their clinical relevance.

This study shows that NK cell receptor signaling and activation vary with cell cycle stages, with some proteins showing non-monotonic abundance changes.

This study introduces MicroCart, a framework for spatial multi-omics analysis of host-microbiome interactions in colitis, revealing tissue-level immune responses and microbial shifts.

The paper introduces tidyomics, an ecosystem bridging Bioconductor and tidy R to streamline omic analysis using data from the Human Cell Atlas.

This paper introduces CellSNAP, a self-supervised method that integrates spatial, transcriptomic, and proteomic data to improve cell population delineation in spatial-omics datasets.

This paper creates a spatial cell atlas of neuroblastoma, revealing tumor heterogeneity through multi-omics data and identifying spatial compartmentalization and signaling patterns.

The paper integrates multiplexed imaging and multiscale modeling to show that T cell phenotype influences tumor phenotype conversion, critical for therapeutic efficacy.

This study reveals that cellular differentiation drives intratumor heterogeneity in B-cell lymphomas, with distinct maturation states associated with different survival outcomes.

The paper introduces MaxFuse, a method for integrating spatial and single-cell omics data with weak linkage, demonstrating its effectiveness in consolidating proteomic, transcriptomic, and epigenomic information.

The paper introduces MARIO, a cell matching algorithm that integrates single-cell proteomic and multi-modal data, revealing orchestrated immune responses in lung tissue.

This paper introduces SpaGFT, a graph Fourier transform model for spatial omics data analysis, enabling accurate identification of tissue modules and their functional relationships in spatial transcriptomics and proteomics data.

STELLAR annotates spatially resolved single-cell data using geometric deep learning, discovering cell types and states while transferring annotations across tissues and donors.

CELESTA identifies cell types in multiplexed in situ images by integrating protein expression and spatial information, revealing novel spatial biology associated with lymph node metastasis.

The paper maps cell organization and function across human intestine regions using single nuclear RNA and open chromatin assays, revealing distinct cell neighborhoods and regulatory differences.

The paper introduces a machine learning method that integrates immunological knowledge to improve predictive power from mass cytometry data.

The paper identifies TNFAIP3 variants in humans, Denisovans, and mice that modulate A20 phosphorylation, revealing a genetic mechanism for balancing immunity and microbial tolerance.

The paper introduces srIBI, a super-resolution imaging technique, to visualize subcellular localization of biomolecules and drugs at the nanometer scale.

This paper introduces ion beam tomography (IBT), a 3D subcellular imaging technique, and demonstrates its ability to visualize genomic regions, RNA transcripts, and protein factors with high resolution.