Bokai Zhu

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.

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

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 identifies early immune responses preceding HIV rebound and links them to viral control outcomes in individuals discontinuing antiretroviral treatment.

The paper introduces two methods to profile functional glycan-lectin interactions in tissues using single-cell and spatial multiomic approaches, revealing stimulus-specific glycan remodeling and spatial glycan programs in immune cells and follicular lymphoma.

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.

This study uses multi-scaled transcriptomics to investigate immune-epithelial dynamics and tissue remodeling in chronic rhinosinusitis, identifying novel cellular interactions and trajectories associated with nasal polyps.

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.

This paper introduces IN-DEPTH and SGCC for spatial multi-omics analysis, revealing EBV-related immunomodulatory differences in DLBCL.

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.

MAPS provides pathologist-level cell type annotation from spatial proteomics data using machine learning, outperforming existing methods in speed and accuracy.

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.

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