Linghua Wang

This study shows that combining stereotactic body radiation therapy with KRAS inhibition synergistically improves preclinical pancreatic cancer treatment by enhancing tumor control and reducing recurrence.

HiCAT improves spatial omics annotation by combining unsupervised clustering with atlas guidance, revealing molecularly defined regions and enhancing downstream analysis.

This study shows TIM3 on M2 macrophages promotes gastric cancer progression and metastasis by suppressing T cell immunity, and TIM3 blockade combined with immunotherapy and chemotherapy improves outcomes.

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

COSIE is a computational framework that integrates multiple spatial omics modalities to generate high-resolution molecular landscapes, outperforming existing methods across diverse datasets.

UTOPIA quantifies confidence in virtual spatial transcriptomics predictions across scales, improving reliability and preventing false conclusions.

REMAP reconstructs spatial tissue architecture from scRNA-seq using deep learning and ST references, revealing microenvironmental features and cancer subtypes.

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.

The paper develops DeMixNB, a deconvolution model for sparse-count RNA sequencing data, showing its utility in analyzing tumor cell plasticity in breast and lung cancer.

This study introduces a deep learning model called spacer to analyze spatially resolved transcriptomics data, revealing novel insights into cellular recruitment mechanisms in tumors and heart tissue during myocarditis.

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

MorphLink identifies disease-related morphological-molecular interplays in multi-modal spatial omics data by linking interpretable morphological features with molecular measurements.

This study identifies cellular and microenvironmental factors contributing to radiation therapy resistance in pancreatic cancer using spatial transcriptomics and single-cell RNA sequencing.

This study investigates how oncogenic mutations in gliomas influence the tumor microenvironment through integrative multiomic and spatial transcriptomic analysis of glioma samples.

This study shows that disrupting the WRAD core in pancreatic cancer leads to DNA replication errors, immune response activation, and improved response to immune checkpoint inhibitors.

The paper develops TimiGP-Response to analyze the tumor immune microenvironment's association with immunotherapy response across multiple cancer types and identifies key immune cell interactions linked to treatment outcomes.

TimiGP infers functional interactions between immune cells in the tumor microenvironment and identifies their prognostic value using bulk gene expression and survival data.

TESLA annotates tumor microenvironment cells and detects spatial transcriptome differences using spatial transcriptomics data.

This study examines DNA methylation changes and intra-tumor heterogeneity in lung adenocarcinoma progression, finding increased methylation aberrations and immunosuppression in later-stage lesions.

The paper analyzes single-cell RNA-seq data to identify ACE2 expression patterns in human lung epithelial cells, finding high ACE2 expression in alveolar type II cells and co-expression with pathogen receptors.

iTALK characterizes and illustrates intercellular communication in tumor microenvironments using single-cell RNA sequencing data.