Sungwoo Bae

This study uses spatial transcriptomics to compare the tumor-stroma border in HBV-related and NBNC HCC, identifying conserved desmoplastic niches and etiology-specific immune signaling differences.

This study identifies spatial tumor microenvironment features of chemoradiotherapy-resistant residual esophageal squamous cell carcinoma using spatial transcriptomics and single-cell analysis, revealing SPP1-associated macrophage programs and microvascular rarefaction as key factors.

This study uses spatial transcriptomics to compare immune and metabolic differences between treatment-responsive and resistant non-small cell lung cancer tumors after neoadjuvant chemoimmunotherapy.

This study uses spatial transcriptomics to identify SPP1+ macrophages as key contributors to CCRT resistance in hypopharyngeal SCC by modulating the tumor microenvironment.

STopover analyzes spatial transcriptomics data to identify cell-cell interactions and colocalization patterns in the tumor microenvironment.

CELLama transforms single-cell and spatial transcriptomics data into embeddings using language model abilities, enabling flexible analysis without manual reference data.

The paper uses spatial transcriptomics to analyze brain cell changes in mouse dementia models after immunomodulation treatments and develops a new analysis tool.

spSeudoMap maps cell types from spatial transcriptomics data using unmatched single-cell RNA-seq data by creating virtual cell mixtures and domain adaptation.

CellDART adapts neural networks to infer cell type spatial distribution from single-cell and spatial transcriptomic data, showing effectiveness in human and mouse tissues.

This study characterizes spatiotemporal glial cell activation in an Alzheimer's disease model, revealing early white matter changes and later gray matter involvement.

The paper introduces SPADE, a method that integrates spatial transcriptomic data with deep learning features from tissue images to identify genes associated with morphological contexts.