Taobo Hu

SIMPlex generates matched spatial and single-nucleus transcriptomic profiles from the same tissue section to resolve cell states with spatial signatures in mouse and human cancer tissues.

COSTE embeds spatial relationships into a hierarchical metric space to reveal tissue architecture in spatial omics data, identifying spatially defined cell states and disease-associated patterns.

gSV improves structural variant detection in long-read sequencing data by integrating alignment and assembly approaches, showing better sensitivity for complex variants and uncovering new SVs in breast cancer.

This paper introduces a method for targeted sequencing of expressed mutations in single cells using RNA-templated gap filling with BstFL polymerase, enabling simultaneous transcriptomic and mutation profiling.

This study identifies molecular features of triple-negative apocrine carcinoma through proteogenomic analysis, revealing resistance mechanisms and suggesting alternative therapeutic strategies.

This study evaluates the Xenium In Situ spatial transcriptomics platform using mouse brain and human breast cancer datasets, comparing it to other technologies and benchmarking analysis tools.

The paper compares Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) in TNBC samples to assess their performance and identify complementary proteomic profiles.

The paper identifies forward-reverse mutation cycles in cancer development by analyzing LOHs and GOHs across non-tumor, paratumor, and tumor tissues in multiple cancer types.