Yan Liang

The paper develops a framework to decode TLS maturation in NSCLC using spatial omics data and deep learning, identifying two distinct spatial ecosystems linked to patient outcomes.

The paper introduces SM3DD, a 3D spatial transcriptomics analysis method, and finds differences in transcript distances between normal and SARS-CoV-2 patient lung tissues.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

This paper presents a 3D spatial atlas of a human lung carcinoma, revealing tumor microenvironment niches and their associated immune interactions and survival outcomes.

An automated in-situ imaging platform reads 980 RNAs and 108 proteins in FFPE tissue at subcellular resolution, with 0.0092 false calls per cell; 800,000-plus cells released publicly.

Multiplexed in situ hybridization pools against every protein-coding gene let Digital Spatial Profiling read whole transcriptomes from regions of ~10-500 cells in human and mouse tissue.