Lidan Wu

The paper uses single-cell spatial transcriptomics to analyze tau pathology in mouse brains, revealing cell-autonomous and non-autonomous effects of tau accumulation on gene expression and pathways.

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

FastReseg improves cell segmentation accuracy in spatial transcriptomics by using transcriptomic data to correct image-based segmentation errors.

The paper introduces a GAN-based method to generate high-quality membranes for cells with poor staining in fluorescent microscopy images, improving segmentation accuracy.

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.

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.