Sha Liao

HESTIA integrates histology and spatial transcriptomics to identify spatial domains in large-scale data, improving clustering accuracy and mapping tumor heterogeneity.

The paper introduces a protocol to integrate spatial transcriptomics and multiplex immunofluorescence in FFPE tissues, enabling multi-omic analysis at single-cell resolution in colorectal cancer samples.

StereoMM integrates spatial transcriptomic data and histological images to improve spatial domain recognition and tumor heterogeneity analysis in cancer tissues.

This study uses spatial transcriptomics to show that cerebral vasculatures, particularly meninges, drive inflammation and tissue damage in Japanese encephalitis by forming a feedback loop involving immune cells and cell death mechanisms.

This study uses spatiotemporal transcriptomics to analyze immune dynamics in E. multilocularis infected mouse liver, identifying key immune cell roles and shifts in immune strategy during infection.

SAW improves the efficiency and accuracy of Stereo-Seq spatial transcriptomics analysis by optimizing key steps and reducing runtime.

The paper creates a high-resolution spatial transcriptomic atlas of the mouse brain using snRNA-seq and Stereo-seq, identifying new cell clusters, region-specific genes, and transcription factor regulons.

Stereo-CITE-seq combines CITE-seq with Stereo-seq to simultaneously profile whole transcriptomes and protein markers on the same fresh-frozen tissue section.

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.

CellBin processes high-resolution spatial transcriptomics data to generate accurate single-cell gene expression profiles, improving accuracy and efficiency in mouse brain tissue analysis.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

The paper introduces a new sequencing chemistry (CoolMPS) that improves signal intensity and eliminates scar interference in massively parallel sequencing.