Ao Chen

HESTIA: Scalable Multimodal Integration of Histology and High-Resolution Spatial Transcriptomics for Robust Spatial Domain Identification

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

CSRefiner: A lightweight framework for fine-tuning cell segmentation models with small datasets

CSRefiner improves cell segmentation accuracy with small datasets by fine-tuning existing models for spatial transcriptomics.

ST-FFPE-mIF: Integrating Spatial Transcriptomics and Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tissues Using Stereo-seq

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.

CellBin:a generalist framework to process spatial omics data to cell level

CellBin processes spatial omics data to create cell-level matrices, outperforming existing methods across multiple platforms and omics types.

Efficient reliability analysis of spatially resolved transcriptomics at varying resolutions using SpaSEG

SpaSEG is a convolutional neural network model for analyzing spatially resolved transcriptomics data, showing superior reliability and efficiency in detecting spatial patterns and intercellular interactions.

An atlas of cell type specific regulatory effects in cattle

The paper constructs a cattle cell-type-specific regulatory variant atlas by integrating bulk and single-cell RNA-seq data, identifying eQTL and linking them to GWAS loci for breeding insights.

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

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

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

Spatial transcriptomics elucidates the central role of cerebral vasculatures in the progression of Japanese encephalitis

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.

Spatiotemporal transcriptomic profiling reveals the dynamic immunological landscape of alveolar echinococcosis

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.

CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models

CellBinDB provides a large-scale multimodal annotated dataset for cell segmentation and benchmarks seven state-of-the-art methods.

Three-dimension transcriptomics maps of whole mouse embryo during organogenesis

The paper fills a gap in 3D spatial transcriptomics by providing data from E9.5 and E11.5 mouse embryos, enabling detailed analysis of organogenesis with a new open-access database.

SCIITensor: A tensor decomposition based algorithm to construct actionable TME modules with spatially resolved intercellular communications

SCIITensor constructs actionable TME modules by analyzing spatially resolved intercellular communications in ST data, identifying immunosuppressive patterns and sample-common immune-infiltration modules.

SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

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

spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

spatiAlign integrates spatially resolved transcriptomics datasets using contrastive learning, improving batch effect removal and enabling downstream analysis across multiple tissue sections.

STOmicsDB: a database of Spatial Transcriptomic data

STOmicsDB provides a database for spatial transcriptomics data with tools for dataset visualization, publication search, and data archiving.

Integrated Spatial Transcriptomic and Proteomic Analysis of Fresh Frozen Tissue Based on Stereo-seq

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

Integrated analysis of spatial multi-omics with SpatialGlue

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

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics

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: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics

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

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis

This study maps gene expression and developmental trajectories in zebrafish embryos using Stereo-seq and scRNA-seq, identifying spatial gene modules and signaling interactions.

Spatiotemporal transcriptome at single-cell resolution reveals key radial glial cell population in axolotl telencephalon development and regeneration

The paper uses Stereo-seq to map single-cell transcriptomes of axolotl telencephalon during development and regeneration, identifying a key radial glial cell subtype involved in early wound healing.

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

This study uses Stereo-seq to generate high-resolution spatiotemporal transcriptomic maps of Drosophila embryos and larvae, revealing new subregions and gene regulatory networks.

Unsupervised Spatial Embedded Deep Representation of Spatial Transcriptomics

SEDR couples a deep autoencoder over expression with a variational graph autoencoder over position, improving clustering in dorsolateral prefrontal cortex and splitting a visually uniform breast tumour region.

DNB-Based On-Chip Motif Finding (DocMF): a High-Throughput Method to Profile Different Types of Protein-DNA Interactions

The paper introduces DocMF, a high-throughput method to profile protein-DNA interactions, identifying PAM sequences for CRISPR systems and other DNA-binding proteins.

Spatially resolved gene regulatory and disease vulnerability map of the adult Macaque cortex

The paper integrates single-cell ATAC and transcriptomic data with spatial transcriptomics to map regulatory features and disease vulnerabilities in macaque cortex.

CoolMPS™: Advanced massively parallel sequencing using antibodies specific to each natural nucleobase

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

Advanced Whole Genome Sequencing Using an Entirely PCR-free Massively Parallel Sequencing Workflow

This paper demonstrates that PCR-free whole genome sequencing using the DNBSEQ platform and machine learning variant calling significantly reduces InDel errors and improves variant calling performance.

A portable and cost-effective microfluidic system for massively parallel single-cell transcriptome profiling

The DNBelab C4 portable, negative-pressure microfluidic device performs high-throughput single-cell transcriptome profiling, resolving species-specific cells and tissue heterogeneity in murine lung and cerebral cortex comparably to existing platforms.