Xiao Wang

The paper profiles the subcellularly resolved 3D translatome in mouse oocytes and early embryos, revealing spatially controlled translation patterns and embryo asymmetry.

This paper introduces in situ graphene-seq, a platform integrating chronic electrophysiology with spatial transcriptomics to analyze tissue microenvironments, showing how spatial heterogeneity affects cell function and gene expression.

The paper uses single-molecule imaging and spatial transcriptomics to show a spatially graded threat-sensing strategy in lung tissue during influenza infection, with higher PRR expression in inner stroma leading to more potent immune responses.

The paper uses spatial omics to analyze translational dysregulation in Grin2a+/- mice, finding gene-specific translation reductions in neurons and non-neuronal cells linked to schizophrenia pathophysiology.

This paper develops Deep-STARmap and Deep-RIBOmap for 3D spatial transcriptomics and translatomics in thick tissue blocks, enabling analysis of gene expression and immune interactions in human skin cancer.

This paper introduces a stretchable microelectrode array that integrates into the developing brain, enabling long-term, stable neural activity tracking without disrupting development.

The paper introduces STAgent, an AI tool that automates spatial transcriptomics analysis, revealing in vivo maturation of human stem cell-derived pancreatic cells.

This study identifies mRNA localization sequences in osteocyte dendrites and shows they are translated locally, revealing a mechanism for regulating dendrite structure and function.

CAST enables spatial-to-spatial searching and matching of single-cell spatial omics data across technologies and conditions, identifying disease-associated pathways and translational efficiency variations.

This paper introduces a harmonic framework to analyze spatial transcriptomics data, showing that high-frequency patterns represent cell-cell interactions while low-frequency patterns represent tissue regions.

This study identifies and characterizes rRNA variants in human tissues and cancer, revealing tissue-specific ribosome subtypes and cancer-specific ribosome variations.

The paper introduces FuseMap, a deep-learning framework for spatial transcriptomics that creates a comprehensive molecular spatial reference atlas of the mouse brain.

The paper creates a high-resolution spatial atlas of the mouse CNS by combining in situ sequencing with scRNA-seq data to map cell types and gene expression across the brain and spinal cord.

The paper introduces SPIN, a method to mitigate autocorrelation in spatial transcriptomics by subsampling neighbors, improving existing methods and enabling joint analysis across datasets.

The paper introduces RIBOmap, a 3D in situ method to profile mRNA translation at single-cell and spatial resolutions, revealing translation dependencies on cell cycle and subcellular localization in brain tissue.

This paper introduces TEMPOmap, a method to profile subcellular RNA kinetics in single cells, revealing kinetic gene clusters with distinct functional roles.

This paper introduces in situ electro-sequencing to map gene expression and electrophysiology in 3D tissues, improving cell type analysis and developmental trajectory reconstruction.

The paper introduces STARmap PLUS to map single-cell transcriptional states and disease markers in AD mouse models, revealing spatial relationships between cell types and pathological features.

This study uses machine learning to optimize organoid morphogenesis, revealing an excitable system driven by WNT and FGF signaling that governs human axial elongation.

This paper introduces UnitedNet, a multi-task deep learning model for analyzing single-cell multi-modality data, showing improved performance in joint group identification and cross-modal prediction.

ClusterMap is a framework for analyzing spatial gene expression data, clustering RNAs into subcellular structures and tissue regions across multiple dimensions.