Jiahao Huang

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.

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

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.

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.

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