The formation and maintenance of tissue integrity requires complex, coordinated activities
by thousands of genes and their encoded products. Until recently, transcript levels could …

Deciphering the principles and mechanisms by which gene activity orchestrates complex
cellular arrangements in multicellular organisms has far-reaching implications for research …

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but
does not capture their spatial distribution nor reveal local networks of intercellular …

Spatial omics technologies generate wealthy but highly complex datasets. Here we present
Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources …

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-
seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with …

Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes
within cell types across tissue context. Challenges to learning DE include changing cell type …

The idea that tumour microenvironment (TME) is organised in a spatial manner will not
surprise many cancer biologists; however, systematically capturing spatial architecture of …

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal
to interpreting spatial transcriptomics data, but the current methods are limited to cell-type …

Transcriptome level expression data connected to the spatial organization of the cells and
molecules would allow a comprehensive understanding of how gene expression is …