Spatial transcriptomics data analysis increasingly depends on artificial intelligence (AI) to convert raw, location-tagged gene expression readings into a usable map of tissue biology. Unlike ...

The field of spatial transcriptomics utilizes technologies that map gene expression data to specific cellular locations within tissues. While traditional RNA sequencing methods generate quantitative ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

Single-cell RNA transcriptomics allows researchers to broadly profile the gene expression of individual cells in a particular tissue. This technique has allowed researchers to identify new subsets of ...

New simulator and computational tools generate realistic ‘virtual tissues’ and map cell-to-cell ‘conversations’ from spatial transcriptomics data, potentially accelerating AI-driven discoveries in ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

Spatial transcriptomics technologies opened the door for new kinds of biological measurements, allowing scientists to generate detailed maps of where genes are expressed in tissue. But most methods ...

Conventional transcriptomic techniques have revealed much about gene expression at the population and single-cell level—but they overlook one crucial factor: spatial context. In musculoskeletal ...