The rapid development of spatial transcriptomics (ST) technologies has greatly advanced the understanding of gene expression, tissue architecture, cellular composition, and disease mechanisms within ...

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 ...

Spatial transcriptomics (ST) has revolutionized biomedical research by enabling scientists to measure gene expression while ...

This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.

Knowing the location of a gene within intact tissue or a single cell allows scientists to unlock unknown cellular functions. This information is often lost in most genetic sequencing techniques, but ...

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 ...

Spatial biology is a rapidly advancing discipline that examines biological molecules (such as DNA, RNA, and proteins) within their native locations in tissues. This approach offers critical insight ...

Computational assessment identifies probe binding errors in a widely used commercial platform for spatial transcriptomics.

Race-specific survival prediction models for de novo metastatic breast cancer using machine learning. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...