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 ...

As the next generation of spatial transcriptomics tools hits the market, researchers are uncovering previously unknown interactions that could transform clinical research. To deeply investigate ...

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 ...

We present SpaHDmap, a deep learning framework that integrates histology images with spatial transcriptomic data to derive high-resolution and interpretable spatial metagenes. We demonstrate that ...

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Technological development is key to improving the way hematologic cancer is diagnosed and treated. With this vision, the Josep Carreras Leukemia Research Institute is committed to the creation and ...

Biological systems are inherently three-dimensional—tissues form intricate layers, networks, and architectures where cells interact in ways that extend far beyond a flat plane. To capture the true ...

A team of Vanderbilt researchers has released a new benchmarking study that aims to assist scientists in selecting the most effective methods for analyzing spatial transcriptomics (ST) data. ST ...

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 genomics is a set of methods for measuring gene activity, DNA-associated features, proteins or other molecular signals while preserving where those signals occur in tissue. In practice, the ...