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 ...

Certain cells in the brain create a nurturing environment, enhancing the health and resilience of their neighbors, while others promote stress and damage. Using spatial transcriptomics and AI, ...

In a recent study published in the journal Nature, researchers developed spatial aging clocks using single-cell transcriptomics to explore cell-type-specific interactions and their impact on brain ...

Illumina is raising the curtain on its upcoming entry into spatial transcriptomics, with tech designed to help researchers explore cellular behavior mapped across complex tissues. The announcement ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper ...

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 ...

(MEMPHIS, Tenn. – December 3, 2025) Spatial transcriptomics provides a unique perspective on the genes that cells express and where those cells are located. However, the rapid growth of the technology ...

Spatial biology is reshaping how researchers study cancer by revealing the architecture and complexity of tumors in extraordinary detail. Through techniques that combine protein- and gene-level ...

“If you have a machine—for example, a bike—all the different components of the bike would need to be fit in the right places for them to function,” said Lambda Moses, a computational biologist at ...

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 ...

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.