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.

The integration of single-cell and spatial transcriptomics with advanced metabolic profiling tools has revolutionized the ...

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 ...

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 ...

Researchers have demonstrated the feasibility of a morphological-based approach to interpreting spatial transcriptomic (ST) data, helping to improve understanding of the lesions that occur in chronic ...

Multi-omics refers to an integrated approach that combines data from multiple "omics" technologies to provide a holistic ...

Researchers reveal the intricate molecular landscape of triple-negative breast cancer (TNBC), uncovering actionable spatial archetypes and gene signatures that pave the way for personalized therapies ...

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 ...

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 ...

In the rapidly developing field of spatial biology, researchers are working to expand our understanding of biological processes within their spatial context. Scientists are taking a holistic approach ...