Spatial transcriptomics and gene expression analysis represent a transformative approach in biomedical research, integrating the spatial context of tissues with high-resolution profiling of gene ...

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.

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 provides a unique perspective on the genes that cells express and where those cells are located. However, the rapid growth of the technology has come at the cost of ...

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 ...

This article explores how researchers are using spatially resolved methods to explore diverse biological processes from ...

Spatial transcriptomics data from osteosarcoma cells. Left) A spatial map of the transcriptome segmented into individual cells using machine learning, with each dot representing a RNA transcript and ...

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 ...

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 ...

This article explores how single-cell multiomics and spatial transcriptomics are illuminating early pregnancy, uncovering ...

The rapid expansion of high-throughput sequencing technologies has generated unprecedented amounts of multi-omics data, ...