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.

Applying single-cell RNA sequencing has led researchers to be able to profile the entire transcriptome of cells. However, these transcriptomes prove difficult to link back to their original location ...

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 ...

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 ...

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 ...

Spatial transcriptomics is a technique that provides information about gene expression patterns within intact tissues. This technology employs various methodologies, including in situ sequencing (ISS) ...

Spatial transcriptomics offers a revolutionary approach to understanding the tumor microenvironment (TME) in solid tumors by preserving the spatial context of gene expression, which is crucial for ...

Spatial transcriptomics (ST) technologies reveal the spatial organization of gene expression in tissues, providing critical insights into development, neurobiology, and cancer. However, the high cost ...