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.

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 ...

This three-part webinar series brings together leading researchers to explore how spatial biology is evolving - from data ...

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 ...

Researchers at the Max Delbrück Center have developed an open-source spatial transcriptomics (ST) platform, called Open-ST, that creates 3D molecular maps from patient tissue samples with subcellular ...

Spatial transcriptomics technologies opened the door for new kinds of biological measurements, allowing scientists to generate detailed maps of where genes are expressed in tissue. But most methods ...

Fei Chen and Chenlei Hu at the Broad Institute of MIT and Harvard have developed a new imaging-free spatial transcriptomics technology that tracks the diffusion of DNA barcodes between beads in an ...

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 ...

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 ...

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 ...