The problem Spatial transcriptomics is rapidly advancing, routinely producing vast numbers of spatial datasets across large tissue areas 1. Several technologies now offer transcriptomic profiling at ...

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 ...

Despite significant advances in understanding lung development, the intricate cellular interactions and spatial organization of the developing human lung remain incompletely defined. Spatial ...

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 ...

The field of spatial transcriptomics utilizes technologies that map gene expression data to specific cellular locations within tissues. While traditional RNA sequencing methods generate quantitative ...

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 ...

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 ...

The rapid development of spatial transcriptomics (ST) technologies has greatly advanced the understanding of gene expression, tissue architecture, cellular composition, and disease mechanisms within ...

A collaborative project has created a comprehensive guide to help standardize spatial transcriptomics practices. Spatial transcriptomics provides a unique perspective on the genes that cells express ...