Andreas Pfenning discusses the techniques being developed and used to study neuronal heterogeneity and the therapeutic potential of his work.

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.

Challenges and Prospects. Challenges and prospects faced by spatial transcriptomics itself and its application to the musculoskeletal system. AI Artificial intelligence, FFPE Formalin fixed and ...

Spatial biology captures molecular complexity by preserving the physical context of cells in their native environments. From deciphering RNA dynamics within stress granules to scaling molecular ...

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 ...

Jasmine Plummer shares the spatial omics techniques she has developed to investigate the cellular processes underlying ...

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 ...

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 ...

Tumors contain many different types of cells organized in complex spatial patterns that can influence how the disease progresses. Because of this, it is hard to predict how a tumor will develop and ...

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 ...