Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis,

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods.

Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and

As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it

Modern microscopy produces massive image datasets that enable detailed multi-scale analysis and can combine several modalities. Visualizing, exploring and sharing such data are challenges both during the execution of a research project and after

Optimal design of spatial transcriptomic experiments allows statistical evaluation of the impact of various biological and technological features on the discovery of cell phenotypes.

Functional properties of cells depend on the underlying molecular profile of these cells. Jia Liu, Xiao Wang and their colleagues from Harvard, MIT and the Broad Institute in Cambridge, Massachusetts, developed in situ electro-sequencing to study how

Organoids derived from stem cells have become an increasingly important tool for studying human development and modeling disease. However, methods are still needed to control and study spatiotemporal patterns of gene expression in organoids. Here we