Artificial intelligence (AI) can acquire characteristics that are not yet known to humans
through extensive learning, enabling to handle large amounts of pathology image data …

Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor
microenvironment. In this environment, cancer cells change their expression profiles to fight …

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities
for biological discovery. In particular, imaging techniques offer single-cell resolutions …

Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to
hundreds of distinct RNA species with high spatial resolution. It presents new avenues for …

Spatial protein expression technologies can map cellular content and organization by
simultaneously quantifying the expression of> 40 proteins at subcellular resolution within …

Background Recent advances in imaging-based spatially resolved transcriptomics (im-SRT)
technologies now enable high-throughput profiling of targeted genes and their locations in …

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with
the power to identify rare cells, discover new cellular subclusters, and describe novel genes …

Background: Spatial transcriptomics (ST) methods provide single cell transcriptome profiles
within the endogenous cell tissue context. Thus, they are ideally suited to resolve intra-tumor …

The glioblastoma tumour microenvironment is characterised by immense heterogeneity, with
malignant and non-malignant cells that interact in a complex ecosystem. Emerging evidence …

Single-cell spatial transcriptomics promises a highly detailed view of a cell's transcriptional
state and microenvironment, yet inaccurate cell segmentation can render this data murky by …