Abstract: Spatial transcriptomics (ST) enables the joint profiling of gene expression and spatial localization, offering new insights into tissue microenvironments and biological processes. However, ...

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

Foster City, Calif. | January 27, 2026 — Signios Bio today announced the launch of a new grant program supporting innovative spatial transcriptomics research using the 10x Genomics Xenium 5K Spatial ...

Lymph nodes are key sites of adaptive immune responses, yet how immune cells coordinate their activities in space and time remains poorly understood. We combined high-resolution spatial ...

Advancements in spatial perturbation transcriptomics (SPT) have revolutionized our understanding of cellular behavior in native tissue contexts by integrating spatial and perturbation data. However, ...

Abstract: Spatial transcriptomics technologies carry out advanced sequencing analysis of molecular profiles with a spatial context, providing multi-source information essential for elucidating ...

IDH-mutant glioma, caused by abnormalities in a specific gene (IDH), is the most common malignant brain tumor among young adults under the age of 50. It is a refractory brain cancer that is difficult ...

Spatial transcriptomics (ST) technologies reveal the spatial organization of gene expression in tissues, providing critical insights into development, neurobiology, and cancer. However, the high cost ...

The preprint is available here. DeepSpot2Cell predicts virtual single-cell spatial transcriptomics as follows: (1) During training, the model takes as input (i) the cropped cell tile defined by the ...