This post outlines key principles for handling FFPE tissues in spatial transcriptomics, emphasizing RNA quality checks, proper tissue block preparation, section quality, and correct orientation. Adhering to these practices ensures reliable data, minimizing variability and enhancing downstream analysis.

To understand how the brain works, researchers need to connect molecular identity with spatial organization - from single cells to entire neural circuits. Stereo-seq enables this connection by providing single-cell resolution transcriptomics across large brain areas, allowing genes, cells, and circuits to be studied within their native spatial context.

In the standard STOmics analysis workflow, SAW generates Cellbin GEF files containing gene expression matrices and spatial coordinates. While SAW provides initial clustering, researchers often perform advanced downstream analysis using third-party tools such as Scanpy, Seurat, or specialized algorithms like cell2location or RCTD (Robust Cell Type Decomposition) to identify precise cell types or functional states.

n the SAW workflow, the built-in automatic segmentation algorithm can typically detect the boundaries of most cell nuclei. However, when working with images with suboptimal nuclear staining, segmentation performance may degrade. In these cases, segmentation still runs, but the results may be suboptimal, potentially leading to over-segmentation, under-segmentation, and other issues. Additionally, in certain situations—such as when image QC fails and automatic registration cannot be performed, or for H&E-stai

Stay ahead of the curve with the latest developments, innovations, and resources in spatial biology from STOmics. We're pushing the boundaries of what's possible with Stereo-seq, offering nanoscale resolution across a large field of view. Dive into recent publications and exciting advancements as we continue to empower groundbreaking discoveries in neuroscience, disease research, and beyond.

Ideal for large-batch sample projects, CSRefiner currently supports three leading models in spatial omics: Cellpose, StarDist, and CellBin (more models planned). With minimal annotated local data, it fine-tunes existing models to specific image features; the fine-tuned model then segments entire/similar images, boosting efficiency while ensuring key-region accuracy.

A recent study published in Molecular Cancer [1] employs single-cell technologies to investigate tumor microenvironment (TME) heterogeneity between primary tumors and liver metastases in CRNET-LM and constructs a single-cell atlas for CRNET tumor samples. The goal is to provide new insights and research avenues for enhancing the prognosis of CRNET-LM.

This data-driven study not only reveals novel therapeutic targets for anti-tumor immunity, but also provides a translatable framework to dissect immune regulation in tumor microenvironments across cancers.

Spatial multi-omics technologies are transforming our understanding of complex biological systems with unprecedented detail. STOmics' Stereo-seq offers nanoscale resolution and an expansive field of view of up to 160 cm². In this issue, we highlight recent advances, key resources, and notable publications that showcase the power of Stereo-seq across neuroscience, disease research, and beyond.

In this blog, we explore the principles of spatial gene co-expression, evaluate the performance of computational algorithms on Stereo-seq datasets, and highlight case studies demonstrating its translational potential.