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.

This blog summarizes key concepts from that session, examining different annotation algorithms and their performance. Our goal is to equip you with foundational knowledge and guide your choice of algorithm for annotating your own datasets.

We present benchmark results comparing eight cell segmentation tools across four typical staining conditions (DAPI, ssDNA, H&E, and mIF). Our data-driven recommendations provide researchers with guidance for selecting optimal segmentation algorithms based on specific experimental conditions.

Building on our previous blog "Stereo-seq Image Processing: Essential Steps and Their Key Points ", which detailed core image processing steps, this blog now explore practical solutions to common analysis challenges using StereoMap's tools and third-party software integration.

This blog distills the webinar (Stereo-seq image analysis)’s key concepts and explains the fundamentals behind these processing stages. Our goal is to provide you with the essential knowledge for Stereo-seq image analysis and to assist in troubleshooting any related issues you may encounter.