Achieving this requires a deeper understanding of the genetic networks that regulate organ development. As a typical legume, soybean forms specialized root nodules that enable nitrogen fixation—key biological processes for yield improvement (Roy et al., 2019). However, the precise regulatory mechanisms underlying these complex organs remain largely unknown. Capturing their spatial and cellular complexity requires not only traditional transcriptomics but also approaches that preserve spatial context..

The human thymus functions as a biological "academy" where T cells develop and learn to distinguish self from non-self. Within this microenvironment, developing thymocytes interact with stromal cells—particularly thymic epithelial cells (TECs)—that guide positive and negative selection (Takahama, 2006). This process relies on a highly organized spatial architecture, especially in the thymic medulla, where rare epithelial populations mimic peripheral tissues to test developing T cells. Recent studies have id

Genomic sequencing has identified key drivers in gastric cancer, including BRAF, KRAS, ARID1A, and the SBS17 mutational signature enriched in IM (Huang et al., 2023). However, conventional approaches face a critical limitation: the loss of spatial context.

The core of regeneration is the formation of a blastema, a region of progenitor cells that drive tissue regrowth. Without spatial data, it’s hard to distinguish between fibroblasts that cause scarring and those with regenerative potential, as they often share similar markers but occupy different tissue regions (Tower et al., 2022). Additionally, comparing mammals with species like salamanders is challenging due to their vast genetic differences. A method to compare regeneration within mammals was needed to

Fan et al. (2023) analyzed 31 treatment-naïve CSCC samples using a combination of single-cell RNA sequencing (scRNA-seq) and Stereo-seq Transcriptomics Solution to map the heterogeneity of CSCC tumors at an unprecedented resolution. The study applied Non-negative Matrix Factorization (NMF) to scRNA-seq data to identify seven distinct transcriptional meta-programs (MP1–MP7) in CSCC cells, and these states were then spatially mapped using Stereo-seq, a high-resolution spatial transcriptomics platform develope

Tumors are highly heterogeneous tissues characterized by intricate spatial architecture, wherein cancer cells, immune cells, and stromal cells collectively constitute distinct and dynamic microenvironments across the tumor landscape (Pattabiram et al., 2025). Understanding spatial heterogeneity is critical for uncovering how tumors work and why treatments succeed or fail, but traditional transcriptomic methods cannot capture this complexity. Stereo-seq enables entire tissue sections to be studied at single-

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.

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.