STOmics' Stereo-CITE Proteo-Transcriptomics Solution is fundamentally reshaping spatial biology by bridging the gap between traditional imaging and high-throughput sequencing. While imaging-based proteomics excels in morphological clarity and protein abundance detection, it often faces bottlenecks in simultaneous whole-transcriptome profiling and scaling plexity due to fluorescence channel limits or tissue damage from stripping.

Despite known drug resistance mechanisms like efflux pump upregulation, many chemotherapy-resistant cases remain unexplained because of the inability to study gene regulation in its spatial context. A key limitation is the inability to map post-transcriptional regulatory events while preserving tumor spatial architecture. Bulk RNA sequencing masks intratumoral heterogeneity, while scRNA-seq destroys spatial context altogether (Ståhl et al., 2016).

These limitations become particularly critical when studying rare or spatially restricted populations, such as LEPR⁺ KCs, whose functional roles depend on precise positioning relative to tumor cells, dying cells, and infiltrating T cells; without sufficient spatial resolution, these closely interacting cell populations are easily averaged together, obscuring key immune–tumor interactions and masking localized immunosuppressive niches. Researchers have thus struggled to pinpoint exactly where these cells res

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.