Paper Review
With the development of spatially resolved single-cell technology, we are able to acknowledge the comprehensive characterization of various systems. However, the increasing complexity of computational methods is still a bottleneck for identifying spatial domains. New methods with high time efficiency, generalizability, and scalability are needed to overcome these challenges.
A study in Nature Communications introduces MENDER (Multi-range cEll coNtext DEciphereR), a paradigm shift that replaces black-box models with a simple yet powerful biology-inspired framework. MENDER leverages the consistent observation of cellular neighborhood structures across spatial technologies (e.g., ~15 μm cell-cell distances) to construct multi-range cellular context representations. By encoding cell state frequencies across concentric spatial rings, MENDER achieves scalable million-cell dataset processing in minutes, automates label harmonization across tissue slices without manual intervention, and enhances interpretability through direct mapping of contextual features. For instance, MENDER uncovered spatially distinct tumor-immune interaction zones in triple-negative breast cancer that stratify patient subtypes, exemplifying its interpretability for therapeutic discovery. MENDER democratizes spatial omics by enabling rapid and reproducible analysis of large-scale datasets—critical for brain atlases, cancer ecosystems, and developmental studies—providing new biological insights.
In this study, researchers validated MENDER using 24 data from 8 different spatial technologies including Stereo-seq, which allowed a comprehensive evaluation of method generalizability across varying spatial resolutions. The results revealed that MENDER, utilizing the single-cell spatial resolution-Stereo-seq, highlighted finer tissue structures. For the bench-leasing code and tutorial, please visit https://mender-tutorial.readthedocs.io/en/latest/
Discover more about the research: https://www.nature.com/articles/s41467-023-44367-9
About STOmics Stereo-seq:
STOmics Stereo-seq enables tissue-to-data analysis by capturing the whole transcriptome in situ with single-cell resolution across centimeter-wide fields. The upgraded v1.3 features optimized reagents, improved probes, and enhanced enzymes for higher efficiency, broader compatibility, and a simpler workflow.
Know more about STOmics Stereo-seq? https://en.stomics.tech/products/stereo-seq-transcriptomics-solution/list.html
