This weeks top spatial transcriptomics paper
🧠 Neurobiology & Brain Evolution
Lamprey 3D single-cell transcriptomics reveals ancestral and specialized features of the vertebrate brain.
Using spatial transcriptomics and single-nucleus RNA-seq, the authors build a 3D molecular atlas of the lamprey brain, resolving 209 cell clusters across 14 regions. Comparative analyses show a deeply conserved spatial architecture alongside lineage-specific neuronal specializations, and suggest that a cerebellum-like organization existed before the evolution of the jawed vertebrate cerebellum.
Impact: Provides an evolutionary blueprint of vertebrate brain organization and cell-type innovation using spatially resolved single-cell data.
Wu H et al., https://doi.org/10.1126/science.aea2535
Multimodal imaging of gene expression, morphology, and activity of the same neuron.
This study introduces a trimodal platform that links in vivo Ca²⁺ activity, whole-brain projection patterns, and gene expression for the very same neuron. By integrating functional imaging with high-resolution anatomical and transcriptomic readouts, the method enables direct mapping from molecular identity to circuit connectivity and behavior.
Impact: Establishes a powerful multimodal framework to connect neuronal gene expression with structure and function at single-cell resolution.
Zhao Y et al., https://doi.org/10.1016/j.cell.2026.05.041
Spatial and network principles behind neural generation of locomotion.
The authors model the mouse spinal cord using cell-type-specific projection patterns derived from single-cell RNA-seq and spatial transcriptomics to explain how locomotor rhythms emerge. An asymmetric “Mexican hat” connectivity pattern and spatial segregation of cell types reproduce key features of walking and predict propagating activity waves during rhythmic movement.
Impact: Reveals general spatial network principles that link spinal cell types and connectivity to locomotor behavior.
Komi S et al., https://doi.org/10.1038/s41467-026-74228-0
🎯 Cancer Research & Tumor Microenvironment
GREM1/FGFR1-activated myofibroblasts induce immunosuppression and accelerate metastasis in high-grade serous ovarian cancer.
By integrating bulk, single-cell, and spatial transcriptomics with functional assays, this work identifies a TGF-β1-driven GREM1⁺ myofibroblast population in high-grade serous ovarian cancer. These CAFs interact with regulatory T cells to create an immunosuppressive niche that accelerates metastasis and reduces responsiveness to PD-1/PD-L1 blockade.
Impact: Pinpoints a spatially defined CAF–Treg axis as a therapeutic target to overcome immune evasion in ovarian cancer.
Li R et al., https://doi.org/10.1073/pnas.2529786123
Integrated Single-Cell and Spatial Analysis Reveals Context-Dependent Myeloid-T Cell Interactions in Response to Immune Checkpoint Blockade in Head and Neck Cancer.
Using combined single-cell and spatial omics in HNSCC patients treated with immune checkpoint blockade, this study systematically dissects in situ interactions between tumor-infiltrating myeloid and T cells. The analysis uncovers context-dependent immune cell neighborhoods linked to treatment response, highlighting specific myeloid–T cell configurations associated with effective or failed ICB.
Impact: Provides a spatially resolved immune interaction map to guide precision immunotherapy in head and neck cancer.
Golfinos-Owens AE et al., https://doi.org/10.1158/1078-0432.CCR-25-2300
Enhancing glioma immunotherapy by disrupting RBP-J-mediated NNMT signaling in tumor microenvironment.
This study integrates scRNA-seq and spatial transcriptomics to identify an NNMT-high CAF subset enriched at the glioma margin and tightly associated with M2 macrophages. Mechanistic work shows that RBP-J drives NNMT expression, triggering epigenetic reprogramming and SAA3 upregulation that recruits and polarizes M2 macrophages, ultimately suppressing CD8⁺ T cells and limiting immunotherapy efficacy.
Impact: Reveals an RBP-J–NNMT–SAA3 axis in spatially localized CAFs as a targetable pathway to boost glioma immunotherapy.
Zhang D et al., https://doi.org/10.1038/s41388-026-03844-3
❤️ Cardiovascular Disease & Inflammation
Single-Cell Studies Advance Understanding of the Genetic and Molecular Basis of Atherosclerosis.
This review synthesizes how single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics are reshaping our view of atherosclerotic plaques. It highlights newly defined vascular and immune cell states, spatially organized signaling circuits within plaques, and emerging CRISPR-based single-cell perturbation tools that link genetic risk variants to causal cell types and regulatory networks.
Impact: Positions spatial and single-cell genomics as central tools for mechanistically dissecting and therapeutically targeting atherosclerosis.
Li DY et al., https://doi.org/10.1161/CIRCRESAHA.125.327472
🧪 Technology & Methods Development
spAttClu: A spatial domain clustering model leveraging spatially-weighted graph attention and contrastive learning.
spAttClu introduces a graph attention network that dynamically weights spatial neighbors based on expression context, coupled with contrastive learning to refine spatial domain assignments. Compared with static-neighbor methods, it improves robustness and accuracy in identifying tissue domains across diverse spatial transcriptomics datasets.
Impact: Offers a next-generation clustering framework that more faithfully captures spatial tissue architecture from ST data.
Zhang T et al., https://doi.org/10.1093/bioinformatics/btag384
SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.
SECTOR jointly infers discrete spatial domains and continuous pseudotemporal gradients directly from spatial transcriptomics, using structural entropy to balance sharp boundaries with smooth within-region transitions. This integrated approach avoids reliance on external trajectory tools and better preserves both spatial compartmentalization and dynamic progression signals.
Impact: Delivers a unified method to decode both spatial structure and temporal evolution from ST sections.
Huang L et al., https://doi.org/10.1093/bioinformatics/btag367
Well-ST-seq: Cost-Effective and Near-Cellular Spatial Transcriptomics Using Deterministic Barcoded Bead Arrays.
Well-ST-seq combines microwell-assembled hydrogel bead arrays with orthogonal microfluidic indexing to generate predefined spatial barcodes at near-cellular resolution. The streamlined workflow produces capture arrays in about two hours at very low consumable cost (∼$0.05–$0.54 per mm²), enabling affordable, scalable spatial transcriptomics for research and translational settings.
Impact: Makes high-resolution spatial transcriptomics more accessible through a low-cost, deterministic barcoded bead platform.
Yu N et al., https://doi.org/10.1021/acs.analchem.6c01070*