This weeks top spatial transcriptomics paper
🎯 Cancer Research & Tumor Microenvironment
Persistent T cell activation and cytotoxicity against glioblastoma following single oncolytic virus treatment in a clinical trial.
Using in situ analyses, this study shows that a single dose of the oncolytic virus rQNestin34.5v.2 induces deep and sustained T cell infiltration into glioblastoma tumors. Persistent T cell–mediated cytotoxicity, captured spatially within tumor regions, is linked to improved survival in treated patients.
Impact: Demonstrates durable, spatially organized anti-tumor immunity after a single oncolytic virotherapy dose in glioblastoma.
Meylan M et al., https://doi.org/10.1016/j.cell.2025.12.055
Spatial transcriptomics reveals tumor microenvironment-driven subtypes of invasive lobular carcinoma.
Spatial transcriptomics of 43 hormone receptor–positive invasive lobular breast cancers uncovers strong spatial and cellular heterogeneity in the tumor microenvironment. Integrating spatial gene expression, histology, and deconvolution, the authors define four microenvironment-driven subtypes (ILC4TME) with distinct biology and prognostic value beyond existing classifiers.
Impact: Provides a spatially informed classification of lobular breast cancer with improved risk stratification and therapeutic targeting potential.
Serra M et al., https://doi.org/10.1073/pnas.2517567123
Chemotherapy triggers immune evasion by fostering LEPR(+) Kupffer cell differentiation in liver metastases.
Cross-species single-cell and spatial transcriptomics reveal that chemotherapy reshapes liver-resident Kupffer cells into a LEPR⁺ immunosuppressive state that promotes immune escape and chemoresistance in liver metastases. These remodeled macrophages orchestrate a protumor microenvironment, undermining the beneficial immunogenic effects of cytotoxic treatment.
Impact: Identifies a chemotherapy-induced, spatially localized Kupffer cell program as a targetable driver of immune evasion in liver metastases.
Wang X et al., https://doi.org/10.1016/j.ccell.2026.01.010
Multimodal spatial-omics reveal co-evolution of alveolar progenitors and proinflammatory niches in progression of lung precursor lesions.
By mapping over 5 million cells across human lung precursor lesions and adenocarcinomas, this work charts how alveolar progenitors co-evolve with proinflammatory micro-niches during early tumor development. Spatially resolved clonal architectures and niche analysis show that IL1B^high macrophage-rich inflammatory environments expand tumor-associated progenitors and can be therapeutically targeted in premalignant stages.
Impact: Defines stage-specific inflammatory–epithelial niches as spatially organized targets for interception of early lung adenocarcinoma.
Peng F et al., https://doi.org/10.1016/j.ccell.2025.10.004
🧠 Neurobiology & Development
Spatiotemporal interplay between epithelial and mesenchymal cells drives human dentinogenesis.
Combining single-cell RNA sequencing with spatial transcriptomics, this study builds a human tooth development atlas from initiation to eruption, focusing on dental epithelium–dental papilla crosstalk. The authors uncover a WNT–NOTCH sequential activation model by which epithelium directs mesenchymal differentiation and identify key signaling molecules controlling dentin formation and defect susceptibility.
Impact: Offers a spatially resolved roadmap of human dentinogenesis that informs regenerative strategies for irreversible dentin defects.
Wei W et al., https://doi.org/10.1038/s41467-026-69545-3
Enhancer dynamics and cellular architecture in the human spinal cord.
This work maps RNA transcription, chromatin accessibility, and histone modifications in the adult human spinal cord at single-cell and spatial resolution. The authors uncover “masked” enhancer states where transcription proceeds independently of accessibility, define region-specific glial regulatory networks along the rostrocaudal axis, and link cellular positioning to paracrine signaling networks.
Impact: Establishes a spatially anchored enhancer-state atlas of the human spinal cord to guide studies of neurodegeneration and repair.
Kandror EK et al., https://doi.org/10.1016/j.neuron.2025.12.035
🧬 Immunology & Vaccinology
Local antibody feedback enforces a checkpoint on affinity maturation in the germinal center and promotes epitope spreading.
Using an mRNA-LNP–encoded membrane-bound HIV Env immunogen in mice with defined BCR affinities, this study dissects how local antibody competition shapes germinal center dynamics. Spatial transcriptomics identifies plasma-like cells in and around germinal centers producing early IgG that shortens high-affinity B cell residency, suppresses competing clones, and redirects responses toward alternative epitopes.
Impact: Reveals a spatially organized local antibody feedback loop that constrains affinity maturation and drives epitope spreading, with implications for vaccine design.
Yan Y et al., https://doi.org/10.1016/j.immuni.2026.01.011
🧪 Technology & Methods Development
Seq-Scope-eXpanded: spatial omics beyond optical resolution.
Seq-Scope-X combines submicrometer-resolution sequencing-based spatial transcriptomics with tissue expansion to surpass traditional optical limits. In liver, brain, colon, spleen, and tonsil, the method resolves nuclear versus cytoplasmic transcriptomes at near single-cell coverage and can be extended to spatial proteomics via barcode-tagged antibodies.
Impact: Delivers ultra–high-resolution spatial transcriptome and proteome mapping, enabling fine-grained dissection of cellular architecture and function.
Anacleto A et al., https://doi.org/10.1038/s41467-026-69346-8
Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.
The ovrlpy computational tool analyzes three-dimensional transcript localization in imaging-based spatial transcriptomics to detect vertically overlapping cells, tissue folds, and segmentation errors. By moving beyond standard 2D segmentation, it flags spatial doublets and misassigned transcripts that can compromise downstream biological interpretation.
Impact: Provides a 3D-aware quality control and correction framework to improve cell segmentation accuracy in spatial transcriptomics datasets.
Tiesmeyer S et al., https://doi.org/10.1038/s41587-026-03004-8
CEMUSA: A Graph-based Integrative Metric for Evaluating Clusters in Spatial Transcriptomics.
CEMUSA introduces a graph-based evaluation metric for spatial clustering that jointly accounts for label agreement, spatial organization, and error severity. By integrating these dimensions, it avoids biases of existing metrics and offers a more faithful assessment of clustering quality across spatial omics methods.
Impact: Supplies a robust, spatially informed benchmark metric to compare and optimize clustering algorithms for spatial transcriptomics.
Hu J et al., https://doi.org/10.1093/bioinformatics/btag056*