🤰 Maternal–Fetal Interface & Development
Single-cell spatiotemporal dissection of the human maternal-fetal interface.
Using single-cell and spatial analyses, this study dissects how maternal and fetal cells are intermixed and organized over time at the human maternal–fetal interface. The work reveals cell-type-specific dynamics and signaling circuits that underpin healthy placentation and immune tolerance in pregnancy.
Impact: Provides a foundational spatiotemporal atlas of the human maternal–fetal interface to inform pregnancy and placental research.
Wang C et al., https://doi.org/10.1038/s41586-026-10316-x
🧠 Cardiovascular & Systems Biology
An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.
By integrating single-nucleus transcriptomic and epigenomic data, this work maps gene regulatory networks in the human left ventricle across development, aging, and cardiac disease. The atlas reveals cell-type-specific regulatory programs and how they remodel in pathology, highlighting potential targets for therapeutic intervention.
Impact: Delivers a multiomic reference map of human ventricular biology to decode regulatory mechanisms over the cardiac lifespan.
Gao W et al., https://doi.org/10.1186/s13059-026-04061-7
🧬 Cancer Research & Tumor Microenvironment
Spatial multiomics profiling reveals ZFP36-mediated immunometabolic reprogramming in bladder cancer.
Combining spatial metabolomics and spatial transcriptomics, this study charts the heterogeneous metabolic and immune landscapes across bladder cancer regions. It identifies ZFP36 as a key regulator that suppresses T cell activation via targeted mRNA degradation, and shows that Zfp36 knockout synergizes with anti–PD-1 therapy in vivo.
Impact: Uncovers ZFP36 as an immunometabolic checkpoint and promising target to boost immunotherapy in bladder cancer.
Ye F et al., https://doi.org/10.1073/pnas.2505125123
🧠🧪 Neurobiology, Aging & Spatial Methods
spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.
spRefine is a deep learning framework that uses genomic language models to jointly denoise and impute spatial transcriptomic data without external references. The method improves spot- and cell-level representations, strengthens data integration, and enhances downstream analyses such as spatial aging clocks, revealing new aging-related biological relationships.
Impact: Introduces a powerful, reference-free toolkit to clean and enrich spatial transcriptomics data, enabling more accurate discovery in aging and beyond.
Liu T et al., https://doi.org/10.1101/gr.281001.125
🧠 Larynx & Stem Cell Biology
Identification of stem cell marker-positive subpopulations in the vocal fold of the larynx through transcriptomic analyses.
Using single-cell RNA-seq, spatial transcriptomics via photo-isolation chemistry, and organoid models, this study maps the cellular ecosystem of the mouse laryngeal mucosa. It uncovers SOX9⁺ basal cells and Lgr5⁺ subpopulations in the vocal fold and establishes multiple epithelial organoid types, while delineating regional epithelial differences along the larynx.
Impact: Provides new stem cell markers and organoid tools to study laryngeal biology and regenerative strategies for vocal fold disorders.
Tamura K et al., https://doi.org/10.1038/s41467-026-71514-9
🧴 Immunology & Inflammatory Skin Disease
CD73(high) fibroblasts orchestrate keratinocyte inflammation in the psoriasis-associated epithelial immune microenvironment.
Integrating metabolomics with single-cell and spatial transcriptomics, this work shows that psoriatic skin exhibits enhanced nucleotide metabolism and elevated adenosine levels correlating with disease severity. It identifies a CD73^high fibroblast population as a key source of adenosine that drives keratinocyte inflammation, illuminating a metabolic-immune axis in psoriasis.
Impact: Pinpoints CD73^high fibroblasts and adenosine metabolism as actionable nodes in the epithelial immune microenvironment of psoriasis.
Tian Y et al., https://doi.org/10.1038/s41467-026-71323-0
🧠 Nephrology & Metabolic Disease
Spatial metabolomics and transcriptomics reveal the metabolic-immune niche associated with renal fibrosis in hyperuricemia.
By combining spatial metabolomics with spatial transcriptomics, this study deciphers how metabolic remodeling and immune cell interactions are organized in hyperuricemic nephropathy. It maps distinct metabolic-immune niches associated with renal fibrosis, clarifying how local metabolite changes align with profibrotic signaling and cellular cross-talk.
Impact: Reveals spatially organized metabolic-immune microenvironments that drive fibrosis in hyperuricemic kidney disease.
Tu C et al., https://doi.org/10.1016/j.freeradbiomed.2026.04.010
🌱 Plant Biology & Climate Resilience
Mapping plant cell-type-specific responses to environmental stresses.
This review synthesizes recent advances in single-cell and spatial transcriptomics applied to plants under biotic and abiotic stresses. It highlights how cell-type-resolved, spatially informed data are reshaping our understanding of plant stress responses, while outlining technical hurdles and future directions for climate-smart agriculture.
Impact: Positions single-cell and spatial omics as key technologies for engineering stress-resilient crops in a changing climate.
Ali M et al., https://doi.org/10.1016/j.tplants.2026.03.003
🧰 Technology & Methods Development
Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing.
DEGAS Spatial Smoothing introduces a method to identify high-risk individual cells and regions directly from single-cell spatial transcriptomics data. By leveraging spatial information to refine risk scores beyond cluster-level associations, it enables more precise localization of disease-relevant cells within tissues.
Impact: Provides a new analytical tool to pinpoint high-risk cells in spatially resolved datasets for disease mechanism and biomarker discovery.
Chatterjee D et al., https://doi.org/10.1093/bioinformatics/btag098
Benchmarking tools for deciphering cellular crosstalk in spatially-resolved transcriptomics.
This study systematically benchmarks methods for inferring cell–cell communication from spatial transcriptomics, comparing performance across datasets, interaction types, and evaluation criteria. It delivers practical guidance on method selection and highlights current limitations and opportunities in modeling ligand–receptor signaling in situ.
Impact: Offers a comprehensive reference for choosing and improving cell–cell interaction inference tools in spatial omics studies.
Ku LT et al., https://doi.org/10.1186/s13059-026-04063-5