🧪 Technology & Methods Development
Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics.
The authors introduce Cellist, a multimodal cell segmentation method that integrates imaging and gene expression to enable robust single-cell analysis across high-resolution spatial transcriptomics platforms. Applied to diverse datasets including mouse brain Stereo-seq and human lung cancer, Cellist improves transcriptomic coherence, spatial domain detection, and cell-type annotation, while scaling efficiently across large fields of view and multiple technologies.
Impact: A cross-platform, scalable solution to the core cell segmentation bottleneck in high-resolution spatial transcriptomics.
Sun D et al., https://doi.org/10.1038/s41588-026-02610-1
Decoding spatial transcriptomics across multicellular and subcellular resolutions.
This study presents STARS, a Vision Transformer–based framework that fuses histology images and spot-level data to reconstruct single-cell gene expression from spatial platforms spanning multicellular to subcellular resolutions. STARS enables high-fidelity identification of cell types, tissue structures, immune niches, and clinically relevant features such as tertiary lymphoid structures in colorectal cancer and infection-driven immune shifts in lung tissue.
Impact: A powerful AI-driven approach to recover single-cell resolution from coarse-spot and ultra-high-resolution spatial datasets.
Zhao C et al., https://doi.org/10.1038/s41467-026-72872-0
DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.
DGAT is a deep learning framework that builds heterogeneous graphs from RNA, protein, and spatial information to accurately impute spatial protein expression from transcriptomic data. Benchmarking across public and in-house datasets shows DGAT outperforms existing approaches, revealing hidden spatial cell states, immune phenotypes, and microenvironmental organization in tissues that lack direct proteomic profiling.
Impact: A graph-attention model that unlocks protein-level insights from transcriptomics-only spatial data.
Wang H et al., https://doi.org/10.1038/s41467-026-73114-z
Accurate delineation of cellular niches via integrated spatial transcriptomics and histological imaging with SYMOL.
SYMOL is a self-supervised multimodal framework that integrates spatial coordinates, gene expression, and rich histology (H&E and multichannel IHC) into unified morphology-aware embeddings. Across multiple datasets, SYMOL consistently improves cellular niche identification, multi-slice integration, label transfer, and gene expression enhancement, enabling precise mapping of tumor microenvironments in lung tissue and fine-grained niche architecture in mouse brain.
Impact: A versatile multimodal engine for extracting high-resolution, morphology-informed cellular niches from spatial omics data.
Wang D et al., https://doi.org/10.1101/gr.281603.125
🧠 Neurobiology
Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer’s disease.
Using an integrated GeoMx DSP and Xenium in situ workflow, this study profiles PV+ and NeuN+ neurons in the retrosplenial cortex of 5xFAD mice to uncover disease-associated transcriptional programs. The authors identify vulnerable PV+ interneuron subpopulations with downregulation of Dner, Gad1, and Pvalb, linking altered GABAergic signaling to early circuit dysfunction in Alzheimer’s disease.
Impact: Demonstrates how dual-platform spatial transcriptomics can pinpoint vulnerable neuronal subtypes and molecular markers in Alzheimer’s disease.
Seo H et al., https://doi.org/10.1038/s41467-026-73474-6
TLR7-induced murine inflammation results in a global neuroinflammatory response driving neural circuit-specific transcriptomic changes.
Combining whole-brain and spatial transcriptomics in a TLR7/8 agonist mouse model, the authors show that systemic immune activation induces widespread neuroinflammatory signatures alongside region-specific transcriptional changes. Brain areas linked to mood and anxiety, including the ventral striatum and amygdala, exhibit reduced expression of synaptic function genes, suggesting circuit-level mechanisms by which inflammation may drive depressive symptoms.
Impact: Provides a spatially resolved molecular link between global neuroinflammation and circuit-specific changes relevant to depression.
Gardner-Stephen K et al., https://doi.org/10.1038/s41598-026-51581-0
🩺 Cancer Research
Concurrent genetic and non-genetic resistance mechanisms to KRAS inhibition in colorectal cancer.
Using targeted exome sequencing and spatial transcriptomics on matched colorectal cancer biopsies after KRAS-targeted combination therapy, this study dissects both genetic mutations and non-genetic cellular programs that underlie drug resistance. Spatially resolved profiles highlight heterogeneous resistant niches and adaptive signaling rewiring within tumors, informing strategies to prevent or overcome resistance to KRAS inhibition.
Impact: Illuminates the spatially organized genetic and phenotypic mechanisms that drive resistance to KRAS inhibitors in colorectal cancer.
Alonso S et al., https://doi.org/10.1016/j.ccell.2026.04.009
Multi-scale transcriptomic integration reveals LINC00152-high tumor cells promote TGCT progression and T cell exhaustion.
The authors perform multi-scale transcriptomic integration, including spatial and single-cell analyses, to investigate lncRNA biology in testicular germ cell tumors. They identify LINC00152-high tumor cell populations associated with aggressive tumor behavior and T cell exhaustion, proposing this lncRNA as a potential biomarker and therapeutic target in TGCT.
Impact: Links spatially defined lncRNA-high tumor cell states to immune exhaustion and progression in testicular germ cell tumors.
Cao J et al., https://doi.org/10.1038/s41416-026-03466-2
❤️ Cardiometabolic & Vascular Disease
Single-Cell Spatial Transcriptomics Reveals Sex-Specific Differences Driving Carotid Atherosclerotic Plaque Instability.
This study applies single-cell spatial transcriptomics to carotid plaques to dissect the cellular ecosystems and molecular pathways associated with plaque instability in males and females. The analysis uncovers sex-specific cell populations and gene programs linked to vulnerability and poststroke outcomes, providing a mechanistic framework for sex-aware risk stratification.
Impact: Offers a high-resolution, sex-specific atlas of carotid plaque biology to better understand and predict ischemic stroke risk.
Byun JH et al., https://doi.org/10.1161/ATVBAHA.125.323505
GLP1-E2 therapy delays autoimmune diabetes in late-stage prediabetic NOD mice and potentiates low-dose anti-CD3 therapy for enhanced disease protection.
In late-stage prediabetic NOD mice, the authors test a beta cell–targeted GLP1–17β-estradiol conjugate (GLP1-E2) in combination with low-dose anti-CD3 therapy, aiming to simultaneously protect beta cells and modulate autoimmunity. The combination yields more durable diabetes protection than either monotherapy, supporting co-targeting of immune dysregulation and beta cell fragility as a strategy for type 1 diabetes prevention.
Impact: Demonstrates a synergistic immuno–beta cell–protective strategy that could translate into more durable intervention for high-risk type 1 diabetes.
Degroote L et al., https://doi.org/10.1007/s00125-026-06750-1