Spatial Transcriptomics
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Related Articles from SNS
SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology
Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or language only pairwise, which limits their capacity to jointly infer molecular states, decode spatial tissue organization, and generate biologically grounded explanations. Here, we show SciCore-Omics, the first tri-modal foundation model linking...
STITCH: Spatial Transcriptomics Imputation via Flow Matching with Internal Learning
Spatial transcriptomics datasets frequently suffer from spatial gaps and missing regions due to sectioning artifacts, tissue damage, and the high cost of sequencing that limits tissue coverage. We present STITCH, a scalable and robust generative framework for multidimensional virtual spatial transcriptomics reconstruction. STITCH models intrinsic spatial-transcriptomic patterns directly from individual tissue samples, enabling reconstruction without requiring external reference atlases or...
Applying Spatial Statistics to Spatial Transcriptomics Reveals Local Association Between M2-like Macrophages and Fibrosis in Diabetic Kidney Disease
Renal fibrosis is the common final pathway of chronic kidney disease (CKD), driven in part by myofibroblast-mediated extracellular matrix deposition. M2 macrophages have been implicated as a source of myofibroblasts through macrophage-to-myofibroblast transition (MMT), yet whether M2 macrophages are pro- or anti-fibrotic remains controversial, and the spatial context in which MAC-M2-fibrosis coupling occurs is unknown. Here, we applied geographically weighted regression (GWR), a spatial...
Cellpin enables reference-based imputation and denoising of spatial transcriptomes
Spatially resolved transcriptomics enables gene expression profiling within tissue architecture, but targeted panels leave much of the transcriptome unmeasured and spatial artifacts such as RNA diffusion and segmentation errors introduce technical noise. These limitations necessitate computational imputation and denoising, yet existing methods typically incorporate spatial measurements during training, limiting scalability and risking the embedding of technology-specific artifacts into...
SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
arXiv:2606.08712v1 Announce Type: new Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context. However, these measurements are often noisy, low-resolution, and sparsely sampled, which limits the recovery of fine spatial structure.
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Announce Type: new Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements. Unlike existing histology-to-ST methods that mainly predict spot-level profiles for local regions containing multiple cells, this task requires modeling cell-to-cell expression variability, which is strongly structured by cell type. We...
Spatial Transcriptomics as Images for Large-Scale Pretraining
arXiv:2603.13432v4 Announce Type: replace Abstract: Spatial Transcriptomics (ST) profiles thousands of gene expression values at discrete spots with precise coordinates on tissue sections, preserving spatial context essential for clinical and pathological studies. With rising sequencing throughput and advancing platforms, the expanding data volumes motivate large-scale ST pretraining. However, the fundamental unit for pretraining, i.e., what constitutes a single training sample, remains...
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Announce Type: new Abstract: Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train...
Single-cell spatial transcriptomics and snRNA-seq decoding the organizational principles of functional modules in the mouse amygdala
The amygdala is a functionally heterogeneous nuclear complex comprising multiple subnuclei that orchestrate diverse behaviors, making it essential for survival and reproduction. However, the precise neural mechanisms underlying these heterogeneous functions remain elusive, primarily due to limited knowledge of the amygdala's cellular heterogeneity, developmental origins, spatial organization, and gene expression profiles. Here, we integrate single-cell-resolution spatial transcriptomics with...
High resolution spatial transcriptomics decodes the microenvironmental determinants of response to Nr-CWS therapy in cervical precancerous lesions
Immunotherapy with Nocardia rubra cell wall skeleton (Nr-CWS) can clear human papillomavirus (HPV) and induce regression of cervical precancerous lesions, yet many patients do not respond. The cellular and molecular basis for this heterogeneous clinical outcome remains unknown. Using high-resolution spatial transcriptomics, we profiled squamous intraepithelial lesions (SIL) from patients stratified by their subsequent response to Nr-CWS therapy.