ArticleNature communications2023
Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning.PLoS computational biology · 2026Article
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- Accurate prediction in reconstructed spatial transcriptomes does not ensure valid biological discovery.bioRxiv : the preprint server for biology · 2026Article
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- Advancement of humanized mice and leading applications in immunological disease models.Inflammation and regeneration · 2026Review
- Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges and future perspectives on transcriptomic profiling.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.Briefings in bioinformatics · 2026Article
- Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.Nature genetics · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Exploring the human brain: spatial transcriptomics challenges and approaches in post-mortem analysis.Brain : a journal of neurology · 2026Review
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- STGNET: extending panel coverage in imaging-based spatial transcriptomics using deep generative adversarial networks.Briefings in bioinformatics · 2026Article
29 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.