ArticleNature communications2023
Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.
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 281 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
281 citing papers in PubMed.
- Spatial transcriptomics unveils the in situ cellular and molecular hallmarks of the lung in fatal COVID-19.Nature communications · 2026Article
- Review
- Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancer.Cell · 2026Article
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Glutathione metabolism-linked ferroptosis in human seminoma: a spatial multi-omics mapping study.Redox biology · 2026Article
- IL-33/IL1RL1 (ST2) signalling drives immune suppression in squamous intraepithelial hyperplasia.Tumour virus research · 2026Article
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Spatial transcriptomics reveals clonal relationships between intraductal carcinoma and adjacent invasive prostate cancer.Histopathology · 2026Article
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal.Bioinformatics (Oxford, England) · 2026Article
- Tissue and cellular spatiotemporal dynamics in colon aging.Nature biotechnology · 2026Article
- ViMST: vision transformer-based dual modality multi-task graph contrastive network for spatial transcriptomics microenvironments investigation.BMC biology · 2026Article
- SHIELD: A weakly supervised graph attention neural network for decoding disease-relevant cell-cell interactions.Patterns (New York, N.Y.) · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.BMC bioinformatics · 2026Article
- Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.Reproduction (Cambridge, England) · 2026Review
- STRESS: spatial transcriptomics resolution enhancing method based on the state-space model.Briefings in bioinformatics · 2026Article
221 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.
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Registered trials
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.