ReviewNature reviews. Molecular cell biology2025
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.
Review in Nature reviews. Molecular cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers.
What it found
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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
171 citing papers in PubMed.
- Multi-omics integration and artificial intelligence for the conservation and utilization of local chicken genetic resources.Poultry science · 2026Review
- Article
- CoexpressDeconvolve enables reference-free single-cell-resolution deconvolution from spot-based spatial transcriptomics.iScience · 2026Article
- Article
- PEBP1 Regulates Ferroptosis in Acute Glaucoma: Targeted Therapy Using Engineered Exosomes.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Long-Term Labeling and Differentiation Monitoring of Mouse Spermatogonial Stem Cells Using CellREADR.Biotech (Basel (Switzerland)) · 2026Article
- Liquid biopsy: current applications and future directions.Molecular biomedicine · 2026Review
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- Article
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- DNA Methylation as a Programmable Information Layer: From Molecular Marks to Disease State Engineering.International journal of molecular sciences · 2026Review
- Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting.Molecular biomedicine · 2026Review
- The immune engram: a spatial model of immunological memory.Nature reviews. Immunology · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies.Nature communications · 2026Article
- Functional Heterogeneity of Immune Cell Subpopulations in Atherosclerosis: From Basic Mechanisms to Precision Therapy.Reviews in cardiovascular medicine · 2026Review
- Plasticity of Non-Apoptotic Residual Tumor Cells After Neoadjuvant Immunochemotherapy: Epigenetic and Microenvironmental Determinants.Biomolecules · 2026Review
111 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
Indexed as
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39169166What OpenQuestion holds
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.