ArticleLife science alliance2023
Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing.
Article in Life science alliance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.Metabolic brain disease · 2026Review
- Review
- Advances in single-cell and spatial multi-omics for plant development and stress responses.Plant diversity · 2026Review
- SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data.PLoS biology · 2026Article
- MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome-proteome data.Bioinformatics (Oxford, England) · 2026Article
- Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.Genome research · 2026Article
- RNA Detection Technologies: A MethodCentric Guide to Principles and Reproducibility.Bio-protocol · 2026Article
- stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling.Interdisciplinary sciences, computational life sciences · 2026Article
- Simultaneous spatial transcriptomics and morphology profiling as tools to explore how microglia change with age.Nature aging · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Integrated scRNA-seq and bulk transcriptomics identify an amino acid metabolism-associated prognostic signature and highlight FUS as a potential driver in prostate cancer progression.Functional & integrative genomics · 2026Article
- Unravelling the progression of the zebrafish primary body axis with reconstructed spatiotemporal transcriptomics.Genome biology · 2026Article
- Beyond Structure: The Dynamic Role of the Extracellular Matrix Components in Immune Evasion.Cancer communications (London, England) · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data.PLoS computational biology · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- SpaBalance: Balanced Learning for Efficient Spatial Multi-Omics Decoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
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Abstract
Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.
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