ArticleNature communications2021
Cell segmentation-free inference of cell types from in situ transcriptomics data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain.Scientific reports · 2023Pooled it
- Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics.Nature genetics · 2026Article
- SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics.NAR genomics and bioinformatics · 2026Article
- Topological Data Analysis of Spatial Protein Expression in Multiplexed Spatial Proteomics Studies.bioRxiv : the preprint server for biology · 2026Article
- Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.Nature biotechnology · 2026Article
- Impact and correction of segmentation errors in spatial transcriptomics.Nature genetics · 2026Article
- Expansion omics: from expansion microscopy to spatial omics.Molecular systems biology · 2026Review
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- GRIDGENE: Guided Region Identification based on Density of GENEs-a transcript density-based approach to characterize tissues by spatial transcriptomics.Bioinformatics advances · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.Bioinformatics advances · 2026Article
- Towards a transparent and reproducible AI-assisted research paper writing.Genomics & informatics · 2025Article
- Spatially organized cellular communities shape functional tissue architecture in the pancreas.Science advances · 2025Article
- Spatial Transcriptomics of Adipose Tissue: Technologies, Applications, and Challenges.Journal of obesity & metabolic syndrome · 2025Review
- Latent plasticity of the human pancreas across development, health, and disease.bioRxiv : the preprint server for biology · 2025Article
- Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.Nature methods · 2025Article
- Using transcripts to refine image based cell segmentation with FastReseg.Scientific reports · 2025Article
- Spatial Transcriptomics Decodes Breast Cancer Microenvironment Heterogeneity: From Multidimensional Dynamic Profiling to Precision Therapy Blueprint Construction.Biomolecules · 2025Review
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.Small methods · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
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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.