ArticleCommunications biology2024
A point cloud segmentation framework for image-based spatial transcriptomics.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Immunophenotyping by Spatial Biology.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Spatial Transcriptomics Analysis of Ewing Sarcoma.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity.PLoS computational biology · 2026Article
- Article
- autoFISH: a modular toolbox for sequential single-molecule RNA FISH experiments.Communications biology · 2026Article
- RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics.Genome biology · 2026Article
- Advancing muscle aging and sarcopenia research through spatial transcriptomics.Osteoporosis and sarcopenia · 2025Review
- Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg .
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