ArticleNature communications2022
Membrane marker selection for segmenting single cell spatial proteomics data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An integrated approach for analyzing spatially resolved multi-omics datasets from the same tissue section.Frontiers in molecular biosciences · 2025Article
- Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.BMC medical informatics and decision making · 2024Article
- Tunable PhenoCycler imaging of the murine pre-clinical tumour microenvironments.Cell & bioscience · 2024Article
- Quantitative image analysis of intracellular protein translocation in 3-dimensional tissues for pharmacodynamic studies of immunogenic cell death.Journal of controlled release : official journal of the Controlled Release Society · 2024Article
- Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance.Frontiers in immunology · 2024Article
- SCS: cell segmentation for high-resolution spatial transcriptomics.Nature methods · 2023Article
- Review
- SCS: cell segmentation for high-resolution spatial transcriptomics.bioRxiv : the preprint server for biology · 2023Article
- Evaluation of cell segmentation methods without reference segmentations.Molecular biology of the cell · 2023Article
- Membrane marker selection for segmenting single cell spatial proteomics data.Nature communications · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells.
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Registered trials
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