ArticleNature communications2025
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.
Article in Nature communications, 2025. 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.
- Patient-derived resources for decoding and targeting brain metastases ecosystems.EMBO molecular medicine · 2026Review
- Cell identity uncertainty and exposure heterogeneity in germ cell-resolved comet assays.Archives of toxicology · 2026Article
- SegJointGene: joint cell segmentation and spatial gene prioritization by information entropy guided convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Elemental mass spectrometry imaging of biomolecules using metal-conjugated probes.Nature reviews. Chemistry · 2025Review
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.PLoS computational biology · 2025Article
- Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.Nature communications · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance, we develop a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a score to quantify the biological plausibility of discovered cellular phenotypes on patient-derived tissue sections. Moreover, we generate spatial expression data of the human tonsil-a densely packed tissue prone to segmentation errors-and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity.
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Registered trials
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