ArticleNature communications2024
BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Kintsugi decides, gene by gene, where spatial transcriptomics borrows information.bioRxiv : the preprint server for biology · 2026Article
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.Nature methods · 2026Article
- Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges and future perspectives on transcriptomic profiling.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.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
- RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics.Genome biology · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.Bioinformatics (Oxford, England) · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis.bioRxiv : the preprint server for biology · 2025Article
- Large language models driven neural architecture search for universal and lightweight disease diagnosis on histopathology slide images.NPJ digital medicine · 2025Article
- Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method.Genome research · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.
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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.