ArticleNature communications2023
CellSighter: a neural network to classify cells in highly multiplexed images.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology.The American journal of pathology · 2026Review
- Integrating Single-Cell Sequencing and Proteomics to Unravel Stem Cell Heterogeneity: Implications for Precision Regenerative Medicine.Stem cell reviews and reports · 2026Review
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- CLEAR-IT, a framework for contrastive learning to capture the immune composition of tumor microenvironments.Communications biology · 2026Article
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data.Bioinformatics advances · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Cell Viability, Drug Screening, and Mechanism Study Under Mild Phototherapy Integrated Chemotherapy (PIC).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis.Nature communications · 2025Article
- Annotation-Free Prediction of Cancer Cells and Glands and Spatial Analysis of Immune Cells.bioRxiv : the preprint server for biology · 2025Article
- Automated classification of cellular expression in multiplexed imaging data with Nimbus.Nature methods · 2025Article
- Spatial multiomics of acute myocardial infarction reveals immune cell infiltration through the endocardium.Nature cardiovascular research · 2025Article
- Article
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.
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Registered trials
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.