ArticleNature methods2022
Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it, 115 citations in OpenAlex.
- Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer.Genome medicine · 2023Pooled it
- Multi-sample and multi-group spatial colocalization analysis using PANORAMIC.Bioinformatics (Oxford, England) · 2026Article
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Spatium: A Protein Language Foundation Model for Spatial Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Genetic and Pharmacologic Targeting of Eya3 in Macrophages Drives Anti-Tumor Immunity in Triple-Negative Breast Cancer.bioRxiv : the preprint server for biology · 2026Article
- CellFuse Enables Multimodal Integration of Single-Cell and Spatial Proteomics Data for Systems-Level Analysis in Cancer.Cancer research · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- 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
- A multi-omics map of diabetic nephropathy reveals c-Jun as a driver of tubular injury and metabolic stress.bioRxiv : the preprint server for biology · 2026Article
- Understanding the spatial determinants of the Oxford Classic prognostic signature for high-grade serous ovarian cancer.Communications medicine · 2026Article
- Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.bioRxiv : the preprint server for biology · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data.Bioinformatics advances · 2026Article
- Spontaneous and experimental models of lymph node metastasis.Nature protocols · 2025Review
- Automated classification of cellular expression in multiplexed imaging data with Nimbus.Nature methods · 2025Article
- Article
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- PRISM: a Python package for interactive and integrated analysis of multiplexed tissue microarrays.NAR genomics and bioinformatics · 2025Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
Abstract
Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.
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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.