ArticleNature communications2023
IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry.
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 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The dual roles of uterine monocytes in regulation of tissue homeostasis throughout reproductive cycles in health and disease.Nature immunology · 2026Article
- CellTune: an integrative software for accurate cell classification in spatial proteomics.Nature methods · 2026Article
- Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation.Molecular systems biology · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.BMC bioinformatics · 2026Article
- OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.Research square · 2026Article
- Microglial-Derived IGF-1 Serves as a Regulator for Neuroimmune Homeostasis During Viral-Induced Demyelination.Viruses · 2026Article
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- Spatially-smoothed quantification improves cell typing in imaging mass cytometry datasets.bioRxiv : the preprint server for biology · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Sensitive detection of minimal residual disease and immunotherapy targets by multi-modal bone marrow analysis in high-risk neuroblastoma - a multi-center study.Journal of experimental & clinical cancer research : CR · 2025Article
- Spatial Profiling Identifies Regionally Distinct Microenvironments and Targetable Immunosuppressive Mechanisms in Pediatric Osteosarcoma Pulmonary Metastases.Cancer research · 2025Article
- Long-Term Dystrophin Replacement Therapy in Duchenne Muscular Dystrophy Causes Cardiac Inflammation.JACC. Basic to translational science · 2025Article
- Spatial mapping of immune cell environments in NF2-related schwannomatosis vestibular schwannoma.Nature communications · 2025Article
- Integrated workflow for analysis of immune enriched spatial proteomic data with IMmuneCite.Scientific reports · 2025Article
- Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.Nature communications · 2025Article
- The spatial biology of HIV infection.PLoS pathogens · 2025Review
- Spatial proteomics of the tumor microenvironment in melanoma: current insights and future directions.Frontiers in immunology · 2025Review
- From surfing to diving into the tumor microenvironment through multiparametric imaging mass cytometry.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments.
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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.