ArticleScientific reports2019
Machine Learning to Quantitate Neutrophil NETosis.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Neutrophils and neutrophil extracellular traps in diabetes mellitus and its complications: Mechanisms and therapeutic implications.iScience · 2026Review
- Predicting DNA damage response using synthetic cell painting profiles and experimental analysis.iScience · 2026Article
- Neutrophil extracellular traps in colorectal cancer: a paradoxical nexus of tumor defense and disease progression.Frontiers in immunology · 2026Review
- Neutrophil-based delivery platforms: from natural mechanisms to engineered therapeutics.Theranostics · 2026Review
- The emerging role of neutrophil extracellular traps in autoimmune and autoinflammatory diseases.MedComm · 2025Review
- Predicting gene signature in breast cancer patients with multiple machine learning models.Discover oncology · 2024Article
- Convergence of nanomedicine and neutrophils for drug delivery.Bioactive materials · 2024Article
- Extracellular DNA traps in a ctenophore demonstrate immune cell behaviors in a non-bilaterian.Nature communications · 2024Article
- Plasma from patients undergoing allogeneic hematopoietic stem cell transplantation promotes NETOSISFrontiers in immunology · 2024Article
- Visualizing neutrophil extracellular traps in septic equine synovial and peritoneal fluid samples using immunofluorescence microscopy.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2023Article
- Single-cell Raman microscopy with machine learning highlights distinct biochemical features of neutrophil extracellular traps and necrosis.Scientific reports · 2023Article
- The perspectives of NETosis on the progression of obesity and obesity-related diseases: mechanisms and applications.Frontiers in cell and developmental biology · 2023Review
- Trapalyzer: a computer program for quantitative analyses in fluorescent live-imaging studies of neutrophil extracellular trap formation.Frontiers in immunology · 2023Article
- Discovery of Biomarkers of Resistance to Immune Checkpoint Blockade in NSCLC Using High-Plex Digital Spatial Profiling.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2022Article
- Macrophage Extracellular Traps: Current Opinions and the State of Research regarding Various Diseases.Journal of immunology research · 2022Review
- Neutrophil extracellular traps in gastrointestinal cancer.World journal of gastroenterology · 2021Review
- A Review of the Neutrophil Extracellular Traps (NETs) from Cow, Sheep and Goat Models.International journal of molecular sciences · 2021Review
- Neutrophil extracellular traps in cancer: not only catching microbes.Journal of experimental & clinical cancer research : CR · 2021Review
- Article
- NETosis in cancer: a critical analysis of the impact of cancer on neutrophil extracellular trap (NET) release in lung cancer patients vs. mice.Cancer immunology, immunotherapy : CII · 2020Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered differences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science.
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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.