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Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
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Machine Learning to Quantitate Neutrophil NETosis.

Laila Elsherif1,2, Noah Sciaky3, Carrington A Metts4

  • 1Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, 27599, USA. lelsheri@uthsc.edu.

Scientific Reports
|November 16, 2019
PubMed
Summary

Machine learning, specifically convolutional neural networks (CNNs), accurately quantitates neutrophil NETosis. This powerful tool aids in disease research and understanding cellular responses in patients.

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Area of Science:

  • Immunology
  • Computational Biology
  • Biomedical Imaging

Background:

  • Neutrophil Extracellular Traps (NETs) are involved in various diseases.
  • Quantifying NETosis, the process of NET formation, is crucial for understanding its role.
  • Current methods for NETosis assessment can be labor-intensive and subjective.

Purpose of the Study:

  • To apply machine learning, particularly CNNs, for automated classification and quantitation of neutrophil NETosis.
  • To evaluate the accuracy and efficiency of CNNs in analyzing nuclear morphology for NETosis detection.
  • To explore the utility of CNNs in dose-response analysis, pathway screening, and patient sample analysis.

Main Methods:

  • Utilized free, open-source software for implementing convolutional neural networks (CNNs).
  • Trained CNNs on images of human blood neutrophil nuclei to differentiate NETotic from non-NETotic cells.
  • Employed CNNs to analyze nuclear morphology features for distinguishing NETosis from necrosis and different signaling pathways.
  • Incorporated object dispersion analysis with CNNs to study NETotic nuclei clustering.

Main Results:

  • CNNs achieved over 94% accuracy in classifying NETotic versus non-NETotic cells.
  • CNNs effectively facilitated dose-response analysis and screening of NETotic responses.
  • Nuclear morphology features alone, as learned by CNNs, distinguished between NETosis and necrosis and distinct signaling pathways.
  • Identified differences in NETotic nuclei clustering between major NETosis pathways.
  • Observed unresponsiveness in neutrophils from sickle cell disease patients to a specific NETosis pathway.

Conclusions:

  • Machine learning, via CNNs, provides a precise and rapid tool for quantitative and qualitative cell analysis.
  • CNNs offer a powerful approach for high-throughput screening and detailed mechanistic studies of NETosis.
  • This ML-based method can reveal novel insights into NETosis signaling and its dysregulation in diseases like sickle cell disease.