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Related Experiment Video

Updated: Jul 22, 2025

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
07:33

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT

Published on: November 27, 2019

6.8K

Comparison of NET quantification methods based on immunofluorescence microscopy: Hand-counting, semi-automated and

Timo Henneck1, Christina Krüger1, Andreas Nerlich2

  • 1Institute of Biochemistry, University of Veterinary Medicine Hannover, Foundation, 30559, Hannover, Germany.

Heliyon
|July 24, 2023
PubMed
Summary

Quantifying neutrophil extracellular traps (NETs) is challenging. Computer-based methods showed significant differences from manual counts, highlighting the need for improved deep-learning algorithms for accurate NETosis assessment.

Keywords:
Cell countingImageJIn vitroNETQUANTNETsQuantification

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Area of Science:

  • Immunology
  • Cell Biology
  • Molecular Biology

Background:

  • Neutrophil extracellular traps (NETs) are crucial in innate immunity.
  • NETs consist of decondensed chromatin, histones, and granule proteins.
  • Quantifying NETs is vital for understanding NETosis, but current methods face challenges.

Purpose of the Study:

  • To compare the accuracy of computer-based NET quantification methods with manual evaluation.
  • To assess the reliability of existing software tools for NET analysis.
  • To identify limitations in current NET quantification techniques.

Main Methods:

  • Overview of recent NET quantification techniques.
  • Comparison of two published computer-based methods (semi-automated and NETQUANT) against manual hand counting.
  • Evaluation of NET-specific immunofluorescence microscopy.

Main Results:

  • Computer-based methods (semi-automated and NETQUANT) significantly differed from manual counts.
  • Automated methods showed limitations in detecting complex NET structures, yielding illogical results.
  • Trained personnel demonstrated adaptability in manual NET evaluation settings.

Conclusions:

  • Current computer-based NET quantification tools require further development and validation.
  • Standardized and reproducible methods for NET analysis are lacking.
  • Deep-learning algorithms are needed for accurate and efficient quantification of NETs.