Related Experiment Video
Updated: Oct 16, 2025

Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT
Published on: November 27, 2019
The nearest neighbor nuclei method to objectify analysis of pertussis toxin-induced clustering
Marieke E Hoonakker1, Ed Remarque2, Jennifer Veth3
1Institute for Translational Vaccinology (Intravacc), Bilthoven, The Netherlands.
Abstract:
The in vivo histamine sensitization test (HIST) has historically been performed to guarantee the safety of acellular pertussis vaccine batches. Non-compliance of batches is primarily associated with the presence of low levels of pertussis toxin (PTx). Because of ethical, standardization and scientific reasons, a variety of alternative in vitro approaches have been studied to replace the lethal HIST. A broadly applied and partially accepted method is the CHO cell clustering test, which is based on the clustered growth pattern of CHO cells when exposed to minute amounts of PTx. One of the major hurdles for global application of the CHO clustering test is the manual assessment of the clusters, which is associated with suboptimal reproducibility of test outcomes and is time-consuming. Here, various parameters of CHO cell nuclei were evaluated in search for a reliable, objective read-out parameter. We demonstrate that the distance between each nucleus and its nearest neighbor (3N method) is the most suitable parameter to assess clustered cell growth. This method detects 2.8 mIU PTx/mL and thereby complies with the requirement set for the sensitivity of the CHO clustering test based on visual reading. In commercial acellular pertussis vaccines spiked with PTx, the method detects 45 mIU/mL PTx, which is substantially lower than the 181-725 mIU/mL PTx detected by visual interpretation. The 3N method thus allows objective and sensitive assessment of CHO clustering and thereby encourages broad and global implementation of the in vitro test as an alternative to the HIST.
More Related Videos
14:10A High Content Imaging Assay for Identification of Botulinum Neurotoxin Inhibitors
Published on: November 14, 2014
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017