Whole organism high-content screening by label-free, image-based Bayesian classification for parasitic diseases.

• Published in: PLoS Neglected Tropical Diseases
• Main Authors: Ross A Paveley, Nuha R Mansour, Irene Hallyburton, Leo S Bleicher, Alex E Benn, Ivana Mikic, Alessandra Guidi, Ian H Gilbert, Andrew L Hopkins, Quentin D Bickle
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2012
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0001762; https://doaj.org/article/4fbeffc48c96427f856dd35cf51c26b1

Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on in vitro cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping.