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

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.
Author Summary Schistosomiasis is a severe helminth infection affecting an estimated 600 million people. The one drug widely available, praziquantel (PZQ), is not ideal. PZQ kills the adult worms but not the developing juveniles so the treated patient may not be cured long-term. In addition, use of repeated mass treatment campaigns with PZQ to control morbidity raises concerns about the development of drug resistance. Our work is aimed at providing starting points for drug discovery programs for schistosomiasis by screening large compound libraries against whole organisms. Praziquantel and several other known anti-schistosomal drugs are also active in vitro against the adult worms and the larval stages, schistosomula. The latter are ideal for novel drug screening as they can be produced in large numbers in vitro, are small and so are amenable to screening in microwell plates. Drug activity can be assessed visually but this is subjective and laborious. We have built an automated system for assessing drug action involving the collection of images of the larvae and the development of computer algorithms to analyze their morphology and motility, defining them as "hits" or "nonhits." The method is reliable, consistent and efficient, making it feasible, for the first time, to screen large compound collections.