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

PLoS Negl Trop Dis. 2012;6(7):e1762. doi: 10.1371/journal.pntd.0001762. Epub 2012 Jul 31.

Abstract

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Anthelmintics / isolation & purification*
  • Anthelmintics / pharmacology*
  • Automation, Laboratory / methods
  • Drug Evaluation, Preclinical / methods*
  • High-Throughput Screening Assays / methods*
  • Image Processing, Computer-Assisted / methods
  • Larva / drug effects
  • Locomotion / drug effects
  • Parasitology / methods*
  • Schistosoma / drug effects*
  • Time-Lapse Imaging / methods

Substances

  • Anthelmintics