title: An image analysis algorithm for malaria parasite stage classification and viability quantification.
abstract: With more than 40% of the world's population at risk, 200-300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays.
authors.affiliation: Image Mining (IM) Group, Institut Pasteur Korea, Seongnam-si, Gyeonggi-do, South Korea.
authors.forename: Seunghyun
authors.initials: S
authors.lastname: Moon
authors.forename: Sukjun
authors.initials: S
authors.lastname: Lee
authors.forename: Heechang
authors.initials: H
authors.lastname: Kim
authors.forename: Lucio H
authors.initials: LH
authors.lastname: Freitas-Junior
authors.forename: Myungjoo
authors.initials: M
authors.lastname: Kang
authors.forename: Lawrence
authors.initials: L
authors.lastname: Ayong
authors.forename: Michael A E
authors.initials: MA
authors.lastname: Hansen
mesh_terms: D000465:Algorithms; D000962:Antimalarials / Q000494:pharmacology; D037621:Artemisinins / Q000494:pharmacology; D002470:Cell Survival / Q000187:drug effects; D002478:Cells, Cultured; D002738:Chloroquine / Q000494:pharmacology; D004912:Erythrocytes / Q000187:drug effects / Q000469:parasitology; D005456:Fluorescent Dyes; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000592:standards*; D007211:Indoles; D020128:Inhibitory Concentration 50; D008856:Microscopy, Fluorescence / Q000379:methods / Q000592:standards*; D008928:Mitochondria / Q000187:drug effects; D009930:Organic Chemicals; D010963:Plasmodium falciparum / Q000187:drug effects* / Q000254:growth & development; D033761:Spores, Protozoan / Q000145:classification* / Q000187:drug effects / Q000254:growth & development
chemical_list: D000962:Antimalarials; D037621:Artemisinins; D005456:Fluorescent Dyes; D007211:Indoles; D009930:Organic Chemicals; C107472:red dye CMXRos; C007293:DAPI; D002738:Chloroquine; C031327:artemisinin