An image analysis algorithm for malaria parasite stage classification and viability quantification.
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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3634010?pdf=render |
| _version_ | 1818510536972697600 |
|---|---|
| author | Seunghyun Moon Sukjun Lee Heechang Kim Lucio H Freitas-Junior Myungjoo Kang Lawrence Ayong Michael A E Hansen |
| author_facet | Seunghyun Moon Sukjun Lee Heechang Kim Lucio H Freitas-Junior Myungjoo Kang Lawrence Ayong Michael A E Hansen |
| author_sort | Seunghyun Moon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T23:21:19Z |
| format | Article |
| id | doaj.art-c75d5bf7d32b4c74a482db255256f034 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T23:21:19Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c75d5bf7d32b4c74a482db255256f0342022-12-22T01:29:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0184e6181210.1371/journal.pone.0061812An image analysis algorithm for malaria parasite stage classification and viability quantification.Seunghyun MoonSukjun LeeHeechang KimLucio H Freitas-JuniorMyungjoo KangLawrence AyongMichael A E HansenWith 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.http://europepmc.org/articles/PMC3634010?pdf=render |
| spellingShingle | Seunghyun Moon Sukjun Lee Heechang Kim Lucio H Freitas-Junior Myungjoo Kang Lawrence Ayong Michael A E Hansen An image analysis algorithm for malaria parasite stage classification and viability quantification. PLoS ONE |
| title | An image analysis algorithm for malaria parasite stage classification and viability quantification. |
| title_full | An image analysis algorithm for malaria parasite stage classification and viability quantification. |
| title_fullStr | An image analysis algorithm for malaria parasite stage classification and viability quantification. |
| title_full_unstemmed | An image analysis algorithm for malaria parasite stage classification and viability quantification. |
| title_short | An image analysis algorithm for malaria parasite stage classification and viability quantification. |
| title_sort | image analysis algorithm for malaria parasite stage classification and viability quantification |
| url | http://europepmc.org/articles/PMC3634010?pdf=render |
| work_keys_str_mv | AT seunghyunmoon animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT sukjunlee animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT heechangkim animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT luciohfreitasjunior animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT myungjookang animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT lawrenceayong animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT michaelaehansen animageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT seunghyunmoon imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT sukjunlee imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT heechangkim imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT luciohfreitasjunior imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT myungjookang imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT lawrenceayong imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification AT michaelaehansen imageanalysisalgorithmformalariaparasitestageclassificationandviabilityquantification |