High-content imaging as a tool to quantify and characterize malaria parasites
Year: 2023
Type: article
Abstract: In 2021, Plasmodium falciparum was responsible for 619,000 reported malaria-related deaths. Resistance has been detected to every clinically used antimalarial, urging the development of novel antimalarials with uncompromised mechanisms of actions. High-content imaging allows researchers to collect and quantify numerous phenotypic properties at the single-cell level, and machine learning-based approaches enable automated classification and clustering of cell populations. By combining these technologies, we developed a method capable of robustly differentiating and quantifying P. falciparum asexual blood stages. These phenotypic properties also allow for the quantification of changes in parasite morphology. Here, we demonstrate that our analysis can be used to quantify schizont nuclei, a phenotype that previously had to be enumerated manually. By monitoring stage progression and quantifying parasite phenotypes, our method can discern stage specificity of new compounds, thus providing insight into the compound’s mode of action.
Source: Cell Reports Methods
Authors: Melissa R. Rosenthal, Caroline L. Ng
Institution: University of Nebraska Medical Center
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,200
Awards: G6160236375, G6878456262, Leveraging insights into the Plasmodium falciparum ubiquitin-proteasome system and the unfolded protein response to combat artemisinin resistance through synergistic combinations, G9000087232, G9000112066, UC San Diego Raising Advancement and Parity for Infectious Disease Researchers (RAPID) Faculty Development Program
OpenAlex ID: W4381952111