Single-cell RNA sequencing (scRNA-seq) is a powerful technology used to investigate cellular heterogeneity. When applied to unicellular eukaryotes such asPlasmodiumparasites, scRNA-seq provides a single-cell resolution particularly valuable to study complex infections which are often comprised of mixed life stages and clones. Until now, the application of scRNA-seq has been mainly limited toin vitroand animal malaria models, despite known transcriptional differences as compared to circulating parasite populations. This is primarily due to the challenges of working withPlasmodiumnatural infections in endemic settings. We validated sample preparation methods and a novel single-cell RNA sequencing technology for the first time inP. knowlesiparasites which can be effectively implemented to analyze natural infections in low-resource settings. We recovered 22,345P. knowlesisingle-cell transcriptomes containing all asexual blood stages from 6in vitroculture samples, with conditions mimicking natural infections, and generated the most extensiveP. knowlesisingle-cell dataset to date. All 6 samples produced reproducible circular UMAP projections with consistent cluster localization and high gene expression correlation, regardless of the sample preparation methods used. Biomarker expression and life stage annotation using the Malaria Cell AtlasP. knowlesireference dataset further confirmed these results. In conclusion, the combination of adaptable sample preparation methods with novel preservation and scRNA-seq technology has the potential to fundamentally transform the study of natural infections. This approach unlocks the use of scRNA-seq in field studies which will lead to new insights intoPlasmodiumparasite biology.ImportanceSequencing unicellular organisms, such as malaria parasites, at the single-cell level is important to understand the diversity present in cell populations. Until now, single-cell sequencing of malaria has been primarily limited to laboratory models. While these models are key to understanding biological processes, there are known differences between lab models and parasite populations circulating in natural human infections. This study presents sample preparation methods and a new single-cell RNA sequencing technology that enables sample collection from natural infections in low-resource settings. Using a mock natural infection, we validated this new single-cell RNA sequencing technology using marker genes with known expression patterns and a reference dataset from the Malaria Cell Atlas. We demonstrate that high-quality single-cell transcriptomes with consistent expression patterns can be recovered using various sample preparation methods, thereby unlocking single-cell sequencing for field studies and leading to additional insights into parasite biology in the future.
