Dataset Information

Novel single-cell preservation and RNA sequencing technology unlocks field studies for Plasmodium natural infections

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a powerful technology that can be used to unravel cellular heterogeneity when applied to unicellular eukaryotes, including Plasmodium parasites. scRNA-seq is particularly useful to study complex infections like malaria, with mixed life stages or clones, yet until now it has been primarily limited to in vitro and animal malaria models due to challenges associated with working with malaria natural infections in endemic settings. We validated a novel single-cell RNA sequencing technology for use with Plasmodium parasites by combining optimized sample preparation methods with RNA preservation integrated in a sample capture device, which allows for the separation of the sample collection and library preparation and sequencing. We recovered 22,345 P. knowlesi single-cell transcriptomes from 6 samples, the most extensive P. knowlesi dataset to date. Regardless of preparation methods used, all samples resulted in reproducible circular UMAP projections with consistent cluster localization and high gene expression correlation, which were confirmed by biomarker expression and annotating life stages using the Malaria Cell Atlas P. knowlesi reference dataset. Some variation in life stage recovery was observed, especially for the ring stage. This could be attributed to the inherent differences between sample preparation methods, or as a result of gene and transcript filtering thresholds which should be further investigated and optimized based on what is known about parasite biology. In conclusion, by combining parasite enrichment with a novel scRNA-seq and preservation technology, scRNA-seq can be expanded to field settings, even when limited resources are available.

ORGANISM(S): Plasmodium knowlesi

PROVIDER: GSE271911 | GEO | 2025/06/24

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Abstract of associated publication: Background: During the latter half of the natural 48-hour intraerythrocytic life cycle of human Plasmodium falciparum infection, parasites sequester deep in endothelium tissues, away from the spleen and inaccessible to peripheral blood. These late-stage parasites may cause tissue damage and likely contribute to clinical disease, and a more complete understanding of their biology is needed. Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge. Methods: We developed a custom nCounter® gene expression platform, and used this platform to measure malaria parasite gene expression profiles in vitro and in vivo. We also used imputation to generate global transcriptional profiles, and assessed differential gene expression between parasites growing in vitro and those recovered from malaria-infected patient tissues collected at autopsy. Results: We demonstrate, for the first time, global transcriptional expression profiles from in vivo malaria parasites sequestered in human tissues. We found that parasite physiology can be correlated with in vitro data from an existing life cycle data set, and that parasites in sequestered tissues show an expected schizont-like transcriptional profile, which is conserved across tissues from the same patient. Imputation based on 60 landmark genes generated global transcriptional profiles that were highly correlated with genome-wide expression patterns from the same sample measured by microarray. Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections. Conclusions: Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues.

Dataset Information

Novel single-cell preservation and RNA sequencing technology unlocks field studies for Plasmodium natural infections

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets