Single-cell RNA sequencing reveals developmental heterogeneity among Plasmodium berghei sporozoites

Abstract

In the malaria-causing parasite’s life cycle, Plasmodium sporozoites must travel from the midgut of a mosquito to the salivary glands before they can infect a mammalian host. However, only a fraction of sporozoites complete the journey. Since salivary gland invasion is required for transmission of sporozoites, insights at the molecular level can contribute to strategies for malaria prevention. Recent advances in single-cell RNA sequencing provide an opportunity to assess sporozoite heterogeneity at a resolution unattainable by bulk RNA sequencing methods. In this study, we use a droplet-based single-cell RNA sequencing workflow to analyze the transcriptomes of over 8000 Plasmodium berghei sporozoites derived from the midguts and salivary glands of Anopheles stephensi mosquitoes. The detection of known marker genes confirms the successful capture and sequencing of samples composed of a mixed population of sporozoites. Using data integration, clustering, and trajectory analyses, we reveal differences in gene expression profiles of individual sporozoites, and identify both annotated and unannotated markers associated with sporozoite development. Our work highlights the utility of a high-throughput workflow for the transcriptomic profiling of Plasmodium sporozoites, and provides new insights into gene usage during the parasite’s development in the mosquito.

Single-cell RNA sequencing ofPlasmodiumvivax sporozoites reveals stage- and species-specific transcriptomic signatures

“…Processing, alignment, and pseudo-bulk assessment of P. vivax sporozoite scRNA-seq data Given its high-throughput capability and prior use for sporozoites from murine-infecting Plasmodium species (Bogale et al, 2021;Ruberto et al, 2021), we used the 10X Genomics gene expression platform to profile the transcriptomes of individual P. vivax sporozoites. We dissected and purified sporozoites from the salivary glands of An.…”

“…berghei midgut sporozoites (Bogale et al, 2021;Ruberto et al, 2021). We interpret the detection of these markers in P. vivax to represent sporozoites that have recently invaded the salivary glands.…”

“…In total, we found 159 markers (adjusted p-value < 0.05, Table S5). Notably, sporozoite-specific protein, S10 (PVP01_0304200), was a marker for sporozoites in C1 (Figure 3C; Table S5), and has previously been shown to be highly transcribed in midgut sporozoites (Bogale et al, 2021;Ruberto et al, 2021). C2 markers included circumsporozoite protein (PVP01_0835600); early transcribed membrane protein (PVP01_0602100); TRAP-like protein (PVP01_1132600); and sporozoite surface protein essential for liver stage development (PVP01_0938800) (Figure 3C;…”

Drug Design for Malaria with Artificial Intelligence (AI)

“…However, bulk RNA-sequencing data does not have the ability to recognize cell to cell variability within a population as a result of which some essential feature may remain undetected. For example, recent single cell RNA-seq experiment was able to differentiate between sexually and asexually committed scizont from a population of parasites [14]. With the launch of such single cell RNA sequencing (RNA-seq), it is now possible to measure RNA levels on the entire genome scale to gain insights into cellular processes and illuminate the specifics of many important molecular events such as alternative splicing, gene fusion, variation of single nucleotide, and differential genes expression.…”

“…Additionally, we can extract many more information such as how many genes can be detected and whether a particular gene of interest is being expressed, or whether there has been differential splicing, depending on the procedure of generating the mRNA data. Several single cell RNA-seq experiments have been performed in the last couple of years for plasmodium parasites which paved a much more sophisticated way to characterise gene expression at different stages of the life cycle [14,[34][35][36]. Both supervised and unsupervised learning methods can be used to identify deferentially expressed genes at different life cycle stages [37,38].…”