A single-cell atlas of Plasmodium falciparum transmission through the mosquito

Malaria parasites have a complex life cycle featuring diverse developmental strategies, each uniquely adapted to navigate specific host environments. Here we use single-cell transcriptomics to illuminate gene usage across the transmission cycle of the most virulent agent of human malaria - Plasmodium falciparum. We reveal developmental trajectories associated with the colonization of the mosquito midgut and salivary glands and elucidate the transcriptional signatures of each transmissible stage. Additionally, we identify both conserved and non-conserved gene usage between human and rodent parasites, which point to both essential mechanisms in malaria transmission and species-specific adaptations potentially linked to host tropism. Together, the data presented here, which are made freely available via an interactive website, provide a fine-grained atlas that enables intensive investigation of the P. falciparum transcriptional journey. As well as providing insights into gene function across the transmission cycle, the atlas opens the door for identification of drug and vaccine targets to stop malaria transmission and thereby prevent disease

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis

Harnessing big data to support the conservation and rehabilitation of mangrove forests globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research. This Perspective reviews the role of global-scale research in stimulating policy action and on-the-ground conservation for mangrove ecosystems. We outline the current state of knowledge in terms of global analyses and examine the challenge of translating this research in action

Novel methods for assembly and quantification of Plasmodium falciparum var transcript expression in laboratory and clinical samples

The pathogenesis of severe Plasmodium falciparum malaria involves cytoadhesive microvascular sequestration of infected erythrocytes, mediated by P. falciparum erythrocyte membrane protein 1 (PfEMP1). PfEMP1 variants are encoded by the highly polymorphic family of var genes, the sequences of which are largely unknown in clinical samples as each parasite isolate contains a unique repertoire of var genes. This has hindered large scale analysis of var genes with previous characterisation attempts presenting a collection of limitations, including the inability to detect novel var sequences and only focussing on small regions of the var gene. Thus, the goal of this project was to develop novel approaches for var transcriptome quantification, which overcome the previous limitations and improve on current methods for var gene expression quantification. To achieve this, I first investigated different technical and biological factors, assembly tools and parameters in the context of var transcript assembly. This revealed the impact of read depth, read length, assembler type, k-mer value and the life cycle stage on var transcript assembly. The integration of these findings led to the development of a novel var transcript assembly and multi-dimensional quantification method. It substantially outperforms a previous approach on both laboratory and clinical isolates across a combination of metrics, including the N50 value, number of mis-assemblies and the ability to characterise lowly expressed var transcripts. It is a powerful tool to interrogate the var transcriptome in context with the rest of the transcriptome and can be applied to enhance our understanding of the role of var genes in malaria pathogenesis. I first applied the approaches to investigate changes in the var and core transcriptome during short-term in vitro cultivation. I then used the methods to assess the var transcriptome in infected individuals, where they previously had to be excluded from analysis. The new approaches lead the way for characterising the relationships between var gene expression and the rest of the parasite transcriptome. They enable large scale studies of associations of var gene expression with clinical outcomes and immunity to malaria. This has the potential to identify targets of novel therapy and identify key mediators of severe versus asymptomatic infections. This would further allow the development of vaccine candidates that would ensure high levels of immunity are achieved.Open Acces

A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture

The pathogenesis of severe Plasmodium falciparum malaria involves cytoadhesive microvascular sequestration of infected erythrocytes, mediated by P. falciparum erythrocyte membrane protein 1 (PfEMP1). PfEMP1 variants are encoded by the highly polymorphic family of var genes, the sequences of which are largely unknown in clinical samples. Previously, we published new approaches for var gene profiling and classification of predicted binding phenotypes in clinical P. falciparum isolates (Wichers et al., 2021), which represented a major technical advance. Building on this, we report here a novel method for var gene assembly and multidimensional quantification from RNA-sequencing that outperforms the earlier approach of Wichers et al., 2021, on both laboratory and clinical isolates across a combination of metrics. Importantly, the tool can interrogate the var transcriptome in context with the rest of the transcriptome and can be applied to enhance our understanding of the role of var genes in malaria pathogenesis. We applied this new method to investigate changes in var gene expression through early transition of parasite isolates to in vitro culture, using paired sets of ex vivo samples from our previous study, cultured for up to three generations. In parallel, changes in non-polymorphic core gene expression were investigated. Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour