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      A single cell atlas of sexual development in Plasmodium falciparum

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          Abstract

          The developmental decision made by malaria parasites to become sexual underlies all malaria transmission. Here, we describe a rich atlas of short- and long-read single-cell transcriptomes of over 37,000 Plasmodium falciparum cells across intraerythrocytic asexual and sexual development. We used the atlas to explore transcriptional modules and exon usage along sexual development and expanded it to include malaria parasites collected from four Malian individuals naturally infected with multiple P. falciparum strains. We investigated genotypic and transcriptional heterogeneity within and among these wild strains at the single-cell level, finding differential expression between different strains even within the same host. These data are a key addition to the Malaria Cell Atlas interactive data resource, enabling a deeper understanding of the biology and diversity of transmission stages.

          Editor’s summary

          The malaria parasite has a complex life cycle that offers many potential targets for control measures. One of the most vulnerable points in the parasite’s life cycle is when it makes the developmental commitment to sexual reproduction. Dogga et al . have built a rich atlas of short- and long-read single-cell transcriptomes of over 37,000 Plasmodium falciparum cells across intraerythrocytic asexual and sexual development (see the Perspective by Carlton and Cunnington). Parasites obtained from an individual infected with multiple strains of P. falciparum were compared with the atlas data. Considerable heterogeneity in gene expression was seen among strains of parasite from the “wild” donor strains compared with the laboratory-cultured strains and, unlike in laboratory culture, unexpected stages in male and female parasite development were observed. —Caroline Ash

          Abstract

          INTRODUCTION

          Plasmodium falciparum malaria is a major contributor to mortality and morbidity in developing nations. The extensive spatiotemporal genetic diversity of P. falciparum presents a challenge to the development of effective diagnostics, drugs, and vaccines. Co-infection with different P. falciparum strains occurs in more than 70% of infections in malaria-endemic populations. Understanding and monitoring parasite evolution in these settings will be necessary for effective control strategies. Additionally, resources that support deeper understanding of the sexual commitment and development of P. falciparum are needed to assist in the development of strategies that block parasite transmission.

          RATIONALE

          We have generated and explored a complete atlas of intraerythrocytic development of P. falciparum , enabling greater insight into its sexual development. This high-resolution reference atlas has great utility for profiling cell types found in natural infections. Single-cell RNA-seq (scRNA-seq) of parasites from natural infections enables extensive characterization of multiple aspects of variation between parasites including differences in gene expression, isoform diversity, genetic variation, relatedness, and transcriptional variation between strains in each stage. These different kinds of variation mediate parasite adaptation to different host conditions and likely underpin parasite evolution.

          RESULTS

          We generated both short- and long-read scRNA-seq data from ~37,000 laboratory malaria parasite cells covering the asexual and sexual developmental stages. Cell and gene clustering revealed a topology reflecting the intraerythrocytic stages, with sexual developmental stages branching off from the asexual replication cycle, progressing to form female and male gametocytes. Notably, a cell cluster at the base of the sexual stages corresponded to parasites expressing sexual commitment signatures. Using trajectory inference analysis, we investigated distinct expression modules as gametocytes differentiated, developed, and matured into late male and female stages. Furthermore, we identified discernible male and female characteristic signatures within the seemingly transcriptionally similar gametocyte developmental stalk. Using long-read data, we captured the intraerythrocytic cell cycle topology, identified novel isoforms, and discovered exon usage differences between life cycle stages. We also profiled and investigated ~8000 parasites obtained from four naturally infected malaria carriers, each of whom carried multiple genotypic strains. Within the sexual stages of these natural infections, we identified cell clusters that showed significantly reduced expression as the parasites aged. Notably, we observed transcriptional differences between genotypically distinct strains within the same host in male and female gametocytes. These differences were mostly in genes that mediate host-parasite interactions. Integrating these natural infection scRNA-seq datasets into the laboratory reference atlas, we created a combined atlas comprising 45,691 cells. This integrated resource will support the mapping and analysis of both donor and laboratory malaria parasites, offering a comprehensive view for the interrogation of gene expression.

          CONCLUSION

          We characterized the intraerythrocytic cycle in lab strains with a focus on sexual development, exploring distinct expression modules underlying gametocyte development. Investigating natural infections at single-cell resolution enabled strain and stage assignment for each parasite and revealed unexpected transcriptomic clusters and differential expression between strains even within the same host. Long-read scRNA-seq exposed differential isoform usage between stages in lab strains and natural infections. The integrated dataset, comprising cells from laboratory strains and natural infections, spanning asexual and sexual development is presented as a new chapter in the interactive Malaria Cell Atlas data resource ( malariacellatlas.org ). Single cell evaluations of malaria parasites from natural infections will enhance our understanding of malaria parasite persistence, pathology, and transmission dynamics, and this atlas will be a key resource underpinning future work.

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          Most cited references109

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Minimap2: pairwise alignment for nucleotide sequences

            Heng Li (2018)
            Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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              Is Open Access

              Integrated analysis of multimodal single-cell data

              Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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                Author and article information

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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                May 03 2024
                May 03 2024
                : 384
                : 6695
                Affiliations
                [1 ]Wellcome Sanger Institute, Hinxton CB10 1SA, UK.
                [2 ]Institute for Computational Biomedicine, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany.
                [3 ]Malaria Research and Training Center (MRTC), Faculty of Pharmacy, Université des Sciences, des Techniques et des Technologies de Bamako (USTTB), Point G, P.O. Box, 1805 Bamako, Mali.
                [4 ]MIVEGEC, University of Montpellier, IRD, CNRS, Montpellier, France.
                Article
                10.1126/science.adj4088
                beb09afb-0297-4123-8e6d-3f343e0b96f6
                © 2024

                Free to read

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