High quality omics datasets for helminth parasites are a relatively recent development

Prior to the current genomic era, helminths were represented in genetic databases by expressed sequence tags, and a few transcriptomes and shotgun sequence libraries (Foster et al., Reference Foster, Zhang, Jumar and Carlow2005). While these were a useful resource at the time, their worth was compromised by being concentrated in a handful of core species, with the majority of parasites poorly represented. Caenorhabditis elegans, a nematode, was the first metazoan genome to be fully sequenced (C. elegans Sequencing Consortium, 1998), a project from which parasitologists undoubtedly benefitted. Many adopted C. elegans as a surrogate system in which to interrogate parasite biology, either as a model (Hashmi et al., Reference Hashmi, Tawe and Lustigman2001; Holden-Dye and Walker, Reference Holden-Dye and Walker2014), or as a heterologous system in which to express and study parasite genes (first described by Kwa et al., Reference Kwa, Veenstra, Van Dijk and Roos1995). More directly, many of the tools developed in C. elegans to help interrogate that organism's genome [gene silencing with RNA interference (RNAi), genetic transformation with green-fluorescent protein, genome editing with CRISPR-Cas9, for example] were later applied to nematode parasites, in some cases (described below) allowing native functional biology studies in parasites, without the need for extrapolation from a non-parasitic model system. Similarly, efforts have been made to understand flatworm parasites using free-living species such as Schmidtea and Dugesia/Girardia, each of which are well-resourced and tractable experimental systems, although these have never been developed into full-fledged models to the extent of C. elegans (Collins and Newmark, Reference Collins and Newmark2013; Wheeler et al., Reference Wheeler, Agbedanu, Kimber, Ribeiro, Day and Zamanian2015). This may be because, as is described below, Schistosoma mansoni, a flatworm blood fluke parasite and cause of human schistosomiasis, is an extremely tractable experimental system in its own right. Using a native parasite model overcomes the obvious key limitation of employing free-living model systems – that they are not parasitic, and are therefore of limited use in understanding native aspects of parasite biology.

The first helminth parasite genome to be sequenced was that of the filarial nematode Brugia malayi, one of the causes of lymphatic filariasis (Ghedin et al., Reference Ghedin, Wang, Spiro, Caler, Zhao, Crabtree, Allen, Delcher, Guiliano, Miranda-Saavedra, Anguiloli, Creasy, Amedeo, Haas, El-Sayed, Wortman, Feldblyum, Tallon, Schatz, Shumway, Koo, Salzberg, Schobel, Pertea, Pop, White, Barton, Carlow, Crawford, Daub, Dimmic, Estes, Foster, Ganatra, Gregory, Johnson, Jin, Komuniecki, Korf, Kumar, Laney, Li, Li, Lindblom, Lustigman, Ma, Maina, Martin, McCarter, McReynolds, Mitreva, Nutman, Parkinson, Peregrin-Alvarez, Poole, Ren, Saunders, Sluder, Smith, Stanke, Unnasch, Ware, Wei, Weil, Williams, Zhang, Williams, Fraser-Liggett, Slatko, Blaxter and Scott2007), closely followed by the root knot nematode Meloidogyne incognita (Abad et al., Reference Abad, Gouzy, Aury, Castagnone-Sereno, Danchin, Deleury, Perfus-Barbeoch, Anthouard, Artiguenave, Blok, Caillaud, Coutinho, Dasilva, De Luca, Deau, Esquibet, Flutre, Goldstone, Hamamouch, Hewzi, Jaillon, Jubin, Leonetti, Magliano, Maier, Markov, McVeigh, Pesole, Poulain, Robinson-Rechavi, Sallet, Segurens, Strinbach, Tytgat, Ugarte, van Ghelder, Veronico, Baum, Blaxter, Bleve-Zacheo, Davis, Ewbank, Favery, Grenier, Henrissat, Jones, Laudet, Maule, Quesneville, Rosso, Schiex, Smant, Weissenbach and Wincker2008), and the human blood flukes S. mansoni and Schistosoma japonicum (Berriman et al., Reference Berriman, Haas, LoVerde, Wilson, Dillon, Cerqueira, Mashiyama, Al-Lazikani, Andrade, Ashton, Aslett, Bartholomeu, Blandin, Caffrey, Coghlan, Coulson, Day, Delcher, DeMarco, Djikeng, Eyre, Gamble, Ghedin, Gu, Hertz-Fowler, Harai, Hirai, Houston, Ivens, Johnston, Lacerda, Macedo, McVeigh, Ning, Oliveira, Overington, Parkhill, Pertea, Pierce, Protasio, Quail, Rajandream, Rogers, Sajid, Salzberg, Stanke, Tivey, White, Williams, Wortman, Wu, Zamanian, Zerlotini, Fraser-Liggett, Barrell and El-Sayed2009; Zhou et al., Reference Zhou, Zheng, Chen, Zhang, Wang, Guo, Huang, Zhang, Huang, Jin, Tonghai, Hasegawa, Wang, Zhang, Zhou, Tao, Cao, Li, Vinar, Brejova, Brown, Li, Miller, Blair, Zhong, Chen, Liu, Hu, Wang, Zhang, Song, Chen, Xu, Xu, Ju, Cheng, Brindley, McManus, Feng, Han, Lu, Ren, Wang, Gu, Kang, Chen, Chen, Chen, Wang, Yan, Wang, Lv, Jin, Wang, Pu, Zhang, Zhang, Hu, Zhu, Wang, Yu, Wang, Yang, Ning, Beriman, Wei, Ruan, Zhao and Wang2009). In the 10 years since, we now have access to high-quality genome data for 81 species of parasitic nematode, and 31 species of parasitic flatworm (WormBase ParaSite release WBPS14; Howe et al., Reference Howe, Bolt, Shafie, Kersey and Berriman2017). These genomes have accelerated studies in each individual species, and also supported large-scale comparative approaches for identifying the evolutionary strategies common to helminth parasitism (Zarowiecki and Berriman, Reference Zarowiecki and Berriman2015; International Helminth Genomes Consortium, 2019). Besides representing important resources in their own right, genome datasets have additional value in supporting the interpretation of other omics datasets. The helminth post-genomic revolution has seen a confluence of expanded genomic data with improved proteomic technologies, where in a two-way relationship, genome-sequencing data have helped to improve protein sequence identification, while proteomics similarly has helped to improve genome annotations (Ruggles et al., Reference Ruggles, Krug, Wang, Clauser, Wang, Payne, Fenyo, Zhang and Mani2017). Importantly, these methods have yielded enhanced understanding of helminth secretomes (i.e. the proportion of the proteome that is secreted by the parasite into the host), augmenting our understanding of helminth immunomodulatory processes and vaccinology (Sotillo et al., Reference Sotillo, Toledo, Mulvenna and Loukas2017). The availability of high-quality genomics datasets has not only enhanced analyses of the protein-coding genome, it has also supported discovery of non-coding (nc)RNAs including micro (mi)RNAs (Fromm et al., Reference Fromm, Ovchinnikov, Hoye, Bernal, Hackenberg and Marcilla2017; Quintana et al., Reference Quintana, Babayan and Buck2017), and long non-coding (lnc)RNAs (Vasconcelos et al., Reference Vasconcelos, DaSilva, Pires, Lavezzo, Pereira, Amaral and Verjovski-Almeida2017; Liao et al., Reference Liao, Zhang, Zhu, Chen, Dong, Tao, He, Liu and Wu2018; Oliveira et al., Reference Oliveira, Moares, Mota, Jannotti-Passos, Coelho, Mattos, Couto, Caffrey, Marsico and Guerra-Sa2018). Although we still have very limited functional data on ncRNAs, it seems reasonable to expect functional insights to flow from the application of functional genomics methods to these sequences.

Functional genomics and reverse genetics – messages in the silence

Functional genomics encompasses the experimental methods that illustrate the phenotypic output of an organism's genotype. These include transcriptomics and proteomics, both of which are used to study aspects of gene expression, and reverse genetics methods, through which we can modify an organism's genome or transcriptome and measure the impact of that manipulation on its phenotype. The development of reverse genetics methodology represented one of the most notable step changes in the history of laboratory-based biological science, revolutionizing our ability to interrogate cellular and organismal functions through induction of targeted transcriptional changes. RNAi mediated by double-stranded (ds)RNA was first developed in C. elegans (Fire et al., Reference Fire, Xu, Montgomery, Kostas, Driver and Mello1998; for which Andrew Fire and Craig Mello were jointly awarded the 2006 Nobel Prize in Physiology or Medicine). RNAi is a gene-silencing method that is now widely adopted across diverse organisms. Triggered by introducing exogenous dsRNA matching the sequence of a target gene, RNAi hijacks endogenous mechanisms to trigger destruction (‘knockdown’) of target transcripts. In C. elegans, this knockdown is specific, can be heritable and triggers suppression of target protein (Ahringer, Reference Ahringer2006), leading to phenotypic changes that can be measured. RNAi has been instrumental in probing the biology of C. elegans, to the point where genome-wide RNAi libraries, capable of knocking down essentially every C. elegans gene, are a freely available community resource (Kamath et al., Reference Kamath, Fraser, Dong, Poulin, Durbin, Gotta, Kanapin, Le Bot, Moreno, Sohrmann, Welchman, Zipperlen and Ahringer2003). While RNAi has not yet been applied at this scale in helminth parasites, it has been employed in several species with varying levels of success (Dalzell et al., Reference Dalzell, Warnock, McVeigh, Marks, Mousley, Atkinson and Maule2012). RNAi is most well advanced in Schistosoma spp. blood fluke, where since first reports in 2003 (Boyle et al., Reference Boyle, Wu, Shoemaker and Yoshino2003; Skelly et al., Reference Skelly, Da'dara and Harn2003) more than 100 publications (at the time of writing) have reported the use of RNAi in S. mansoni or S. japonicum. These include application throughout the schistosome life cycle – in eggs (Rinaldi et al., Reference Rinaldi, Morales, Alrefaei, Cancela, Castillo, Dalton, Tort and Brindley2009), in vitro-derived intra-molluscan larvae (Boyle et al., Reference Boyle, Wu, Shoemaker and Yoshino2003; Dinguirard and Yoshino, Reference Dinguirard and Yoshino2006; Mourão et al., Reference Mourão, Dinguirard, Franco and Yoshino2009a, Reference Mourão, Dinguirard, Franco and Yoshino2009b, Reference Mourão, Bitar, Lobo, Peconick, Grynberg, Prsdocimi, Waisberg, Cerqueria, Macedo, Machado, Yoshino and Franco2013; Taft and Yoshino, Reference Taft and Yoshino2011), in-vitro-maintained intra-mammalian larvae, ex vivo adult parasites (reviewed by Da'dara and Skelly, Reference Da'dara and Skelly2015) and even through intra-venous delivery of RNAi triggers to schistosomes in vivo (Pereira et al., Reference Pereira, Pascoal, Marchesini, Maia, Magalhaes, Zanotti-Magalhaes and Lopes-Cendes2008). In illustrating the application of RNAi to different life cycle stages, these studies highlight the ability to manipulate gene function throughout the many physiologically distinct developmental stages of the schistosome life cycle. This permits in-depth studies of parasite gene function, enabling exploitation of schistosome genome data towards the identification of new treatments for human schistosomiasis. Such research could contribute to the WHO's stated goal of schistosomiasis elimination as a public health issue by 2025 (Deol et al., Reference Deol, Fleming, Calvo-Urbano, Walker, Bucimi, Gnandou, Tukahebwa, Jumu, Mwingira, Alkohlani, Traore, Ruberanziza, Toure, Basanez, French and Webster2019).

Although RNAi is a powerful technique, a key limitation is its inability to generate gain of function (‘knock-in’) alterations. This need is met by the ongoing genome editing revolution, through the application of CRISPR-Cas9 methods to helminths. CRISPR employs components of a prokaryotic adaptive immune system, including a Cas nuclease enzyme, and a guide RNA to target the enzyme's cleavage activity (Jacinto et al., Reference Jacinto, Link and Ferreira2020). When these components are introduced into a eukaryotic system, they can mediate exquisitely precise genome edits. The first applications of CRISPR-Cas9 technology to helminth parasites have described its use in Strongyloides spp. nematodes (Gang et al., Reference Gang, Castelletto, Bryant, Yang, Mancuso, Lopez, Pellegrini and Hallem2017), and in the flatworms S. mansoni (Ittiprasert et al., Reference Ittiprasert, Mann, Larinshak, Coghlan, Rinaldi, Sankaranarayanan, Chaidee, Tanno, Kumkhaek, Prangtaworn, Mentink-Kane, Cochran, Driguez, Holroyd, Tracey, Rodpai, Everts, Hokke, Hoffmann, Berriman and Brindley2019) and Opisthorchis viverrini (Arunsan et al., Reference Arunsan, Ittiprasert, Smout, Cochran, Mann, Chaiyadet, Karinshak, Sripa, Young, Sotillo, Loukas, Brindley and Laha2019). All three of these studies demonstrated the existence of non-homologous end joining mechanisms, enabling specific disruption and knockout of target genes. Homology-directed repair was also employed in Strongyloides and Schistosoma to introduce new genetic information via a repair template (Gang et al., Reference Gang, Castelletto, Bryant, Yang, Mancuso, Lopez, Pellegrini and Hallem2017; Ittiprasert et al., Reference Ittiprasert, Mann, Larinshak, Coghlan, Rinaldi, Sankaranarayanan, Chaidee, Tanno, Kumkhaek, Prangtaworn, Mentink-Kane, Cochran, Driguez, Holroyd, Tracey, Rodpai, Everts, Hokke, Hoffmann, Berriman and Brindley2019). This illustrates the possibility of labelling edited organisms with a marker (such as a fluorescent protein), or introducing an anthelmintic resistance selection gene. These approaches could streamline the selection and analysis of edited individuals. If taken up by the wider community, this suite of genome editing tools should revolutionize our ability to probe and interrogate the biology of helminth parasites, and to identify new control targets in these globally important pathogens.

Anthelmintic resistance – using big data to understand a big problem

Anthelmintics are used worldwide in parasite control programmes for both human and veterinary helminths, where widespread reliance has led to a five-decade struggle against anthelmintic resistance in helminth parasites (Sangster et al., Reference Sangster, Cowling and Woodgate2018). This is a particular problem in veterinary parasites, such that in the Southern Hemisphere it is not uncommon to find sheep/goat farms colonized by nematodes that are resistant to all available anthelmintics (Kaplan and Vidyashankar, Reference Kaplan and Vidyashankar2012). This situation has stimulated much research interest in the mechanisms and markers of selection for anthelmintic resistance (Tinker, Reference Tinker2019; Kaplan, Reference Kaplan2020). Much of the research in these areas has centred on individual drug-resistant candidate genes, selected either from potential anthelmintic targets (Gilleard, Reference Gilleard2006) or from hypotheses around drug deactivation/efflux mechanisms (Matoušková et al., Reference Matoušková, Vokrál, Lamka and Skálová2016; Whittaker et al., Reference Whittaker, Carlson, Jones and Brewer2017). This approach has not been widely successful, probably because of the narrow and limited assumptions upon which candidate gene selection was based (Gilleard, Reference Gilleard2006). The availability of genome data now allows genome wide approaches for studying the genetics of anthelmintic resistance, where phenotypically distinct strains can be compared across the entirety of the genome to identify distinct regions that correlate with resistance (Doyle and Cotton, Reference Doyle and Cotton2019). These regions are known as quantitative trait loci (QTL), and resistance-associated QTL have been identified in Haemonchus contortus in response to benzimidazoles and monepantel (Doyle et al., Reference Doyle, Illingworth, Laing, Bartley, Redman, Martinelli, Holroyd, Morrison, Rezansoff, Tracey, Devaney, Berriman, Sargison, Cotton and Gilleard2019; Niciura et al., Reference Niciura, Tizioto, Moraes, Cruvinel, de Albuquerque, Santana, de Souza Chagas, Esteves, Benavides and do Amarante2019) and to benzimidazoles in C. elegans (Zamanian et al., Reference Zamanian, Cook, Zdraljevic, Brady, Lee, Lee and Andersen2018). These studies have the potential to identify resistance-associated single-nucleotide polymorphisms within individual alleles, therefore linking genotype to phenotype. The hope for such data is that they will almost certainly improve our ability to identify drug-resistant genotypes in parasite populations, and they may also assist efforts to overcome resistance (Doyle and Cotton, Reference Doyle and Cotton2019).

Secreted nucleic acids in host–parasite interactions and molecular diagnostics

One promising application of post-genomic tools to diagnostics is the use of RNA sequencing to identify secretion of small non-coding RNAs by helminths. Genome data are an essential element in the identification of ncRNAs, because their discovery hinges upon accurate mapping of millions of short-sequencing reads to a high-quality genome. These mapping data are then computationally analysed for similarity with known ncRNA sequences and/or the prediction of their existence on feasible precursor sequences. Secreted small RNAs, predominantly micro (mi)RNAs, have been reported from 15 helminth species. Since miRNAs are negative regulators of gene expression, the prevailing hypothesis posits that these molecules are released by helminths to modulate the function of host cells, and the host environment, in their favour. This is a relatively newly recognized aspect of the host–parasite interface, which has traditionally been focused on helminth-secreted proteins (Siles-Lucas et al., Reference Siles-Lucas, Morchon, Simon and Manzano-Roman2015; Cai et al., Reference Cai, Gobert and McManus2016; Quintana et al., Reference Quintana, Babayan and Buck2017). We are beginning to see the application of post-genomic tools to test this hypothesis, and indeed the latest evidence supports defined functional roles for helminth-secreted miRNAs in modulation of specific host cell functions (Lin et al., Reference Lin, Zhu, Hu, Wang, Jiang, Zhu, Li, Wang, Zhang, Xu and Pan2019; Liu et al., Reference Liu, Zhu, Wang, Qiu, Chen, Davis and Cheng2019).

Secreted miRNAs are also of obvious appeal as new molecular biomarkers for diagnosis of helminth infections. This appeal comes from the fact that as nucleic acids, they: (i) can be detected with exquisite sensitivity and specificity using polymerase chain reaction (PCR); (ii) are systemically distributed and are detectable in blood and other biofluids and (iii) are generally stable because most are encapsulated in extracellular vesicles (Quintana et al., Reference Quintana, Babayan and Buck2017; Ghalenoei et al., Reference Ghalenoei, Bagheri, Fakhar and Mishan2020). Besides being useful for detecting the presence of parasite infection, dysregulation of host miRNA profiles can identify pathology – for example plasma miRNAs can quantify the extent of liver fibrosis caused by schistosomiasis (Chen et al., Reference Chen, Zhang, Zheng, Chen, Nie, Zheng and Gong2019). Diagnostics is one sub-field of parasitology that stands to benefit appreciably from the genomic revolution, due to the improvements in sensitivity, specificity and throughput that genomic data and tools could bring to diagnostic tests. All of the existing ‘gold standard’ tests for diagnosis of helminth parasites rely on visual identification of helminth life stages in feces, blood or tissue samples, or immunological detection of anti-parasite antibodies (McCarthy et al., Reference McCarthy, Lustigman, Yang, Barakat, Garcia, Sripa, Willingham, Prichard and Basanez2012; Charlier et al., Reference Charlier, de Waele, Ducheyne, van der Voort, Vande Velde and Claerebout2016; Gomez-Morales et al., Reference Gomez-Morales, Garate, Blocher, Devleesschauwer, Smit, Schmidt, Perteguer, Ludovisi, Pozio, Dorny, Gabriel and Winkler2017; Pfister and Van Doorn, Reference Pfister and van Doorn2018). These methods are largely subjective, relatively insensitive and/or are capable of detecting only mature, patent infections. Since many helminth pathologies are the result of infections by larval parasites, the next generation of diagnostic tools must aim to provide early detection of infection. Despite apparent progress in research laboratories, there has been little translation of molecular diagnostic tools into the field (Papaiakovou et al., Reference Papaiakovou, Gasser and Littlewood2019).

Recent years have seen the development of metabarcoding sequence analysis methods, where DNA extracted from a complex mixture is amplified by PCR for phylogenetically informative sequences, such as mitochondrial or ribosomal genes. The resulting amplicons are subjected to next-generation sequencing (NGS) analysis, followed by bioinformatic deconvolution to identify the breadth of sequences (and therefore species) present in the original sample. Metabarcoding has transformed our ability to perform non-invasive biodiversity surveys in terrestrial, freshwater and marine environments (Deiner et al., Reference Deiner, Bik, Machler, Seymour, Lacoursiere-Roussel, Altermatt, Creer, Bista, Lodge, de Vere, Pfrender and Bernatchez2017), and these methods have now been adapted for the speciation of complex mixtures of parasites in fecal samples. The gold standard for the detection of gastrointestinal nematodes of sheep, cattle and goats has, for many decades, been fecal egg counting, where eggs are separated from a homogenized fecal sample by flotation in a sugar or salt solution, then quantified and speciated by microscopy. Many eggs can be speciated by experienced microscopists, but strongylids, one of the major groupings of veterinary gastrointestinal nematodes, cannot be visually distinguished to species level. The traditional solution to this has been to perform a larval development assay, since the third larval stage that develops on pasture after hatching from strongylid eggs do exhibit individual diagnostic morphologies. However, this is a laborious method that relies on highly trained and experienced personnel to perform. Helminth genome data have permitted the adaptation of metabarcoding methods for simple (and automatable) molecular differentiation of strongylid larvae following nematode collection from fecal samples. Through amplification of the ITS-2 ribosomal DNA locus using conserved nematode primer sets, and NGS of those amplicons, accurate quantification of complex parasitic nematode communities is possible. This method is termed ‘nemabiome’ sequencing (Avramenko et al., Reference Avramenko, Redman, Lewis, Yazwinski, Wasmuth and Gilleard2015), first used in the field to illustrate the differences in gut nematode composition between cattle herds in Canada and Brazil, as well as highlighting changes in nematode communities following anthelmintic treatment (Avramenko et al., Reference Avramenko, Redman, Lewis, Bichuette, Palmeira, Yazwinski and Gilleard2017). Nemabiome sequencing has since been used to quantify nematode community composition in Canadian Bison (Avramenko et al., Reference Avramenko, Bras, Redman, Woodbury, Wagner, Shury, Licciolo, Windeyer and Gilleard2018), dairy cows (Scott et al., Reference Scott, Gilleard, Jelinski, Barkema, Redman, Avramenko, Luby, Kelton, Bauman, Keefe, Dubuc and Uehlinger2019a, Reference Scott, Avramenko, Redman, Jelinski, Luby, Henderson, Wagner, Gilleard and Uehlingerb), UK sheep farms (Redman et al., Reference Redman, Queiroz, Bartley, Levy, Avramenko and Gilleard2019) and horses (Mitchell et al., Reference Mitchell, O'Sullivan, Pinloche, Wilkinson, Morphew and McEwan2019). These methods enable high-throughput, objective surveillance of nematode parasite prevalence in veterinary herds, providing molecular support for large-scale parasite prevalence surveys and parasite risk forecasting. Although not yet applied to human parasites, nemabiome sequencing could be useful for epidemiological analysis of soil-transmitted helminth populations in the developing world.

Single-cell transcriptomics

Following the trend for ‘big data’ in biology, the transcriptomics field has now developed methods for sequencing the RNA transcriptome of every cell in a complex cell population containing thousands of individual cells (Hwang et al., Reference Hwang, Lee and Bang2018). Single-cell RNA sequencing (scRNA-Seq) generally involves the dissociation of tissues into individual cells, followed by the encapsulation of those cells into individual lipid vesicles containing sequencing reagents and unique molecular barcodes. After cell lysis, library construction and sequencing is performed, with extensive subsequent deconvolution using custom-scripted bioinformatics analyses. This approach allows the elucidation of the transcriptome of every cell in a tissue, organ or entire organism. The resulting datasets can be clustered by cell type, permitting large scale delineation of cell type-specific transcription patterns. These methods are uniquely informative for studies as diverse as developmental biology, transcriptional regulation and stem cell research (Hwang et al., Reference Hwang, Lee and Bang2018). ScRNA-Seq has been applied to large numbers of C. elegans cells (Cao et al., Reference Cao, Packer, Ramani, Cusanovich, Huynh, Daza, Qiu, Lee, Furlan, Steemers, Adey, Waterston, Trapnell and Shendure2017), but has been developed to its highest potential in the acoelomate flatworms, where scRNA-Seq is useful for ‘dissection’ of cell populations, overcoming the considerable technical challenge of physically separating closely packed cells/tissues (Hahnel et al., Reference Hahnel, Lu, Wilson, Grevelding and Quack2013). Reflecting this challenge, the first studies to sequence essentially every cell in a complete metazoan organism were performed in the flatworm model system, S. mediterranea (Fincher et al., Reference Fincher, Wurtzel, de Hoog, Kravarik and Reddien2018; Plass et al., Reference Plass, Solana, Wolf, Ayoub, Misios, Glazar, Obermayer, Theis, Kocks and Rajewsky2018). These datasets are useful for parasitologists since they can inform conserved aspects of flatworm biology, particularly those of nerve, muscle, reproductive and stem cells, all of which are key foci for flatworm parasitologists interested in parasite control. At the time of writing, two studies, both published as pre-prints, have applied scRNA-Seq to S. mansoni, describing 68 distinct cell populations and the majority of the adult parasite's tissue types (Wendt et al., Reference Wendt, Zhao, Chen, Liu, O'Donoghue, Caffrey and Collins2020), and 11 cell types in the schistosomulum, the first intra-mammalian larval stage of the life cycle (Soria et al., Reference Soria, Lee, Chong, Coghlin, Tracey, Young, Andrews, Hall, Ling, Ng, Rawlinson, Doyle, Leonard, Lu, Bennett, Rinaldi, Newmark and Berriman2019). These studies describe comprehensive cellular transcriptional maps for two important stages of the intra-mammalian schistosome life cycle, providing a source of hypotheses and targets for new anti-schistosomal therapeutics.