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Here we apply single cell transcriptomics to unravel the program inducing sexual differentiation in <ns4:italic>Plasmodium falciparum<\/ns4:italic>. Parasites have to make this essential life-cycle decision in preparation for human-to-mosquito transmission.<\/ns4:p><ns4:p> <ns4:bold>Methods:<\/ns4:bold> By combining transcriptional profiling with quantitative imaging and genetics, we defined a transcriptional signature in sexually committed cells.<\/ns4:p><ns4:p> <ns4:bold>Results:<\/ns4:bold> We found this transcriptional signature to be distinct from general changes in parasite metabolism that can be observed in response to commitment-inducing conditions.<\/ns4:p><ns4:p> <ns4:bold>Conclusions:<\/ns4:bold> This proof-of-concept study provides a template to capture transcriptional diversity in parasite populations containing complex mixtures of different life-cycle stages and developmental programs, with important implications for our understanding of parasite biology and the ongoing malaria elimination campaign.<\/ns4:p>","DOI":"10.12688\/wellcomeopenres.14645.4","type":"journal-article","created":{"date-parts":[[2018,10,17]],"date-time":"2018-10-17T14:51:02Z","timestamp":1539787862000},"page":"70","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["Probing Plasmodium falciparum sexual commitment at the single-cell level"],"prefix":"10.12688","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0655-3266","authenticated-orcid":false,"given":"Nicolas M.B.","family":"Brancucci","sequence":"first","affiliation":[]},{"given":"Mariana","family":"De Niz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5009-0778","authenticated-orcid":false,"given":"Timothy J.","family":"Straub","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5553-1763","authenticated-orcid":false,"given":"Deepali","family":"Ravel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2151-2470","authenticated-orcid":false,"given":"Lauriane","family":"Sollelis","sequence":"additional","affiliation":[]},{"given":"Bruce W.","family":"Birren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1464-4988","authenticated-orcid":false,"given":"Till S.","family":"Voss","sequence":"additional","affiliation":[]},{"given":"Daniel E.","family":"Neafsey","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-9566","authenticated-orcid":false,"given":"Matthias","family":"Marti","sequence":"additional","affiliation":[]}],"member":"2560","published-online":{"date-parts":[[2018,10,17]]},"reference":[{"key":"ref-1","article-title":"WHO Malaria Report 2016","year":"2016"},{"key":"ref-2","doi-asserted-by":"publisher","first-page":"2025-2037","DOI":"10.1056\/NEJMoa1505819","article-title":"Genetic Diversity and Protective Efficacy of the RTS,S\/AS01 Malaria Vaccine.","volume":"373","author":"D Neafsey","year":"2015","journal-title":"N Engl J Med."},{"key":"ref-3","doi-asserted-by":"publisher","first-page":"2453-2464","DOI":"10.1056\/NEJMoa1513137","article-title":"A Worldwide Map of Plasmodium falciparum K13-Propeller Polymorphisms.","volume":"374","author":"D M\u00e9nard","year":"2016","journal-title":"N Engl J Med."},{"key":"ref-4","doi-asserted-by":"publisher","first-page":"1091-1095","DOI":"10.1038\/nature06311","article-title":"Distinct physiological states of Plasmodium falciparum in malaria-infected patients.","volume":"450","author":"J Daily","year":"2007","journal-title":"Nature."},{"key":"ref-5","doi-asserted-by":"publisher","first-page":"431-435","DOI":"10.1126\/science.1260403","article-title":"Drug resistance. 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Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T07:39:12Z","timestamp":1589960352000},"score":1,"resource":{"primary":{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,17]]},"references-count":46,"URL":"https:\/\/doi.org\/10.12688\/wellcomeopenres.14645.4","relation":{},"ISSN":["2398-502X"],"issn-type":[{"value":"2398-502X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,17]]},"assertion":[{"value":"Indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.15947.r33320, Lyn-Marie Birkholtz, Riette van Biljon, Roelof van Wyk, Malaria Parasite Molecular Laboratory, Department of Biochemistry, Institute for Sustainable Malaria Control, Medical Research Council Collaborating Centre for Malaria Research, University of Pretoria, Pretoria, South Africa, 04 Jul 2018, version 1, 1 approved, 1 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v1#referee-response-33320","order":0,"name":"referee-response-33320","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Matthias Marti<\/b>; \n<i>Posted: 23 Jul 2018<\/i>; Figure 1a: The explanation as to the experimental setup needs clarification, including cell lines used for the single-cell transcriptome study, as this is not clearly defined in the methodology section. To avoid confusion, please clearly indicate if the single-cell transcriptomics was performed on non-modified lines, the Pf2004\/164dTom line or the&nbsp;\n<i>pmt<\/i>&nbsp;knockout line. It is also not clear from the figure or legend what the \u2018green\u2019 population for FACS sorting and microscopic evaluation refers to, besides indicating the populations tagged for DGE?  \n<b>The line used for flow sorting and scRNAseq is Pf2004\/164tdTom, hence the same as used in the previous cell paper by Brancucci et al. This has been clarified in the methods section and the figure legend. The green population represents sexually committed cells. This has also been added to figure legend 1.<\/b>  Figure 1d: The platform is validated through transcriptional differences observed between time points and a statement that \u2018single-cell transcriptomes generally recapitulate overall transcriptional profiles from population-level RNAseq experiments\u2019. However, care should be taken in as to the extent that correlations can be used to support these conclusions. The data presented in Fig 1d, is used to justify a major conclusion from figure 1- that the scRNA profiling is able to discriminate 4 h of temporal development. However, the single correlation (at R2&nbsp;of 0.43) with the complete bulk transcriptome time course from Bozdech 2003 (only the top 100 genes) is not entirely convincing (should perhaps just be included in supplementary data?).  \n<b>The single cell data are much sparser than the bulk transcriptomics data, and gene dropout effects may lower the correlation. Using a standard linear regression will not be robust to gene dropout and the sparseness of these data, but nevertheless show a modest, significant correlation. We acknowledge that the plot illustrating the correlation between scRNA and bulk profiles is not compelling (Fig 1f) and have consequently removed it.<\/b> &nbsp; Fig 1f. Poor correlations of 0.1 and 0.15 is observed for the top 100 most transcribed genes between the scRNA-seq data and population-level data (from Brancucci 2017), and the authors conclude this to be \u2018significantly through weakly\u2019 correlated.&nbsp; However, such low correlations are almost indicative of no correlation, even though significant. Could the authors elaborate why such low correlation is seen between the scRNA-seq and their own population-seq data produced under the same experimental conditions, but a correlation of 0.4 is seen when compared with the Bozdech 2003 population-seq data? As the authors rightly state, this could be a reflection of the loss of genes in single-cell sequencing, and should be left at that.  \n<b>Again, gene dropout is a likely culprit here for why the single cell data only weakly correlates with bulk transcriptomics data. We agree the significance is driven by numbers, rather than a strong correlation. However, in general, especially for the 100 most expressed genes, a higher relative expression in the population data led to a higher relative expression in the single cell data, which was our point with this panel.<\/b> &nbsp; Fig 2a. This figure has some findings that could be really important that is only presented in the scRNA-seq dataset and lacking in the bulk RNA dataset. Genes with an induction score of 0, but with positive pseudobulk log2FC values are therefore only seen at the scRNA-seq levels and potentially lost or obscured in bulk datasets. Could the authors elaborate on these genes as potential novel aspects of their dataset?  \n<b>Some of those genes were not significantly differentially expressed by single cell, but those that were include ETRAMP14, CLAG3.1, and RhopH3. After multiple hypothesis correction, only CLAG3.1 remains. The single-cell data were unbalanced, with more induced than uninduced cells; this, combined with gene dropout, may lead to some false discovery, though p-value correction would in theory overcome this issue. We instead chose to focus on genes that were corroborated by the bulk dataset, as we have higher confidence in those results than discordant genes.<\/b> &nbsp; Fig 2b: The relevance of the additional FPKM axis is not clear? Please clarify in the figure legend.  \n<b>FPKM is the axis for the population data. We acknowledge this is a confusing presentation and have separated the first plot in Fig 2b into two separate plots.<\/b> &nbsp; Fig 2c: The signature value is not properly explained.  \n<b>The signature value is explained in the methods, but was unfortunately omitted from the legend. We have now added it to the legend to correct this.&nbsp;<\/b> &nbsp; Fig 2d: The activity scale used is not sufficient to show genes that express, with white indicating high expression even though it appears mostly inactive. The use of a 5-colour system may be more appropriate or transformations that place the values in a more comparable range.  \n<b>Thank you for your suggestions. We adjusted the scale to improve clarity.&nbsp;<\/b> &nbsp; Table 2. The comparison with the Poran 2017 data clearly highlights the genes that correlate, but also indicates that the data here identifies genes involved in the processes upstream of AP2-G activation. It would be important to highlight the numbers and individual genes involved in these processes, which currently are not clearly stipulated, but do present a novel finding of this study.  \n<b>Please see our response below to REVIEWER 1, in which we have further investigated the genes which may be upstream vs. downstream of AP2-G activation through an analysis of AP2-G binding sites in promotor regions. We have included a more detailed description of the genes in the revised draft.<\/b>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v4#referee-comment-3655","order":1,"name":"referee-comment-3655","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.16113.r33800, Bj\u00f6rn F.C. Kafsack, Department of Microbiology\u00a0and\u00a0Immunology, Weill Cornell Medicine, New York City, NY, USA, 17 Sep 2018, version 3, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/3-70\/v3#referee-response-33800","order":2,"name":"referee-response-33800","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The project was supported by Senior Investigator Award 172862 and IRS Award 172805 from the Wellcome Trust and a career development award from the Burroughs Wellcome Fund to M.M., and a Centre Award 104111 to the Wellcome Trust Centre for Molecular Parasitology, University of Glasgow. M.M. is supported by a Royal Society Wolfson Research Merit Award. The project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Grant Number U19AI110818 to the Broad Institute. The project was further supported by Swiss National Science Foundation grants (31003A_163258 and BSCGI0_157729) to T.S.V.. N.M.B.B and M.D.N. received PostDoc.Mobility fellowships from the Swiss National Science Foundation (P300PA_160975 and P2BEP3_165396, respectively), and M.D.N. received a Long Term EMBO postdoctoral fellowship (EMBO ALTF 1048-2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}