Abstract
Plasmodium falciparum evades antibody recognition through transcriptional switching between members of the var gene family, which encodes the major virulence factor and surface antigen on infected red blood cells. Previous work with clonal P. falciparum populations revealed var gene expression profiles inconsistent with uniform single var gene expression. However, the mechanisms underpinning this and how it might contribute to chronic infections were unclear. Here, using single-cell transcriptomics employing enrichment probes and a portable microwell system, we analysed var gene expression in clonal 3D7 and IT4 parasite lines. We show that in addition to mono-allelic var gene expression, individual parasites can simultaneously express multiple var genes or enter a state in which little or no var gene expression is detectable. Reduced var gene expression resulted in greatly decreased antibody recognition of infected cells. This transcriptional flexibility provides parasites with greater adaptive capacity and could explain the antigenically ‘invisible’ parasites observed in chronic asymptomatic infections.
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Materials availability
All unique/stable reagents generated in this study are available from the lead contact without restriction.
Data availability
All sequencing data produced for this study are deposited in the NCBI Sequence Read Archive available at https://www.ncbi.nlm.nih.gov/sra under the study accession code PRJNA1075333. P. falciparum genome and transcriptome data are available through PlasmoDB v. 57. Source data are provided with this paper.
Code availability
All codes utilized for analysis and figure production as well as count matrices are available on GitHub at https://github.com/DeitschLab/SingleCell (ref. 90).
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Acknowledgements
We thank K. Kim (University of South Florida) and A. Craig (Liverpool School of Tropical Medicine) for providing access to hyperimmune IgG; J. Smith (University of Washington) for providing the IT4 parasite line; and A. Vishwanatha for assistance with figure generation. This work was supported by the National Institutes of Health (AI 52390 and AI99327 to K.W.D). K.W.D. is a Stavros S. Niarchos Scholar and a recipient of a William Randolf Hearst Endowed Faculty Fellowship. F.F. received support from the Swiss NSF (Early Postdoc.Mobility grant P2BEP3_191777). J.E.V. received support from F31 Predoctoral Fellowship F31AI164897 from the NIH. The Department of Microbiology and Immunology at Weill Medical College of Cornell University acknowledges the support of the William Randolph Hearst Foundation. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the paper.
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F.F., J.E.V., E.H. and S.M. designed and performed the experiments, collected and analysed the data. B.F.C.K. aided in designing custom scripts for data analysis. F.F., J.E.V., E.H., C.N., B.F.C.K. and K.W.D. wrote the paper.
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Extended data
Extended Data Fig. 1 Detection of ‘high-single’ and ‘low-many’ var expression states in IT4.
(a) Clone tree of wildtype IT4 parasites. Each pie chart represents the var profile of an individual subcloned population determined by qRT-PCR, each slice of the pie represents the expression level of a single var gene. Expression of each gene is determined as relative to seryl-tRNA synthetase (PfIT_020011400). Populations are classified as ‘low-many’ if the expression level of no individual gene makes up more the 50% of the total var signal. The annotation number of the most highly expressed var gene is shown below the pie chart for populations expressing a dominant var gene. Vertical and horizontal lines delineate sequential rounds of subcloning by limiting dilution. (b) Total var expression levels as determined by qRT-PCR for all the subclones in (A). The mean ± SD interval is shown, and an unpaired two-tailed t-test indicates a ****p < 0.0001. (c) Relative expression of control genes as determined by quantitative real-time RT-PCR (qRT-PCR) and represented as relative to seryl-tRNA synthetase (PF3D7_0717700). The mean ± SD interval is shown, and an unpaired two-tailed t-test indicates non-significant differences (ns) between selected high-singles (n = 6) and low-many populations (n = 6). The control genes are: SBP1 (Skeleton Binding Protein 1, PfIT_050006700), MSP1 (Merozoite Surface Protein 1, PfIT_090034900), RFC (Replication Factor C, Subunit 1, PfIT_020024100), DNA Pol alpha (DNA Polymerase Alpha, Subunit A, PfIT_040016500).
Extended Data Fig. 2 Comparison of RNA sequencing techniques reveals similar single-cell profiles.
Percentage of individual cells in the ‘single’ state (different colors depending on the expressed var gene), ‘null or undetected’ state (black) or ‘multiple’ (green) in each of the populations as analyzed by different sequencing techniques. Genes were considered expressed with at least 2 UMIs and over 15% of total var signal. If no var gene meets the criteria, the cell is counted as ‘null or undetected’, if only one var gene meets the criteria, the cell is counted as ‘single’, if more than one var gene meets the criteria, the cell is counted as ‘multiple’.
Extended Data Fig. 3 PfSAMS knockdown induces high level expression of multiple var genes.
Total var expression levels as determined by quantitative real-time RT-PCR (qRT-PCR) and represented as relative to seryl-tRNA synthetase (PF3D7_0717700). Biological replicate of the experiments shown in Fig. 2b and c starting with an independent transfection of a 3D7 parasite population with a different initial var expression profile.
Extended Data Fig. 4 var genes are the main cluster-drivers in HIVE scRNA-Seq data from the IT4 P. falciparum strain.
(a) Cumulative var expression profiles of two IT4 populations represented as histograms and pie charts. Expression of each gene is determined by quantitative RT-PCR and is represented as relative to seryl-tRNA synthetase (PfIT_020011400). var genes are ordered by type. (b) UMAP of the HIVE single-cell transcriptomes obtained from the IT4 populations with cells colored according to their clustering (see Supplementary Table 4 for cluster information). (c) UMAP of the HIVE single-cell transcriptomes with cells colored according to the parasite population that was sampled. (d) UMAP of the HIVE single-cell transcriptomes obtained from the IT4 populations excluding clonally-variant genes from the analysis. Cells are colored according to their clustering (see Supplementary Table 4 for cluster information). (e) UMAP of the HIVE single-cell transcriptomes obtained excluding clonally-variant genes from the analysis. Cells are colored according to the parasite population that was sampled. (f) UMAP graphs as in (B, C) with cells colored according to expression level of different var genes. (g) UMAP graphs as in (B, C) with cells colored according to expression level of different ring-expressed genes.
Extended Data Fig. 5 Comparison of read depth and sequence uniqueness across two highly expressed var genes.
(a) Integrated genome viewer (IGV) was used to visualize the distribution of all mapped reads from the HIVE single cell transcriptomes for the 3D7 High Single A (top) and 3D7 High single B (bottom) populations. The distribution of reads across the most highly expressed var gene in each population is shown as ‘Coverage’. The gene model for each gene according to PlasmoDB release 68 is indicated as ‘CDS’. Genes were parsed into 50 bp sliding windows and queried using NCBI blast using the blastn algorithm without low complexity filtering. Heatmaps and line charts indicating levels of similarity were generated using the bit-score of the top, non-self, transcript model for each of these sliding windows. Blue signifies low sequence identity to other genes within the genome, thereby enabling unambiguous mapping of sequencing reads. (b) Proportion of exon1 vs exon2 reads (normalized to exon length) detected in samples obtained from populations with differing numbers of ‘single’ and ‘multiple’ var expression phenotypes (HS: High Single, LM: Low Many). In all cases, exon1 reads meets or exceeded exon2 reads, indicating the transcripts were obtained from mRNAs rather than exon2 associated noncoding RNAs. The greater detection of exon1 reads likely results from the more efficient unique mapping of reads to this portion of the gene, as displayed in (A).
Extended Data Fig. 6 Relative var abundance/cell in the different var expression states across experiments.
Number of total var UMIs relative to total UMIs per individual cell in the HIVE (A), and Drop-Seq (B, C) experiments for the different 3D7 samples. The number of cells in each category is listed under the graphs. Genes were considered expressed if they had at least 2 UMIs, at least 0.1% of the total UMIs and over 15% of total var signal. If no var gene meets the criteria, the cell is counted as ‘null’, if only one var gene meets the criteria, the cell is counted as ‘single’, if more than one var gene meets the criteria, the cell is counted as ‘multiple’. The mean ± standard deviation is shown.
Extended Data Fig. 7 HIVE scRNA-Seq confirms multiple var transcripts in individual cells.
var gene expression is displayed for the top-100 cells for ‘high-single’ (A,C) or ‘low-many’ (B,D) 3D7 populations according to total UMI detected by HIVE scRNA-Seq. Each color in a bar represents a single var gene and each bar represents an individual cell. var UMIs over total UMIs are displayed for individual cells obtained from ‘high-single’ (a) and ‘low-many’ (b) populations. Relative UMI counts are shown as percentage of total var UMI in cells obtained from ‘high-single’ (c) and ‘low-many’ (d) populations.
Extended Data Fig. 8 3D7 and NF54 lines exhibit low immunogenicity.
(a) Flow-cytometry with hyperimmune IgG on NF54 (blue, gated infected RBC), 3D7 (red, gated infected RBCs) and uninfected RBCs (orange). (b) Flow-cytometry with hyperimmune IgG on NF54 (blue, gated infected RBC), IT4 (red, gated infected RBCs) and uninfected RBCs (orange). Histograms show normalized cell count over FITC intensity. (c) Example of flow-cytometry gating strategy applied to all experiments shown in Figure 6 and Extended Data Fig. 8a and b. FSC vs SSC is initially used to identify singlets. The DNA content measured by staining with Hoechst 33342 vs FSC is used to distinguish uninfected red blood cells (uRBC) from infected red blood cells (iRBC). These gating parameters are then used directly to detect antibody recognition as displayed in the associated histograms.
Extended Data Fig. 9 Percentage of var UMIs is consistent through a broad range of total UMIs.
Graphs displaying all cells analyzed for each 3D7 population in the HIVE experiments, arranged in order of most total UMIs to least per cell (left to right, red line). The relative number of var UMIs (var UMIs/total UMIs) is shown as a vertical line for each cell. The left y-axis shows the total amount of UMI per cell. The right y-axis (black) displays the percentage of var UMI per cell.
Extended Data Fig. 10 Shannon Diversity Index scores for var transcript diversity for all single cell transcriptomes obtained using HIVE.
(a) The Shannon Diversity Index for var gene expression was determined for each single cell transcriptome obtained by HIVE for each of the populations shown on the horizontal axis. Cells categorized as ‘single’ or ‘many’ are displayed in the columns marked S and M, respectively. The Shannon Index was calculated based on the number of UMIs for each var gene within the transcriptome of each cell using the formula \({H}^{{\prime} }=-{\sum }_{i=1}^{S}{p}_{i}{\log }_{2}{p}_{i}\). In this formula, H’: Index score; i: each category, in this case each var gene; S: number of categories, in this case the number of var genes; pi: proportion of the total made up of category i. Cells in which transcripts were detected from only one var gene have no diversity and a score of zero. A diversity score of 0.175 (shown as a red horizontal line) separates the vast majority of ‘single’ cells from ‘many’ cells. There is overlap however, particularly in samples with exceptional sequencing depth when very low levels of reads could be detected from nearly silent genes (for example the 3D7 High Single A population). In such instances, a minority of cells that we categorized as ‘singles’ display a Shannon Index score similar to ‘multiples’, however in these cells only one gene reaches the 15% threshold, a property that we believe is biologically relevant for the reasons described in detail in the Methods section and that is not reflected in the Shannon Index score. (b) Table showing the average index score for each category and the number of cells scoring either above or below the cutoff of 0.175.
Supplementary information
Supplementary Information
Supplementary Figs. 1 and 2.
Supplementary Tables
Supplementary Tables 1–7.
Source data
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Gene expression source data and fluorescence intensity.
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Florini, F., Visone, J.E., Hadjimichael, E. et al. scRNA-seq reveals transcriptional plasticity of var gene expression in Plasmodium falciparum for host immune avoidance. Nat Microbiol 10, 1417–1430 (2025). https://doi.org/10.1038/s41564-025-02008-5
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DOI: https://doi.org/10.1038/s41564-025-02008-5
