Introduction

Despite progress made to reduce the global malaria burden, Plasmodium falciparum (Pf) remains one of the leading causes of mortality among children under 5 years of age.¹ Unfortunately, progress has been impeded by a plateau in malaria control since 2015. Anti-malarial drug resistance,^2,3 and unprecedented logistical challenges during the COVID-19 pandemic that dramatically impacted the distribution of insecticide-impregnated mosquito nets led to an increase in malaria cases in children under 5 years of age from 17.6 million in 2015 to 19.2 million in 2021 in East and Southern African countries,^1,4 thus, contributing to reinvigorated prioritization of malaria vaccine initiatives.

After 30 years of development, the first malaria vaccine—RTS,S/AS01E—was approved by the World Health Organization in 2021 for use in children residing in malaria-endemic regions. However, the limited efficacy of RTS,S,⁵ particularly against severe malaria⁵, has motivated the search for additional vaccine candidate antigens, including blood-stage Pf-schizont egress antigen-1 (PfSEA-1A)⁶ and Pf-glutamic acid-rich protein (PfGARP).⁷ Antibodies against PfSEA-1A correlate with significantly lower parasite densities in Kenyan adults and adolescents and substantially reduce schizont replication in vitro.^6,8 Whereas PfGARP-specific antibodies kill trophozoite-infected erythrocytes in culture and confer partial protection against Pf-challenge in vaccinated non-human primates.⁷ The presence of antibodies against PfGARP was associated with a 2-fold-lower parasite density in Kenyan adults and adolescents.⁷ Due to the complexity of the parasites’ life cycle, there is consensus that a multi-valent vaccine would be more efficacious⁹. However, determining which vaccine candidate antigens hold the most promise for inclusion in next-generation malaria vaccines remains a challenge.

Many pathogens and vaccines engender protective antibody responses after a single or few exposures¹⁰, characterized by the production of long-lived plasma cells and memory B cells.¹¹ Affinity maturation takes place in the germinal center (GC), where antigen-specific CD4^pos T-follicular helper (T_FH) cells are required not only to provide cellular (CD40L) and molecular (IL-21, IL-4, and IL-13) signals to trigger B-cell proliferation but also to promote GC maintenance and plasmablast differentiation. The GC is also where higher affinity B cells outcompete B cells with lower affinity for T_FH help.^12,13 T_FH cells are defined by the expression of a combination of markers, starting with chemokine receptor CXCR5, which directs CD4^pos T cells from the T-cell zone to engage with follicular B cells.^14,15 Once antigen-experienced T_FH cells leave the GC, they become circulating T_FH (cT_FH) cells and correlate with the generation of long-lasting antibody responses. Interestingly, cT_FH cells can also come from peripheral cT_FH precursor CCR7^lowPD1^highCXCR5^pos cells; thus, they also have a GC-independent origin¹⁶. Expression profiles of CCR6 and CXCR3 categorize cT_FH subsets as follows: cT_FH1 (CCR6^negCXCR3^pos), cT_FH2 (CCR6^negCXCR3^neg), and cT_FH17 (CCR6^posCXCR3^neg)¹⁷, whereas the expression of PD-1, ICOS, CD127, and CCR7 define their functional status: quiescent/central memory cT_FH (CCR7^highPD-1^negICOS^negCD127^pos) or activated/effector memory cT_FH (CCR7^lowPD-1^posICOS^posCD127^low/neg).^17–19 In addition, transcription factors (i.e., Bcl6 and cMAF) and cytokines (i.e., interferon-gamma [IFNγ], IL-4, and IL-21) are crucial to further characterizing the role of each cT_FH subset within the context of their interactions with B cells to promote antibody production.^20–23 Bcl6 facilitates the production of IL-21 by T cells which aids B-cell affinity maturation and antibody production.^24–26 T_FH cells also secrete cytokines that align with their subset classifications (but are not limited to them), such as IFNγ (T_FH1), IL-4/IL-13 (T_FH2), IL-4/IL-5/IL-13 (T_FH13), and IL-17 (T_FH17), that direct antibody isotype class-switching and mediate effector functions.^{12,18,20–22,27}

Several studies have characterized cT_FH subsets within the context of both adult and childhood Pf-malaria infections, as well as in healthy malaria-naïve volunteers under controlled malaria infection conditions.²⁸ In Mali, where malaria is seasonal, Obeng-Adjei and colleagues showed that T_H1-polarized cT_FH PD1^posCXCR5^posCXCR3^pos cells were preferentially activated in children and less efficient than CXCR3^neg cT_FH cells in helping autologous B cells produce antibodies; yet they used U.S. healthy adult cT_FH cells for their in vitro assays. Therefore, the authors suggested that promoting T_H2-like CXCR3^neg cT_FH subsets could improve antimalarial vaccine efficacy.²⁹ Similarly, a recent study in Papua, Indonesia, where malaria is perennial, found that all cT_FH subsets from adults showed higher activation and proliferation compared to cT_FH cells from children.³⁰ After in vitro stimulation with infected red blood cells, they found that all cT_FH subsets were activated in adults accompanied by IL-4 production, whereas only the T_H1-polarized cT_FH subset responded in children.³⁰ One study in Uganda, where malaria is holoendemic with seasonal peaks, showed that a shift from T_H2- to T_H1-polarized cT_FH subsets occurred during the first 6 years of life and was associated with the development of functional antibodies against Pf-malaria, yet appeared to be independent of malaria exposure as this age-associated shift was also observed in a malaria-naïve population.³¹ Interestingly, this study also found that a higher proportion of T_H17-cT_FH cells was associated with a decreased risk of P. falciparum infection the following year; however, the authors postulated that this phenomenon could have been driven by previous exposure to the parasite. The observed higher abundance of T_H2-like cT_FH cells in children younger than 6 years old and the age-associated increase in T_H1-cT_FH cells achieving the same proportion of T_H1-cT_FH cells by 6 years of age and into adulthood appeared to be independent of malaria exposure. Of note, these studies were limited by the use of a 2-dimensional gating strategy to classify cT_FH subsets and whole parasite activation conditions, either during infections or in vitro stimulation assays, leaving the malaria-antigen specificity of the different cT_FH subsets responses undefined. However, these studies highlight the progression in the field of evaluating the role of human T_FH cells in anti-malarial immunity.

Antibody levels are an unreliable predictor of malaria vaccine efficacy.³² Thus, establishing a combined immune profile to incorporate other surrogates of protection is warranted. Here, we examined the profile of cT_FH subsets using multiparameter spectral flow cytometry^33,34 against two malaria vaccine candidate antigens (PfSEA-1A⁶ and PfGARP⁷) in a cross-section of children and adults residing in a malaria-holoendemic region of Kenya. Our findings revealed significant differences in cT_FH subsets between children and adults, where children had more abundant cT_FH1-like cells and showed antigen-specific responses from all cT_FH types, whereas these responses were limited to cT_FH17- and 1/17-like cells in adults. Moreover, this study showed that PfGARP triggered Bcl6 expression across cT_FH subsets, whereas PfSEA-1A induced more cMAF expression, demonstrating the feasibility of implementing T-cell immune correlates to down-select new malaria candidates.

Results

Children had lower anti-PfSEA-1A antibodies compared to adults but similar levels of anti-PfGARP antibodies

This cross-sectional study selected a convenience sample of 7-year-old children and adults with a mean age of 22.67 years (ranging from 19 to 30 years old). No statistical difference was observed (p=0.35 after Welch’s test) regarding the absolute lymphocyte count (ALC) between children (mean of 373.1; standard deviation [SD] of 118.1) and adults (mean of 335.2; SD of 99.08). Children had significantly lower hematocrit values (median of 37%, interquartile range [IQR] of 32.7–40.5) compared to adults (median of 44.4%, IQR of 40.5– 46), p=0.002 after a two-tailed Mann-Whitney t-test; however, these values were within normal ranges after adjusting for age.³⁵ Both males and females (sex assigned at birth) were enrolled, with 43% (6/14) and 53% (8/15) being female within the children and adult groups, respectively. Seroprofiles against a panel of commonly used malaria antigens were generated to confirm the history of previous malaria infections for the selected children (Figure 1a). All participants had high IgG levels against merozoite antigens, apical membrane antigen 1 (AMA-1) and merozoite surface protein (MSP1), confirming at least one malaria infection within their lifetime.^36,37 IgG antibodies against circumsporozoite protein (CSP) and CelTOS (liver-stage antigens) were characteristically lower than against blood-stage antigens yet were present in all study children. We observed a clear bimodal distribution in antibody levels against histidine-rich protein 2 (HRP2) with half of the children having “high-HRP2” versus “low-HRP2” IgG levels, possibly a reflection of recent malaria history since it has been suggested that HRP2-specific antibodies are short-lived and could serve as a surrogate for a recent infection.³⁸ We assessed serological profiles for two Pf-malaria antigens being considered as potential vaccine candidates,^6,7 PfSEA-1A and PfGARP (Figure 1b). We found that children had a significantly lower median level of IgG against PfSEA-1A compared to adults (p<0.0001), whereas median levels to PfGARP were similarly high for adults and children, yet with a broad range of reactivity.

Figure 1:

The overall abundance of CD4^posCXCR5^pos cells is unaltered by in vitro antigen stimulation

To determine whether in vitro antigen stimulation with PfSEA-1A or PfGARP altered the abundance of total cT_FH cells, we compared cT_FH cells from adults and children using a FlowSOM unbiased clustering analysis and EMBEDSOM dimensional reduction based on common lineage markers assessed by spectral flow cytometry from 87,812 live lymphocytes from each sample: CD8^pos, CD4^pos, CD4^posCXCR5^pos, and CD4^posCD25^pos (Figure 2a). As expected, after a short stimulation (6 hrs), overall abundances of cT_FH cells were similar across conditions for both adults (Figure 2b) and children (Figure 2c). This observation was confirmed by EdgeR statistical analysis (Supplemental Figure 2) and demonstrated that in vitro stimulation did not preferentially expand the T cell populations on which we based our subsequent analyses.

Figure 2:

An unbiased clustering analysis identifies twelve distinct cT_FH meta-clusters

Numerous markers and a two-dimensional gating strategy have previously been used to determine the frequency of cT_FH subsets.^29–31 To simultaneously account for the expression of 17 markers required to define cT_FH subsets, we used FlowSOM unbiased clustering analysis to determine the frequency of cT_FH subsets from a pool of 1,000 CD3^posCD4^posCXCR5^posCD25^neg cells from each sample (total of 13,000 CD3^posCD4^posCXCR5^posCD25^neg cells in both children and adults). Based on CXCR3 and CCR6 expression as well as the expression of effector/memory/activation markers (CCR7, CD127, PD1, and ICOS), cytokines (IFNγ, IL-4, and IL-21), and transcription factors (Bcl6 and cMAF), we initially identified 15 meta-clusters within the CD4^posCXCR5^pos T cells (Figure 3a). First, we found different CXCR5 expression levels between meta-clusters (Figure 3b); CXCR5 is essential for cT_FH cells to migrate to the lymph nodes and interact with B-cells. Data presented in Figure 3 are from children; however, we found similar observations in the adult group (Supplemental Figure 3). Because CD45RA^posCXCR5^pos cells are likely naïve cells with transient low expression of CXCR5 yet high expression of CD45RA, we excluded 3 meta-clusters using these criteria (i.e., MC12, MC14, and MC15) (Figure 3c). Then, using the overall expression of CXCR3 and CCR6 across the cT_FH subsets (Figure 3d and 3e), we identified the remaining 12 clusters as follows: MC01 and MC02 were cT_FH2-like; MC06 and MC07 were cT_FH1-like; MC09 and MC11 were cT_FH1/17-like; MC10 and MC13 were cT_FH17-like. However, CXCR3 expression was not clearly delineated for some subsets and did not align with the conventional CCR6 vs. CXCR3 cytoplot (Figure 3f and 3g, Supplemental Figure 4). Based on the heatmap (Figure 3f), MC03, MC04, MC05, and MC08 clusters appear closer to cT_FH2-like MC01 and MC02 clusters than cT_FH1-like clusters MC06 and MC07, suggesting that they might be part of the cT_FH2-like subset. But, based on their intensity of CXCR3 expression and their distribution across the CCR6 vs. CXCR3 cytoplot (Supplemental Figure 4), we defined MC03, MC04, MC05, and MC08 clusters as “undetermined” and would require additional cytokine and transcription factor analyses to fully categorize them.

Figure 3:

Heterogeneity of activation/maturation markers within cT_FH subsets

By assessing the expression of CCR7, PD1, CD127, and ICOS (Figure 4) and following the three-dimensional expression patterns adapted from Schmitt et al.¹⁷ (Supplemental Figure 5), we determined the activation state of each cT_FH meta-cluster and which markers created novel subsets. Data in Figure 4 are from children; however, similar observations were made for adults (Supplemental Figure 6). As expected, none of the extracellular markers showed significant differences in expression patterns after a short 6-hour stimulation, thus, representing the cT_FH repertoire present within our study participants. Interestingly, the cT_FH1or2 subset (MC03) was the only meta-cluster with high expression of PD1 (Figure 4a) accompanied by high ICOS (Figure 4b), low CCR7 (Figure 4c), and low CD127 expression (Figure 4d), suggesting that MC03 was an activated/effector cT_FH1or2 subset. The MC04 subset had low CCR7 and high ICOS expression but low CD127 and intermediate PD1 expression, indicating that this cluster was a less activated/effector cT_FH1or2 cluster compared to MC03.

Figure 4:

We also observed additional nuances in the expression pattern for other cT_FH1-like meta-clusters. The cT_FH1-like MC06 cluster had an activated/effector profile, whereas the cT_FH1-like MC07 cluster had a quiescent/effector profile. The cT_FH1or2-like MC05 and MC08 clusters were defined as quiescent/memory (overall high CCR7 expression) yet with very low expression of ICOS for the MC05 cluster.

Likewise, cT_FH2-like and cT_FH17-like meta-clusters also displayed heterogeneity. The cT_FH2-like MC01 cluster had an activated/effector profile, whereas the cT_FH2-like MC02 cluster seemed to be a quiescent/effector subset. The cT_FH1/17-like subsets MC09 and MC11 overall had an activated profile, although they had higher expression of CCR7 and CD127 compared to other subsets, suggesting an activated/memory phenotype. Finally, the cT_FH17-like MC10 cluster seemed quiescent, whereas cT_FH17-like MC13 had an activated/effector profile. Of note, because of our short stimulation time (6 hrs), we were unable to find statistical differences in the CD40L expression between groups as only few individuals responded through it (Supplemental Figure 7). However, our analysis methods revealed a higher degree of previously unrecognized heterogeneity within circulating cT_FH cells.

Activated cT_FH1or2-, cT_FH1-, and quiescent cT_FH1or2-like subsets were more abundant in children

After having deconvoluted cT_FH cells into 12 subsets, we next wanted to determine whether their abundance differed by age or after antigen stimulation. Using Uniform Manifold Approximation and Projection (UMAP) visualization, we found that cT_FH dimensional reduction was contiguous as meta-clusters merged with each other; in addition, there were notable differences between adults and children (Figure 5a). We found that activated PD1^high cT_FH1or2-like (MC03), activated cT_FH1-like (MC06), and quiescent ICOS^high cT_FH1or2-like (MC08) subsets were significantly more abundant in children compared to adults regardless of the stimulation conditions (Figure 5b, 5c, and 5d, p<0.05). In contrast, the quiescent PfSEA-1A- and PfGARP-specific cT_FH2-like cluster (MC02) was significantly more abundant in adults compared to children (Figure 5c and 5d, p<0.05). Interestingly, following PfGARP stimulation, the activated cT_FH1/17-like subset (MC09) became more abundant in children compared to adults (Figure 5d, p<0.05 with a False Discovery Rate=0.08), but no additional subsets shifted phenotype after PfSEA-1A stimulation (Figure 5c). Of note, the activated PD1^low cT_FH1or2-like cells (MC04) seemed more abundant in non-stimulated adults and PfGARP-stimulated children, but, because these observations were not present in all the participants, they did not achieve statistical significance in EdgeR.

Figure 5:

The abundance heatmap (Figure 5e) reiterates the differences observed between children and adults and highlights important considerations when assessing the potential role of each cT_FH subset in assisting with cognate antibody production. Overall, the most common cT_FH subset in both children and adults is the quiescent cT_FH2-like cells (MC02, 24.1% and 41.2% respectively). However, the antigen-specific differences in the cT_FH subset abundance for children (MC09 for PfGARP) and for adults (MC02 for both PfSEA-1A and PfGARP) suggest that children engage different cT_FH cells as they are developing immunity. Of note, only the activated PD1^low cT_FH1or2-like cells (MC04) was less abundant in children with low compared to high HRP2 antibody levels (Supplemental Figure 8), suggesting that this subset may be involved in short-term antibody production. Overall, this comprehensive examination of the abundance of cT_FH subsets demonstrates important diversity based on age and malaria-antigen specificity.

PfSEA-1A and PfGARP induced IL-4, Bcl6, and cMAF from a broad range of cT_FH subsets in children

Because the children in this cohort were 7 years of age and resided in a malaria-holoendemic area, they had ample time to develop premunition. To assess antigen-specific cytokine and transcription factor expression signatures and further characterize cT_FH subsets, we generated clustered heatmaps of the median fluorescence intensity (MFI) of each analyte (IFN, IL-4, IL-21, Bcl6, and cMAF) for children (Supplemental Figure 9a) and adults (Supplemental Figure 9b). Next, using these MFI data, we performed Wilcoxon paired two-tailed t-tests to compare PfSEA-1A and PfGARP stimulation to unstimulated cells (Figure 6a and 6b, respectively). Significant differences in these expression profiles allowed us to further characterize meta-clusters into three main groups. Group 1: cT_FH2-like (activated MC01 and quiescent MC02) and activated cT_FH1or2-like (PD1^highMC03 and PD1^lowMC04); Group 2: activated and quiescent cT_FH1-like (MC06 and MC07); Group 3: activated cT_FH1/17-like (MC11) and cT_FH17-like (quiescent MC10 and activated MC13). For children, PfSEA-1A and PfGARP induced robust IL-4 expression in 9 out of 12 cT_FH meta-clusters (p-values ≤ 0.0105); although the composition of which cT_FH subsets were engaged differed slightly by antigen (quiescent cT_FH1or2-like ICOS^low MC05 versus quiescent cT_FH2-like MC02, respectively). In contrast to IL-4, we observed no change in expression for IFNγ or IL-21 after in vitro antigen stimulation (except for quiescent cT_FH1or2-like ICOS^high MC08, p- value=0.0391), suggesting that these cytokines are not informative to define the development of antigen-specific cT_FH subsets in children.

Figure 6:

We found that PfSEA-1A (Figure 6a) and PfGARP (Figure 6b) induced similar Bcl6 and cMAF expression profiles from some of the same cT_FH subsets: both transcription factors were expressed by activated cT_FH2-like MC01 and quiescent cT_FH17-like MC10, but only cMAF was expressed in activated and quiescent cT_FH1-like MC06, MC07, and activated cT_FH1/17-like MC11 subsets. However, PfSEA-1A induced Bcl6 and cMAF from activated PD1^highMC03, whereas PfGARP only induced Bcl6. In contrast, the quiescent cT_FH2-like MC02 subset did not seem to respond to PfSEA-1A (only cMAF was significant, p=0.0266, Figure 6a, Supplemental Figures 10 and 11), whereas PfGARP stimulation induced significantly more IL-4, Bcl6, and cMAF compared to unstimulated cells (p=0.0081, p=0.0017, and p=0.0081, respectively, Figure 6b, Supplemental Figures 10 and 11). We then compared the response intensity between PfSEA-1A and PfGARP stimulations and found significant differences in IFNγ, IL-21, Bcl6, and cMAF expression levels (Figure 6c, 6d, 6e, and 6f, respectively). In Group 1, Bcl6 expression was significantly higher within activated and quiescent cT_FH2-like subsets (MC01 and MC02) as well as activated PD1^high cT_FH1or2-like (MC03) cells after PfGARP compared to PfSEA-1A stimulation (p=0.0266, p=0.0134 and p=0.0327, respectively, Figure 6e). Within Group 2, IFNγ and Bcl6 were highly expressed by activated cT_FH1-like (MC06) after PfGARP stimulation compared to PfSEA-1A stimulation (p=0.0093 and p=0.0024, respectively, Figure 6c and 6e). Finally, within Group 3, the activated cT_FH1/17-like cells (MC11) expressed higher cMAF after PfSEA-1A stimulation compared to PfGARP (p=0.0186, Figure 6f). Interestingly, PfGARP induced significantly more IL-21 within the quiescent ICOS^low cT_FH1or2-like (MC05) meta-cluster compared to PfSEA-1A stimulation (p=0.0186, Figure 6d). Non-significant differences in cytokines and transcription factors expressed by cT_FH subsets between conditions are shown in Supplemental Figures 12 and 13, respectively.

PfGARP induced IL-4, Bcl6, and cMAF expression in activated cT_FH1/17- and cT_FH17-like subsets in adults

A similar heatmap was generated for adult expression profiles comparing PfSEA-1A and PfGARP stimulated to unstimulated cells (Figure 7). Here, we found that both PfSEA-1A and PfGARP induced significant expression of both IFNγ and IL-4 for activated (MC01) and quiescent (MC02) cT_FH2-like cells (Figure 7a and 7b, Supplemental Figure 12). Whereas PfGARP also induced IL-4 and IFNγ expression from quiescent and activated cT_FH17-like cells (MC10 and MC13), in addition to Bcl6 and cMAF for MC13 (Figure 7b, Supplemental Figure 13). This observation was surprising because IFNγ expression is commonly used to categorize the cT_FH1 subset (Group 2); however, as shown earlier, quiescent and activated cT_FH17-like cells (MC10 and MC13) did not express CXCR3 (Figure 3d and 3e). In contrast, activated the cT_FH1/17-like cells (MC11) only responded to PfGARP (Figure 7b, Supplemental Figure 12), expressing higher levels of IL-4, IL-21, Bcl6, and cMAF. Finally, while assessing the differences between the two malaria antigens, we found that PfGARP induced more Bcl6 expression than PfSEA-1A within the quiescent cT_FH2-like subset (MC02, p=0.0327) and the quiescent ICOS^low cT_FH1or2-like cells (MC05, p=0.0105), as well as within the quiescent cT_FH17-like subset (MC10, p=0.0479) and the activated cT_FH1/17-like cells (MC11, p=0.0020) (Figure 7d). The MC11 cells also expressed higher IL-21 levels after PfGARP compared to PfSEA-1A stimulation (p=0.0273, Figure 7c); whereas PfSEA-1A induced cMAF within the activated cT_FH2-like subset (MC01, p=0.0134) but PfGARP did not (Figure 7e). Overall, the main observation for adults is that PfSEA-1A predominantly induced IL-4 from slightly more than half of the cT_FH clusters, whereas PfGARP induced a broader range of cytokines and transcription factors but within the activated cT_FH1/17-like cells (MC11) and quiescent and activated cT_FH17-like subset (MC10 and MC13) similar to children. This analysis shows clear differences in cT_FH subset specificity by malaria antigen and cT_FH subset engagement by age group, with the cT_FH repertoire becoming more restricted in adults compared to children.

Figure 7:

The activated cT_FH1or2-like subset is more abundant in participants with high anti-PfGARP antibodies

As shown in Figure 1b, a broad range of anti-PfGARP IgG antibody levels were found in both children and adults. Thus, we wanted to determine whether the abundance of any of the cT_FH subsets was associated with the level of anti-PfGARP IgG antibodies. When stratifying by high versus low anti-PfGARP IgG antibody levels, we found that an activated cT_FH1or2-like subset (MC04) was more abundant in participants with high levels of anti-PfGARP IgG for both children (Figure 8a) and adults (Figure 8b) after PfGARP stimulation compared to participants with low levels or an absence of anti-PfGARP IgG. However, even though the p- values were significant for both children and adults (p=0.02 and p=0.004, respectively), the FDR was less than 0.05 only for the adults (FDR=0.018). This suggests that this particular subset might be important for the generation of anti-PfGARP antibodies. Interestingly, activated cT_FH1-like (MC06) and quiescent cT_FH1-like (MC07) cells were more abundant for adults with low or no anti-PfGARP IgG antibodies compared to those with high levels (p=0.0024 with FDR=0.0185 and p=0.0034 with FDR=0.0185, respectively) consistent with previous observations describing T_FH1 subsets as inefficient help for antibody production²⁹.

Figure 8:

Discussion

The overall aim of this study was to define cT_FH subsets using unbiased clustering analysis and assess their malaria antigen-specific (PfSEA-1A and PfGARP)^6,7,39 profiles for adults and children residing in a malaria-holoendemic area of Kenya. Contrary to previous publications,^29,30 our study found that children not only respond via their cT_FH1-like subsets but also engage a broader spectrum of cT_FH subsets. In fact, cytokine and transcription factor profiles for children involved cT_FH1-, cT_FH2-, cT_FH17-, and cT_FH1/17-like subsets (summarized in Figure 9), whereas in adults, the dominant antigen-specific memory response was from cT_FH17- and cT_FH1/17-like subsets but only for PfGARP. Thus, revealing a potential difference in engaging cT_FH help between the two malaria vaccine candidates evaluated in this study.

Figure 9:

Several key points can be made from our results. First, it may be too soon in the field of cT_FH biology to select only one or two types of cT_FH subset(s) to measure when evaluating malaria vaccine candidates, especially if we miss essential, albeit transient, correlates of protection for children which may differ in adults. Longitudinal studies with clinical outcomes are needed to determine the impact of the changing dynamics of cT_FH subset adaptations after repeated malaria infections that lead to premunition and parasite clearance. A Ugandan study by Chan et al.³¹ demonstrated an age-associated change in cT_FH subsets independent of malaria and, thus, supports the importance of accounting for age when evaluating antigen-specific cT_FH profiles. Second, our study reveals that adults have a robust cT_FH17- and cT_FH1/17-like subset response to PfGARP but not to PfSEA-1A. In addition to demonstrating differences between malaria antigens, this finding supports the premise that cT_FH17 cells are important for maintaining immunological memory,⁴⁰ which has intriguing implications for evaluating malaria vaccine efficacy. Third, we found a correlation between cT_FH1or2-like (MC04) and high PfGARP antibodies for both children and adults, indicating that a cT_FH subset accompanied by high antibody levels could serve as a potential biomarker of protection. More studies are needed to explore this association; however, we postulate that a coalition of cT_FH subsets might be engaged to develop long-lived antibody responses and, therefore, categorizing subsets as efficient or inefficient might be context dependent.²⁹

As the field of computational immunology and unbiased clustering analyses evolves, it presents an ongoing challenge to meaningfully define cT_FH subsets. Here, we intentionally chose to use cT_FH1or2-like nomenclature for meta-clusters MC03, MC04, MC05, and MC08 (Figure 9) because of their low (but not null) CXCR3 and robust IL-4 expression along with Bcl6 and cMAF). CXCR3 expression leans toward a cT_FH1-like polarization, whereas IL-4 expression indicates a cT_FH2-like profile when we follow the most commonly used cT_FH classification.^17,21,22,33 This difference is crucial as cT_FH2 cells are described as good promoters for functional GC antibodies^28,30, whereas cT_FH1 cells are not.^29,30,41 Therefore, their definitive classification will require a more in-depth investigation. Although we used malaria antigen-specific stimulation, and not a Pf-lysate or infected red blood cells (iRBCs) as previously described,^29–31 adults had significantly more of the quiescent cT_FH2-like subset (MC02) compared to children who, instead, had significantly more of the activated cT_FH1- like subset (MC06), the latest being consistent with previous publications,^29,30 and more PD1^high activated and ICOS^high quiescent cT_FH1or2-like subsets (MC03 and MC08, respectively). However, even when classifying the cT_FH meta-clusters as cT_FH1, cT_FH2, cT_FH17, or cT_FH1/17, the most abundant cT_FH subset was cT_FH2 cells for adults (more than 50%) contradicting a previous study showing only ∼20% of cT_FH2 cells for the same age range.³¹ As suggested by Gowthaman’s T_FH model,²⁷ T_FH1 cells are involved in the development of neutralization antibody responses to viruses and bacteria, whereas parasites, such as helminths, lead to a T_FH2 response. These observations reinforce the need to assess T_FH subset profiles stratified by exposure to potentially immune-modulating co-infections.

Transcription factors cMAF and Bcl6 play essential roles in T_FH development and function.^24,42,43 cMAF induces the expression of various molecules, such as ICOS, PD1, CXCR5, IL-4, and IL-21, which are all essential for T_FH function.⁴² It was, therefore, not surprising to find that cMAF significantly increased after antigen stimulation for most of the cT_FH subsets in children. However, again, this expression profile was only observed in the activated cT_FH1/17-like subset (MC11) after PfGARP stimulation in cells from adults, supporting a role for T_FH17 cells in the maintenance of immunological memory.⁴⁰ Importantly, cMAF cooperates with Bcl6 in T_FH development and function and is essential to establish an efficient GC response.^24,44 Bcl6 is also described as being expressed by mature T_FH cells, inducing the expression of CXCR5 and PD1, but it can also regulate IFNγ and IL-17 production.^12,24,44,45 In our study, PfGARP induced a highly significant increase of Bcl6 in cT_FH2-like and cT_FH17-like subsets in children, suggesting that PfGARP may be a better candidate to trigger an efficient humoral response in children compared to PfSEA-1A. Of note, Blimp1, a Bcl6 antagonist,⁴⁵ was absent from our flow panel and would be of interest to assess in future studies.

There are several limitations of human immune profiling studies. Similar to other such studies, we used peripheral blood and, thus, were only able to provide a snapshot of the cT_FH cells. Study participants did not have blood-stage malaria infections at the time of blood collection, and children were 7 years of age; thus, our profiles were by design meant to reflect cT_FH memory recall responses and demonstrate differences between children and adults. As this was not a birth cohort study design, the number of cumulative malaria infections could have confounded the association between malaria and cT_FH subsets, causing a certain cT_FH meta-cluster to arise. Most human immunology studies are unable to assess tissue-resident T_FH or T_FH in the lymph nodes. Therefore, we can only speculate on the composition of T_FH cells observed in children that were perhaps short-lived helper cells and not maintained as T cell memory cells or may have trafficked from the blood into tissues over time.⁴⁶ Using an in vivo non-human primate model, Potter et al., showed an entry rate of lymphocyte subsets into peripheral lymph nodes per hour of 1.54% and 2.17% for CD4^pos central memory (CD45^negCCR7^pos) and effector memory (CD45^negCCR7^neg) T cells, respectively,⁴⁶ both of which may include central and effector memory cT_FH cells. With this in mind, cT_FH cells seem to be at a crossroads with multiple possible fates. The first one being the recirculation of cT_FH cells from lymph node to lymph node, whereby a peripheral blood sampling captures migratory cT_FH subsets, which could explain the cT_FH subsets we observed still expressing cMAF and Bcl6. A decade ago, it was hypothesized that once a T_FH cell leaves the lymph node, it can become a PD1^low memory cT_FH cell with the possibility of returning to a GC T_FH stage after a secondary recall response.¹² Other possible fates include becoming a PD1^neg memory cT_FH with the same path after recall or progressing to a non-T_FH cell.¹² More recently, a few studies showed circulating CXCR5^negCD4^pos cells with T_FH functions, such as production of IL-21 and the capability to help B cells in systemic lupus erythematosus and HIV-infected individuals,^47,48 and importantly researchers mapped these circulating CXCR5^negCD4^pos cells to an original lymph node CXCR5^pos T_FH subset,⁴⁸ suggesting another possible outcome for T_FH cells outside secondary lymphoid organs. To date, no clear destiny has been established for cT_FH cells in humans, thus, highlighting the importance of including a complete panel of cT_FH subsets to continue to improve our understanding of their respective roles against different pathogens and eliciting long-lived vaccine-induced antibody responses.

Other limitations of this study include not measuring other cytokines (i.e., IL-5, IL-13, and IL-17) and transcription factors (i.e., T-bet, BATF, GATA3, and RORγt) that have been used in other studies to fully characterize cT_FH subsets^12,27. Although the heterogeneity in the response of CD40L and IFNγ suggests that our tested malaria antigens did not induce significant differences in the expression of these markers in all our participants, our panel did not include other activated induced markers, such as OX40, 4-1BB, and CD69. However, even with our small sample size, we demonstrated significant age-associated differences in malaria antigen-specific responses from different cT_FH subsets. To minimize false-positive results that can arise when using algorithms for computational analyses, we ran the statistical tests in triplicate and the clustering algorithm in duplicate, to validate our findings.

In summary, our study provides additional justification for the resources needed to conduct cellular immunological studies of cT_FH cell signatures and provides insight into which types of cT_FH subsets during a person’s lifespan assist B cells in the GC to produce long-lived plasmocytes and functional antibodies⁴⁹ against malaria. This is particularly important when selecting immune correlates of protection that could be used to predict the efficacy of the next generation of Pf-malaria vaccine candidates within various study populations.

Methods

Study populations and ethical approvals

Adults and children were recruited from Kisumu County, Kenya, which is holoendemic for Pf-malaria. Written informed consent was obtained from each adult participant and every child’s guardian. An abbreviated medical history, physical examination, and blood film were used to ascertain health and malaria infection status at the time of blood sample collection. Participants were also life-long residents of the study area with an assumption that they naturally acquired immunity to malaria. This study was conducted before the implementation of any malaria vaccines. Participants were eligible if they were healthy and not experiencing any symptoms of malaria at the time venous blood was collected. For this cross-sectional immunology study, we selected fourteen 7-year-old children from a larger age-structured prospective cohort study (enrollment age range 3–7 years) and fifteen Kenyan adults. We selected 7-year-olds because of the age-dependent shift in major cT_FH subsets occurring after 6 years of age³¹, and as a comparable age published by other studies²⁹, to maximize our ability to measure antigen-specific differences in T_FH subsets.

Ethical approvals were obtained from the Scientific and Ethics Review Unit (SERU) at the Kenya Medical Research Institute (KEMRI) reference number 3542, and the Institutional Review Board at the University of Massachusetts Chan Medical School, Worcester, MA, USA, IRB number H00014522. Brown University, Providence, RI, USA signed a reliance agreement with KEMRI.

Plasma and peripheral blood mononuclear cell (PBMC) isolation

Venous blood was collected in sodium heparin BD Vacutainers and processed within 2 hours at the Center for Global Health Research, KEMRI, Kisumu. Absolute lymphocyte counts (ALC) were determined from whole blood using BC-3000 Plus Auto Hematology Analyzer, 19 parameters (Shenzhen Mindray Bio-Medical Electronics Co.). After 10 mins at 1,000g spin, plasma was removed and stored at −20 °C, and an equivalent volume of 1X PBS was added to the cell pellet. PBMCs were then isolated using Ficoll-Hypaque density gradient centrifugation on SepMate (StemCell). PBMCs were frozen at 5×10⁶ cells/ml in a freezing medium (90% heat-inactivated and filter-sterilized fetal bovine serum [FBS] and 10% dimethyl sulfoxide [DMSO, Sigma]) and chilled overnight in Mr. Frosty™ containers at −80 °C before being transferred to liquid nitrogen. For transport to the USA, an MVE vapor shipper (MVE Biological Solutions) was used to maintain the cold chain.

In-vitro stimulation assay

PBMCs were thawed in 37 °C filtered-complete media (10% FBS, 2 mM L-Glutamine, 10 mM HEPES, 1X Penicillin/Streptomycin) and spun twice before resting overnight in a 37 °C, 5% CO₂ incubator. PBMCs were counted using Trypan Blue (0.4%) and a hemocytometer, and the cell survival was calculated. Our samples showed a median of 94.6% live cells (25% percentile of 92%; 75% percentile of 97%). Using a P96 U-bottom plate, 1×10⁶ PBMCs per well were placed in culture with one of the following stimulation conditions: PfSEA-1A⁶ (5 µg/ml) or PfGARP⁷ (10 µg/ml) both produced in the Kurtis lab (Brown University); SEB (1 µg/ml; EMD Millipore) was used as a positive control; sterile water (10 µl, the same volume used to reconstitute PfSEA and PfGARP) was used as a negative control. A pool of anti-CD28/anti-CD49d (BD Fast-Immune Co-Stim following the manufacturer’s instructions), GolgiSTOP (0.7 µg/ml), and GolgiPLUG (0.1 µg/ml) (BD Biosciences) were added to each well before incubating cells at 37 °C for 6 hours.

Cell staining and flow cytometry

A multiparameter spectral flow cytometry panel was used to characterize cT_FH cell subsets: CCR6-BV421 (RRID: AB_2561356), CD14-Pacific Blue (RRID: AB_830689), CD19-Pacific Blue (RRID: AB_2073118), CCR7-BV480 (RRID: AB_2739502), IFNγ-BV510 (RRID: AB_2563883), CD127-BV570 (RRID: AB_10900064), CD45RA-BV605 (RRID: AB_2563814), PD1-BV650 (RRID: AB_2738746), CXCR3-BV711 (RRID: AB_2563533), CD25-BV750 (RRID: AB_2871896), CXCR5-BV785 (RRID: AB_2629528), Bcl6-AF488 (RRID: AB_10716202), CD3-Spark Blue 550 (RRID: AB_2819985), CD8-PerCP-Cy5.5 (RRID: AB_2044010), IL-21-PE (RRID: AB_2249025), IL-4-PE-Dazzle (RRID: AB_2564036), CD4-PE-Cy5 (RRID: AB_314078), ICOS-PE-Cy7 (RRID: AB_10643411), cMAF-eFluor 660 (RRID: AB_2574388), CD40L-AF700 (RRID: AB_2750053), and Zombie NIR (BioLegend cat# 423106) for Live/Dead staining. Cells were fixed and permeabilized for 45 mins using the transcription factor buffer set (BD Pharmingen) followed by a wash with the perm-wash buffer. Intracellular staining was performed at 4 °C for 45 more mins followed by two washes using the kit’s perm-wash buffer. Data was acquired on a Cytek Aurora with 4-lasers (UMass Chan Flow Core Facility) using SpectroFlo® software (Cytek) and compensation for unmixing and fluorescence-minus-one controls. Quality control of the data was performed using SpectroFlo®, and the multi-parameter analysis was performed with OMIQ data analysis software (www.omiq.ai). Thus, we assessed the expression of markers commonly used to define the following different cT_FH (CD4^posCD25^negCXCR5^pos) subsets: cT_FH1-like (CCR6^negCXCR3^pos), cT_FH2-like (CCR6^negCXCR3^neg), and cT_FH17-like (CCR6^posCXCR3^neg),¹⁷ as well as quiescent/central memory cT_FH (CCR7^highPD-1^negICOS^neg) or activated/effector memory cT_FH cells (CCR7^lowPD-1^posICOS^pos).^17,18 Representative cytoplots can be found in Supplemental Figure 1.

Multiplex suspension bead-based serology assay

To measure plasma IgG antibody levels to PfSEA-1A⁶ and PfGARP⁷, we used a Luminex bead-based suspension assay as previously published.^50,51 In addition, previous Pf exposure was determined using recombinant proteins to blood-stage malaria antigens: AMA1, MSP1, HRP2, CelTos, and CSP (gifts from Sheetji Dutta, Evelina Angov, and Elke Bergmann from the Walter Reed Army Institute of Research). Briefly, 100 μg of each antigen or BSA (Sigma), as a background control, were coupled to ∼12×10⁶ non-magnetic microspheres (Bio-Rad carboxylated beads) and then incubated with study participant plasma (spun down 10,000g for 10 mins and diluted at 1:100 in the assay dilution buffer) for 2 hrs, followed by incubation with biotinylated anti-human IgG (BD #555785) diluted 1:1000 for 1 hr and streptavidin (BD #554061) diluted 1:1000 for 1 hr following the manufacturer’s instructions. The mean fluorescence intensity (MFI) of each conjugated bead (minimum of 50 beads per antigen) was quantified on a FlexMap3D Luminex multianalyte analyzer (Xponent software). Results are reported as antigen-specific MFI after subtracting the BSA value for each individual since background levels can vary between individuals.

OMIQ analysis

The fcs files were uploaded into the OMIQ platform after passing quality control under SpectroFlo® (Cytek) where compensation was re-checked. In OMIQ, we arcsinh-transformed the scale to allow downstream analysis and then gated on singlet live lymphocytes and sub-sampled the data to yield 87,712 live lymphocytes per sample. Using only lineage markers CD3, CD4, CD8, CD14, CD19, CXCR5, and CD25, FlowSOM consensus meta-clustering was run on 100 clusters based on the 87,712 live lymphocytes per sample with a comma-separated k-value of 75 and Euclidean distance metric. Using these 75 meta-clusters, we defined subsets of cells based on lineage markers, such as CD3^posCD8^pos, CD3^negCD14^posCD19^pos, and CD3^posCD4^pos, and then distinguished CD4^posCXCR5^posCD25^neg (cT_FH) and CD4^posCXCR5^negCD25^pos (T regulatory [T_reg] or T follicular regulatory [T_FR]) subsets. CD25 was used to exclude T_reg and T_FR cells which share numerous markers with cT_FH cells.^52–54 EmbedSOM dimensional reduction was used to visualize the different groups of cells and EdgeR analysis was run to assess the significance of their differences. A clustering heatmap was used to visualize cytokine expression and transcription factor profiles for each subset.

Focusing on the CD4^posCXCR5^posCD25^neg T_FH cells, we ran another FlowSOM analysis based on the 1,000 CXCR5^pos cells per sample (two samples from the adult group and one sample from the children group were excluded from the analysis as they had less than 1,000 CXCR5^pos cells), using extracellular markers CXCR5, CXCR3, CCR6, ICOS, CCR7, CD45RA, CD127, CD40L, and PD1 enabled the identification of 15 meta-clusters. If the fcs file had more than 1,000 CXCR5^pos cells, the down-sampling was done randomly by the OMIQ platform algorithm to select only 1,000 CXCR5^pos cells within this specific fcs file. From there, we performed Uniform Manifold Approximation and Projection (UMAP) dimensional reduction, heatmaps, and EdgeR analyses; the latter allowed statistical analysis of the cT_FH abundances. To demonstrate the reproducibility of these results, statistical analysis algorithms were run at least three times downstream of the same clustering algorithm and downstream of repeated clustering algorithms. To assess statistical differences in cytokines and transcription factor expression, we exported the statistics dataset from OMIQ containing MFI values from each marker (IFNγ, IL-4, IL-21, Bcl6, and cMAF) per cluster and for each sample and stimulation condition. To assess cytokines and transcription factors without bias, we chose to use the total MFI expression per meta-cluster with the assumption that cells with increased production of the desired analyte trigger an increase in the overall meta-cluster MFI compared to unstimulated cells, and if there is no production of the desired analyte, the overall MFI will not differ. However, the percentage of positive IFNγ, IL-4, IL-21, Bcl6, or cMAF using manual gating can be found in Supplemental Figures 14, 15, and 16 along with the overlay of the gated positive cells on the CD4^posCXCR5^posCD25^neg UMAP and the cytoplots of the gated positive cells for each meta-cluster (Supplemental Figures 14, 15, and 16).

Statistical analysis

For this cross-sectional immunology study, we selected both male and female study participants (sex defined at birth). There were fourteen 7-year-old children and fifteen adults. Using GraphPad Prism software (version 7.0), age, sex, ALC, and serological data were compared between adults and children. Because the number of participants within each group was too low to verify the normality of the underlying distributions (adults n=15 and children n=14), we chose to use non-parametric tests, including the Mann-Whitney U test (for unpaired analysis) and Wilcoxon signed-rank test (for paired analysis). When data passed the normality test (D’Agostino and Pearson test), we used Welch’s parametric test. All tests were two-tailed with a p-value < 0.05 for significance. Because of the exploratory nature of the analysis, we did not use any adjustment of the p-value for multiple comparisons. The tests used are indicated in the legend of each figure. Results were expressed as the mean with standard deviation and exact p-values for dot plots.

To perform a statistical analysis of the cytokine and transcription factor expression from each cT_FH subset, the exported data file from OMIQ was integrated into GraphPad Prism, and a non-parametric Wilcoxon paired two-tailed t-test analysis was done (n=13 in each group). As this analysis generated numerous bar plots (all included in the Supplemental Figures 12 to 15), to better visualize the cytokines and transcription factor patterns, the p- values obtained from each analysis are presented using a non-clustering heatmap.

Role of the funding source

The funders were not involved in the study design, sample collection, data analysis, interpretation of the data, or the writing of the manuscript and its decision to be submitted for publication.

Data availability

Deidentified raw data from this manuscript are available from the ImmPort platform under the accession study number SDY2534.

Acknowledgements

The authors would like to thank the children and their families for participating in this study. We thank the field and lab teams for their work collecting data and processing blood samples. We also thank Dr. Melanie Trombly from UMass Chan for proofreading our manuscript. This manuscript was approved for publication by KEMRI.

This study was supported by NIH R01 AI127699 (Kurtis).

Additional information

Contributors

CN, JMO, JK, and AMM designed the research study; CF and SPT conducted experiments; CF, JK, and AMM analyzed data; CF, SPT, JK, and AMM contributed to experimental design; HWW, BO, JMO, JK, and AMM conducted and supervised the clinical study and the sampling; CF generated figures; HWW and BO verified underlying data; CF and AMM led manuscript preparation with feedback from all the authors. All authors have read and approved the final version of the manuscript.

Additional files

Supplemental figures 1 to 16