Tick hemocytes have a pleiotropic role in microbial infection and arthropod fitness

Rolandelli, Agustin; Laukaitis-Yousey, Hanna J.; Bogale, Haikel N.; Singh, Nisha; Samaddar, Sourabh; O’Neal, Anya J.; Ferraz, Camila R.; Butnaru, Matthew; Mameli, Enzo; Xia, Baolong; Mendes, M. Tays; Butler, L. Rainer; Marnin, Liron; Cabrera Paz, Francy E.; Valencia, Luisa M.; Rana, Vipin S.; Skerry, Ciaran; Pal, Utpal; Mohr, Stephanie E.; Perrimon, Norbert; Serre, David; Pedra, Joao H. F.

doi:10.1038/s41467-024-46494-3

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Published: 08 March 2024

Tick hemocytes have a pleiotropic role in microbial infection and arthropod fitness

Nature Communications volume 15, Article number: 2117 (2024) Cite this article

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Abstract

Uncovering the complexity of systems in non-model organisms is critical for understanding arthropod immunology. Prior efforts have mostly focused on Dipteran insects, which only account for a subset of existing arthropod species in nature. Here we use and develop advanced techniques to describe immune cells (hemocytes) from the clinically relevant tick Ixodes scapularis at a single-cell resolution. We observe molecular alterations in hemocytes upon feeding and infection with either the Lyme disease spirochete Borrelia burgdorferi or the rickettsial agent Anaplasma phagocytophilum. We reveal hemocyte clusters exhibiting defined signatures related to immunity, metabolism, and proliferation. Depletion of phagocytic hemocytes affects hemocytin and astakine levels, two I. scapularis hemocyte markers, impacting blood-feeding, molting behavior, and bacterial acquisition. Mechanistically, astakine alters hemocyte proliferation, whereas hemocytin affects the c-Jun N-terminal kinase (JNK) signaling pathway in I. scapularis. Altogether, we discover a role for tick hemocytes in immunophysiology and provide a valuable resource for comparative biology in arthropods.

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Introduction

The evolution of arthropod immune systems is shaped by the environment, longevity, nutrition, microbial exposure, development, and reproduction^1,2. Yet, our current understanding of immunology is centered around Dipteran insects, which reflects an incomplete depiction of arthropod biological networks existing in nature^{3,4,5,6,7,8,9}. Divergent signaling modules prevail between Dipterans and other arthropods, highlighting the complex and dynamic features occurring across evolutionarily distant species. One example is the immune deficiency (IMD) pathway, a network analogous to the tumor necrosis factor receptor (TNFR) in mammals¹⁰. Some components of the canonical IMD pathway are not observed in arachnids, although this immune pathway remains functional and responsive to microbial infection^3,4,5,6,7. Another instance is the Janus Kinase/Signal Transducer and Activator of Transcription (JAK/STAT) signaling pathway, which in flies is elicited by unpaired cytokine-like molecules¹¹, whereas in ticks host-derived interferon (IFN)-γ facilitates Dome1-mediated activation^8,9. This biochemical network enhances tick blood meal acquisition and development while inducing the expression of antimicrobial components^8,9. Thus, investigating the plasticity of immune networks in evolutionarily divergent organisms may reveal discrete aspects of arthropod biology.

Hard ticks are ancient arthropods that serve as major vectors of human and animal pathogens, including the Lyme disease spirochete Borrelia burgdorferi and the rickettsial agent Anaplasma phagocytophilum¹². Selective pressure caused by longevity, exclusive hematophagy in all life stages, long-term dispersal (adaptation to multiple environments) and exposure to an array of microbes make ticks distinct from other hematophagous arthropods^13,14. While it is acknowledged that the immune system plays an important role in vector competence, our understanding of the mechanisms by which immune resistance balances microbial infection in ticks remains fragmented^5,6,7,8,9.

Hemocytes are specialized arthropod immune cells that function in both cellular and humoral responses. These cells circulate through the hemolymph and are in contact with tissues within the arthropod body cavity¹⁵. Historically, tick hemocytes have been categorized according to their cellular morphology and ultrastructural features^16,17,18. Although useful, this classification is incomplete because the ontogeny, plasticity, and molecular features of hemocytes during hematophagy and infection remain ill-defined. Here, we used bulk and single-cell RNA sequencing (scRNA-seq) coupled with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) activation, RNA interference (RNAi), RNA-fluorescence in situ hybridization (RNA-FISH), and immune cell depletion through treatment with clodronate liposomes to reveal distinct features of hemocyte clusters in the blacklegged tick. We identify marker genes for each hemocyte cluster, predict lineages, and unveil specific biological signatures related to immunity, proliferation, and metabolism during hematophagy. We further characterize an immune cluster that expresses hemocytin (hmc) and astakine (astk). Manipulation of phagocytic hemocytes and the expression of hmc or astk affects bacterial acquisition, feeding and molting. Overall, we highlight the exquisite biology of ticks, demonstrate the canonical and non-canonical roles of immune cells in an obligate hematophagous ectoparasite, and provide a critical resource for comparative biology of arthropods.

Results

Blood-feeding induces molecular signatures in I. scapularis hemocytes related to immunity, metabolism, and proliferation

Ticks rely solely on blood as a source of essential metabolites, ingesting ~100 times their body mass per meal¹⁹. During feeding, extensive modifications and tissue rearrangements are necessary to accommodate and digest such large volumes of blood¹⁹. Considering that hemocytes are the circulating cells in the hemolymph, we hypothesized that these immune cells sense and respond to physiological and microbial exposure during tick infestation on vertebrate hosts. We optimized a protocol to collect hemocytes from I. scapularis nymphs, a clinically relevant stage in the blacklegged tick, and identified three common hemocyte morphotypes reported in the literature (Fig. 1a, Supplementary Fig. 1)^16,17,18. Prohemocytes, considered the stem cell-like hemocyte population, were the smallest cells, with a round or oval shape between 5.7 and 12.5 µm (average diameter of 8.5 µm). The cytoplasm was minute (high nuclear/cytoplasmic ratio) and homogeneous, with no apparent protrusions or granules (Supplementary Fig. 1a, d). Granulocytes were round or oval shaped cells with diameters ranging from 10.4 to 22.8 µm (average of 16.7 µm). The position of the nucleus varied, appearing most often near the periphery of the cell. Their cytoplasm was filled with dark-blue or violet stained granules or vacuoles (Supplementary Fig. 1b, d). Plasmatocytes varied in size (ranged from 14.8 to 31.2 µm, with an average of 21.3 µm) and shape (oval, ameboid-like, pyriform), and had cytoplasmic protrusions or pseudopodia-like structures. The cytoplasm was clear and had few dark-blue or violet stained granules or vacuoles. The nucleus was located either near the center or the periphery of the cell (Supplementary Fig. 1c, d). We then investigated the impact of blood-feeding on hemocytes originating from I. scapularis nymphs. We observed an increased quantity of total hemocytes upon mammalian feeding (Fig. 1b). The percentage of plasmatocytes increased in engorged ticks, whereas we noticed a decline in the proportion of prohemocytes and granulocytes in repleted nymphs compared to unfed (Fig. 1b). Altogether, we demonstrate the impact of hematophagy on the distribution of tick hemocyte morphotypes.

**Fig. 1: Blood-feeding induces alterations in *I. scapularis* hemocytes.**

Next, we aimed to examine global transcriptional changes induced by hematophagy through bulk RNA-seq. We collected hemocyte-enriched samples from unfed and engorged nymphs and observed drastic changes in gene expression, with a total of 6134 differentially expressed genes (DEGs) (Fig. 1c; Supplementary Data 1). Samples from unfed I. scapularis nymphs were enriched for housekeeping genes, including those involved in mRNA transcription (e.g., RNA splicing, mRNA processing, histone modification), protein synthesis (e.g., peptide biosynthetic process, cellular protein modification process, ribosome biogenesis) and membrane receptor signaling pathways (e.g., transmembrane signaling receptor activity, G protein-coupled receptor signaling pathway, protein kinase activity) (Fig. 1c; Supplementary Data 2). In contrast, hemocyte-enriched samples from engorged ticks exhibited an overrepresentation of gene signatures related to immunity, metabolic pathways, cell proliferation/growth, and arthropod molting/development (Fig. 1c; Supplementary Fig. 2; Supplementary Data 2). Key genes modulated during feeding, including 4-coumarate-CoA ligase 1 (4cl1), adenosylhomocysteinase B (ahcy), glucose-6-phosphatase 2 (g6pc), inosine-5’-monophosphate dehydrogenase 1 (impdh), Brahma chromatin remodeling complex subunit osa (osa), runt-related transcription factor 1 (runx) and frizzled-5 (frizzled), were independently validated using RT-qPCR (Fig. 1d; Supplementary Fig. 3). Notably, the overall expression levels of these genes in hemocyte-enriched samples from partially fed ticks were intermediate compared to those in unfed and engorged ticks, suggesting a transitional phenotype. Collectively, this dataset indicated that I. scapularis hemocytes exhibit a dynamic genetic program during hematophagy.

Defining I. scapularis hemocyte clusters by scRNA-seq

To uncover whether the transcriptional changes observed through bulk RNA-seq are accompanied with heterogeneity among hemocytes, we performed scRNA-seq. We collected hemocyte-enriched samples from (1) unfed nymphs, (2) engorged nymphs fed on uninfected mice, and engorged nymphs fed on mice infected with either (3) the rickettsial pathogen A. phagocytophilum or (4) the Lyme disease spirochete B. burgdorferi. After stringent quality controls, we profiled a total of 20,432 cells (unfed = 4630; engorged uninfected = 6000; engorged A. phagocytophilum-infected = 6287; engorged B. burgdorferi-infected = 3515), with a median of 744 unique molecular identifiers (UMIs), 261 genes and 6.2% of mitochondrial transcripts per cell across conditions (Supplementary Fig. 4). Consistent with the bulk RNA-seq results, the principal component analysis (PCA) identified distinct distributions between unfed and fed conditions, reinforcing the notion that significant cellular and/or transcriptional changes occur following a blood meal (Supplementary Fig. 5).

Following unsupervised clustering, we identified seven clusters in unfed ticks and thirten clusters in engorged nymphs (Supplementary Fig. 6). Based on similarities in marker gene profiles, two clusters in the unfed and two clusters in the engorged datasets were merged (Supplementary Data 3, 4). Thus, six and twelve clusters remained, respectively, with each cluster expressing a unique set of cell type-defining genes (Fig. 2a–c, Supplementary Data 5–7). One cluster in each dataset showed high expression of gut-associated genes cathepsinB, cathepsinD and boophilinH2 (Fig. 2a–c, Supplementary Data 5–7) and two additional clusters in the engorged dataset had gene expression profiles indicative of cuticle and salivary glands tissues (Fig. 2a–c, Supplementary Data 5 and 7). Therefore, these clusters were excluded from subsequent analysis. We assigned putative functions to the top 50 DEGs per cluster using information for tick genes in VectorBase, the sequence homology to D. melanogaster in FlyBase and the presence of functionally annotated protein domains in InterPro (Fig. 2c–e, Supplementary Data 6, 7). We also performed functional enrichment analysis based on gene ontology (GO) using the entire list of DEGs for each cluster (Supplementary Data 8, 9). Altogether, we defined five hemocyte clusters from unfed and nine clusters in engorged I. scapularis nymphs.

**Fig. 2: scRNA-seq uncovers hemocytes with immune, proliferative and metabolic signatures in *I. scapularis*.**

scRNA-seq uncovers hemocyte adaptations to hematophagy in I. scapularis

The molecular features and differentiation process of I. scapularis hemocytes is currently unknown. Thus, based on GO enrichment and marker gene profiles, we were able to characterize clusters of hemocytes shared by both unfed and engorged ticks (Supplementary Figs. 7, 8, Supplementary Data 5–7). The Immune 1 cluster showed high expression of genes related to phagocytosis or cytoskeleton organization; coagulation and agglutination functions, such as lectins (hemocytin, techylectin-5A), chitin-binding and clotting proteins; and secreted proteins related to immunity, such as astakine, microplusin, mucins and cystatin domain peptides (Fig. 2c–e, Supplementary Data 5–7). The Immune 2 cluster displayed genes encoding secreted proteins involved in immunity, such as antimicrobial peptides (AMPs) and clotting related peptides (Fig. 2c–e, Supplementary Data 5–7). The Proliferative 1 cluster was enriched with mitochondrial genes, characteristic of stem cells in ancient arthropods, such as crayfish²⁰. This cell cluster also had high expression of genes related to actin polymerization, cell proliferation and differentiation (Fig. 2c–e, Supplementary Data 5–7). The Proliferative 2 cluster displayed a high percentage of transcription factors, RNA binding proteins and genes related to actin dynamics. Marker genes for this cluster included several genes involved in hormone-related responses, suggesting they may be responsive to ecdysteroids synthesized after a blood meal (Fig. 2c–e, Supplementary Data 5–7). Lastly, both datasets had a Transitional cluster indicative of intermediate subtypes (Fig. 2c–e, Supplementary Data 5–7).

Four hemocyte clusters were only observed in engorged I. scapularis ticks. The Immune 3 cluster displayed an enrichment in secreted proteins and genes related to immune functions. This cluster was enriched for chitinases, matrix and zinc metalloproteinases, peptidases, and actin binding proteins, which have roles related to wound healing or tissue rearrangement. Several glycine-rich proteins (GRPs), commonly associated with antimicrobial properties or structural proteins, were also present (Fig. 2c–e, Supplementary Data 5, 7). The Immune 4 cluster showed an overrepresentation of genes related to protein degradation, immune function, and cell proliferation (Fig. 2c–e, Supplementary Data 5, 7). Thus, we posit that the Immune 4 cluster represents an intermediate state between the Immune 2 and the Proliferative 2 clusters. Two clusters displayed an enrichment for genes related to metabolic functions. The Metabolism 1 cluster represented genes involved in sulfonation of proteins, lipids and glycosaminoglycans, transmembrane solute transporter, nucleotide, and protein metabolism (Fig. 2c–e, Supplementary Data 5, 7). The Metabolism 2 cluster displayed genes related to detoxification, histamine binding, lipid metabolism, methionine and juvenile hormone metabolism (Fig. 2c–e, Supplementary Data 5, 7).

Based on these findings, we predicted hemocyte ontogeny using pseudotime analysis, which orders hemocyte clusters based on their gene expression profiles, enabling us to infer developmental lineages (Fig. 2f)²¹. We found six trajectories considering the cluster Proliferative 1 as a stem cell-like subpopulation. Lineage 1 and 2 ended with the Immune 2 and Proliferative 2 clusters, respectively, with the Immune 4 cluster serving as an intermediate state. Lineages 3 and 4 gave rise to the Immune 3 and Immune 1 clusters, respectively. Finally, two lineage trajectories ended with the metabolic clusters, Metabolism 1 and Metabolism 2. Overall, our findings suggest the presence of an oligopotent subpopulation that differentiates into more specialized subtypes involved in immune and metabolic functions, a process evoked by hematophagy.

Bacterial infection of I. scapularis ticks alters the molecular profile of hemocytes

The impact of bacterial infection on subtypes of tick hemocytes remains elusive. Thus, we collected hemocytes from I. scapularis nymphs fed on uninfected, A. phagocytophilum- or B. burgdorferi-infected mice and determined morphotype percentages. During infection with the rickettsial agent A. phagocytophilum, a relative decrease in prohemocytes and increase in plasmatocytes was noted (Fig. 3a). Conversely, only a slight decrease in the proportion of prohemocytes was observed during infection with the Lyme disease spirochete B. burgdorferi (Fig. 3a). No difference in total hemocyte numbers was observed across infection conditions (Supplementary Fig. 9). However, partitioning the engorged scRNA-seq datasets by treatments revealed a reduction in the Transitional cluster with an expansion in the Metabolism 1 cluster during B. burgdorferi infection (Fig. 3b, c). We next analyzed transcriptional changes at the cellular level in engorged uninfected nymphs compared to engorged infected ticks. Hemocyte clusters were grouped according to three molecular programs: “Immune” (Immune 1-4), “Proliferative” (Proliferative 1-2 and Transitional) and “Metabolism” (Metabolism 1-2). During A. phagocytophilum infection, we identified 177 DEGs within the Proliferative clusters, 53 DEGs in the Metabolism clusters, and 5 DEGs among the Immune clusters (Fig. 3d, Supplementary Data 10). Conversely, during B. burgdorferi infection, we detected 244 DEGs within the Proliferative clusters, 81 DEGs among the Metabolism clusters, and 12 DEGs associated with the Immune clusters (Fig. 3d, Supplementary Data 10). In total, we identified 188 and 257 unique DEGs across all hemocyte clusters during A. phagocytophilum and B. burgdorferi infection, respectively. Notably, 11 genes were differentially expressed during both bacterial infections and shared amongst all cluster groupings, in which 36.4% (4 out of 11) were marker genes of the Immune 1 cluster (Fig. 3e). Collectively, our results defined specific hemocyte subpopulations and genes that are differentially expressed in I. scapularis in response to A. phagocytophilum or B. burgdorferi infection.

**Fig. 3: Bacterial infection alters hemocyte subtypes and their gene expression.**

Phagocytic hemocytes contribute to A. phagocytophilum infection and tick fitness

The preceding findings suggested that the Immune 1 hemocyte cluster represented a subpopulation of cells that responded to bacterial infections in ticks (Fig. 3d). Therefore, our focus shifted to two marker genes from the Immune 1 hemocyte cluster: hemocytin and astakine (Fig. 4a, Supplementary Data 6, 7). Hemocytin is homologous to hemolectin in D. melanogaster and von Willebrand factors of mammals^22,23. Hemocytin encodes for a large multidomain adhesive protein involved in clotting, microbial agglutination and hemocyte aggregation^22,23,24,25. Conversely, astakine is a cytokine-like molecule present in chelicerates and crustaceans and is homologous to vertebrate prokineticins^26,27,28,29. Astakine also induces hemocyte proliferation and differentiation of immune cells^26,27,28,29. Hemocytin and astakine were broadly expressed in the Immune 1 cluster of I. scapularis hemocytes (Fig. 4a). Validating our scRNA-seq results, we confirmed the expression of hemocytin and astakine in hemocyte-enriched samples obtained from unfed ticks using RNA-FISH, illustrating that these genes serve as markers for the Immune 1 cluster (Fig. 4b, Supplementary Fig. 10). Furthermore, we noted an upregulation of hemocytin and astakine in hemocyte-enriched samples collected from engorged ticks, a pattern also observed in other markers of the Immune 1 cluster (Fig. 4c, Supplementary Data 11). These findings suggested that blood-feeding expanded this hemocyte subtype and/or upregulated the expression of its marker genes in I. scapularis ticks.

**Fig. 4: Phagocytic hemocytes contribute to *A. phagocytophilum* infection and fitness in ticks.**

Hemolectin is a known marker of phagocytic plasmatocytes in Drosophila^30,31,32, and phagocytic hemocytes expressing hemocytin are present in crustaceans^20,33,34. Therefore, we explored the phagocytic potential of the Immune 1 cluster by employing clodronate liposomes (CLD), which have been used to deplete phagocytic hemocytes in flies, mosquitoes, and, more recently, ticks^35,36,37,38. We found that CLD treatment led to a 36% reduction in the total number of hemocytes in I. scapularis nymphs (Fig. 4d). We observed an increase in the proportion of granulocytes along with a decrease in the percentages of prohemocytes and plasmatocytes compared to ticks treated with empty liposomes (Fig. 4e). Interestingly, the expression of both Immune 1 marker genes, hemocytin and astakine, also decreased after CLD treatment, implying the Immune 1 cluster is phagocytic (Fig. 4f). Taken together, CLD treatment effectively reduced the number of phagocytic hemocytes in I. scapularis, particularly impacting prohemocytes, plasmatocytes and cells associated with the Immune 1 cluster.

Phagocytosis serves as a pivotal immune mechanism against invading microbes. Previous studies have demonstrated that the depletion of phagocytic immune cells can influence the survival of arthropods following infection with either Gram-positive or Gram-negative bacteria^35,36,37,38. However, the impact of depleting phagocytic hemocytes on the acquisition of tick-borne bacteria remains unclear. Thus, we further investigated the effects of CLD treatment during A. phagocytophilum infection, as this intracellular microbe is known to interact with I. scapularis hemocytes shortly after uptake³⁹. Our findings revealed a reduction in A. phagocytophilum load in engorged ticks after injection with CLD compared to controls (Fig. 4g), with no differences in tick attachment (Supplementary Fig. 11), suggesting that phagocytic hemocytes promote either the acquisition or proliferation of A. phagocytophilum during blood-feeding.

Reports on Dipteran model organisms have demonstrated that hemocytes play roles beyond immunity, including tissue communication, clearing apoptotic cells during molting and development, and serving as vehicles for molecules^{40,41,42,43,44}. However, whether hemocytes have non-immune roles in ticks remains unknown. Our sequencing results revealed an enrichment in functions related to metabolism, cell proliferation, and development after a blood meal, suggesting that hemocytes may participate in the feeding or molting processes in ticks (Figs. 1, 2). To investigate this further, we recorded these physiological processes after injecting ticks with CLD. Although no differences in attachment were observed (Supplementary Fig. 11), engorged ticks treated with CLD weighed significantly less compared to the control treatment, indicating that phagocytic hemocytes are required for proper hematophagy (Fig. 4h). Furthermore, the number of nymphs that successfully molted to adults was significantly lower in CLD-treated ticks (Fig. 4i). Collectively, these results indicated that phagocytic hemocytes pleiotropically impacted tick immunity, feeding, and molting in I. scapularis. Importantly, the observed correlation between changes in physiological parameters and the reduced expression of the Immune 1 markers, hemocytin and astakine, during CLD treatment suggested a potential regulatory role for these genes in various aspects of tick physiology, prompting us to explore this hypothesis further.

Astakine induces hemocyte proliferation and differentiation in I. scapularis

We found that hematophagy induces hemocyte proliferation and differentiation (Fig. 1b) while upregulating astakine expression (Fig. 4c). Thus, we investigated whether astakine was directly implicated in the proliferation or differentiation of I. scapularis hemocytes. We microinjected increasing amounts of recombinant astakine (rAstk) in unfed nymphs and observed a dose-dependent increase in the total number of hemocytes (Fig. 5a). Specifically, we measured a decrease in the percentage of prohemocytes and an increase in plasmatocytes (Fig. 5b). These findings matched alterations observed during normal blood-feeding (Fig. 1b). Interestingly, we also detected an increase in tick IDE12 cell numbers in vitro following treatment with rAstk, supporting a role of astakine in inducing cell proliferation in hemocyte-like cells (Supplementary Fig. 12). To corroborate these results, we then silenced astakine by microinjecting unfed nymphs with small-interfering RNA (siRNA) before feeding on uninfected mice. We utilized siRNA to knockdown gene expression, as genome editing through CRISPR has only recently been introduced for adult germline manipulation and possesses low efficiency in ticks⁴⁵. We recovered 41% less hemocytes and observed an increase in the percentage of prohemocytes with a decrease in plasmatocytes from engorged ticks when astakine was silenced (Fig. 5c–e). Therefore, we determined that astakine acts on hematopoietic processes in the ectoparasite I. scapularis.

**Fig. 5: *Astakine* (*astk*) promotes hemocyte proliferation and differentiation in *I. scapularis*.**

Our previous results showed alterations in the percentage of hemocytes by the CLD treatment, which influenced bacterial infection and tick physiology. Given that astakine also alters hemocyte composition in ticks, we then investigated whether decreasing the expression of astakine affects A. phagocytophilum infection, hematophagy or ecdysis in I. scapularis. Accordingly, we observed a significant reduction in weight, molting success, and A. phagocytophilum burden in ticks silenced for astakine compared to the control treatment, without differences in tick attachment (Fig. 5f–h; Supplementary Fig. 13). Corroborating these findings, A. phagocytophilum load was also lower in astakine-silenced tick cells in vitro (Supplementary Fig. 14). Overall, we demonstrated that astakine regulates hemocyte composition in I. scapularis, which affects not only bacterial infection but also feeding and ecdysis, supporting a pleiotropic role of hemocytes in ticks.

Hemocytin affects the JNK pathway of I. scapularis

The broad expression of hemocytin detected in I. scapularis hemocytes (Fig. 4a), and its homology to a Drosophila gene used as a marker of phagocytic immune cells, prompted us to explore whether hemocytin could be involved in hemocyte proliferation or differentiation, phagocytosis or immune signaling in ticks. Surprisingly, no differences were observed in hemocyte numbers or subtype proportions between ticks injected with siRNA targeting hemocytin, nor in the phagocytic capacity of hemocytin-silenced tick cells (Supplementary Figs. 15, 16). We then asked whether hemocytin could act as an immune pathway regulator in I. scapularis. We focused on the IMD and the c-Jun N-terminal kinase (JNK) pathways, as previous reports indicated the importance of these molecular networks during bacterial infection of ticks^5,6.

After transfection with siRNA targeting hemocytin, we found a decrease in JNK phosphorylation in hemocytin-silenced tick cells, without alteration in Relish cleavage (Fig. 6a, b). To complement our findings, we overexpressed hemocytin in tick cells through CRISPR activation (CRISPRa). CRISPRa has been widely used to enhance the expression of an endogenous locus, employing a catalytically inactive Cas9 (dCas9) fused with transcription activators and single guide RNAs (sgRNAs) that direct the modified enzyme to the promoter region of a gene of interest⁴⁶. However, so far none of the CRISPRa effectors have been tested in tick cell lines and no endogenous promoter has been identified for genetic expression of sgRNAs. We optimized reagents and developed a protocol to up-regulate hemocytin in ISE6 cells using two rounds of nucleofection with different expression plasmids. The first plasmid expressed dCas9-VPR paired with neomycin resistance under the control of the CMV promoter. The second plasmid expressed either a sgRNA specific to the promoter region of hemocytin (hmc-sgRNA) or a control sgRNA (ctrl-sgRNA) driven by an endogenous RNA polymerase III promoter (Fig. 6c). Tick ISE6 cells were used as a platform for CRISPRa given the lower expression of hemocytin compared to IDE12 cells (Supplementary Fig. 17). Strikingly, we detected a 277% increase in the expression of hemocytin in dCas9⁺ cells transfected with the hmc-sgRNA compared to dCas9⁺ cells transfected with the ctrl-sgRNA (Fig. 6d). Importantly, we also noticed elevated levels of both jun (the transcription factor for the JNK pathway) and jnk expression (Fig. 6d).

**Fig. 6: *Hemocytin* (*hmc*) positively impacts the JNK pathway in *I. scapularis*.**

To corroborate our results in vivo, we microinjected unfed ticks with a scrambled control or siRNA targeting hemocytin and allowed them to feed on naïve mice. Upon repletion, we measured the expression of jun and relish. Consistently, we found that reduction in hemocytin expression led to a decrease in jun levels in ticks, without affecting relish expression (Fig. 6e), uncovering a role for hemocytin in the activation of the JNK pathway in I. scapularis.

Finally, building on our previous findings demonstrating that blood-feeding and infection alter the expression of hemocytin in ticks (Fig. 4c, Supplementary Data 9), coupled with the documented roles of hemocytin as an agglutinating factor and the JNK pathway in enhancing organismal growth and metabolism in insects⁴⁷, we delved deeper into the relationship between hemocytin expression and physiological parameters in I. scapularis. We detected a decrease in weight and molting success to adulthood in hemocytin-silenced ticks that fed on uninfected mice compared to scrambled controls, with no differences in tick attachment (Fig. 6f, g; Supplementary Fig. 18). Additionally, we noted that silencing hemocytin increased A. phagocytophilum load both in vivo and in vitro (Fig. 6h; Supplementary Fig. 19), supporting an antimicrobial role for hemocytin. Collectively, our findings indicated that tick hemocyte subtypes and their associated marker genes play a critical role in I. scapularis immunophysiology.

Discussion

The immune response against microbial infection has been extensively studied in Dipteran insects, but these species do not fully account for the variation in fundamental processes that exists in arthropods^{3,4,5,6,7,8,9,14}. In this study, we expand on the previous morphological classification of tick hemocytes. We characterized hemocytes at a single-cell resolution in combination with orthogonal approaches to uncover hemocyte subtypes and molecular markers in I. scapularis. Our findings revealed immune and metabolic alterations of tick hemocytes in response to blood-feeding and microbial infection. In contrast to mosquitoes^48,49, I. scapularis hematophagy prompts the emergence of new hemocyte clusters and distinct biological signatures.

Hemocytes with immune-related roles exhibited overrepresentation of genes involved in peptide secretion, agglutination, clotting, extracellular matrix remodeling and protein degradation. These data suggest that these clusters not only participate in antimicrobial responses, but also aid in the extensive internal remodeling of tissues required during blood meal acquisition¹⁹. Given the established immunological function of plasmatocytes and granulocytes in ticks, we speculate that the immune clusters represent these morphotypes^16,17,18. Metabolic hemocytes were enriched in solute transport, lipid and protein metabolism, histamine binding and detoxification genes. These molecular features posit an involvement in the feeding process by metabolizing nutrients and xenobiotics present in the blood meal. We also identified hemocytes specialized in cellular proliferation, including cells expressing genes involved in ecdysteroid biosynthesis, essential for regulating molting and development^6,50. We considered these proliferative clusters, prohemocytes, which have been categorized as a stem cell-like hemocyte subpopulation^16,17,18.

With the advent of cutting-edge technologies, hemocyte subtypes have been characterized at the molecular level in various arthropods, facilitating a broad comparative analysis of the evolution of the immune system^{20,30,31,32,33,34,48,49,51,52,53}. In the model organism D. melanogaster, three hemocyte subtypes exist: (1) plasmatocytes, responsible for phagocytosis, clotting, and AMP secretion; (2) crystal cells, involved in the melanization process by synthesizing pro-phenoloxidase (proPO); and (3) lamellocytes, which encapsulate foreign bodies^30,31,32. Interestingly, ticks do not seem to trigger melanization involving the proPO cascade, as proPO genes have not been found in the I. scapularis genome⁵⁴. Furthermore, markers typically associated with specific hemocyte subtypes in D. melanogaster were not detected in I. scapularis hemocyte clusters, except for hemocytin. These discrepancies in marker genes are also observed in closely related species. For instance, there is a lack of hemocytin expression or lamellocyte markers in hemocytes from Anopheles gambiae compared to D. melanogaster. Additionally, there is the presence of ProPO genes in several hemocyte subtypes of mosquitoes as opposed to the specific pattern in crystal cells of flies^{30,31,32,48,49}. Collectively, genetic divergences appear to be common occurrences in hemocytes during evolution, likely arising from differences in longevity, metabolic resources and exposure to microbes.

Despite the absence of universally conserved genetic markers, hemocytes in arthropods exhibit shared subtypes with functions related to immunity, proliferation or metabolism^{20,30,31,32,33,34,48,49,51,53}. For instance, specialized immune clusters of hemocytes involved in combating infection have been identified, such as “megacytes” in A. gambiae⁵⁵. Furthermore, the specialization of hemocyte subtypes in activities related to proliferation and metabolism is evident in both Hexapoda and Crustacea^{20,30,31,32,34,51}. Accordingly, we and others have noted significant changes in the numbers of tick hemocyte and subtype distributions following a blood meal³⁵. This is also the case for mosquitoes, in which blood-feeding increases granulocyte numbers, likely by triggering granulocyte proliferation and prohemocyte differentiation^49,56,57. Therefore, while hemocytes are recognized as specialized immune cells in arthropods, it is evident that they have acquired distinct roles through evolution that extend beyond immunity.

For I. scapularis, the presence of A. phagocytophilum or B. burgdorferi does not cause adverse effects on tick viability, suggesting that co-evolutionary mechanisms have developed^1,2. This was further exemplified by the lack of new hemocyte clusters emerging during infection, highlighting an opportunity for comparative biology in arthropods. However, we established specific molecular programs in hemocytes altered during bacterial infection of I. scapularis. Avoidance of strong cellular responses upon microbial infection may conceivably be related to a discrete evolutionary balance between immunity and survival in hematophagous arthropods. Using CLD to deplete phagocytic hemocytes, we observed a reduction in the percentage of prohemocytes and plasmatocytes, which correlated with a decrease in A. phagocytophilum load. Consistent with previous evidence showing that blocking phagocytosis in hemocytes reduces A. phagocytophilum dissemination from the midgut to other tick tissues³⁹, our results affirm the essential role of plasmatocytes in the infection process by this intracellular bacterium. Supporting our findings, it was recently reported that Ehrlichia chaffeensis relies on phagocytic hemocytes for systemic infection in the Lone Star tick, Amblyomma americanum³⁸. Hence, tick-borne intracellular bacteria have evolved mechanisms to exploit hemocytes for dissemination, unveiling an essential process for successful vector transmission.

CLD treatment resulted in a decrease of hemocytin and astakine levels, two marker genes of the Immune 1 cluster. Mechanistically, we uncovered a function for astakine in tick hematopoiesis, similar to observations in crustaceans²⁶. Consistent with the CLD experiments, silencing astakine led to reduced percentages of plasmatocytes and decreased A. phagocytophilum load in I. scapularis, emphasizing this morphotype as a key participant during bacterial infection. Future studies determining the cellular source, receptors, and signaling pathways of astakine will reveal mechanisms driving proliferation and differentiation of immune cells in ticks. Conversely, silencing of hemocytin in ticks led to an elevated A. phagocytophilum burden during hematophagy independent of phagocytosis. Supporting these findings, knockdown of hemocytin in the hard tick A. americanum led to an increase in the intracellular bacterium E. chaffeensis⁵³. Interestingly, hemocytin was shown to be involved in microbial agglutination in insects^23,24,25. Whether hemocytin acts directly as an agglutination factor in ticks is yet to be determined. Furthermore, both hemocytin and astakine were decreased in hemocytes from the tick Amblyomma maculatum during infection with the intracellular bacterium Rickettsia parkeri, suggesting that these genes may contribute to microbial infection in other tick species³⁵.

Hemocytes are important for organogenesis, development, clearing of apoptotic cells and tissue communication in insects^{40,41,42,43,44}. We showed that changes in phagocytic hemocytes, as well as alterations in astakine and hemocytin levels, impacted the fitness of I. scapularis. Acari exclusively utilize ecdysteroids, synthesized from cholesterol, for molting^6,50. Considering the transcriptional profile induced by feeding and the circulatory nature of hemocytes, we hypothesize that these cells are involved in the internal reorganization of tissues needed during hematophagy and ecdysis. Our findings indicated the involvement of hemocytes in ecdysteroid synthesis and transport, illustrated by alterations in lipid metabolism-related genes after a blood meal. Specifically, our dataset highlights an essential role of plasmatocytes in tick feeding and molting. This assertion is supported by a decrease in plasmatocytes upon astakine knockdown or CLD treatment, which led to a diminished tick weight and a reduced number of nymphs transitioning to adulthood. Conversely, hemocytin knockdown disrupted these processes without altering hemocyte numbers or their phagocytic capacity. Intriguingly, we demonstrated a role of hemocytin in modulating the JNK pathway in ticks. Given its conservation across arthropods and its recognized function as a pro-survival mechanism in Drosophila⁴⁷, we postulate that the JNK pathway plays a key role in tick organismal growth. Future investigations should focus on identifying the molecular connections between hemocytin and JNK signaling.

In summary, we leveraged the power of systems biology in combination with reductionist approaches to shed light on the complexity and dynamic attributes of biological processes associated with immune cells of an obligate hematophagous ectoparasite. Our findings uncovered canonical and non-canonical roles of tick hemocytes, which might have evolved because of evolutionary associations of I. scapularis with its vertebrate hosts and microbial exposure. The hemocyte dataset presented in this work, along with the identified cell type-specific marker genes and experimental tools, will facilitate future mechanistic studies in ticks. A few examples may include: (1) the use of marker genes to identify specific hemocyte subsets susceptible to infection or capable of microbial phagocytosis; (2) CRISPRa to study a gene of interest in ticks through overexpression; and (3) the development of antibodies against specific cell clusters for sorting hemocyte subtypes during distinct physiological conditions. Collectively, our open-access resource will foster hypothesis-driven research that could be adapted to construct an integrated atlas of the medically relevant tick I. scapularis.

Methods

Ethics statement

Mouse breeding, weaning and tick experiments were performed under guidelines from the NIH (Office of Laboratory Animal Welfare (OLAW) assurance numbers A3200-01) and pre-approved by the Institutional Biosafety (IBC-00002247) and Animal Care and Use (IACUC-0119012) committee of the University of Maryland School of Medicine.

Cell lines

The I. scapularis IDE12 and ISE6 cell lines were obtained from Dr. Ulrike Munderloh at University of Minnesota. Cells were cultured in L15C300 medium supplemented with 10% heat inactivated fetal bovine serum (FBS, Sigma), 10% tryptose phosphate broth (TPB, Difco), 0.1% bovine cholesterol lipoprotein concentrate (LPPC, MP Biomedicals) at 34 °C in a non-CO₂ incubator. ISE6 cells were grown to confluency in capped T25 flasks (Greiner bio-one) and either seeded at 3 × 10⁶ cells/well in 6-well plates (Millipore Sigma) or 3 × 10⁵ cells/well in 24-well plates (Corning). IDE12 cells were also grown to confluency in T25 flasks and either seeded at 1 × 10⁶ cells/well in 6-well plates (Millipore Sigma) or 3 × 10⁵ cells/well in 24-well plates (Corning). The human leukemia cell line, HL-60, was obtained from ATCC and maintained in RPMI-1640 medium with L-Glutamine (Quality Biological) supplemented with 10% FBS (Gemini Bio-Products) and 1% GlutaMax (Gibco), in vented T25 flasks (CytoOne) at 37 °C and 5% CO₂. All cell cultures were confirmed to be Mycoplasma-free (Southern Biotech).

Bacteria, ticks, and mice

A. phagocytophilum strain HZ was grown as previously described in HL-60 cells at 37 °C, using RPMI medium supplemented with 10% FBS and 1% Glutamax⁵⁸. Bacterial numbers were calculated using the following formula: number of infected HL-60 cells × 5 morulae/cell × 19 bacteria/morulae × 0.5 recovery rate⁵⁹. To determine the percentage of infection, cells were spun onto microscope slides using a Epredia™ Cytospin™ 4 Cytocentrifuge (Thermo Scientific), stained using the Richard-Allan Scientific™ three-step staining (Thermo Scientific) and visualized by light microscopy using a Primo Star™ HAL/LED Microscope (Carl Zeiss). For in vitro experiments, bacteria were purified in L15C300 media by passing infected cells through a 27-gauge bent needle and centrifugation, as previously described⁵⁸. Isolated bacteria were inoculated onto tick cells at a multiplicity of infection (MOI) of 50 for 48 h. For in vivo experiments, a total of 1 × 10⁷ A. phagocytophilum-infected cells/mL was resuspended in 100 µL of 1X phosphate-buffered saline (PBS) and intraperitonially injected into mice. Infection progressed for 6 days prior to tick placement.

B. burgdorferi B31 clone MSK5 (passage 3–5) was cultured in Barbour-Stoenner Kelly (BSK)-II medium supplemented with 6% normal rabbit serum (Pel-Freez) at 37 °C. Before mouse infection, plasmid profiling was performed as described elsewhere⁶⁰. To determine the infectious inoculum, spirochetes were enumerated under a 40X objective lens using a dark-field microscope (Carl Zeiss™ Primo Star™ Microscope) by multiplying the number of bacteria detected per field x dilution factor x 10⁶^58,60. For intradermal inoculation, spirochetes were washed, resuspended in 50% normal rabbit serum at a concentration of 1 × 10⁶ B. burgdorferi/mL and anesthetized C3H/HeJ mice were injected with 100 µL of inoculum (1 × 10⁵ total B. burgdorferi). Infected mice were maintained for at least 14 days prior to tick placement.

I. scapularis nymphs were obtained from Oklahoma State University and University of Minnesota. Upon arrival, ticks were maintained in a Percival I-30BLL incubator at 23 °C with 85% relative humidity and a 12/10 h light/dark photoperiod regimen. Age matched (6-10 weeks), male C57BL6 and C3H/HeJ mice were supplied by Jackson Laboratories or the University of Maryland Veterinary Resources. For all tick experiments, capsules were attached to the back of mice using a warmed adhesive solution made from 3 parts gum rosin (Millipore-Sigma) and 1 part beeswax (Thermo Scientific) 24 h prior to tick placement. All experiments were done using C57BL6 mice, except for the ones including B. burgdorferi infection (C3H/HeJ genetic background).

Hemocyte collection

Hemolymph was collected from I. scapularis nymphs in L15C300 + 50% FBS on ice from individual unfed, partially fed, or engorged ticks placed on uninfected, A. phagocytophilum- or B. burgdorferi-infected mice, using non-stick RNase-free 1.5 mL microtubes (Ambion) and siliconized pipet tips. Briefly, ticks were immobilized on a glass slide using double-sided tape (3 M) and covered in a sphere of 10 µL of L15C300 + 50% FBS. Forelegs were incised at the tarsus, and forceps were used to gently apply pressure to the tick body to release hemolymph into the media. Enumeration of total hemocytes was determined for each tick using a Neubauer chamber. For differentiation of hemocyte morphotypes, 100 µL of L15C300 was added in a microtube for each tick before preparation on Fisherbrand™ Superfrost™ Plus microscope slides (Fisher Scientific) using a Epredia™ Cytospin™ 4 Cytocentrifuge (Thermo Scientific). Afterwards, hemocyte-enriched samples were stained using the Richard-Allan Scientific™ three-step staining (Thermo Scientific) and evaluated morphologically under a Primo Star™ HAL/LED Microscope (Carl Zeiss). Images were acquired using an Axiocam 305 color camera (Carl Zeiss) with the ZEN software (Carl Zeiss), which was used for measuring cell diameters.

For RNA extraction, hemocyte-enriched samples were collected as mentioned above using PBS (4 µL for unfed and 6 µL for engorged ticks) instead of media. For each biological replicate, hemocytes from 80 unfed or 40 engorged ticks (retrieved immediately post-detachment from the host) were pooled in 700 µL of TRIzol.

Bulk RNA sequencing

Three independent hemocyte-enriched sample collections were performed for each condition (unfed and engorged), as mentioned above. After collection, RNA was extracted from TRIzol following the manufacturer’s instruction with the addition of 1 µg of glycogen per sample. The RNA integrity (RIN) was assessed for each sample using a Eukaryote Total RNA Nano Chip Assay on a Bioanalyzer (Agilent). RIN values ranged between 7.6 and 9.4. Strand-specific, dual unique indexed libraries for sequencing on Illumina platforms were generated using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® (New England Biolabs). The manufacturer’s protocol was modified by diluting adapters 1:30 and using 3 µL of this dilution. The size selection of the library was performed with SPRI-select beads (Beckman Coulter Genomics). Glycosylase digestion of the adapter and 2^nd strand was performed in the same reaction as the final amplification. The sample input was polyA-enriched. Libraries were assessed for concentration and fragment size using the DNA High Sensitivity Assay on the LabChip GX Touch (Perkin Elmer). The resulting libraries were sequenced on a multiplexed paired end 100 bp Illumina NovaSeq 6000 S4 flowcell, generating an average of 92 million read pairs per sample. RIN analysis, library preparation and sequencing were completed by Maryland Genomics at the Institute for Genome Sciences, University of Maryland School of Medicine. One sample was excluded from subsequent analysis due to considerably smaller library size compared to other biological replicates.

After sequencing, all reads were aligned to the I. scapularis genome (GCF_016920785.2_ASM1692078v2) using HISAT v2.0.4^14,61. Potential PCR duplicates were removed by keeping only one of the read pairs mapped to the same genomic location of the same strand and then tallied the number of “unique reads” mapped to each annotated I. scapularis gene. The generated count matrix was used as input for differential expression analysis with edgeR v3.36.0⁶². During data normalization, only transcripts with at least 10 counts per million (cpm) in two or more samples were kept for downstream analysis. Differentially expressed genes (DEGs) were defined based on the following thresholds: (1) a false discovery rate (FDR) < 0.05, and (2) -0.5146<logFC>0.3785, corresponding to at least a 30% reduction or increase in gene expression. All sequencing reads were deposited into the NCBI Sequence Read Archive under the BioProject accession PRJNA906572.

Single-cell RNA sequencing

For single-cell RNA sequencing (scRNA-seq), hemocyte-enriched samples were collected from individual unfed, engorged, or A. phagocytophilum- or B. burgdorferi-infected ticks and each condition pooled in 300 µL of L15C300 + 50% FBS (n = 90 for unfed and n = 50 for engorged ticks retrieved immediately post-detachment from the host). Cells were filtered through a 40 µm cell strainer (Millipore Sigma) and enriched for live cells using OptiPrep density gradient centrifugation³⁰. Briefly, 200–300 µL of the filtered solution was overlaid onto 2 mL of OptiPrep™ Density Gradient Medium (1.09 g/mL in 1X PBS; Sigma) and centrifugated at 1000 x g for 10 min at 4 °C, using a swinging bucket without breaking. After centrifugation, 200 µL of the interphase was collected in a new microtube and spun at 1000 x g for 10 min at 4 °C. Cells were concentrated in 30 µL of 1X PBS + 0.2 U RNasin® Plus Ribonuclease Inhibitor (Promega), from which 5 µL were used for measuring viability and cell numbers by trypan blue exclusion assay. The final concentration of cells ranged from 350 to 500 cells/µL, with viability ranging between 95.7% and 98.5%.

An estimated 5000–7000 cells per condition were loaded on a 10X Genomics Chromium controller and four individually barcoded 3’-end scRNA-seq libraries were prepared according to the manufacturer’s protocol. Each library was then sequenced on an Illumina high-output sequencer to generate a total of 816,376,790 75-bp paired-end reads. To process all scRNA-seq reads, a custom pipeline previously used to analyze single-cell data from Plasmodium parasites similar to 10X Genomics Cell Ranger software was adapted⁶³. First, only reads longer than 40 bp after trimming any sequences downstream of 3’ polyadenylation tails were kept. These reads were then mapped to the I. scapularis genome (GCF_016920785.2_ASM1692078v2) using HISAT version 2.0.4^14,61. Out of the mapped reads, “unique reads” were identified by keeping only one of the reads that had identical 16-mer 10X Genomics barcode, 12-mer unique molecular identifier, and mapped to the same genomic location of the same strand. Finally, the 10X Genomics barcodes were used to separate reads derived from each individual cell and count the number of unique reads mapped to each annotated I. scapularis gene (from transcription start site to 500 bp after the annotated 3’-end). All sequencing reads are deposited into the NCBI Sequence Read Archive under the BioProject accession PRJNA905678 and the code for mapping is available through the GitHub website (https://github.com/Haikelnb/scRNASeq_Analysis).

The scRNA-seq libraries were combined into a single dataset using scran v1.26.2⁶⁴. Genes containing “40s_ribosomal” and “60s_ribosomal” were removed from the entire dataset. Based on Principal Component Analysis (PCA), unfed and engorged hemocyte-enriched samples were analyzed independently for downstream analyses. Low quality cells were removed with the following thresholds: less than 600 unique reads, less than 150 mapped genes, and 30% or higher mitochondrial transcripts. The remaining transcriptomes were normalized by first calculating size factors via scran functions quickCluster and computeSumFactors and computing normalized counts for each cell with logNormCounts function in scater v1.22.0⁶⁵. For downstream analysis, highly variable genes were selected using getTopHVGs before performing PCA and t-distributed Stochastic Neighbor Embedding (t-SNE) projections. Clustering was conducted using a kmeans value of 20. Differential gene expression between clusters was calculated using the find marker function in scran v1.26.2⁶⁴. The R package slingshot v2.2.1 was used to perform pseudotime inference where trajectories began from the “Proliferative 1” cluster, based on predicted function²¹. To determine differences in gene expression between each infected condition compared to the reference factor (uninfected), clusters were grouped based on annotated function (e.g., metabolism, proliferation, immune) and MAST v1.24.1 was used to test for significance under the Hurdle model adjusting for the cellular detection rate⁶⁶. The code for the analysis is available through the GitHub website (https://github.com/HLaukaitisJ/PedraLab_hemocyte_scRNAseq).

Functional annotation and enrichment analysis

Functional annotation and Gene Ontology Enrichment analysis were done through VectorBase, using the LOC number of all DEGs (bulk RNA-seq) or marker genes (scRNA-seq) as input, and the following parameters: (1) Organism: “Ixodes scapularis PalLabHiFi”; (2) Ontology: “Molecular Function” or “Biological Process”; (3) adjusted p-value cutoff (Benjamini) < 0.05⁶⁷. Additionally, the putative function of the top 50 marker genes in each single cell cluster were manually assigned based on 10 categories (“ncRNA/Pseudogene”, “detoxification”, “secreted protein/extracellular matrix”, “metabolism”, “immunity”, “hormone related”, “cell proliferation/differentiation”, “protein synthesis”, “actin polymerization/cell rearrangement”, “unknown”). First, the protein coding sequence of each gene was retrieved from the NCBI website. Next, homologues from each gene were searched in the D. melanogaster genome using the BLAST tool from Flybase⁶⁸. If a match was found with an E value < 10⁻²⁰, then the genes were assumed to have some (E value was between < 10⁻²⁰ and 10⁻³⁹) or complete (E value < 10⁻⁴⁰) homology. The function of the I. scapularis gene was interpreted based on the description for D. melanogaster. Comparison to gene expression profiles in specific D. melanogaster hemocyte subtypes was assessed using the DRSC/TRiP Functional Genomics Resources dataset³⁰. Alternatively, if a high match to known D. melanogaster genes was not found, then the complete coding sequence of the I. scapularis gene was used to search for protein domains using InterPro and the functional information retrieved from Pubmed or UniProt^69,70. Otherwise, the gene function was assigned as “unknown”.

RNA-fluorescence in situ hybridization (FISH)

The expression of specific genes in hemocytes was determined by RNA-FISH using the RNAScope-Multiplex Fluorescent Reagent Kit v2 Assay (Advanced Cell Diagnostics), following the manufacturer’s instructions with slight modifications. Briefly, hemocytes were pooled from five unfed ticks in microtubes containing 100 µL of L15C300. Then, cells were spun onto microscope slides using a Cytospin 2 and fixed with 4% paraformaldehyde (PFA, Millipore-Sigma) for 20 min. After three washing steps with 1X PBS, two drops of hydrogen peroxide were applied to the samples for 10 min and washed three times with 1X PBS. Subsequently, two drops of Protease III were added to the cells and the samples incubated in the HybEZ^TM Oven (Advanced Cell Diagnostics) for 30 min at 40 °C. Samples were washed three times with 1X PBS before the addition of two drops of the following RNAScope probes (Advanced Cell Diagnostics): hemocytin RNA probe (regions 2778-3632 of XM_042287086.1) conjugated with C1, astakine probe (regions 47-800 of XM_040222953.1) conjugated with C2, actin5c probe (regions 2-1766 of XM_029977298.4) conjugated with C1 as a positive control and gfp probe (regions 12-686 of AF275953.1) conjugated with C1 or C2 as negative controls. Slides were incubated in a HybEZ^TM Oven for two hours at 40 °C. Samples were then washed three times with 1X PBS and stored overnight in 5X SSC buffer (Millipore Sigma). Next, slides were washed using 1X PBS and incubated with the AMP reagents as instructed by the manufacturer. To develop the probe signals, samples were washed three times with 1X PBS and three drops of the corresponding RNAscope Multiplex FLv2 HRP was added for 15 min at 40 °C. Then, slides were washed again with 1X PBS and incubated with ~50 µL of 1:1500 dilution of Opal^TM 520 and 570 dyes (Akoya Biosciences), for C1 and C2 respectively, before the addition of two drops of the RNAscope Multiplex FLv2 HRP blocker for 15 min at 40 °C. Slides were counterstained with two drops of DAPI for 30 s and mounted using two drops of ProLong™ Gold Antifade Mountant (Thermo Scientific). Slides were allowed to dry overnight before examined under a Nikon W-1 spinning disk confocal microscope. For imaging, the following laser channels were used: 405 nm (DAPI), 488 nm (GFP, C1) and 561 nm (RFP, C2).

RNA interference

For in vitro experiments, small interfering RNAs (siRNAs) and scrambled controls (scRNAs) designed for astakine and hemocytin were synthesized by Millipore Sigma with UU overhangs. Tick cells were seeded at 3 × 10⁵ cells/well (24-well plate) for RNA extraction or the phagocytosis assay and 1 × 10⁶ cells/well (6-well plate) for protein extraction. siRNAs were transfected into tick cells using Lipofectamine 3000 (Thermo Scientific) at 1 µg per mL. After 7 days, cells were either harvested or infected with A. phagocytophilum for 48 h. For protein extraction, cells were washed with 1X PBS, resuspended in a solution of Radio-immunoprecipitation assay (RIPA) buffer (Merck Millipore) containing protease inhibitor cocktail (Roche) and stored at −80 °C. For RNA extraction, cells were harvested in TRIzol and stored at −80 °C.

For in vivo experiments, siRNAs and scRNAs designed for astakine and hemocytin were synthesized using the Silencer™ siRNA Construction Kit (Thermo Scientific) with the primers listed in Supplementary Data 12, following the manufacturer’s instruction. Tick microinjections were performed using 40–60 ng of siRNA or scRNA per tick. Ticks were allowed to recover overnight before being placed on uninfected or A. phagocytophilum-infected mice. After placement, the percentage of ticks attached to mice was calculated. Fully fed ticks were collected 4 days post-placement, weighed, and either maintained in a humidified chamber for molting experiments or frozen at −80 °C in 200 µL TRIzol for RNA extraction.

Phagocytosis assay

IDE12 were transfected with siRNA or scRNA designed for hemocytin, as previously described in ref. ⁶. Briefly, 7 days post-transfection, cells were re-plated at a density of 4 × 10⁵ cells/well in a 24-well plate and left to adhere overnight. The following day, media was replaced with 500 μL of L15C300 containing a 1:10,000 dilution of FluoSpheres™ Carboxylate 1.0 µm beads (yellow-green, Invitrogen) for 24 h. Cells were then washed five times with 1X PBS and fixed in 200 mL of 4% PFA for 20 min. Fluorescence and bright field microscopy images were acquired with an EVOS FL Digital Inverted Microscope (Advanced Microscopy Group) and merged using ImageJ software. Negative and positive cells for fluorescent microspheres and the average number of phagocytosed beads per cell were calculated from the number of green puncta detected in the total cells counted in each field ( > 100 cells). Two independent experiments were performed, with fields taken from three different wells per replicate.

Recombinant astakine

I. scapularis recombinant Astakine (rAstk) was produced by GeneScript, based on the coding sequence of the XM_040222953.1 astakine transcript (LOC8032444). For in vitro cell proliferation assays, 1 × 10⁵ IDE12 cells/well were seeded in L15C300 complete medium (24-well plate). The following day, cells were treated with 0.05 µg/mL of rAstk for a total of 5 days. Cells were collected in a 1.5 mL microtube and 10 µL of the suspension was used for live cell counting using the trypan blue exclusion assay with a TC20™ automated cell counter (Bio-Rad). BSA (0.05 µg/mL) was used as negative control. For in vivo experiments, nymphs were microinjected with 50 nL of PBS containing 0.05 ng, 0.5 ng or 5 ng of rAstk^26,27,28. Afterwards, ticks were allowed to rest for 24 h before hemolymph was collected, as described above. Microinjection of BSA (5 ng) was used as a negative control.

Clodronate depletion of phagocytic hemocytes

To determine the effect of phagocytic hemocytes in I. scapularis nymphs, we used clodronate liposomes (CLD) to deplete phagocytic cells^35,36,37. Ticks were immobilized on a glass slide using double-sided tape and microinjected (Nanojet III microinjector; Drummond Scientific) through the anal pore with 69 nL of either CLD (Standard macrophage depletion kit, Encapsula NanoSciences LLC) or control liposomes (LP) at a 1:5 dilution in 1X PBS. Ticks were allowed to recover overnight before being placed on uninfected or A. phagocytophilum-infected mice. After placement, the percentage of ticks attached to mice was calculated. Fully fed ticks were collected 4 days post-placement, weighed, and either maintained in a humidified chamber for molting experiments or frozen at −80 °C in 200 µL TRIzol for RNA extraction.

RNA extraction and quantitative real-time PCR (qPCR)

Total RNA from cell lines or engorged ticks was isolated from samples preserved in TRIzol using the PureLink RNA Mini kit (Ambion). Total RNA from tick hemocytes was purified following the manufacturer’s instruction for TRIzol RNA extraction with the addition of 1 µg of glycogen (RNA grade, Thermo Scientific). For cDNA synthesis, 400–600 ng of RNA was used in the Verso cDNA Synthesis Kit (Thermo Scientific). To measure gene expression by RT-qPCR, 2X Universal SYBR Green Fast qPCR Mix (ABclonal) was used with the addition of 1 µL of cDNA per well and quantified using a CFX96 Touch Real-Time PCR Detection System (Bio-rad). All genes of interest were amplified at 54 °C, except for amplification of A. phagocytophilum 16 S, which was run at 46 °C. No-template controls were included to verify the absence of primer-dimers formation and/or contamination. Reactions on each sample and controls were run in duplicate. Gene expression was determined by relative quantification normalized to tick actin, using the primers described in Supplementary Data 12.

Western blotting

To extract proteins, wells were washed with cold 1X PBS, and resuspended in 1X RIPA buffer (Merck Millipore) containing protease inhibitor cocktail (Roche). Protein extracts were stored at −80 °C and their concentration was estimated using Bicinchoninic acid (BCA) assay (Thermo Scientific). For sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), equal amounts of protein were boiled in 6X Laemmli sample buffer (Alfa Aesar) containing 5% β-mercaptoethanol. The proteins were then transferred onto PVDF membranes (Biorad) and blocked for 1 h using a solution of 5% skim milk prepared in 1X PBS and 0.1% Tween® 20 Detergent (PBS-T). Primary antibodies were incubated overnight at 4 °C, followed by four washes in PBS-T and incubation with secondary antibodies for at least 1 h at room temperature with gentle agitation. The blots were washed four times in PBST, incubated with enhanced chemiluminescence (ECL) substrate solution for 1 min (Millipore), and then imaged. A complete list of antibodies used can be found in Supplementary Data 13.

CRISPR activation (CRISPRa)

The SP-dCas9-VPR plasmid was a gift from George Church (Addgene plasmid, #63798). For expression of sgRNAs, the sgRNA for the hemocytin promoter or a control sequence (random sequence that does not match with any region in the I. scapularis genome) was cloned between the BbsI sites of a derivative of the pLib8 vector (pLibTB.1_ISCW_025) under the control of an I. scapularis U6 promoter (ISCW025025) (Supplementary Fig. 20). ISE6 cells (7 × 10⁷ cells) were nucleofected with 15 µg of SP-dCas9-VPR plasmid using the buffer SF, pulse code EN150 (Lonza Biosciences) and subsequently incubated for 3 days. Following incubation, the cells were selected with neomycin (250 µg/mL) for 2 weeks. After antibiotic selection, the cells were nucleofected with 1 µg of the pLibTB.1_ISCW_025 vector containing the experimental or scrambled control sgRNA and were harvested 3 days later. Cells were collected in TRIzol for RNA extraction, as previously described.

Statistics

Statistical significance for quantitative variables was tested with an unpaired two-tailed t-test with Welch’s correction, two-tailed Mann–Whitney U test or one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test when appropriate. Gaussian distribution was assessed using the D’Agostino-Pearson omnibus K2 test, and homogeneity of variance was determined by F-test when comparing two conditions or the Brown-Forsythe test when comparing three conditions. Statistical significance for categorical variables was assessed using Fisher’s exact test. GraphPad PRISM® (version 9.1.2) was used for all statistical analyses. Outliers were detected by a Grubbs’ Test, using the GraphPad Quick Cals Outlier Calculator. p values of < 0.05 were considered statistically significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All primers, reagents, resources, and software used in this study, together with their manufacturer’s information and catalog numbers are available in Supplementary Data 12 and 13. Source data are provided with this article. All sequencing reads for the bulk RNA sequences were deposited into the NCBI Sequence Read Archive under the BioProject accession PRJNA906572. All scRNA sequences were deposited into the NCBI Sequence Read Archive under the BioProject accession PRJNA905678. Further information and request for resources and reagents should be directed to and will be honored by the corresponding author: Joao HF Pedra (jpedra@som.umaryland.edu). Source data are provided with this paper.

Code availability

The codes for the scRNA sequence analysis are available through the GitHub websites: https://github.com/Haikelnb/scRNASeq_Analysis and https://github.com/HLaukaitisJ/PedraLab_hemocyte_scRNAseq⁷¹.

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Acknowledgements

This work was funded by the National Institutes of Health (NIH) grants T32AI162579 (HJL-Y), F31AI152215 (A.J.O.), F31AI167471 (L.R.B.), R01AI134696 (J.H.F.P.), R01AI116523 (J.H.F.P.), P01AI138949 (J.H.F.P., U.P.) and R21 AI168592 (N.P., S.E.M., J.H.F.P.). Funding was also provided by the Fairbairn Family Lyme Research Initiative (N.P., S.E.M., and J.H.F.P.) and the Howard Hughes Medical Institute (N.P.). The authors acknowledge members of the Pedra laboratory for providing insightful discussions and colleagues for manuscript feedback. We thank Jon Skare (Texas A&M University Health Science Center) for providing the B. burgdorferi B31 strain, clone MSK5; Ulrike G. Munderloh (University of Minnesota) for supplying ISE6 and IDE12 tick cells; Joseph Mauban (University of Maryland School of Medicine) for aiding in the microscopy analysis; Jonathan Oliver (University of Minnesota) for providing available I. scapularis ticks; Dana Shaw (Washington State University) and Adela Oliva Chavez (Texas A&M University) for information regarding cultures of B. burgdorferi and A. phagocytophilum; the University of Maryland, Baltimore Confocal Microscopy Core and the Maryland Genomics Core at the Institute for Genome Sciences for the services provided in microscopy and next generation sequencing, respectively. Some images were created with BioRender.com. The content is solely the responsibility of the authors and does not represent the official views of the NIH, the Department of Health and Human Services or the United States government.

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Haikel N. Bogale
Present address: Rancho BioSciences, San Diego, CA, USA
Nisha Singh
Present address: Department of Biotechnology, School of Energy Technology, Pandit Deendayal Energy University; Knowledge Corridor, Gandhinagar, Gujarat, India
Anya J. O’Neal
Present address: Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
L. Rainer Butler
Present address: Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

Authors and Affiliations

Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
Agustin Rolandelli, Hanna J. Laukaitis-Yousey, Nisha Singh, Sourabh Samaddar, Anya J. O’Neal, Camila R. Ferraz, M. Tays Mendes, L. Rainer Butler, Liron Marnin, Francy E. Cabrera Paz, Luisa M. Valencia, Ciaran Skerry, David Serre & Joao H. F. Pedra
Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
Haikel N. Bogale & David Serre
Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
Matthew Butnaru, Enzo Mameli, Baolong Xia, Stephanie E. Mohr & Norbert Perrimon
Howard Hughes Medical Institute, Chevy Chase, MD, USA
Matthew Butnaru & Norbert Perrimon
Department of Microbiology, National Emerging Infectious Diseases Laboratories, Boston University School of Medicine, Boston, MA, USA
Enzo Mameli
Department of Veterinary Medicine, University of Maryland, College Park, MD, USA
Vipin S. Rana & Utpal Pal

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Agustin Rolandelli
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Contributions

A.R. and J.H.F.P. designed the study. A.R., H.J.L.-Y., H.N.B., N.S., S.S., M.B., C.R.F., A.J.O., M.T.M. and L.M. performed the experiments. A.R., H.J.L.-Y., and H.N.B. performed computational analysis. M.B., N.S., E.M., and B.X. established the CRISPRa in the tick system. A.R., H.J.L.-Y., A.J.O. and J.H.F.P. wrote the manuscript. L.R.B., L.M.V., V.S.R. and F.E.C.P. aided with experimentation. L.R.B. created some schematics. All authors analyzed the data, provided intellectual input into the study, and contributed to editing of the manuscript. C.S., S.E.M., U.P., N.P. and D.S. supervised experiments and provided instruments and reagents. J.H.F.P. supervised the study.

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Correspondence to Joao H. F. Pedra.

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Rolandelli, A., Laukaitis-Yousey, H.J., Bogale, H.N. et al. Tick hemocytes have a pleiotropic role in microbial infection and arthropod fitness. Nat Commun 15, 2117 (2024). https://doi.org/10.1038/s41467-024-46494-3

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Received: 20 October 2023
Accepted: 28 February 2024
Published: 08 March 2024
DOI: https://doi.org/10.1038/s41467-024-46494-3

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