Single-cell RNA-seq revealed heterogeneous responses and functional differentiation of hemocytes against white spot syndrome virus infection in Litopenaeus vannamei

ABSTRACT

Shrimp hemocytes are the vital immune cells participating in innate immune response to defend against viruses. However, the lack of specific molecular markers for shrimp hemocyte hindered the insightful understanding of their functional clusters and differential roles in combating microbial infections. In this study, we used single-cell RNA sequencing to map the transcriptomic landscape of hemocytes from the white spot syndrome virus (WSSV)-infected Litopenaeus vannamei and conjointly analyzed with our previous published single-cell RNA sequencing technology data from the healthy hemocytes. A total of 16 transcriptionally distinct cell clusters were identified, which occupied different proportions in healthy and WSSV-infected hemocytes and exerted differential roles in antiviral immune response. Following mapping of the sequencing data to the WSSV genome, we found that all types of hemocytes could be invaded by WSSV virions, especially the cluster 8, which showed the highest transcriptional levels of WSSV genes and exhibited a cell type-specific antiviral response to the viral infection. Further evaluation of the cell clusters revealed the delicate dynamic balance between hemocyte immune response and viral infestation. Unsupervised pseudo-time analysis of hemocytes showed that the hemocytes in immune-resting state could be significantly activated upon WSSV infection and then functionally differentiated to different hemocyte subsets. Collectively, our results revealed the differential responses of shrimp hemocytes and the process of immune-functional differentiation post-WSSV infection, providing essential resource for the systematic insight into the synergistic immune response mechanism against viral infection among hemocyte subtypes.

IMPORTANCE

Current knowledge of shrimp hemocyte classification mainly comes from morphology, which hinder in-depth characterization of cell lineage development, functional differentiation, and different immune response of hemocyte types during pathogenic infections. Here, single-cell RNA sequencing was used for mapping hemocytes during white spot syndrome virus (WSSV) infection in Litopenaeus vannamei, identifying 16 cell clusters and evaluating their potential antiviral functional characteristics. We have described the dynamic balance between viral infestation and hemocyte immunity. And the functional differentiation of hemocytes under WSSV stimulation was further characterized. Our results provided a comprehensive transcriptional landscape and revealed the heterogeneous immune response in shrimp hemocytes during WSSV infection.

INTRODUCTION

Hemocytes are the vital immune cells in crustaceans, including shrimps, that could mount humoral and cellular immune responses, and are principally involved in the production and release of immune effector molecules, apoptosis, and encapsulation, among other immune functions (1, 2). Considering the critical importance of hemocytes, omics approaches including metabolomic and transcriptome have been applied to study the functional characteristics at the overall hemocyte level (3, 4). However, these studies have not focused on the heterogeneity of hemocyte subpopulations. Traditionally, the hemocytes of shrimp are classified as granulocytes, semi-granulocytes, and hyalinocytes, depending on cell morphology and dyeing characteristics of intracellular granules (5, 6). Although some studies have explored the functional roles of three hemocyte subpopulations at the molecular level, the identification of rare cell types and the distinction of cell transient states are hampered by the low resolution of cell morphology, and the analysis based on gene transcription data of rough classified hemocyte populations would omit the transcriptional characteristics of some low abundance cell types, resulting in some controversy over the hemocyte subpopulation classification and their immune functions (7, 8). Additionally, the knowledge on the developmental trajectory of the hemocyte lineage and the intermediate states in the differentiation process of hemocytes remains ambiguous (9). Therefore, it is necessary to clarify the molecular signatures of hemocytes at a single-cell resolution.

Shrimp hemocytes are capable of rapidly initiating the immune response process upon pathogen invasion to defend against infection. As the only circulating cell type in the shrimp, hemocytes also serve as the primary target cells for external viruses and facilitate the spread of viruses to other tissues (1, 10). White spot syndrome virus (WSSV) is the most threatening viral pathogen for crustacean aquaculture, which has been listed as one of the aquatic animal viral viruses that need to be reported by the World Organization of Animal Health (11–13). WSSV is a large rod-shaped virus with an envelope structure and belongs to the only member of the genus Whispovirus in the virus family Nimaviridae (14, 15). There have been numerous studies attempting to elucidate the interaction between shrimp hemocytes and WSSV and further efforts to reveal differences in the immune response to viral infection at the cell subpopulations level, as well as differences in the molecular characteristics and mechanisms of WSSV infestation (10, 16–19). However, consistent results can be hardly obtained due to the discrepancies in cell classification and study methods, and the lack of genetic and marker tools for hemocyte subpopulations. Hence, it is essential to thoroughly characterize the molecular features and responses of all hemocyte subtypes after exposure to pathogenic microorganisms, which also facilitated our better understanding of shrimp immune defense mechanisms.

The development of single-cell RNA sequencing technology (scRNA-seq) has enabled the analysis of dynamic gene expression patterns in complex tissue cell at single-cell resolution to identify cell types, resolve cell functions, and delineate cell differentiation characteristics (20). At present, this technique has been widely utilized for the taxonomic identification of invertebrate hemocytes, such as Drosophila, mosquitoes, oyster, and shrimp (21–25). The scRNA-seq has also been used in hemocyte immune response studies to clarify the host-pathogen interactions within individual cells. For example, scRNA-seq of Drosophila larvae hemocytes under different inflammatory conditions found the differences in immune activation characteristics and differentiation relationships among different hemocyte types (26), and scRNA-seq of silkworm hemocytes revealed differential immune responses to baculovirus infection in cell populations (27). Compared with the common omics techniques, scRNA-seq could identify the highly sensitive and specific immunoreactive cells, providing more accurate and detailed characterization of different cell types.

In this study, we have performed scRNA-seq on hemocytes of healthy and WSSV-infected Litopenaeus vannamei to comprehensively identify hemocyte types and characterize their specific molecular and cellular responses post viral infection. We identified the different types of hemocytes and described the immune status of each cell cluster. The results of scRNA-seq also clarified the specific cell types sensitized to WSSV, and further elucidated the balance of virus-host interactions at the single-cell level. Moreover, we have precisely delineated the dynamics of hemocyte lineage differentiation under viral infection. The extensive single-cell transcriptional data sets generated during this study will be a useful resource for detailed resolution of the functional characteristics of shrimp hemocytes.

RESULTS

Identification of cell clusters of L. vannamei hemocytes by scRNA-seq

In order to reveal the hemocyte functional subtypes, differentiation characteristic, and their differential immune response to WSSV infection, the hemocytes of L. vannamei were collected at 24h post-WSSV infection and submitted for scRNA-seq. The sequencing results obtained were co-analyzed with our previous scRNA-seq data from healthy shrimp hemocytes (SRR18899521), and the unified data set was created and analyzed by the official 10X Genomics analysis software Cell Ranger (Fig. 1A). After filtering out invalid or low-quality cells, 9,043 and 8,414 validated hemocytes were obtained from healthy and WSSV-infected L. vannamei, respectively, and the information of nGenes, nUMIs, and mitochondrial mRNA percentages in each sample were provided in Table S1. After analysis using t-Distributed Stochastic Neighbor Embedding (tSNE) dimensionality reduction and unsupervised cell clustering, a total of 16 hemocyte clusters with distinct gene expression profiles were identified based on the scRNA-seq data of two samples (Fig. 1B and C; Fig. S1A through C). The cell number in different hemocyte cluster gradually decreases from cluster 0 to cluster 15, and there is a significant difference of the proportion for each hemocyte cluster before and post-WSSV infection (Fig. 1D; Table S1). The amounts of upregulated genes detected in each hemocyte cluster also exhibited an obvious difference (Fig. S1D). Among these differentially expressed genes (DEGs), top five genes in each hemocyte cluster were screened and shown in dot plot with their expression abundances (Fig. 1E). The gene expression heat maps of the top five genes in the 16 clusters were also generated (Fig. S1E), and the detailed information for the top five genes are provided in Table S1. Given that compared with clearly defined subpopulations in Drosophila hemocytes, no cell type-specific marker genes are available for specifying cell types of shrimp hemocytes (23, 26), therefore, the top expression genes we identified have a potential to be employed as the marker genes for shrimp hemocyte clustering.

Fig 1.

Single-cell profiling of hemocyte populations in Litopenaeus vannamei. (A)The schematic of scRNA-seq and its data analyses. (B)t-SNE plot of integration of data sets from health (yellow) and at 24h post-WSSV infection (blue). (C)Clustering of shrimp hemocytes from both conditions reveals a total of 16 clusters. Different cell clusters are color coded. (D)Proportion of cells in each cluster and their distribution in the healthy and WSSV-infected samples. (E)Dot plot representing the top five genes enriched per cluster based on avg. logFC. Dot size represents percentage of cells expressing any gene in cluster. Dot color gradient represents the expression level, relative to other clusters.

Gene ontologies (GOs) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) analysis on hemocyte clusters

The DEGs in each cluster were separately annotated using GO and KEGG. The details of the functional annotation for each cluster were presented in Table S1 and Fig. S2 The genes associated with “stimulated response” and “immune system processes” in the GO term were enriched in clusters 0–5, 8–11, and 13–15, aligning with the pathways associated with the “immune system” in the KEGG (Fig. 2A and B). In clusters 0–4, 9, 10, and 13–15, genes involved in cellular metabolism exhibited higher levels of enrichment (Fig. 2C and D). However, each cell cluster was enriched for no more than 15% of its total number of upregulated differential genes in both GO and KEGG functional analyses, and most of the genes enriched were multifunctional cytokines. As a result, the distinctive antiviral function of the cell clusters was not emphasized, aligning with previously reported findings (23, 28). Additionally, the presence of cell proliferation of the GO term and KEGG pathway was found in cluster 9, while genes related to DNA replication, repair, and nucleotide metabolism functions were highly enriched in cluster 15, suggesting potential cell proliferation functions for both types of clusters (Fig. 2C and D).

Fig 2.

Plot profiling of the KEGG pathway enrichment analysis and GO analyses in each cluster. (A)The numbers of genes involved in “immune system process” and “response to stimulus” of GO terms as estimated in each cluster. (B)The numbers of genes involved in “Immune system,” “Infectious diseases,” and “Signal transduction” of KEGG pathways as analyzed in each cluster. (C)Dot plot enrichening of the cellular metabolism and cell proliferation of GO terms in each cluster. (D)Dot plot enrichening of the cellular metabolism and cell proliferation of KEGG pathways in each cluster. Color gradient of dots represents the different terms or pathways, while the size represents the number of genes estimated as distinct function of GOs or KEGGs.

Functions of hemocyte clusters during WSSV infection

The GO and KEGG enrichment analyses described above did not identify the distinct immune response function of shrimp cell clusters, nor did they elucidate the specific antiviral roles of each cluster. This might be due to incomplete annotation of the shrimp innate immune response pathway in the database (29). Therefore, a detailed analysis of the differentially upregulated genes in each cell cluster was conducted to characterize their expression levels and predict the immune function of each cluster during viral infection (Table S1). The results showed that the 16 hemocyte clusters were divided into five main categories, and they function cooperatively in the anti-WSSV response.

Clusters 9 and 15 exhibited the highest specificity, showing significantly greater enrichment of genes involved in cell proliferation/growth pathways and nucleic acid metabolism pathways in GO and KEGG analyses compared to other clusters. Additionally, the analysis of upregulated genes showed significantly elevated expression of genes involved in cytokinesis, specifically cytoplasmic division, in cluster 9, suggesting that cells in this cluster are in the late stage of mitosis (Fig. 3A). Genes involved in DNA replication and repair were enriched in cluster 15, indicating that it is likely in the early stage of mitosis (Fig. 3B). It is notable that the GO and KEGG enrichment revealed an enrichment of “response-stimulating” related genes in both two clusters, but the genes enriched in the process were apparently multifunctional cytokines, also involved in cell division, metabolism, and other processes. Also, a detailed gene analysis revealed the absence of shrimp-specific immune-associated molecules in both cell clusters. Thus, clusters 9 and 15 should evidently be cell types that are in the cell cycle and might not perform characteristic immune functions. A portion of mitotic cells were also found in the circulating hemocytes of the shrimp, and these cells might be critical for the hemocyte self-renewal (23, 30).

Fig 3.

Heat map showing the top functional genes by various cell clusters. (A)Heat map profile of the cell division genes in cluster 9, relative to other clusters. (B)Heat map profile of the DNA replication and repair genes in cluster 15, relative to other clusters. (C)Heat map profile of the coagulation-related genes in clusters 0 and 12, relative to other clusters. (D–F)Heat map profile of the molecules of cellular metabolism and stress in clusters 3 and 4 (D),clusters 10 and 13 (E),and cluster 2 (F),relative to other clusters. Color gradient represents the expression level of each cluster.

In clusters 0 and 12, the characteristic genes that were significantly upregulated were two hemocyte transglutaminases (TGases/TGMs) (Fig. 3C). Calcium-dependent TGM, a core protein involved in the coagulation of shrimp hemocytes, is released from hemocytes in response to external stimuli and forms protein complexes by cross-linking with coagulation proteins. These complexes trap viruses, preventing their spread in the hemolymph (31, 32). Highly expressed profiles of some prophenoloxidase-activating factors such as PPAF2 and PPAF1 were also found in clusters 0 and 12 (Fig. 3C). These prophenoloxidase molecules synergistically stimulate the release of TGM during hemocyte agglutination (33). Additionally, a number of genes acting in clot formation and cell adhesion, including NINJ, CNN3, Smad3, and so on, were found to be upregulated in the two cell clusters (Fig. 3C), and all of these proteins appear to be capable of assisting in the hemocyte coagulation process. The initial step of hemocyte coagulation involves pathogen recognition (34), as supported by the upregulations of CTLDcp2, RNase H2, and Lrrfip2 in clusters 0 and 12 (Fig. 3C). Based on these findings, it is hypothesized that clusters 0 and 12 synthesize and release a large quantity of agglutination-associated molecules upon recognition of WSSV, facilitating hemolymph coagulation to prevent WSSV diffusion in the hemolymph.

In clusters 2, 3, 4, 10, and 13, only a few immune molecules were expressed detectably, suggesting that these clusters might not be the main antiviral cell types during WSSV infection. However, in these clusters, molecules of cellular metabolism and stress were significantly enriched. Cellular metabolism is closely intertwined with the state of cellular stress. Generally, mitochondrial metabolism intensifies after a cellular stimulus, promoting the production of oxidative stress and further inducing processes such as endoplasmic reticulum stress and DNA stress to counteract the stimulus for protecting the cell (35, 36). In clusters 3 and 4, mitochondrial metabolism-related genes were significantly up-expressed, especially those related to the mitochondrial electron transport chain, indicating that this cluster might serve primarily as a source of energy to meet the metabolic demands of hemocytes, while the cluster also exhibited a strong oxidative stress state (Fig. 3D). In clusters 10 and 13, mitochondrial oxidative stress and endoplasmic reticulum stress-related genes were both upregulated (Fig. 3E). Characteristic genes of the endoplasmic reticulum stress state were dominantly enriched in cluster 2, such as PRP25 and TMEM258 (Fig. 3F). Altogether, mitochondria and the endoplasmic reticulum, the central organelles of cellular metabolism and stress, were significantly enriched in these five cell types for their associated molecules, implying that these hemocytes were mainly engaged in stress response during WSSV infection, with cellular rescue via reactive oxygen species or endoplasmic reticulum stress molecules (1, 37).

The most noteworthy immune functions of clusters 1, 7, 11, and 14 are associated closely with pathogen recognition, antigen processing, and immune signaling, which seems to suggest that these four clusters are the main types of immune recognition cells in shrimp. Unlike the immune recognition molecules described in the previous clusters, the cells in these clusters primarily utilize lectin-like molecules for pathogen recognition, particularly C-type lectins (Fig. 4A). Lectin-like molecules have been demonstrated to be the predominant pattern recognition molecules for pathogens in shrimp and are currently the most intensively studied pattern recognition molecules in hemocytes (38, 39). Antigen processing-related molecules and signal transduction-related genes were also significantly highly expressed in these four cell types, suggesting that these cells seem to be capable of processing viruses and further transmitting immune signals to other hemocytes (Fig. 4C and D). Although the presence of antigen-presenting cells in invertebrates is not found, the appearance of molecules related to antigen processing in Drosophila and shrimp suggests that antigen presentation processes are probably conserved in animals (40–42). Meanwhile, immune effector molecules, such as ALF, ZNFX1, and Lyz1, appeared to be upregulated in the four clusters (Fig. 4B). Based on the presence of genes in the above four types of cell clusters, we venture to speculate that clusters 1, 7, 11, and 14 have functions analogous to those of antigen-presenting cells, recognizing foreign viruses, initiating antigen processing, and transmitting immune signals to other cells while also activating classical immune signaling pathways in the body to a certain extent, releasing immune effector molecules and thus resisting viral invasion.

Fig 4.

Molecular characterization of the key antiviral hemocyte clusters. (A–D) Violin plot showing the genes profiles of pathogen recognition (A), anti-virus (B), immune signaling (C), and antigen processing (D) in clusters 1, 7, 11, and 14, relative to other clusters.

According to the upregulated molecules of antimicrobial peptides (Crustin and Penaeidin/PEN) and genes related to the phenoloxidase system (Prophenoloxidase/PPO and Kazal-type proteinase inhibitor/KPI), clusters 5 and 6 are expected to have the most significant antiviral immune function (Fig. 5A and B). These two clusters are likely the key cell types involved in the synthesis and release of antimicrobial peptides and phenoloxidase-mediated melanization, which contributes to viral elimination (18). Cluster 8 showed upregulated expression of anti-lipopolysaccharide molecule (ALF5) type antimicrobial peptides, while the most significant antiviral molecules were primarily zinc finger proteins and RNAi-related molecules (Fig. 5C). Zinc finger protein ZFAND6 and ZnF-CCCH are primarily involved in RNA metabolism and play important roles in regulating immune cell activation and antiviral responses (43, 44). Dicers are members of the RNAase III family that catalyzes the cleavage of double-stranded RNA into small interfering RNAs and micro RNAs, leading to sequence-specific gene silencing (45, 46). Therefore, it is probable that cluster 8 mediates its antiviral immune response through the process of RNA interference. Both clusters 5 and 6 had upregulation of pathogen recognition-related molecules such as FCN1, Spon2, and FREP-1 (47–50). Similarly, cluster 8 also displayed upregulated expression of these pattern recognition molecules, indicating their involvement in the activation of immune functions dependent on pathogen recognition (51). Considering the characterization of signature antiviral molecules in shrimp, clusters 5, 6, and 8 are identified as the primary cell types involved in the antiviral immune response.

Fig 5.

Molecular characterization of the clusters 5, 6, and 8. (A, B) Violin plot showing the genes profiles of AMPs (A) and proPO (B) in clusters 5 and 6, relative to other clusters. (C) Dot plot representing the top immune genes enriched in cluster 8 based on avg. logFC. (D) Schematic diagram depicting the immune response function of different types of hemocyte clusters during WSSV infection.

Post-WSSV infection, clusters 0 and 12 might serve as the primary cell types involved in hemocyte coagulation and cell adhesion to impede the spread of WSSV. Clusters 1, 7, 11, and 14 are likely to represent the major immune recognition cells in the hemolymph, capable of recognizing WSSV, transmitting immune signals to other cell clusters, and performing specific immune response functions. Clusters 5, 6, and 8 should be pivotal immune response cells that play crucial roles in combating WSSV. Clusters 2, 3, 4, 10, and 13 are probably the predominant cell types playing crucial roles in the cellular stress response to WSSV infection. Clusters 9 and 15 represent cell types characterized by high proliferation and replication capacity (Fig. 5D).

Differential susceptibility of hemocyte clusters to WSSV

The scRNA-seq data of hemocytes were mapped to the WSSV genome to characterize the profiles of viral genes in distinct hemocyte clusters. The t-SNE plot depicted the transcriptional abundances of WSSV genes in 16cell clusters, highlighting the distinct transcription patterns of WSSV genes among different clusters following viral infection (Fig. 6A). The most highly expressed viral genes were screened and identified however, which were not found to be expressed in the Clusters 0, 3, 4, 6, 12, and 13, suggesting the cells of these clusters were insensitive to WSSV despite presence of several viral transcripts. This is supported by the heat map results for the top 20 highly expressed viral genes in different cell clusters (Fig. 6C; Table S1). Within the remaining hemocyte clusters, there were variable amounts of highly expressed viral genes; to be noted, the highest transcription level of WSSV genes were found in cluster 8, which is significantly higher than those in other cell clusters, indicating that hemocytes of cluster 8 were most susceptible to WSSV (Fig. 6B). The result was also confirmed by the analysis of the cellular viral load, given that cluster 8 was found to have the highest level of viral load (Fig. 6D; Table S1). Meanwhile, the infected cells with moderate to high viral load were also present in the remaining cell clusters, indicating that all hemocyte clusters were capable of being infiltrated by WSSV (Fig. 6D).

Fig 6.

Analysis of viral gene expression and viral load in hemocyte clusters post-WSSV infection. (A)t-SNE displaying the average expression of all detected WSSV genes in each hemocyte cluster. (B)Statistics on the number of specific viral genes in each cluster. (C)Heat maps showing the normalized expression of the top 20 highly expressed WSSV genes in each cell cluster. (D)The proportion of hemocytes with different viral load in each cluster. Shown are the percentages of low (green), medium (orange), and high (red) viral load states within the population of infected cells in each cluster. (E)Dot plot representing the critical WSSV genes enriched per cluster based on avg. logFC. (F)Validation of the highly specific cell cluster gene of cluster 8 among hemocyte subpopulations by fluorescent in situ hybridization, and WSSV distribution detected by immunofluorescent assay (IFA), bar = 10µm.

The expression of WSSV genes was partitioned into three temporal phases: immediate early (IE), early, and late (52), and we have characterized the expression of signature genes for the different viral infection stages in each cell cluster to further elucidate the viral infection status in different clusters. IE genes are the first class of WSSV genes expressed after primary infection or reactivation of latent infection that initiate viral replication and/or regulate cellular functions to aid viral replication (53). The WSSV-IE genes, IE1 and IE2, were highly expressed in most hemocyte clusters except clusters 0, 3, 4, 6, 12, and 13, which is consistent with previous speculation that clusters 0, 3, 4, 6, 12, and 13 are insensitive to WSSV, while other cell clusters are more susceptible to viral infestation (Fig. 6E). The signature genes of the early stages of WSSV infection, including latency-related genes, chimeric thymidine and thymidylate kinase, viral protein kinase, ribonucleotide reductase, and nonspecific nuclease, are highly significantly up-expressed in cluster 8 (Fig. 6E), suggesting that viral replication is more active in this cluster, which also explains the highest viral load present in cluster 8 (14). Meanwhile, the signature envelope protein genes of late WSSV infection, VP24, VP26, and VP28, were similar in distribution characteristics to the IE genes (Fig. 6E), which might be due to the timing of sampling and the inconsistent viral infection process (52). Overall, the differences in the distribution of WSSV signature genes for different infection stages reaffirm the ability of WSSV to invade all hemocyte types, while highlighting significant variations in viral tropism. Subsequently, using RNA-fluorescent in situ hybridization (FISH) combined with immunofluorescent assay (IFA), it was observed that potential marker genes of cluster 8 co-localized significantly with WSSV, and the virus infection level (virus-positive signal) in cluster 8 is significantly higher than in other cell types, which further corroborated our analysis (Fig. 6F; Fig. S3).

To further elucidate the susceptibility mechanism of cluster 8 to WSSV, we conducted a comprehensive characterization of the specific genes in this cluster. Cluster 8 was significantly increased in number and proportion of cells following viral infection (Fig. 7A). The specific upregulated genes totaled 374 and the most significant KEGG pathways identified by functional annotation included proteasomal, viral infection, gene expression, and metabolism-related pathways (Fig. 7B; Table S1). Previous analysis revealed significant upregulation of RNAi-related immune molecules in cluster 8, which serves as a major defense against WSSV infection. Additionally, lectin-like molecules, including FBP-1, were identified in cluster 8 and potentially contribute to virus recognition (Fig. 5C). Further analysis of the significantly upregulated genes in cluster 8 revealed that molecules of cell membrane/cytoskeleton, such as ARF6, CD63, and Rab3, were upregulated in expression. These genes are known to be involved in cellular endocytosis, confirming the attachment of WSSV to the membrane through cell membrane proteins and its subsequent entry into the cell via endocytosis (Fig. 7C). Genes involved in protein transport/vesicular transport processes also show significant upregulation, which might act in either the viral endosomal transport process or the viral release process (Fig. 7D). The high expression of mitochondrial metabolism-related molecules in this cell cluster is probably a result of virus-induced host metabolic reprogramming to facilitate its own replication (Fig. 7E). A large number of gene expression-associated nucleoproteins or transcription factors were found to be upregulated in cell cluster 8, and they could be utilized by WSSV to facilitate the replication, transcription, and assembly of viral genes in the nucleus (Fig. 7F). Genes related to the ubiquitin proteasome system (UPS) also appear significantly upregulated in cluster 8 (Fig. 7G). The UPS process is critical in the WSSV life cycle, assisting in viral immune escape, viral decapsidation, viral replication, transcription, and viral protein processing and assembly of progeny viruses (54).

Fig 7.

Reciprocal balance of viral infection and cellular immune response. (A) t-SNE showing the proportional change in cluster 8 pre- and post-WSSV infection. (B) KEGG enrichment pathway for highly expressed genes of cluster 8. (C–G) Heat maps displaying the high expressed genes involved in cell membrane/cytoskeleton (C), protein transport (D), mitochondrial metabolism-related molecules (E), expression-associated nucleoproteins or transcription factors (F), and ubiquitin proteasome system (G) in cluster 8, relative to other clusters.

Functional differentiation of hemocytes during WSSV infection

We next sought to investigate the effect of WSSV infection on hemocyte differentiation. For this purpose, the pseudo-times were constructed from the scRNA-seq data using Monocle 2, and the results showed that significant changes in differentiation status occurred in hemocytes from the WSSV-infected and healthy groups (Fig. 8A and B). Fig. 8C and D illustrated the status of each hemocyte cluster on the pseudo-time differentiation trajectory. To further characterize the transcriptional program underlying hemocyte differentiation, we identified cell differentiation-related genes in 16 hemocyte clusters (Fig. 9; Table S1). The results showed that clusters 9 and 15 were at the beginning of pseudo-time differentiation trajectory, but no expression of differentiation genes was found, and these two cell clusters have also been proven to be in a cell cycle state. Clusters 0 and 12 were in the initial differentiation and exhibited high expression of invertebrate hemocyte differentiation regulators such as Notch1 and HDC (Fig. 9A), whose signature gene TGM is also thought to be a marker of undifferentiated mature hemocytes (55, 56). Meanwhile, the presence of some genes that restrict cell division indicates that these two types of cells are the ones that are starting to functionally differentiate (26). In terms of cell function, clusters 0 and 12 are key types that perform immunological functions for hemocyte coagulation and cell adhesion. Clusters 2, 3, 4, 10, and 13 represented hemocytes at the intermediate stage of differentiation, as indicated by their placement in the middle of a pseudo-temporal trajectory by Monocle (Fig. 8C and D). And a large number of differentiation-related genes were present in these clusters (Fig. 9B), while we also found that few immune factors such as antimicrobial peptides were beginning to be upregulated in these cells. The significant expression of differentiation genes and the upregulation of immune molecules, such as antimicrobial peptides, validate that these five cell types are in an intermediate state (Fig. 9D). Clusters 5, 6, and 8 and clusters 1, 7, 11, and 14 exhibited the lowest levels of differentiation-related genes and were positioned at the end of the Monocle differentiation trajectory, representing terminally differentiated hemocytes. Considering their characteristic antiviral function, it is evident that these clusters represent mature hemocyte types (Fig. 9C and D). Interestingly, clusters 5 and 6 were located in different branches of the differentiation trajectory and exhibited high expression of crustacean hematopoietic factor (CHF) and Astakine 2 (Ast2) that act on the maturation of crustacean hemocytes (Fig. 9C), implying that the two types of clusters are likely to have different differentiation potential (23, 30, 57).

Fig 8.

Pseudo-time trajectories of hemocyte clusters. (A) Pseudo-time trajectory calculated from hemocytes in healthy and WSSV-infected groups. Dark colors of dots represent shorter pseudo-time and earlier differentiation periods. (B) Distribution of healthy and WSSV-infected hemocyte samples in the pseudo-time trajectory. (C, D) Pseudo-temporal ordering of different hemocyte clusters based on their gene profiles.

Fig 9.

Potential functional differentiation relationships among hemocyte clusters. (A, B) Dot plot representing the expression levels of differentiation-related genes in clusters 0 and 12 (A)and clusters 2, 3, 4, 10, and 13 (B).(C)Dot plot showing the expression profiles of genes in clusters 5 and 6 that regulate the direction of cell differentiation. Dot size represents percentage of cells expressing any gene in clusters. Dot color gradient represents the expression level, relative to other clusters. (D) Schematic showing potential lineage flow from the oligopotent state of clusters to mature cell types with their intermediates.

DISCUSSION

From morphological observations, shrimp hemocytes have traditionally been described as granulocytes, semi-granulocytes, and hyalinocytes (6, 58). However, with advancements in the study of shrimp hemocyte immune function, it has become evident that the complexity of the hemocyte population extends well beyond these broad cell subtype classifications due to the emergence of techniques such as monoclonal antibody-based assays, lectin binding, and media isolation (17, 59–61). Research utilizing single-cell sequencing techniques has now identified distinct hemocyte types in shrimp, providing further evidence that the complexity of hemocyte populations extends beyond the three traditional morphological subpopulation classifications. ScRNA-seq of hemocytes from Marsupenaeus japonicus demonstrated six classes of cells that differed in immune function, and differentiation trajectories (23, 62). The scRNA-seq of hemocytes for healthy Litopenaeus vannamei also identified three types of hemocyte clusters, and the re-classification of clusters reflected functional diversity within a wide range of cell subtypes or represented different stages of maturation (30). The scRNA-seq for rCREG (recombinant protein of cellular repressor of E1A-stimulated genes)-treated hemocytes of L. vannamei characterized prohemocytes, monocytic hemocytes, and granulocytes and also revealed the presence of a novel macrophage-like subset (63). Here, we used joint analysis of scRNA-seq data from hemocytes pre- and post-WSSV infection, aiming to gain a deeper understanding of distinct cell types and their transitional states. Importantly, our scRNA-seq could distinguish cell types in different states of differentiation and maturation, which are less well understood and for which marker genes were not previously available. Therefore, the relevant potential marker genes identified in this study should facilitate further investigations into these specific cell types.

In our previous study, it was shown that acute infection with high doses of WSSV caused significant reductions in the densities of hemocyte and its morphological subpopulations in the hemolymph, which disrupted the normal physiological functions of hemocytes and resulted in shrimp mortality (16, 64). Our scRNA-seq results revealed that the hemocyte types did not show changes pre- and post-WSSV infection, which is consistent with that reported in scRNA-seq of hemocytes from M. japonicus (62). This consistency indicates that our study offers a detailed and precise characterization of hemocyte status under these circumstances. However, our results differ from research by Xin et al., in which they found that new cell types emerged in shrimp hemocytes post-WSSV or -LPS (lipopolysaccharide) stimulation (65). The discrepancy could be attributed to variations in the dosage of virus infection or the timing of sampling (66). It could also stem from differences in the analytical approach, that they mapped scRNA-seq data to the shrimp genome and then performed full-length transcriptome mapping, possibly characterizing certain upregulated genes that we did not map (65). In common, all scRNA-seq results for hemocytes found significant differences in the number or percentage of different hemocyte taxa pre- and post-WSSV infection, implying that viral infection induces profound alterations in physiological and immunological features of hemocytes.

The integrated pseudo-time analysis of the hemocytes pre- and post-WSSV infection was performed to investigate the hemocyte functional differentiation, which showed a close similarity to the fast differentiation of immune cells in vertebrates that occurred in response to pathogen invasion (67). In our results, clusters 9 and 15 had the ability to divide continuously but had not yet shown high expression of differentiation genes, which closely resemble the prohemocytes identified in other arthropod hemocytes (22, 26). In crustaceans, hemocytes are matured from hematopoietic tissues and released into the hemolymph circulation (9), while a small number of hemocytes in active proliferation are also found in shrimp hemolymph (23). The notable increase in the proportion of clusters 9 and 15 following WSSV infection indicated that they are the prohemocytes released from hematopoietic tissues to to maintain the number of hemocytes and possess the potential to differentiate into diverse functional subsets in response to viral infection (26). The clusters 0 and 12 located at the beginning of cell differentiation are the main cell types responsible for the coagulation and cell adhesion, which exhibited significant decrease post-WSSV infection. The reduced proportion of these two cell types might be attributed to their migration to other tissues for tissue repair, as they are the main contributors to cellular agglutination (68, 69). Moreover, the arthropod hematopoietic differentiation factors, such as Notch, were significantly upregulated in clusters 0 and 12 and viral infection was likely to induce the migration and differentiation of these cell types toward other cell types, leading to a decrease in their numbers (26). This is supported by the increased proportion of clusters 2 and 10, which was located at the later differentiation compared to clusters 0 and 12.

Monocle trajectory analysis showed that clusters 2, 3, 4, 10, and 13 exhibit a less advanced developmental stage compared to clusters 0 and 12, placing them at intermediate states in the differentiation process. Importantly, these intermediate clusters displayed high expression levels of genes associated with differentiation, indicating their potential to further differentiate into different functional clusters. Following WSSV infection, a notable increase in the proportions of clusters 2 and 10 was observed, while cluster 13 exhibited a decrease. However, there were no significant changes in the proportions of clusters 3 and 4. These findings suggest that the functional differentiation of hemocytes in response to WSSV is a continuous and dynamic process, regulated and influenced by multiple factors. Based on the pseudo-time trajectory analysis, it was determined that clusters 1, 7, 11, and 14 represent cell types at the terminal stage of lineage development. These clusters are characterized by their roles in immune recognition, immune signal transduction, and antiviral effects. Notably, compared to cluster 7, clusters 1, 11, and 14 are considered potential precursor cell types in the terminal differentiation pathway. Moreover, following viral infection, there was a significant increase in the proportion of these three clusters, indicating their heightened involvement in the immune response against WSSV infection. Clusters 5, 6, and 8 represent a distinct group of cells that are crucial for mounting the primary immune response against WSSV. Among these clusters, cluster 8 exhibited the most pronounced increase in proportion, while cluster 5 showed no significant change, and cluster 6 displayed a significant decrease following viral infection. Interestingly, both clusters 5 and 6 exhibited upregulated expression of hemocyte maturation-related genes, including CHF. This suggests that both clusters, particularly cluster 6, may serve as precursor cells capable of maturing into cluster 8, thereby contributing to the observed increase in the proportion of cluster 8 and decrease in cluster 6. Furthermore, previous studies have indicated that cell types that express high levels of antimicrobial peptides are likely to migrate to heavily WSSV-infected tissues (62, 70, 71). This observation may explain the divergent shifts in the proportions of cluster 6, as it is possible that these cells migrated to specific infected tissues to combat the viral infection.

Our results showed that the transcripts of viral genes could be detected in all the hemocyte clusters with different levels, suggesting that WSSV could invade all hemocyte types and initiate the transcription of viral genes to different extent in different hemocyte clusters. The differences of viral genes expression might be closely related to the differential immune status and differentiation characteristics among hemocyte clusters (72). It was not surprising that the cells in clusters 9 and 15 with active proliferative potential were selected as target cells by WSSV, as DNA viruses are likely to utilize host cell cycle-related genes for their own replication (73). The high-level transcription of the late WSSV genes VP26 and VP28 indicated that the virus has completed replication and proliferation in these two cell clusters. Previous studies have also shown that crayfish hematopoietic tissue cells and shrimp lymphoid tissue cells, which are in an active mitotic state and produced circulating hemocytes, could be infected by WSSV and allowed effective viral replication (74, 75). The clusters 0 and 12 that represent the early differentiation stages displayed the lowest transcription levels of viral genes, including IE genes, early and replication-related genes, and late genes. This might be due to the high expression of the genes involved in hemocyte coagulation and adhesion, which facilitate the formation of cellular aggregation and adhesion complex as a “protective layer” to prevent the virus entry at the initial stage (76, 77). Clusters 2, 3, 4, 10, and 13 in the intermediate differentiation stages exhibited significant differences in viral gene transcription. Among them, cells in clusters 3, 4, and 13, which were in the slightly earlier differentiation stage, had the lowest levels of viral load, and the transcription levels of viral functional genes were relatively low. We speculated that on one hand, these three cell clusters differentiated from clusters 0 and 12 still retain partial transcriptional characteristics of extracellular-related molecules, thus also delaying the entry of WSSV to some extent. On the other hand, in clusters 3, 4, and 13, mitochondrial stress-related genes are most dramatically upregulated, which led to the generation of a large amount of reactive oxygen species (ROS) and other stress products, and these stress responses could potentially kill WSSV and block its transcription within the cells (78–80). In contrast, cells in clusters 2 and 10, which were at a slightly later stage in the pseudo-time differentiation trajectory, exhibited high transcription levels of viral genes, especially the IE genes and late genes, suggesting that WSSV has successfully infected these cells. In these subsets, the significantly enriched genes were primarily related to endoplasmic reticulum (ER) stress and the unfolded protein response (UPR), which is in accordance with the state of the cellular environment during viral infection. Upon virus invasion, the abundant translation and synthesis of viral proteins in the endoplasmic reticulum can trigger ER stress, and to protect host cells from ER stress-induced cell death, allowing for the translation of viral proteins and effective viral replication, the virus may induce and manipulate the UPR (81–83).

Cells in different terminal differentiation pathways demonstrated high transcription levels of WSSV genes. Notably, the highest transcription level of viral IE and replication-related genes was detected in cluster 8, indicating the cluster that represents the cell type at late differentiation stage are the most permissive target cells for WSSV. Based on our findings, it seems that WSSV primarily infects late-stage differentiated hemocytes, although the immature or early-stage differentiated hemocytes could be also infected by WSSV. Supportedly, results of transmission electron microscope revealed that following WSSV infection, intact virus particles are prominently present within the nuclei of granular hemocytes (late-stage differentiated cells) in Cherax quadricarinatus, Marsupenaeus japonicus, Penaeus merguiensis, Penaeus monodon, and Palaemon serratus, but hyalinocytes (early-stage differentiated cells) show minimal occurrences of WSSV particles (84–86). And our previous research findings also indicated a notably higher WSSV infection rate in granular hemocytes of Fenneropenaeus chinensis (16). However, these findings seem to contradict some reported conclusions, which believed that WSSV only could complete replication and proliferation in immature cells or early-stage differentiated hemocytes, such as crayfish hematopoietic tissue cells (75, 87). Taking into account several aspects related to viral replication and hemocyte differentiation, there is a considerable likelihood of viral infection targeting the late-stage differentiated hemocytes. Firstly, among all cells with high transcription levels of viral genes, especially in late-stage differentiated hemocytes, enrichment of different IE genes had been observed, which play crucial roles in initiating viral replication and/or regulating cell functions (53, 88). It was documented that successful transcription of viral IE genes would lead to the upregulation of host cell cycle and/or differentiation-related genes to regulate the cellular state, making it more suitable for viral replication (89, 90). Thus, these evidences indicated that WSSV could efficiently utilize the host cell’s machinery to replicate and propagate in late-stage differentiated hemocytes upon invasion. Secondly, as the largest DNA virus, the gene-encoded products of WSSV might include all or part of the proteins required for its own DNA replication. Although the characterization and functional studies of WSSV proteins are not yet fully understood (91), it has been confirmed that some WSSV proteins are involved in its DNA replication (92, 93). This suggested that the replication of the nucleic acid by WSSV itself might not be dependent wholly on the host DNA replication machinery, and therefore, viral replication might still occur in the late-stage differentiated hemocytes with arrested DNA replication (62, 94). Until now, due to the inability to accurately identify and separate the hemocyte subpopulations in different differentiation stages, the susceptibility and vulnerability of different hemocyte subtypes to WSSV was still needed to be further explored.

There is a very complex interaction between viruses and their hosts. During viral infection, the host must respond by activating multiple defense mechanisms, and as intracellular specialized parasites, viruses adopt various strategies to hijack host cell mechanisms for their own benefit (95, 96). In the past decades, considerable efforts have been devoted to understanding WSSV-host molecular interactions. When placed in the context of previously published scRNA-seq studies in shrimp hemocytes (62, 63, 65), our results provide additional resolution and perspective to the burgeoning study of shrimp immune cells. We have described a detailed description of the functional characteristics in different cell clusters, which provided a theoretical basis for a deeper understanding of the synergistic immune interactions among different types of shrimp hemocytes (Fig. 10A). Upon entry of WSSV into the hemolymph, clusters 1, 7, 11, and 14, which primarily perform immune recognition functions, were the first to recognize the virus through lectin-like molecules. Subsequently, they processed and transmitted immune signals through a pathway similar to antigen presentation to activate the immune processes of other cell types, thereby triggering systemic immune responses. Clusters 0 and 12, on receiving the immune signals transmitted by the aforementioned cells, rapidly initiated the coagulation process through signal transduction pathways and released molecules such as TGase and cross-linking proteins to prevent the spread of WSSV in the hemolymph. Clusters 5, 6, and 8 enhanced the immune processes by receiving immune signals and through traditional immune signaling pathways such as Toll and IMD (97, 98), promoting the expression and release of immune molecules such as antimicrobial peptides and activating the phenoloxidase system. The molecular expression pattern in cluster 8 suggested that upon viral entry into host cells, the ubiquitin proteasome system degrades viral proteins, while the RNAi pathway inhibits viral replication and transcription processes (99). During this period, immune signals and the virus stimulated mitochondrial stress and endoplasmic reticulum stress processes in clusters 2, 3, 4, 10, and 13, leading to the production of ROS to counteract the virus. Multiple cell types collaborate in their antiviral actions to maintain the homeostasis of hemocytes after viral infection. It has also been demonstrated that WSSV utilizes various host molecules and pathways for infection and proliferation, eventually leading to its release (12).

Fig 10.

The interactive balance of hemocyte immunity and WSSV invasion. (A) The schematic showing the synergistic immune pathways by which hemocyte clusters resist WSSV infection. (B) The schematic showing the presumed mechanism of WSSV infestation into hemocytes and the viral life cycle.

Our scRNA-seq results further reveal the molecular characteristics of cluster 8, which was the primary cell type for WSSV proliferation and replication. Through the analysis of upregulated genes in cluster 8 and in combination with previous studies, we inferred the mechanism of WSSV infection in cluster 8 (Fig. 10B). Under the action of pattern recognition molecules, WSSV successfully adheres to the hemocyte membrane and enters the cell via endocytic pathways, involving the participation of the cytoskeleton for intracellular trafficking (100). During this process, the virus stimulates mitochondrial metabolic reprogramming, facilitating immune evasion (101). Once near the cell nucleus, the virus undergoes uncoating, and the viral genome is released into the nucleus for replication and transcription (100). The replicated viral genome remains in the cell nucleus, while the translated viral proteins are further processed in the endoplasmic reticulum and Golgi apparatus before translocating into the nucleus (102). Finally, the viral genome and viral proteins assemble in the nucleus and are transported to the cell membrane using the cellular cytoskeleton for release (103). The success of viral invasion is closely associated with its ability to disrupt the host’s immune balance, which is crucial for the host survival (19, 34, 104).

In addition to the identification of various states of cell types and characterization of cellular functions, our study also suggests several new insights for a number of genes and pathways in hemocyte biology that warrant further investigation. The first includes the process of antigen presentation, which is the key process for delivering immune signals and initiating the immune response in higher animals (105). Our results characterized genes related to the presentation of immune signaling in clusters 1, 7, 11, and 14, and they exhibit significant upregulation of a large number of C-type lectin-like immune recognition molecules and immune signaling molecules, similar to the antigen presenting cells in vertebrate (106). Previous studies in Drosophila have found that the fat body production of antimicrobial peptides is dependent on antigen processing by hemocytes (40). Also, numerous transcriptomic studies of invertebrate hemocytes following pathogen attack have found significant enrichment of antigen-processing molecules (107, 108). These studies suggested that antigen-presenting cell types are likely to be present in invertebrates such as shrimps for activating the systemic immune response process (109). An increasing number of studies have shown that the UPS could be exploited by host cells and viruses as an immune response strategy to outwit each other (54). The UPS is the primary protein degradation mechanism for the selective degradation of various proteins that promote viral infestation, including viral proteins, thereby limiting viral infection in the host cell (110, 111). Viruses could manipulate the system to degrade intracellular proteins that are detrimental to viral infection or modify their function directly by encoding viral ubiquitination/de-ubiquitination enzymes, as well as indirectly by using endogenous molecules of the ubiquitin system. Up to now, UPS has been reported to function significantly in the different life cycle stages of a variety of viruses, including viral uncoating, viral replication, viral envelopment, viral protein expression and progeny release (112–114). We further analyzed the clusters that are sensitive to WSSV and those that play a key antiviral response. Notably, there was a significant upregulation of UPS-related genes in cluster 8, implying that the system might have a dual function, both in modulating the immune response process in host cells to exert antiviral effects and in being manipulated by WSSV to act on viral infestation. Previous studies have also confirmed our hypothesis that the viral proteins WSSV277 and WSSV304 are ubiquitinated during WSSV infestation, thereby inhibiting viral replication (115). Viral proteins, including WSSV199, WSSV222, WSSV249, and WSSV403, could act as E3 ligases in concert with the ubiquitin proteasome pathway of shrimp to promote viral replication (116, 117). However, the functional mechanisms of the shrimp UPS and its major participating molecules have not yet been fully identified, and how WSSV utilizes the UPS to resist host antiviral processes has not been systematically elucidated.

Zinc finger proteins are among the most abundant protein family in the body and are broadly involved in DNA repair, gene transcription, signal transduction, and innate immune response (118). An array of zinc finger proteins, in particular the tripartite motif family proteins, has been demonstrated to play an important role in antiviral immunity through the ubiquitination of target proteins, which has been intensively studied and systematically reviewed (119), and the critical role of the ubiquitin proteasome system in WSSV infection was also characterized in the present study in the genes analysis for cluster 8. However, it is noteworthy that other than ubiquitin modification, zinc finger proteins employ a variety of antiviral strategies by their nucleic acid-binding property and various protein-interaction domains, mediating direct and rapid antiviral innate immune responses, particularly the CCCH-type members, could detect viral nucleic acid, and elicit subsequent antiviral immune responses (44). ZnF-CCCH type molecules were also characterized in our results, and their expression was significantly upregulated in cluster 8 with high viral load, most likely acting on key antiviral genes. Of course, numerous zinc finger protein molecules like ZnFX1 were identified in other cell clusters, which might play a broader role. It is important to mention that we found that WSSV appears to be released using the host vesicle transport process, which could also be considered the exosome pathway, since some of the tetraspanins involved in vesicle transport were significantly upregulated in cluster 8. Recent studies in shrimp and crab have also shown the presence of infectious WSSV in either extracellular vesicles or exosomes (120, 121), which confirms our results. However, in previous reports, WSSV was thought to release progeny virus by lysing cells (103). It is unclear whether this contradiction is due to technical differences in experimental methods or other reasons. Nevertheless, the systematic investigation of the mechanisms of WSSV release from hemocytes would provide new thoughts for studies related to the interruption of virus transmission. Future studies involving a comprehensive analysis of these potentially functional genes or pathways would advance our understanding of shrimp hemocytes in the context of immune response, provide new insights into the replication and pathogenicity of WSSV in hemocytes, and possibly lead to a new avenue of therapeutic intervention for the virus.

In summary, this work provided the first elucidation of functional differentiation characteristics of L. vannamei hemocytes post-WSSV infection and revealed differential viral susceptibilities and antiviral responses of different hemocytes clusters. Moreover, our scRNA-seq also uncovered multiple critical functional pathways against WSSV infection, which provided useful information for the future remediation efforts. Due to current constraints and limitations, we have to acknowledge the lack of accurate validation experiments, such as the marker genes that were assigned to different clusters need to be confirmed by independent methods, the roles of key pathways/genes in viral infection, and the correlation between hemocyte functional clusters and morphological subtypes. To address these experimental questions, there is an inherent need to develop additional tools and resources for the study of shrimp immune cell populations, including in-depth analysis and characterization of relevant marker genes and their functions, development of specific marker antibodies, and cultivation of stable hemocytes in passaging cultures (23, 63, 65). Even with regard to WSSV infection, earlier time points or lower doses of virus might provide new insights into the vulnerability of hemocyte types and the mounting of an immune response. Our scRNA-seq data of L. vannamei hemocytes would serve as a platform providing the necessary information for the continuous study of shrimp genes and their functions, lay a solid foundation for further research on cellular differentiation and immune defense mechanism of shrimp hemocytes, and also provide the valuable clues for the prevention and control of shrimp diseases.

MATERIALS AND METHODS

Shrimp, viral infection, and hemocytes preparation

The healthy adult shrimp (Litopenaeus vannamei), 30–40 g, were obtained from an aquaculture farm in Qingdao, Shandong Province. Then, the shrimp were temporarily maintained at 23°C in tanks containing aerated filtered seawater for 1 week and used for the following studies.

After acclimation, 1 mL of hemolymph was withdrawn from the pericardial cavity of one healthy shrimp individual using a 5 mL syringe which contains sterile cold Alsever anticoagulant (27 mM Na citrate, 336 mM NaCl, 115 mM glucose, 9 mM EDTA, pH 7.2, AS). The hemolymph of three healthy shrimp were mixed into one sample (i.e., the healthy shrimp hemocyte sample) and centrifuged at 400 g for 5 min at 4°C, then the supernatant was discarded. For WSSV-infected shrimp group, 10 shrimp were randomly selected as the WSSV-infected group and intramuscularly injected on the flanks of the second abdominal segment with 100 µL WSSV inoculum per shrimp (containing 10⁴ viral copies/μL). The WSSV inoculum was prepared and quantified according to the previous method of our lab (16, 122). At 24 h post-WSSV infection, the hemolymph were withdrawn from three WSSV-infected shrimp to collect hemocytes and mixed into one sample (i.e., the virus-infected shrimp hemocyte sample). The hemocytes collected from healthy shrimp and WSSV- infected shrimp were respectively resuspended with AS, followed by passing through a 45 µm cell strainer (Corning, USA) to remove adherent cells. Hemocytes used for scRNA-seq must ensure a cell concentration of approximately 1,000 cells/µL and cell viability of ≥95%. Hemocytes were collected from the remaining seven WSSV-infected shrimps at 24 h post-infection and settled onto glass slides for subsequent experiments according to our previous procedure (30).

Single hemocyte encapsulation and reverse transcribe reaction

Single hemocytes capture of the healthy shrimp hemocyte sample and the WSSV-infected group shrimp hemocyte sample was performed using the 10X Genomics Chromium Controller system (10X Genomics, Pleasanton, CA, USA), respectively. Briefly, hemocytes of each sample were first resuspended using Chromium Single Cell 3´ Reagent v3 Kits (10X Genomics), and then the generated mixtures of each sample were transferred separately to the Chromium Controller system to generate nanoliter-scale single-cell gel bead-in-emulsions (GEMs). The gel beads in the Chromium Single Cell 3´ Reagent v3 Kits contain tens of thousands of cell barcodes to separately index individual cells, unique molecular identifiers (UMI) to label mRNA transcription profiles and poly(dT) sequences. According to the 10X Genomics Chromium Controller system protocol, each group of generated GEMs was reverse transcribed using the Library and Gel Bead Kit (10X Genomics, USA) to achieve barcoded cDNA amplification of each cell individually. The amplified cDNA products of each sample were purified separately using the SPRIselect Reagent Kit (Beckman Coulter, Brea, CA, USA). Quality of purified cDNA form each sample was tested using the Agilent Bioanalyzer High Sensitivity assay (Agilent, USA) and Qubit dsDNA BR Assay (Invitrogen, USA).

CDNA library construction and sequencing

The indexed sequencing library of amplified cDNA from each cell in each sample was constructed separately using the reagents in the Chromium Single Cell 3´ Library v3 Kit. In brief, the obtained cDNAs of each sample were sequentially modified by fragmentation, end-repair, and A-tailing according to the instructions. Then, the SPRIselect beads were applied to the size selection, adaptor ligation, and the final purification process. The obtained single-cell 3´ library was subjected to Illumina bridge amplification using P5 and P7 primers to generate the standard Illumina paired-end constructs. Post quantified and characterized, the final barcoded sequencing libraries of each sample were separately uploaded to HiSeq2500 (Illumina, San Diego, CA), and each sample was sequenced individually with a custom paired-end sequencing mode (26 base pairs for read 1 and 98 base pairs for read 2).

ScRNA-seq data processing

Through the above process, the raw single-cell RNA sequencing data sets were obtained on the healthy shrimp hemocyte sample and the virus-infected shrimp hemocyte sample. The two raw single-cell RNA sequencing data sets (SRR18899520 and SRR18899521) were performed through Cell Ranger for data quality statistics, aligned reads, and generated gene-cell matrices, respectively (122). Read1 and Read2 were both obtained by Illumina double-end sequencing, and Read1 was used for distinguishing individual cells and UMI, while Read2 was compared with the reference genome of L. vannamei (ASM378908v1) by the STAR software. The reads located in exons were compared to transcripts containing annotation information and are annotated as transcriptomic reads if they were in the same direction and were considered to be uni-mapped if the transcriptomic reads match only one gene. UMI counting was only available using uni-mapped transcriptomic reads. The filtered and quantified scRNA-seq data of each sample were generated gene-cell matrices by Cell Ranger.

Cell clustering and DEGs analysis

In order to comprehensively investigate the changes in hemocytes pre- and post-WSSV infection, the obtained single-cell sequencing data of hemocytes from healthy L. vannamei were combined with the scRNA-seq data of hemocytes post 24 h WSSV infection. The scRNA-seq results were further analyzed using Seurat (v.4.0.3) function to identify cell clusters and to characterize DEGs. Seurat is a popular R package that can perform quality control (QC), analysis, and exploration of scRNA-seq data, which was originally developed as a clustering tool for scRNA-seq data (123). In this study, the cell subset was grouped by graph-based clustering based on the gene expression profile of each cell in Seurat. After removal of low-quality cells, data were normalized. Harmony was used for data merging and batch effect correction to stabilize the clustering, and the normalized expression levels were used to perform canonical correspondence analysis to correct for the batch effect which was followed by data integration to carry out Z-score normalization (124). The normalized data were clustered using principal component analysis (PCA) and visualized with t-SNE (125) or Uniform Manifold Approximation and Projection (UMAP) (126). Other data analyses including standardization, difference of gene expression, and marker gene screening were also achieved by Seurat. The scRNA-seq data were validated for analysis according to the following QC criteria: (i) 500 < gene counts < 4,000 per cell; (ii) UMI counts <8,000 per cell; (iii) the percentage of mitochondrial genes <10%. Following QC, the number of unique UMIs was calculated as the expression level of the cellular gene. It should be noted that when performing gene quantification, “UMI Total” does not include “UMI WSSV.” Firstly, the normalized data were compared to the distribution of P-values by the “JackStrawPlot” function and the most significant principal components (P < 10⁻⁵) were selected for downstream clustering and cluster analysis in the PCA results to reduce the number of feature dimensions. Subsequently, graph-based clustering was then run to “FindClusters” function to group cells with similar expression profiles, thereby constructing a sparse nearest neighbor graph. Finally, a resolution of 0.1 was chosen as the clustering parameter, which identified the clusters, then the t-SNE and UMAP were performed to cluster visualization in a two-dimensional space. To identify special genes that were enriched in each cluster, the variations between cells were regressed by counting the number of target molecules in each cell (UMI). The DEGs analysis was performed on different cell populations using the Seurat “bimod” function, and the log2 fold-change of DEGs was calculated. The upregulated genes in each hemocyte cluster were screened based on the conditions |logFC| ≥ 0.25, P-value ≤0.05, and the percentages of cells in specific cluster for which the gene was detected was >25% (127). According to the upregulated genes obtained above, potential marker genes (cluster-enriched genes) of each cluster were identified and tested by Seurat function “FindAllMarkers” and “roc,” respectively (128).

Functional prediction of hemocyte clusters

On the basis of the differentially upregulated gene expression profiles in each of the cell clusters, we first used GO enrichment and KEGG pathway enrichment to analyze the biological function in each hemocyte cluster and the important differentially expressed transcripts (129, 130). The hypergeometric tests were applied to define GO enrichment terms that were significantly enriched in peak-associated genes relative to the corresponding database. Using the KEGG pathway as a unit, the significantly over-represented KEGG terms in differentially expressed transcripts were extracted by hypergeometric distribution. Enrichment of a GO or KEGG term was determined based on P-value adj ≤0.05.

Impact of WSSV infection on hemocyte clusters

The composition of the hemocyte clusters pre- and post-WSSV infection was further compared to identify the cell types that responded dramatically to WSSV infestation. Based on the results of functional enrichment, the genes and functional terms of immune-related processes, disease-related processes, and stimulus response-related processes in each cell cluster were statistically analyzed. In addition, the immune-related genes were significantly enriched in each cell cluster, the gene expression differences were then calculated, and the key molecular functions were resolved to compare the differences in antiviral defense mechanisms among hemocyte clusters.

Viral genes analysis in hemocyte clusters

To determine the state of WSSV infection in each hemocyte cluster, the expression of viral genes and viral load were analyzed in the different cell clusters. Viral gene expression was analyzed using Cell Ranger, based on the WSSV genome (GenBank: AF332093.3). The “viral load” of a cell in scRNA-seq analysis was based on the number of UMIs that map to the WSSV genome and expressed as a percentage of total UMI content of a given cell. In the present study, infected hemocytes were divided into three categories of viral load states: low (<5%), medium (between 5% and 20%), and high (>20%) (131).

Pseudo-time inference

The cell differentiation process and cell fate were analyzed using a matrix of cells and gene expression levels by Monocle 2 (Version2.6.4). According to the previously identified cell clusters and differentially expressed genes, the data were transferred to Monocle package， then the differentiation and cell fate-related genes were selected to define the cell differentiation process. We assigned the start point based on the expression of cell proliferation-related genes and the cell cycle-related genes. Monocle reduces the space in which the cells were embedded to two dimensions and orders the cells (parameters used: sigma = 0.001, lambda = NULL, param. gamma = 10, tol = 0.001). After the cells were ordered according to the criteria mentioned above, the hemocyte trajectory (with tree-like structure, including tips and branches) could be visualized in the reduced dimensional space.

FISH

The FISH procedure was performed in strict accordance with the manufacturer’s protocol of FISH probes kit (GenePharam, Shanghai). The 24-h-virus-infected hemocytes precipitated on slides as described above were first fixed by 4% paraformaldehyde. Then, the hemocytes were sequentially dehydrated with 100%, 95%, 85%, and 75% ethanol for 2 min, washed with hybridization buffer solution, and digested with pepsin for 5 min. After rinsing as above, the pre-denatured nucleic acid probes with fluorescent markers were incubated overnight at 37°C. The sensing probe was used as a negative control. The nucleic acid probes used for the experiments are seen in Table 1. Following three rinses with buffer, the hemocytes were incubated with a mixture of mouse anti-WSSV monoclonal antibodies (Mabs 2D2, 3B7, 2G3) previously prepared in the laboratory for 1 h at 37°C and protected from light (132, 133). Then, the hemocytes were incubated with goat anti-mouse IgG Alexa Fluor488 antibody (Invitrogen) for 45 min at 37°C in the dark. Finally, the cell nucleus was stained with 4,6-diamidino-2-phenylindole dihydrochloride, and the hemocytes were observed by the fluorescence microscopy.

TABLE 1.

Sequences used for synthesis primers of RNA-FISH

Gene name (gene ID)	Sequence
Negative control	TGCTTTGCACGGTAACGCCTGTTTT
GuaD (ncbi_113805350)	T+TCCGT+TACAAACACCAA+TAGCC
SINHCAF (ncbi_113804936)	TC+TCGTCCTACT+TTCCGAACAT+AC

DATA AVAILABILITY

Sequencing data from scRNA-seq are available in the NCBI. The accession numbers for the scRNA-seq data reported in this paper are SRR18899520 and SRR18899521.

ETHICS APPROVAL

This study was carried out in agreement with the International Guiding Principles for Biomedical Research Involving Animals documented by Guide for the Use of Experimental Animals and the Committee of the Ethics on Animal Care and Experiments at Ocean University of China (permit number: 20180101)

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/jvi.01805-23.

Supplemental figures. jvi.01805-23-s0001.pdf.

Figures S1 to S3.

Table S1. jvi.01805-23-s0002.xlsx.

Data related to single-cell sequencing results.

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