Single-Cell Transcriptomic Analysis of Specific Responses of Different Cell Populations of Hemocytes to the Re-infection of Bacteria, a Case Study in Abalone

Single-Cell Transcriptomic Analysis of Specific Responses of Different Cell Populations of Hemocytes to the Re-infection of Bacteria, a Case Study in Abalone | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-Cell Transcriptomic Analysis of Specific Responses of Different Cell Populations of Hemocytes to the Re-infection of Bacteria, a Case Study in Abalone Ziping Zhang, Xin Zhang, Yulong Sun, Jianjun Feng, Yilei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4675005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract It is commonly believed that invertebrates lack immune memory due to the absence of immunoglobulins, related molecules, cells, and organs. However, our previous research demonstrated that Haliotis discus hannai , a prominent abalone species cultivated in China, often faces substantial economic losses due to diseases, particularly those caused by Vibrio sp . exhibited higher survival rates upon re-infection with Vibrio parahaemolyticus compared to the initial infection, implying the existence of immune memory. We hypothesized that hemocytes, which play a critical role in pathogen resistance in abalone, might be involved in the immune memory process. Therefore, we aimed to investigate the hemocyte response mechanism to V. parahaemolyticus re-infection to provide valuable insights for preventing and controlling abalone vibriosis and advancing sustainable abalone aquaculture. Additionally, our research aimed to contribute to understanding the origin and evolution of immune memory mechanisms. This study constructed a transcriptome map of abalone hemocytes using 10× Genomics single-cell RNA sequencing (scRNA-seq). Traditionally, abalone hemocytes were categorized into three cell types: hyalinocytes, semi-granulocytes, and granulocytes. The initial cell division resulted in the formation of 15 clusters further through subsequent analysis using scRNA-seq. Among these clusters, cluster_11 exhibited unique characteristics, indicating a more mature cluster of GRCs. This specific subpopulation displayed significant functionality as a core immune regulator, manifesting robust phagocytic and endocytic activities and substantial involvement in signal transduction and immune regulatory processes. Furthermore, we analyzed and detailed functional variances among different hemocyte types. Through the implementation of RNA interference technology, we validated the interplay between key signaling pathways. Interestingly, our findings suggested the potential existence of a classical TLR/NF-κB signaling pathway in abalone hemocytes, which may contribute to the immune regulation process in response to V. parahaemolyticus re-infection, as preliminarily confirmed in our study. Biological sciences/Immunology/Adaptive immunity/Cellular immunity/Lymphocyte differentiation Biological sciences/Immunology/Innate immune cells/Innate lymphoid cells Haliotis discus hannai hemocyt༛Vibrio parahaemolyticus༛secondary infection༛immune memory Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction Different pathogens affect invertebrates living in various habitats 1 . It is generally believed that invertebrates lack key molecules, cells, and organs for immune memory, so they rely on innate immunity to fight infections 2 . However, some invertebrates can reject transplants more strongly after repeated exposure, suggesting they have immunomodulatory mechanisms similar to vertebrate-specific immunity 3–5 . Recent studies also show that prior exposure to a pathogen can lower mortality and boost cellular and humoral immunity upon re-infection, indicating a form of innate immune memory 6,7 . This phenomenon is called immune priming, innate immune memory, or trained immunity by researchers who want to distinguish it from vertebrate immune memory 8–14 . The mechanism of immune memory in invertebrates is still poorly understood, but evidence supports its existence. The main difference is that invertebrates respond faster and stronger to re-infection by pathogens, which helps them eliminate infections and survive better. For example, Exaiptasia pallida exposed to sublethal and then lethal doses of Vibrio coralliilyticus had lower mortality and higher levels of immune proteins than controls 13 . Similar results have been reported in crustaceans 8,11,15–20 and molluscs 9,10,21–24 . Some invertebrates also showed recognition specificity and time dependence when re-infected by different pathogens 15,16,25 . Vertebrate immunology has data, but invertebrate immunology could be stronger. In China, shellfish and crustaceans are important for aquaculture, but bacterial and viral infections can cause massive deaths and economic losses 26–28 . Antibiotics and other drugs can leave residues in their bodies, raising food safety concerns 18 . Crustaceans and shellfish have skin defenses against most pathogens, but their innate immunity becomes crucial when pathogens invade their body 29,30 . Hemocytes are the main cells involved in the innate immunity of invertebrates. They participate in various cellular processes, such as phagocytosis, cysts, apoptosis, wound repair, and blood coagulation. Hemocytes of shellfish can be classified into three types based on their size, shape, and function: hyalinocytes (HCs), semi-granulocytes (SGRCs), and granulocytes (GRCs) 31,32 . Hemocyte differentiation in invertebrates differs from that in vertebrates but is still essential for cellular immunity against pathogens. Hemocytes recognize pathogen-associated molecular patterns (PAMPs) through pattern recognition receptors (PRRs), such as lectins and toll-like receptors (TLRs), on the surface of bacteria, fungi, or viruses 33,34 . Then, they clear the pathogen by phagocytosis 35 . Previous studies have shown that immunization with killed or inactivated pathogens can enhance the phagocytic activity of hemocytes in different invertebrates 8,9,22,36 . This suggests that increased phagocytosis is important for immune memory in invertebrates. Moreover, studies of Anopheles gambiae and Litopenaeusvannamei have shown that immune priming can induce directional differentiation and mitosis of hemocytes, indicating that hemocyte proliferation and regeneration may also be a key mechanism of immune memory production 8,37,38 . Recognition of PAMPs by PRRs activates downstream signaling pathways that lead to the secretion of lectins, activation of prophenoloxidase, and production of various antimicrobial peptides (AMPs). The TLR pathway is a well-studied innate immune pathway in invertebrates 39,40 . Research has shown that the TLR pathway is directly involved in regulating immune priming in Drosophila and may also be involved in the immune memory process of other invertebrates to pathogens 11,21,41 . Single-cell RNA sequencing (scRNA-seq) is a technique that measures gene expression differences between single cells, revealing the functional diversity within different cell clusters in the same tissue. Unlike bulk RNA-seq, scRNA-seq can resolve cellular heterogeneity 42 . This technique has advanced the research of many marine invertebrates, such as identifying the marker genes of 20 cell clusters in the adductor muscle of Patinopectenyessoensis 43 and showing that hemocytes of Parribacus japonicus differentiated from a single population 44 . The analysis of scRNA-seq and bulk RNA-seq of hemocytes of Crassostrea hongkongensis discussed the diversity and heterogeneity of hemocytes and their responses to copper ion exposure 45,46 . Haliotis discus hannai is the main abalone species cultured in Fujian, accounting for about 79.1% of China’s total output 47 . However, abalone aquaculture faces diseases, especially those caused by Vibrio. Hemocytes are important for resisting pathogens in abalone. Previous studies have used whole hemolymph as materials and could not address cell heterogeneity. They also had different classification standards for abalone hemocytes. We have shown that hemocytes are involved in the immune memory process of H. discus hannai to V. parahaemolyticus re-infection, but the exact mechanism is unclear 48–56 . Using scRNA-seq, we aim to explore hemocyte clusters' functional differences and roles in H. discus hannai immune memory from a cellular heterogeneous perspective. This will help us control infectious disease outbreaks through vaccination and support aquaculture's healthy and sustainable development. 2 Materials and Methods 2.1 Ethics statement All the study designs and animal experiments were conducted following the Animal Care and Use Committee of the Fisheries College of Jimei University guidelines. 2.2Animals and bacterial challenge Adult Pacific abalones (body length 6.30 ± 0.70 cm, body weight 19.45 ± 4.50 g; n = 100) were purchased from Jinjiang Fuda Abalone Fisheries Co. Ltd (Jinjiang, Fujian, China) in September 2021. Abalones were then maintained in a recirculating system with a sand-filter at constant temperatures (26°C) and dissolved oxygen levels (6.2 mg/L) and were fed sea tangle ( Laminaria japonica ) once daily 57 . V. parahaemolyticus (isolated from diseased Pacific abalones and preserved in our laboratory) was cultured in LB media (containing 10 g/L peptone, 5 g/L yeast extract and 10 g/L NaCl) at 37°C for 24 h and harvested via centrifugation (6000 × g , 10 min, 4°C). The pellet was washed and re-suspended in 0.9% normal saline (NS). Through the analysis of the results of the pre-experimental stage, the dosage of V. parahaemolyticus was set to 1.0 × 10 8 CFU/mL. The experiment was split into the immune phase (IP) and the secondary infection phase (SSP). For the IP, 50 abalones received an injection of NS (20 µL) and were employed as the N group, and another 50 abalones received an injection of V . parahaemolyticus suspension with the dosage of 1.0×10 8 CFU/mL (20 µL) and were employed as the V group. Hemolymph was collected from 36 abalones of the N and V groups, respectively, at 3 h, 12 h, and 24 h during the IP (N = 6). After 168 h of the IP, in the SSP, all the remaining abalones were randomly allocated to the following groups, and the experimental design is shown in Fig. 1 : VV group: the V group received an injection of 100µL V . parahaemolyticus (1.0×10 8 CFU/ml). NV group: part of the remaining abalones from the N group received an injection of 100µL1.0×10 8 CFU/ml V . parahaemolyticus. NN group: the other part of the remaining abalones from the N group received an injection of 100 µL NS. 2.3 Hemocyte collection for single-cell RNA sequencing The hemolymph of H. discus hannai from different groups was extracted from the abdominal foot muscles, and 2 mL of hemolymph was mixed with 15 ml of pre-cooled anticoagulant containing glucose (20.5 g/L), citrate dihydrate (8.0 g/L), citrate (0.8 g/L), sodium chloride (4.2 g/L), and HEPES (2.3 g/L) at pH 6.1. Hemocytes were collected by centrifugation at 4°C, 300g for 5 minutes, and then separated and washed twice with a pre-cooled anticoagulant. Cell viability was assessed by adding 20 µL trypan blue dye to a 20 µL cell suspension and re-suspended with an anticoagulant. Dead cells were dyed blue, and the aggregation rate was observed using a cell counter. The target cell concentration for each sample was set at ≥ 1000 cells/µL, while cell viability had to be ≥ 90%. 2.4 Library preparation and single-cell RNA sequencing Library synthesis and scRNA-seq were done by Gene Denovo Biotechnology Co. (Guangzhou, China). All the hemocyte samples (N_group, V_group, NV_group, NN_group and VV_group) were analyzed using the 10× Genomics single-cell capturing system, which partitioned thousands of cells into nanoliter-scale Gel Bead-In-Emulsions (GEMs) with a standard 10× Barcode using Chromium Single Cell 3 'GEM V3 kit (10 × Genomics). The libraries were generated from the cDNA and sequenced, with individual reads associated back to their respective GEM partitions by the 10× Barcodes. The process comprised four steps: ( 1 ) GEM generation and barcoding, wherein the single-cell 3′ gel bead was dissolved in a GEM, essential primers were released and mixed with cell lysate and Master Mix, and pooled fractions were recovered after breaking the GEMs; ( 2 ) GEM-RT cleanup and cDNA amplification, in which biochemical reagents and primers from the post-GEM mixture were removed by magnetic beads, followed by PCR amplification of the cDNA for library construction; ( 3 ) Library construction, which involved adding primers to the final libraries containing the P5 and P7 primers to facilitate Illumina bridge amplification; and ( 4 ) Sequencing, where a Single-Cell 3′ Library was composed of standard Illumina paired-end constructs that started and ended with the sequencing primer P7 and P5 primer. The raw data of single-cell transcriptome sequencing were screened using Cell Ranger software from the 10× Genomics Chromium platform ( https://www.10× genomics.com/). This software aligned, filtered, and counted barcodes and UMIs for each library. Low-quality cell data with fewer than 4000 genes and 8000 UMIs were eliminated, ensuring high-quality scRNA-seq data for downstream analysis. The data were compared with a reference genome sequence using the STAR package of Cell Ranger. However, due to poor genome splicing of H. discus hannai , a full-length transcriptome from previous experiments was used as a reference genome sequence to compare and annotate all single-cell sequencing data 58 . UMIs were counted for reads that were uniquely mapped to the transcriptome. Cells were identified based on barcodes that revealed total UMI counts surpassing m/10. 2.5 Bioinformatics Analysis of the Single-Cell RNA Sequencing Database. Cell cluster analysis with Seurat. An expression matrix was obtained that delineated the relationship between cells and genes by establishing an index for all clean data using Cell Ranger and after quantifying and aligning the data to a reference sequence. This expression matrix was read by the Seurat package ( https://satijalab.org/seurat/ ) 59 , which enabled high-quality cells to be attained by integrating multiple selection criteria. Subsequently, cells were grouped using principal component analysis (PCA). T-distributed stochastic neighbor embedding (t-SNE) is a non-linear dimensionality reduction algorithm that is currently one of the most popular algorithms for data visualization, particularly for reducing high-dimensional data into a two- or three-dimensional space. After clustering was completed, t-SNE was used to visualize the data in a lower dimension. Cells with similar gene expression patterns were placed closer to each other in the t-SNE plot. Therefore, using the Seurat package for single-cell data of hemocytes from the H. discus hannai , t-SNE clustering analysis can be indispensable in the investigation of functional relationships and differences between various cell clusters. Differently expressed genes (DEGs) analysis in each cluster. Using the Seurat package 60 , differential gene expression analysis was performed on distinct cell subtypes of hemocytes from the H. discus hannai that were obtained. Genes with upregulated expression in each subtype were selected ( p < 0.05, |log2FC|≥ 0.585) to identify subtype-specific marker genes to reveal the differences in regulatory patterns among different clusters. Thereafter, GO analysis was used to comprehensively describe the functional properties of differentially upregulated genes in each subtype of hemocytes. Based on the enrichment results from the KEGG database, biological functions of relevant genes were explored via significantly enriched pathways, thereby contributing toward a theoretical foundation for further functional identification of cell clusters. Functional analysis of specific hemocyte types. The Weighted Gene Co-expression Network Analysis (WGCNA) method clusters genes with similar expression patterns across multiple samples into modules and performs association analysis between different modules and specific traits or phenotypes. In this study, we utilized the WGCNA package (v1.47) in R to construct a co-expression network for a particular type of hemocytes to identify key regulatory genes and related signaling pathways involved in abalone immune regulation. Finally, we visualized the co-expression network of the selected key genes using the Cytoscape 3.7.1 software ( https://cytoscape.org/ ). 2.6 Pseudo-temporal ordering of cells using Monocle Monocle( http://coletrapnell-lab.github.io/monocle-release/tutorials/)i s an R package software that utilizes key gene expression patterns to arrange cells in a pseudo-temporal order along a cellular trajectory, allowing for the simulation and visualization of cell differentiation relationships during development 61 . By selecting differentially expressed genes and performing dimensionality reduction analysis between different subgroups of cells, the software can fit the best cell differentiation trajectory and display it through visualizations, which enables the reconstruction of a cell's temporal changes, revealing alterations in the cellular state as a response to external stimuli within an organism. 2.7 Cell communication analysis Activation of extracellular-specific cell signaling pathways relies on ligand-receptor binding, and analyzing the interaction between different ligands and receptors can enhance our understanding of diverse cell behaviors. CellphoneDB is a database that contains a wealth of information on ligand-receptor interactions, and it can be utilized to construct a cellular communication network among different cell clusters based on the single-cell transcriptome gene expression matrix 62 . The number of ligand-receptor pairs in different cell pairs can be obtained by analyzing the expression abundance of ligand-receptor pairs in different cell pairs. The application of CellphoneDB software to pairwise comparisons between all cell clusters in the dataset enables the screening of significantly enriched ligand-receptor pairs and the construction of an intercellular interaction network map, revealing the behavior of a specific cell and providing preliminary insights into the communication relationships between different cells. Furthermore, based on the above, NicheNet software can be used to analyze the activity of ligands and their regulatory potential on specific target genes, thereby further elucidating the mechanisms of interaction between ligands and receptors 63 . 2.8 Suppression of NFκB and TLR2 gene expression by double-stranded RNA (dsRNA) Based on the above analysis, the TLR signaling pathway and NF-κB signaling pathway may play a key role in regulating the response of wrinkled abalone lymphocytes to secondary infection by V. parahaemolyticus . To further analyze the gene interactions in these two pathways, we used RNAi to interfere with the key genes NFκB and TLR2 in the two pathways, respectively. Also, the green fluorescent protein (GFP) gene from the pEGFP-N1 vector was amplified by PCR. The sequences of these primers are listed in Table S1. Single-stranded RNA (ssRNA) was transcribed from the templates using T7 phage RNA polymerases (Promega, Madison, WI, USA) after the PCR products were purified and sequenced. After being purified, the sense ssRNA and antisense ssRNA were mixed and annealed at 75°C for 15 min, at 65°C for 15 min, and then down to room temperature at the rate of 0.1°C /s. The dsRNAs of NFκB and TLR2 were used in the silence experiment at a final concentration of 5 µg/mL directly to the hemocytes culture medium without any vehicle 64 with GFP dsRNA as control. The medium without any modifications was regarded as the blank control group. For each treatment, six replicates were produced. All samples were incubated at 27°C for 6 h, 12 h and 24 h, and the hemocytes were harvested to detect the mRNA expression by qRT-PCR. 3 Results 3.1 The quality inspection results of all samples The scRNA-Seq data of different samples of hemocytes from H. discus Hanna were subjected to quality control and preliminary statistical analysis using Cell Ranger(Table S2). The V_group, N_group, NN_group, NV_group and VV_group libraries obtained 396,266,876, 414,300,518, 447,684,335, 421,542,139 and 368,963,729 raw reads respectively. All Q30 values were above 90.50%, indicating good quality of the sequencing data. BioProject accession number PRJNA979786 has been assigned to all raw reads submitted to the NCBI Short Read Archive database. Owing to the deficient quality of the published abalone genome assembly, this investigation employed the full-length transcriptome of H. discus hannai as a reference sequence for aligning and annotating the obtained sequences 58 . Ultimately, the number of high-quality cells captured in the V_group, N_group, NN_group, NV_group, and VV_group samples, which can be utilized for subsequent analyses, were 9,483, 9,547, 8,702, 8,947, and 6,200, respectively (Table S3, Figure S1 ). The number of genes detected in each treatment group surpassed 30,000, and according to the results of full-length transcriptome data alignment, the alignment rate of the data extracted from the five samples was higher than 66.7% (Table S3). 3.2 Cluster analysis of hemocytes of abalone H. discus hannai The Seurat package was utilized to amalgamate and scrutinize multiple filtered datasets. Following the implementation of UMAP dimensionality visualization analysis and t-SNE clustering, a total of fifteen clusters (cluster_0–14) (Fig. 2 A-B) have been identified. Each dot on the graph signifies a unique cell, distinguished by color based on subgroup classification. Moreover, clustering outcomes indicate considerable differences in cell quantities between different clusters, which suggests that functional variations exist among each cluster (Fig. 2 C). In addition, in order to further scrutinize the functional disparities among different clusters of resting hemocytes in H. discus hannai , we have conducted comparative analyses on the differential genes between various hemocyte clusters in the N_group sample. The results have revealed that crucial immune-regulating genes such as allograft inflammatory factor 1 ( AIF1 ), an inhibitor of NF-κB ( NFKBIA ), CD63 , and some members of the caspase gene family alongside certain heat shock proteins ( HSPs ), are significantly upregulated in different hemocyte clusters (Fig. 2 D). 3.3 Identification of cell clusters Due to the late start of research on marine invertebrates, insufficient information on marker genes for different cell types, coupled with poor genome assembly quality, resulting in a large number of omissions 65 , it is difficult to identify different blood cell clusters with marker genes of H. discus hannai . Notwithstanding this, there are similarities in the expression patterns of differential genes within the different hemocyte clusters, hinting at them being discrete components of a certain type of cell. In light of this, we undertook KEGG enrichment analysis on the upregulated genes in distinct cell clusters (Fig. 3 A). By combining the outcomes of functional enrichment analysis of diverse clusters and consulting existing studies on the functions of different hemocyte types 45,51 , we further classified the original 15 clusters into three cell types: hyalinocytes (cluster_1, cluster_4, cluster_7), semi-granulocytes (cluster_2, cluster_5, cluster_6, cluster_8, cluster_9, cluster_13, cluster_14), and granulocytes (cluster_0, cluster_3, cluster_10, cluster_11, cluster_12). Granulocytes constitute a proportion of 60.12% (in which GRCs amount to 28.13% of the total cells and SGRCs account for 31.99%)(Fig. 3 B). Hemocytes (HCs) make up roughly 39.88%, which aligns with previously documented findings. The distribution of diverse cellular types of hemocytes in H. diversicolor was identified through flow cytometry 49,50 . Furthermore, the marker gene was identified in conjunction with varying gene expression patterns among different types of cells. The results revealed that allograft inflammatory factor 1 ( AIF1 ), Cell Division Cycle 42 ( CDC42 ), matrix metalloproteinase-18 ( MMP18 ), and CD63 exhibited significantly high expressions in GRCs cells. In contrast, Thioredoxin-2 ( TRX2 ), glutathione s-transferase 7 ( GST7 ), and caspase-3 ( CASP3 ) showed substantial overexpression in SGRCs cells. For HCs, genes such as complement C3-like ( C3 ), proliferating cell nuclear antigen ( PCNA ), and histone H2A-beta ( HIS2A ) were highly expressed (Fig. 3 C-D). 3.4 Functional analysis of marker gene in different cell clusters Fifteen hemocyte clusters from all samples were re-clustered using the aforementioned classification method. Statistical results suggest that there was no significant alteration in the overall clustering results of three cell types (GRCs, SGRCs and HCs) among different treatment groups of V. parahaemolyticus . Still, the number of cells of different types varied (Fig. 4 A). After exposure to distinct treatments with V. parahaemolyticus , the number of granulocytes in V_group, NV_group and VV_group samples changed to varying degrees compared to 60.12% in the N_group, with the most substantial increase observed in V_group, where the proportion of granulocytes rose to 74.53%. In contrast, proportions in NV_group and VV_group increased to 67.88% and 69.61%, respectively. Moreover, the proportion of HCs in the three treatment groups decreased relative to the control group (Fig. 4 A). The identification of marker genes was performed on various samples after re-clustering. In V_group, significantly upregulated GRCs marker genes such as AIF1 , AAH2 , IP6K1 , CPNE8 and ALOX5 were identified; SGRCs marker genes included TRX2 , GIMAP9 , GST7 , CASP3 and PRDX6 ; and HCs marker genes comprised of NIP7 , C3 , DKC1 , FKBP46 and DDX51 (Fig. 4 B). In NV_group, significantly upregulated GRCs marker genes included AIF1 , CDC42 , SQSTM1 , PSTPIP1 and NOCT ; SGRCs marker genes comprised of TRX2 , LRP1 , GST7 , FSTL3 and PFE ; and HCs marker genes included DDX21 , C3 , SLC6A9 , Nop2 and HIS2A (Fig. 4 C). In VV_group, significantly upregulated GRCs marker genes were AIF1 , CDC42 , ELF3 , NFKB1 and TLR4 ; SGRCs marker genes included LCP1 , PDIK1B , GS2 , FSTL3 and PFE ; and HCs marker genes included FKBP46 , C3 , SLC6A9 , RELN and PCNA (Fig. 4 D). Hemocytes represent the primary immune regulatory organs in invertebrates. Various hemocyte types also respond differently to external stimuli. Marker genes of diverse hemocyte types function as labels for identifying them, characterized by stable high expression characteristics that should not vary substantially with changes in the external environment. Therefore, we comprehensively analyzed relevant genes identified in the aforementioned different samples and coupled them with results obtained in the above study from marker gene screening under non-stress conditions. We determined some marker genes that can be stably expressed at a high level in various cell types in different treatment samples, including AIF1 and CDC42 in GRCs, TRX2 and GST7 in SGRCs, and C3 in HCs. The expression heatmap of these genes is depicted in Fig. 4 E. Additionally, the t-SNE graph represents their expression patterns in different cells (Fig. 4 F). 3.5 Analysis of weighted gene co-expression network In order to further investigate the important regulatory mechanisms of GRCs in the immune modulation process of H. discus hannai in a resting state, we conducted WGCNA on 5 clusters of GRCs belonging to N_group and ultimately identified 8 clustering modules with significant differences in gene expression patterns that were allocated to different clusters comprising GRCs (cluster_0, cluster_3, cluster_10, cluster_11, and cluster_12). Among these modules, the turquoise and blue modules exhibited a higher level of gene enrichment, which consisted of 6,475 and 4,341 genes, respectively (Fig. 5 A-B). In addition, a heatmap of gene clustering was generated for different modules to visualize the expression matrix of related genes between modules (Fig. 5 C). The correlation analysis results showed a significant positive correlation (p < 0.05) between the brown, blue, and yellow modules (Fig. 5 D), with high expression levels observed in cluster_11 (Fig. 5 E). The brown, blue, yellow, and red modules were selected as target modules to explore the functional differences of GRCs further, and KEGG enrichment analysis was carried out on the genes grouped in these modules. The results indicated that immune-related signaling pathways such as NF-κB signaling pathway, Endocytosis, Toll and Imd signaling pathway, NLR signaling pathway, CLR signaling pathway, and Fc gamma R-mediated phagocytosis were significantly enriched in the brown module. Energy metabolism-related signaling pathways, such as Oxidative phosphorylation and Thermogenesis, were significantly enriched in the red module. Moreover, some immune-related signaling pathways, such as the PI3K-Akt signaling pathway, IL-17 signaling pathway, and Phagosome, also showed significant enrichment ( p < 0.05) in the red module (Fig. 5 F). Hub genes are pivotal genes with significant roles in biological processes and can be identified within different modules based on their K.in values that indicate the level of connectivity and regulatory function of the gene within the module. The higher the K.in value of a gene, the greater its connectivity and the more central its regulatory function. Based on this theory, 5–6 genes with high K.in values were selected as hub genes for each target module, and a molecular interaction network was constructed using Cytoscape to visualize the relationship between these hub genes and their associated genes (Fig. 6 A-D). The results revealed that calcium-binding protein genes ( CALM ), which play roles in signal transduction and transcriptional regulation, ubiquitin-conjugating enzyme genes ( EFF ) responsible for ubiquitination regulation, and toll-like receptor genes ( TLR3 ) were identified as hub genes in the blue module(Fig. 6 A). In the related network of the brown module, the core transcription factor NF-κB of the NF-κB signaling pathway and immune-related genes such as GST, Tollo, and Perlucin were identified as hub genes (Fig. 6 B). For the yellow module, signal transduction-related genes such as CAP-Gly domain-containing linker protein 1-like ( CLIP1 ) and phosphatidylinositol 3-kinase regulatory subunit alpha ( PIK3R1 ), as well as genes involved in intracellular engulfment such as SMURF2 were identified as hub genes (Fig. 6 C). Key immunoregulatory factors such as CD109, MMP18, and HSP90 were also identified as hub genes in the red module (Fig. 6 D). 3.6 Response of different cell types to V. parahaemolyticus infection The results of KEGG enrichment analysis of upregulated genes of different hemocyte types in different treatment groups after V parahaemolyticus infection showed that in the V_group, signaling pathways associated with energy metabolism, such as oxidative phosphorylation, were notably enriched in both SGRCs and HCs; however, HCs possessed a higher number of enriched genes. Conversely, signaling pathways involved in energy metabolism, such as thermogenesis and protein processing in the endoplasmic reticulum, were solely significantly enriched in HCs. In addition, the phagocytosis-related signaling pathway, Phagosome, was significantly enriched in both GRCs and HCs. Contrarily, the commonly enriched signaling pathways in SGRCs and GRCs were primarily related to immunity, such as the NLR signaling pathway, consistent with the biological functions of granulocytes. Meanwhile, signaling pathways involved in immune regulation, such as Endocytosis and IL-17 signaling pathways, were exclusively enriched in GRCs (Fig. 7 A). In the NV_group, signaling pathways related to energy metabolism and phagocytosis, such as oxidation phosphorylation and Phagosome, were significantly enriched in SGRCs and HCs. Certain signaling pathways involved in transcription, translation, folding, and degradation processes, such as Ribosome, Proteasome, and Spliceosome, were specifically enriched in HCs. In SCRGs, signaling pathways involved in signal transduction, such as the Hippo signaling pathway and Rap1 signaling pathway, were specifically enriched. In contrast, signaling pathways specifically enriched in GRCs, those involved in immune regulation, such as IL-17 signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, Toll and Imd signaling pathway, were all prominently present (Fig. 7 B). Compared to the previously mentioned two groups, there were no signal pathways commonly enriched in HCs of VV_group with the other two cell types—the significantly enriched pathways in HCs primarily involved translation, folding, and degradation processes. Additionally, signal pathways associated with energy metabolism, such as oxidative phosphorylation and Thermogenesis, were specifically enriched in HCs. Signaling pathways involved in the degradation and metabolism of exogenous substances, such as the Metabolism of xenobiotics by cytochrome P450 and Phagosome, were significantly enriched in SGRCs. GRCs displayed a significant increase in the number of enriched signal pathways compared to HCs and SGRCs, with most of the genes significantly enriched in signaling pathways involved in signal transduction such as MAPK signaling pathway, NF-κB signaling pathway, TNF signaling pathway, signal pathways involved in degradation such as Endocytosis and Phagosome, and signal pathways involved in immune regulation such as NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, CLR signaling pathway, Toll and Imd signaling pathway, IL-17 signaling pathway, and Fc gamma R-mediated phagocytosis (Fig. 7 C). 3.7 Response of GRCs to V. parahaemolyticus infection In the previous study, we have functionally annotated and defined the gene sets that may be involved in the immune regulation response of abalone hemocytes to V. parahaemolyticus infection. These gene sets include immune response genes (IRGs), potential immune-enhancing genes (PEGs), immune-enhancing regulatory genes (ERGs), and essential immune-enhancing genes (EEGs) 56 . Our analysis found that GRCs from different treatment groups were enriched with numerous immune-related signaling pathways, suggesting their critical regulatory role in abalone's response to V. parahaemolyticus infection. Therefore, we employed the previous gene set screening approach to identify DEGs in GRCs from different comparison groups and performed KEGG functional enrichment analysis. The results indicated that 801 DEGs in GRCs responded to both V. parahaemolyticus infections simultaneously, defining them as co-response genes of GRCs (CRGs-b) (Fig. 8 A), whereas 489 common DEGs were obtained from the comparison groups of NN_group vs NV_group, V_group vs VV_group, and N_group vs V_group, which might relate to GRCs' faster immune response after secondary V. parahaemolyticus infection and were designated as immune response genes of GRCs (IRGs-b) (Fig. 8 B). Moreover, by comparing 801 CRGs-b with the putative immune-enhancing gene set, we identified 361 common DEGs that were classified as potential immune-enhancing genes of GRCs (PEGs-b) (Fig. 8 C). Finally, 246 immune-enhancing genes of GRCs (ERGs-b) were identified via comparison of IRGs-b and PEGs-b using the Venn diagram, which may possess specific immune memory regulatory functions. The KEGG enrichment results revealed that 801 CRGs-b mainly enriched immune-related signaling pathways such as the NF-κB signaling pathway, IL-17 signaling pathway, TLR signaling pathway, and NLR signaling pathway (Fig. 8 A), and 361 PEGs-b were primarily enriched in phagocytosis-related pathways, such as Fc gamma R-mediated phagocytosis and Phagosome (Fig. 8 B). The 489 IRGs-b were significantly enriched in immune-related pathways, including the TLR signaling pathway, NF-κB signaling pathway, and Fc gamma R-mediated phagocytosis (Fig. 8 C). In addition, the 246 ERGs-b were significantly enriched in phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis and Phagosome, as well as apoptosis-related pathways like Apoptosis (Fig. 8 D). To reveal the molecular interactions among the DEGs in the significantly enriched pathways, we used Cytoscape software to construct network diagrams of some key signaling pathways in abalone GRCs (Fig. 9 ). As shown in the figure, as important signaling pathways involved in abalone innate immune regulation, NF-κB signaling pathway, TLR signaling pathway and NLR signaling pathway have complex molecular interactions among them, and among them, IRAK4 and NFKB1 act as core factors and participate in the regulation of these three signaling pathways (Fig. 9 ). At the same time, there is also an interaction between NF-κB signaling pathway and Fc gamma R-mediated phagocytosis (Fig. 9 ). 3.8 Pseudo-temporal analysis of GRCs stimulated by V. parahaemolyticus Monocle2 constructed a developmental trajectory in pseudo-time to study the differentiation pattern of GRCs in samples stimulated by V. parahaemolyticus and control samples. The results indicate that the hemocytes of H. discus hannai infected by V. parahaemolyticus (V_group, NV_group, and VV_group) and those treated with normal saline (N_group and NN_group) were evidently differentiating along distinct branches of the pseudo-time trajectory, exhibiting three distinct differentiation states (Fig. 10 A-C). Based on cell trajectory analysis, we defined the concentrated distribution of state_3 in the GRCs of H. discus hannai hemocytes treated with normal saline as the starting point of differentiation. After infection with V. parahaemolyticus , these cells could differentiate into state_1 and state_2, respectively (Fig. 10 B). Furthermore, all three V_group, NV_group, and VV_group samples were observed to be distributed among three different differentiation states, mainly concentrated in state_1 and state_2 (Fig. 10 C). The expression heat map of differentially differentiated genes revealed an increase in the expression of granulocyte marker genes such as AIF1 , MMP18 , and TRX2 in Gene_cluster2 with deepening cell differentiation. However, highly expressed genes in granulocytes, such as CD63 and CDC42 , gradually decreased in Gene_cluster1 with the deepening of cell differentiation (Fig. 10 D). As state_1 and state_2 were two directions of granulocyte differentiation with obvious differentiation boundaries with state_3, responding to different stages of V. parahaemolyticus infection, we analyzed the upregulated genes in state_3 and state 1_2, respectively. The results showed that granulocyte high-expression genes such as AIF1 , GST7 , CDC42 , and TRX2 were upregulated in state1_2. The important regulatory factors of immune signaling pathways, such as MAPK14 and MyD88 , were also significantly upregulated (Fig. 10 A-E). KEGG enrichment results of these upregulated genes showed significant enrichment of energy metabolism-related pathways, such as Oxidative phosphorylation and Thermogenesis, in state_3. At the same time, the IL-17 signaling pathway, NLR signaling pathway, TLR signaling pathway, and other pathways involved in innate immune regulation were significantly enriched in state1_2. Moreover, Fc gamma R-mediated phagocytosis pathways involved in phagocytosis were significantly enriched by differential genes in state1_2 and state_3, respectively (Fig. 10 F). 3.9 Re-clustering of GRCs The outcomes of pseudo-temporal analysis disclose that despite being the same type of cells, there exist dissimilarities in functional differentiation and also that the differentiation status of the identical cell type differs in diverse physiological states. The KEGG enrichment results revealed significant distinctions in signaling pathways of notable enrichment in varying differentiation conditions. All of these findings indicate that GRCs themselves are somewhat heterogeneous. It has been previously reported in bivalve-related research that there are three different developmental conditions in GRCs 66 . Through re-clustering analysis of GRCs from H. discus hannai hemocytes, further examination of the heterogeneity of the same cell type and the similarities and variances of cellular functions at the molecular level is anticipated. In this study, using the subgrouping technique for GRCs from H. discus hannai , three separate subgroups (Sub-cluster_0–2) displaying elevated cell segregation between GRCs were identified (Fig. 11 A). Analyzing the existing state of every subgroup in dissimilar treatment groups showed that two subsets, Sub-cluster_0 and Sub-cluster_1, coexisted in different treatment groups simultaneously. However, Sub-cluster_0 mainly existed in V. parahaemolyticus stimulated samples and had a greater number of cells in it, while Sub-cluster_1 predominantly contained more cells in the control group and accounted for less in different experimental groups. Notably, Sub-cluster_2 primarily existed in samples without infection of V. parahaemolyticus (Fig. 11 B). The findings of differential gene analysis demonstrated that 1,784 DEGs were significantly upregulated in all three subclusters ( p < 0.05, | log2FC | ≥ 0.585). The heatmap of gene expression selected from each subpopulation indicates a significant difference, as depicted in Fig. 11 C. The key marker genes of GRCs, namely AIF1 and CDC42 , and immune-regulation-related genes, such as MyD88 and IRAK4 , were notably upregulated in Sub-cluster_0, while significant genes like CD109 , MMP18 , CASP3 , and A2ML1 were upregulated in Sub-cluster_1 and Sub-cluster_2, respectively, which aligns with the overall expression pattern. There is a certain intersection between Sub-cluster_1 and Sub-cluster_2, implying some similarities in the biological functions of the two subclusters (Fig. 11 C). The KEGG enrichment results of each subcluster showed that there are several mutual signaling pathways in Sub-cluster_1 and Sub-cluster_2, including oxidative phosphorylation involved in energy metabolism and Fc gamma R-mediated phagocytosis involved in cell phagocytosis, further indicating the functional similarity between the two subclusters (Fig. 11 D). In Sub-cluster_0, most of the signaling pathways are specifically enriched, including various immune-related signaling pathways such as the IL-17 signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, and Toll and Imd signaling pathway (Fig. 11 D). Finally, we investigated the quantity and function of differential genes belonging to the same sub-cluster under varying V. parahaemolyticus treatments. Sub-cluster_2 was absent in some treatment groups, so our analysis only focused on Sub-cluster_0 and Sub-cluster_1. Within Sub-cluster_0, a total of 811 DEGs were upregulated when stimulated with high dosages of V. parahaemolyticus (V_group vs NV_group). These genes were primarily associated with immune signaling pathways such as NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, CLR signaling pathway, and NF- κB signaling pathway, as well as phagocytosis-related pathways including Fc gamma R-mediated phagocytosis, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 A). In the hypothetical immune enhancement gene cluster (VV_group vs NV_group), we identified 660 upregulated DEGs that were mainly involved in phagocytosis-related pathways, such as Phagosome and Fc gamma R-mediated phagocytosis, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 B). Additionally, upon re-infection with V. parahaemolyticus (V_group vs VV_group), 660 DEGs were detected, showing significant upregulation; these genes were primarily linked to immune signaling pathways such as NLR signaling pathway, NF-κB signaling pathway, RLR signaling pathway, TLR signaling pathway, and IL-17 signaling pathway, phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis and Autophagy, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 C). In Sub-cluster_1, a total of 245 DEGs showed up-regulation upon infection with high dosages of V. parahaemolyticus (V_group vs NV_group), and these genes were primarily associated with phagocytosis-related pathways, including Endocytosis, as well as the synthesis of amino acids, such as Biosynthesis of amino acids (Fig. S3 A). Moreover, in the hypothetically assumed immune enhancement gene cluster (VV_group vs NV_group), we detected 468 upregulated DEGs that were primarily enriched in phagocytosis-related pathways, such as Endocytosis, apoptosis-related pathways like Apoptosis, and immune-related signaling pathways, including NLR signaling pathway (Fig. S3B). Finally, upon further infection of the organism with V. parahaemolyticus (V_group vs VV_group), 613 DEGs were found to be significantly upregulated, and these genes were mainly enriched in immune signaling pathways such as Toll and Imd signaling pathway, NF-κB signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, and IL-17 signaling pathway, phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis, as well as apoptosis-related pathways such as Apoptosis (Fig. S3C). 3.10 Cell communication of different clusters By selecting receptors and ligands from various cell clusters across multiple samples, we created molecular interaction networks among these cell clusters in diverse samples via the utilization of Cytoscape software, ultimately depicting intricate interactions between these cell clusters (Fig. 12 A). Although three different cell types were involved, overall cellular communication between clusters 5, 6, 11, 12, and 14 was found to be stronger compared to other clusters (Fig. 12 A-D). The expression abundance heatmap of ligand-receptor pairs among subpopulations in different treatment groups is illustrated in Fig. 11 E-H. Ultimately, 74 interacting receptor-ligand pairs were identified and screened from the 15 hemocyte clusters detected in disparate samples (Table S4). Upon analysis of all ligand and receptor genes, it was revealed that 15 correlated genes were present in each sample. Additionally, the maximum number of ligand genes were observed to be screened in VV_group(Fig. 12 I). The GO enrichment analysis of all receptor-ligand genes showcased that transmembrane receptor activity, signaling receptor activity, and receptor activity were meaningfully enriched across different samples, with receptor binding only displaying significant enrichment in VV_group. Notably, the enrichment degree of cell communication in the NV_group was lower in comparison to other groups, whereas the enrichment degree of receptor agonist activity, receptor activator activity, and receptor regulator activity was higher (Fig. 12 J). KEGG enrichment results illustrated that the primary signaling pathways relevant to H. discus hannai hemocyte communication included Cytokine-cytokine receptor interaction, Axon guidance, Wnt signaling pathway, mTOR signaling pathway, PI3K-Akt signaling pathway, and Melanogenesis (Fig. 12 K). To analyze the communication relationship between GRCs and SGRCs, cluster_11, possessing the strongest correlation among all cell clusters from the cell interaction network diagram presented in Fig. 12 , was utilized as the ligand signaling cell, while cluster_14, having the strongest communication relationship with it, acted as the receptor signaling cell. Four classic signaling pathways were selected: PI3K-Akt signaling pathway and NF-κB signaling pathway, which relate to immune signaling transmission and cell-based regulation, and NLR signaling pathway and TLR signaling pathway, found to be enriched multiple times in prior analysis and involved in innate immune regulation. Ligand activity assays were subsequently performed on ligand-signaling cells using specific gene sets in receptor-signaling cells. There were some differences in the number of detected ligands within different treatment groups, with the smallest quantity observed in N_group and the greatest in VV_group; overall, the ligand activity in VV_group was generally robust (Fig. S4). Under various treatments, ligands regulating the PI3K-Akt signaling pathway gene set were highly active, while those regulating the NF-κB signaling pathway gene set had a low activity rate in N_group and V_group. Following exposure to high dosages of V. parahaemolyticus , viability was enhanced, generally displaying higher viability within VV_group(Fig. S4). In addition, ligands regulating the NLR signaling pathway and TLR signaling pathway gene sets displayed the most activity in VV_group(Fig. S4). In addition, the NicheNet software was utilized to screen ligand-target gene pairs in different target cell pairs for the aforementioned four signaling pathway gene sets and score the regulatory potential of the ligand-target genes. Based on the score, a ligand-target gene regulatory potential heatmap was created (Fig. S5). Results displayed that under identical screening conditions, the number of ligand-regulated target genes was relatively small in both N_group and NV_group, while the largest number of ligand-regulated target genes occurred within the VV_group(Fig. S5). In terms of regulatory potential, HSP90 exhibited the strongest regulatory activity on TLR2, which existed across all four treatment groups. Apart from HSP90's potent regulatory effect on TLR2, the regulatory activities of all ligands on target genes within the PI3K-Akt signaling pathway were weak as a whole, while the regulatory activities on target genes in the NF-κB signaling pathway, NLR signaling pathway, and TLR signaling pathway were comparative across various treatments (Fig. S5). 3.11 Effect of dsRNA exposure assay for NFκB and TLR2 gene expression The expression of genes associated with the TLR signaling pathway and NF-κB signaling pathway in hemocytes after TLR2 silencing by dsRNA was assessed by qPCR. The results indicated that TLR2 gene expression in the experimental group was substantially lowered at all time points relative to the control group ( p < 0.05) (Fig. 13 A). Similarly, other genes in the pathway: HIF1A , NFκB , EIF4E , FADD , TRAF6 , IRAK4 , MyD88 , CASP8 , Akirin2 , 14-3-3ζ , MKK4 , RIP1 , and MAPK14 demonstrated significant downregulation at different time points in the experimental group compared with the control group ( p < 0.05) (Fig. 13 A). In particular, TRAF6 , IRAK4 , NFκB , FADD and MyD88 showed robust interference effects and were persistently downregulated at each time point of the experiment ( p < 0.05) (Fig. 13 A). The expression of genes related to the two target pathways in blood lymphocytes after NFκB was interfered with by dsRNA was further detected by qPCR. The results showed that compared with the control group, the expression level of the NFκB gene in the experimental group was significantly down-regulated at different time points ( p < 0.05) (Fig. 13 B). At the same time, other related genes in the pathway: HIF1A , EIF4E , Akirin2 , 14-3-3ζ , MKK4 , and MAPK14 were also significantly down-regulated to varying degrees at different time points in the experimental group compared with the control group ( p < 0.05) (Fig. 13 B), while the expression patterns of TLR2 , FADD , TRAF6 , IRAK4 , MyD88 , CASP8 , and RIP1 did not change due to NFκB gene interference (Fig. 13 B). 4 Discussion As a typical invertebrate, mollusks are sensitive to environmental and pathogenic factors that affect their health 67 . Their hemocytes have various functions: immunity, digestion, wound healing, detoxification, shell formation, and excretion 68 . Previous studies have shown that hemocytes can enhance their phagocytic activity after exposure to pathogens and maintain it for a long time 8,9 . These works suggest that Mollusks and other invertebrates have immune memory that differs from vertebrates 9–11 . However, the function and classification of abalone hemocytes are unclear 48,49,51 . ScRNA-seq technology can help us understand the different cell types and their roles in organisms. This technology is useful for non-model species that lack genomic data 69 . We used the full-length transcriptome data of Epinepheluscoioides to annotate most transcripts from scRNA-seq, which enabled us to apply this technology in non-reference genome species 70 . The full-length transcriptome data from H. discus hannai was used to perform scRNA-seq of hemocytes 58 . We found 15 hemocyte clusters, which were grouped into three cell types based on previous studies and functional enrichment results: GRCs, SGRCs, and HCs 43,45,48,51 . The proportions of these cell types in the N_group were similar to those reported by flow cytometry in abalone hemocytes 49,50 , confirming our clustering results. The proportions of these cell types did not change significantly in different treatment groups, but the number of cells did. This suggests that different hemocyte types have different roles in the immune response to V. parahaemolyticus infection. Previous studies have shown that viral infection can increase GRCs and decrease HCs in abalone 71 . We observed similar changes in V_group, NV_group, and VV_group samples compared to the control group. We also identified potential marker genes for each cell type: AIF1 and CDC42 for GRCs, TRX2 and GST7 for SGRCs, and C3 for HCs. These genes were highly and stably expressed in all treated samples. The immune regulation mechanism of 5 GRCs clusters in H. discus hannai was investigated by WGCNA. Eight modules were identified, with the red module and the brown module being highly expressed in cluster_3 and cluster_12 but lowly expressed in cluster_11. This indicated that cluster_11 might be regulated by other modules, such as the brown, blue, and yellow modules, and that cluster_11 might differ in differentiation degree from cluster_3 and cluster_12. The immune-related pathways were enriched in the brown module, while the energy metabolism pathways were enriched in the red module. The key genes in the brown module were NFκB , GST , Tollo and Perlucin , which regulated the TLR and NF-κB signaling pathways. These pathways might be important for the immune regulation of cluster_11, which was a mature GRCs cluster. In the red module, CD109 , CD63 , MMP18 and HSP90 were important genes, among which CD109 regulated HSP90 , MMP18 and CD63 . CD109 was a TEP superfamily member that mediated phagocytosis in innate immunity 72,73 . 14 isoforms of CD109 were found in H. discus hannai 58 . Therefore, cluster_11 might regulate immunity by modulating the TLR signaling pathway and NF-κB signaling pathway, as well as phagocytosis. The cluster_3 and cluster_12 in the red module might mainly use phagocytosis for immune regulation, which also requires energy metabolism pathways. The ratio of distinct hemocyte types was altered by V. parahaemolyticus infection. The upregulated genes of different hemocyte types after V. parahaemolyticus infection were analyzed using KEGG functional enrichment analysis. HCs had fewer but more specific pathways and genes enriched than granulocytes, suggesting that HCs had more specific functions. Granulocytes had more diverse pathways and enriched genes, indicating that granulocytes had more complex functions and regulation modes. The Oxidative phosphorylation pathway related to energy metabolism was enriched in HCs of all treatment groups. This pathway might provide oxidative energy and kill pathogens by oxidative burst for HCs 74,75 . Granulocytes, especially GRCs, were mainly enriched in immune-related pathways. In the VV_group, GRCs were also enriched in NF-κB signaling pathways related to signal transduction and Fc gamma R-mediated phagocytosis related to cellular phagocytosis. This suggests that abalone might have a stronger immune response and a possible immune memory effect when exposed to a high dosage of V. parahaemolyticus after a low dosage exposure. This is consistent with previous studies in other mollusks that showed increased phagocytic activity after repeated infection by pathogens 9,10,24 . The comparison of DEGs of GRCs in each treatment group was based on the previous identification methods of different gene sets. Fc gamma R-mediated phagocytosis was enriched in all comparisons, indicating that phagocytosis is the main response mechanism of GRCs to pathogen infection. The TLR pathway of Drosophila was shown to regulate immune priming 41 . In Pacific oysters and Scyllaparamamosain , the expression of TLR signaling molecules and other immune-related factors increased in their second response to V. splendidus and V. parahaemolyticus infection, respectively 11,21 . This suggested that the TLR pathway might be involved in the immune memory process of invertebrates to pathogens. The KEGG enrichment of each gene set showed that the NF-κB signaling pathway and TLR signaling pathway were enriched in CRGs-b and IRGs-b, respectively. These pathways might play an essential role in the initial and re-infection immune response to V. parahaemolyticus , and might be related to the immune memory process of abalone. Hemocytes are the main cellular immune response executors in invertebrates, which clear foreign pathogens and their own infected and damaged cells by phagocytosis 76,77 . Studies have shown that the immune priming of Drosophila and silkworms depends on phagocytic cells 41,78 . In mollusks, the number and phagocytic activity of hemocytes increased after re-infection by pathogens in snails, scallops, and oysters 9,10,24 . In oysters, Fc gamma R-mediated phagocytosis was enriched in the differential genes, indicating that phagocytosis plays a critical immune defense role in oyster hemocytes responding to re-infection by V. splendidus 21 . Many genes related to phagocytosis were differentially expressed in abalone after re-infection by V. harveyi , suggesting that phagocytosis plays an important role in preventing abalone from being reinfected by the same pathogen 23 . In this study, Fc gamma R-mediated phagocytosis was also enriched in PEGs-b, IRGs-b and ERGs-b, indicating that phagocytosis not only plays a key regulatory role in abalone hemocytes responding to re-infection by V. parahaemolyticus , but also participates in the immune memory process of abalone hemocytes. In addition, in the molecular interaction network analysis between different immune signal pathways, NFκB and IRAK4 commonly regulated the three immune signal pathways, and the NF-κB signaling pathway interacted with Fc gamma R-mediated phagocytosis through PLCG1 . PLCG1 is a phospholipase C gamma family member that can regulate various physiological and pathological responses of cells. In Octopus ocellatus , PLCG1 might be involved in a more complex immune regulation process that only exists in hatchlings with egg protection from maternal incubation 79 . Our results also suggested that NF-κB signaling pathway might participate in the immune response process of abalone hemocytes to V. parahaemolyticus re-infection under the coordination of PLCG1 , and induce a more substantial phagocytic effect to clear the invading pathogens. This also implied a possible immune memory effect existing in abalone hemocytes. The pseudotime trajectory analysis of the GRCs differentiation mode in each sample was performed. Compared with the control group samples, the GRCs of samples after V. parahaemolyticus infection were found to be in different branches and states of the pseudotime differentiation trajectory. Under the differentiation mode with state_3 as the starting point, the potential marker genes of GRCs and some classic immune-related genes and pathways were upregulated in state1_2. This suggested that V. parahaemolyticus infection could induce the differentiation and maturation of GRCs, making them more stable in immune regulation function and that different dosages of V. parahaemolyticus infection could also affect this differentiation potential. In previous studies, we have discussed different views on hemocyte differentiation, and in abalone, our results support the view that different types of hemocytes are differentiated from a single type, that is, HCs and GRCs are the initial and final stages of differentiation of the same cell type 80,81 . In this study, we found that C3 , a potential marker gene of HCs, was highly expressed in state_3, which might indicate that state_3 had many cells in an immature state of differentiation from HCs to GRCs, which also verified our conclusion. The latest research shows that PCNA , as a proliferation marker, is expressed in the hemocytes and gill of H. discus hannai 82 . In this study, PCNA was expressed at a higher level in the HCs of the untreated group, which may indicate its involvement in the differentiation process of hemocytes from HCs to GRCs.Moreover, the enrichment of energy metabolism-related pathways in state_3 also indicated that cellular differentiation was an energy-dependent process. Although this study proposed a cellular differentiation model of abalone GRCs under V. parahaemolyticus infection, the results still need further experimental validation, which will be the focus of future research. A re-clustering analysis of GRCs was performed to investigate their functional differentiation further. Three different sub-clusters were obtained, among which Sub-cluster_1 was distributed in each treatment group but mainly enriched in the control group, while Sub-cluster_2 mainly existed in the control group samples without V. parahaemolyticus infection. Further analysis showed that these two sub-clusters had many common pathways, mainly Oxidative phosphorylation related to energy metabolism and Fc gamma R-mediated phagocytosis related to cellular phagocytosis. This indicated that these two sub-clusters had similar functions but different participation modes. Sub-cluster_1 might be widely involved, while Sub-cluster_2 might be mainly involved in energy provision and cellular phagocytosis under a cellular resting state. Similar to Sub-cluster_1, Sub-cluster_0 also existed widely in each treatment group sample, but their distribution states were different. Sub-cluster_0 mainly existed in each sample after V. parahaemolyticus infection, while Sub-cluster_1 was mainly distributed in the control group. There was also some overlap in gene expression patterns between them, indicating that they might only differ in the degree of differentiation. The up-regulation of GRCs key marker genes and some immune regulatory genes in Sub-cluster_0 indicated that this sub-cluster had a higher degree of differentiation and might be at the end of GRCs differentiation. The DEGs in the same sub-cluster between different treatment groups after V. parahaemolyticus infection were analyzed for their function. It was shown that both high-concentration V. parahaemolyticus infection (V_group vs NV_group) and V. parahaemolyticus re-infection (V_group vs VV_group) upregulated immune-related pathways such as TLR signaling pathway and NF-κB signaling pathway, and phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis in Sub-cluster_0. This indicated that Sub-cluster_0 was the main GRCs regulation type in response to V. parahaemolyticus infection and that some classic immune pathways and phagocytosis of hemocytes were involved in this regulation process. In contrast, in Sub-cluster_1, after high concentration V. parahaemolyticus infection, and in VV_group vs NV_group, only some processes, such as Endocytosis, were enriched, indicating the difference in immune regulation capacity between the two sub-clusters. Moreover, in V_group vs VV_group, the NF-κB signaling pathway, TLR signaling pathway, Fc gamma R-mediated phagocytosis, and other pathways were enriched in Sub-cluster_1 again. Due to the small number of cells in each group after V. parahaemolyticus infection in Sub-cluster_1, we speculated that these cells might be induced to differentiate by V. parahaemolyticus re-infection and that they might have stronger phagocytosis capacity and immune response mechanism through NF-κB signaling pathway and TLR signaling pathway, participating in the immune memory process of abalone hemocytes. The activation of specific cell signals depends on the binding of ligands and their receptors. However, this part of research is rarely reported in marine invertebrates due to the lack of reference data. In the latest study of oysters, it was proved that there was a complex cell communication relationship between GRCs and SGRCs, and that copper ions would affect this relationship 46 . Using similar methods, we analyzed the cell communication relationship between different cell clusters in different treatment groups after V. parahaemolyticus infection. We found that V. parahaemolyticus infection could enhance the interaction between different cell clusters, making them have stronger communication relationships with each other. Among the 74 pairs of receptor-ligand pairs that interacted, COPA and SORT1 interacted in different treatment samples, and in NV_group and VV_group, SORT1 also interacted with GRN. Studies have shown that GRN can enhance the proliferation of various cell types, regulate inflammatory response and wound healing, and participate in endocytosis by binding with SORT1 83 . Our results suggest that high-dosage V. parahaemolyticus infection, either in the initial or re-infection process, involves endocytosis. In addition, studies have shown that L1cam is a marker of NF-κB signal pathway activation 84 . In this study, we found that L1cam interactions were found in V_group and VV_group compared with NV_group, indicating that V. parahaemolyticus re-infection can activate NF-κB signal pathway again significantly, which might be one of the key regulatory factors for stronger immune effect. In addition, more ligand gene numbers in VV_group indicate that V. parahaemolyticus re-infection can enhance the communication relationship between ligand-receptor cells. There was a strong communication relationship between cell clusters 5, 6, 11, 12 and 14, indicating that GRCs and SGRCs might interact with each other in a more complex way. To further analyze this communication relationship, we selected cluster_11 and cluster_14 as ligand signal cells and receptor signal cells respectively, and analyzed the ligand activity in ligand signal cells and the regulation potential of ligands on specific genes in receptor cells. The results showed that the overall activity and number of each ligand were generally higher in VV_group and that high dosage V. parahaemolyticus infection could enhance the ligand activity of regulating the NF-κB signaling pathway, NLR signaling pathway and TLR signaling pathway gene set, which were highest in the VV_group. The regulation analysis results of the ligand-target gene showed that the number of target genes regulated by ligands in the VV_group was also the highest. These results indicate that V. parahaemolyticus re-infection can increase the communication exchange between ligand-receptor cells and induce a stronger immune communication effect in abalone hemocytes, which might be the communication basis for abalone immune memory. RNAi has been successfully performed in marine invertebrates such as H. diversicolor 85 . NFκB and TLR2 are the core factors of NF-κB signaling pathway and TLR signaling pathway, respectively. To study their regulatory effects on other molecules of these pathways and their interactions in H. discus hannai , we performed RNAi experiments of NFκB and TLR2 genes in hemocytes of H. discus hannai using dsRNA soaking method. The results showed that the expression levels of NFκB and TLR2 in the experimental group were significantly lower than those in the control group at each time point after RNAi treatment, indicating obvious interference effects. When TLR2 was interfered with, the expression levels of genes related to NF-κB and TLR signaling pathway in the experimental group were also significantly down-regulated at different time points compared with the control group. TRAF6 , IRAK4 , NFκB , FADD and MyD88 had obvious interference effects, which were significantly inhibited from 6 h after interference. These results indicated that TLR2 had a key positive regulatory role on these genes. However, there might be differences in the regulation intensity, which might cause the inconsistency of expression patterns of different genes at each time point. When NFκB was interfered with, some genes, such as HIF1A , EIF4E , Akirin2 , 14-3-3ζ , MKK4 , and MAPK14 , were also significantly down-regulated at different time points in the experimental group compared with the control group, indicating that NFκB had a positive regulatory role on these genes. Among them, Akirin2 was inhibited from 6 h to 24 h after interference; HIF1A , EIF4E , 14-3-3ζ and MKK4 were inhibited from 12 h after interference; and MAPK14 was only down-regulated at 24 h compared with the control group. In addition, several other genes, such as TLR2 , FADD , TRAF6 , IRAK4 , MyD88 , CASP8 , and RIP1 , did not change their expression patterns after NFκB interference. This result indicated that these genes might be upstream of NFκB and not directly regulated by NFκB . The molecular interaction network diagrams of the above genes were constructed by Cytoscape, which showed the interaction relationships among these genes after NFκB and TLR2 interference. We observed that TRAF6 was at the top of the network and regulated a complex network. Previous studies have found that TRAF6 is an important regulator in signal transduction during innate immunity 86 . When pathogens infected the organism, TRAF6 was upregulated in C. farreri 87 , Portunustrituberculatus 88 , Apostichopus japonicus 89 and Pinctada martensii 90 . Moreover, TRAF6 has been proven to connect TLR and MyD88 with NF-κB signaling pathway. In this study, TRAF6 was also in the core position of TLR and NF-κB signaling pathway network diagram. This indicated that in H. discus hannai , TRAF6 might be a key link hub of TLR and NF-κB signaling pathway. Moreover, RNAi results showed that TLR2 interference significantly inhibited TRAF6 expression at all time points, indicating that TLR2 was upstream of TRAF6 and firmly regulated its expression. However, NFκB interference did not affect TRAF6 expression, which confirmed that in hemocytes of H. discus hannai , the NF-κB signaling pathway should be downstream of TRAF6 and might be regulated by TRAF6 and that this regulation was one-way. Thus, a classical TLR/NF-κB signaling pathway exists in hemocytes of H. discus hannai , and this pathway participates in the immune regulation process of abalone hemocytes in response to V. parahaemolyticus re-infection. When the organism was challenged by pathogens, the TLR family members on the cell membrane captured the signals, and with the help of regulators such as TRAF6, they relayed the signals to NFκB, which played a key immune regulatory role. In conclusion, we explored the response mechanism of H. discus hannai hemocytes to V. parahaemolyticus re-infection by using scRNA-seq. The 15 hemocyte clusters were re-clustered into three cell types: GRCs, SGRCs, and HCs, which were consistent with traditional views, and their potential marker genes were screened. Pseudotime analysis showed that HCs and GRCs were in the early and late stages of differentiation of the same cell type. GRCs might be the main cellular immunity participants in H. discus hannai , as they were enriched in phagocytosis and immune regulation-related pathways. We identified potential marker genes such as AIF1 and CDC42 in GRCs, TRX2 and GST7 in SGRCs, and C3 in HCs. GRCs might have more complex functions than HCs. Through WGCNA, we identified that cluster_11 might be a more mature subpopulation of GRCs, primarily functioning as a core immune regulator with strong phagocytic, endocytic, signal transduction, and immune regulatory capabilities. On the other hand, cluster_3 and cluster_12 might be in the early stages of GRCs differentiation, and the involvement of energy metabolism-related pathways accompany their differentiation process. Secondary infection by V. parahaemolyticus might induce GRCs to produce a stronger immune response through the NF-κB signaling pathway, TLR signaling pathway and phagocytosis. It could also accelerate the differentiation process of HCs to GRCs. GRCs had heterogeneity, and different clusters had different functions at different differentiation stages. The higher the differentiation degree, the stronger the immune regulation ability. The immune memory of abalone hemocytes to V. parahaemolyticus might not involve all cells, but a few cells produced an immune regulation mechanism after re-infection. Secondary infection by V. parahaemolyticus could also increase the communication between ligand-receptor cells, indicating a stronger immune communication effect in the organism, which might be the communication basis for abalone immune memory. RNAi results showed that a classical Toll/NF-κB signaling pathway existed in hemocytes of H. discus hannai , and this pathway was involved in the immune regulation process of abalone hemocytes responding to secondary infection by V. parahaemolyticus. Based on these results, we summarized the possible immune memory regulation mechanism in hemocytes of H. discus hannai (Fig. 13 ) Declarations Conflicts of Interest: The authors declare no conflict of interest. Author Contributions: Yilei Wang and Ziping Zhang conceived the study and designed the experiments. Xin Zhang conducted the experiments and wrote the manuscript. Yulong Sun conducted the experiments and analyzed the data. Yilei Wang, Jianjun Feng and Ziping Zhang checked and modified the manuscript. All authors read and approved the final manuscript. Acknowledgments The work was supported by the National Key R&D Program of China (NO. 2021YFE0106100, 2018YFD0900304-5), Fujian Innovation and Industrialization Development of Abalone Seed Industry (2021FJSCZY02), the Natural Science Foundation of China (No. 31672681), Open fund project of Fujian Engineering Research Center of Aquatic Breeding and Healthy Aquaculture (No. DF20902), Open fund project of Key Laboratory of Healthy Mariculture for the East China Sea (No. 2020ESHML12). References Cerenius KS (1992) Crustacean immunity. Annu Rev Fish Dis 2:3–23 Zeng X, Zhang Z, Wang Y (2017) Progress in immunological memory of invertebrates. Chin Bull Life Sci (in Chinese), 1174–1184 Hildemann WH, Raison RL, Cheung G, Hull CJ, Okamoto J (1977) Immunological specificity and memory in a scleractinian coral. Nature 270:219–223 Kinetics C, Cooper EL, Roar P (1986) Second-set allograft responses in the earthworm Lumbricus terrestris . 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Genet Mol Res 13:10545–10555 Additional Declarations There is NO Competing Interest. Supplementary Files TableS.docx Tables S1-S4 FiguresS15.docx Fig. S1 Single cell quality inspection results. A-E: Barcodes line graphs of V_group (A), N_group (B), NN_group (C), VV_group (D) and NV_group (E) samples. Fig. S2 Comparison of Sub-cluster_0 in different V. parahaemolyticus treatments. A: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 under high dosage of V. parahaemolyticus infection (V_group vs NV_group); B: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 in the presumed immune-enhanced gene set (VV_group vs NV_group); C: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 under V. parahaemolyticus re-infection (V_group vs VV_group). Fig. S3 Comparison of Sub-cluster_1 in different V. parahaemolyticus treatments. A: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 under high dosage of V. parahaemolyticus infection (V_group vs NV_group); B: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 in the presumed immune-enhanced gene set (VV_group vs NV_group); C: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 under V. parahaemolyticus re-infection (V_group vs VV_group). Fig. S4 Heatmaps of ligand activity in different samples of H. discus hannai. A, B, C, D: Heatmap of ligand activity in N_group (A), V_group (B), NV_group (C) and VV_group (D) samples. The vertical axis is all the ligands in the ligand signaling cells that regulate the specific gene set in the receptor signaling cells, the horizontal axis is the different signaling pathways that are regulated in the receptor signaling cells, and the ligand activity is indicated by different colors, the darker the color, the stronger the ligand activity in this pair of cells. Fig. S5 Heatmaps of regulatory potential of ligand-target genes. A, B, C, D: Heatmap of ligand-target gene regulatory potential in N_group (A), V_group (B), NV_group (C) and VV_group (D) samples. The horizontal axis is the specific signaling pathway in the receptor signaling cells, the vertical axis is the ligand-target gene pair in the ligand-receptor signaling cell pair, and the ligand’s regulatory potential on the target gene is indicated by different colors, red indicates that the ligand has strong regulatory potential on the target gene, and blue indicates weak. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4675005","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":327805800,"identity":"9ff6129c-3073-42b5-8cf9-361792183f66","order_by":0,"name":"Ziping Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDACCSjJxsDA+BjMZmZuIFoLszEDgwGQYiRKCxiwSYO1MBDQIj+7+dnDL38s8vik269VF1T8ieZvB2r5UbENpxbGOcfMjWV4JIrZZM6U3Z5xxiB3xmHGBsaeM7dxamGWSDCTlpCQSGyTyEm7zdtmkNsA1MLM2IZbC5tE+jdpCQOIlmKQlvmEtPBI5JhJfkgAaUk/xgzSsoGQFgmJnDJphgNAv0jkMEvznDHO3QjUchCfX+RnpG+T/PGnLg/IePiZp0Iud975wwcf/KjArQUcBDwMDAlANxrARQ7gVQ8EjD/AWtgfEFI4CkbBKBgFIxQAAMQmUXJ36W9mAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Ziping","middleName":"","lastName":"Zhang","suffix":""},{"id":327805801,"identity":"a6605f1d-04dc-41b0-aa54-e2d3cc63a328","order_by":1,"name":"Xin Zhang","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":327805802,"identity":"16669410-50d1-4af6-9ced-67ab5659e135","order_by":2,"name":"Yulong Sun","email":"","orcid":"","institution":"Fujian Agriculture And Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"Sun","suffix":""},{"id":327805803,"identity":"8ffee431-219a-4ed5-8026-df4d3f99eb44","order_by":3,"name":"Jianjun Feng","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Feng","suffix":""},{"id":327805804,"identity":"9aa021e5-870d-4336-a6ad-4010b5b6033e","order_by":4,"name":"Yilei Wang","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Yilei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-02 14:36:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4675005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4675005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61811605,"identity":"897bda0a-11b6-43ec-bdd7-209fd8e44cbe","added_by":"auto","created_at":"2024-08-05 20:28:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1291039,"visible":true,"origin":"","legend":"\u003cp\u003e10× Genomics single-cell transcriptome analysis experimental flow chart of hemocytes stimulated and these secondary infected with \u003cem\u003eV. parahaemolyticus\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/4f63e0a736bf5b0fa57422c8.png"},{"id":61811916,"identity":"1fc34e0a-c644-4506-ba88-1f11f7eb6d18","added_by":"auto","created_at":"2024-08-05 20:36:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":821716,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and clustering results of cell clusters in hemocytes. A: Cell clusters clustering in t-SNE dimensionality reduction outcomes of hemocytes; B: Cell cluster clustering in t-SNE of hemocytes from different samples; C: Stacked plot of the number of different clusters of hemocytes in each sample; D: Expression bubble plot of the top 5 highly expressed genes enriched in each cluster of N_group.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/0c133ae101ccf975fd87a20d.png"},{"id":61810450,"identity":"3ea931e7-2ad9-4f23-ada0-1eb8463fa32f","added_by":"auto","created_at":"2024-08-05 20:20:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":519829,"visible":true,"origin":"","legend":"\u003cp\u003eRe-classification of hemocyte clusters of \u003cem\u003eH. discus hannai\u003c/em\u003e. A: KEGG enrichment of different cell clusters; B: The number of cells contained in three types of hemocytes in N_group of \u003cem\u003eH. discus hannai\u003c/em\u003e; C: Heat map of the top 10 highly expressed genes enriched in three types of hemocytes based on scRNA-seq data; D: Expression bubble plot of the top 10 highly expressed genes enriched in each type of hemocytes.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/9f03e7579246ddc00af7315b.png"},{"id":61811606,"identity":"ac80a555-4420-4496-907d-563282f3e3d4","added_by":"auto","created_at":"2024-08-05 20:28:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":843822,"visible":true,"origin":"","legend":"\u003cp\u003eMarker gene identification of different samples infected with \u003cem\u003eV. parahaemolyticus. \u003c/em\u003eA: Stacked chart of the number of different types of hemocytes in each sample; B: V_group expression distribution bubble plot of marker genes for different cell types; C: NV_group expression distribution bubble plot of marker genes for different cell types; D: VV_group expression distribution bubble plot of marker genes for different cell types; E: Heat map of the expression of common marker genes; F: t-SNE maps of different common marker genes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/b1fd6dcb7faec4bb3da0de53.png"},{"id":61810452,"identity":"754e6e43-f9c2-42d0-9f5e-3e5a0fd0f03b","added_by":"auto","created_at":"2024-08-05 20:20:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":473603,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA analysis of different clusters of N_groupGRCs. A: Hierarchical clustering tree of the correlated modules obtained by WGCNA analysis. Each main branch represents a module, marked with different colors; B: Number of genes clustered in different modules; C: Heat map of expression distribution of genes for different modules; D: Heat map of correlation clustering between different modules. The positive and negative numbers in each square represent the Pearson correlation coefficient between the corresponding modules. Positive values indicate positive correlation, negative values indicate negative correlation, and the numbers in parentheses are P values. E: Heat map of expression patterns of different modules in different clusters of GRCs; F: Bubble plot of KEGG enrichment results for different module genes.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/27a559c8cc8f279c6521952d.png"},{"id":61811609,"identity":"53138f92-35c3-4ae0-bd56-f4e2879dfeb3","added_by":"auto","created_at":"2024-08-05 20:28:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478139,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction network between the top 5-6 genes with high connectivity in the objective module. A: Hub genes and their regulatory network diagrams screened in the blue module; B: Hub genes and their regulatory network diagrams screened in the brown module; C: Hub genes and their regulatory network diagrams screened in the yellow module; D: Hub genes and their regulatory network diagrams screened in the red module. The nodes marked with gene name abbreviations represent a gene, hub genes are shown as triangles, and associated correlated genes are circles.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/85b6d4b5a6562c1cc960cb2d.png"},{"id":61810455,"identity":"6f12ddd8-6877-4aa9-969e-fac8471d28fe","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2119585,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment of upregulated genes in all cell types of different samples stimulated with \u003cem\u003eV. parahaemolyticus. \u003c/em\u003eA: Bubble plot of enriched pathways for differentially upregulated genes in different types of hemocytes in V_group; B: Bubble plot of enriched pathways for differentially upregulated genes in different types of hemocytes in NV_group; C: Bubble plot of enriched pathways for differentially upregulated genes in different types of hemocytes in VV_group.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/87e0f4d847d6bbee2ffeb616.png"},{"id":61811611,"identity":"6224941d-1532-406d-a9c8-eea89e9a4bf6","added_by":"auto","created_at":"2024-08-05 20:28:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":441710,"visible":true,"origin":"","legend":"\u003cp\u003eSpecific and common DEGs in GRCs among different comparison groups of \u003cem\u003eH. discus hannai\u003c/em\u003eafter infection with \u003cem\u003eV. parahaemolyticus. \u003c/em\u003eA: Screening and KEGG enrichment results of 801 CRGs-b; B: Screening and KEGG enrichment results of 361 PEGs-b; C: Screening and KEGG enrichment results of 489 IRGs-b; D: Screening and KEGG enrichment results of 246 ERGs-b.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/289043202724266f75998d72.png"},{"id":61810461,"identity":"32edf8b8-14bc-4486-a43c-60ecb6389807","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":402166,"visible":true,"origin":"","legend":"\u003cp\u003eAn overview of immune signaling pathways and gene interactions in GRCs.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/505dc3a94ee293f894469301.png"},{"id":61811607,"identity":"55a90cd6-8078-413c-ad1f-c9b0e5169d57","added_by":"auto","created_at":"2024-08-05 20:28:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":590137,"visible":true,"origin":"","legend":"\u003cp\u003eThe pseudo-temporal ordering of GRCs from different samples. A: Pseudotemporal trajectory of GRCs development, yellow indicates early pseudotime, and the color gradually turns blue as time extends; B: Scatter plot of GRCs differentiation states under different treatments, arrows indicate differentiation direction; C: Scatter plot of GRCs differentiation states under different treatment groups; D: Heatmap of DEGs along pseudotime axis for different differentiation states; E: Bubble plot of expression of differential upregulated genes for different differentiation states; F: KEGG enrichment results of key upregulated genes for different differentiation states of GRCs.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/af0134ee1d8e34f057ade05a.png"},{"id":61810464,"identity":"93bc1c8f-8a68-44aa-ac7d-5386817b8d54","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":618823,"visible":true,"origin":"","legend":"\u003cp\u003eThe t-SNE clustering results and functional analysis of different sub-clusters of GRCs. A: t-SNE clustering results of GRCs re-clustering; B: t-SNE clustering results of different sub-clusters of GRCs under different treatment groups; C: Heatmap of distribution of upregulated genes in each sub-cluster; D: KEGG enrichment bubble plot of upregulated genes in sub-clusters.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/df285e797cc3c16208ec1448.png"},{"id":61810458,"identity":"ed8d6498-28d0-4516-864c-cc2f9d1cf524","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":4553025,"visible":true,"origin":"","legend":"\u003cp\u003eCell communication analysis among 15 clusters of hemocytes in \u003cem\u003eH. discus hannai. \u003c/em\u003eA, B, C, D: Interaction graphs of 15 hemocyte clusters in N_group (A), V_group (B), NV_group(C) and VV_group (D) samples. Circles represent different cell clusters. Circle size is determined by the number of significantly enriched ligand pairs between clusters and all their interacting clusters. The larger the circle, the stronger the correlation between clusters. The thickness of the line represents the number of significantly enriched ligand-receptor pairs between two clusters; E, F, G, H: Heatmap analysis of ligand-receptor pairs in each cell cluster in N_group (E), V_group (F), NV_group (G) and VV_group (H) samples. Blue indicates that the cell has fewer ligands and receptor pairs, red indicates more; I: Upset-Venn diagram of ligand-receptor numbers in different samples; J: GO enrichment analysis of receptor and ligand genes in each cell cluster in different samples; K: KEGG enrichment analysis of receptor and ligand genes in each cell cluster in different samples.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/2e6d38afa4e4dc660ba70e41.png"},{"id":61810456,"identity":"6e8e05a7-08a1-42f4-b1e0-7afaf1beab14","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":614865,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction network of TLRand NF-κB signal pathway-related genes. A: The interaction network of TLRand NF-κB signal pathway-related genes after \u003cem\u003eTLR2\u003c/em\u003ewas interfered; B: The interaction network of TLRand NF-κB signal pathway-related genes after \u003cem\u003eNFκB\u003c/em\u003e was interfered.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/a95d213056af1809b1404603.png"},{"id":61810462,"identity":"864e0ee7-52a6-498b-9bb3-cad08cc2fd50","added_by":"auto","created_at":"2024-08-05 20:20:38","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1398629,"visible":true,"origin":"","legend":"\u003cp\u003eImmune regulation of \u003cem\u003eH. discus hannai\u003c/em\u003e hemocytes in response to the secondary infection of \u003cem\u003eV. parahaemolyticus.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/2c1834144a89f49c1fc4f028.png"},{"id":86630777,"identity":"1a3d7919-5251-474c-8e4c-de3791d23577","added_by":"auto","created_at":"2025-07-14 06:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15203227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/dcbcec38-36af-4adc-8f54-9677f95faf1a.pdf"},{"id":61810449,"identity":"72e8fff6-4e70-40fe-95c5-ae681f8a280f","added_by":"auto","created_at":"2024-08-05 20:20:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25537,"visible":true,"origin":"","legend":"Tables S1-S4","description":"","filename":"TableS.docx","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/255bfd2e11d5ccb1061ba683.docx"},{"id":61811917,"identity":"7ba7d91e-1b98-40d3-9580-fca13e525edf","added_by":"auto","created_at":"2024-08-05 20:36:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22190306,"visible":true,"origin":"","legend":"\u003cp\u003eFig. S1 Single cell quality inspection results. A-E: Barcodes line graphs of V_group (A), N_group (B), NN_group (C), VV_group (D) and NV_group (E) samples.\u003c/p\u003e\n\u003cp\u003eFig. S2 Comparison of Sub-cluster_0 in different \u003cem\u003eV. parahaemolyticus\u003c/em\u003e treatments. A: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 under high dosage of \u003cem\u003eV. parahaemolyticus \u003c/em\u003einfection (V_group vs NV_group); B: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 in the presumed immune-enhanced gene set (VV_group vs NV_group); C: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_0 under \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection (V_group vs VV_group).\u003c/p\u003e\n\u003cp\u003eFig. S3 Comparison of Sub-cluster_1 in different \u003cem\u003eV. parahaemolyticus\u003c/em\u003e treatments. A: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 under high dosage of \u003cem\u003eV. parahaemolyticus \u003c/em\u003einfection (V_group vs NV_group); B: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 in the presumed immune-enhanced gene set (VV_group vs NV_group); C: Number of DEGs and KEGG enrichment circle and bar graphs of Sub-cluster_1 under \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection (V_group vs VV_group).\u003c/p\u003e\n\u003cp\u003eFig. S4 Heatmaps of ligand activity in different samples of \u003cem\u003eH. discus hannai. \u003c/em\u003eA, B, C, D: Heatmap of ligand activity in N_group (A), V_group (B), NV_group (C) and VV_group (D) samples. The vertical axis is all the ligands in the ligand signaling cells that regulate the specific gene set in the receptor signaling cells, the horizontal axis is the different signaling pathways that are regulated in the receptor signaling cells, and the ligand activity is indicated by different colors, the darker the color, the stronger the ligand activity in this pair of cells.\u003c/p\u003e\n\u003cp\u003eFig. S5 Heatmaps of regulatory potential of ligand-target genes. A, B, C, D: Heatmap of ligand-target gene regulatory potential in N_group (A), V_group (B), NV_group (C) and VV_group (D) samples. The horizontal axis is the specific signaling pathway in the receptor signaling cells, the vertical axis is the ligand-target gene pair in the ligand-receptor signaling cell pair, and the ligand’s regulatory potential on the target gene is indicated by different colors, red indicates that the ligand has strong regulatory potential on the target gene, and blue indicates weak.\u003c/p\u003e","description":"","filename":"FiguresS15.docx","url":"https://assets-eu.researchsquare.com/files/rs-4675005/v1/31fb64f2b80889463c74f207.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-Cell Transcriptomic Analysis of Specific Responses of Different Cell Populations of Hemocytes to the Re-infection of Bacteria, a Case Study in Abalone","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDifferent pathogens affect invertebrates living in various habitats \u003csup\u003e1\u003c/sup\u003e. It is generally believed that invertebrates lack key molecules, cells, and organs for immune memory, so they rely on innate immunity to fight infections \u003csup\u003e2\u003c/sup\u003e. However, some invertebrates can reject transplants more strongly after repeated exposure, suggesting they have immunomodulatory mechanisms similar to vertebrate-specific immunity \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. Recent studies also show that prior exposure to a pathogen can lower mortality and boost cellular and humoral immunity upon re-infection, indicating a form of innate immune memory \u003csup\u003e6,7\u003c/sup\u003e. This phenomenon is called immune priming, innate immune memory, or trained immunity by researchers who want to distinguish it from vertebrate immune memory \u003csup\u003e8\u0026ndash;14\u003c/sup\u003e. The mechanism of immune memory in invertebrates is still poorly understood, but evidence supports its existence. The main difference is that invertebrates respond faster and stronger to re-infection by pathogens, which helps them eliminate infections and survive better. For example, \u003cem\u003eExaiptasia pallida\u003c/em\u003e exposed to sublethal and then lethal doses of \u003cem\u003eVibrio coralliilyticus\u003c/em\u003e had lower mortality and higher levels of immune proteins than controls \u003csup\u003e13\u003c/sup\u003e. Similar results have been reported in crustaceans \u003csup\u003e8,11,15\u0026ndash;20\u003c/sup\u003e and molluscs \u003csup\u003e9,10,21\u0026ndash;24\u003c/sup\u003e. Some invertebrates also showed recognition specificity and time dependence when re-infected by different pathogens \u003csup\u003e15,16,25\u003c/sup\u003e. Vertebrate immunology has data, but invertebrate immunology could be stronger. In China, shellfish and crustaceans are important for aquaculture, but bacterial and viral infections can cause massive deaths and economic losses \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Antibiotics and other drugs can leave residues in their bodies, raising food safety concerns \u003csup\u003e18\u003c/sup\u003e. Crustaceans and shellfish have skin defenses against most pathogens, but their innate immunity becomes crucial when pathogens invade their body \u003csup\u003e29,30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHemocytes are the main cells involved in the innate immunity of invertebrates. They participate in various cellular processes, such as phagocytosis, cysts, apoptosis, wound repair, and blood coagulation. Hemocytes of shellfish can be classified into three types based on their size, shape, and function: hyalinocytes (HCs), semi-granulocytes (SGRCs), and granulocytes (GRCs) \u003csup\u003e31,32\u003c/sup\u003e. Hemocyte differentiation in invertebrates differs from that in vertebrates but is still essential for cellular immunity against pathogens. Hemocytes recognize pathogen-associated molecular patterns (PAMPs) through pattern recognition receptors (PRRs), such as lectins and toll-like receptors (TLRs), on the surface of bacteria, fungi, or viruses \u003csup\u003e33,34\u003c/sup\u003e. Then, they clear the pathogen by phagocytosis \u003csup\u003e35\u003c/sup\u003e. Previous studies have shown that immunization with killed or inactivated pathogens can enhance the phagocytic activity of hemocytes in different invertebrates \u003csup\u003e8,9,22,36\u003c/sup\u003e. This suggests that increased phagocytosis is important for immune memory in invertebrates. Moreover, studies of \u003cem\u003eAnopheles gambiae\u003c/em\u003e and \u003cem\u003eLitopenaeusvannamei\u003c/em\u003ehave shown that immune priming can induce directional differentiation and mitosis of hemocytes, indicating that hemocyte proliferation and regeneration may also be a key mechanism of immune memory production \u003csup\u003e8,37,38\u003c/sup\u003e. Recognition of PAMPs by PRRs activates downstream signaling pathways that lead to the secretion of lectins, activation of prophenoloxidase, and production of various antimicrobial peptides (AMPs). The TLR pathway is a well-studied innate immune pathway in invertebrates \u003csup\u003e39,40\u003c/sup\u003e. Research has shown that the TLR pathway is directly involved in regulating immune priming in Drosophila and may also be involved in the immune memory process of other invertebrates to pathogens \u003csup\u003e11,21,41\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) is a technique that measures gene expression differences between single cells, revealing the functional diversity within different cell clusters in the same tissue. Unlike bulk RNA-seq, scRNA-seq can resolve cellular heterogeneity \u003csup\u003e42\u003c/sup\u003e. This technique has advanced the research of many marine invertebrates, such as identifying the marker genes of 20 cell clusters in the adductor muscle of \u003cem\u003ePatinopectenyessoensis\u003c/em\u003e \u003csup\u003e43\u003c/sup\u003e and showing that hemocytes of \u003cem\u003eParribacus japonicus\u003c/em\u003e differentiated from a single population \u003csup\u003e44\u003c/sup\u003e. The analysis of scRNA-seq and bulk RNA-seq of hemocytes of \u003cem\u003eCrassostrea hongkongensis\u003c/em\u003e discussed the diversity and heterogeneity of hemocytes and their responses to copper ion exposure \u003csup\u003e45,46\u003c/sup\u003e. \u003cem\u003eHaliotis discus hannai\u003c/em\u003e is the main abalone species cultured in Fujian, accounting for about 79.1% of China\u0026rsquo;s total output \u003csup\u003e47\u003c/sup\u003e. However, abalone aquaculture faces diseases, especially those caused by Vibrio. Hemocytes are important for resisting pathogens in abalone. Previous studies have used whole hemolymph as materials and could not address cell heterogeneity. They also had different classification standards for abalone hemocytes. We have shown that hemocytes are involved in the immune memory process of \u003cem\u003eH. discus hannai\u003c/em\u003e to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection, but the exact mechanism is unclear \u003csup\u003e48\u0026ndash;56\u003c/sup\u003e. Using scRNA-seq, we aim to explore hemocyte clusters' functional differences and roles in \u003cem\u003eH. discus hannai\u003c/em\u003e immune memory from a cellular heterogeneous perspective. This will help us control infectious disease outbreaks through vaccination and support aquaculture's healthy and sustainable development.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethics statement\u003c/h2\u003e \u003cp\u003eAll the study designs and animal experiments were conducted following the Animal Care and Use Committee of the Fisheries College of Jimei University guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Animals and bacterial challenge\u003c/h2\u003e \u003cp\u003eAdult Pacific abalones (body length 6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 cm, body weight 19.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 g; n\u0026thinsp;=\u0026thinsp;100) were purchased from Jinjiang Fuda Abalone Fisheries Co. Ltd (Jinjiang, Fujian, China) in September 2021. Abalones were then maintained in a recirculating system with a sand-filter at constant temperatures (26\u0026deg;C) and dissolved oxygen levels (6.2 mg/L) and were fed sea tangle (\u003cem\u003eLaminaria japonica\u003c/em\u003e) once daily \u003csup\u003e57\u003c/sup\u003e.\u003cem\u003eV. parahaemolyticus\u003c/em\u003e (isolated from diseased Pacific abalones and preserved in our laboratory) was cultured in LB media (containing 10 g/L peptone, 5 g/L yeast extract and 10 g/L NaCl) at 37\u0026deg;C for 24 h and harvested \u003cem\u003evia\u003c/em\u003e centrifugation (6000 \u0026times; \u003cem\u003eg\u003c/em\u003e, 10 min, 4\u0026deg;C). The pellet was washed and re-suspended in 0.9% normal saline (NS). Through the analysis of the results of the pre-experimental stage, the dosage of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e was set to 1.0 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e CFU/mL.\u003c/p\u003e \u003cp\u003eThe experiment was split into the immune phase (IP) and the secondary infection phase (SSP). For the IP, 50 abalones received an injection of NS (20 \u0026micro;L) and were employed as the N group, and another 50 abalones received an injection of \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eparahaemolyticus\u003c/em\u003e suspension with the dosage of 1.0\u0026times;10\u003csup\u003e8\u003c/sup\u003e CFU/mL (20 \u0026micro;L) and were employed as the V group. Hemolymph was collected from 36 abalones of the N and V groups, respectively, at 3 h, 12 h, and 24 h during the IP (N\u0026thinsp;=\u0026thinsp;6). After 168 h of the IP, in the SSP, all the remaining abalones were randomly allocated to the following groups, and the experimental design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVV group: the V group received an injection of 100\u0026micro;L \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eparahaemolyticus\u003c/em\u003e (1.0\u0026times;10\u003csup\u003e8\u003c/sup\u003e CFU/ml).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNV group: part of the remaining abalones from the N group received an injection of 100\u0026micro;L1.0\u0026times;10\u003csup\u003e8\u003c/sup\u003e CFU/ml \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eparahaemolyticus.\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNN group: the other part of the remaining abalones from the N group received an injection of 100 \u0026micro;L NS.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Hemocyte collection for single-cell RNA sequencing\u003c/h2\u003e \u003cp\u003eThe hemolymph of \u003cem\u003eH. discus hannai\u003c/em\u003e from different groups was extracted from the abdominal foot muscles, and 2 mL of hemolymph was mixed with 15 ml of pre-cooled anticoagulant containing glucose (20.5 g/L), citrate dihydrate (8.0 g/L), citrate (0.8 g/L), sodium chloride (4.2 g/L), and HEPES (2.3 g/L) at pH 6.1. Hemocytes were collected by centrifugation at 4\u0026deg;C, 300g for 5 minutes, and then separated and washed twice with a pre-cooled anticoagulant. Cell viability was assessed by adding 20 \u0026micro;L trypan blue dye to a 20 \u0026micro;L cell suspension and re-suspended with an anticoagulant. Dead cells were dyed blue, and the aggregation rate was observed using a cell counter. The target cell concentration for each sample was set at \u0026ge;\u0026thinsp;1000 cells/\u0026micro;L, while cell viability had to be \u0026ge;\u0026thinsp;90%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Library preparation and single-cell RNA sequencing\u003c/h2\u003e \u003cp\u003eLibrary synthesis and scRNA-seq were done by Gene Denovo Biotechnology Co. (Guangzhou, China). All the hemocyte samples (N_group, V_group, NV_group, NN_group and VV_group) were analyzed using the 10\u0026times; Genomics single-cell capturing system, which partitioned thousands of cells into nanoliter-scale Gel Bead-In-Emulsions (GEMs) with a standard 10\u0026times; Barcode using Chromium Single Cell 3 'GEM V3 kit (10 \u0026times; Genomics). The libraries were generated from the cDNA and sequenced, with individual reads associated back to their respective GEM partitions by the 10\u0026times; Barcodes. The process comprised four steps: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) GEM generation and barcoding, wherein the single-cell 3\u0026prime; gel bead was dissolved in a GEM, essential primers were released and mixed with cell lysate and Master Mix, and pooled fractions were recovered after breaking the GEMs; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) GEM-RT cleanup and cDNA amplification, in which biochemical reagents and primers from the post-GEM mixture were removed by magnetic beads, followed by PCR amplification of the cDNA for library construction; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Library construction, which involved adding primers to the final libraries containing the P5 and P7 primers to facilitate Illumina bridge amplification; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Sequencing, where a Single-Cell 3\u0026prime; Library was composed of standard Illumina paired-end constructs that started and ended with the sequencing primer P7 and P5 primer.\u003c/p\u003e \u003cp\u003eThe raw data of single-cell transcriptome sequencing were screened using Cell Ranger software from the 10\u0026times; Genomics Chromium platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.10\u0026times;\u003c/span\u003e\u003cspan address=\"https://www.10\u0026times;\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e genomics.com/). This software aligned, filtered, and counted barcodes and UMIs for each library. Low-quality cell data with fewer than 4000 genes and 8000 UMIs were eliminated, ensuring high-quality scRNA-seq data for downstream analysis. The data were compared with a reference genome sequence using the STAR package of Cell Ranger. However, due to poor genome splicing of \u003cem\u003eH. discus hannai\u003c/em\u003e, a full-length transcriptome from previous experiments was used as a reference genome sequence to compare and annotate all single-cell sequencing data \u003csup\u003e58\u003c/sup\u003e. UMIs were counted for reads that were uniquely mapped to the transcriptome. Cells were identified based on barcodes that revealed total UMI counts surpassing m/10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Bioinformatics Analysis of the Single-Cell RNA Sequencing Database.\u003c/h2\u003e \u003cp\u003e \u003cb\u003eCell cluster analysis with Seurat.\u003c/b\u003e An expression matrix was obtained that delineated the relationship between cells and genes by establishing an index for all clean data using Cell Ranger and after quantifying and aligning the data to a reference sequence. This expression matrix was read by the Seurat package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/\u003c/span\u003e\u003cspan address=\"https://satijalab.org/seurat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e59\u003c/sup\u003e, which enabled high-quality cells to be attained by integrating multiple selection criteria. Subsequently, cells were grouped using principal component analysis (PCA). T-distributed stochastic neighbor embedding (t-SNE) is a non-linear dimensionality reduction algorithm that is currently one of the most popular algorithms for data visualization, particularly for reducing high-dimensional data into a two- or three-dimensional space. After clustering was completed, t-SNE was used to visualize the data in a lower dimension. Cells with similar gene expression patterns were placed closer to each other in the t-SNE plot. Therefore, using the Seurat package for single-cell data of hemocytes from the \u003cem\u003eH. discus hannai\u003c/em\u003e, t-SNE clustering analysis can be indispensable in the investigation of functional relationships and differences between various cell clusters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferently expressed genes (DEGs) analysis in each cluster.\u003c/b\u003e Using the Seurat package \u003csup\u003e60\u003c/sup\u003e, differential gene expression analysis was performed on distinct cell subtypes of hemocytes from the \u003cem\u003eH. discus hannai\u003c/em\u003e that were obtained. Genes with upregulated expression in each subtype were selected (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC|\u0026ge; 0.585) to identify subtype-specific marker genes to reveal the differences in regulatory patterns among different clusters. Thereafter, GO analysis was used to comprehensively describe the functional properties of differentially upregulated genes in each subtype of hemocytes. Based on the enrichment results from the KEGG database, biological functions of relevant genes were explored via significantly enriched pathways, thereby contributing toward a theoretical foundation for further functional identification of cell clusters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional analysis of specific hemocyte types.\u003c/b\u003e The Weighted Gene Co-expression Network Analysis (WGCNA) method clusters genes with similar expression patterns across multiple samples into modules and performs association analysis between different modules and specific traits or phenotypes. In this study, we utilized the WGCNA package (v1.47) in R to construct a co-expression network for a particular type of hemocytes to identify key regulatory genes and related signaling pathways involved in abalone immune regulation. Finally, we visualized the co-expression network of the selected key genes using the Cytoscape 3.7.1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Pseudo-temporal ordering of cells using Monocle\u003c/h2\u003e \u003cp\u003eMonocle(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://coletrapnell-lab.github.io/monocle-release/tutorials/)i\u003c/span\u003e\u003cspan address=\"http://coletrapnell-lab.github.io/monocle-release/tutorials/)i\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003es an R package software that utilizes key gene expression patterns to arrange cells in a pseudo-temporal order along a cellular trajectory, allowing for the simulation and visualization of cell differentiation relationships during development \u003csup\u003e61\u003c/sup\u003e. By selecting differentially expressed genes and performing dimensionality reduction analysis between different subgroups of cells, the software can fit the best cell differentiation trajectory and display it through visualizations, which enables the reconstruction of a cell's temporal changes, revealing alterations in the cellular state as a response to external stimuli within an organism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Cell communication analysis\u003c/h2\u003e \u003cp\u003eActivation of extracellular-specific cell signaling pathways relies on ligand-receptor binding, and analyzing the interaction between different ligands and receptors can enhance our understanding of diverse cell behaviors. CellphoneDB is a database that contains a wealth of information on ligand-receptor interactions, and it can be utilized to construct a cellular communication network among different cell clusters based on the single-cell transcriptome gene expression matrix \u003csup\u003e62\u003c/sup\u003e. The number of ligand-receptor pairs in different cell pairs can be obtained by analyzing the expression abundance of ligand-receptor pairs in different cell pairs. The application of CellphoneDB software to pairwise comparisons between all cell clusters in the dataset enables the screening of significantly enriched ligand-receptor pairs and the construction of an intercellular interaction network map, revealing the behavior of a specific cell and providing preliminary insights into the communication relationships between different cells. Furthermore, based on the above, NicheNet software can be used to analyze the activity of ligands and their regulatory potential on specific target genes, thereby further elucidating the mechanisms of interaction between ligands and receptors \u003csup\u003e63\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Suppression of \u003cem\u003eNFκB\u003c/em\u003eand \u003cem\u003eTLR2\u003c/em\u003e gene expression by double-stranded RNA (dsRNA)\u003c/h2\u003e \u003cp\u003eBased on the above analysis, the TLR signaling pathway and NF-κB signaling pathway may play a key role in regulating the response of wrinkled abalone lymphocytes to secondary infection by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e. To further analyze the gene interactions in these two pathways, we used RNAi to interfere with the key genes \u003cem\u003eNFκB\u003c/em\u003e and \u003cem\u003eTLR2\u003c/em\u003e in the two pathways, respectively. Also, the green fluorescent protein (GFP) gene from the pEGFP-N1 vector was amplified by PCR. The sequences of these primers are listed in Table S1. Single-stranded RNA (ssRNA) was transcribed from the templates using T7 phage RNA polymerases (Promega, Madison, WI, USA) after the PCR products were purified and sequenced. After being purified, the sense ssRNA and antisense ssRNA were mixed and annealed at 75\u0026deg;C for 15 min, at 65\u0026deg;C for 15 min, and then down to room temperature at the rate of 0.1\u0026deg;C /s. The dsRNAs of \u003cem\u003eNFκB\u003c/em\u003e and \u003cem\u003eTLR2\u003c/em\u003e were used in the silence experiment at a final concentration of 5 \u0026micro;g/mL directly to the hemocytes culture medium without any vehicle \u003csup\u003e64\u003c/sup\u003e with GFP dsRNA as control. The medium without any modifications was regarded as the blank control group. For each treatment, six replicates were produced. All samples were incubated at 27\u0026deg;C for 6 h, 12 h and 24 h, and the hemocytes were harvested to detect the mRNA expression by qRT-PCR.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The quality inspection results of all samples\u003c/h2\u003e \u003cp\u003eThe scRNA-Seq data of different samples of hemocytes from \u003cem\u003eH. discus Hanna\u003c/em\u003e were subjected to quality control and preliminary statistical analysis using Cell Ranger(Table S2). The V_group, N_group, NN_group, NV_group and VV_group libraries obtained 396,266,876, 414,300,518, 447,684,335, 421,542,139 and 368,963,729 raw reads respectively. All Q30 values were above 90.50%, indicating good quality of the sequencing data. BioProject accession number PRJNA979786 has been assigned to all raw reads submitted to the NCBI Short Read Archive database.\u003c/p\u003e \u003cp\u003eOwing to the deficient quality of the published abalone genome assembly, this investigation employed the full-length transcriptome of \u003cem\u003eH. discus hannai\u003c/em\u003e as a reference sequence for aligning and annotating the obtained sequences \u003csup\u003e58\u003c/sup\u003e. Ultimately, the number of high-quality cells captured in the V_group, N_group, NN_group, NV_group, and VV_group samples, which can be utilized for subsequent analyses, were 9,483, 9,547, 8,702, 8,947, and 6,200, respectively (Table S3, Figure\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The number of genes detected in each treatment group surpassed 30,000, and according to the results of full-length transcriptome data alignment, the alignment rate of the data extracted from the five samples was higher than 66.7% (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cluster analysis of hemocytes of abalone \u003cem\u003eH. discus hannai\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe Seurat package was utilized to amalgamate and scrutinize multiple filtered datasets. Following the implementation of UMAP dimensionality visualization analysis and t-SNE clustering, a total of fifteen clusters (cluster_0\u0026ndash;14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B) have been identified. Each dot on the graph signifies a unique cell, distinguished by color based on subgroup classification. Moreover, clustering outcomes indicate considerable differences in cell quantities between different clusters, which suggests that functional variations exist among each cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn addition, in order to further scrutinize the functional disparities among different clusters of resting hemocytes in \u003cem\u003eH. discus hannai\u003c/em\u003e, we have conducted comparative analyses on the differential genes between various hemocyte clusters in the N_group sample. The results have revealed that crucial immune-regulating genes such as allograft inflammatory factor 1 (\u003cem\u003eAIF1\u003c/em\u003e), an inhibitor of NF-κB (\u003cem\u003eNFKBIA\u003c/em\u003e), \u003cem\u003eCD63\u003c/em\u003e, and some members of the caspase gene family alongside certain heat shock proteins (\u003cem\u003eHSPs\u003c/em\u003e), are significantly upregulated in different hemocyte clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of cell clusters\u003c/h2\u003e \u003cp\u003eDue to the late start of research on marine invertebrates, insufficient information on marker genes for different cell types, coupled with poor genome assembly quality, resulting in a large number of omissions \u003csup\u003e65\u003c/sup\u003e, it is difficult to identify different blood cell clusters with marker genes of \u003cem\u003eH. discus hannai\u003c/em\u003e. Notwithstanding this, there are similarities in the expression patterns of differential genes within the different hemocyte clusters, hinting at them being discrete components of a certain type of cell. In light of this, we undertook KEGG enrichment analysis on the upregulated genes in distinct cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). By combining the outcomes of functional enrichment analysis of diverse clusters and consulting existing studies on the functions of different hemocyte types \u003csup\u003e45,51\u003c/sup\u003e, we further classified the original 15 clusters into three cell types: hyalinocytes (cluster_1, cluster_4, cluster_7), semi-granulocytes (cluster_2, cluster_5, cluster_6, cluster_8, cluster_9, cluster_13, cluster_14), and granulocytes (cluster_0, cluster_3, cluster_10, cluster_11, cluster_12). Granulocytes constitute a proportion of 60.12% (in which GRCs amount to 28.13% of the total cells and SGRCs account for 31.99%)(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Hemocytes (HCs) make up roughly 39.88%, which aligns with previously documented findings. The distribution of diverse cellular types of hemocytes in \u003cem\u003eH. diversicolor\u003c/em\u003e was identified through flow cytometry \u003csup\u003e49,50\u003c/sup\u003e. Furthermore, the marker gene was identified in conjunction with varying gene expression patterns among different types of cells. The results revealed that allograft inflammatory factor 1 (\u003cem\u003eAIF1\u003c/em\u003e), Cell Division Cycle 42 (\u003cem\u003eCDC42\u003c/em\u003e), matrix metalloproteinase-18 (\u003cem\u003eMMP18\u003c/em\u003e), and \u003cem\u003eCD63\u003c/em\u003e exhibited significantly high expressions in GRCs cells. In contrast, Thioredoxin-2 (\u003cem\u003eTRX2\u003c/em\u003e), glutathione s-transferase 7 (\u003cem\u003eGST7\u003c/em\u003e), and caspase-3 (\u003cem\u003eCASP3\u003c/em\u003e) showed substantial overexpression in SGRCs cells. For HCs, genes such as complement C3-like (\u003cem\u003eC3\u003c/em\u003e), proliferating cell nuclear antigen (\u003cem\u003ePCNA\u003c/em\u003e), and histone H2A-beta (\u003cem\u003eHIS2A\u003c/em\u003e) were highly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional analysis of marker gene in different cell clusters\u003c/h2\u003e \u003cp\u003eFifteen hemocyte clusters from all samples were re-clustered using the aforementioned classification method. Statistical results suggest that there was no significant alteration in the overall clustering results of three cell types (GRCs, SGRCs and HCs) among different treatment groups of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e. Still, the number of cells of different types varied (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). After exposure to distinct treatments with \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, the number of granulocytes in V_group, NV_group and VV_group samples changed to varying degrees compared to 60.12% in the N_group, with the most substantial increase observed in V_group, where the proportion of granulocytes rose to 74.53%. In contrast, proportions in NV_group and VV_group increased to 67.88% and 69.61%, respectively. Moreover, the proportion of HCs in the three treatment groups decreased relative to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe identification of marker genes was performed on various samples after re-clustering. In V_group, significantly upregulated GRCs marker genes such as \u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eAAH2\u003c/em\u003e, \u003cem\u003eIP6K1\u003c/em\u003e, \u003cem\u003eCPNE8\u003c/em\u003e and \u003cem\u003eALOX5\u003c/em\u003e were identified; SGRCs marker genes included \u003cem\u003eTRX2\u003c/em\u003e, \u003cem\u003eGIMAP9\u003c/em\u003e, \u003cem\u003eGST7\u003c/em\u003e, \u003cem\u003eCASP3\u003c/em\u003e and \u003cem\u003ePRDX6\u003c/em\u003e; and HCs marker genes comprised of \u003cem\u003eNIP7\u003c/em\u003e, \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eDKC1\u003c/em\u003e, \u003cem\u003eFKBP46\u003c/em\u003e and \u003cem\u003eDDX51\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In NV_group, significantly upregulated GRCs marker genes included \u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eCDC42\u003c/em\u003e, \u003cem\u003eSQSTM1\u003c/em\u003e, \u003cem\u003ePSTPIP1\u003c/em\u003e and \u003cem\u003eNOCT\u003c/em\u003e; SGRCs marker genes comprised of \u003cem\u003eTRX2\u003c/em\u003e, \u003cem\u003eLRP1\u003c/em\u003e, \u003cem\u003eGST7\u003c/em\u003e, \u003cem\u003eFSTL3\u003c/em\u003e and \u003cem\u003ePFE\u003c/em\u003e; and HCs marker genes included \u003cem\u003eDDX21\u003c/em\u003e, \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eSLC6A9\u003c/em\u003e, \u003cem\u003eNop2\u003c/em\u003e and \u003cem\u003eHIS2A\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In VV_group, significantly upregulated GRCs marker genes were \u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eCDC42\u003c/em\u003e, \u003cem\u003eELF3\u003c/em\u003e, \u003cem\u003eNFKB1\u003c/em\u003e and \u003cem\u003eTLR4\u003c/em\u003e; SGRCs marker genes included \u003cem\u003eLCP1\u003c/em\u003e, \u003cem\u003ePDIK1B\u003c/em\u003e, \u003cem\u003eGS2\u003c/em\u003e, \u003cem\u003eFSTL3\u003c/em\u003e and \u003cem\u003ePFE\u003c/em\u003e; and HCs marker genes included \u003cem\u003eFKBP46\u003c/em\u003e, \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eSLC6A9\u003c/em\u003e, \u003cem\u003eRELN\u003c/em\u003e and \u003cem\u003ePCNA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Hemocytes represent the primary immune regulatory organs in invertebrates. Various hemocyte types also respond differently to external stimuli. Marker genes of diverse hemocyte types function as labels for identifying them, characterized by stable high expression characteristics that should not vary substantially with changes in the external environment. Therefore, we comprehensively analyzed relevant genes identified in the aforementioned different samples and coupled them with results obtained in the above study from marker gene screening under non-stress conditions. We determined some marker genes that can be stably expressed at a high level in various cell types in different treatment samples, including \u003cem\u003eAIF1\u003c/em\u003e and \u003cem\u003eCDC42\u003c/em\u003e in GRCs, \u003cem\u003eTRX2\u003c/em\u003e and \u003cem\u003eGST7\u003c/em\u003e in SGRCs, and \u003cem\u003eC3\u003c/em\u003e in HCs. The expression heatmap of these genes is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eE. Additionally, the t-SNE graph represents their expression patterns in different cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of weighted gene co-expression network\u003c/h2\u003e \u003cp\u003eIn order to further investigate the important regulatory mechanisms of GRCs in the immune modulation process of \u003cem\u003eH. discus hannai\u003c/em\u003e in a resting state, we conducted WGCNA on 5 clusters of GRCs belonging to N_group and ultimately identified 8 clustering modules with significant differences in gene expression patterns that were allocated to different clusters comprising GRCs (cluster_0, cluster_3, cluster_10, cluster_11, and cluster_12). Among these modules, the turquoise and blue modules exhibited a higher level of gene enrichment, which consisted of 6,475 and 4,341 genes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). In addition, a heatmap of gene clustering was generated for different modules to visualize the expression matrix of related genes between modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The correlation analysis results showed a significant positive correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the brown, blue, and yellow modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), with high expression levels observed in cluster_11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe brown, blue, yellow, and red modules were selected as target modules to explore the functional differences of GRCs further, and KEGG enrichment analysis was carried out on the genes grouped in these modules. The results indicated that immune-related signaling pathways such as NF-κB signaling pathway, Endocytosis, Toll and Imd signaling pathway, NLR signaling pathway, CLR signaling pathway, and Fc gamma R-mediated phagocytosis were significantly enriched in the brown module. Energy metabolism-related signaling pathways, such as Oxidative phosphorylation and Thermogenesis, were significantly enriched in the red module. Moreover, some immune-related signaling pathways, such as the PI3K-Akt signaling pathway, IL-17 signaling pathway, and Phagosome, also showed significant enrichment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the red module (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eHub genes are pivotal genes with significant roles in biological processes and can be identified within different modules based on their K.in values that indicate the level of connectivity and regulatory function of the gene within the module. The higher the K.in value of a gene, the greater its connectivity and the more central its regulatory function. Based on this theory, 5\u0026ndash;6 genes with high K.in values were selected as hub genes for each target module, and a molecular interaction network was constructed using Cytoscape to visualize the relationship between these hub genes and their associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D).\u003c/p\u003e \u003cp\u003eThe results revealed that calcium-binding protein genes (\u003cem\u003eCALM\u003c/em\u003e), which play roles in signal transduction and transcriptional regulation, ubiquitin-conjugating enzyme genes (\u003cem\u003eEFF\u003c/em\u003e) responsible for ubiquitination regulation, and toll-like receptor genes (\u003cem\u003eTLR3\u003c/em\u003e) were identified as hub genes in the blue module(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the related network of the brown module, the core transcription factor NF-κB of the NF-κB signaling pathway and immune-related genes such as GST, Tollo, and Perlucin were identified as hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). For the yellow module, signal transduction-related genes such as CAP-Gly domain-containing linker protein 1-like (\u003cem\u003eCLIP1\u003c/em\u003e) and phosphatidylinositol 3-kinase regulatory subunit alpha (\u003cem\u003ePIK3R1\u003c/em\u003e), as well as genes involved in intracellular engulfment such as \u003cem\u003eSMURF2\u003c/em\u003e were identified as hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Key immunoregulatory factors such as CD109, MMP18, and HSP90 were also identified as hub genes in the red module (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Response of different cell types to \u003cem\u003eV. parahaemolyticus\u003c/em\u003einfection\u003c/h2\u003e \u003cp\u003eThe results of KEGG enrichment analysis of upregulated genes of different hemocyte types in different treatment groups after \u003cem\u003eV parahaemolyticus\u003c/em\u003e infection showed that in the V_group, signaling pathways associated with energy metabolism, such as oxidative phosphorylation, were notably enriched in both SGRCs and HCs; however, HCs possessed a higher number of enriched genes. Conversely, signaling pathways involved in energy metabolism, such as thermogenesis and protein processing in the endoplasmic reticulum, were solely significantly enriched in HCs. In addition, the phagocytosis-related signaling pathway, Phagosome, was significantly enriched in both GRCs and HCs. Contrarily, the commonly enriched signaling pathways in SGRCs and GRCs were primarily related to immunity, such as the NLR signaling pathway, consistent with the biological functions of granulocytes. Meanwhile, signaling pathways involved in immune regulation, such as Endocytosis and IL-17 signaling pathways, were exclusively enriched in GRCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In the NV_group, signaling pathways related to energy metabolism and phagocytosis, such as oxidation phosphorylation and Phagosome, were significantly enriched in SGRCs and HCs. Certain signaling pathways involved in transcription, translation, folding, and degradation processes, such as Ribosome, Proteasome, and Spliceosome, were specifically enriched in HCs. In SCRGs, signaling pathways involved in signal transduction, such as the Hippo signaling pathway and Rap1 signaling pathway, were specifically enriched.\u003c/p\u003e \u003cp\u003eIn contrast, signaling pathways specifically enriched in GRCs, those involved in immune regulation, such as IL-17 signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, Toll and Imd signaling pathway, were all prominently present (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Compared to the previously mentioned two groups, there were no signal pathways commonly enriched in HCs of VV_group with the other two cell types\u0026mdash;the significantly enriched pathways in HCs primarily involved translation, folding, and degradation processes.\u003c/p\u003e \u003cp\u003eAdditionally, signal pathways associated with energy metabolism, such as oxidative phosphorylation and Thermogenesis, were specifically enriched in HCs. Signaling pathways involved in the degradation and metabolism of exogenous substances, such as the Metabolism of xenobiotics by cytochrome P450 and Phagosome, were significantly enriched in SGRCs. GRCs displayed a significant increase in the number of enriched signal pathways compared to HCs and SGRCs, with most of the genes significantly enriched in signaling pathways involved in signal transduction such as MAPK signaling pathway, NF-κB signaling pathway, TNF signaling pathway, signal pathways involved in degradation such as Endocytosis and Phagosome, and signal pathways involved in immune regulation such as NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, CLR signaling pathway, Toll and Imd signaling pathway, IL-17 signaling pathway, and Fc gamma R-mediated phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Response of GRCs to \u003cem\u003eV. parahaemolyticus\u003c/em\u003einfection\u003c/h2\u003e \u003cp\u003eIn the previous study, we have functionally annotated and defined the gene sets that may be involved in the immune regulation response of abalone hemocytes to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. These gene sets include immune response genes (IRGs), potential immune-enhancing genes (PEGs), immune-enhancing regulatory genes (ERGs), and essential immune-enhancing genes (EEGs) \u003csup\u003e56\u003c/sup\u003e. Our analysis found that GRCs from different treatment groups were enriched with numerous immune-related signaling pathways, suggesting their critical regulatory role in abalone's response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. Therefore, we employed the previous gene set screening approach to identify DEGs in GRCs from different comparison groups and performed KEGG functional enrichment analysis. The results indicated that 801 DEGs in GRCs responded to both \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infections simultaneously, defining them as co-response genes of GRCs (CRGs-b) (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), whereas 489 common DEGs were obtained from the comparison groups of NN_group vs NV_group, V_group vs VV_group, and N_group vs V_group, which might relate to GRCs' faster immune response after secondary \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection and were designated as immune response genes of GRCs (IRGs-b) (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Moreover, by comparing 801 CRGs-b with the putative immune-enhancing gene set, we identified 361 common DEGs that were classified as potential immune-enhancing genes of GRCs (PEGs-b) (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Finally, 246 immune-enhancing genes of GRCs (ERGs-b) were identified via comparison of IRGs-b and PEGs-b using the Venn diagram, which may possess specific immune memory regulatory functions. The KEGG enrichment results revealed that 801 CRGs-b mainly enriched immune-related signaling pathways such as the NF-κB signaling pathway, IL-17 signaling pathway, TLR signaling pathway, and NLR signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), and 361 PEGs-b were primarily enriched in phagocytosis-related pathways, such as Fc gamma R-mediated phagocytosis and Phagosome (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The 489 IRGs-b were significantly enriched in immune-related pathways, including the TLR signaling pathway, NF-κB signaling pathway, and Fc gamma R-mediated phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). In addition, the 246 ERGs-b were significantly enriched in phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis and Phagosome, as well as apoptosis-related pathways like Apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo reveal the molecular interactions among the DEGs in the significantly enriched pathways, we used Cytoscape software to construct network diagrams of some key signaling pathways in abalone GRCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003e). As shown in the figure, as important signaling pathways involved in abalone innate immune regulation, NF-κB signaling pathway, TLR signaling pathway and NLR signaling pathway have complex molecular interactions among them, and among them, IRAK4 and NFKB1 act as core factors and participate in the regulation of these three signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003e). At the same time, there is also an interaction between NF-κB signaling pathway and Fc gamma R-mediated phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Pseudo-temporal analysis of GRCs stimulated by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eMonocle2 constructed a developmental trajectory in pseudo-time to study the differentiation pattern of GRCs in samples stimulated by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e and control samples. The results indicate that the hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003e infected by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (V_group, NV_group, and VV_group) and those treated with normal saline (N_group and NN_group) were evidently differentiating along distinct branches of the pseudo-time trajectory, exhibiting three distinct differentiation states (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). Based on cell trajectory analysis, we defined the concentrated distribution of state_3 in the GRCs of \u003cem\u003eH. discus hannai\u003c/em\u003e hemocytes treated with normal saline as the starting point of differentiation. After infection with \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, these cells could differentiate into state_1 and state_2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Furthermore, all three V_group, NV_group, and VV_group samples were observed to be distributed among three different differentiation states, mainly concentrated in state_1 and state_2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThe expression heat map of differentially differentiated genes revealed an increase in the expression of granulocyte marker genes such as \u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eMMP18\u003c/em\u003e, and \u003cem\u003eTRX2\u003c/em\u003e in Gene_cluster2 with deepening cell differentiation. However, highly expressed genes in granulocytes, such as \u003cem\u003eCD63\u003c/em\u003e and \u003cem\u003eCDC42\u003c/em\u003e, gradually decreased in Gene_cluster1 with the deepening of cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). As state_1 and state_2 were two directions of granulocyte differentiation with obvious differentiation boundaries with state_3, responding to different stages of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, we analyzed the upregulated genes in state_3 and state 1_2, respectively. The results showed that granulocyte high-expression genes such as \u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eGST7\u003c/em\u003e, \u003cem\u003eCDC42\u003c/em\u003e, and \u003cem\u003eTRX2\u003c/em\u003e were upregulated in state1_2. The important regulatory factors of immune signaling pathways, such as \u003cem\u003eMAPK14\u003c/em\u003e and \u003cem\u003eMyD88\u003c/em\u003e, were also significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-E). KEGG enrichment results of these upregulated genes showed significant enrichment of energy metabolism-related pathways, such as Oxidative phosphorylation and Thermogenesis, in state_3. At the same time, the IL-17 signaling pathway, NLR signaling pathway, TLR signaling pathway, and other pathways involved in innate immune regulation were significantly enriched in state1_2. Moreover, Fc gamma R-mediated phagocytosis pathways involved in phagocytosis were significantly enriched by differential genes in state1_2 and state_3, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e10\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Re-clustering of GRCs\u003c/h2\u003e \u003cp\u003eThe outcomes of pseudo-temporal analysis disclose that despite being the same type of cells, there exist dissimilarities in functional differentiation and also that the differentiation status of the identical cell type differs in diverse physiological states. The KEGG enrichment results revealed significant distinctions in signaling pathways of notable enrichment in varying differentiation conditions. All of these findings indicate that GRCs themselves are somewhat heterogeneous. It has been previously reported in bivalve-related research that there are three different developmental conditions in GRCs \u003csup\u003e66\u003c/sup\u003e. Through re-clustering analysis of GRCs from \u003cem\u003eH. discus hannai\u003c/em\u003e hemocytes, further examination of the heterogeneity of the same cell type and the similarities and variances of cellular functions at the molecular level is anticipated. In this study, using the subgrouping technique for GRCs from \u003cem\u003eH. discus hannai\u003c/em\u003e, three separate subgroups (Sub-cluster_0\u0026ndash;2) displaying elevated cell segregation between GRCs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). Analyzing the existing state of every subgroup in dissimilar treatment groups showed that two subsets, Sub-cluster_0 and Sub-cluster_1, coexisted in different treatment groups simultaneously. However, Sub-cluster_0 mainly existed in \u003cem\u003eV. parahaemolyticus\u003c/em\u003e stimulated samples and had a greater number of cells in it, while Sub-cluster_1 predominantly contained more cells in the control group and accounted for less in different experimental groups. Notably, Sub-cluster_2 primarily existed in samples without infection of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). The findings of differential gene analysis demonstrated that 1,784 DEGs were significantly upregulated in all three subclusters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, | log2FC | \u0026ge; 0.585). The heatmap of gene expression selected from each subpopulation indicates a significant difference, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eC. The key marker genes of GRCs, namely \u003cem\u003eAIF1\u003c/em\u003e and \u003cem\u003eCDC42\u003c/em\u003e, and immune-regulation-related genes, such as \u003cem\u003eMyD88\u003c/em\u003e and \u003cem\u003eIRAK4\u003c/em\u003e, were notably upregulated in Sub-cluster_0, while significant genes like \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eMMP18\u003c/em\u003e, \u003cem\u003eCASP3\u003c/em\u003e, and \u003cem\u003eA2ML1\u003c/em\u003e were upregulated in Sub-cluster_1 and Sub-cluster_2, respectively, which aligns with the overall expression pattern. There is a certain intersection between Sub-cluster_1 and Sub-cluster_2, implying some similarities in the biological functions of the two subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eC). The KEGG enrichment results of each subcluster showed that there are several mutual signaling pathways in Sub-cluster_1 and Sub-cluster_2, including oxidative phosphorylation involved in energy metabolism and Fc gamma R-mediated phagocytosis involved in cell phagocytosis, further indicating the functional similarity between the two subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). In Sub-cluster_0, most of the signaling pathways are specifically enriched, including various immune-related signaling pathways such as the IL-17 signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, and Toll and Imd signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFinally, we investigated the quantity and function of differential genes belonging to the same sub-cluster under varying \u003cem\u003eV. parahaemolyticus\u003c/em\u003e treatments. Sub-cluster_2 was absent in some treatment groups, so our analysis only focused on Sub-cluster_0 and Sub-cluster_1. Within Sub-cluster_0, a total of 811 DEGs were upregulated when stimulated with high dosages of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (V_group vs NV_group). These genes were primarily associated with immune signaling pathways such as NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, CLR signaling pathway, and NF- κB signaling pathway, as well as phagocytosis-related pathways including Fc gamma R-mediated phagocytosis, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 A). In the hypothetical immune enhancement gene cluster (VV_group vs NV_group), we identified 660 upregulated DEGs that were mainly involved in phagocytosis-related pathways, such as Phagosome and Fc gamma R-mediated phagocytosis, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 B). Additionally, upon re-infection with \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (V_group vs VV_group), 660 DEGs were detected, showing significant upregulation; these genes were primarily linked to immune signaling pathways such as NLR signaling pathway, NF-κB signaling pathway, RLR signaling pathway, TLR signaling pathway, and IL-17 signaling pathway, phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis and Autophagy, and energy metabolism-related pathways such as Oxidative phosphorylation and Thermogenesis (Fig. S2 C).\u003c/p\u003e \u003cp\u003eIn Sub-cluster_1, a total of 245 DEGs showed up-regulation upon infection with high dosages of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (V_group vs NV_group), and these genes were primarily associated with phagocytosis-related pathways, including Endocytosis, as well as the synthesis of amino acids, such as Biosynthesis of amino acids (Fig. S3 A). Moreover, in the hypothetically assumed immune enhancement gene cluster (VV_group vs NV_group), we detected 468 upregulated DEGs that were primarily enriched in phagocytosis-related pathways, such as Endocytosis, apoptosis-related pathways like Apoptosis, and immune-related signaling pathways, including NLR signaling pathway (Fig. S3B). Finally, upon further infection of the organism with \u003cem\u003eV. parahaemolyticus\u003c/em\u003e (V_group vs VV_group), 613 DEGs were found to be significantly upregulated, and these genes were mainly enriched in immune signaling pathways such as Toll and Imd signaling pathway, NF-κB signaling pathway, NLR signaling pathway, RLR signaling pathway, TLR signaling pathway, and IL-17 signaling pathway, phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis, as well as apoptosis-related pathways such as Apoptosis (Fig. S3C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Cell communication of different clusters\u003c/h2\u003e \u003cp\u003eBy selecting receptors and ligands from various cell clusters across multiple samples, we created molecular interaction networks among these cell clusters in diverse samples via the utilization of Cytoscape software, ultimately depicting intricate interactions between these cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). Although three different cell types were involved, overall cellular communication between clusters 5, 6, 11, 12, and 14 was found to be stronger compared to other clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003eA-D). The expression abundance heatmap of ligand-receptor pairs among subpopulations in different treatment groups is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e11\u003c/span\u003eE-H. Ultimately, 74 interacting receptor-ligand pairs were identified and screened from the 15 hemocyte clusters detected in disparate samples (Table S4).\u003c/p\u003e \u003cp\u003eUpon analysis of all ligand and receptor genes, it was revealed that 15 correlated genes were present in each sample. Additionally, the maximum number of ligand genes were observed to be screened in VV_group(Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003eI). The GO enrichment analysis of all receptor-ligand genes showcased that transmembrane receptor activity, signaling receptor activity, and receptor activity were meaningfully enriched across different samples, with receptor binding only displaying significant enrichment in VV_group. Notably, the enrichment degree of cell communication in the NV_group was lower in comparison to other groups, whereas the enrichment degree of receptor agonist activity, receptor activator activity, and receptor regulator activity was higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003eJ). KEGG enrichment results illustrated that the primary signaling pathways relevant to \u003cem\u003eH. discus hannai\u003c/em\u003e hemocyte communication included Cytokine-cytokine receptor interaction, Axon guidance, Wnt signaling pathway, mTOR signaling pathway, PI3K-Akt signaling pathway, and Melanogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003eTo analyze the communication relationship between GRCs and SGRCs, cluster_11, possessing the strongest correlation among all cell clusters from the cell interaction network diagram presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e12\u003c/span\u003e, was utilized as the ligand signaling cell, while cluster_14, having the strongest communication relationship with it, acted as the receptor signaling cell. Four classic signaling pathways were selected: PI3K-Akt signaling pathway and NF-κB signaling pathway, which relate to immune signaling transmission and cell-based regulation, and NLR signaling pathway and TLR signaling pathway, found to be enriched multiple times in prior analysis and involved in innate immune regulation. Ligand activity assays were subsequently performed on ligand-signaling cells using specific gene sets in receptor-signaling cells. There were some differences in the number of detected ligands within different treatment groups, with the smallest quantity observed in N_group and the greatest in VV_group; overall, the ligand activity in VV_group was generally robust (Fig. S4). Under various treatments, ligands regulating the PI3K-Akt signaling pathway gene set were highly active, while those regulating the NF-κB signaling pathway gene set had a low activity rate in N_group and V_group. Following exposure to high dosages of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, viability was enhanced, generally displaying higher viability within VV_group(Fig. S4). In addition, ligands regulating the NLR signaling pathway and TLR signaling pathway gene sets displayed the most activity in VV_group(Fig. S4).\u003c/p\u003e \u003cp\u003eIn addition, the NicheNet software was utilized to screen ligand-target gene pairs in different target cell pairs for the aforementioned four signaling pathway gene sets and score the regulatory potential of the ligand-target genes. Based on the score, a ligand-target gene regulatory potential heatmap was created (Fig. S5). Results displayed that under identical screening conditions, the number of ligand-regulated target genes was relatively small in both N_group and NV_group, while the largest number of ligand-regulated target genes occurred within the VV_group(Fig. S5). In terms of regulatory potential, HSP90 exhibited the strongest regulatory activity on TLR2, which existed across all four treatment groups. Apart from HSP90's potent regulatory effect on TLR2, the regulatory activities of all ligands on target genes within the PI3K-Akt signaling pathway were weak as a whole, while the regulatory activities on target genes in the NF-κB signaling pathway, NLR signaling pathway, and TLR signaling pathway were comparative across various treatments (Fig. S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Effect of dsRNA exposure assay for \u003cem\u003eNFκB and TLR2\u003c/em\u003e gene expression\u003c/h2\u003e \u003cp\u003eThe expression of genes associated with the TLR signaling pathway and NF-κB signaling pathway in hemocytes after \u003cem\u003eTLR2\u003c/em\u003e silencing by dsRNA was assessed by qPCR. The results indicated that \u003cem\u003eTLR2\u003c/em\u003e gene expression in the experimental group was substantially lowered at all time points relative to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). Similarly, other genes in the pathway: \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eNFκB\u003c/em\u003e, \u003cem\u003eEIF4E\u003c/em\u003e, \u003cem\u003eFADD\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eMyD88\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, \u003cem\u003eAkirin2\u003c/em\u003e, \u003cem\u003e14-3-3ζ\u003c/em\u003e, \u003cem\u003eMKK4\u003c/em\u003e, \u003cem\u003eRIP1\u003c/em\u003e, and \u003cem\u003eMAPK14\u003c/em\u003e demonstrated significant downregulation at different time points in the experimental group compared with the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). In particular, \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eNFκB\u003c/em\u003e, \u003cem\u003eFADD\u003c/em\u003e and \u003cem\u003eMyD88\u003c/em\u003e showed robust interference effects and were persistently downregulated at each time point of the experiment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). The expression of genes related to the two target pathways in blood lymphocytes after \u003cem\u003eNFκB\u003c/em\u003e was interfered with by dsRNA was further detected by qPCR. The results showed that compared with the control group, the expression level of the \u003cem\u003eNFκB\u003c/em\u003e gene in the experimental group was significantly down-regulated at different time points (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). At the same time, other related genes in the pathway: \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eEIF4E\u003c/em\u003e, \u003cem\u003eAkirin2\u003c/em\u003e, \u003cem\u003e14-3-3ζ\u003c/em\u003e, \u003cem\u003eMKK4\u003c/em\u003e, and \u003cem\u003eMAPK14\u003c/em\u003e were also significantly down-regulated to varying degrees at different time points in the experimental group compared with the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eB), while the expression patterns of \u003cem\u003eTLR2\u003c/em\u003e, \u003cem\u003eFADD\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eMyD88\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003eRIP1\u003c/em\u003e did not change due to \u003cem\u003eNFκB\u003c/em\u003e gene interference (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAs a typical invertebrate, mollusks are sensitive to environmental and pathogenic factors that affect their health \u003csup\u003e67\u003c/sup\u003e. Their hemocytes have various functions: immunity, digestion, wound healing, detoxification, shell formation, and excretion \u003csup\u003e68\u003c/sup\u003e. Previous studies have shown that hemocytes can enhance their phagocytic activity after exposure to pathogens and maintain it for a long time \u003csup\u003e8,9\u003c/sup\u003e. These works suggest that Mollusks and other invertebrates have immune memory that differs from vertebrates \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. However, the function and classification of abalone hemocytes are unclear \u003csup\u003e48,49,51\u003c/sup\u003e. ScRNA-seq technology can help us understand the different cell types and their roles in organisms. This technology is useful for non-model species that lack genomic data \u003csup\u003e69\u003c/sup\u003e. We used the full-length transcriptome data of \u003cem\u003eEpinepheluscoioides\u003c/em\u003e to annotate most transcripts from scRNA-seq, which enabled us to apply this technology in non-reference genome species \u003csup\u003e70\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe full-length transcriptome data from \u003cem\u003eH. discus hannai\u003c/em\u003e was used to perform scRNA-seq of hemocytes \u003csup\u003e58\u003c/sup\u003e. We found 15 hemocyte clusters, which were grouped into three cell types based on previous studies and functional enrichment results: GRCs, SGRCs, and HCs \u003csup\u003e43,45,48,51\u003c/sup\u003e. The proportions of these cell types in the N_group were similar to those reported by flow cytometry in abalone hemocytes \u003csup\u003e49,50\u003c/sup\u003e, confirming our clustering results. The proportions of these cell types did not change significantly in different treatment groups, but the number of cells did. This suggests that different hemocyte types have different roles in the immune response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. Previous studies have shown that viral infection can increase GRCs and decrease HCs in abalone \u003csup\u003e71\u003c/sup\u003e. We observed similar changes in V_group, NV_group, and VV_group samples compared to the control group. We also identified potential marker genes for each cell type: \u003cem\u003eAIF1\u003c/em\u003e and \u003cem\u003eCDC42\u003c/em\u003e for GRCs, \u003cem\u003eTRX2\u003c/em\u003e and \u003cem\u003eGST7\u003c/em\u003e for SGRCs, and \u003cem\u003eC3\u003c/em\u003e for HCs. These genes were highly and stably expressed in all treated samples.\u003c/p\u003e \u003cp\u003eThe immune regulation mechanism of 5 GRCs clusters in \u003cem\u003eH. discus hannai\u003c/em\u003ewas investigated by WGCNA. Eight modules were identified, with the red module and the brown module being highly expressed in cluster_3 and cluster_12 but lowly expressed in cluster_11. This indicated that cluster_11 might be regulated by other modules, such as the brown, blue, and yellow modules, and that cluster_11 might differ in differentiation degree from cluster_3 and cluster_12. The immune-related pathways were enriched in the brown module, while the energy metabolism pathways were enriched in the red module. The key genes in the brown module were \u003cem\u003eNFκB\u003c/em\u003e, \u003cem\u003eGST\u003c/em\u003e, \u003cem\u003eTollo\u003c/em\u003e and \u003cem\u003ePerlucin\u003c/em\u003e, which regulated the TLR and NF-κB signaling pathways. These pathways might be important for the immune regulation of cluster_11, which was a mature GRCs cluster. In the red module, \u003cem\u003eCD109\u003c/em\u003e, \u003cem\u003eCD63\u003c/em\u003e, \u003cem\u003eMMP18\u003c/em\u003e and \u003cem\u003eHSP90\u003c/em\u003e were important genes, among which \u003cem\u003eCD109\u003c/em\u003e regulated \u003cem\u003eHSP90\u003c/em\u003e, \u003cem\u003eMMP18\u003c/em\u003e and \u003cem\u003eCD63\u003c/em\u003e. CD109 was a TEP superfamily member that mediated phagocytosis in innate immunity \u003csup\u003e72,73\u003c/sup\u003e. 14 isoforms of \u003cem\u003eCD109\u003c/em\u003e were found in \u003cem\u003eH. discus hannai\u003c/em\u003e \u003csup\u003e58\u003c/sup\u003e. Therefore, cluster_11 might regulate immunity by modulating the TLR signaling pathway and NF-κB signaling pathway, as well as phagocytosis. The cluster_3 and cluster_12 in the red module might mainly use phagocytosis for immune regulation, which also requires energy metabolism pathways.\u003c/p\u003e \u003cp\u003eThe ratio of distinct hemocyte types was altered by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. The upregulated genes of different hemocyte types after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection were analyzed using KEGG functional enrichment analysis. HCs had fewer but more specific pathways and genes enriched than granulocytes, suggesting that HCs had more specific functions. Granulocytes had more diverse pathways and enriched genes, indicating that granulocytes had more complex functions and regulation modes. The Oxidative phosphorylation pathway related to energy metabolism was enriched in HCs of all treatment groups. This pathway might provide oxidative energy and kill pathogens by oxidative burst for HCs \u003csup\u003e74,75\u003c/sup\u003e. Granulocytes, especially GRCs, were mainly enriched in immune-related pathways. In the VV_group, GRCs were also enriched in NF-κB signaling pathways related to signal transduction and Fc gamma R-mediated phagocytosis related to cellular phagocytosis. This suggests that abalone might have a stronger immune response and a possible immune memory effect when exposed to a high dosage of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e after a low dosage exposure. This is consistent with previous studies in other mollusks that showed increased phagocytic activity after repeated infection by pathogens \u003csup\u003e9,10,24\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe comparison of DEGs of GRCs in each treatment group was based on the previous identification methods of different gene sets. Fc gamma R-mediated phagocytosis was enriched in all comparisons, indicating that phagocytosis is the main response mechanism of GRCs to pathogen infection. The TLR pathway of Drosophila was shown to regulate immune priming \u003csup\u003e41\u003c/sup\u003e. In Pacific oysters and \u003cem\u003eScyllaparamamosain\u003c/em\u003e, the expression of TLR signaling molecules and other immune-related factors increased in their second response to \u003cem\u003eV. splendidus\u003c/em\u003e and \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, respectively\u003csup\u003e11,21\u003c/sup\u003e. This suggested that the TLR pathway might be involved in the immune memory process of invertebrates to pathogens. The KEGG enrichment of each gene set showed that the NF-κB signaling pathway and TLR signaling pathway were enriched in CRGs-b and IRGs-b, respectively. These pathways might play an essential role in the initial and re-infection immune response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, and might be related to the immune memory process of abalone.\u003c/p\u003e \u003cp\u003eHemocytes are the main cellular immune response executors in invertebrates, which clear foreign pathogens and their own infected and damaged cells by phagocytosis \u003csup\u003e76,77\u003c/sup\u003e. Studies have shown that the immune priming of Drosophila and silkworms depends on phagocytic cells \u003csup\u003e41,78\u003c/sup\u003e. In mollusks, the number and phagocytic activity of hemocytes increased after re-infection by pathogens in snails, scallops, and oysters\u003csup\u003e9,10,24\u003c/sup\u003e. In oysters, Fc gamma R-mediated phagocytosis was enriched in the differential genes, indicating that phagocytosis plays a critical immune defense role in oyster hemocytes responding to re-infection by \u003cem\u003eV. splendidus\u003c/em\u003e\u003csup\u003e21\u003c/sup\u003e. Many genes related to phagocytosis were differentially expressed in abalone after re-infection by \u003cem\u003eV. harveyi\u003c/em\u003e, suggesting that phagocytosis plays an important role in preventing abalone from being reinfected by the same pathogen \u003csup\u003e23\u003c/sup\u003e. In this study, Fc gamma R-mediated phagocytosis was also enriched in PEGs-b, IRGs-b and ERGs-b, indicating that phagocytosis not only plays a key regulatory role in abalone hemocytes responding to re-infection by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, but also participates in the immune memory process of abalone hemocytes. In addition, in the molecular interaction network analysis between different immune signal pathways, \u003cem\u003eNFκB\u003c/em\u003e and \u003cem\u003eIRAK4\u003c/em\u003e commonly regulated the three immune signal pathways, and the NF-κB signaling pathway interacted with Fc gamma R-mediated phagocytosis through \u003cem\u003ePLCG1\u003c/em\u003e. PLCG1 is a phospholipase C gamma family member that can regulate various physiological and pathological responses of cells. In \u003cem\u003eOctopus ocellatus\u003c/em\u003e, PLCG1 might be involved in a more complex immune regulation process that only exists in hatchlings with egg protection from maternal incubation \u003csup\u003e79\u003c/sup\u003e. Our results also suggested that NF-κB signaling pathway might participate in the immune response process of abalone hemocytes to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection under the coordination of \u003cem\u003ePLCG1\u003c/em\u003e, and induce a more substantial phagocytic effect to clear the invading pathogens. This also implied a possible immune memory effect existing in abalone hemocytes.\u003c/p\u003e \u003cp\u003eThe pseudotime trajectory analysis of the GRCs differentiation mode in each sample was performed. Compared with the control group samples, the GRCs of samples after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection were found to be in different branches and states of the pseudotime differentiation trajectory. Under the differentiation mode with state_3 as the starting point, the potential marker genes of GRCs and some classic immune-related genes and pathways were upregulated in state1_2. This suggested that \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection could induce the differentiation and maturation of GRCs, making them more stable in immune regulation function and that different dosages of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection could also affect this differentiation potential. In previous studies, we have discussed different views on hemocyte differentiation, and in abalone, our results support the view that different types of hemocytes are differentiated from a single type, that is, HCs and GRCs are the initial and final stages of differentiation of the same cell type \u003csup\u003e80,81\u003c/sup\u003e. In this study, we found that \u003cem\u003eC3\u003c/em\u003e, a potential marker gene of HCs, was highly expressed in state_3, which might indicate that state_3 had many cells in an immature state of differentiation from HCs to GRCs, which also verified our conclusion. The latest research shows that \u003cem\u003ePCNA\u003c/em\u003e, as a proliferation marker, is expressed in the hemocytes and gill of \u003cem\u003eH. discus hannai\u003c/em\u003e\u003csup\u003e82\u003c/sup\u003e. In this study, \u003cem\u003ePCNA\u003c/em\u003e was expressed at a higher level in the HCs of the untreated group, which may indicate its involvement in the differentiation process of hemocytes from HCs to GRCs.Moreover, the enrichment of energy metabolism-related pathways in state_3 also indicated that cellular differentiation was an energy-dependent process. Although this study proposed a cellular differentiation model of abalone GRCs under \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, the results still need further experimental validation, which will be the focus of future research.\u003c/p\u003e \u003cp\u003eA re-clustering analysis of GRCs was performed to investigate their functional differentiation further. Three different sub-clusters were obtained, among which Sub-cluster_1 was distributed in each treatment group but mainly enriched in the control group, while Sub-cluster_2 mainly existed in the control group samples without \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. Further analysis showed that these two sub-clusters had many common pathways, mainly Oxidative phosphorylation related to energy metabolism and Fc gamma R-mediated phagocytosis related to cellular phagocytosis. This indicated that these two sub-clusters had similar functions but different participation modes. Sub-cluster_1 might be widely involved, while Sub-cluster_2 might be mainly involved in energy provision and cellular phagocytosis under a cellular resting state. Similar to Sub-cluster_1, Sub-cluster_0 also existed widely in each treatment group sample, but their distribution states were different. Sub-cluster_0 mainly existed in each sample after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, while Sub-cluster_1 was mainly distributed in the control group. There was also some overlap in gene expression patterns between them, indicating that they might only differ in the degree of differentiation. The up-regulation of GRCs key marker genes and some immune regulatory genes in Sub-cluster_0 indicated that this sub-cluster had a higher degree of differentiation and might be at the end of GRCs differentiation.\u003c/p\u003e \u003cp\u003eThe DEGs in the same sub-cluster between different treatment groups after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection were analyzed for their function. It was shown that both high-concentration \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection (V_group vs NV_group) and \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection (V_group vs VV_group) upregulated immune-related pathways such as TLR signaling pathway and NF-κB signaling pathway, and phagocytosis-related pathways such as Fc gamma R-mediated phagocytosis in Sub-cluster_0. This indicated that Sub-cluster_0 was the main GRCs regulation type in response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection and that some classic immune pathways and phagocytosis of hemocytes were involved in this regulation process. In contrast, in Sub-cluster_1, after high concentration \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, and in VV_group vs NV_group, only some processes, such as Endocytosis, were enriched, indicating the difference in immune regulation capacity between the two sub-clusters. Moreover, in V_group vs VV_group, the NF-κB signaling pathway, TLR signaling pathway, Fc gamma R-mediated phagocytosis, and other pathways were enriched in Sub-cluster_1 again. Due to the small number of cells in each group after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection in Sub-cluster_1, we speculated that these cells might be induced to differentiate by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection and that they might have stronger phagocytosis capacity and immune response mechanism through NF-κB signaling pathway and TLR signaling pathway, participating in the immune memory process of abalone hemocytes.\u003c/p\u003e \u003cp\u003eThe activation of specific cell signals depends on the binding of ligands and their receptors. However, this part of research is rarely reported in marine invertebrates due to the lack of reference data. In the latest study of oysters, it was proved that there was a complex cell communication relationship between GRCs and SGRCs, and that copper ions would affect this relationship \u003csup\u003e46\u003c/sup\u003e. Using similar methods, we analyzed the cell communication relationship between different cell clusters in different treatment groups after \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection. We found that \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection could enhance the interaction between different cell clusters, making them have stronger communication relationships with each other. Among the 74 pairs of receptor-ligand pairs that interacted, COPA and SORT1 interacted in different treatment samples, and in NV_group and VV_group, SORT1 also interacted with GRN. Studies have shown that GRN can enhance the proliferation of various cell types, regulate inflammatory response and wound healing, and participate in endocytosis by binding with SORT1 \u003csup\u003e83\u003c/sup\u003e. Our results suggest that high-dosage\u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection, either in the initial or re-infection process, involves endocytosis. In addition, studies have shown that L1cam is a marker of NF-κB signal pathway activation \u003csup\u003e84\u003c/sup\u003e. In this study, we found that L1cam interactions were found in V_group and VV_group compared with NV_group, indicating that \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection can activate NF-κB signal pathway again significantly, which might be one of the key regulatory factors for stronger immune effect. In addition, more ligand gene numbers in VV_group indicate that \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection can enhance the communication relationship between ligand-receptor cells. There was a strong communication relationship between cell clusters 5, 6, 11, 12 and 14, indicating that GRCs and SGRCs might interact with each other in a more complex way. To further analyze this communication relationship, we selected cluster_11 and cluster_14 as ligand signal cells and receptor signal cells respectively, and analyzed the ligand activity in ligand signal cells and the regulation potential of ligands on specific genes in receptor cells. The results showed that the overall activity and number of each ligand were generally higher in VV_group and that high dosage \u003cem\u003eV. parahaemolyticus\u003c/em\u003e infection could enhance the ligand activity of regulating the NF-κB signaling pathway, NLR signaling pathway and TLR signaling pathway gene set, which were highest in the VV_group. The regulation analysis results of the ligand-target gene showed that the number of target genes regulated by ligands in the VV_group was also the highest. These results indicate that \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection can increase the communication exchange between ligand-receptor cells and induce a stronger immune communication effect in abalone hemocytes, which might be the communication basis for abalone immune memory.\u003c/p\u003e \u003cp\u003eRNAi has been successfully performed in marine invertebrates such as \u003cem\u003eH. diversicolor\u003c/em\u003e\u003csup\u003e85\u003c/sup\u003e. NFκB and TLR2 are the core factors of NF-κB signaling pathway and TLR signaling pathway, respectively. To study their regulatory effects on other molecules of these pathways and their interactions in \u003cem\u003eH. discus hannai\u003c/em\u003e, we performed RNAi experiments of \u003cem\u003eNFκB\u003c/em\u003e and \u003cem\u003eTLR2\u003c/em\u003e genes in hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003eusing dsRNA soaking method. The results showed that the expression levels of \u003cem\u003eNFκB\u003c/em\u003eand \u003cem\u003eTLR2\u003c/em\u003e in the experimental group were significantly lower than those in the control group at each time point after RNAi treatment, indicating obvious interference effects. When \u003cem\u003eTLR2\u003c/em\u003e was interfered with, the expression levels of genes related to NF-κB and TLR signaling pathway in the experimental group were also significantly down-regulated at different time points compared with the control group. \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eNFκB\u003c/em\u003e, \u003cem\u003eFADD\u003c/em\u003e and \u003cem\u003eMyD88\u003c/em\u003e had obvious interference effects, which were significantly inhibited from 6 h after interference. These results indicated that \u003cem\u003eTLR2\u003c/em\u003e had a key positive regulatory role on these genes. However, there might be differences in the regulation intensity, which might cause the inconsistency of expression patterns of different genes at each time point. When \u003cem\u003eNFκB\u003c/em\u003e was interfered with, some genes, such as \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eEIF4E\u003c/em\u003e, \u003cem\u003eAkirin2\u003c/em\u003e, \u003cem\u003e14-3-3ζ\u003c/em\u003e, \u003cem\u003eMKK4\u003c/em\u003e, and \u003cem\u003eMAPK14\u003c/em\u003e, were also significantly down-regulated at different time points in the experimental group compared with the control group, indicating that \u003cem\u003eNFκB\u003c/em\u003e had a positive regulatory role on these genes. Among them, \u003cem\u003eAkirin2\u003c/em\u003e was inhibited from 6 h to 24 h after interference; \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eEIF4E\u003c/em\u003e, \u003cem\u003e14-3-3ζ\u003c/em\u003e and \u003cem\u003eMKK4\u003c/em\u003e were inhibited from 12 h after interference; and \u003cem\u003eMAPK14\u003c/em\u003e was only down-regulated at 24 h compared with the control group. In addition, several other genes, such as \u003cem\u003eTLR2\u003c/em\u003e, \u003cem\u003eFADD\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e, \u003cem\u003eIRAK4\u003c/em\u003e, \u003cem\u003eMyD88\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003eRIP1\u003c/em\u003e, did not change their expression patterns after \u003cem\u003eNFκB\u003c/em\u003e interference. This result indicated that these genes might be upstream of \u003cem\u003eNFκB\u003c/em\u003e and not directly regulated by \u003cem\u003eNFκB\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe molecular interaction network diagrams of the above genes were constructed by Cytoscape, which showed the interaction relationships among these genes after \u003cem\u003eNFκB\u003c/em\u003eand \u003cem\u003eTLR2\u003c/em\u003e interference. We observed that \u003cem\u003eTRAF6\u003c/em\u003e was at the top of the network and regulated a complex network. Previous studies have found that \u003cem\u003eTRAF6\u003c/em\u003e is an important regulator in signal transduction during innate immunity \u003csup\u003e86\u003c/sup\u003e. When pathogens infected the organism, \u003cem\u003eTRAF6\u003c/em\u003e was upregulated in \u003cem\u003eC. farreri\u003c/em\u003e \u003csup\u003e87\u003c/sup\u003e, \u003cem\u003ePortunustrituberculatus\u003c/em\u003e \u003csup\u003e88\u003c/sup\u003e, \u003cem\u003eApostichopus japonicus\u003c/em\u003e \u003csup\u003e89\u003c/sup\u003e and \u003cem\u003ePinctada martensii\u003c/em\u003e \u003csup\u003e90\u003c/sup\u003e. Moreover, \u003cem\u003eTRAF6\u003c/em\u003e has been proven to connect \u003cem\u003eTLR\u003c/em\u003e and \u003cem\u003eMyD88\u003c/em\u003e with NF-κB signaling pathway. In this study, TRAF6 was also in the core position of TLR and NF-κB signaling pathway network diagram. This indicated that in \u003cem\u003eH. discus hannai\u003c/em\u003e, \u003cem\u003eTRAF6\u003c/em\u003e might be a key link hub of TLR and NF-κB signaling pathway. Moreover, RNAi results showed that \u003cem\u003eTLR2\u003c/em\u003e interference significantly inhibited \u003cem\u003eTRAF6\u003c/em\u003e expression at all time points, indicating that \u003cem\u003eTLR2\u003c/em\u003e was upstream of \u003cem\u003eTRAF6\u003c/em\u003e and firmly regulated its expression. However, \u003cem\u003eNFκB\u003c/em\u003einterference did not affect \u003cem\u003eTRAF6\u003c/em\u003e expression, which confirmed that in hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003e, the NF-κB signaling pathway should be downstream of \u003cem\u003eTRAF6\u003c/em\u003e and might be regulated by \u003cem\u003eTRAF6\u003c/em\u003e and that this regulation was one-way. Thus, a classical TLR/NF-κB signaling pathway exists in hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003e, and this pathway participates in the immune regulation process of abalone hemocytes in response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection. When the organism was challenged by pathogens, the TLR family members on the cell membrane captured the signals, and with the help of regulators such as TRAF6, they relayed the signals to NFκB, which played a key immune regulatory role.\u003c/p\u003e \u003cp\u003eIn conclusion, we explored the response mechanism of \u003cem\u003eH. discus hannai\u003c/em\u003e hemocytes to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection by using scRNA-seq.\u0026nbsp;The 15 hemocyte clusters were re-clustered into three cell types: GRCs, SGRCs, and HCs, which were consistent with traditional views, and their potential marker genes were screened. Pseudotime analysis showed that HCs and GRCs were in the early and late stages of differentiation of the same cell type. GRCs might be the main cellular immunity participants in \u003cem\u003eH. discus hannai\u003c/em\u003e, as they were enriched in phagocytosis and immune regulation-related pathways. We identified potential marker genes such as \u003cem\u003eAIF1\u003c/em\u003e and \u003cem\u003eCDC42\u003c/em\u003e in GRCs, \u003cem\u003eTRX2\u003c/em\u003e and \u003cem\u003eGST7\u003c/em\u003e in SGRCs, and \u003cem\u003eC3\u003c/em\u003e in HCs. GRCs might have more complex functions than HCs. Through WGCNA, we identified that cluster_11 might be a more mature subpopulation of GRCs, primarily functioning as a core immune regulator with strong phagocytic, endocytic, signal transduction, and immune regulatory capabilities. On the other hand, cluster_3 and cluster_12 might be in the early stages of GRCs differentiation, and the involvement of energy metabolism-related pathways accompany their differentiation process. Secondary infection by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e might induce GRCs to produce a stronger immune response through the NF-κB signaling pathway, TLR signaling pathway and phagocytosis. It could also accelerate the differentiation process of HCs to GRCs. GRCs had heterogeneity, and different clusters had different functions at different differentiation stages. The higher the differentiation degree, the stronger the immune regulation ability. The immune memory of abalone hemocytes to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e might not involve all cells, but a few cells produced an immune regulation mechanism after re-infection. Secondary infection by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e could also increase the communication between ligand-receptor cells, indicating a stronger immune communication effect in the organism, which might be the communication basis for abalone immune memory. RNAi results showed that a classical Toll/NF-κB signaling pathway existed in hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003e, and this pathway was involved in the immune regulation process of abalone hemocytes responding to secondary infection by \u003cem\u003eV. parahaemolyticus.\u003c/em\u003e Based on these results, we summarized the possible immune memory regulation mechanism in hemocytes of \u003cem\u003eH. discus hannai\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eYilei Wang and Ziping Zhang conceived the study and designed the experiments. Xin Zhang conducted the experiments and wrote the manuscript. Yulong Sun conducted the experiments and analyzed the data. Yilei Wang, Jianjun Feng and Ziping Zhang checked and modified the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe work was supported by the National Key R\u0026amp;D Program of China (NO. 2021YFE0106100, 2018YFD0900304-5), Fujian Innovation and Industrialization Development of Abalone Seed Industry (2021FJSCZY02), the Natural Science Foundation of China (No. 31672681), Open fund project of Fujian Engineering Research Center of Aquatic Breeding and Healthy Aquaculture (No. DF20902), Open fund project of Key Laboratory of Healthy Mariculture for the East China Sea (No. 2020ESHML12).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCerenius KS (1992) Crustacean immunity. 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Genet Mol Res 13:10545\u0026ndash;10555\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Haliotis discus hannai, hemocyt༛Vibrio parahaemolyticus༛secondary infection༛immune memory","lastPublishedDoi":"10.21203/rs.3.rs-4675005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4675005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is commonly believed that invertebrates lack immune memory due to the absence of immunoglobulins, related molecules, cells, and organs. However, our previous research demonstrated that \u003cem\u003eHaliotis discus hannai\u003c/em\u003e, a prominent abalone species cultivated in China, often faces substantial economic losses due to diseases, particularly those caused by \u003cem\u003eVibrio sp\u003c/em\u003e. exhibited higher survival rates upon re-infection with \u003cem\u003eVibrio parahaemolyticus\u003c/em\u003e compared to the initial infection, implying the existence of immune memory. We hypothesized that hemocytes, which play a critical role in pathogen resistance in abalone, might be involved in the immune memory process. Therefore, we aimed to investigate the hemocyte response mechanism to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection to provide valuable insights for preventing and controlling abalone vibriosis and advancing sustainable abalone aquaculture. Additionally, our research aimed to contribute to understanding the origin and evolution of immune memory mechanisms.\u003c/p\u003e \u003cp\u003eThis study constructed a transcriptome map of abalone hemocytes using 10\u0026times; Genomics single-cell RNA sequencing (scRNA-seq). Traditionally, abalone hemocytes were categorized into three cell types: hyalinocytes, semi-granulocytes, and granulocytes. The initial cell division resulted in the formation of 15 clusters further through subsequent analysis using scRNA-seq.\u0026nbsp;Among these clusters, cluster_11 exhibited unique characteristics, indicating a more mature cluster of GRCs. This specific subpopulation displayed significant functionality as a core immune regulator, manifesting robust phagocytic and endocytic activities and substantial involvement in signal transduction and immune regulatory processes. Furthermore, we analyzed and detailed functional variances among different hemocyte types. Through the implementation of RNA interference technology, we validated the interplay between key signaling pathways. Interestingly, our findings suggested the potential existence of a classical TLR/NF-κB signaling pathway in abalone hemocytes, which may contribute to the immune regulation process in response to \u003cem\u003eV. parahaemolyticus\u003c/em\u003e re-infection, as preliminarily confirmed in our study.\u003c/p\u003e","manuscriptTitle":"Single-Cell Transcriptomic Analysis of Specific Responses of Different Cell Populations of Hemocytes to the Re-infection of Bacteria, a Case Study in Abalone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 20:20:32","doi":"10.21203/rs.3.rs-4675005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"391433b9-9eb7-41b8-84a6-111833ebd6a5","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34696148,"name":"Biological sciences/Immunology/Adaptive immunity/Cellular immunity/Lymphocyte differentiation"},{"id":34696149,"name":"Biological sciences/Immunology/Innate immune cells/Innate lymphoid cells"}],"tags":[],"updatedAt":"2025-07-14T05:52:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 20:20:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4675005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4675005","identity":"rs-4675005","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}