Abstract

   Jellyfish stings induce a range of symptoms, from localized irritation
   to life-threatening systemic reactions, yet the underlying immune
   mechanisms remain poorly understood. This study employed single-cell
   RNA sequencing (scRNA-seq) to analyze peripheral blood mononuclear
   cells (PBMCs) from a severely affected patient and healthy controls,
   uncovering the immune landscape at single-cell resolution and
   identifying the signaling pathways. We identified 11 major immune cell
   types, with a marked increase in CD14+ monocytes (81.86% of total
   cells) and significant reductions in T cells, B cells, and CD16+
   monocytes in the envenomated patient. Subclustering revealed six
   monocyte and four neutrophil subsets, each displaying distinct
   functional profiles. Patient monocytes were enriched for MMP9+ and
   RETN+ subsets, associated with leukocyte migration and inflammation,
   whereas healthy controls exhibited CD74+ monocytes linked to oxidative
   phosphorylation. Neutrophils in the patient were predominantly LTF+ and
   S100A12+, implicating inflammatory and immune regulatory pathways.
   These findings provide a detailed single-cell atlas of immune
   dysregulation post-jellyfish sting, highlighting the pivotal roles of
   MMP9+ monocytes and S100A12+ neutrophils in driving inflammation. This
   study offers potential therapeutic targets for mitigating severe immune
   responses in jellyfish envenomation.

   Keywords: jellyfish stings, scRNA-seq, cell heterogeneity, CD74+
   monocytes, MMP9+ monocytes, S100A12+ neutrophils

1. Introduction

   Jellyfish, belonging to the phylum Cnidaria, are widely distributed
   marine organisms known for their ecological abundance and taxonomic
   diversity. Characterized by a bell-shaped body adorned with tentacles
   and oral arms, they harbor specialized stinging cells called
   nematocysts, which deliver a complex cocktail of bioactive toxins upon
   contact with predators or prey. The venom composition is highly
   heterogeneous, comprising proteins, peptides, enzymes, tetramine,
   potent anesthetics, histamines, serotonin, and other components
   [[36]1,[37]2]. These toxins exhibit a broad spectrum of biological
   activities, such as hemolytic, cardiotoxic, neurotoxic, dermal-muscular
   toxicities, as well as enzymatic and antioxidant properties [[38]3].
   Envenomation in humans can lead to a range of clinical
   manifestations—from localized pain, erythema, and blistering to
   systemic complications such as cardiovascular instability,
   neurotoxicity, acute kidney injury, and, in severe cases, multi-organ
   failure and death [[39]4,[40]5].

   Despite the increasing incidence of jellyfish stings worldwide, the
   molecular and immunological mechanisms underlying their pathophysiology
   remain poorly understood. This knowledge gap has significantly impeded
   the development of targeted and effective therapeutic interventions. A
   deeper understanding of the host immune response to cnidarian toxins is
   therefore essential for identifying key cellular and molecular drivers
   of disease progression. Recent advances in single-cell transcriptomics
   have opened new avenues for dissecting complex immune responses with
   unprecedented resolution. Single-cell RNA sequencing (scRNA-seq)
   enables the profiling of gene expression at the individual cell level,
   revealing cellular heterogeneity, rare cell populations, and dynamic
   transcriptional states that are often masked in bulk RNA sequencing
   analyses [[41]6,[42]7,[43]8,[44]9,[45]10,[46]11,[47]12,[48]13,[49]14].

   In the context of envenomation, traditional bulk transcriptomic
   approaches provide only an averaged view of gene expression across
   heterogeneous cell populations, obscuring critical cell-type-specific
   responses. In contrast, scRNA-seq offers a powerful platform to uncover
   the precise immune cell subsets and signaling pathways perturbed by
   jellyfish toxins. Previous studies have demonstrated that jellyfish
   venom components trigger robust inflammatory responses, including the
   upregulation of pro-inflammatory cytokines and chemokines, which
   promote leukocyte recruitment and activation
   [[50]15,[51]16,[52]17,[53]18,[54]19]. Moreover, systemic manifestations
   such as acute myocardial injury, pulmonary edema, and arrhythmias
   suggest a profound dysregulation of immune and vascular homeostasis
   following envenomation [[55]20]. However, the specific immune cell
   types and intercellular communication networks involved in these
   processes remain largely uncharacterized.

   Recent applications of scRNA-seq in immunology have revolutionized our
   understanding of host responses in infectious, autoimmune, and
   inflammatory diseases, enabling the discovery of novel cell states,
   regulatory circuits, and therapeutic targets
   [[56]14,[57]21,[58]22,[59]23,[60]24,[61]25,[62]26,[63]27,[64]28]. In
   the context of toxin exposure, this technology holds particular promise
   for identifying distinct immune subpopulations that drive inflammation
   or tissue damage. For instance, scRNA-seq can reveal differentially
   expressed marker genes, reconstruct signaling pathways, and infer
   cell–cell interactions that underlie pathological immune activation
   [[65]6,[66]29,[67]30,[68]31,[69]32,[70]33,[71]34,[72]35,[73]36,[74]37,[
   75]38].

   In this study, we applied scRNA-seq to peripheral blood mononuclear
   cells (PBMCs) from a patient with severe jellyfish envenomation and
   matched healthy controls to comprehensively characterize the immune
   response at single-cell resolution. We aimed to (i) define alterations
   in immune cell composition, with a focus on monocytes, neutrophils, and
   lymphocytes; (ii) identify differentially expressed genes and their
   functional enrichment in inflammatory and migratory pathways; and (iii)
   infer cell–cell communication networks that may contribute to systemic
   immune dysregulation.

   Our findings reveal a profound shift in immune homeostasis following
   jellyfish stings, dominated by the expansion of pro-inflammatory MMP9+
   and RETN+ monocyte subsets and the activation of S100A12+
   neutrophils—cell populations strongly associated with tissue damage and
   inflammation. This work not only advances our understanding of the
   pathophysiology of jellyfish envenomation but also highlights potential
   molecular targets for therapeutic intervention. By combining marine
   toxin biology with cutting-edge immunogenomics, this study illustrates
   how marine-derived challenges can contribute to the discovery of novel
   immune mechanisms and, ultimately, the development of bioinspired
   therapeutics.

2. Results

2.1. Overview of Single-Cell Transcriptomic Profiling of PBMCs

   To investigate the immune landscape following severe jellyfish
   envenomation, we performed single-cell RNA sequencing (scRNA-seq) on
   peripheral blood mononuclear cells (PBMCs) from one patient with
   systemic symptoms and three healthy individuals. A total of four PBMC
   samples were collected, processed via density gradient centrifugation,
   and subjected to high-throughput scRNA-seq. This approach enabled
   high-resolution profiling of gene expression at the single-cell level,
   allowing for the identification of cell-type-specific transcriptional
   signatures.

   Bioinformatic analyses included quality control, read alignment, count
   matrix generation, normalization, and dimensionality reduction.
   Unsupervised clustering was applied to delineate distinct cell
   populations within the integrated dataset. Based on canonical marker
   gene expression, we identified 11 major immune cell types: T cells, B
   cells, natural killer (NK) cells, CD14+ monocytes, CD16+ monocytes,
   dendritic cells (DCs), neutrophils, immature neutrophils,
   megakaryocytes, mast cells, and proliferating cells ([76]Figure 1A,B).
   The clustering and annotation workflow is summarized in [77]Figure 1A,
   and the Uniform Manifold Approximation and Projection (UMAP)
   visualization revealed well-separated populations, reflecting the
   cellular heterogeneity present in human PBMCs.

Figure 1.

   [78]Figure 1
   [79]Open in a new tab

   Overview and cell type analysis of PBMC single-cell transcriptomics.
   (A) Schematic representation of the study design and workflow. The
   figure outlines the steps from patient sample processing to single-cell
   RNA sequencing and downstream analysis. (B) UMAP projection of sampled
   cell types. A UMAP plot illustrating the dimensionality reduction of
   different cell types identified within the PBMC samples. Each dot
   represents a single cell, colored by its annotated cell type to
   visualize the distribution and clustering of cellular diversity. (C)
   Dot plot of canonical marker genes. A dot plot displaying the
   expression of classical marker genes across various cell types. The
   size of each dot indicates the percentage of cells expressing the gene,
   while the color intensity indicates the average expression level. (D)
   Pie charts illustrating the proportion of 11 major cell types across
   the four samples. Each bar represents a sample, and the colors
   correspond to different cell types. Pie charts showing the relative
   proportions of eleven major cell types identified in the PBMCs of the
   four individual samples. (E) Feature plots for selected canonical
   marker genes providing a spatial representation of gene expression
   levels on the UMAP plot. These plots help to identify the specific cell
   types associated with the expression of these marker genes.

   To validate the accuracy of cell type annotations, we generated a dot
   plot showing the average expression and detection frequency of
   established lineage-specific markers. This analysis confirmed robust
   expression of CD3E in T cells, CD19 in B cells, CD14 in classical
   monocytes, FCGR3A (CD16) in non-classical monocytes and neutrophils,
   KLRD1 (CD94) in NK cells, and S100A8/A9 in myeloid populations
   ([80]Figure 1C). These results support the reliability of our cell type
   classification and provide a solid foundation for downstream analyses.

2.2. Altered Immune Cell Composition in Jellyfish Envenomation

   Comparative analysis of cell type proportions revealed dramatic shifts
   in the immune landscape of the jellyfish sting patient compared to
   healthy controls. In the control group, T cells were the predominant
   population (37.68% of total cells), followed by B cells (18.80%), NK
   cells (17.80%), and CD14+ monocytes (16.58%), with neutrophils
   representing only a minor fraction (2.62%)—consistent with typical PBMC
   composition.

   In striking contrast, the patient exhibited a profound skewing toward
   myeloid lineage expansion. CD14+ monocytes became the dominant cell
   type, constituting 81.86% of all PBMCs—an over fivefold increase
   compared to controls. Immature neutrophils were the second-most
   abundant population (6.42%), suggesting active myelopoiesis or
   emergency granulopoiesis in response to envenomation. The proportions
   of T cells, B cells, and CD16+ monocytes were significantly reduced in
   the patient compared to the healthy controls ([81]Figure 1D).

   These findings demonstrate a systemic immune imbalance characterized by
   monocytic expansion and lymphopenia, indicative of a strong innate
   immune activation and potential immunosuppressive state in the adaptive
   compartment. This shift mirrors patterns observed in severe
   inflammatory or sepsis-like conditions, highlighting the intensity of
   the host response to jellyfish toxins.

2.3. Feature Plot of Canonical Marker Genes

   To further validate the identity and spatial distribution of annotated
   cell clusters, we visualized the expression of key lineage-defining
   genes on the UMAP embedding. Feature plots confirmed the specific and
   restricted expression of classical markers within their expected
   clusters. These plots confirmed the localized expression of marker
   genes within their respective cell clusters, further validating the
   cell type annotations ([82]Figure 1E).

2.4. Subclustering and Functional Annotation of Monocytes

   To further dissect the heterogeneity within monocyte populations, we
   performed subclustering analysis using UMAP, which revealed six
   distinct monocyte subtypes ([83]Figure 2A). These subtypes included
   MMP9+ monocytes and CD74+ monocytes, among others, each showing clear
   spatial separation in the UMAP embedding, indicating significant
   transcriptional diversity within the monocyte lineage.

Figure 2.

   [84]Figure 2
   [85]Open in a new tab

   Subclustering and functional analysis of monocytes. (A) UMAP plot
   depicting the distribution of monocyte (Mono) subtypes across the four
   samples. Each color represents a distinct monocyte subtype. (B) Dot
   plot showing the expression of marker genes for each monocyte subtype.
   The dot size and color intensity represent the percentage of cells
   expressing the gene and the average expression level, respectively. (C)
   Pie charts display the proportion of six monocyte subtypes across the
   four samples. Each bar represents a sample, and the colors correspond
   to different monocyte subtypes. (D) Feature plot illustrating the
   spatial distribution of marker genes for each monocyte subtype on the
   UMAP plot. UMAP_1 and UMAP_2 represent the two primary dimensions
   resulting from dimensionality reduction. (E) Volcano plot highlighting
   differentially expressed genes between MMP9+ monocytes and CD74+
   monocytes. Significantly upregulated genes are marked in red. (F)
   Pathway enrichment analysis of upregulated genes in MMP9+ monocytes.
   The bar plot shows the top enriched pathways. (G) Pathway enrichment
   analysis of upregulated genes in CD74+ monocytes. The bar plot shows
   the top enriched pathways.

2.4.1. Subtype-Specific Gene Signatures of Monocytes

   To characterize the gene expression profiles specific to each subtype,
   a dot plot analysis was conducted ([86]Figure 2B). MMP9+ monocytes
   uniquely expressed MMP9, while CD74+ monocytes showed elevated levels
   of CD74. Additionally, other subtypes such as CDKN1C+ monocytes, CCL5+
   monocytes, RETN+ monocytes, and IL32+ monocytes exhibited
   characteristic marker genes indicative of their functional roles.
   Quantitative assessment of the proportions of these subtypes across
   samples demonstrated variability, with MMP9+ monocytes being
   particularly enriched in the patient sample ([87]Figure 2C). Feature
   plots further validated the distinct spatial localization of these
   marker genes within the UMAP coordinates, confirming the accuracy of
   our annotations. Conversely, CD74+ monocytes were more prevalent in
   healthy controls, with upregulation of genes like LYZ and CD74, which
   are involved in oxidative phosphorylation and aerobic respiration
   ([88]Figure 2D).

2.4.2. Differential Gene Expression Analysis

   To explore the functional differences between MMP9+ and CD74+
   monocytes, differential gene expression analysis was performed and
   visualized using a volcano plot ([89]Figure 2E). MMP9+ monocytes
   exhibited significant upregulation of inflammatory response genes,
   including IL1R2 and IFITM1, suggesting their involvement in acute
   inflammation. In contrast, CD74+ monocytes were enriched in genes
   related to antigen processing and presentation pathways, indicative of
   their role in immune surveillance and regulation.

   Gene set enrichment analysis (GSEA) corroborated these findings,
   revealing that MMP9+ monocytes were significantly enriched in cytokine
   signaling and immune response pathways ([90]Figure 2F), whereas CD74+
   monocytes displayed activation of oxidative phosphorylation and aerobic
   respiration pathways ([91]Figure 2G). These results highlight the
   distinct functional roles of these monocyte subtypes in orchestrating
   immune responses.

2.4.3. Classification of Monocyte Subclusters

   The six identified monocyte subclusters were classified based on their
   gene expression profiles and functional annotations.

   CD74+ monocytes (Clusters 0, 7): Dominant in healthy controls,
   associated with oxidative phosphorylation and aerobic respiration,
   reflecting their role in maintaining cellular homeostasis. MMP9+
   monocytes (Clusters 1, 2): Enriched in patients, linked to leukocyte
   migration (MMP9, IL1R2) and inflammatory responses, highlighting their
   involvement in acute immune reactions. CDKN1C+ monocytes (Cluster 3):
   Marked by cell cycle regulatory genes, suggesting a role in
   proliferative processes or tissue repair. CCL5+ monocytes (Cluster 4):
   Featured chemokine-related gene expression, indicating their
   involvement in chemotactic recruitment of immune cells. RETN+ monocytes
   (Cluster 5): Promoted myeloid/neutrophil chemotaxis, likely
   contributing to the mobilization of neutrophils during inflammation.
   IL32+ monocytes (Cluster 6): Enriched in interleukin-associated
   cytotoxicity pathways, potentially playing a role in enhancing
   immune-mediated cytotoxic effects.

2.5. Subclustering and Functional Annotation of Neutrophils

   To investigate the heterogeneity within neutrophil populations, we
   performed subclustering analysis using UMAP, which identified four
   distinct neutrophil subtypes ([92]Figure 3A). These subtypes included
   IFITM3+, LTF+, S100A12+, and PPBP+ neutrophils, each exhibiting unique
   transcriptional signatures.

Figure 3.

   [93]Figure 3
   [94]Open in a new tab

   Subclustering and functional analysis of neutrophils. (A) UMAP plot
   showing the distribution of neutrophil subtypes across the four
   samples. Each color represents a distinct neutrophil subtype. (B) Dot
   plot displaying the expression of marker genes for each neutrophil
   subtype. The dot size and color intensity represent the percentage of
   cells expressing the gene and the average expression level,
   respectively. (C) Pie charts illustrate the proportion of four
   neutrophil subtypes across the four samples. Each bar represents a
   sample, and the colors correspond to different neutrophil subtypes. (D)
   Feature plot showing the spatial distribution of marker genes for each
   neutrophil subtype on the UMAP plot. UMAP_1 and UMAP_2 represent the
   two primary dimensions resulting from dimensionality reduction. (E) Dot
   plot summarizing the functional pathways associated with each
   neutrophil subtype. The dot size represents the significance of the
   pathway, and the color indicates the enrichment score. (F) Monocle
   pseudotime analysis of neutrophil subtypes. A trajectory analysis using
   Monocle to infer the potential differentiation paths or states of
   neutrophil subtypes over pseudotime. Component 1 and Component 2 in the
   pseudotime analysis reflect the dominant trajectory components.

2.5.1. Subtype-Specific Gene Signatures of Neutrophils

   Dot plot analysis was conducted to identify subtype-specific markers
   ([95]Figure 3B). The pie chart highlights the inter-sample variability
   in subtype abundance, demonstrating significant differences between the
   jellyfish sting patient and healthy controls. Differential expression
   analysis revealed that MMP9+ monocytes were significantly enriched in
   the jellyfish sting patient ([96]Figure 3C). Feature plots further
   confirmed the spatial segregation of these marker genes within the UMAP
   space ([97]Figure 3D).

2.5.2. Functional Pathway Enrichment Analysis

   Pathway enrichment analysis highlighted several key signaling pathways
   activated or suppressed in response to jellyfish toxins. Notably,
   pathways associated with leukocyte migration, cytokine signaling, and
   inflammatory responses were upregulated in the patient’s immune cells.
   Additionally, there was evidence of suppression in pathways related to
   metabolic processes and oxidative phosphorylation, suggesting a shift
   in cellular energy metabolism following envenomation ([98]Figure 3E).
   These findings underscore the systemic impact of jellyfish toxins on
   immune cell function and metabolism.

   Specifically, IFITM3+ neutrophils (Cluster 0) are enriched in antiviral
   defense and leukocyte migration. LTF+ neutrophils (Cluster 1) are
   associated with granule formation and antiviral gene expression,
   indicating an active role in innate immunity. S100A12+ neutrophils
   (Cluster 2) are implicated in leukocyte adhesion, T-cell proliferation,
   and myeloid differentiation, suggesting a critical role in inflammatory
   amplification. PPBP+ neutrophils (Cluster 3) are linked to platelet
   activation and granulocyte motility, contributing to vascular repair
   and immune cell trafficking.

2.5.3. Pseudotime Trajectory Analysis

   Pseudotime trajectory analysis (Monocle) suggested a differentiation
   continuum from immature to mature neutrophil states ([99]Figure 3F). In
   the patient sample, neutrophils were predominantly LTF+ and S100A12+,
   with S100A12+ subsets driving leukocyte adhesion and inflammatory
   responses. Additionally, our exploration of the temporal dynamics and
   differentiation trajectories of neutrophil subpopulations suggested
   that LTF+ neutrophils might represent an immature state, which was
   primarily found in the patient. S100A12+ neutrophils appeared to be an
   intermediate state, while IFITM3+ neutrophils were considered mature.
   This differentiation trajectory implies that jellyfish toxins may
   disrupt normal neutrophil maturation, leading to an accumulation of
   immature cells in the patient.

2.6. Altered Cell–Cell Communication Networks

   To investigate the changes in cell–cell communication networks
   following jellyfish envenomation, we performed comprehensive
   ligand–receptor interaction analysis. This analysis revealed
   significant alterations in key signaling pathways, particularly those
   involving chemokines and cytokines, within the monocytes and
   neutrophils of the patient.

2.6.1. Heatmap Visualization of Interaction Strength and Specificity

   We performed cell–cell communication analysis to identify potential
   interactions between different cell types. A heatmap was generated to
   visualize the strength and specificity of interactions in healthy
   control (HC) and patient samples. Notable differences in interaction
   patterns are observed between the HC and patient groups, suggesting
   altered cell communication in disease conditions. Specifically, MMP9+
   monocytes and S100A12+ neutrophils showed increased interactions,
   potentially contributing to enhanced inflammatory responses and
   impaired immune function. These findings highlight the importance of
   considering cell–cell interactions when investigating the
   pathophysiology of jellyfish stings. Additionally, several interactions
   were upregulated or downregulated in the patient, suggesting a shift in
   immune regulatory mechanisms ([100]Figure 4A).

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   Cell–cell communication analysis in PBMC samples. (A) Heatmap showing
   the interaction strength between cell types in healthy controls (HCs,
   left) and the patient (right). The color intensity represents the
   interaction strength. (B) Dot plot summarizing the key interaction
   pairs in healthy controls (HCs). The dot size represents the
   interaction strength, and the color indicates the significance. (C) Dot
   plot summarizing the key interaction pairs in the patient. The dot size
   represents the interaction strength, and the color indicates the
   significance.

2.6.2. Dot Plot Analysis of Key Interaction Pairs in Healthy Controls

   To further characterize these interactions, we generated a dot plot to
   highlight key ligand–receptor pairs that are highly active in healthy
   controls ([103]Figure 4B). This analysis identified specific
   interactions predominantly involved in immune regulation and
   homeostasis, which underscores the importance of maintaining immune
   homeostasis in healthy individuals.

2.6.3. Dot Plot Analysis of Key Interaction Pairs in Patient Samples

   Similarly, a dot plot was generated to highlight key interaction pairs
   in the patient sample ([104]Figure 4C). This analysis revealed
   significant alterations in cell–cell communication in patient samples,
   with certain interactions being upregulated or downregulated compared
   to healthy controls.

   These findings indicate that altered cell–cell communication may play a
   crucial role in the pathogenesis of systemic inflammation following
   jellyfish envenomation.

3. Discussion

   Our study presents the first comprehensive single-cell transcriptomic
   atlas of the human immune response following severe jellyfish
   envenomation. By leveraging single-cell RNA sequencing (scRNA-seq), we
   achieved unprecedented resolution in characterizing immune cell
   heterogeneity, uncovering profound alterations in cellular composition,
   functional states, and intercellular communication networks in a
   patient with systemic toxicity. While bulk RNA sequencing has been
   instrumental in profiling immune responses in envenomation and
   inflammatory diseases, it inherently masks cellular diversity and
   transitional states [[105]39]. In contrast, scRNA-seq enables the
   dissection of rare subsets, differentiation trajectories, and
   cell-type-specific signaling pathways [[106]40], making it uniquely
   suited to unravel the complexity of venom-induced immune
   dysregulation—a domain previously unexplored in human marine
   envenomation.

   Although scRNA-seq has advanced our understanding of immune dynamics in
   conditions such as sepsis [[107]41], its application to toxin-mediated
   injuries remains limited. Our work bridges this critical gap by
   systematically analyzing PBMCs from a severe jellyfish sting patient
   and three healthy controls, revealing key immunological perturbations
   that underlie the pathophysiology of envenomation.

3.1. Immune Cell Composition Remodeling and Pro-Inflammatory Monocyte
Expansion

   One of the most striking findings was a dramatic shift in immune cell
   composition: CD14+ monocytes expanded to constitute 81.86% of total
   PBMCs in the patient, while T cells, B cells, and classical neutrophil
   populations were markedly reduced. This profound reprogramming suggests
   that jellyfish toxins drive a systemic skewing of hematopoiesis or
   peripheral mobilization toward myeloid lineage dominance, consistent
   with emergency myelopoiesis observed in severe infection or trauma
   [[108]42,[109]43,[110]44,[111]45,[112]46,[113]47].

   The expansion of MMP9+ monocytes—a subset characterized by high
   expression of IL1R2, IFITM1, and MMP9—points to their central role in
   amplifying inflammation and tissue remodeling. MMP9 encodes a matrix
   metalloproteinase capable of degrading extracellular matrix components,
   facilitating leukocyte infiltration into injured tissues [[114]48]. Its
   co-expression with IL1R2, a decoy receptor that modulates IL-1
   signaling, suggests a dual regulatory mechanism, promoting early
   inflammatory recruitment while potentially dampening excessive
   IL-1-driven pathology. This finding resolves longstanding uncertainty
   about the cellular sources of proteolytic activity in envenomation
   [[115]49,[116]50], implicating monocytes—not just neutrophils—as key
   mediators of tissue damage.

   In contrast, healthy controls exhibited a dominance of CD74+ monocytes,
   which is enriched in genes involved in oxidative phosphorylation and
   antigen processing (LYZ, CD74, HLA class II), reflecting a homeostatic,
   immunoregulatory phenotype. The replacement of this population by
   pro-inflammatory MMP9+ and RETN+ monocytes in the patient underscores a
   fundamental shift from immune surveillance to inflammatory activation.

3.2. Neutrophil Subsets and Impaired Maturation Trajectory

   Subclustering revealed distinct neutrophil subpopulations with
   divergent functional profiles. Healthy individuals showed higher
   proportions of IFITM3+ and PPBP+ neutrophils, associated with antiviral
   defense, granulocyte motility, and vascular repair. Conversely, the
   patient exhibited a striking accumulation of LTF+ and S100A12+
   neutrophils.

   Notably, pseudotime analysis suggests that LTF+ neutrophils represent
   an immature state, S100A12+ cells an intermediate stage, and IFITM3+
   cells a mature, terminally differentiated phenotype. The predominance
   of immature and intermediate subsets in the patient implies that
   jellyfish toxins may inhibit neutrophil maturation, possibly through
   direct cytotoxic effects or disruption of differentiation signals in
   the bone marrow or circulation [[117]51,[118]52]. Immature neutrophils
   are known to exhibit reduced phagocytic capacity and microbial killing
   efficiency [[119]53,[120]54], which may contribute to secondary
   infection risk and prolonged inflammation.

   Moreover, S100A12+ neutrophils—known to activate TLR4/NF-κB signaling
   via RAGE binding [[121]55,[122]56]—were not only expanded but also
   functionally primed for leukocyte adhesion, myeloid proliferation, and
   cytokine production. Their enrichment suggests a role in inflammatory
   amplification and immune cell crosstalk, further destabilizing immune
   homeostasis.

3.3. Altered Cell–Cell Communication and Inflammatory Synergy

   Cell communication analysis revealed a rewired interaction network in
   the patient. Most notably, enhanced ligand–receptor interactions
   between MMP9+ monocytes and S100A12+ neutrophils suggest a synergistic
   pro-inflammatory circuit. These cells co-express molecules involved in
   leukocyte migration (CCL3/4–CCR1/5), cytokine signaling (IL1–IL1R2),
   and extracellular matrix remodeling (MMP9–TIMP2), forming a positive
   feedback loop that sustains inflammation.

   For example, MMP9 facilitates tissue penetration by degrading basement
   membranes, while S100A12 acts as a chemoattractant and adhesion
   promoter, collectively enhancing immune cell recruitment and
   activation. This crosstalk likely contributes to the systemic
   inflammatory response syndrome (SIRS)-like phenotype observed in severe
   stings.

   Additionally, interactions between neutrophils and dendritic cells were
   significantly upregulated in the patient, potentially promoting
   aberrant antigen presentation and adaptive immune activation. Such
   dysregulated crosstalk may underlie the persistent inflammation and
   delayed recovery seen in some envenomation cases.

3.4. Therapeutic Implications and Future Directions

   Our findings highlight several promising therapeutic targets. First,
   MMP9 inhibition could mitigate tissue damage and leukocyte
   infiltration, with existing inhibitors (e.g., doxycycline, marimastat)
   offering potential for repurposing [[123]57,[124]58]. Second, targeting
   the IL-1 pathway, particularly via blockade of IL-1R2 signaling or
   S100A12–RAGE interaction, may help break the inflammatory cycle
   [[125]59,[126]60]. Third, interventions that promote neutrophil
   maturation—such as G-CSF or modulation of key transcription factors
   (e.g., C/EBPε, PU.1)—could restore immune competence and accelerate
   recovery [[127]61,[128]62].

   Furthermore, disrupting the MMP9+ monocyte–S100A12+ neutrophil axis may
   represent a novel strategy to dampen inflammation without broad
   immunosuppression. Validating these targets in larger cohorts and in
   vivo models will be essential for translation.

3.5. Limitations and Outlook

   While this study provides deep mechanistic insights, it is based on a
   single patient. Future work should expand to multi-patient cohorts
   across different envenomation severities and time points to capture
   dynamic immune trajectories. Additionally, integrating proteomic and
   functional assays will be crucial to validate transcriptomic findings
   and assess cellular behavior ex vivo.

   Nonetheless, our data establish a foundational framework for
   understanding how marine toxins reprogram the immune system at
   single-cell resolution. The identification of specific pathogenic
   subsets and their communication networks opens new avenues for
   precision immunomodulation in envenomation and other toxin-mediated
   inflammatory conditions.

4. Materials and Methods

4.1. Human Sample Collection

   Blood was obtained from a jellyfish-sting-induced CF patient and three
   healthy human donors. The stings were caused by a Sand Sea Jellyfish
   (Rhopilema esculentum). Multiple lower limb regions (thighs and calves)
   were exposed. The incident occurred while swimming in coastal waters.
   The study was conducted in accordance with the Declaration of Helsinki
   and approved by the Institutional Ethics Committees of the PLA Naval
   Medical Center (Clinical Trial No. AF-HEC-054) and Ocean University of
   China (OUC-HM-2025-042). Written informed consent was obtained from all
   participants prior to sample collection.

4.2. Isolation of PBMCs and Preparation of Single-Cell Suspensions

   PBMCs were isolated by density gradient centrifugation using
   Ficoll-Paque Plus medium (GE Healthcare) (Cytiva, Marlborough, MA, USA)
   and washed with Ca/Mg-free PBS. To remove the red blood cells, 2 mL red
   blood cell lysis buffer was added at 25 °C for 10 min. The solution was
   then centrifuged at 500× g for 5 min and suspended in PBS. The blood
   samples were centrifuged at 400× g for 5 min at 4 °C, and the
   supernatant was discarded. After the red blood cells had been removed,
   PBMCs were isolated by centrifugation at 400× g for 10 min at 4 °C. The
   supernatant was discarded and the PBMCs were resuspended by
   phosphate-buffered saline to obtain a single-cell suspension. Cells
   were processed immediately for 10× Genomics single-cell capture and
   cDNA synthesis. Libraries were sequenced on an Illumina NovaSeq 6000
   platform following quality control assessment.

4.3. Processing scRNA-Seq Data

   Single-cell suspensions were processed using the 10× Genomics Chromium
   platform. Libraries were prepared according to the manufacturer’s
   protocol and sequenced on an Illumina NovaSeq 6000 platform. Raw
   sequencing data were aligned to the human reference genome using Cell
   Ranger (v7.0.1) to obtain the Unique Molecular Identifier (UMI) matrix,
   which was further imported into R (v4.2.2) and processed with the
   Seurat package (v4.3.2). Cells with a detected gene number of <200 or
   >5000 or a high mitochondrial transcript ratio (>10%) were excluded.
   The top 2000 highly variable genes were extracted to perform principal
   component analysis (PCA), and the top 30 principal components (PCs)
   were used for cluster analysis. After normalization and scaling, the
   batch effect between patient samples was then removed using harmony.
   The batch-corrected matrix was used for further analysis and
   visualization. Cell types were annotated by the SingleR package
   (v1.2.4) and then checked manually.

4.4. Identification of Marker Genes

   To identify differentially expressed marker genes for each cell type,
   the FindAllMarkers function in Seurat was used under the default
   parameters. Marker genes were selected as those with adjusted p values
   less than 0.05, an average logFC larger than 0.25, and a percentage of
   cells with expression higher than 0.25. Gene Ontology (GO), Kyoto
   Encyclopedia of Genes and Genomes (KEGG), and GSEA were performed with
   the R package clusterProfiler v3.18.0, using all detected genes from
   the entire scRNA-seq library as background. Terms were enriched with
   the nominal p value < 0.05 and false discovery rate (q value) < 0.05.

4.5. Cell Type Annotation

   The cell type identity of each cluster was determined according to the
   expression of canonical markers found in the DEGs using well-known
   cell-type-specific markers. Dot plots displaying the expression of
   markers used to identify each cell type were generated by Seurat v3.1.2
   DotPlot.

4.6. Pathway Enrichment Analysis

   To investigate the potential functions of DEGs, Gene Ontology (GO) and
   Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used with
   the “clusterProfiler” R package 3.16.1. Pathways with a p_adj value of
   less than 0.05 were considered significantly enriched. Gene Ontology
   gene sets including molecular function (MF), biological process (BP),
   and cellular component (CC) categories were used as reference.

4.7. Quality Control, Dimension Reduction, and Clustering

   Seurat v 3.1.2 was used for quality control, dimensionality reduction,
   and clustering. For each sample dataset, we filtered the expression
   matrix by the following criteria: (1) cells with gene counts of less
   than 200 or with a gene count in the top 2% were excluded; (2) cells
   with a UMI count in the top 2% were excluded; (3) cells with a
   mitochondrial content in the top 10% were excluded; (4) genes expressed
   in less than 5 cells were excluded. After filtering, 93,143 cells were
   retained for the downstream analyses, with on average 1524 genes and
   2875 UMIs per cell. The gene expression matrix was normalized and
   scaled using the functions NormalizeData and ScaleData. The top 2000
   variable genes were selected by FindVariableFeatures for PCA. Cells
   were separated into 18 clusters by FindClusters, using the top 20
   principal components and a resolution parameter of 0.1. Cell clusters
   were visualized using UMAP with the Seurat functions RunTSNE and
   RunUMAP.

4.8. Differentially Expressed Gene (DEG) Analysis (Seurat)

   To identify differentially expressed genes (DEGs), we used the Seurat
   FindMarkers function based on the Wilcoxon rank sum test with the
   default parameters and selected the genes expressed in more than 10% of
   the cells in both of the compared groups of cells and with an average
   log (Fold Change) value greater than 0.25, identifying these as DEGs.
   The adjusted p value was calculated using the Bonferroni correction and
   the value 0.05 was used as the criterion to evaluate the statistical
   significance.

4.9. UCell Gene Set Scoring

   Gene set activity scores were calculated using the UCell R package
   (v1.1.0), a non-parametric, rank-based method robust to batch effects
   and library size variation. For each predefined gene signature, UCell
   computes a Mann–Whitney U statistic based on the relative expression
   ranks of the genes within each cell. This approach is particularly
   suitable for large-scale, multi-sample scRNA-seq datasets. Signature
   scores were used to infer activation states of biological pathways
   across cell types.

4.10. Cell–Cell Communication Analysis

   We used cellphoneDB (v.4.0.0) to infer cell–cell interactions of
   selected ligand–receptor pairs between cells. The potential interaction
   strength between two cell subsets was predicted based on the expression
   of ligand–receptor pairs. The enriched ligand–receptor interactions
   between two cell subsets were calculated based on a permutation test.
   We extracted significant ligand–receptor pairs with a p value < 0.01.
   Key signaling circuits (e.g., inflammatory, chemotactic) were
   visualized using chord plots and hierarchical clustering.

5. Conclusions

   In conclusion, our single-cell transcriptomic analysis provides a
   high-resolution portrait of the immune landscape in peripheral blood
   following jellyfish envenomation. We reveal substantial alterations in
   the abundance and activation states of monocytes and neutrophils,
   underscoring the potent immunomodulatory impact of jellyfish venom
   components. These findings extend the current understanding of
   venom-induced inflammatory responses and highlight key cellular
   pathways that may contribute to tissue injury and systemic symptoms. By
   identifying dysregulated immune mechanisms at single-cell resolution,
   this study not only advances the pathophysiological knowledge of
   jellyfish stings but also opens new avenues for the development of
   targeted therapeutics—potentially informing the design of anti-venom
   strategies or host-directed interventions. As marine toxins continue to
   serve as rich sources of bioactive molecules, our work reinforces the
   value of integrating single-cell technologies into marine drug and
   toxin research.

Abbreviations

   The following abbreviations are used in this manuscript:
   scRNA-seq Single-cell RNA sequencing
   PBMC      Peripheral blood mononuclear cells
   CF        Cardiac function
   UMAP      Uniform Manifold Approximation and Projection
   NK        Natural killer
   DC        Dendritic cells
   GSEA      Gene set enrichment analysis
   GO        Gene Ontology
   KEGG      Kyoto Encyclopedia of Genes and Genomes
   HC        Healthy control
   S100A12   S100 calcium-binding protein A12
   MMP9      Matrix metalloproteinase
   DEGs      Differentially expressed genes
   [129]Open in a new tab

Author Contributions

   Conceptualization, Z.Q.; methodology, Z.Q. and C.W.; project
   administration, R.Y.; software, Z.H. and P.Q.; supervision, R.Y.;
   writing—original draft, Z.Q. and N.L.; writing—review and editing, S.W.
   and R.Y. All authors have read and agreed to the published version of
   the manuscript.

Institutional Review Board Statement

   Blood was obtained from a jellyfish-sting-induced CF patient and three
   healthy human donors. The study was conducted in accordance with the
   Declaration of Helsinki, and approved by the institutional ethics
   committee of PLA Naval Medical Center and Ocean University of China
   (AF-HEC-054 approved on 11 October 2023 and OUC-HM-2025-042 approved on
   16 July 2025, respectively).

Informed Consent Statement

   Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

   The original contributions presented in this study are included in the
   article. Further inquiries can be directed to the corresponding
   authors.

Conflicts of Interest

   There are no conflicts of interest.

Funding Statement

   This study was sponsored by the National Natural Science Foundation of
   China (32271177 to Z.Q.) and Postdoctoral Fellowship Program of CPSF
   under Grant Number GZB20250733, Fundamental Research Funds for the
   Central Universities (NO. 202441004) and Shandong Natural Science Fund
   (Grant No. ZR2023MH210).

Footnotes

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References