Single-cell sequencing reveals that neutrophils mediate the inflammatory response in gestational diabetes

Single-cell sequencing reveals that neutrophils mediate the inflammatory response in gestational diabetes | 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 Research Article Single-cell sequencing reveals that neutrophils mediate the inflammatory response in gestational diabetes Shi Yin, Na Zhuo, Qing Duan, Jinyu Song, Chunrui Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7589196/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Journal of Molecular Histology → Version 1 posted 12 You are reading this latest preprint version Abstract Gestational diabetes mellitus (GDM) is a pregnancy-associated metabolic disorder characterized by insulin resistance and hyperglycemia, posing significant risks to maternal-fetal health. While placental inflammation contributes to GDM pathology, its cellular mechanisms remain incompletely understood. Leveraging public single-cell RNA sequencing (scRNA-seq) data, we constructed a cellular atlas of GDM and control placental tissues, identifying 14 transcriptionally distinct populations. Neutrophil_1 emerged as the dominant pro-inflammatory subset, exhibiting amplified interactions with macrophages and T cells that exacerbate inflammatory responses. Pseudotime trajectory analysis revealed Neutrophil_2 as a precursor state differentiating toward the Neutrophil_1 lineage through transcriptionally regulated programming. Critically, Arginine catabolism and Nicotinate/nicotinamide metabolism were found to modulate Neutrophil_1's pro-inflammatory functions. Our findings implicate pathogenic neutrophil subsets in GDM progression and nominate arginase and NAMPT inhibition as potential therapeutic strategies for mitigating neutrophil-mediated placental inflammation. Gestational diabetes mellitus Single-cell RNA sequencing Neutrophil heterogeneity Placental inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Gestational Diabetes Mellitus (GDM), defined as glucose intolerance first recognized during pregnancy, is characterized by maternal hyperglycemia and exhibits a global prevalence ranging from 1% to 14%, with a significant upward trend(Xie, He et al. 2025 ). Although GDM often resolves postpartum and may present with subtle clinical manifestations, it is strongly associated with serious maternal-fetal perinatal complications, including preeclampsia, macrosomia (and related birth injuries), neonatal hypoglycemia, and postpartum hemorrhage(Yin, Zhang et al. 2024 ). Notably, the maternal hyperglycemic intrauterine environment not only directly impairs fetal development but also mediates adverse pregnancy outcomes by disrupting placental morphogenesis and functional differentiation(Gauster, Desoye et al. 2012 ). Therefore, a deeper understanding of GDM pathogenesis is critical for developing targeted therapeutic strategies. The placenta, as the core organ at the maternal-fetal interface, exhibits high dynamism and cellular heterogeneity. Beyond trophoblast cells, its immune microenvironment is enriched with diverse immune populations—including dendritic cells, macrophages, natural killer cells, T cells, neutrophils, and monocytes—collectively maintaining placental immune homeostasis(Yang, Guo et al. 2021 ). Recent research implicates chronic low-grade inflammation as a key pathological basis for GDM development, manifested by increased trophoblast apoptosis, neutrophil infiltration, impaired trophoblast differentiation and invasiveness, and elevated pro-inflammatory cytokine production(Pantham, Aye et al. 2015 , Wang, Li et al. 2025 ). Neutrophils, the most abundant innate immune effector cells in peripheral blood, play a central role in inflammatory responses(Raftopoulou, Valadez-Cosmes et al. 2022 ). During normal pregnancy, neutrophils participate in multiple critical processes, from embryo implantation and placentation to tissue remodeling and parturition initiation(Aslanian-Kalkhoran, Mehdizadeh et al. 2024 ). Functional plasticity is a hallmark of leukocytes; under specific microenvironmental signals, neutrophils can polarize into distinct functional subsets—analogous to macrophage M1/M2 polarization—exhibiting divergent or even opposing functions(Yang and Zhang 2017 , Guerriero 2018 ). For instance, in the tumor microenvironment, anti-tumor N1 neutrophils demonstrate enhanced tumor cell cytotoxicity and immune activation, while pro-tumor N2 neutrophils promote angiogenesis and metastasis(Raftopoulou, Valadez-Cosmes et al. 2022 ). Within the healthy placental microenvironment, neutrophils typically maintain maternal-fetal tolerance by promoting activated T-cell apoptosis and polarizing towards an immunoregulatory phenotype(Oravecz, Romero et al. 2022 ). However, this balance is disrupted in GDM. Studies indicate that GDM patients exhibit significantly elevated peripheral white blood cell, neutrophil, and monocyte counts, as well as an increased neutrophil-to-lymphocyte ratio as early as the first trimester, correlating positively with disease risk(Ye, Wang et al. 2023 ). Placental tissue also exhibits abnormal, persistent pro-inflammatory activation of monocytes and neutrophils(Yin, Zhang et al. 2024 ). Critically, recent studies reveal aberrant deposition of Neutrophil Extracellular Traps (NETs) in the placental tissue of GDM patients. NETs are web-like structures composed of decondensed chromatin DNA decorated with antimicrobial proteins (e.g., myeloperoxidase and neutrophil elastase), functioning physiologically to trap and kill pathogens(Sorensen and Borregaard 2016 ). However, in the hyperglycemic environment of GDM, high glucose is a potent trigger of NETs, primarily through activation of the NADPH oxidase complex leading to reactive oxygen species (ROS) burst and upregulation of peptidylarginine deiminase 4 (PAD4)-mediated histone citrullination(Takei, Araki et al. 1996 , Berthelot, Le Goff et al. 2017 , Vokalova, van Breda et al. 2018 ). Excessive NETs production or impaired clearance results in placental accumulation, linked to placental hypoperfusion, trophoblast damage, and dysfunction. Nevertheless, the precise cellular and molecular networks driving abnormal neutrophil activation, subset polarization imbalance, and pathological NETs release in the GDM placenta—particularly the intrinsic metabolic regulatory mechanisms—remain incompletely elucidated. Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented powerful tool to dissect the pathological remodeling of the placenta—especially its complex immune microenvironment—in GDM at single-cell resolution. It enables precise identification of diverse cell types, characterization of their unique transcriptomic signatures, functional states, intercellular communication networks, and metabolic activities, thereby uncovering pathogenic mechanisms obscured by traditional bulk sequencing. Therefore, this study leverages publicly available scRNA-seq datasets to systematically investigate neutrophils—key mediators of placental immune dysregulation in GDM. We focus on deciphering their heterogeneity, pathogenic mechanisms, and metabolic reprogramming to elucidate neutrophil-driven contributions to GDM pathophysiology. 2. Method and Material 2.1. Patients and placenta preparation Placental tissues were obtained from 12 pregnant women admitted to the Department of Obstetrics, Tianjin Medical University Second Hospital between January 2025 and April 2025. Six women were diagnosed with GDM and six served as healthy controls. Participants were randomly selected. Prior to delivery, all participants underwent transabdominal color Doppler ultrasonography (GE Healthcare, LOGIQ V2). Examinations were performed with the pulse repetition frequency set at 2.8 kHz. Scans were carefully conducted to ensure complete visualization of the placenta from the chorionic plate to the basal plate. Placental thickness was measured while the participant was in a quiet, resting state without respiratory movement. Following placental expulsion, the gross morphology was examined. A representative placental tissue block, approximately 1 cm × 1 cm × 1 cm in size, was excised. The tissue sample was thoroughly rinsed with sterile water for injection to remove residual blood. Subsequently, the sample was immersed in 4% phosphate-buffered paraformaldehyde (P0099, Beyotime, Shanghai, China) for fixation and then routinely processed for paraffin embedding. 2.2 Hematoxylin and eosin staining Placental tissue samples from both GDM and control subjects were fixed in 4% phosphate-buffered paraformaldehyde, processed for paraffin embedding, and sectioned at 4 µm thickness. Sections were then stained with hematoxylin and eosin (H&E; BA4025, BaSO, Zhuhai, China) using standard protocols. Morphological features of the target tissues were examined and imaged using light microscopy. 2.3 Quality control, integration, and cell annotation For the published dataset GSE173193, cells were retained for downstream analysis only after stringent quality control. We excluded cells expressing fewer than 200 genes or exhibiting > 10% mitochondrial gene content. Potential doublets were identified and removed using DoubletFinder (v2.0.3). Batch effects across sequencing runs were corrected using the Harmony algorithm. Following removal of genes overlapping genomic blacklist regions, the top 3,000 highly variable genes were selected via the FindVariableFeatures function in the Seurat package. Cell clustering was performed using Seurat (v5) with default parameters. Cell type annotation was determined by canonical marker gene expression: Neutrophil subsets were identified by FCGR3B/CXCL8/TREM1 (Neutrophil_1) and CAMP/LYZ/S100A8 (Neutrophil_2); Hofbauer cells by MRC1/CD163/CD14; Stromal cells by FN1/LAMA3/COL5A1; Syncytiotrophoblast (STB) subsets by KRT7/ERVW-1/SPINT1 (STB1), ERVW-1/CDH1/SLC2A1 (STB2), and CYP19A1/CGA/ERVFRD-1 (STB3); T cells by CD3D/IL7R/CCR7; Macrophages by FCN1/HLA-DRA/CD86; Trophoblast progenitors by BIRC5/CDK1/TOP2A; Endothelial cells by VWF/CLDN5/CD34; Mast cells by CPA3/FCER1A/CLC; Hematopoietic stem/progenitor cells (HSPCs) by KIT/CD34/MYB; and B cells by IGHM/CD79A/MS4A1. 2.4 Single-cell metabolomics analysis Metabolic activity was computationally inferred from scRNA-seq data using the scMetabolism R package (v0.2.0). Pathway enrichment scores for KEGG metabolic pathways (release 2023.2) were quantified through the VISION algorithm, which employs a signature projection approach on non-imputed expression data. This method weights genes by: (1) expression magnitude (log-normalized counts), (2) pathway topology connectivity (KEGG edge information), and (3) transcriptional coherence (local neighborhood variance). Null distributions were established from 1,000 permutations of randomized gene sets. Cells were subsequently classified into functional metabolic subtypes by hierarchical clustering based on pathway activity z-scores > 1.5 standard deviations from the cohort mean. 2.5 Trajectory analysis Developmental relationships between transcriptionally distinct neutrophil subtypes (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were resolved using Monocle3 (v1.3.4). The top 1,500 highly variable genes identified by Seurat's FindVariableFeatures (v2.3) within these clusters served as ordering genes for pseudotemporal reconstruction. Trajectories were learned through the DDRTree algorithm with default parameters (dimensions = 2; max iterations = 20), and branch significance was validated by permutation testing (n = 500). Pseudotime alignments were mapped onto Uniform Manifold Approximation and Projection (UMAP) embeddings (Seurat v5-derived) for spatial visualization. Branch-dependent expression patterns were illustrated through heatmaps of core neutrophil markers (FCGR3B, CXCL8, CAMP, LYZ, S100A8), with genes clustered by k-means partitioning (k = 6) along pseudotime. 2.6 Pseudotemporal Trajectory and Co-Expression Dynamics Developmental trajectories of neutrophil subtypes (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were reconstructed using Monocle3 (v1.3.4) with the top 1,500 HVGs (Seurat FindVariableFeatures, v2.3) as ordering genes. DDRTree-derived pseudotime (parameters: dimensions = 2; max_iter = 20; branch significance validated by permutation testing, n = 500) was mapped onto Seurat v5 UMAP embeddings. 2.7 Transcription Factor Regulatory Analysis Transcr iption factor (TF) netwo rks underlying neutrophil subtype identities (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were resolved using SCENIC (v1.3.2). GRNBoost2-inferred co-expression modules were refined via cis-regulatory motif enrichment (hg38 mc9nr database; AUC > 0.005, FDR 1; adj. p 0.6) and enriched in neutrophil effector pathways (KEGG FDR < 0.001). Differential TF activity identified CEBPD/STAT3/STAT1 (Neutrophil_1) and E2F4/MyC (Neutrophil_2) as master regulators. Regulons underwent Gene Ontology (GO) enrichment (clusterProfiler v4.0; org.Hs.eg.db v3.17) with semantic simplification (GOSemSim, cutoff = 0.7). 2.8 Statistical analysis Statistical analyses were conducted in R v4.2.1. Two-sided tests with α = 0.05 significance threshold were applied throughout. Parametric tests (t-test/ANOVA) required validation of normality (Shapiro-Wilk p > 0.1) and homoscedasticity (Levene's p > 0.05); otherwise, non-parametric alternatives (Wilcoxon/Kruskal-Wallis) were used. Multiple testing correction employed Benjamini-Hochberg FDR for hypothesis sets (n ≥ 10). Effect sizes were reported (Cohen's d for pairwise comparisons; η² for ANOVA). Heatmaps were generated via pheatmap v1.0.12 with row-scaled values and ward.D2 hierarchical clustering. 3. Results 3.1 Structural and pathological features of the placenta in GDM Prenatal ultrasound examination revealed significant placental abnormalities in GDM pregnancies compared to controls. GDM placentae exhibited increased volume, greater thickness, accelerated maturation, and focal calcification (Fig. 1 A). Quantitative analysis confirmed significantly increased placental thickness in the GDM cohort (Fig. 1 B, P < 0.05). Postpartum macroscopic evaluation of placentae corroborated ultrasound findings, demonstrating enlarged dimensions, thickened parenchyma, and visible calcific deposits in GDM specimens (Fig. 1 C). Histopathological analysis via H&E staining identified hallmark features of placental insufficiency in GDM, including, prominent syncytial knot formation, widespread calcification, stromal fibrosis. Notably, substantial inflammatory cell infiltration was observed within GDM placental tissues (Fig. 1 D). Collectively, these structural and histological alterations demonstrate placental dysfunction compounded by inflammatory pathology in GDM, indicating compromised fetal support. 3.2 scRNA-seq reveals cellular heterogeneity in GDM-affected placenta To investigate the pathological mechanisms underlying GDM in placental tissue, we collected scRNA-seq data of GDM and matched control placentas. Following stringent quality control, 22,233 high-quality cells were retained for downstream analysis. UMAP dimensionality reduction and clustering identified 14 transcriptionally distinct cellular populations, including: Neutrophil_1, Neutrophil_2, Hofbauer cells (M2-polarized macrophages), STBs, T cells, Macrophages, Trophoblast progenitor cells, Endothelial cells, Mast cells, HSPCs, and B cells (Fig. 2 A-C). Immune profiling revealed neutrophils, Hofbauer cells, and macrophages as dominant placental leukocytes. Notably, the GDM group exhibited, increased proportions of Neutrophil_1 (GDM vs control) and macrophages cell (Fig. 2 D-E). Neutrophil_1 showed elevated expression of pro-inflammatory mediators (IL1B, CXCL8), while macrophages demonstrated antigen-presenting function (HLA-DRA, HLA-DRB1, and CD86). Hofbauer cells displayed an immunosuppressive M2 phenotype (CD163 and CD206) despite reduced frequency in GDM (Fig. 2 C). Collectively, these alterations compose a disordered immune microenvironment characterized by pro-inflammatory neutrophil/macrophage expansion and diminished regulatory cell populations, potentially driving GDM pathogenesis. 3.3 Neutrophil_1 is characterized as a pro-inflammatory subtype Prior studies implicate neutrophils in GDM-associated inflammatory pathology. Building on this evidence, we performed focused characterization of placental neutrophil heterogeneity through subset analysis of our scRNA-seq data. Neutrophil_1 demonstrated significantly increased abundance in GDM placentas (Fig. 3 A-B). Differential expression analysis revealed Neutrophil_1 specifically upregulated pro-inflammatory mediators (IL1B, TREM1, AQP9) compared to Neutrophil_1 (Fig. 3 C). Functional annotation via Metascape identified Neutrophil_1 enrichment in inflammatory response and chemotaxis, Conversely, Neutrophil_2 exhibited enrichment in negative regulation of leukocyte activation and neutrophil degranulation(Fig. 3 D). Similarly, GO enrichment results also indicated neutrophil_1 were enriched in cytokines meditated signaling pathway, immune repsonse activating, and leukocyte chemotaxis, while neutrophil_2 were enriched in cytoplasmic transilation, aerobic respiration, and oxidative phosphorylation (Fig. 3 E). To confirm this, we also performed Single-sample Gene Set Enrichment Analysis(ssGSEA) pathway enrichment analysis. The results uncovered that neutrophil_1 were enriched in inflammatory response, IL-6 JAK-STAT3 signaling and TNF-α and IFN-γ response, neutrophil_2 were enriched in oxidative phosphorylation, G2M checkpoint and E2F target (Fig. 3 F). Taken together, these multi-modal analyses establish Neutrophil_1 as a pro-inflammatory subtype characterized by chemotactic and cytokine-signaling capabilities, suggesting its pathogenic role in GDM placental inflammation. 3.4 Intercellular signaling between pro-inflammatory neutrophils and macrophages To investigate how the pro-inflammatory Neutrophil_1 subset drives inflammatory pathology in GDM, we analyzed cellular interactions using CellChat. This revealed dominant signaling between Neutrophil_1 and macrophage populations - particularly immunosuppressive Hofbauer cells (Fig. 4 A-B). Pathway analysis identified four key communication axes, including Annexin pathway (primarily Neutrophil_1-Macrophages/T cells), IL-1 signaling (Neutrophil_1-Macrophages), Resistin pathway (Neutrophil_1-Macrophages), SPP1-CD44 axis (Hofbauer cells -Macrophages) (Fig. 4 C-F). Notably, the Annexin pathway (known to promote inflammation) mediated Neutrophil_1 interactions with both macrophages and T cells (Fig. 4 C). Ligand-receptor analysis further demonstrated that Hofbauer cells suppress macrophage activation via SPP1-CD44 signaling, Neutrophil_1 drives pro-inflammatory macrophage polarization through ANXA1-FPR1 binding (Fig. 4 G). These results uncover a pathogenic signaling network where Neutrophil_1 amplifies inflammation while Hofbauer cells attempt compensatory immunosuppression, revealing potential therapeutic targets for GDM. 3.5 Neutrophil_2 represents a precursor state committed to neutrophil_1 lineage Neutrophil_1 cells demonstrated pro-inflammatory properties and significantly influenced inflammatory responses in GDM. To elucidate why Neutrophil_1 frequency was elevated in GDM compared to controls, we investigated the developmental trajectory of Neutrophil_1 and Neutrophil_2 subpopulations. We identified Neutrophil_1 cells by high expression of FCGR3B and CXCL8, and Neutrophil_2 cells by markers CAMP and S100A8. Pseudotime trajectory analysis using Monocle3 revealed that Neutrophil_2 functions as a precursor state that differentiates into Neutrophil_1 subsets (Fig. 5 A-B). To characterize transcriptional dynamics during this differentiation, we performed Gene Co-Expression Network Analysis. This identified 19 gene modules dynamically regulated along the trajectory, with Modules 3, 4, and 5 exhibiting significant expression changes during the Neutrophil_2 to Neutrophil_1 transition (Fig. 5 C). GO enrichment analysis showed Module 3 was enriched for leukocyte migration in inflammatory responses, Module 4 for NAD biosynthesis via nicotinamide riboside salvage pathway, and Module 5 for iron ion transport (Fig. 5 D-F), indicating fundamental shifts in biological functions during neutrophil differentiation. We further analyzed TF regulatory networks, revealing distinct profiles: Neutrophil_1 showed enriched activity of pro-inflammatory TFs (STAT1, STAT3, NFKB1, CEBPB, FOS, RELA), while Neutrophil_2 exhibited elevated stem/progenitor TFs (E2F4, MYC, GATA1, NR2C2, TFDP1) that regulate differentiation (Fig. 5 G). TF-associated GO terms confirmed these identities, with Neutrophil_1 terms including lymphocyte differentiation and myeloid cell differentiation regulation, whereas Neutrophil_2 terms involved ossification, G1/S cell cycle transition, mesenchymal cell proliferation, and epithelial tube branching morphogenesis (Fig. 5 H-I). Collectively, these results demonstrate Neutrophil_2 serves as a progenitor population that differentiates into the pro-inflammatory Neutrophil_1 subset, explaining its increased frequency in GDM. 3.6 Arginine biosynthesis and Nicotinate/nicotinamide metabolism modulates neutrophil_1 pro-inflammatory function Metabolic profiling via scMetabolism revealed distinct functional specialization between neutrophil subsets. Neutrophil_1 demonstrated significant enrichment in nitrogen metabolism, nicotinate/nicotinamide metabolism, and arginine biosynthesis (KEGG, Fig. 6 A). This metabolic reprogramming sustains inflammatory effector functions through two key mechanisms: (1) Arginine serves as both structural component and signaling substrate for nitric oxide production, ROS generation, and NET formation (NETosis); (2) NAD + precursors (niacin/nicotinamide) modulate chemotaxis, phagocytosis, and oxidative burst via NAD + -dependent pathways. Conversely, Neutrophil_2 exhibited pentose phosphate pathway dominance, consistent with proliferative states. Differential gene expression further distinguished subsets: Neutrophil_1 overexpressed pro-inflammatory mediators (SAT1, CXCL8, G0S2, NAMPT), while Neutrophil_2 displayed elevated S100A8/A9 and immunosuppressive CAMP (Fig. 6 B). Collectively, arginine-catabolizing and NAD⁺-generating metabolism maintains Neutrophil_1's inflammatory phenotype in GDM pathogenesis. 4. Discussion The placenta, as the core organ at the maternal-fetal interface, relies critically on the effective functioning of trophoblast cells. Trophoblast dysfunction constitutes a key pathological basis for placental insufficiency(Jiao, Wang et al. 2023). In the placentas of mothers with GDM, we observed manifestations of placental insufficiency. Both prenatal ultrasound and postpartum placental examination revealed increased placental thickness, accelerated maturation, and focal calcification (Fig.1A-C). Pathological analysis of GDM placentas further identified features indicative of insufficiency, including syncytial knots, calcification, and stromal fibrosis (Fig.1D). These findings demonstrate the detrimental impact of the hyperglycemic environment on the placenta, the underlying mechanisms of which warrant further exploration. Pregnancy itself represents a unique physiological state characterized by the activation of systemic low-grade inflammation(Hahn, Giaglis et al. 2013). Within this context, the precise regulation of maternal-fetal immune tolerance within the placenta is essential for maintaining gestational homeostasis. However, the chronic hyperglycemia associated with GDM can severely disrupt this immune equilibrium. Studies have found significant elevations of IL-1β (200%) and TNF-α (58%) in GDM placental tissue(Marseille-Tremblay, Ethier-Chiasson et al. 2008). Consistent with this, H&E staining in our study revealed substantial inflammatory cell infiltration within the placenta (Fig.1D), collectively pointing towards a significant dysregulation of the local inflammatory microenvironment. To further investigate the mechanisms, we performed scRNA-seq on GDM and matched control placental tissues. This analysis successfully identified 14 major cell populations within the placental microenvironment, including neutrophils, Hofbauer cells (M2-polarized macrophages), STBs, T cells, macrophages, trophoblast progenitor cells, endothelial cells, mast cells, HSPCs, and B cells (Fig.2A-C). These cells collectively constitute the placental microenvironment. Neutrophils, as pioneers of innate immunity, colonize the decidua early in pregnancy and gradually increase during healthy gestation(Aslanian-Kalkhoran, Mehdizadeh et al. 2024). Traditionally recognized for their role in antimicrobial defense, recent evidence highlights their central function in sterile inflammation and tissue repair/injury(Papayannopoulos 2018). During acute injury repair, neutrophils play beneficial roles by clearing debris and releasing growth factors and pro-angiogenic molecules. However, in chronic inflammatory states like GDM, neutrophil function can undergo pathological transformation, exacerbating tissue stress and damage through the release of mediators such as matrix metalloproteinases and ROS(Lagnado, Leslie et al. 2021). Our scRNA-seq data identified two functionally distinct neutrophil subpopulations within the placenta: neutrophil_1 and neutrophil_2 (Fig.2D). Notably, the proportion of the neutrophil_1 subpopulation was significantly higher in GDM placental tissue compared to healthy controls (Fig.2E). This subpopulation was characterized by high expression of potent pro-inflammatory mediators (e.g., IL1B, TREM1, AQP9) (Fig.3C). GO enrichment analysis revealed that neutrophil_1 was highly enriched in terms related to "cytokine-mediated signaling pathway," "immune response-activating signal transduction," and "leukocyte chemotaxis" (Fig.3E). ssGSEA further confirmed that neutrophil_1 exhibited high activity in pro-inflammatory pathways, including "inflammatory response," "IL-6/JAK-STAT3 signaling," "TNF-α response," and "IFN-γ response" (Fig.3F). These data collectively indicate that neutrophil_1 is a key pro-inflammatory driver within the placental inflammatory microenvironment. Crucially, our cell-cell communication analysis revealed a complex pro-inflammatory signaling network between neutrophil_1 and other immune cells, particularly macrophages (Fig.4A-B). Neutrophil_1 likely interacts with macrophages through multiple pathways, such as Annexin signaling, IL-1 signal transduction, Resistin pathways, and the SPP1-CD44 axis (Fig.4D-F). Of particular significance, neutrophil_1 may drive macrophage polarization towards a pro-inflammatory (M1-like) phenotype via the ANXA1-FPR1 ligand-receptor pair (Fig.4C), thereby establishing a self-amplifying inflammatory loop that perpetuates local placental inflammation. In contrast, the neutrophil_2 subpopulation exhibited distinct functional properties. Its gene expression profile suggested potential involvement in the "negative regulation of leukocyte activation" and "negative regulation of neutrophil degranulation" (Fig.3D), implying a potential anti-inflammatory or homeostatic role. However, pseudotime trajectory analysis using Monocle3 revealed a critical and potentially pathologically significant finding: neutrophil_2 may represent a precursor state to neutrophil_1 (Fig.5). This suggests that within the pathological microenvironment of GDM, placental neutrophils undergo pathological differentiation or polarization from a relatively quiescent/regulatory state (neutrophil_2) towards a highly pro-inflammatory state (neutrophil_1), further amplifying the inflammatory response. Core mechanisms for neutrophil pathogen clearance include phagocytosis, degranulation, and the release of NETs, via a process termed NETosis (Shen, Lu et al. 2021). While NETs serve as an important innate immune barrier under physiological conditions, their excessive production or impaired clearance leads to aberrant accumulation in tissues, causing damage, organ dysfunction, uncontrolled inflammation, and coagulopathy(Ravindran, Khan et al. 2019). Previous research has unequivocally established hyperglycemia as a potent trigger for NETs release in diabetes. Our metabolic analysis (scMetabolism) revealed that positioned Arginine biosynthesis and NAD + precursors as topological hubs within the Neutrophil-1 metabolome(Fig.6). Critically, niacin/NAM-mediated control of NAD⁺/NADPH homeostasis paradoxically gates ROS dynamics, enabling either: (1) ROS constraint to prevent oxidative stress, or (2) ROS amplification to fuel microbicidal activity-a context-instructed switch governing redox adaptation (Minhas, Liu et al. 2019). Hyperglycemia-induced ROS activates PAD4, triggering histone H3 citrullination that decondenses chromatin to drive NETosis. Critically, citrullinated histones feed back to potentiate NADPH oxidase, establishing a self-amplifying ROS-PAD4 circuit that exacerbates NET release(Wong, Demers et al. 2015, Tatsiy and McDonald 2018). Locally, this cascade directly compromises placental integrity by inducing syncytiotrophoblast stress and barrier dysfunction-mechanistically linking aberrant NETosis to histopathological sequelae (syncytial knots, fibrosis) and functional insufficiency in GDM. Consequently, We speculate that Neutrophil-1 exhibits sustained intracellular ROS elevation through NOX2/DUOX activation, driving NETotic cascades that compromise placental integrity. In summary, this study reveals a significant imbalance in neutrophil subpopulations within the placental microenvironment of GDM, characterized by an abnormally elevated proportion and hyperactivation of the pro-inflammatory neutrophil_1 subset. This subpopulation not only releases copious pro-inflammatory mediators but also drives macrophage polarization towards a pro-inflammatory phenotype via pathways such as the ANXA1-FPR1 axis, collectively establishing and amplifying a local inflammatory network. Critically, based on metabolic profiling and established mechanisms, we propose that dysregulated metabolism of core metabolites (Arginine, NAD⁺ precursors) within neutrophil_1, under GDM hyperglycemic conditions, may drive excessive NETosis through activation of the ROS-PAD4 self-amplifying loop. The aberrant release of NETs and concomitant ROS burst represent key effector mechanisms underlying trophoblast damage and placental dysfunction. Consequently, therapeutically targeting the polarization state of specific neutrophil subsets, blocking their pro-inflammatory interactions (e.g., the ANXA1-FPR1 axis), or inhibiting pathological NETosis (e.g., by intervening in the ROS-PAD4 pathway) represent emerging therapeutic directions for exploring protective strategies against GDM-associated placental injury. Declarations Acknowledgements Not applicable. Authors Contributions S.Y performed the research work, analyzed the results, and wrote this manuscript; N.Z and Q.D collected the information of pregnant women and retain placental tissues. J.S analyzed data and prepared figures. C.R.Y designed the study, analyzed data, prepared figures, wrote the paper, administrative support, and final approval of manuscript. All authors read and approved the final manuscript. Funding This research was supported by grant from Youth Fund of the Second Hospital of Tianjin Medical University (2020ydey06). Conflict of interest The authors declare that there is no conflict of interests. Ethical approval and informed consent All experiments in this study were performed in the Medical Ethics Committee of Tianjin Medical University(No. KY2022K044). Availability of data and materials The data that support the findings of this study are available from the corresponding authors upon reasonable request. 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Wang, A. B. Goldfine, C. R. Kahn and D. D. Wagner (2015). "Diabetes primes neutrophils to undergo NETosis, which impairs wound healing." Nat Med 21 (7): 815-819. Xie, J., L. He and S. Qin (2025). "Integrated bioinformatics analysis identifies neutrophils and villous cytotrophoblasts infiltration characterized by BLC6 upregulation as associated with the co-occurrence of gestational diabetes mellitus and pre-eclampsia." Hypertens Pregnancy 44 (1): 2475814. Yang, L. and Y. Zhang (2017). "Tumor-associated macrophages: from basic research to clinical application." J Hematol Oncol 10 (1): 58. Yang, Y., F. Guo, Y. Peng, R. Chen, W. Zhou, H. Wang, J. OuYang, B. Yu and Z. Xu (2021). "Transcriptomic Profiling of Human Placenta in Gestational Diabetes Mellitus at the Single-Cell Level." Front Endocrinol (Lausanne) 12 : 679582. Ye, Y. X., Y. Wang, P. Wu, X. Yang, L. Wu, Y. Lai, J. Ouyang, Y. Li, P. Li, Y. Hu, Y. X. Wang, X. Song, S. Yan, C. Lv, G. Liu, A. Pan and X. F. Pan (2023). "Blood Cell Parameters From Early to Middle Pregnancy and Risk of Gestational Diabetes Mellitus." J Clin Endocrinol Metab 108 (12): e1702-e1711. Yin, M., Y. Zhang, X. Li, S. Liu, J. Huang, H. Yu and X. Li (2024). "Adverse effects of gestational diabetes mellitus on fetal monocytes revealed by single-cell RNA sequencing." iScience 27 (1): 108637. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Journal of Molecular Histology → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 11 Sep, 2025 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. 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06:33:04","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96456,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/5e14266c5942d92423280199.html"},{"id":91815893,"identity":"63508065-1a43-43c5-bb2a-ed6cdbcdd532","added_by":"auto","created_at":"2025-09-22 06:33:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1400651,"visible":true,"origin":"","legend":"\u003cp\u003eStructural and histopathological alterations in placental tissues from control versus GDM pregnancies\u003c/p\u003e\n\u003cp\u003eA.Representative prenatal ultrasound images demonstrating placental morphology in control (NC) and GDM groups. B. Quantitative analysis of placental thickness measured by ultrasonography (* P \u0026lt; 0.05; Mann-Whitney U test). C. Macroscopic comparison of delivered placental specimens. D.H\u0026amp;E staining revealing histopathological features: syncytial knot formation\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/2bc55ab1d5eb2727534df5c4.jpg"},{"id":91816449,"identity":"6eef0bce-e6e6-4c85-9ed2-4954665a01fc","added_by":"auto","created_at":"2025-09-22 06:41:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1781693,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell transcriptomic landscape of GDM and control placental tissues\u003c/p\u003e\n\u003cp\u003eA. UMAP projection colored by sample group: GDM (red) versus matched controls (blue). B. UMAP projection annotated by cell type assignment, highlighting 14 transcriptionally distinct populations. C. Canonical marker expression across cell types (dot size: detection rate; color: mean expression). D. The bar chart shows the cell number in distinct populations. E. Proportional cell-type abundances in GDM (n=2) vs control (n=2).\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/f47225675f7a4b46be2bf526.jpg"},{"id":91815895,"identity":"38f7449a-f3db-47a7-9b68-ce301341d010","added_by":"auto","created_at":"2025-09-22 06:33:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2899024,"visible":true,"origin":"","legend":"\u003cp\u003eNeutrophil_1 subpopulations demonstrate distinct functional programs\u003c/p\u003e\n\u003cp\u003eA. UMAP projection of all neutrophils colored by clinical status: GDM (blue) vs control (red). B.UMAP embedding partitioned by neutrophil subset: Neutrophil_1 and Neutrophil_2. C. Heatmap of top 30 differentially expressed genes between subsets. Rows: genes; columns: cells. Expression z-scores scaled from -2 (blue) to 2 (red). D. Metascape functional enrichment heatmap. E. GO enrichment bubble plot. Circle size: enriched gene count; color gradient: mean expression level in Neutrophil-1 (blue: low; red: high). F. Volcano plot of ssGSEA pathway enrichment. X-axis: enrichment score difference (Neutrophil_1 - Neutrophil_2); Blue: Neutrophil_1 enriched (FDR\u0026lt;0.05);orange: Neutrophil_2 enriched (FDR\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/1b9565254803478c644b2ad3.jpg"},{"id":91815894,"identity":"b36bf07b-5184-4256-b885-1bdfbf182bb7","added_by":"auto","created_at":"2025-09-22 06:33:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1671630,"visible":true,"origin":"","legend":"\u003cp\u003eNeutrophil_1 crosstalk with immune cell\u003c/p\u003e\n\u003cp\u003eA-B. Cellular interaction networks depicting communication between immune cell types. Node size is proportional to the number of interactions each cell type engages in with other cell types. C-F. Cellular interaction networks depicting biological pathways associated with interactions between Neutrophil_1 and macrophages or T cells. G. Dot plot visualizing significant ligand-receptor pairs mediating crosstalk between Neutrophil_1 and other immune cell types. Dot size represents interaction strength (e.g., expression level or significance) and color indicates interaction specificity or pathway association\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/8750e6685ef42f86b8620812.jpg"},{"id":91816452,"identity":"75ef8ffe-7847-419b-921c-e9c3487981d9","added_by":"auto","created_at":"2025-09-22 06:41:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2651586,"visible":true,"origin":"","legend":"\u003cp\u003eNeutrophil_2 represents a precursor state committed to neutrophil_1 lineage\u003c/p\u003e\n\u003cp\u003eA.UMAP show the development trajectory of Neutrophil_1 and Neutrophil_2. left indicative the development trajectory, right show the cell type. B. Vlinplot show the pseudotime distribution of cell type. C.UMAP show the gene module of the development trajectory of Neutrophil_1 and Neutrophil_2. D-F. GO analysis of differentially gene module function of 3, 4 and 5. The size of the circles represents the number of significantly enriched genes in each item. The color key from blue to red indicates low to high average gene expression. G.Volcano plot representing the TF of neutrophil 1 and neutrophil. H-I. GO analysis the TF function. The size of the circles represents the number of significantly enriched genes in each item. The color key from blue to red indicates low to high average gene expression.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/b09291bfa64654c18ea04668.jpg"},{"id":91815897,"identity":"609ac94b-4ecf-40aa-a49a-2094e3648662","added_by":"auto","created_at":"2025-09-22 06:33:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1520216,"visible":true,"origin":"","legend":"\u003cp\u003eArginine biosynthesis and Nicotinate/nicotinamide metabolism modulates neutrophil_1 pro-inflammatory function\u003c/p\u003e\n\u003cp\u003eA. Metabolic profiling via scMetabolism revealed median metabolic pathway scores across neutrophil subsets. Both the circle size and color of the dot plot represent the scaled metabolic score. B. Comparative analysis of metabolic gene expression signatures in Neutrophil_1 and Neutrophil_2.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/65bce19bd9dea011c67f8727.jpg"},{"id":100616104,"identity":"e2950131-7ebd-41f8-9e6d-af2f12996d8f","added_by":"auto","created_at":"2026-01-19 17:39:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12803672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7589196/v1/60fcdb69-12c6-4721-935f-44490c8ef759.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell sequencing reveals that neutrophils mediate the inflammatory response in gestational diabetes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGestational Diabetes Mellitus (GDM), defined as glucose intolerance first recognized during pregnancy, is characterized by maternal hyperglycemia and exhibits a global prevalence ranging from 1% to 14%, with a significant upward trend(Xie, He et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although GDM often resolves postpartum and may present with subtle clinical manifestations, it is strongly associated with serious maternal-fetal perinatal complications, including preeclampsia, macrosomia (and related birth injuries), neonatal hypoglycemia, and postpartum hemorrhage(Yin, Zhang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, the maternal hyperglycemic intrauterine environment not only directly impairs fetal development but also mediates adverse pregnancy outcomes by disrupting placental morphogenesis and functional differentiation(Gauster, Desoye et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, a deeper understanding of GDM pathogenesis is critical for developing targeted therapeutic strategies.\u003c/p\u003e\u003cp\u003eThe placenta, as the core organ at the maternal-fetal interface, exhibits high dynamism and cellular heterogeneity. Beyond trophoblast cells, its immune microenvironment is enriched with diverse immune populations\u0026mdash;including dendritic cells, macrophages, natural killer cells, T cells, neutrophils, and monocytes\u0026mdash;collectively maintaining placental immune homeostasis(Yang, Guo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent research implicates chronic low-grade inflammation as a key pathological basis for GDM development, manifested by increased trophoblast apoptosis, neutrophil infiltration, impaired trophoblast differentiation and invasiveness, and elevated pro-inflammatory cytokine production(Pantham, Aye et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Wang, Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNeutrophils, the most abundant innate immune effector cells in peripheral blood, play a central role in inflammatory responses(Raftopoulou, Valadez-Cosmes et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). During normal pregnancy, neutrophils participate in multiple critical processes, from embryo implantation and placentation to tissue remodeling and parturition initiation(Aslanian-Kalkhoran, Mehdizadeh et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Functional plasticity is a hallmark of leukocytes; under specific microenvironmental signals, neutrophils can polarize into distinct functional subsets\u0026mdash;analogous to macrophage M1/M2 polarization\u0026mdash;exhibiting divergent or even opposing functions(Yang and Zhang \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Guerriero \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, in the tumor microenvironment, anti-tumor N1 neutrophils demonstrate enhanced tumor cell cytotoxicity and immune activation, while pro-tumor N2 neutrophils promote angiogenesis and metastasis(Raftopoulou, Valadez-Cosmes et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within the healthy placental microenvironment, neutrophils typically maintain maternal-fetal tolerance by promoting activated T-cell apoptosis and polarizing towards an immunoregulatory phenotype(Oravecz, Romero et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, this balance is disrupted in GDM. Studies indicate that GDM patients exhibit significantly elevated peripheral white blood cell, neutrophil, and monocyte counts, as well as an increased neutrophil-to-lymphocyte ratio as early as the first trimester, correlating positively with disease risk(Ye, Wang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Placental tissue also exhibits abnormal, persistent pro-inflammatory activation of monocytes and neutrophils(Yin, Zhang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCritically, recent studies reveal aberrant deposition of Neutrophil Extracellular Traps (NETs) in the placental tissue of GDM patients. NETs are web-like structures composed of decondensed chromatin DNA decorated with antimicrobial proteins (e.g., myeloperoxidase and neutrophil elastase), functioning physiologically to trap and kill pathogens(Sorensen and Borregaard \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, in the hyperglycemic environment of GDM, high glucose is a potent trigger of NETs, primarily through activation of the NADPH oxidase complex leading to reactive oxygen species (ROS) burst and upregulation of peptidylarginine deiminase 4 (PAD4)-mediated histone citrullination(Takei, Araki et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Berthelot, Le Goff et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Vokalova, van Breda et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Excessive NETs production or impaired clearance results in placental accumulation, linked to placental hypoperfusion, trophoblast damage, and dysfunction. Nevertheless, the precise cellular and molecular networks driving abnormal neutrophil activation, subset polarization imbalance, and pathological NETs release in the GDM placenta\u0026mdash;particularly the intrinsic metabolic regulatory mechanisms\u0026mdash;remain incompletely elucidated.\u003c/p\u003e\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) technology provides an unprecedented powerful tool to dissect the pathological remodeling of the placenta\u0026mdash;especially its complex immune microenvironment\u0026mdash;in GDM at single-cell resolution. It enables precise identification of diverse cell types, characterization of their unique transcriptomic signatures, functional states, intercellular communication networks, and metabolic activities, thereby uncovering pathogenic mechanisms obscured by traditional bulk sequencing. Therefore, this study leverages publicly available scRNA-seq datasets to systematically investigate neutrophils\u0026mdash;key mediators of placental immune dysregulation in GDM. We focus on deciphering their heterogeneity, pathogenic mechanisms, and metabolic reprogramming to elucidate neutrophil-driven contributions to GDM pathophysiology.\u003c/p\u003e"},{"header":"2. Method and Material","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Patients and placenta preparation\u003c/h2\u003e\u003cp\u003ePlacental tissues were obtained from 12 pregnant women admitted to the Department of Obstetrics, Tianjin Medical University Second Hospital between January 2025 and April 2025. Six women were diagnosed with GDM and six served as healthy controls. Participants were randomly selected. Prior to delivery, all participants underwent transabdominal color Doppler ultrasonography (GE Healthcare, LOGIQ V2). Examinations were performed with the pulse repetition frequency set at 2.8 kHz. Scans were carefully conducted to ensure complete visualization of the placenta from the chorionic plate to the basal plate. Placental thickness was measured while the participant was in a quiet, resting state without respiratory movement. Following placental expulsion, the gross morphology was examined. A representative placental tissue block, approximately 1 cm \u0026times; 1 cm \u0026times; 1 cm in size, was excised. The tissue sample was thoroughly rinsed with sterile water for injection to remove residual blood. Subsequently, the sample was immersed in 4% phosphate-buffered paraformaldehyde (P0099, Beyotime, Shanghai, China) for fixation and then routinely processed for paraffin embedding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Hematoxylin and eosin staining\u003c/h2\u003e\u003cp\u003ePlacental tissue samples from both GDM and control subjects were fixed in 4% phosphate-buffered paraformaldehyde, processed for paraffin embedding, and sectioned at 4 \u0026micro;m thickness. Sections were then stained with hematoxylin and eosin (H\u0026amp;E; BA4025, BaSO, Zhuhai, China) using standard protocols. Morphological features of the target tissues were examined and imaged using light microscopy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Quality control, integration, and cell annotation\u003c/h2\u003e\u003cp\u003eFor the published dataset GSE173193, cells were retained for downstream analysis only after stringent quality control. We excluded cells expressing fewer than 200 genes or exhibiting\u0026thinsp;\u0026gt;\u0026thinsp;10% mitochondrial gene content. Potential doublets were identified and removed using DoubletFinder (v2.0.3). Batch effects across sequencing runs were corrected using the Harmony algorithm. Following removal of genes overlapping genomic blacklist regions, the top 3,000 highly variable genes were selected via the FindVariableFeatures function in the Seurat package. Cell clustering was performed using Seurat (v5) with default parameters.\u003c/p\u003e\u003cp\u003eCell type annotation was determined by canonical marker gene expression: Neutrophil subsets were identified by FCGR3B/CXCL8/TREM1 (Neutrophil_1) and CAMP/LYZ/S100A8 (Neutrophil_2); Hofbauer cells by MRC1/CD163/CD14; Stromal cells by FN1/LAMA3/COL5A1; Syncytiotrophoblast (STB) subsets by KRT7/ERVW-1/SPINT1 (STB1), ERVW-1/CDH1/SLC2A1 (STB2), and CYP19A1/CGA/ERVFRD-1 (STB3); T cells by CD3D/IL7R/CCR7; Macrophages by FCN1/HLA-DRA/CD86; Trophoblast progenitors by BIRC5/CDK1/TOP2A; Endothelial cells by VWF/CLDN5/CD34; Mast cells by CPA3/FCER1A/CLC; Hematopoietic stem/progenitor cells (HSPCs) by KIT/CD34/MYB; and B cells by IGHM/CD79A/MS4A1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Single-cell metabolomics analysis\u003c/h2\u003e\u003cp\u003eMetabolic activity was computationally inferred from scRNA-seq data using the scMetabolism R package (v0.2.0). Pathway enrichment scores for KEGG metabolic pathways (release 2023.2) were quantified through the VISION algorithm, which employs a signature projection approach on non-imputed expression data. This method weights genes by: (1) expression magnitude (log-normalized counts), (2) pathway topology connectivity (KEGG edge information), and (3) transcriptional coherence (local neighborhood variance). Null distributions were established from 1,000 permutations of randomized gene sets. Cells were subsequently classified into functional metabolic subtypes by hierarchical clustering based on pathway activity z-scores\u0026thinsp;\u0026gt;\u0026thinsp;1.5 standard deviations from the cohort mean.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Trajectory analysis\u003c/h2\u003e\u003cp\u003eDevelopmental relationships between transcriptionally distinct neutrophil subtypes (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were resolved using Monocle3 (v1.3.4). The top 1,500 highly variable genes identified by Seurat's FindVariableFeatures (v2.3) within these clusters served as ordering genes for pseudotemporal reconstruction. Trajectories were learned through the DDRTree algorithm with default parameters (dimensions\u0026thinsp;=\u0026thinsp;2; max iterations\u0026thinsp;=\u0026thinsp;20), and branch significance was validated by permutation testing (n\u0026thinsp;=\u0026thinsp;500). Pseudotime alignments were mapped onto Uniform Manifold Approximation and Projection (UMAP) embeddings (Seurat v5-derived) for spatial visualization. Branch-dependent expression patterns were illustrated through heatmaps of core neutrophil markers (FCGR3B, CXCL8, CAMP, LYZ, S100A8), with genes clustered by k-means partitioning (k\u0026thinsp;=\u0026thinsp;6) along pseudotime.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Pseudotemporal Trajectory and Co-Expression Dynamics\u003c/h2\u003e\u003cp\u003eDevelopmental trajectories of neutrophil subtypes (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were reconstructed using Monocle3 (v1.3.4) with the top 1,500 HVGs (Seurat FindVariableFeatures, v2.3) as ordering genes. DDRTree-derived pseudotime (parameters: dimensions\u0026thinsp;=\u0026thinsp;2; max_iter\u0026thinsp;=\u0026thinsp;20; branch significance validated by permutation testing, n\u0026thinsp;=\u0026thinsp;500) was mapped onto Seurat v5 UMAP embeddings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Transcription Factor Regulatory Analysis\u003c/h2\u003e\u003cp\u003eTranscr\u003cb\u003eiption factor (TF) netwo\u003c/b\u003erks underlying neutrophil subtype identities (Neutrophil_1: FCGR3B+/CXCL8+; Neutrophil_2: CAMP+/S100A8+) were resolved using SCENIC (v1.3.2). GRNBoost2-inferred co-expression modules were refined via cis-regulatory motif enrichment (hg38 mc9nr database; AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.005, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Differential TF activity (|log₂FC| \u0026gt;1; adj. p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) identified subtype-defining regulators (CEBPD/STAT3/STAT1 in Neutrophil_1; E2F4/MyC in Neutrophil_2), validated by target coherence (Pearson r\u0026thinsp;\u0026gt;\u0026thinsp;0.6) and enriched in neutrophil effector pathways (KEGG FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Differential TF activity identified CEBPD/STAT3/STAT1 (Neutrophil_1) and E2F4/MyC (Neutrophil_2) as master regulators. Regulons underwent Gene Ontology (GO) enrichment (clusterProfiler v4.0; org.Hs.eg.db v3.17) with semantic simplification (GOSemSim, cutoff\u0026thinsp;=\u0026thinsp;0.7).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted in R v4.2.1. Two-sided tests with α\u0026thinsp;=\u0026thinsp;0.05 significance threshold were applied throughout. Parametric tests (t-test/ANOVA) required validation of normality (Shapiro-Wilk p\u0026thinsp;\u0026gt;\u0026thinsp;0.1) and homoscedasticity (Levene's p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); otherwise, non-parametric alternatives (Wilcoxon/Kruskal-Wallis) were used. Multiple testing correction employed Benjamini-Hochberg FDR for hypothesis sets (n\u0026thinsp;\u0026ge;\u0026thinsp;10). Effect sizes were reported (Cohen's d for pairwise comparisons; η\u0026sup2; for ANOVA). Heatmaps were generated via pheatmap v1.0.12 with row-scaled values and ward.D2 hierarchical clustering.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Structural and pathological features of the placenta in GDM\u003c/h2\u003e\u003cp\u003ePrenatal ultrasound examination revealed significant placental abnormalities in GDM pregnancies compared to controls. GDM placentae exhibited increased volume, greater thickness, accelerated maturation, and focal calcification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Quantitative analysis confirmed significantly increased placental thickness in the GDM cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Postpartum macroscopic evaluation of placentae corroborated ultrasound findings, demonstrating enlarged dimensions, thickened parenchyma, and visible calcific deposits in GDM specimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Histopathological analysis via H\u0026amp;E staining identified hallmark features of placental insufficiency in GDM, including, prominent syncytial knot formation, widespread calcification, stromal fibrosis. Notably, substantial inflammatory cell infiltration was observed within GDM placental tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Collectively, these structural and histological alterations demonstrate placental dysfunction compounded by inflammatory pathology in GDM, indicating compromised fetal support.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 scRNA-seq reveals cellular heterogeneity in GDM-affected placenta\u003c/h2\u003e\u003cp\u003eTo investigate the pathological mechanisms underlying GDM in placental tissue, we collected scRNA-seq data of GDM and matched control placentas. Following stringent quality control, 22,233 high-quality cells were retained for downstream analysis. UMAP dimensionality reduction and clustering identified 14 transcriptionally distinct cellular populations, including: Neutrophil_1, Neutrophil_2, Hofbauer cells (M2-polarized macrophages), STBs, T cells, Macrophages, Trophoblast progenitor cells, Endothelial cells, Mast cells, HSPCs, and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Immune profiling revealed neutrophils, Hofbauer cells, and macrophages as dominant placental leukocytes. Notably, the GDM group exhibited, increased proportions of Neutrophil_1 (GDM vs control) and macrophages cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). Neutrophil_1 showed elevated expression of pro-inflammatory mediators (IL1B, CXCL8), while macrophages demonstrated antigen-presenting function (HLA-DRA, HLA-DRB1, and CD86). Hofbauer cells displayed an immunosuppressive M2 phenotype (CD163 and CD206) despite reduced frequency in GDM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Collectively, these alterations compose a disordered immune microenvironment characterized by pro-inflammatory neutrophil/macrophage expansion and diminished regulatory cell populations, potentially driving GDM pathogenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Neutrophil_1 is characterized as a pro-inflammatory subtype\u003c/h2\u003e\u003cp\u003ePrior studies implicate neutrophils in GDM-associated inflammatory pathology. Building on this evidence, we performed focused characterization of placental neutrophil heterogeneity through subset analysis of our scRNA-seq data. Neutrophil_1 demonstrated significantly increased abundance in GDM placentas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). Differential expression analysis revealed Neutrophil_1 specifically upregulated pro-inflammatory mediators (IL1B, TREM1, AQP9) compared to Neutrophil_1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Functional annotation via Metascape identified Neutrophil_1 enrichment in inflammatory response and chemotaxis, Conversely, Neutrophil_2 exhibited enrichment in negative regulation of leukocyte activation and neutrophil degranulation(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Similarly, GO enrichment results also indicated neutrophil_1 were enriched in cytokines meditated signaling pathway, immune repsonse activating, and leukocyte chemotaxis, while neutrophil_2 were enriched in cytoplasmic transilation, aerobic respiration, and oxidative phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). To confirm this, we also performed Single-sample Gene Set Enrichment Analysis(ssGSEA) pathway enrichment analysis. The results uncovered that neutrophil_1 were enriched in inflammatory response, IL-6 JAK-STAT3 signaling and TNF-α and IFN-γ response, neutrophil_2 were enriched in oxidative phosphorylation, G2M checkpoint and E2F target (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Taken together, these multi-modal analyses establish Neutrophil_1 as a pro-inflammatory subtype characterized by chemotactic and cytokine-signaling capabilities, suggesting its pathogenic role in GDM placental inflammation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Intercellular signaling between pro-inflammatory neutrophils and macrophages\u003c/h2\u003e\u003cp\u003eTo investigate how the pro-inflammatory Neutrophil_1 subset drives inflammatory pathology in GDM, we analyzed cellular interactions using CellChat. This revealed dominant signaling between Neutrophil_1 and macrophage populations - particularly immunosuppressive Hofbauer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Pathway analysis identified four key communication axes, including Annexin pathway (primarily Neutrophil_1-Macrophages/T cells), IL-1 signaling (Neutrophil_1-Macrophages), Resistin pathway (Neutrophil_1-Macrophages), SPP1-CD44 axis (Hofbauer cells -Macrophages) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-F). Notably, the Annexin pathway (known to promote inflammation) mediated Neutrophil_1 interactions with both macrophages and T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Ligand-receptor analysis further demonstrated that Hofbauer cells suppress macrophage activation via SPP1-CD44 signaling, Neutrophil_1 drives pro-inflammatory macrophage polarization through ANXA1-FPR1 binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). These results uncover a pathogenic signaling network where Neutrophil_1 amplifies inflammation while Hofbauer cells attempt compensatory immunosuppression, revealing potential therapeutic targets for GDM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Neutrophil_2 represents a precursor state committed to neutrophil_1 lineage\u003c/h2\u003e\u003cp\u003eNeutrophil_1 cells demonstrated pro-inflammatory properties and significantly influenced inflammatory responses in GDM. To elucidate why Neutrophil_1 frequency was elevated in GDM compared to controls, we investigated the developmental trajectory of Neutrophil_1 and Neutrophil_2 subpopulations. We identified Neutrophil_1 cells by high expression of FCGR3B and CXCL8, and Neutrophil_2 cells by markers CAMP and S100A8. Pseudotime trajectory analysis using Monocle3 revealed that Neutrophil_2 functions as a precursor state that differentiates into Neutrophil_1 subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo characterize transcriptional dynamics during this differentiation, we performed Gene Co-Expression Network Analysis. This identified 19 gene modules dynamically regulated along the trajectory, with Modules 3, 4, and 5 exhibiting significant expression changes during the Neutrophil_2 to Neutrophil_1 transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). GO enrichment analysis showed Module 3 was enriched for leukocyte migration in inflammatory responses, Module 4 for NAD biosynthesis via nicotinamide riboside salvage pathway, and Module 5 for iron ion transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F), indicating fundamental shifts in biological functions during neutrophil differentiation.\u003c/p\u003e\u003cp\u003eWe further analyzed TF regulatory networks, revealing distinct profiles: Neutrophil_1 showed enriched activity of pro-inflammatory TFs (STAT1, STAT3, NFKB1, CEBPB, FOS, RELA), while Neutrophil_2 exhibited elevated stem/progenitor TFs (E2F4, MYC, GATA1, NR2C2, TFDP1) that regulate differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). TF-associated GO terms confirmed these identities, with Neutrophil_1 terms including lymphocyte differentiation and myeloid cell differentiation regulation, whereas Neutrophil_2 terms involved ossification, G1/S cell cycle transition, mesenchymal cell proliferation, and epithelial tube branching morphogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH-I). Collectively, these results demonstrate Neutrophil_2 serves as a progenitor population that differentiates into the pro-inflammatory Neutrophil_1 subset, explaining its increased frequency in GDM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Arginine biosynthesis and Nicotinate/nicotinamide metabolism modulates neutrophil_1 pro-inflammatory function\u003c/h2\u003e\u003cp\u003eMetabolic profiling via scMetabolism revealed distinct functional specialization between neutrophil subsets. Neutrophil_1 demonstrated significant enrichment in nitrogen metabolism, nicotinate/nicotinamide metabolism, and arginine biosynthesis (KEGG, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This metabolic reprogramming sustains inflammatory effector functions through two key mechanisms: (1) Arginine serves as both structural component and signaling substrate for nitric oxide production, ROS generation, and NET formation (NETosis); (2) NAD\u003csup\u003e+\u003c/sup\u003eprecursors (niacin/nicotinamide) modulate chemotaxis, phagocytosis, and oxidative burst via NAD\u003csup\u003e+\u003c/sup\u003e-dependent pathways. Conversely, Neutrophil_2 exhibited pentose phosphate pathway dominance, consistent with proliferative states. Differential gene expression further distinguished subsets: Neutrophil_1 overexpressed pro-inflammatory mediators (SAT1, CXCL8, G0S2, NAMPT), while Neutrophil_2 displayed elevated S100A8/A9 and immunosuppressive CAMP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Collectively, arginine-catabolizing and NAD⁺-generating metabolism maintains Neutrophil_1's inflammatory phenotype in GDM pathogenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe placenta, as the core organ at the maternal-fetal interface, relies critically on the effective functioning of trophoblast cells. Trophoblast dysfunction constitutes a key pathological basis for placental insufficiency(Jiao, Wang et al. 2023). In the placentas of mothers with GDM, we observed manifestations of placental insufficiency. Both prenatal ultrasound and postpartum placental examination revealed increased placental thickness, accelerated maturation, and focal calcification (Fig.1A-C). Pathological analysis of GDM placentas further identified features indicative of insufficiency, including syncytial knots, calcification, and stromal fibrosis (Fig.1D). These findings demonstrate the detrimental impact of the hyperglycemic environment on the placenta, the underlying mechanisms of which warrant further exploration.\u003c/p\u003e\n\u003cp\u003ePregnancy itself represents a unique physiological state characterized by the activation of systemic low-grade inflammation(Hahn, Giaglis et al. 2013). Within this context, the precise regulation of maternal-fetal immune tolerance within the placenta is essential for maintaining gestational homeostasis. However, the chronic hyperglycemia associated with GDM can severely disrupt this immune equilibrium. Studies have found significant elevations of IL-1β (200%) and TNF-α (58%) in GDM placental tissue(Marseille-Tremblay, Ethier-Chiasson et al. 2008). Consistent with this, H\u0026amp;E staining in our study revealed substantial inflammatory cell infiltration within the placenta (Fig.1D), collectively pointing towards a significant dysregulation of the local inflammatory microenvironment. To further investigate the mechanisms, we performed scRNA-seq on GDM and matched control placental tissues. This analysis successfully identified 14 major cell populations within the placental microenvironment, including neutrophils, Hofbauer cells (M2-polarized macrophages), STBs, T cells, macrophages, trophoblast progenitor cells, endothelial cells, mast cells, HSPCs, and B cells (Fig.2A-C). These cells collectively constitute the placental microenvironment.\u003c/p\u003e\n\u003cp\u003eNeutrophils, as pioneers of innate immunity, colonize the decidua early in pregnancy and gradually increase during healthy gestation(Aslanian-Kalkhoran, Mehdizadeh et al. 2024). Traditionally recognized for their role in antimicrobial defense, recent evidence highlights their central function in sterile inflammation and tissue repair/injury(Papayannopoulos 2018). During acute injury repair, neutrophils play beneficial roles by clearing debris and releasing growth factors and pro-angiogenic molecules. However, in chronic inflammatory states like GDM, neutrophil function can undergo pathological transformation, exacerbating tissue stress and damage through the release of mediators such as matrix metalloproteinases and ROS(Lagnado, Leslie et al. 2021). Our scRNA-seq data identified two functionally distinct neutrophil subpopulations within the placenta: neutrophil_1 and neutrophil_2 (Fig.2D). Notably, the proportion of the neutrophil_1 subpopulation was significantly higher in GDM placental tissue compared to healthy controls (Fig.2E). This subpopulation was characterized by high expression of potent pro-inflammatory mediators (e.g., IL1B, TREM1, AQP9) (Fig.3C). GO enrichment analysis revealed that neutrophil_1 was highly enriched in terms related to \"cytokine-mediated signaling pathway,\" \"immune response-activating signal transduction,\" and \"leukocyte chemotaxis\" (Fig.3E). ssGSEA further confirmed that neutrophil_1 exhibited high activity in pro-inflammatory pathways, including \"inflammatory response,\" \"IL-6/JAK-STAT3 signaling,\" \"TNF-α response,\" and \"IFN-γ response\" (Fig.3F). These data collectively indicate that neutrophil_1 is a key pro-inflammatory driver within the placental inflammatory microenvironment. Crucially, our cell-cell communication analysis revealed a complex pro-inflammatory signaling network between neutrophil_1 and other immune cells, particularly macrophages (Fig.4A-B). Neutrophil_1 likely interacts with macrophages through multiple pathways, such as Annexin signaling, IL-1 signal transduction, Resistin pathways, and the SPP1-CD44 axis (Fig.4D-F). Of particular significance, neutrophil_1 may drive macrophage polarization towards a pro-inflammatory (M1-like) phenotype via the ANXA1-FPR1 ligand-receptor pair (Fig.4C), thereby establishing a self-amplifying inflammatory loop that perpetuates local placental inflammation.\u003c/p\u003e\n\u003cp\u003eIn contrast, the neutrophil_2 subpopulation exhibited distinct functional properties. Its gene expression profile suggested potential involvement in the \"negative regulation of leukocyte activation\" and \"negative regulation of neutrophil degranulation\" (Fig.3D), implying a potential anti-inflammatory or homeostatic role. However, pseudotime trajectory analysis using Monocle3 revealed a critical and potentially pathologically significant finding: neutrophil_2 may represent a precursor state to neutrophil_1 (Fig.5). This suggests that within the pathological microenvironment of GDM, placental neutrophils undergo pathological differentiation or polarization from a relatively quiescent/regulatory state (neutrophil_2) towards a highly pro-inflammatory state (neutrophil_1), further amplifying the inflammatory response.\u003c/p\u003e\n\u003cp\u003eCore mechanisms for neutrophil pathogen clearance include phagocytosis, degranulation, and the release of NETs, via a process termed NETosis (Shen, Lu et al. 2021). While NETs serve as an important innate immune barrier under physiological conditions, their excessive production or impaired clearance leads to aberrant accumulation in tissues, causing damage, organ dysfunction, uncontrolled inflammation, and coagulopathy(Ravindran, Khan et al. 2019). Previous research has unequivocally established hyperglycemia as a potent trigger for NETs release in diabetes. Our metabolic analysis (scMetabolism) revealed that positioned Arginine biosynthesis and NAD\u003csup\u003e+\u003c/sup\u003eprecursors as topological hubs within the Neutrophil-1 metabolome(Fig.6). Critically, niacin/NAM-mediated control of NAD⁺/NADPH homeostasis paradoxically gates ROS dynamics, enabling either: (1) ROS constraint to prevent oxidative stress, or (2) ROS amplification to fuel microbicidal activity-a context-instructed switch governing redox adaptation (Minhas, Liu et al. 2019). Hyperglycemia-induced ROS activates PAD4, triggering histone H3 citrullination that decondenses chromatin to drive NETosis. Critically, citrullinated histones feed back to potentiate NADPH oxidase, establishing a self-amplifying ROS-PAD4 circuit that exacerbates NET release(Wong, Demers et al. 2015, Tatsiy and McDonald 2018). Locally, this cascade directly compromises placental integrity by inducing syncytiotrophoblast stress and barrier dysfunction-mechanistically linking aberrant NETosis to histopathological sequelae (syncytial knots, fibrosis) and functional insufficiency in GDM. Consequently, We speculate that Neutrophil-1 exhibits sustained intracellular ROS elevation through NOX2/DUOX activation, driving NETotic cascades that compromise placental integrity.\u003c/p\u003e\n\u003cp\u003eIn summary, this study reveals a significant imbalance in neutrophil subpopulations within the placental microenvironment of GDM, characterized by an abnormally elevated proportion and hyperactivation of the pro-inflammatory neutrophil_1 subset. This subpopulation not only releases copious pro-inflammatory mediators but also drives macrophage polarization towards a pro-inflammatory phenotype via pathways such as the ANXA1-FPR1 axis, collectively establishing and amplifying a local inflammatory network. Critically, based on metabolic profiling and established mechanisms, we propose that dysregulated metabolism of core metabolites (Arginine, NAD⁺ precursors) within neutrophil_1, under GDM hyperglycemic conditions, may drive excessive NETosis through activation of the ROS-PAD4 self-amplifying loop. The aberrant release of NETs and concomitant ROS burst represent key effector mechanisms underlying trophoblast damage and placental dysfunction. Consequently, therapeutically targeting the polarization state of specific neutrophil subsets, blocking their pro-inflammatory interactions (e.g., the ANXA1-FPR1 axis), or inhibiting pathological NETosis (e.g., by intervening in the ROS-PAD4 pathway) represent emerging therapeutic directions for exploring protective strategies against GDM-associated placental injury.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;S.Y performed the research work, analyzed the results, and wrote this manuscript; N.Z and Q.D collected the information of pregnant women and retain placental tissues. J.S analyzed data and prepared figures. C.R.Y designed the study, analyzed data, prepared figures, wrote the paper, administrative support, and final approval of manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grant from Youth Fund of the Second Hospital of Tianjin Medical University (2020ydey06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments in this study were performed in the Medical Ethics Committee of Tianjin Medical University(No. KY2022K044).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAslanian-Kalkhoran, L., A. 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Li (2024). \u0026quot;Adverse effects of gestational diabetes mellitus on fetal monocytes revealed by single-cell RNA sequencing.\u0026quot; \u003cu\u003eiScience\u003c/u\u003e \u003cstrong\u003e27\u003c/strong\u003e(1): 108637.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-histology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hijo","sideBox":"Learn more about [Journal of Molecular Histology](https://www.springer.com/journal/10735)","snPcode":"10735","submissionUrl":"https://submission.springernature.com/new-submission/10735/3","title":"Journal of Molecular Histology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gestational diabetes mellitus, Single-cell RNA sequencing, Neutrophil heterogeneity, Placental inflammation","lastPublishedDoi":"10.21203/rs.3.rs-7589196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7589196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGestational diabetes mellitus (GDM) is a pregnancy-associated metabolic disorder characterized by insulin resistance and hyperglycemia, posing significant risks to maternal-fetal health. While placental inflammation contributes to GDM pathology, its cellular mechanisms remain incompletely understood. Leveraging public single-cell RNA sequencing (scRNA-seq) data, we constructed a cellular atlas of GDM and control placental tissues, identifying 14 transcriptionally distinct populations. Neutrophil_1 emerged as the dominant pro-inflammatory subset, exhibiting amplified interactions with macrophages and T cells that exacerbate inflammatory responses. Pseudotime trajectory analysis revealed Neutrophil_2 as a precursor state differentiating toward the Neutrophil_1 lineage through transcriptionally regulated programming. Critically, Arginine catabolism and Nicotinate/nicotinamide metabolism were found to modulate Neutrophil_1's pro-inflammatory functions. Our findings implicate pathogenic neutrophil subsets in GDM progression and nominate arginase and NAMPT inhibition as potential therapeutic strategies for mitigating neutrophil-mediated placental inflammation.\u003c/p\u003e","manuscriptTitle":"Single-cell sequencing reveals that neutrophils mediate the inflammatory response in gestational diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 06:32:58","doi":"10.21203/rs.3.rs-7589196/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T18:57:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T04:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282574663673692097037016602767572984532","date":"2025-09-26T01:55:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T10:59:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170047689088101111627526099527304859193","date":"2025-09-24T22:09:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246460590112478929975482924013591142816","date":"2025-09-15T16:12:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101510085859217345254707945783356514121","date":"2025-09-13T22:30:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216025367729116588710773278458691968848","date":"2025-09-13T16:20:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T01:34:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T01:32:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T11:40:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Histology","date":"2025-09-11T07:49:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-histology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hijo","sideBox":"Learn more about [Journal of Molecular Histology](https://www.springer.com/journal/10735)","snPcode":"10735","submissionUrl":"https://submission.springernature.com/new-submission/10735/3","title":"Journal of Molecular Histology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cb60b51e-3325-4d5f-82aa-0c49ed2f563c","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:04:36+00:00","versionOfRecord":{"articleIdentity":"rs-7589196","link":"https://doi.org/10.1007/s10735-025-10707-w","journal":{"identity":"journal-of-molecular-histology","isVorOnly":false,"title":"Journal of Molecular Histology"},"publishedOn":"2026-01-17 16:29:20","publishedOnDateReadable":"January 17th, 2026"},"versionCreatedAt":"2025-09-22 06:32:58","video":"","vorDoi":"10.1007/s10735-025-10707-w","vorDoiUrl":"https://doi.org/10.1007/s10735-025-10707-w","workflowStages":[]},"version":"v1","identity":"rs-7589196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7589196","identity":"rs-7589196","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}