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Single cell sequencing and spatial multiomics of diabetic kidney segmentation insights zonation-specific therapeutic metabolic pathways
糖尿病肾分割的单细胞测序和空间多组学洞察分区特异性治疗代谢途径

Shi Qiu a a ^(a){ }^{\mathrm{a}}, Zhibo Wang a a ^(a){ }^{\mathrm{a}}, Sifan Guo a a ^(a){ }^{\mathrm{a}}, Dandan Xie a a ^(a){ }^{\mathrm{a}}, Ying Cai a , b a , b ^(a,b){ }^{\mathrm{a}, \mathrm{b}}, Xian Wang a a ^(a){ }^{\mathrm{a}}, Chunsheng Lin b b ^(b){ }^{\mathrm{b}}, Songqi Tang a , ∗ a , ∗ ^(a,**){ }^{\mathrm{a}, *}, Yiqiang Xie a , ∗ ∗ a , ∗ ∗ ^(a,****){ }^{\mathrm{a}, * *}, Aihua Zhang a , b , ∗ ∗ ∗ a , b , ∗ ∗ ∗ ^(a,b,******){ }^{\mathrm{a}, \mathrm{b}, * * *}
Shi qiu a a ^(a){ }^{\mathrm{a}} ， 王 a a ^(a){ }^{\mathrm{a}} 志波 ， 郭思凡 a a ^(a){ }^{\mathrm{a}} ， 谢丹丹 a a ^(a){ }^{\mathrm{a}} ， 蔡颖 a , b a , b ^(a,b){ }^{\mathrm{a}, \mathrm{b}} ， 王 a a ^(a){ }^{\mathrm{a}} 贤 ， 林春生 b b ^(b){ }^{\mathrm{b}} ， 唐松琪 a , ∗ a , ∗ ^(a,**){ }^{\mathrm{a}, *} ， 谢一强 a , ∗ ∗ a , ∗ ∗ ^(a,****){ }^{\mathrm{a}, * *} ， 张 a , b , ∗ ∗ ∗ a , b , ∗ ∗ ∗ ^(a,b,******){ }^{\mathrm{a}, \mathrm{b}, * * *} 爱华 a a  ^("a "){ }^{\text {a }} International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China
a a  ^("a "){ }^{\text {a }} 海南医科大学海南省重大疾病生物样本资源工程研究中心，海南省重大疾病生物样本资源工程研究中心，海南省海口市 571199 园路 3 号，国际先进功能组学平台 b b ^(b){ }^{\mathrm{b}} Graduate School, Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China
b b ^(b){ }^{\mathrm{b}} 黑龙江中医药大学附属第二医院研究生院，黑龙江 哈尔滨 150040

H I G H L I G H T S

  • Spatial multi-omics maps DN’s metabolic zonation & actionable targets.
    空间多组学绘制了 DN 的代谢分区和可作的靶点。
  • Cross-species validation bridges murine models to human DN pathology.
    跨物种验证将小鼠模型与人类 DN 病理学联系起来。
  • ASIV restores compartment-specific glutathione & glycolytic pathways.
    ASIV 恢复隔室特异性谷胱甘肽和糖酵解途径。
  • Single-cell atlas links fibroblasts to metabolic dysregulation via GPX3.
    单细胞图谱通过 GPX3 将成纤维细胞与代谢失调联系起来。
  • GPX3 identified as fibroblast-enriched biomarker ( ( (( AUC = 0.995 ) = 0.995 ) =0.995)=0.995) for DN.
    GPX3 被鉴定为富含成纤维细胞的 DN 生物标志物 ( ( (( AUC = 0.995 ) = 0.995 ) =0.995)=0.995) 。

A R T I C L E I N F O

Keywords:  关键字：

Spatial proteomics  空间蛋白质组学
Spatial metabolomics  空间代谢组学
Metabolic zonation  代谢分区
Metabolism  新陈代谢
Metabolic pathway  代谢途径
Target  目标

GRAPHICALABSTRACT  图形摘要

Abstract  抽象

Diabetic nephropathy (DN) exhibits profound spatial metabolic heterogeneity across kidney regions, yet how compartmentalized pathways drive disease progression remains poorly defined. A deeper understanding of the organizational spatial environment and metabolic pathways of diabetic kidney damage will provide new insights to develop new therapies. By integrating high-resolution spatial multi-omics and single-cell transcriptomics, we mapped region-specific metabolic dysregulation in diabetic kidneys, identifying glutathione metabolism, pentose phosphate, and glycolytic pathways as zonally disrupted in cortical and medullary regions. Spatial metabolomics revealed distinct anatomical clustering of ten clinically associated metabolites, while spatial proteomic profiling uncovered sixty-four region-enriched proteins linked to these pathways. Specifically, depending on anatomic location, spatial protein signatures across multiple regions of diabetic mouse kidneys were enriched in each segmentation, respectively. Cross-species integration identified GPX3 as a fibroblast-enriched biomarker strongly correlated with kidney dysfunction and closely related to clinical indicators. Notably, astragaloside IV (ASIV) treatment reversed spatial metabolic perturbations in diabetic mice, restoring glutathione and glycolytic pathway
糖尿病肾病 （DN） 在肾脏区域表现出深刻的空间代谢异质性，但分室途径如何驱动疾病进展仍不清楚。对糖尿病肾损伤的组织空间环境和代谢途径的深入了解将为开发新疗法提供新的见解。通过整合高分辨率空间多组学和单细胞转录组学，我们绘制了糖尿病肾脏中区域特异性代谢失调的图谱，确定了谷胱甘肽代谢、磷酸戊糖和糖酵解途径在皮质和髓质区域中被区域性破坏。空间代谢组学揭示了 10 种临床相关代谢物的不同解剖学聚类，而空间蛋白质组学分析发现了与这些途径相关的 64 种区域富集蛋白质。具体来说，根据解剖位置，糖尿病小鼠肾脏多个区域的空间蛋白质特征分别在每个分割中富集。跨物种整合鉴定出 GPX3 是一种富含成纤维细胞的生物标志物，与肾功能障碍密切相关，与临床指标密切相关。值得注意的是，黄芪甲苷 IV （ASIV） 治疗逆转了糖尿病小鼠的空间代谢紊乱，恢复了谷胱甘肽和糖酵解途径

activity in a compartment-specific manner. Single-cell analyses identified five cell types-endothelial cells, fibroblasts, epithelial cells, macrophages and neutrophils-and further revealed fibroblasts as key contributors to regulatory effects via GPX3 overexpression. Importantly, the higher expression of Gpx3 in fibroblasts compared to other cell types, Gpx3 (AUC = 0.995), was further validated, demonstrating the high sensitivity and specificity for DN patients. This multimodal atlas establishes the spatially resolved metabolic blueprint of DN, bridging molecular zoning with anatomical localization of renal tissue to unveil actionable therapeutic targets for metabolic disorders in kidney disease.
以特定于隔间的方式进行活动。单细胞分析确定了五种细胞类型——内皮细胞、成纤维细胞、上皮细胞、巨噬细胞和中性粒细胞——并进一步揭示了成纤维细胞是通过 GPX3 过表达调节作用的关键贡献者。重要的是，与其他细胞类型 Gpx3 （AUC = 0.995） 相比，成纤维细胞中 Gpx3 的表达更高，进一步验证了对 DN 患者的高敏感性和特异性。该多模态图谱建立了 DN 的空间分辨代谢蓝图，将分子分区与肾组织的解剖定位联系起来，从而揭示了肾脏疾病代谢紊乱的可行治疗靶点。

1. Introduction  1. 简介

Diabetic nephropathy (DN), a metabolic disorder-driven complication of diabetes characterized by progressive kidney damage (Cefalu et al., 2024; Chai et al., 2025; Suzuki et al., 2024), arises from spatially heterogeneous dysfunction across renal subregions with distinct metabolic roles (Guo, Dong, et al., 2024; Lees et al., 2019). The structural complexity of the kidney and multifaceted injury mechanisms underlie current therapeutic limitations, necessitating multidimensional approaches to unravel its pathology. Recent advances in high-resolution spatial mapping integrate omics and imaging to decode the molecular networks governing disease progression (Asowata et al., 2024; Govind et al., 2022; Li & Humphreys, 2024b), offering dual promise and challenge to analyzing the comprehensive molecular profile of kidney tissue across mechanism and treatment.
糖尿病肾病 （DN） 是一种代谢紊乱驱动的糖尿病并发症，其特征是进行性肾损伤（Cefalu 等人，2024 年;Chai 等人，2025 年;Suzuki 等人，2024 年），源于具有不同代谢作用的肾亚区域的空间异质性功能障碍（Guo， Dong， et al.， 2024;Lees 等人，2019 年）。肾脏的结构复杂性和多方面的损伤机制是当前治疗局限性的基础，需要多维方法来揭示其病理。高分辨率空间映射的最新进展整合了组学和成像来解码控制疾病进展的分子网络（Asowata 等人，2024 年;Govind 等人，2022 年;Li & Humphreys，2024b），为分析肾组织跨机制和治疗的综合分子谱提供了双重希望和挑战。
Spatial organization of metabolites within the kidney’s 3D architecture critically governs DN progression, as compartmentalized metabolic activity directly shapes renal function and pathology (Addario et al., 2024; Kadotani et al., 2024; Lee et al., 2024). This spatial regulation integrates multi-omic networks with tissue morphology, where metabolomics serves as the functional endpoint, synthesizing transcriptomics and proteomics to define disease-associated cellular states (Cai et al., 2023; Qiu et al., 2023). While spatially resolved metabolomics enables systematic mapping of metabolic perturbations and pathway dysregulation, current analyses remain fragmented across renal subregions, limiting mechanistic insights and therapeutic target identification. Single-cell transcriptomics has unveiled cell-type-specific injury mechanisms (Juliar et al., 2024; Li & Humphreys, 2024b; Polonsky et al., 2024; Zhang et al., 2024), yet its lack of spatial context obscured microenvironmental interactions. Recent advances in spatial multi-omics now enable multimodal reconstruction of regulatory networks across anatomical zones (Cao et al., 2024; Gopee et al., 2024; Iglesia et al., 2024; Ounadjela et al., 2024; Qian et al., 2024). Nevertheless, the interplay between zonal metabolic reprogramming, cellular pathophysiology, and drug-responsive pathways in DN remains poorly resolved, highlighting the need for integrative frameworks to decode spatially targeted therapeutic opportunities.
肾脏 3D 结构内代谢物的空间组织对 DN 的进展至关重要，因为分室代谢活动直接影响肾功能和病理学（Addario 等人，2024 年;Kadotani 等人，2024 年;Lee 等人，2024 年）。这种空间调控将多组学网络与组织形态学相结合，其中代谢组学作为功能终点，合成转录组学和蛋白质组学来定义与疾病相关的细胞状态（Cai et al.， 2023;Qiu 等人，2023 年）。虽然空间分辨代谢组学能够系统地绘制代谢扰动和通路失调的图谱，但目前的分析在肾脏亚区域仍然分散，限制了机制洞察力和治疗靶点识别。单细胞转录组学揭示了细胞类型特异性损伤机制（Juliar 等人，2024 年;Li & Humphreys，2024b;Polonsky 等人，2024 年;Zhang et al.， 2024），但其缺乏空间背景掩盖了微环境相互作用。空间多组学的最新进展现在使得跨解剖区域的调控网络的多模态重建成为可能（Cao et al.， 2024;Gopee 等人，2024 年;Iglesia 等人，2024 年;Ounadjela 等人，2024 年;Qian et al.， 2024）。然而，DN 中区域代谢重编程、细胞病理生理学和药物反应途径之间的相互作用仍然没有得到很好的解决，这凸显了需要综合框架来解码空间靶向治疗机会。
While spatial omics resolves metabolite localization in 2D tissue sections, 3D functional zonation mapping remains unachieved. Mass spectrometry imaging-based spatial metabolomics (Vandergrift et al., 2025; Ponzoni et al., 2024) advances multiplexed metabolite profiling while retaining tissue architecture, enabling systematic characterization of injury-associated microenvironmental remodeling. Integrating single-cell RNA sequencing (scRNA-seq) with spatial multi-omics bridges molecular gradients to cellular behaviors, offering unprecedented resolution to decoding the kidney injury mechanisms. These multimodal frameworks not only uncover spatially defined therapeutic metabolites but also pioneer next-generation strategies for renal compartment-specific diagnosis and intervention.
虽然空间组学解决了 2D 组织切片中的代谢物定位，但 3D 功能分区图仍然无法实现。基于质谱成像的空间代谢组学（Vandergrift 等人，2025 年;Ponzoni 等人，2024 年）在保留组织结构的同时推进了多重代谢物分析，从而能够系统地表征与损伤相关的微环境重塑。将单细胞 RNA 测序 （scRNA-seq） 与空间多组学相结合，将分子梯度与细胞行为联系起来，为解码肾损伤机制提供了前所未有的分辨率。这些多模式框架不仅揭示了空间定义的治疗代谢物，而且还开创了肾室特异性诊断和干预的下一代策略。
Our current understanding of DN molecular mechanisms remains incomplete, necessitating integrative multi-omics and scRNA-seq approaches to elucidate disease progression pathways. Through spatial multi-omics mapping and renal zonation analysis, we systematically characterized protein-metabolic pathway enrichment patterns in diabetic murine kidneys. Crucially, spatial resolution of metabolic pathway localization within renal tissue compartments represents a pivotal advancement for deciphering DN pathogenesis.
我们目前对 DN 分子机制的了解仍然不完整，需要综合多组学和 scRNA-seq 方法来阐明疾病进展途径。通过空间多组学图谱和肾脏分区分析，系统地表征了糖尿病小鼠肾脏的蛋白质代谢途径富集模式。至关重要的是，肾组织隔室内代谢途径定位的空间分辨率代表了破译 DN 发病机制的关键进步。

2. Results  2. 结果

2.1. Machine learning-enhanced metabolomic profiling identifies diagnostic biomarkers and dysregulated metabolic pathways in diabetic nephropathy
2.1. 机器学习增强的代谢组学分析可识别糖尿病肾病的诊断生物标志物和失调的代谢途径

Integrated strategy of scRNA-seq analyses and spatial metabolomics as well as spatial proteomics was shown in Fig. S1. Principal-component analysis (PCA) scores revealed significant differences in urine metabolite expression between DN patients and healthy controls (HCs) (Fig. 1(A)). In addition, hierarchical analysis of the metabolite signals in urine samples from DN patients and HCs also presented clear inter-group clustering (Fig. 1(B)). We screened and analyzed the differentially expressed metabolites in DN patients and a total of 10 abundant metabolites were found (Fig. 1©, Table S2). Heatmap plot illustrated the relative abundance of the differentially abundant metabolites between the DN and HCs (Fig. 1(D)). The KEGG pathway analysis results showed that citrate cycle, glutathione metabolism, pentose phosphate pathway, glycerophospholipid metabolism, fructose and mannose metabolism, fatty acid biosynthesis, glycolysis/gluconeogenesis were significantly enriched (Fig. 1(E), Table S3). Correlations analysis (Mantel test) on differentially abundant metabolites and the clinical biochemical indicators revealed homocitric acid and pyroglutamic acid were positive correlation ( p < 0.05 p < 0.05 p < 0.05p<0.05 ) with BUN, serum creatinine, eGFR and blood glucose, myristic acid was positive correlation ( p < 0.05 p < 0.05 p < 0.05p<0.05 ) with blood glucose, serum creatinine and eGFR (Fig. 1(F), Table S4). The predictive performances of each metabolite between the DN and HCs were illustrated via ROC curves (Fig. 1(G)). Intriguingly, we observed AUC values on the prediction model demonstrated the high diagnostic capability of metabolite combination panel from potential biomarkers (Fig. 1(H)). In Fig. 1(I), the bar chart showed that top feature contribution on the performance evaluation of the differentially abundant metabolites in DN patients. Furthermore, Fig. S2 showed the expression status on feature contribution of potential biomarkers for the prediction model evaluation.
scRNA-seq 分析与空间代谢组学以及空间蛋白质组学的综合策略如图 S1 所示。主成分分析（PCA）评分显示，DN 患者和健康对照（HC）的尿液代谢物表达存在显著差异[图 1（A）]。此外，对 DN 患者和 HC 尿液样本中代谢物信号的分层分析也呈现出明显的组间聚类[图 1（B）]。我们筛选和分析了 DN 患者中差异表达的代谢物，共发现了 10 种丰富的代谢物（图 1©，表 S2）。热图图说明了 DN 和 HCs 之间差异丰度代谢物的相对丰度[图 1（D）]。KEGG 通路分析结果显示，枸橼酸循环、谷胱甘肽代谢、磷酸戊糖通路、甘油磷脂代谢、果糖和甘露糖代谢、脂肪酸生物合成、糖酵解/糖异生显著富集[图 1（E），表 S3]。差异丰度代谢物的相关性分析（Mantel 试验）和临床生化指标显示，高柠檬酸和焦谷氨酸与 BUN、血清肌酐、eGFR 和血糖呈正相关（ p < 0.05 p < 0.05 p < 0.05p<0.05 ），肉豆蔻酸与血糖、血清肌酐和 eGFR 呈正相关（ p < 0.05 p < 0.05 p < 0.05p<0.05 ）[图 1（F），表 S4]。通过 ROC 曲线说明了 DN 和 HCs 之间每种代谢物的预测性能[图 1（G）]。有趣的是，我们观察到预测模型上的 AUC 值表明了代谢物组合组合对潜在生物标志物的高诊断能力[图 1（H）]。在图中。 1（I），条形图显示，对 DN 患者差异丰度代谢物的性能评估具有最大的特征贡献。此外，图。S2 显示了潜在生物标志物对特征贡献的表达状态，用于预测模型评估。

2.2. Spatially resolved metabolomics unveils anatomical zonation and pathway-specific metabolic remodeling in diabetic nephropathy mouse kidneys
2.2. 空间分辨代谢组学揭示了糖尿病肾病小鼠肾脏的解剖分区和通路特异性代谢重塑

Principal component analysis of metabolites can significantly distinguish kidney tissue samples from db / m db / m db//m\mathrm{db} / \mathrm{m} mice and transgenic db / db db / db db//db\mathrm{db} / \mathrm{db} mice (Fig. 2(A)). Targeted metabolomics revealed group clustering of the metabolite signals in kidney tissue samples from db / m db / m db//m\mathrm{db} / \mathrm{m} mice and transgenic db / db db / db db//db\mathrm{db} / \mathrm{db} mice (Fig. 2(B)). Heatmap of the clustering analysis of tissue samples indicated metabolites changes between db / m db / m db//m\mathrm{db} / \mathrm{m} mice and transgenic db / db db / db db//db\mathrm{db} / \mathrm{db} mice (Fig. 2©). High-spatial-resolution MALDI-MSI was applied to detect the differentially abundant metabolites from mice kidney tissue at spatial resolution. Spatially resolved metabolomics mass spectrometry imaging (MSI) revealed the unique metabolite biomarkers signature (Fig. 2(D)). Fig. 2(E) displays the schematic diagram for kidney organizational annotations (Cor, kidney cortex including its vasculature; OM, outer stripe of kidney medulla including its vasculature; IM, the inner stripe of kidney medulla consisting of some vasculature, the main arteries and veins within the renal parenchyma, papilla and pelvis, part of the external renal vessels, part of the ureter, and uniform surrounding tissue). Heatmap of spatial segmentation analysis then highlighted
代谢物的主成分分析可以显着区分肾组织样本与 db / m db / m db//m\mathrm{db} / \mathrm{m} 小鼠和转基因 db / db db / db db//db\mathrm{db} / \mathrm{db} 小鼠[图 2（A）]。靶向代谢组学揭示了小鼠和转基因 db / db db / db db//db\mathrm{db} / \mathrm{db} 小鼠肾 db / m db / m db//m\mathrm{db} / \mathrm{m} 组织样本中代谢物信号的组聚[图 2（B）]。组织样本聚类分析的热图表明小鼠和转基因 db / db db / db db//db\mathrm{db} / \mathrm{db} 小鼠之间的 db / m db / m db//m\mathrm{db} / \mathrm{m} 代谢物变化（图 2©）。采用高空间分辨率 MALDI-MSI 检测小鼠肾组织中差异丰度代谢物的空间分辨率。空间分辨代谢组学质谱成像（MSI）揭示了独特的代谢物生物标志物特征[图 2（D）]。图 2（E）显示了肾脏组织注释的示意图（Cor，肾皮层，包括其脉管系统;OM，肾髓质的外条纹，包括其脉管系统;IM，肾髓质的内条纹，由一些脉管系统、肾实质内的主要动脉和静脉、和骨盆、部分肾外血管、部分输尿管和均匀的周围组织组成）。然后突出显示空间分割分析的热图
    • Corresponding author.  通讯作者。
      ** Corresponding author.
      ** 通讯作者。
      *** Corresponding author. Hainan Medical University, Xueyuan Road 3, Haikou 571199, Heilongjiang, China.
      通讯作者。海南医科大学，中国黑龙江省海口571199学园路3号。
      E-mail address: aihuatcm@163.com (A. Zhang).
      电子邮件地址：aihuatcm@163.com （A. Zhang）。