The single-cell RNA sequencing (scRNA-seq) transcriptome profile of pancreatic islets in three types of diabetic mice and its pathological changes

The single-cell RNA sequencing (scRNA-seq) transcriptome profile of pancreatic islets in three types of diabetic mice and its pathological changes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The single-cell RNA sequencing (scRNA-seq) transcriptome profile of pancreatic islets in three types of diabetic mice and its pathological changes Yijun Zhou, Xiu Yan, Ning Wang, Jianlei Bi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7225641/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate diabetic mouse models are very necessary for basic and clinical diabetes research. Different induction conditions and genetic changes in mouse diabetes models may lead to different classification and expression of islet cell sub-populations. In this study, we first examined the pathologic phenotypic differences of wild C57BL/6J mice (WT) and three types of diabetic mice: high-fat diet (HFD) fed mice (DIO), db/db mice (T2D) and streptozotocin (STZ) induced mice (T1D). Then we described a complete islet cell landscape and identified robust marker genes for each endocrine cell type by analyzing the gene expression of islet cells in the above three types of diabetic mice using publicly available scRNA-Seq data. We also identified α, β, δ and PP cell sub-populations genes expression profile and KEGG and GO analysis in three diabetic mouse models, and explored their similarities and differences. Furthermore, we classified the β-cell populations of three types of diabetic mice and humans with type 2 diabetes and identified differential genes. Experimental verification was conducted on β-cell differential genes such as Scg2 and G6pc2 between different models. We found Scg2 High beta cells may represent in energy-hyperactive diabetes. G6pc2 High beta cells showed an enrichment pattern in the HFD group. In summary, our work provides a deeper understanding of the pathogenesis and usage scenarios of three commonly used diabetic mouse models through single-cell sequencing analysis of pancreatic islets. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Diabetes is a disorder of chronic hyperglycaemia, which is classified into two types: type 1 diabetes(T1D), which is caused by autoimmune destruction of β cells responsible for insulin secretion, and type 2 diabetes (T2D), characterized by insulin resistance and features of metabolic syndrome [1–3] . Islet dysfunction is a hallmark of Diabetes [4, 5] . The islets of Langerhans consist of complex micro-organs of five endocrine cells including alpha (glucagon), beta (insulin), gamma (pancreatic polypeptide), delta (somatostatin), and epsilon (ghrelin) cells, which secrete insulin, glucagon, pancreatic polypeptide, somatostatin and ghrelin, account for only 5% of the pancreatic mass [6–9] . Various research studies have estimated that the islets cells are characterized by their delicate secretion capacity and cellular mass control ability such as Hormone-producing endocrine cells play major roles in glucose homeostasis [7] . These ability enables islets cells to adapt effectively to various metabolic stress or pathology such as obesity [10] . For a long period of time, rats and mice have been the primary objects to study the composition and structure of pancreatic islet cells [7] . The HFD mouse model is frequently used to study obesity and its related diseases. HFD mice may develop obesity-related complications such as insulin resistance and dyslipidemia. DB/DB mice exhibit hyperphagia and obesity from four weeks of age, with increasing age leading to pronounced hyperglycemia, hyperlipidemia, insulin resistance, and other characteristics. The course of their disease progression is very similar to that of patients with type 2 diabetes, making them an ideal animal model for studying type 2 diabetes. STZ can destroy pancreatic beta cells, leading to insulin deficiency and thus inducing hyperglycemic symptoms. STZ-induced diabetic models are commonly used to study type 1 diabetes but can also be combined with a high-fat diet to induce type 2 diabetes. Although the HFD, DB/DB, and STZ mouse models each have their unique features in various disease studies, the differences in diabetic gene expression among these three mouse models remain unclear. In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for transcriptome analysis [7] . This technology can simultaneously measure the expression of tens of thousands of genes in a single cell, ensuring high accuracy, and classify cells based on their expression profiles to detect and characterize cell types and states [11] . Some studies have reported that single-cell RNA sequencing is capable of identifying and validating the core genes of T2D [12] . Another study has demonstrated that cross-species single-cell RNA sequencing can reveal the cellular landscape of early changes in type 2 diabetic mice [13] . In summary, single-cell analysis holds significant importance in medicine because it can identify changes in cellular composition related to diseases and differences in gene expression of cell subtypes in parallel [11] . Through this method, it is possible to study intercellular heterogeneity, intercellular interactions, and unique functions of individual cells in the islets of different diabetic mouse models. β-cell dysfunction or loss exists in both T1D and T2D [14] . Research shows that there is significant heterogeneity in human islets endocrine cells, with beta cells accounting for only about 54% [15] . With the development of scRNA-seq technology and diabetic research progresses, it has been shown that α-cells and rare cells also play important roles in the occurrence and development of diabetes [16] . Although islet separation is a common operation, these auxiliary and endocrine cells in islets connected closely, this makes the separation and purification process of homogenous groups more complex. With the development of the scRNA-seq, high-throughput analysis of transcriptomes across cell types, subpopulations, and states has become feasible [17] . Jin Li et al. identified human-specific expression patterns in alpha and beta cells [18] . Some studies also showed that in diabetic patients corresponding functional and transcriptional changes also occur in α-cells, rare cells, and the vascular system [19, 20] . In addition, δ cell dysfunction is also a potential factor in the development of monogenic diabetes [21] . However, here are still various unresolved questions urgently need further research regarding the role of rare cells in the islet of the pancreas. In the present study, We selected three types of diabetic mouse models: STZ-induced diabetic mice, HFD-induced diabetic mice, and DB/DB mice. We identified α, β, δ and PP cell subpopulations in three diabetic mouse models using publicly available scRNA-Seq data, and explored their similarities and differences. Through this study, we will provide theoretical basis for the selection of different diabetic mouse models in diabetes research, so as to more accurately meet the needs of diabetes theoretical research, and provide theoretical basis for more accurate treatment of diabetes. RESULTS Pathological alterations of diabetes of three diabetic model In order to further understand diabetes, we analyzed the pathological changes in three types of mice and clarify the metabolic characterization and clinical correspondence of different types of mice to illustrate the applicable research directions of mice. First of all, we know that the three types of mice present different nutritional status, which leads to obvious changes in body weight ( Fig. 1 A ) . The most obvious changes in body weight were found in DB/DB mice. However, the changes of fasting blood glucose in STZ group were the most obvious ( Fig. 1 B ) . Corresponds to a large functional loss of pancreatic beta cells, the changes in GTT (Glucose Tolerance Test ) were most obvious in the DB/DB and STZ group. The change in ITT (Insulin Tolerance Test) was more significant in the STZ group ( Fig. 1 C-F ) . Next, we isolated pancreatic islets from three types of diabetic mice and control mice (Fig. 1 G ) . We found that the number of pancreatic islets was reduced in all three types of diabetic mice, especially in the presence of STZ-induced injury. At the same time, the number of larger islet cells increased in HFD and DB/DB mice. This may be due to compensatory proliferation over a certain period of time. scRNA-seq analysis reveals landscape of STZ-induced, HFD-fed, and DB/DB diabetic mouse islets To depict the cellular landscape of the islets of Langerhans in three different diabetic mouse model, three scRNA-seq datasets of pancreatic islets were obtained from GEO databases. A total of 22269 cells from three diabetic mouse models: HFD-fed ( 60% fat; D12492; Research Diets, USA for 8weeks)( 9501 cells), STZ-induced (5562 cells), DB/DB (7206 cells), were obtained after filtering out the low-quality cells respectively ( Figure S1 A, B). Islets cells were clustered into four principal endocrine cells(α、 β、 δ、 pp), annotated based on predominant hormone gene expression (Gcg、Ins1、Sst、Ppy). In addition, small numbers of endothelial, immune, mesenchymal and ductal cells were also identified based on previously reported marker genes (Fig. 2 A). Marker genes expression in untreated WT mouse were showed for landscape depiction (Fig. 2 C, D), Diabetic mouse models maker genes expression in different sub-populations also showed (Fig S2, 3, 4). Compared with the cell composition of WT mice, we found that the ratio of β cells in the HFD group and STZ group was reduced, especially in the STZ group, as a typical chemically induced type 1 diabetes. At the same time, the α cells and δ cells increased significantly in STZ group. However, there was no significant change in the proportion of β cells in DB/DB mice, and in contrast to the other two groups, there appears to be a concomitant decrease in α-cell ratio(Fig. 2 B). While both the DB/DB and HFD groups were nutritionally over-induced models of type 2 diabetes, some of the differences in cell proportions may be due to the different effects on islets caused by the trade-off between high-fat induced diabetes and excessive intake of non-high-fat foods. This suggests that there may be some differences in the mechanism of diabetes under different excessive energy supplies. Endocrine signatures across three different diabetic mouse model To further identify the differences in changes between different subpopulations of cells in the three diabetic mice, we characterized the expression profiles of differential genes among the four major endocrine cells(α、β、δ、pp). We evaluated the overlap of difference gene expression between three diabetic mouse model in four principal endocrine cells and marked some differential genes with conserved changes ( Fig. 3 A ) . We found that HFD and DB/DB group showed significant changes in the gene expression profile of α cells compare to WT respectively, while STZ group had fewer differentially expressed genes. The three had a total of 75 conserved genes. Focusing on the changes in β cells, we found that there are a large number of differential genes in the β cells of STZ mice compared with the other two mice. Among the changes in δ cell genes, the HFD group and the DB/DB mouse group showed more obvious changes compared with the STZ group, and a total of 449 genes were changed together. All three types of mice showed obvious changes in gene expression profiles in PP cells. Volcano plots were used to present the four main cell groups in the three groups of mice in more detail, and the genes with significant changes were annotated in detail (FigS8A-C) . Islets cells expressed established islet hormones and transcription factors defining endocrine cell identities. Therefore, we selected previously reported identified hormone genes and some transcription factor genes [22] to characterize the endocrine signatures among different mice ( Fig. 3 B ) . Generally, the selected genes maintained a relatively stable expression trend. However, the transcription factor Mafa showed significant changes among the four groups. The expression level increased significantly in the HFD group, while it decreased significantly in the STZ group(Fig. 3 C). Mafa is a basic leucine zipper family transcription factor that can activate the expression of insulin in β-cells with PDX1 and NEUROD1. MAFA is indeed indispensable for the maintenance of not only insulin expression but also function of adult β-cells. Therefore, we focused on the similarity of transcription factor (TF) expression patterns. TFs are key to regulating gene expression networks that determine endocrine cell identity during development and maintain the function in mouse islets. Therefore, we focused on finding transcription factors whose functions have been determined in four major endocrine cell populationsto further describe endocrine signatures (Fig. 3 D-F ) . Our analysis identified a number of transcription factors that vary significantly across various cell types in different diabetic models. For example, Nuclear factor erythroid 2-related factor 1 (NFE2L1, also known as Nrf1) is a crucial member of the CNC-bZIP subfamily of transcription factors expressed ubiquitously throughout our body. NFE2L1 exerts regulatory control by modulating pancreatic β cells and insulin production. DNA damage-inducible transcript 3 (Ddit3), a member of the CCAAT/enhancer-binding protein (C/EBP) family, serves as a pivotal transcription factor that plays a crucial role in the endoplasmic reticulum stress response of pancreatic β-cells, influencing cellular function and insulin secretion. We found the Nfe2l1 and Ddit3 was not only highly expressed in β cells in the HFD group, but it also showed significant expression enrichment and increased expression levels in α cells and PP cells. Homeobox protein NKX6-1 is a transcription factor (TF) that plays a critical role in pancreatic β cell function and proliferation. In human pancreatic islet, NKX6-1 expression is exclusive to β cells and is undetectable in other islet cells. We have observed high expression of NKX6-1 in β-cells of high-fat diet (HFD) mice, while its expression is decreased in β-cells of DB/DB and streptozotocin (STZ)-induced diabetic mice. However, there is a small amount of NKX6-1 expression in the PP cells of all four types of mice, which may be due to the fact that the data we selected are derived from mice rather than humans. In addition, some other transcription factors also show expression changes among different subgroups of diabetic mice, thereby delineating different endocrine signatures. Through comparison and screening, it provides a good data support for researchers to choose an appropriate diabetes related tf research model. Analysis of differentially expressed genes (DEGs) expression profiles and enrichment analysis of four major endocrine cells β cells, as insulin-secreting cells, play an important role in the function of pancreatic islets. Therefore, we performed further gene expression analysis and grouping of β-cell populations. We used UAMP analysis to further classify the different types of β cells in each group of diabetic mice. We further divided the β cells in the HFD group into 9 subpopulation (Fig. 4 A), 8 subpopulation in DB/DB group(Fig. 4 B), 6 subpopulation in STZ group(Fig. 4 C). According to the grouping situation, we found that the DB/DB group and the STZ group had a very obvious boundary of cell group composition, while the boundary between the HFD group and the WT group was not obvious. This suggests that the diversification distance of β-cell populations in the HFD group may be closer than that of the other two groups. In order to further clarify the changes in its gene expression profile, we annotated the top 30 up- and down-regulated genes in each group(Fig. 5 A-C). We found that the up- and down-regulation trends of many genes in each group were inconsistent. This prompts us to choose a more reasonable model when study related genes. For example, Secretogranin II(Scg2), is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins, was significantly increased in HFD and DB/DB mouse, but significantly decreased in STZ group. Glucose-6-phosphatase catalytic subunit 2 (G6pc2), an enzyme belonging to the glucose-6-phosphatase catalytic subunit family, was increased in HFD group, but decreased in other two groups. In order to further explore the functions of these changed genes and the changes in signals that may be mediated, we performed KEGG and GO analysis on the changed genes of the three groups of mice. We found that the PI3K-AKT signaling pathway is enriched in the HFD group, which echoed changes in CCND2 and other genes mediating the cell cycle found in the previous gene change screening, suggesting that cell proliferation may occur during 8 weeks of HFD feeding (Fig. 5 D). In the DB/DB group, the islet-change genes were more enriched in inflammation-related pathways, such as TNF and IL7 signaling pathway, at the same time, MAPK pathway was also enriched, suggesting the cell proliferation in DB/DB mouse β cells (Fig. 5 E). The islet change genes in the STZ group are more enriched in related molecular pathways leading to changes in islet function(Fig. 5 F). At the same time, we used GO analysis to enrich and annotate the differentially expressed genesin each group (Fig. 5 G-I). Our gene enrichment analysis provides researchers with a very good resource when studying various signaling pathways in diabetes and choose a scientific mouse model. α cells, is an endocrine gland epithelial cell in the pancreatic islets. Under normal physiological conditions, islet α cells account for approximately 20% of the total number of islet cells. In the case of beta cell insufficiency, alpha cells will transform into beta cells, which is also a good entry point for the treatment of diabetes. We examined genes differentially expressed in alpha cells of three types of diabetic mice. We found that in HFD mice and DB/DB mice, the pancreatic β-cell marker genes Ins1, Ins2, NKX6-1, and Mafa were significantly up-regulated, suggesting that the transformation of α cells into β cells occurred in these two mice ( FigS5A, C ). In contrast, in STZ group, these markers were down-regulated in α cells, suggesting that there may be problems with the conversion of α cells to β cells under STZ-induced conditions( FigS5E ). Very interestingly, in HFD δ cells, the expression of these four genes is still up-regulated, suggesting that delta cells also tended to transform into beta cells in HFD mice ( FigS6A ). Overexpression of Ins1 and Ins2 genes also occurred in delta cells in the DB/DB group ( FigS6B ). Like alpha cells, delta cells did not show a tendency to transform into beta cells in the STZ group( FigS6C ). Next, We also found overexpression of Ins1, Ins2, and Mafa genes in HFD PP cells( FigS7A ), which suggests that HFD mice may be a very good model for studying β-cell transformation. Scg2 High beta cells may represent in energy-hyperactive diabetes DB/DB mice express mutations in leptin receptor that lead to obesity, decreased insulin sensitivity and β-cell function subsequent increased levels of blood glucose, decreased [23] . HFD treatment also lead to obesity and decreased β-cell function. These two models represent energy-hyperactive induced type 2 diabetes. Regardless of type 1 diabetes or type 2 diabetes, loss of β-cell function is the most important terminal change. In this study, we found that secretogranin II(Scg2), which is involved in the packaging or sorting of peptide hormones and neuropeptides into secretory vesicles, was increased in the DB/DB and HFD groups, and decreasedin STZ group. In the HFD group, except for subgroup 5, Scg2 High Cells showed a large amount of enriched expression in other subgroups. In DB/DB mice, the Scg2 High cell population is enriched in 1, 4, 5, and 6 cell subpopulations and overlaps with the DB/DB cluster, while in the STZ group, it approximately overlaps with the cell population of the WT cluster, and the Scg2 low cell population appears in large numbers in the STZ cluster ( Fig. 6 C ) . In order to further confirm this phenomenon, we performed immunohistochemical staining on pancreatic tissue sections of four types of mice and found that Scg2 was mainly expressed in pancreatic islets and showed roughly the same location as insulin expression. We also found that its expression was higher in the HFD group and the DB/DB group ( Fig. 6 A,B ) . At the same time, we isolated pancreatic islets from four types of mice and detected protein expression, and the results were consistent with what we found in omics and immunohistochemical staining (Fig. 6 D,E ) . Therefore, in metabolic-related type 2 diabetes caused by excessive energy supply, we believe that pancreatic beta cells increase insulin secretion to a certain extent through excessive proliferation or even differentiation. For the first time, we found an overexpressed β subpopulation of Scg2 High , which may be a represent event in this type of diabetes. To further verify the changes of Scg2 in human diabetes, we downloaded single-cell sequencing data from diabetic patients and mapped the expression profile of Scg2 in the β-cell population. First, we use the conventional BMI as the standard to define overnutrition. We selected samples with diabetes, prediabetes, and controls and found that increasing BMI was positively correlated with the progression of diabetes ( Fig. 7 A ) . We grouped β cells from samples at three different stages and obtained a total of 18 subpopulations ( Fig. 7 B ) . We also screened the TOP30 genes that were up- and down-regulated in β cells for annotation. As expected, Scg2 is also up-regulated in the diabetic stage, and we also found that it is up-regulated to a certain extent in the prediabetic stage ( Fig. 7 C ) . Next, we mapped the expression profile of Scg2 in specific subpopulations of β cells. We found that the cell population of Scg2 High cells also overlaps with prediabetes and progressive diabetes ( Fig. 7 D-H ) , suggesting that Scg2 High cells are marker cells in type 2 diabetes under high nutritional conditions. G6pc2 High beta cells showed an enrichment pattern in the HFD group G6pc2 is predominantly expressed in islets, encodes a glucose-6-phosphatase catalytic subunit that converts glucose-6-phosphate (G6P) to glucose. We found that G6pc2 showed an overall up-regulation trend in the HFD group, with low expression only in subpopulations 5 and 8 ( Fig. 8 A ) , which was consistent with the work of Jeffre R Millman et al [24] . At the same time, our data analyzes the specific changing trends in each subgroup in more detail. On the contrary, G6pc2 showed an overall downward trend in both the DB/DB group and the STZ group, and we also presented the changes in each subgroup ( Fig. 8 B,C ) . We also analyzed the expression of G6pc2 in human pancreatic islet β cells. Like the expression trend in HFD, G6pc2 High Cells also appeared in β cells in obese type 2 diabetes ( Fig. 8 D ) . Therefore, we believe that when studying the impact of G6pc2 gene on pancreatic islet function, the HFD-induced model may be more consistent with type 2 diabetes in human obesity. In order to better verify the expression of G6pc2 in single-cell sequencing, we performed immunohistochemical staining on pancreatic tissue sections of four types of mice and found that G6pc2 was expressed in pancreatic islets. It can be seen from the results that G6pc2 High cells appeared in large quantities under the induction of HFD. However, there was a significant reduction in the STZ group and the DB/DB group (Fig. 8 E ) . This is consistent with the overall trend in our previous single-cell sequencing analysis. Disscussion Many genetic alterations and chemically or nutritionally induced models of type 1 and type 2 diabetes have played a pivotal role in the study of diabetes [25] . However, further discussion and research are still needed on the scientificity and appropriateness of model application. To develop or apply the most appropriate models for various types of diabetes in the clinic, the development of scRNA-seq technology and the discovery of new cell markers deepened the understanding of beta cell heterogeneity in health and disease [26, 27] . At present, other researchers have performed single-cell sequencing on the pancreatic islets of three commonly used diabetic mice (DB/DB, HFD, STZ). We have further compared and analyzed these data, and found out the differences in islet cell composition and gene expression (α, β, δ and PP) at the single-cell level. In particular, further exploration on the most important β cells provides good data support for elucidating its pathogenesis and clinical application. In our research, we found that even for the same type of diabetes, such as type 2 diabetes, there are many differences in the cell group compositions in the pancreatic islets between different models. For example, large groups such as α-cell groups appear in different groups of diabetic mice. The ratios are different, which may indicate the mutual transformation between α cells and β cells [28, 29] . Even changes in many transcription factors such as Mafa, Nfe2l1 and Ddit3 may regulate the expression of different gene networks, which also prompts us to choose appropriate models in molecular biology research. Our analysis at the single-cell level may be the molecular basis for the diverse phenotypes exhibited by several diabetes models. β cells are the main cells for insulin secretion. We further analyzed their differences in three different diabetic mice. In this reasearch, we found that the expression changes of Scg2 are very noticeable. But we found few reports on the relationship between diabetes and Scg2. This gene may be a very potential diabetes gene worthy of further exploration. In our analysis, we found that the change of Scg2 in various diabetes mellitus was not consistent. It was significantly up-regulated in energy-excess diabetes mellitus of HFD and DB/DB mice, while it was significantly down-regulated in STZ-induced diabetes mellitus. We also analyzed human diabetes and found that Scg2 was up-regulated in both the early and progressive stages of obese type 2 diabetes,which is consistent with the trends we observed in mice. Some researchers have found Scg2 was highly expressed in the hypothalamic nuclei, PVN, LHA, ARC, and amygdaloid nucleus, which are known to form the main centre of appetite regulation [30] . Toshiyuki Takeuchi et.al found that Scg2 binds to Scg3, which based on SNP analysis, have an association with obesity [31] . It is suggested that Scg2 may play an important role in energy metabolism. Whether the appearance of Scg2 High cells may be related to the proliferation of islet cell clusters under high-energy conditions may require further exploration. In view of the present data, we propose that Scg2 is a potential regulator of food intake and energy homeostasis in energy-excess diabetes. Another gene we paid attention to was G6pc2, which showed differences in the islet cells of three different diabetic mice. Significant upregulation occurs upon HFD induction and in the context of obese type 2 diabetes in humans. However, downregulation occurred in DB/DB mice and STZ mice. Our study is consistent with that of Karin J. Bosma [32] , who found that G6pc2 expression was markedly induced in response to high fat diet feeding. Jason M. Tonne [33] found STZ treatment also suppressed expression of a wide range of genes linked with key β-cell functions or diabetes development, such as G6pc2, Slc2a2 (Glut2), Slc30a8, Neurod1, Ucn3, Gad1, Isl1, Foxa2, Vdr, Pdx1, Fkbp1b and Abcc8, suggesting global β-cell defects in STZ-treated islets. Lipeng Ren found G6pc2 was reduced in DB/DB islets [34] . As we know, G6pc2 acts in conjunction with β-cell glucose sensors and glucokinase to produce ineffective substrate recycling and determine the rate of β-cell glycolytic flux, it is an important regulatory gene for fasting blood glucose (FBG). This brings up a very interesting point, especially the difference in the expression of G6pc2 in diabetes between DB/DB and HFD. Is it the effect of fat metabolism on pancreatic islets that causes the difference? Mice with excess energy metabolism may have completely opposite effects on the pancreatic islets of mice due to differences in the nutrients they consume. This also illustrates the complexity of diabetes to a certain extent, and our data may have some hints on how to configure a healthy diabetes diet. Conclusion In general, we integrated data from single-cell sequencing of islet cells from three types of diabetic mice(HFD, DB/DB, STZ). We specifically classified and compared the four main types of secretory cells(α, β, δ and PP). We indeed found that there are significant differences in the types of islet cells in these diabetic mice, and the gene expression profiles in various types of cells are different. Currently, studying the relationship between genes and diabetes is the mainstream direction. When selecting diabetic mice, our data laid the foundation for it and would help everyone to choose a more appropriate model for related genetic research. Besides, we found that the changes in the relationship between Scg2 and nutritional diabetes are very interesting, and we also conducted preliminary verification. At present, there are not many studies on Scg2, and it may be a new clinical target. Through this study, we will provide theoretical basis for the selection of different diabetic mouse models in diabetes research, so as to more accurately meet the needs of diabetes research, and provide theoretical basis for more accurate treatment of diabetes. Declarations Author contributions XY and NW download single cell sequencing data and conduct data analysis, conduct mouse experiments, and wrote the manuscript. Jl B and YJZ helped in writing-review, discussion and editing the manuscript. All authors reviewed the manuscript. Acknowledgments This work was funded by the Basic scientific research project of Liaoning Provincial Department of Education (LJKMZ20221290). Declaration of Interest Statement The authors declare no potential conflicts of interest. Material and methods Source of raw data The T2D samples were downloaded from the GSE200531 series with 10× scRNA-seq data, the samples included 7356 cells(DB/m) and 7206 cells(DB/DB). The HFD samples were downloaded from the GSE203376 series with 10× scRNA-seq data, the samples included 9501 cells(HFD) and 9306 cells(CD). The T1D samples were downloaded from the GSE128565 series with 10× scRNA-seq data, the samples included 9360 cells(WT) and 5562 cells(STZ). Data processing of 10 × scRNA-Seq We processed single-cell sequencing data of diabetes using the following methods. First, we transformed the 10× scRNA-seq data into a Seurat object using the "Seurat" R package, and excluded cells with poor quality by calculating the percentage of mitochondrial or ribosomal genes as quality control (QC). The "FindVariableFeatures" program identified the top 2000 highly variable genes, and we performed dimensionality reduction and cell subpopulation identification using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) with these 2000 additional genes. To find marker genes for each cluster, we used the "Find All Markers" tool with |Log2FC| and min. pct cutoff values set to 0.25 and 0.25, respectively. We annotated the cell subpopulations based on the marker genes identified in the study and performed GO and KEGG enrichment analysis using the R package "clusterProfiler". Mice and treatment All mices were under specific pathogen-free conditions with a 12-hour light/dark cycle in Institute for Genome Engineered Animal Models of Human Diseases. HFD group were fed a HFD( 60% fat; D12492) or a chow diet(CD; 13.5% fat; 5001) for eight weeks. The STZ mouse model uses C57BL/6J mice and starts continuous injections of STZ (50mg/kg) for five days from the 8-week-old. Two weeks later, fasting blood glucose should be measured to be greater than 11.1 mmol/L. 12-weeks-old DB/DB mouse was used for the experiment. All experimental animal procedures were approved by DaLian medical University, DaLian, LiaoNing, China. Glucose and insulin tolerance tests GTT: The experimental mice were fasted for 13 hours with normal water intake. Based on the body weight, an appropriate amount of 20% glucose solution was injected into the peritoneal cavity. The timer started after the first mouse was injected. Blood glucose levels were measured at 15 minutes, 30 minutes, 60 minutes, and 120 minutes after glucose injection, and the results were recorded. ITT: The experimental mice were fasted for 5 hours with normal water intake. After fasting. Based on the body weight, an appropriate amount of 0.075 U/mL insulin solution was injected into the peritoneal cavity. Timing started after the first mouse was injected. Blood glucose levels were measured at 15 minutes, 30 minutes, and 60 minutes after injecting the glucose solution, and the results were recorded. Western blotting Proteins were isolated from mouse islet cells using TRIzol reagent. Equal amount of protein samples (10 μg) were loaded on SDS-PAGE gel and then transferred to PVDF membranes for 2 h. Subsequently, the PVDF membrane was sealed with 5% skimmed milk powder for 2 h at room temperature. Pimary antibodies: anti-GAPDH (1:10000, 10494-1-AP, proteintech, China); anti-Scg2 (1:1000, 20357-1-AP, proteintech, China) were incubation overnight at 4 ℃, following by HRP-conjugated secondary antibodies: Goat Anti-Rabbit IgG (1:10000, A21020, Abbkine, China) for 2 h at room temperature after washing. The protein blots were visualized using SuperSignal West Pico Chemiluminescent Substrate (Pierce Biotechnology, USA) in ChemiDoc XRS+ system (BioRad, USA). Immunofluorescence Fix pancreatic tissue in formalin, embed in paraffin after dehydration in ethanol and xylene, cut into 3mm sections, deparaffinize in xylene, rehydrate using a series of graded alcohols, perform antigen retrieval and block with serum. Then incubate the sections with primary antibody: anti-insulin (1:400, 66198-1-Ig, proteintech, China); anti-Scg2 (1:400, 20357-1-AP, proteintech, China); anti-G6pc2(1:300, bs-13386R, bioboss, China) overnight at 4 ℃ in the dark. After warming up to room temperature, wash the sections and incubate with secondary antibody: Goat Anti-Mouse IgG(1：1000, A23210-1, Abbkine, China); Goat Anti-Rabbit IgG(1: 300, Y6107L, UElandy YF, China) for 2 hours at room temperature in the dark. Visualize using fluorescence microscopy (Zeiss, Germany). Islet isolation We used liver bile duct microinjection collagenase technique to isolate mouse islets. The mice were weighed and injected with anesthetic according to their body weight. The position of the duodenal papilla was identified and clamped with hemostatic forceps to block the bile and collagenase solution from entering the duodenum. 4mL of collagenase solution was extracted using a 5mL syringe, and a 30G 1/2 needle was attached. Pancreatic digestion: The centrifuge tube was placed in a 37 ℃ water bath and digested for 16 minutes, gently shaking the tube two to three times during the process. After digestion, The islets were manually selected under a dissecting microscope and placed in the culture medium. References Tuomi, T., et al., The many faces of diabetes: a disease with increasing heterogeneity. Lancet, 2014. 383 (9922): p. 1084-1094. Cohrs, C.M., et al., Dysfunction of Persisting β Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis. Cell Reports, 2020. 31 (1): p. 107469. Kramer, C.K., et al., Glucagon response to oral glucose challenge in type 1 diabetes: lack of impact of euglycemia. Diabetes Care, 2014. 37 (4): p. 1076-82. Weir, G.C. and S. Bonner-Weir, Islet β cell mass in diabetes and how it relates to function, birth, and death. Annals of the New York Academy of Sciences, 2013. 1281 (1): p. 92-105. De Jesus, D.F. and R.N. Kulkarni, "Omics" and "epi-omics" underlying the β-cell adaptation to insulin resistance. Mol Metab, 2019. 27s (Suppl): p. S42-s48. Mastracci, T.L. and L. Sussel, The endocrine pancreas: insights into development, differentiation, and diabetes. WIREs Developmental Biology, 2012. 1 (5): p. 609-628. Bosco, D., et al., Unique arrangement of alpha- and beta-cells in human islets of Langerhans. Diabetes, 2010. 59 (5): p. 1202-10. Orci, L. and R.H. Unger, Functional subdivision of islets of Langerhans and possible role of D cells. Lancet, 1975. 2 (7947): p. 1243-4. Da Silva Xavier, G., The Cells of the Islets of Langerhans. Journal of Clinical Medicine, 2018. 7 (3): p. 54. Ellenbroek, J.H., et al., Pancreatic α-cell mass in obesity. Diabetes Obes Metab, 2017. 19 (12): p. 1810-1813. Tritschler, S., et al., Systematic single-cell analysis provides new insights into heterogeneity and plasticity of the pancreas. Mol Metab, 2017. 6 (9): p. 974-990. Yang, T.T., et al., Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing. European Journal of Medical Research, 2023. 28 (1). Chen, K., et al., Cross-species scRNA-seq reveals the cellular landscape of retina and early alterations in type 2 diabetes mice. Genomics, 2023. 115 (4). Johnson, J.D., The quest to make fully functional human pancreatic beta cells from embryonic stem cells: climbing a mountain in the clouds. Diabetologia, 2016. 59 (10): p. 2047-57. Bonner-Weir, S., B.A. Sullivan, and G.C. Weir, Human Islet Morphology Revisited: Human and Rodent Islets Are Not So Different After All. J Histochem Cytochem, 2015. 63 (8): p. 604-12. Fu, H., et al., Discoveries in Pancreatic Physiology and Disease Biology Using Single-Cell RNA Sequencing. Frontiers in Cell and Developmental Biology, 2022. 9 . Sandberg, R., Entering the era of single-cell transcriptomics in biology and medicine. Nat Methods, 2014. 11 (1): p. 22-4. Li, J., et al., Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Rep, 2016. 17 (2): p. 178-87. Brissova, M., et al., α Cell Function and Gene Expression Are Compromised in Type 1 Diabetes. Cell Rep, 2018. 22 (10): p. 2667-2676. Lam, C.J., et al., Highly Proliferative α-Cell-Related Islet Endocrine Cells in Human Pancreata. Diabetes, 2018. 67 (4): p. 674-686. Lawlor, N., et al., Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res, 2017. 27 (2): p. 208-222. Kim, S., et al., Molecular and genetic regulation of pig pancreatic islet cell development. Development, 2020. 147 (6). Ghilardi, N., et al., Defective STAT signaling by the leptin receptor in diabetic mice. Proceedings of the National Academy of Sciences of the United States of America, 1996. 93 (13): p. 6231-5. Augsornworawat, P., et al., Single-cell transcriptome profiling reveals β cell maturation in stem cell-derived islets after transplantation. Cell Rep, 2021. 34 (10): p. 108850. Alpers, C.E. and K.L. Hudkins, Mouse models of diabetic nephropathy. Current Opinion in Nephrology and Hypertension, 2011. 20 (3): p. 278-284. Avrahami, D., et al., Single-cell transcriptomics of human islet ontogeny defines the molecular basis of β-cell dedifferentiation in T2D. Molecular Metabolism, 2020. 42 . Bao, K., et al., Pseudotime Ordering Single-Cell Transcriptomic of beta Cells Pancreatic Islets in Health and Type 2 Diabetes. Phenomics (Cham, Switzerland), 2021. 1 (5): p. 199-210. Chung, C.H., et al., Pancreatic β-cell neogenesis by direct conversion from mature α-cells. Stem Cells, 2010. 28 (9): p. 1630-8. Piran, R., et al., Pharmacological induction of pancreatic islet cell transdifferentiation: relevance to type I diabetes. Cell Death Dis, 2014. 5 (7): p. e1357. Hotta, K., et al., Secretogranin II binds to secretogranin III and forms secretory granules with orexin, neuropeptide Y, and POMC. Journal of Endocrinology, 2009. 202 (1): p. 111-121. Hotta, K., et al., Secretogranin II binds to secretogranin III and forms secretory granules with orexin, neuropeptide Y, and POMC. J Endocrinol, 2009. 202 (1): p. 111-21. Bosma, K.J., et al., G6PC2 confers protection against hypoglycemia upon ketogenic diet feeding and prolonged fasting. Mol Metab, 2020. 41 : p. 101043. Tonne, J.M., et al., Global gene expression profiling of pancreatic islets in mice during streptozotocin-induced β-cell damage and pancreatic Glp-1 gene therapy. Dis Model Mech, 2013. 6 (5): p. 1236-45. Ren, L., et al., Adjudin improves beta cell maturation, hepatic glucose uptake and glucose homeostasis. Diabetologia, 2024. 67 (1): p. 137-155. Additional Declarations There is NO conflict of interest to disclose Supplementary Files SupplementalFigure.docx Supplemental Figure Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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19:11:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22602258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7225641/v1/8c42c776-884a-453e-8db7-eeb50064fc34.pdf"},{"id":96366791,"identity":"37917ce8-2e32-4289-9301-6d08d40d3bf1","added_by":"auto","created_at":"2025-11-20 10:11:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5596877,"visible":true,"origin":"","legend":"Supplemental Figure","description":"","filename":"SupplementalFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7225641/v1/243e6b986ef925e40f79dc24.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"The single-cell RNA sequencing (scRNA-seq) transcriptome profile of pancreatic islets in three types of diabetic mice and its pathological changes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes is a disorder of chronic hyperglycaemia, which is classified into two types: type 1 diabetes(T1D), which is caused by autoimmune destruction of β cells responsible for insulin secretion, and type 2 diabetes (T2D), characterized by insulin resistance and features of metabolic syndrome\u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e. Islet dysfunction is a hallmark of Diabetes\u003csup\u003e[4, 5]\u003c/sup\u003e. The islets of Langerhans consist of complex micro-organs of five endocrine cells including alpha (glucagon), beta (insulin), gamma (pancreatic polypeptide), delta (somatostatin), and epsilon (ghrelin) cells, which secrete insulin, glucagon, pancreatic polypeptide, somatostatin and ghrelin, account for only 5% of the pancreatic mass \u003csup\u003e[6\u0026ndash;9]\u003c/sup\u003e. Various research studies have estimated that the islets cells are characterized by their delicate secretion capacity and cellular mass control ability such as Hormone-producing endocrine cells play major roles in glucose homeostasis\u003csup\u003e[7]\u003c/sup\u003e. These ability enables islets cells to adapt effectively to various metabolic stress or pathology such as obesity\u003csup\u003e[10]\u003c/sup\u003e. For a long period of time, rats and mice have been the primary objects to study the composition and structure of pancreatic islet cells\u003csup\u003e[7]\u003c/sup\u003e. The HFD mouse model is frequently used to study obesity and its related diseases. HFD mice may develop obesity-related complications such as insulin resistance and dyslipidemia. DB/DB mice exhibit hyperphagia and obesity from four weeks of age, with increasing age leading to pronounced hyperglycemia, hyperlipidemia, insulin resistance, and other characteristics. The course of their disease progression is very similar to that of patients with type 2 diabetes, making them an ideal animal model for studying type 2 diabetes. STZ can destroy pancreatic beta cells, leading to insulin deficiency and thus inducing hyperglycemic symptoms. STZ-induced diabetic models are commonly used to study type 1 diabetes but can also be combined with a high-fat diet to induce type 2 diabetes. Although the HFD, DB/DB, and STZ mouse models each have their unique features in various disease studies, the differences in diabetic gene expression among these three mouse models remain unclear.\u003c/p\u003e\u003cp\u003eIn recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for transcriptome analysis\u003csup\u003e[7]\u003c/sup\u003e. This technology can simultaneously measure the expression of tens of thousands of genes in a single cell, ensuring high accuracy, and classify cells based on their expression profiles to detect and characterize cell types and states\u003csup\u003e[11]\u003c/sup\u003e. Some studies have reported that single-cell RNA sequencing is capable of identifying and validating the core genes of T2D\u003csup\u003e[12]\u003c/sup\u003e. Another study has demonstrated that cross-species single-cell RNA sequencing can reveal the cellular landscape of early changes in type 2 diabetic mice\u003csup\u003e[13]\u003c/sup\u003e. In summary, single-cell analysis holds significant importance in medicine because it can identify changes in cellular composition related to diseases and differences in gene expression of cell subtypes in parallel \u003csup\u003e[11]\u003c/sup\u003e. Through this method, it is possible to study intercellular heterogeneity, intercellular interactions, and unique functions of individual cells in the islets of different diabetic mouse models.\u003c/p\u003e\u003cp\u003eβ-cell dysfunction or loss exists in both T1D and T2D \u003csup\u003e[14]\u003c/sup\u003e. Research shows that there is significant heterogeneity in human islets endocrine cells, with beta cells accounting for only about 54%\u003csup\u003e[15]\u003c/sup\u003e. With the development of scRNA-seq technology and diabetic research progresses, it has been shown that α-cells and rare cells also play important roles in the occurrence and development of diabetes\u003csup\u003e[16]\u003c/sup\u003e. Although islet separation is a common operation, these auxiliary and endocrine cells in islets connected closely, this makes the separation and purification process of homogenous groups more complex. With the development of the scRNA-seq, high-throughput analysis of transcriptomes across cell types, subpopulations, and states has become feasible\u003csup\u003e[17]\u003c/sup\u003e. Jin Li et al. identified human-specific expression patterns in alpha and beta cells\u003csup\u003e[18]\u003c/sup\u003e. Some studies also showed that in diabetic patients corresponding functional and transcriptional changes also occur in α-cells, rare cells, and the vascular system\u003csup\u003e[19, 20]\u003c/sup\u003e. In addition, δ cell dysfunction is also a potential factor in the development of monogenic diabetes\u003csup\u003e[21]\u003c/sup\u003e. However, here are still various unresolved questions urgently need further research regarding the role of rare cells in the islet of the pancreas.\u003c/p\u003e\u003cp\u003eIn the present study, We selected three types of diabetic mouse models: STZ-induced diabetic mice, HFD-induced diabetic mice, and DB/DB mice. We identified α, β, δ and PP cell subpopulations in three diabetic mouse models using publicly available scRNA-Seq data, and explored their similarities and differences. Through this study, we will provide theoretical basis for the selection of different diabetic mouse models in diabetes research, so as to more accurately meet the needs of diabetes theoretical research, and provide theoretical basis for more accurate treatment of diabetes.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003ePathological alterations of diabetes of three diabetic model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to further understand diabetes, we analyzed the pathological changes in three types of mice and clarify the metabolic characterization and clinical correspondence of different types of mice to illustrate the applicable research directions of mice. First of all, we know that the three types of mice present different nutritional status, which leads to obvious changes in body weight \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The most obvious changes in body weight were found in DB/DB mice. However, the changes of fasting blood glucose in STZ group were the most obvious \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Corresponds to a large functional loss of pancreatic beta cells, the changes in GTT (Glucose Tolerance Test ) were most obvious in the DB/DB and STZ group. The change in ITT (Insulin Tolerance Test) was more significant in the STZ group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F\u003cb\u003e)\u003c/b\u003e. Next, we isolated pancreatic islets from three types of diabetic mice and control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. We found that the number of pancreatic islets was reduced in all three types of diabetic mice, especially in the presence of STZ-induced injury. At the same time, the number of larger islet cells increased in HFD and DB/DB mice. This may be due to compensatory proliferation over a certain period of time.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003escRNA-seq analysis reveals landscape of STZ-induced, HFD-fed, and DB/DB diabetic mouse islets\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo depict the cellular landscape of the islets of Langerhans in three different diabetic mouse model, three scRNA-seq datasets of pancreatic islets were obtained from GEO databases. A total of 22269 cells from three diabetic mouse models: HFD-fed ( 60% fat; D12492; Research Diets, USA for 8weeks)( 9501 cells), STZ-induced (5562 cells), DB/DB (7206 cells), were obtained after filtering out the low-quality cells respectively ( Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, B). Islets cells were clustered into four principal endocrine cells(α、 β、 δ、 pp), annotated based on predominant hormone gene expression (Gcg、Ins1、Sst、Ppy). In addition, small numbers of endothelial, immune, mesenchymal and ductal cells were also identified based on previously reported marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Marker genes expression in untreated WT mouse were showed for landscape depiction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D), Diabetic mouse models maker genes expression in different sub-populations also showed \u003cb\u003e(Fig S2, 3, 4).\u003c/b\u003e Compared with the cell composition of WT mice, we found that the ratio of β cells in the HFD group and STZ group was reduced, especially in the STZ group, as a typical chemically induced type 1 diabetes. At the same time, the α cells and δ cells increased significantly in STZ group. However, there was no significant change in the proportion of β cells in DB/DB mice, and in contrast to the other two groups, there appears to be a concomitant decrease in α-cell ratio(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). While both the DB/DB and HFD groups were nutritionally over-induced models of type 2 diabetes, some of the differences in cell proportions may be due to the different effects on islets caused by the trade-off between high-fat induced diabetes and excessive intake of non-high-fat foods. This suggests that there may be some differences in the mechanism of diabetes under different excessive energy supplies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndocrine signatures across three different diabetic mouse model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further identify the differences in changes between different subpopulations of cells in the three diabetic mice, we characterized the expression profiles of differential genes among the four major endocrine cells(α、β、δ、pp). We evaluated the overlap of difference gene expression between three diabetic mouse model in four principal endocrine cells and marked some differential genes with conserved changes\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. We found that HFD and DB/DB group showed significant changes in the gene expression profile of α cells compare to WT respectively, while STZ group had fewer differentially expressed genes. The three had a total of 75 conserved genes. Focusing on the changes in β cells, we found that there are a large number of differential genes in the β cells of STZ mice compared with the other two mice. Among the changes in δ cell genes, the HFD group and the DB/DB mouse group showed more obvious changes compared with the STZ group, and a total of 449 genes were changed together. All three types of mice showed obvious changes in gene expression profiles in PP cells. Volcano plots were used to present the four main cell groups in the three groups of mice in more detail, and the genes with significant changes were annotated in detail\u003cb\u003e(FigS8A-C)\u003c/b\u003e. Islets cells expressed established islet hormones and transcription factors defining endocrine cell identities. Therefore, we selected previously reported identified hormone genes and some transcription factor genes\u003csup\u003e[22]\u003c/sup\u003e to characterize the endocrine signatures among different mice \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Generally, the selected genes maintained a relatively stable expression trend. However, the transcription factor Mafa showed significant changes among the four groups. The expression level increased significantly in the HFD group, while it decreased significantly in the STZ group(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Mafa is a basic leucine zipper family transcription factor that can activate the expression of insulin in β-cells with PDX1 and NEUROD1. MAFA is indeed indispensable for the maintenance of not only insulin expression but also function of adult β-cells. Therefore, we focused on the similarity of transcription factor (TF) expression patterns. TFs are key to regulating gene expression networks that determine endocrine cell identity during development and maintain the function in mouse islets. Therefore, we focused on finding transcription factors whose functions have been determined in four major endocrine cell populationsto further describe endocrine signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F\u003cb\u003e)\u003c/b\u003e. Our analysis identified a number of transcription factors that vary significantly across various cell types in different diabetic models. For example, Nuclear factor erythroid 2-related factor 1 (NFE2L1, also known as Nrf1) is a crucial member of the CNC-bZIP subfamily of transcription factors expressed ubiquitously throughout our body. NFE2L1 exerts regulatory control by modulating pancreatic β cells and insulin production. DNA damage-inducible transcript 3 (Ddit3), a member of the CCAAT/enhancer-binding protein (C/EBP) family, serves as a pivotal transcription factor that plays a crucial role in the endoplasmic reticulum stress response of pancreatic β-cells, influencing cellular function and insulin secretion. We found the Nfe2l1 and Ddit3 was not only highly expressed in β cells in the HFD group, but it also showed significant expression enrichment and increased expression levels in α cells and PP cells. Homeobox protein NKX6-1 is a transcription factor (TF) that plays a critical role in pancreatic β cell function and proliferation. In human pancreatic islet, NKX6-1 expression is exclusive to β cells and is undetectable in other islet cells. We have observed high expression of NKX6-1 in β-cells of high-fat diet (HFD) mice, while its expression is decreased in β-cells of DB/DB and streptozotocin (STZ)-induced diabetic mice. However, there is a small amount of NKX6-1 expression in the PP cells of all four types of mice, which may be due to the fact that the data we selected are derived from mice rather than humans. In addition, some other transcription factors also show expression changes among different subgroups of diabetic mice, thereby delineating different endocrine signatures. Through comparison and screening, it provides a good data support for researchers to choose an appropriate diabetes related tf research model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of differentially expressed genes (DEGs) expression profiles and enrichment analysis of four major endocrine cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ cells, as insulin-secreting cells, play an important role in the function of pancreatic islets. Therefore, we performed further gene expression analysis and grouping of β-cell populations. We used UAMP analysis to further classify the different types of β cells in each group of diabetic mice. We further divided the β cells in the HFD group into 9 subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), 8 subpopulation in DB/DB group(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), 6 subpopulation in STZ group(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). According to the grouping situation, we found that the DB/DB group and the STZ group had a very obvious boundary of cell group composition, while the boundary between the HFD group and the WT group was not obvious. This suggests that the diversification distance of β-cell populations in the HFD group may be closer than that of the other two groups. In order to further clarify the changes in its gene expression profile, we annotated the top 30 up- and down-regulated genes in each group(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). We found that the up- and down-regulation trends of many genes in each group were inconsistent. This prompts us to choose a more reasonable model when study related genes. For example, Secretogranin II(Scg2), is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins, was significantly increased in HFD and DB/DB mouse, but significantly decreased in STZ group. Glucose-6-phosphatase catalytic subunit 2 (G6pc2), an enzyme belonging to the glucose-6-phosphatase catalytic subunit family, was increased in HFD group, but decreased in other two groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn order to further explore the functions of these changed genes and the changes in signals that may be mediated, we performed KEGG and GO analysis on the changed genes of the three groups of mice. We found that the PI3K-AKT signaling pathway is enriched in the HFD group, which echoed changes in CCND2 and other genes mediating the cell cycle found in the previous gene change screening, suggesting that cell proliferation may occur during 8 weeks of HFD feeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In the DB/DB group, the islet-change genes were more enriched in inflammation-related pathways, such as TNF and IL7 signaling pathway, at the same time, MAPK pathway was also enriched, suggesting the cell proliferation in DB/DB mouse β cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The islet change genes in the STZ group are more enriched in related molecular pathways leading to changes in islet function(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). At the same time, we used GO analysis to enrich and annotate the differentially expressed genesin each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-I). Our gene enrichment analysis provides researchers with a very good resource when studying various signaling pathways in diabetes and choose a scientific mouse model.\u003c/p\u003e\u003cp\u003eα cells, is an endocrine gland epithelial cell in the pancreatic islets. Under normal physiological conditions, islet α cells account for approximately 20% of the total number of islet cells. In the case of beta cell insufficiency, alpha cells will transform into beta cells, which is also a good entry point for the treatment of diabetes. We examined genes differentially expressed in alpha cells of three types of diabetic mice. We found that in HFD mice and DB/DB mice, the pancreatic β-cell marker genes Ins1, Ins2, NKX6-1, and Mafa were significantly up-regulated, suggesting that the transformation of α cells into β cells occurred in these two mice (\u003cb\u003eFigS5A, C\u003c/b\u003e). In contrast, in STZ group, these markers were down-regulated in α cells, suggesting that there may be problems with the conversion of α cells to β cells under STZ-induced conditions(\u003cb\u003eFigS5E\u003c/b\u003e). Very interestingly, in HFD δ cells, the expression of these four genes is still up-regulated, suggesting that delta cells also tended to transform into beta cells in HFD mice (\u003cb\u003eFigS6A\u003c/b\u003e). Overexpression of Ins1 and Ins2 genes also occurred in delta cells in the DB/DB group (\u003cb\u003eFigS6B\u003c/b\u003e). Like alpha cells, delta cells did not show a tendency to transform into beta cells in the STZ group(\u003cb\u003eFigS6C\u003c/b\u003e). Next, We also found overexpression of Ins1, Ins2, and Mafa genes in HFD PP cells(\u003cb\u003eFigS7A\u003c/b\u003e), which suggests that HFD mice may be a very good model for studying β-cell transformation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScg2\u003c/b\u003e\u003csup\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/sup\u003e \u003cb\u003ebeta cells may represent in energy-hyperactive diabetes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDB/DB mice express mutations in leptin receptor that lead to obesity, decreased insulin sensitivity and β-cell function subsequent increased levels of blood glucose, decreased \u003csup\u003e[23]\u003c/sup\u003e. HFD treatment also lead to obesity and decreased β-cell function. These two models represent energy-hyperactive induced type 2 diabetes. Regardless of type 1 diabetes or type 2 diabetes, loss of β-cell function is the most important terminal change. In this study, we found that secretogranin II(Scg2), which is involved in the packaging or sorting of peptide hormones and neuropeptides into secretory vesicles, was increased in the DB/DB and HFD groups, and decreasedin STZ group. In the HFD group, except for subgroup 5, Scg2\u003csup\u003eHigh\u003c/sup\u003e Cells showed a large amount of enriched expression in other subgroups. In DB/DB mice, the Scg2\u003csup\u003eHigh\u003c/sup\u003e cell population is enriched in 1, 4, 5, and 6 cell subpopulations and overlaps with the DB/DB cluster, while in the STZ group, it approximately overlaps with the cell population of the WT cluster, and the Scg2\u003csup\u003elow\u003c/sup\u003e cell population appears in large numbers in the STZ cluster \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In order to further confirm this phenomenon, we performed immunohistochemical staining on pancreatic tissue sections of four types of mice and found that Scg2 was mainly expressed in pancreatic islets and showed roughly the same location as insulin expression. We also found that its expression was higher in the HFD group and the DB/DB group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B\u003cb\u003e)\u003c/b\u003e. At the same time, we isolated pancreatic islets from four types of mice and detected protein expression, and the results were consistent with what we found in omics and immunohistochemical staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD,E\u003cb\u003e)\u003c/b\u003e. Therefore, in metabolic-related type 2 diabetes caused by excessive energy supply, we believe that pancreatic beta cells increase insulin secretion to a certain extent through excessive proliferation or even differentiation. For the first time, we found an overexpressed β subpopulation of Scg2\u003csup\u003eHigh\u003c/sup\u003e, which may be a represent event in this type of diabetes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further verify the changes of Scg2 in human diabetes, we downloaded single-cell sequencing data from diabetic patients and mapped the expression profile of Scg2 in the β-cell population. First, we use the conventional BMI as the standard to define overnutrition. We selected samples with diabetes, prediabetes, and controls and found that increasing BMI was positively correlated with the progression of diabetes\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. We grouped β cells from samples at three different stages and obtained a total of 18 subpopulations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. We also screened the TOP30 genes that were up- and down-regulated in β cells for annotation. As expected, Scg2 is also up-regulated in the diabetic stage, and we also found that it is up-regulated to a certain extent in the prediabetic stage\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Next, we mapped the expression profile of Scg2 in specific subpopulations of β cells. We found that the cell population of Scg2\u003csup\u003eHigh\u003c/sup\u003e cells also overlaps with prediabetes and progressive diabetes\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-H\u003cb\u003e)\u003c/b\u003e, suggesting that Scg2\u003csup\u003eHigh\u003c/sup\u003ecells are marker cells in type 2 diabetes under high nutritional conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eG6pc2\u003c/b\u003e\u003csup\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ebeta cells showed an enrichment pattern in the HFD group\u003c/b\u003e\u003c/p\u003e\u003cp\u003eG6pc2 is predominantly expressed in islets, encodes a glucose-6-phosphatase catalytic subunit that converts glucose-6-phosphate (G6P) to glucose. We found that G6pc2 showed an overall up-regulation trend in the HFD group, with low expression only in subpopulations 5 and 8 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, which was consistent with the work of Jeffre R Millman et al\u003csup\u003e[24]\u003c/sup\u003e. At the same time, our data analyzes the specific changing trends in each subgroup in more detail. On the contrary, G6pc2 showed an overall downward trend in both the DB/DB group and the STZ group, and we also presented the changes in each subgroup \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB,C\u003cb\u003e)\u003c/b\u003e. We also analyzed the expression of G6pc2 in human pancreatic islet β cells. Like the expression trend in HFD, G6pc2\u003csup\u003eHigh\u003c/sup\u003e Cells also appeared in β cells in obese type 2 diabetes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Therefore, we believe that when studying the impact of G6pc2 gene on pancreatic islet function, the HFD-induced model may be more consistent with type 2 diabetes in human obesity. In order to better verify the expression of G6pc2 in single-cell sequencing, we performed immunohistochemical staining on pancreatic tissue sections of four types of mice and found that G6pc2 was expressed in pancreatic islets. It can be seen from the results that G6pc2\u003csup\u003eHigh\u003c/sup\u003e cells appeared in large quantities under the induction of HFD. However, there was a significant reduction in the STZ group and the DB/DB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. This is consistent with the overall trend in our previous single-cell sequencing analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Disscussion","content":"\u003cp\u003eMany genetic alterations and chemically or nutritionally induced models of type 1 and type 2 diabetes have played a pivotal role in the study of diabetes\u003csup\u003e[25]\u003c/sup\u003e. However, further discussion and research are still needed on the scientificity and appropriateness of model application. To develop or apply the most appropriate models for various types of diabetes in the clinic, the development of scRNA-seq technology and the discovery of new cell markers deepened the understanding of beta cell heterogeneity in health and disease\u003csup\u003e[26, 27]\u003c/sup\u003e. At present, other researchers have performed single-cell sequencing on the pancreatic islets of three commonly used diabetic mice (DB/DB, HFD, STZ). We have further compared and analyzed these data, and found out the differences in islet cell composition and gene expression (α, β, δ and PP) at the single-cell level. In particular, further exploration on the most important β cells provides good data support for elucidating its pathogenesis and clinical application. In our research, we found that even for the same type of diabetes, such as type 2 diabetes, there are many differences in the cell group compositions in the pancreatic islets between different models. For example, large groups such as α-cell groups appear in different groups of diabetic mice. The ratios are different, which may indicate the mutual transformation between α cells and β cells\u003csup\u003e[28, 29]\u003c/sup\u003e. Even changes in many transcription factors such as Mafa, Nfe2l1 and Ddit3 may regulate the expression of different gene networks, which also prompts us to choose appropriate models in molecular biology research. Our analysis at the single-cell level may be the molecular basis for the diverse phenotypes exhibited by several diabetes models.\u003c/p\u003e\u003cp\u003eβ cells are the main cells for insulin secretion. We further analyzed their differences in three different diabetic mice. In this reasearch, we found that the expression changes of Scg2 are very noticeable. But we found few reports on the relationship between diabetes and Scg2. This gene may be a very potential diabetes gene worthy of further exploration. In our analysis, we found that the change of Scg2 in various diabetes mellitus was not consistent. It was significantly up-regulated in energy-excess diabetes mellitus of HFD and DB/DB mice, while it was significantly down-regulated in STZ-induced diabetes mellitus. We also analyzed human diabetes and found that Scg2 was up-regulated in both the early and progressive stages of obese type 2 diabetes,which is consistent with the trends we observed in mice. Some researchers have found Scg2 was highly expressed in the hypothalamic nuclei, PVN, LHA, ARC, and amygdaloid nucleus, which are known to form the main centre of appetite regulation\u003csup\u003e[30]\u003c/sup\u003e. Toshiyuki Takeuchi et.al found that Scg2 binds to Scg3, which based on SNP analysis, have an association with obesity\u003csup\u003e[31]\u003c/sup\u003e. It is suggested that Scg2 may play an important role in energy metabolism. Whether the appearance of Scg2\u003csup\u003eHigh\u003c/sup\u003e cells may be related to the proliferation of islet cell clusters under high-energy conditions may require further exploration. In view of the present data, we propose that Scg2 is a potential regulator of food intake and energy homeostasis in energy-excess diabetes.\u003c/p\u003e\u003cp\u003eAnother gene we paid attention to was G6pc2, which showed differences in the islet cells of three different diabetic mice. Significant upregulation occurs upon HFD induction and in the context of obese type 2 diabetes in humans. However, downregulation occurred in DB/DB mice and STZ mice. Our study is consistent with that of Karin J. Bosma \u003csup\u003e[32]\u003c/sup\u003e, who found that G6pc2 expression was markedly induced in response to high fat diet feeding. Jason M. Tonne \u003csup\u003e[33]\u003c/sup\u003efound STZ treatment also suppressed expression of a wide range of genes linked with key β-cell functions or diabetes development, such as G6pc2, Slc2a2 (Glut2), Slc30a8, Neurod1, Ucn3, Gad1, Isl1, Foxa2, Vdr, Pdx1, Fkbp1b and Abcc8, suggesting global β-cell defects in STZ-treated islets. Lipeng Ren found G6pc2 was reduced in DB/DB islets\u003csup\u003e[34]\u003c/sup\u003e. As we know, G6pc2 acts in conjunction with β-cell glucose sensors and glucokinase to produce ineffective substrate recycling and determine the rate of β-cell glycolytic flux, it is an important regulatory gene for fasting blood glucose (FBG). This brings up a very interesting point, especially the difference in the expression of G6pc2 in diabetes between DB/DB and HFD. Is it the effect of fat metabolism on pancreatic islets that causes the difference? Mice with excess energy metabolism may have completely opposite effects on the pancreatic islets of mice due to differences in the nutrients they consume. This also illustrates the complexity of diabetes to a certain extent, and our data may have some hints on how to configure a healthy diabetes diet.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn general, we integrated data from single-cell sequencing of islet cells from three types of diabetic mice(HFD, DB/DB, STZ). We specifically classified and compared the four main types of secretory cells(α, β, δ and PP). We indeed found that there are significant differences in the types of islet cells in these diabetic mice, and the gene expression profiles in various types of cells are different. Currently, studying the relationship between genes and diabetes is the mainstream direction. When selecting diabetic mice, our data laid the foundation for it and would help everyone to choose a more appropriate model for related genetic research. Besides, we found that the changes in the relationship between Scg2 and nutritional diabetes are very interesting, and we also conducted preliminary verification. At present, there are not many studies on Scg2, and it may be a new clinical target. Through this study, we will provide theoretical basis for the selection of different diabetic mouse models in diabetes research, so as to more accurately meet the needs of diabetes research, and provide theoretical basis for more accurate treatment of diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXY and NW download single cell sequencing data and conduct data analysis, conduct mouse experiments, and wrote the manuscript. Jl B and YJZ helped in writing-review, discussion and editing the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Basic scientific research project of Liaoning Provincial Department of Education (LJKMZ20221290).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eSource of raw data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe T2D samples were downloaded from the\u0026nbsp;GSE200531 series\u0026nbsp;with 10×\u0026nbsp;scRNA-seq data, the samples included 7356 cells(DB/m) and 7206 cells(DB/DB). The HFD samples were downloaded from the GSE203376 series with 10× scRNA-seq data, the samples included 9501 cells(HFD) and 9306 cells(CD). The T1D samples were downloaded from the\u0026nbsp;GSE128565 series\u0026nbsp;with 10×\u0026nbsp;scRNA-seq data, the samples included 9360 cells(WT) and 5562 cells(STZ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing of 10\u003c/strong\u003e\u003cstrong\u003e×\u003c/strong\u003e\u003cstrong\u003escRNA-Seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe processed single-cell sequencing data of diabetes using the following methods. First, we transformed the 10×\u0026nbsp;scRNA-seq data into a Seurat object using the \"Seurat\" R package, and excluded cells with poor quality by calculating the percentage of mitochondrial or ribosomal genes as quality control (QC). The \"FindVariableFeatures\" program identified the top 2000 highly variable genes, and we performed dimensionality reduction and cell subpopulation identification using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) with these 2000 additional genes. To find marker genes for each cluster, we used the \"Find All Markers\" tool with |Log2FC| and min. pct cutoff values set to 0.25 and 0.25, respectively. We annotated the cell subpopulations based on the marker genes identified in the study and performed GO and KEGG enrichment analysis using the R package \"clusterProfiler\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMice and treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll mices were under specific pathogen-free conditions with a 12-hour light/dark cycle in Institute for Genome Engineered Animal Models of Human Diseases. HFD group were fed a HFD( 60% fat; D12492) or a chow diet(CD; 13.5% fat; 5001) for eight weeks. The STZ mouse model uses C57BL/6J mice and starts continuous injections of STZ (50mg/kg) for five days from the 8-week-old. Two weeks later, fasting blood glucose should be measured to be greater than 11.1 mmol/L. 12-weeks-old DB/DB mouse was used for the experiment. All experimental animal procedures were approved by DaLian medical University, DaLian, LiaoNing, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlucose and insulin tolerance tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;GTT: The experimental mice were fasted for 13 hours with normal water intake. Based on the body weight, an appropriate amount of 20% glucose solution was injected into the peritoneal cavity. The timer started after the first mouse was injected. Blood glucose levels were measured at 15 minutes, 30 minutes, 60 minutes, and 120 minutes after glucose injection, and the results were recorded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eITT: The experimental mice were fasted for 5 hours with normal water intake. After fasting. Based on the body weight, an appropriate amount of 0.075 U/mL insulin solution was injected into the peritoneal cavity. Timing started after the first mouse was injected. Blood glucose levels were measured at 15 minutes, 30 minutes, and 60 minutes after injecting the glucose solution, and the results were recorded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteins were isolated from mouse islet cells using TRIzol reagent. Equal amount of protein samples (10 μg) were loaded on SDS-PAGE gel and then transferred to PVDF membranes for 2 h. Subsequently, the PVDF membrane was sealed with 5% skimmed milk powder for 2 h at room temperature. Pimary antibodies: anti-GAPDH (1:10000, 10494-1-AP, proteintech, China); anti-Scg2 (1:1000, 20357-1-AP, proteintech, China) were incubation overnight at 4 ℃, following by HRP-conjugated secondary antibodies: Goat Anti-Rabbit IgG (1:10000, A21020, Abbkine, China) for 2 h at room temperature after washing. The protein blots were visualized using SuperSignal West Pico Chemiluminescent Substrate (Pierce Biotechnology, USA) in ChemiDoc XRS+ system (BioRad, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFix pancreatic tissue in formalin, embed in paraffin after dehydration in ethanol and xylene, cut into 3mm sections, deparaffinize in xylene, rehydrate using a series of graded alcohols, perform antigen retrieval and block with serum. Then incubate the sections with primary antibody: anti-insulin (1:400, 66198-1-Ig, proteintech, China); anti-Scg2 (1:400, 20357-1-AP, proteintech, China); anti-G6pc2(1:300, bs-13386R, bioboss, China) overnight at 4 ℃ in the dark. After warming up to room temperature, wash the sections and incubate with secondary antibody: Goat Anti-Mouse IgG(1：1000, A23210-1, Abbkine, China); Goat Anti-Rabbit IgG(1: 300, Y6107L, UElandy YF, China) for 2 hours at room temperature in the dark. Visualize using fluorescence microscopy (Zeiss, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIslet isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used liver bile duct microinjection collagenase technique to isolate mouse islets. The mice were weighed and injected with anesthetic according to their body weight. \u0026nbsp; The position of the duodenal papilla was identified and clamped with hemostatic forceps to block the bile and collagenase solution from entering the duodenum. 4mL of collagenase solution was extracted using a 5mL syringe, and a 30G 1/2 needle was attached. Pancreatic digestion: The centrifuge tube was placed in a 37 ℃ water bath and digested for 16 minutes, gently shaking the tube two to three times during the process. After digestion, The islets were manually selected under a dissecting microscope and placed in the culture medium.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTuomi, T., et al., \u003cem\u003eThe many faces of diabetes: a disease with increasing heterogeneity.\u003c/em\u003e Lancet, 2014. \u003cstrong\u003e383\u003c/strong\u003e(9922): p. 1084-1094.\u003c/li\u003e\n\u003cli\u003eCohrs, C.M., et al., \u003cem\u003eDysfunction of Persisting \u0026beta; Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis.\u003c/em\u003e Cell Reports, 2020. \u003cstrong\u003e31\u003c/strong\u003e(1): p. 107469.\u003c/li\u003e\n\u003cli\u003eKramer, C.K., et al., \u003cem\u003eGlucagon response to oral glucose challenge in type 1 diabetes: lack of impact of euglycemia.\u003c/em\u003e Diabetes Care, 2014. \u003cstrong\u003e37\u003c/strong\u003e(4): p. 1076-82.\u003c/li\u003e\n\u003cli\u003eWeir, G.C. and S. Bonner-Weir, \u003cem\u003eIslet \u0026beta; cell mass in diabetes and how it relates to function, birth, and death.\u003c/em\u003e Annals of the New York Academy of Sciences, 2013. \u003cstrong\u003e1281\u003c/strong\u003e(1): p. 92-105.\u003c/li\u003e\n\u003cli\u003eDe Jesus, D.F. and R.N. Kulkarni, \u003cem\u003e\"Omics\" and \"epi-omics\" underlying the \u0026beta;-cell adaptation to insulin resistance.\u003c/em\u003e Mol Metab, 2019. \u003cstrong\u003e27s\u003c/strong\u003e(Suppl): p. S42-s48.\u003c/li\u003e\n\u003cli\u003eMastracci, T.L. and L. Sussel, \u003cem\u003eThe endocrine pancreas: insights into development, differentiation, and diabetes.\u003c/em\u003e WIREs Developmental Biology, 2012. \u003cstrong\u003e1\u003c/strong\u003e(5): p. 609-628.\u003c/li\u003e\n\u003cli\u003eBosco, D., et al., \u003cem\u003eUnique arrangement of alpha- and beta-cells in human islets of Langerhans.\u003c/em\u003e Diabetes, 2010. \u003cstrong\u003e59\u003c/strong\u003e(5): p. 1202-10.\u003c/li\u003e\n\u003cli\u003eOrci, L. and R.H. Unger, \u003cem\u003eFunctional subdivision of islets of Langerhans and possible role of D cells.\u003c/em\u003e Lancet, 1975. \u003cstrong\u003e2\u003c/strong\u003e(7947): p. 1243-4.\u003c/li\u003e\n\u003cli\u003eDa Silva Xavier, G., \u003cem\u003eThe Cells of the Islets of Langerhans.\u003c/em\u003e Journal of Clinical Medicine, 2018. \u003cstrong\u003e7\u003c/strong\u003e(3): p. 54.\u003c/li\u003e\n\u003cli\u003eEllenbroek, J.H., et al., \u003cem\u003ePancreatic \u0026alpha;-cell mass in obesity.\u003c/em\u003e Diabetes Obes Metab, 2017. \u003cstrong\u003e19\u003c/strong\u003e(12): p. 1810-1813.\u003c/li\u003e\n\u003cli\u003eTritschler, S., et al., \u003cem\u003eSystematic single-cell analysis provides new insights into heterogeneity and plasticity of the pancreas.\u003c/em\u003e Mol Metab, 2017. \u003cstrong\u003e6\u003c/strong\u003e(9): p. 974-990.\u003c/li\u003e\n\u003cli\u003eYang, T.T., et al., \u003cem\u003eIdentification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing.\u003c/em\u003e European Journal of Medical Research, 2023. \u003cstrong\u003e28\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eChen, K., et al., \u003cem\u003eCross-species scRNA-seq reveals the cellular landscape of retina and early alterations in type 2 diabetes mice.\u003c/em\u003e Genomics, 2023. \u003cstrong\u003e115\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eJohnson, J.D., \u003cem\u003eThe quest to make fully functional human pancreatic beta cells from embryonic stem cells: climbing a mountain in the clouds.\u003c/em\u003e Diabetologia, 2016. \u003cstrong\u003e59\u003c/strong\u003e(10): p. 2047-57.\u003c/li\u003e\n\u003cli\u003eBonner-Weir, S., B.A. Sullivan, and G.C. Weir, \u003cem\u003eHuman Islet Morphology Revisited: Human and Rodent Islets Are Not So Different After All.\u003c/em\u003e J Histochem Cytochem, 2015. \u003cstrong\u003e63\u003c/strong\u003e(8): p. 604-12.\u003c/li\u003e\n\u003cli\u003eFu, H., et al., \u003cem\u003eDiscoveries in Pancreatic Physiology and Disease Biology Using Single-Cell RNA Sequencing.\u003c/em\u003e Frontiers in Cell and Developmental Biology, 2022. \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eSandberg, R., \u003cem\u003eEntering the era of single-cell transcriptomics in biology and medicine.\u003c/em\u003e Nat Methods, 2014. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 22-4.\u003c/li\u003e\n\u003cli\u003eLi, J., et al., \u003cem\u003eSingle-cell transcriptomes reveal characteristic features of human pancreatic islet cell types.\u003c/em\u003e EMBO Rep, 2016. \u003cstrong\u003e17\u003c/strong\u003e(2): p. 178-87.\u003c/li\u003e\n\u003cli\u003eBrissova, M., et al., \u003cem\u003e\u0026alpha; Cell Function and Gene Expression Are Compromised in Type 1 Diabetes.\u003c/em\u003e Cell Rep, 2018. \u003cstrong\u003e22\u003c/strong\u003e(10): p. 2667-2676.\u003c/li\u003e\n\u003cli\u003eLam, C.J., et al., \u003cem\u003eHighly Proliferative \u0026alpha;-Cell-Related Islet Endocrine Cells in Human Pancreata.\u003c/em\u003e Diabetes, 2018. \u003cstrong\u003e67\u003c/strong\u003e(4): p. 674-686.\u003c/li\u003e\n\u003cli\u003eLawlor, N., et al., \u003cem\u003eSingle-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.\u003c/em\u003e Genome Res, 2017. \u003cstrong\u003e27\u003c/strong\u003e(2): p. 208-222.\u003c/li\u003e\n\u003cli\u003eKim, S., et al., \u003cem\u003eMolecular and genetic regulation of pig pancreatic islet cell development.\u003c/em\u003e Development, 2020. \u003cstrong\u003e147\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eGhilardi, N., et al., \u003cem\u003eDefective STAT signaling by the leptin receptor in diabetic mice.\u003c/em\u003e Proceedings of the National Academy of Sciences of the United States of America, 1996. \u003cstrong\u003e93\u003c/strong\u003e(13): p. 6231-5.\u003c/li\u003e\n\u003cli\u003eAugsornworawat, P., et al., \u003cem\u003eSingle-cell transcriptome profiling reveals \u0026beta; cell maturation in stem cell-derived islets after transplantation.\u003c/em\u003e Cell Rep, 2021. \u003cstrong\u003e34\u003c/strong\u003e(10): p. 108850.\u003c/li\u003e\n\u003cli\u003eAlpers, C.E. and K.L. Hudkins, \u003cem\u003eMouse models of diabetic nephropathy.\u003c/em\u003e Current Opinion in Nephrology and Hypertension, 2011. \u003cstrong\u003e20\u003c/strong\u003e(3): p. 278-284.\u003c/li\u003e\n\u003cli\u003eAvrahami, D., et al., \u003cem\u003eSingle-cell transcriptomics of human islet ontogeny defines the molecular basis of \u0026beta;-cell dedifferentiation in T2D.\u003c/em\u003e Molecular Metabolism, 2020. \u003cstrong\u003e42\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eBao, K., et al., \u003cem\u003ePseudotime Ordering Single-Cell Transcriptomic of beta Cells Pancreatic Islets in Health and Type 2 Diabetes.\u003c/em\u003e Phenomics (Cham, Switzerland), 2021. \u003cstrong\u003e1\u003c/strong\u003e(5): p. 199-210.\u003c/li\u003e\n\u003cli\u003eChung, C.H., et al., \u003cem\u003ePancreatic \u0026beta;-cell neogenesis by direct conversion from mature \u0026alpha;-cells.\u003c/em\u003e Stem Cells, 2010. \u003cstrong\u003e28\u003c/strong\u003e(9): p. 1630-8.\u003c/li\u003e\n\u003cli\u003ePiran, R., et al., \u003cem\u003ePharmacological induction of pancreatic islet cell transdifferentiation: relevance to type I diabetes.\u003c/em\u003e Cell Death Dis, 2014. \u003cstrong\u003e5\u003c/strong\u003e(7): p. e1357.\u003c/li\u003e\n\u003cli\u003eHotta, K., et al., \u003cem\u003eSecretogranin II binds to secretogranin III and forms secretory granules with orexin, neuropeptide Y, and POMC.\u003c/em\u003e Journal of Endocrinology, 2009. \u003cstrong\u003e202\u003c/strong\u003e(1): p. 111-121.\u003c/li\u003e\n\u003cli\u003eHotta, K., et al., \u003cem\u003eSecretogranin II binds to secretogranin III and forms secretory granules with orexin, neuropeptide Y, and POMC.\u003c/em\u003e J Endocrinol, 2009. \u003cstrong\u003e202\u003c/strong\u003e(1): p. 111-21.\u003c/li\u003e\n\u003cli\u003eBosma, K.J., et al., \u003cem\u003eG6PC2 confers protection against hypoglycemia upon ketogenic diet feeding and prolonged fasting.\u003c/em\u003e Mol Metab, 2020. \u003cstrong\u003e41\u003c/strong\u003e: p. 101043.\u003c/li\u003e\n\u003cli\u003eTonne, J.M., et al., \u003cem\u003eGlobal gene expression profiling of pancreatic islets in mice during streptozotocin-induced \u0026beta;-cell damage and pancreatic Glp-1 gene therapy.\u003c/em\u003e Dis Model Mech, 2013. \u003cstrong\u003e6\u003c/strong\u003e(5): p. 1236-45.\u003c/li\u003e\n\u003cli\u003eRen, L., et al., \u003cem\u003eAdjudin improves beta cell maturation, hepatic glucose uptake and glucose homeostasis.\u003c/em\u003e Diabetologia, 2024. \u003cstrong\u003e67\u003c/strong\u003e(1): p. 137-155.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7225641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7225641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate diabetic mouse models are very necessary for basic and clinical diabetes research. Different induction conditions and genetic changes in mouse diabetes models may lead to different classification and expression of islet cell sub-populations. In this study, we first examined the pathologic phenotypic differences of wild C57BL/6J mice (WT) and three types of diabetic mice: high-fat diet (HFD) fed mice (DIO), db/db mice (T2D) and streptozotocin (STZ) induced mice (T1D). Then we described a complete islet cell landscape and identified robust marker genes for each endocrine cell type by analyzing the gene expression of islet cells in the above three types of diabetic mice using publicly available scRNA-Seq data. We also identified α, β, δ and PP cell sub-populations genes expression profile and KEGG and GO analysis in three diabetic mouse models, and explored their similarities and differences. Furthermore, we classified the β-cell populations of three types of diabetic mice and humans with type 2 diabetes and identified differential genes. Experimental verification was conducted on β-cell differential genes such as Scg2 and G6pc2 between different models. We found Scg2\u003csup\u003eHigh\u003c/sup\u003e beta cells may represent in energy-hyperactive diabetes. G6pc2\u003csup\u003eHigh\u003c/sup\u003e beta cells showed an enrichment pattern in the HFD group. In summary, our work provides a deeper understanding of the pathogenesis and usage scenarios of three commonly used diabetic mouse models through single-cell sequencing analysis of pancreatic islets.\u003c/p\u003e","manuscriptTitle":"The single-cell RNA sequencing (scRNA-seq) transcriptome profile of pancreatic islets in three types of diabetic mice and its pathological changes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 19:33:05","doi":"10.21203/rs.3.rs-7225641/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"180650cb-bac7-4d54-92ee-68e10a69a306","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57690396,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes"},{"id":57690397,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes"},{"id":57690398,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2026-03-20T19:10:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 19:33:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7225641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7225641","identity":"rs-7225641","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}