Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes.
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ABSTRACT:
SUBMITTER: Bosi E
PROVIDER: S-EPMC7679065 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:β cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic β cells are heterogeneous and demonstrated the transcript alterations of β cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the β cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of β cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. β cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that β cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of β cells in T2D.Supplementary informationThe online version contains supplementary material is available at 10.1007/s43657-021-00024-z.
| S-EPMC9590480 | biostudies-literature
Project description:Aims/introductionIn the development of type 1 diabetes, metabolites are significantly altered and might be involved in β-cell destruction and protection. We aimed to identify new metabolic markers of β-cell destruction in type 1 diabetes patients.Materials and methodsA total of 33 participants were recruited for this cross-sectional observational study: 23 with type 1 diabetes, seven with type 2 diabetes and three healthy controls. Those with type 1 diabetes were further subdivided into three groups: new-onset, microsecretors and complete lack of endogenous insulin in type 1 diabetes.ResultsMetabolomic analysis identified a total of 737 peaks, and partial least square analysis was successful in discriminating between the three groups of type 1 diabetes. Among the factor loadings discriminating type 1 diabetes, 3-phenylpropionic acid (r = 0.80, P = 4.7E-6 ) and hypotaurine (r = -0.484, P = 1.9E-2 ) strongly contributed to identifying new-onset type 1 diabetes, and 5-methylcytosine to identifying complete-lack type 1 diabetes (r = 0.586, P = 6.5E-3 ). Reporter operating characteristics analysis, including all type 1 diabetes, type 2 diabetes and healthy controls, showed that high 3-phenylpropionic acid (Pc <0.0001) and low hypotaurine (Pc <0.0001) were useful for identifying new-onset type 1 diabetes, and high 5-methylcytosine (Pc = 0.002) for the complete-lack type 1 diabetes.ConclusionsIn the present study, metabolic signatures were shown to be useful in identifying type 1 diabetes at different clinical stages, and 3-phenylpropionic acid and hypotaurine are novel biomarkers for identifying new-onset type 1 diabetes, suggesting the involvement of the gut bacterial environment, anti-oxidant mechanisms through the hypotaurine-taurine pathway and methylated deoxyribonucleic acid fragmentation in the process of β-cell destruction.
| S-EPMC9807153 | biostudies-literature
Project description:BackgroundType 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing technology can provide additional information about the molecular mechanisms of T2D at single-cell level.ResultsIn this work, we analyze three datasets of single-cell transcriptomes to reveal β-cell dysfunction and deficit mechanisms in T2D. Focused on the expression levels of key genes, we conduct discrimination of healthy and T2D β-cells using five machine learning classifiers, and extracted major influential factors by calculating correlation coefficients and mutual information. Our analysis shows that T2D β-cells are normal in insulin gene expression in the scenario of low cellular stress (especially oxidative stress), but appear dysfunctional under the circumstances of high cellular stress. Remarkably, oxidative stress plays an important role in affecting the expression of insulin gene. In addition, by analyzing the genes related to apoptosis, we found that the TNFR1-, BAX-, CAPN1- and CAPN2-dependent pathways may be crucial for β-cell apoptosis in T2D. Finally, personalized analysis indicates cell heterogeneity and individual-specific insulin gene expression.ConclusionsOxidative stress is an important influential factor on insulin gene expression in T2D. Based on the uncovered mechanism of β-cell dysfunction and deficit, targeting key genes in the apoptosis pathway along with alleviating oxidative stress could be a potential treatment strategy for T2D.
| S-EPMC6311914 | biostudies-literature
Project description:Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.
| S-EPMC2848542 | biostudies-literature
Project description:In the context of infectious diseases, cell population transcriptomics are useful to gain mechanistic insight into protective immune responses, which is not possible using traditional whole-blood approaches. In this study, we applied a cell population transcriptomics strategy to sorted memory CD4 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and gain insight into the phenotype of tuberculosis (TB)-specific CD4 T cells. We found a 74-gene signature that could discriminate between memory CD4 T cells from healthy latently Mycobacterium tuberculosis-infected subjects and noninfected controls. The gene signature presented a significant overlap with the gene signature of the Th1* (CCR6+CXCR3+CCR4-) subset of CD4 T cells, which contains the majority of TB-specific reactivity and is expanded in LTBI. In particular, three Th1* genes (ABCB1, c-KIT, and GPA33) were differentially expressed at the RNA and protein levels in memory CD4 T cells of LTBI subjects compared with controls. The 74-gene signature also highlighted novel phenotypic markers that further defined the CD4 T cell subset containing TB specificity. We found the majority of TB-specific epitope reactivity in the CD62L-GPA33- Th1* subset. Thus, by combining cell population transcriptomics and single-cell protein-profiling techniques, we identified a CD4 T cell immune signature of LTBI that provided novel insights into the phenotype of TB-specific CD4 T cells.
| S-EPMC5991485 | biostudies-literature
Project description:Although both physiological hypertrophy (PHH) and pathological hypertrophy (PAH) of the heart have similar morphological appearances, only PAH leads to fatal heart failure. In the present study, we used RNA sequencing (RNA-Seq) to determine the transcriptomic signatures for both PHH and PAH. Approximately 13-20 million reads were obtained for both models, among which PAH showed more differentially expressed genes (DEGs) (2,041) than PHH (245). The expression of 417 genes was barely detectable in the normal heart but was suddenly activated in PAH. Among them, Foxm1 and Plk1 are of particular interest, since Ingenuity Pathway Analysis (IPA) using DEGs and upstream motif analysis showed that they are essential hub proteins that regulate the expression of downstream proteins associated with PAH. Meanwhile, 52 genes related to collagen, chemokines, and actin showed opposite expression patterns between PHH and PAH. MAZ-binding motifs were enriched in the upstream region of the participating genes. Alternative splicing (AS) of exon variants was also examined using RNA-Seq data for PAH and PHH. We found 317 and 196 exon inclusions and exon exclusions, respectively, for PAH, and 242 and 172 exon inclusions and exclusions, respectively for PHH. The AS pattern was mostly related to gains or losses of domains, changes in activity, and localization of the encoded proteins. The splicing variants of 8 genes (i.e., Fhl1, Rcan1, Ndrg2, Synpo, Ttll1, Cxxc5, Egfl7, and Tmpo) were experimentally confirmed. Multilateral pathway analysis showed that the patterns of quantitative (DEG) and qualitative (AS) changes differ depending on the type of pathway in PAH and PHH. One of the most significant changes in PHH is the severe downregulation of autoimmune pathways accompanied by significant AS. These findings revealed the unique transcriptomic signatures of PAH and PHH and also provided a more comprehensive understanding at both the quantitative and qualitative levels.
| S-EPMC3327670 | biostudies-literature
Project description:Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.
| S-EPMC11361826 | biostudies-literature
Project description:INTRODUCTION:Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive. OBJECTIVES:This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms. METHODS:Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods. RESULTS:In total, 72 (0.9%) metabolite features were associated (p<0.01) with preeclampsia after adjustment for maternal age, race, and gestational age. These features had moderate to good discriminatory ability; in ROC curve analyses a summary score based on these features displayed an area under the curve (AUC) of 0.794 (95%CI 0.700, 0.888). This profile retained the ability to distinguish preeclamptic from healthy pregnancies in the third trimester (AUC:0.762 (95% CI 0.663, 0.860)). Additionally, metabolite set enrichment analysis identified common pathways, including glycerophospholipid metabolism, at the two time-points. Integration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system. CONCLUSIONS:These findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.
| S-EPMC5458629 | biostudies-literature
Project description:BackgroundPlasmodium vivax sporozoites reside in the salivary glands of a mosquito before infecting a human host and causing malaria. Previous transcriptome-wide studies in populations of these parasite forms were limited in their ability to elucidate cell-to-cell variation, thereby masking cellular states potentially important in understanding malaria transmission outcomes.Methodology/principal findingsIn this study, we performed transcription profiling on 9,947 P. vivax sporozoites to assess the extent to which they differ at single-cell resolution. We show that sporozoites residing in the mosquito's salivary glands exist in distinct developmental states, as defined by their transcriptomic signatures. Additionally, relative to P. falciparum, P. vivax displays overlapping and unique gene usage patterns, highlighting conserved and species-specific gene programs. Notably, distinguishing P. vivax from P. falciparum were a subset of P. vivax sporozoites expressing genes associated with translational regulation and repression. Finally, our comparison of single-cell transcriptomic data from P. vivax sporozoite and erythrocytic forms reveals gene usage patterns unique to sporozoites.Conclusions/significanceIn defining the transcriptomic signatures of individual P. vivax sporozoites, our work provides new insights into the factors driving their developmental trajectory and lays the groundwork for a more comprehensive P. vivax cell atlas.
| S-EPMC9380936 | biostudies-literature
Project description:BackgroundThe treatment of diabetic foot ulcers (DFUs) poses a challenging medical problem that has long plagued individuals with diabetes. Clinically, wounds that fail to heal for more than 12 weeks after the formation of DFUs are referred to as non-healing/chronic wounds. Among various factors contributing to the non-healing of DFUs, the impairment of skin microvascular endothelial cell function caused by high glucose plays a crucial role. Our study aimed to reveal the transcriptomic signatures of non-healing DFUs endothelial cells, providing novel intervention targets for treatment strategies.MethodsBased on the GEO dataset (GSE165816), we selected DFU-Healer, DFU-Non-healer, and healthy non-diabetic controls as research subjects. Single-cell RNA transcriptomic sequencing technology was employed to analyze the heterogeneity of endothelial cells in different skin tissue samples and identify healing-related endothelial cell subpopulations. Immunofluorescence was applied to validate the sequencing results on clinical specimens.ResultsThe number of endothelial cells and vascular density showed no significant differences among the three groups of skin specimens. However, endothelial cells from non-healing DFUs exhibited apparent inhibition of angiogenesis, inflammation, and immune-related signaling pathways. The expression of CCND1, ENO1, HIF1α, and SERPINE1 was significantly downregulated at the transcriptomic and histological levels. Further analysis demonstrated that healing-related endothelial cell subpopulations in non-healing DFUs has limited connection with other cell types and weaker differentiation ability.ConclusionAt the single-cell level, we uncovered the molecular and functional specificity of endothelial cells in non-healing DFUs and highlighted the importance of endothelial cell immune-mediated capability in angiogenesis and wound healing. This provides new insights for the treatment of DFUs.
| S-EPMC10722230 | biostudies-literature