{"title":"健康和2型糖尿病患者胰岛β细胞的伪时间顺序单细胞转录组学研究","authors":"Kaixuan Bao, Zhicheng Cui, Hui Wang, Hui Xiao, Ting Li, Xingxing Kong, Tiemin Liu","doi":"10.1007/s43657-021-00024-z","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.</p>","PeriodicalId":74435,"journal":{"name":"Phenomics (Cham, Switzerland)","volume":"1 5","pages":"199-210"},"PeriodicalIF":3.7000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9590480/pdf/43657_2021_Article_24.pdf","citationCount":"4","resultStr":"{\"title\":\"Pseudotime Ordering Single-Cell Transcriptomic of β Cells Pancreatic Islets in Health and Type 2 Diabetes.\",\"authors\":\"Kaixuan Bao, Zhicheng Cui, Hui Wang, Hui Xiao, Ting Li, Xingxing Kong, Tiemin Liu\",\"doi\":\"10.1007/s43657-021-00024-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.</p>\",\"PeriodicalId\":74435,\"journal\":{\"name\":\"Phenomics (Cham, Switzerland)\",\"volume\":\"1 5\",\"pages\":\"199-210\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9590480/pdf/43657_2021_Article_24.pdf\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Phenomics (Cham, Switzerland)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s43657-021-00024-z\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Phenomics (Cham, Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s43657-021-00024-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Pseudotime Ordering Single-Cell Transcriptomic of β Cells Pancreatic Islets in Health and Type 2 Diabetes.
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 information: The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.