Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis

被引:5
|
作者
Ye, Yuting [1 ]
Song, Hui [1 ]
Zhang, Jiong [1 ]
Shi, Shaolin [1 ]
机构
[1] Nanjing Univ, Natl Clin Res Ctr Kidney Dis, Jinling Hosp, Sch Med, 305 East Zhongshan Rd, Nanjing 210002, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Single-cell RNA-seq; Gene expression dynamics; Kidney; Cell type identification; Cell subpopulation; RNA-SEQUENCING REVEALS; GENE-EXPRESSION; MESSENGER-RNA; RT-PCR; SEQ; HETEROGENEITY; DIFFERENTIATION; LANDSCAPE; PATHWAYS; RESOLUTION;
D O I
10.1159/000492470
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Background: Single-cell RNA-seq (scRNA-seq) has recently emerged as a revolutionary and powerful tool for biomedical research. However, there have been relatively few studies using scRNA-seq in the field of kidney study. Summary: scRNA-seq achieves gene expression profiling at single-cell resolution in contrast with the conventional methods of gene expression profiling, which are based on cell population and give averaged values of gene expression of the cells. Single-cell transcriptomic analysis is crucial because individual cells of the same type are highly heterogeneous in gene expression, which reflects the existence of subpopulations, different cellular states, or molecular dynamics, of the cells, and should be resolved for further insights. In addition, gene expression analysis of tissues or organs that usually comprise multiple cell types or subtypes results in data that are not fully applicable to any given cell type. scRNA-seq is capable of identifying all cell types and subtypes in a tissue, including those that are new or present in small quantity. With these unique capabilities, scRNA-seq has been used to dissect molecular processes in cell differentiation and to trace cell lineages in development. It is also used to analyze the cells in a lesion of disease to identify the cell types and molecular dynamics implicated in the injury. With continuous technical improvement, scRNA-seq has become extremely high throughput and cost effective, making it accessible to all laboratories. In the present review article, we provide an overall review of scRNA-seq concerning its history, improvements, and applications. In addition, we describe the available studies in which scRNA-seq was employed in the field of kidney research. Lastly, we discuss other potential uses of scRNA-seq for kidney research. Key Message: This review article provides general information on scRNA-seq and its various uses. Particularly, we summarize the studies in the field of kidney diseases in which scRNA-seq was used and discuss potential additional uses of scRNA-seq for kidney research.
引用
收藏
页码:214 / 225
页数:12
相关论文
共 50 条
  • [1] Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis
    Huang, Changyuan
    Zeng, Bin
    Zhou, Bo
    Chen, Guanming
    Zhang, Qi
    Hou, Wenhong
    Xiao, Guozhi
    Duan, Li
    Hong, Ni
    Jin, Wenfei
    GENES & DISEASES, 2025, 12 (02)
  • [2] Single-cell Transcriptomic Analysis
    Zheng, Zhihong
    Chen, Enguo
    Lu, Weiguo
    Mouradian, Gary
    Hodges, Matthew
    Liang, Mingyu
    Liu, Pengyuan
    Lu, Yan
    COMPREHENSIVE PHYSIOLOGY, 2020, 10 (02) : 767 - 783
  • [3] Cancer biology deciphered by single-cell transcriptomic sequencing
    Yanmeng Li
    Jianshi Jin
    Fan Bai
    Protein & Cell, 2022, 13 (03) : 167 - 179
  • [4] Cancer biology deciphered by single-cell transcriptomic sequencing
    Li, Yanmeng
    Jin, Jianshi
    Bai, Fan
    PROTEIN & CELL, 2022, 13 (03) : 167 - 179
  • [5] Single-cell transcriptomic analysis of endometriosis
    Fonseca, Marcos A. S.
    Haro, Marcela
    Wright, Kelly N.
    Lin, Xianzhi
    Abbasi, Forough
    Sun, Jennifer
    Hernandez, Lourdes
    Orr, Natasha L.
    Hong, Jooyoon
    Choi-Kuaea, Yunhee
    Maluf, Horacio M.
    Balzer, Bonnie L.
    Fishburn, Aaron
    Hickey, Ryan
    Cass, Ilana
    Goodridge, Helen S.
    Truong, Mireille
    Wang, Yemin
    Pisarska, Margareta D.
    Dinh, Huy Q.
    EL-Naggar, Amal
    Huntsman, David G.
    Anglesio, Michael S.
    Goodman, Marc T.
    Medeiros, Fabiola
    Siedhoff, Matthew
    Lawrenson, Kate
    NATURE GENETICS, 2023, 55 (02) : 255 - 267
  • [6] Single-cell transcriptomic analysis of endometriosis
    Marcos A. S. Fonseca
    Marcela Haro
    Kelly N. Wright
    Xianzhi Lin
    Forough Abbasi
    Jennifer Sun
    Lourdes Hernandez
    Natasha L. Orr
    Jooyoon Hong
    Yunhee Choi-Kuaea
    Horacio M. Maluf
    Bonnie L. Balzer
    Aaron Fishburn
    Ryan Hickey
    Ilana Cass
    Helen S. Goodridge
    Mireille Truong
    Yemin Wang
    Margareta D. Pisarska
    Huy Q. Dinh
    Amal EL-Naggar
    David G. Huntsman
    Michael S. Anglesio
    Marc T. Goodman
    Fabiola Medeiros
    Matthew Siedhoff
    Kate Lawrenson
    Nature Genetics, 2023, 55 : 255 - 267
  • [7] Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology
    Ortega-Batista, Ana
    Jaen-Alvarado, Yanelys
    Moreno-Labrador, Dilan
    Gomez, Natasha
    Garcia, Gabriela
    Guerrero, Erika N.
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2025, 26 (05)
  • [8] Deciphering Cardiac Biology and Disease by Single-Cell Transcriptomic Profiling
    Wang, Le
    Hu, Shengshou
    Zhou, Bingying
    BIOMOLECULES, 2022, 12 (04)
  • [9] Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling
    Ermann, Joerg
    Lefton, Micah
    Wei, Kevin
    Gutierrez-Arcelus, Maria
    CURRENT RHEUMATOLOGY REPORTS, 2024, 26 (04) : 144 - 154
  • [10] Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling
    Joerg Ermann
    Micah Lefton
    Kevin Wei
    Maria Gutierrez-Arcelus
    Current Rheumatology Reports, 2024, 26 : 144 - 154