TRANSCRIPTOME ANALYSIS OF SHEEP EMBRYOS IN VIVO BASED ON SINGLE CELL RNA-SEQ

被引:2
|
作者
Fang, Di [1 ]
Tao, Weikun [2 ]
Wang, Jie [1 ]
Huang, Fei [2 ]
Gao, Qinghua [2 ,3 ]
机构
[1] Tarim Univ, Coll Life Sci, Alar 843300, Xinjiang, Peoples R China
[2] Tarim Univ, Coll Anim Sci & Technol, Alar 843300, Xinjiang, Peoples R China
[3] Xinjiang Prod & Construct Corps, Key Lab Tarim Anim Husb Sci & Technol, Alar 843300, Xinjiang, Peoples R China
来源
ACTA MEDICA MEDITERRANEA | 2022年 / 38卷 / 02期
基金
中国国家自然科学基金;
关键词
sheep; embryo; transcriptome; RNA-Seq; GENOME;
D O I
10.19193/0393-6384_2022_2_192
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Objective: The purpose of the present study was to explore the transcriptome differences of sheep embryos. Embryos were at different developmental stages in order to assess the differences of the function, classification and metabolic pathway of differentially expressed genes and to provide a theoretical basis for revealing the regulatory mechanism of sheep early embryo development. Materials and methods: 8-cell, 16-cell, morula and blastocysts were collected and the sequencing library was constructed by the Smart-Seq2 amplification technology. The transcriber was sequenced by Illumina HiSeqXten high-throughput sequencing technology and the effective sequences were analyzed by functional annotation and related bioinformatic analysis. Results: The results indicated that the Clean reads of 8-cell, 16-cell, morula and early blastocysts embryos were 44169859048957974, of which 93.71-95.29% reads were compared with the reference genome sequence of sheep; We used FDR 0.05 and Fold Change 2 as the criteria to screen for differential genes by comparing pairwise differences at four stage during embryo development. A total of 8281 differentially expressed mRNAs were identified, including 840 in E16vsE8, 6631 in E32vsE16, 810 in BlavsE32. Using the GO enrichment analysis, we explored the function of the DEGs.No significant difference was found at E16 vs E8.At E32 vs E16,Cellular components contained 127 significance terms (P < 0.05), 92 terms were significant enriched in molecular function, And biological processes involved 338 significance terms; At Bla vs E32,Cellular components contained 7 significance terms (P <0.05). A total of 40 significance KEGG pathway terms were enriched in E32 vs E16. Conclusions: In this study, individual embryonic transcriptome sequencing of sheep was established for high-throughput sequencing and analysis of the transcriptomes of sheep 8-cell, 16-cell, morula, and early blastocysts. The number of differentially expressed genes was identified at different stages of sheep embryo development and the function, classification and metabolic pathway of differentially expressed genes were obtained. The current study offers substantial information on the identification of the sheep embryo transcriptome, revealing the molecular regulatory mechanism of sheep embryo development. and selects 30 key genes, and its function needs further exploration and research.
引用
收藏
页码:1263 / 1272
页数:10
相关论文
共 50 条
  • [21] Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing
    Fan, Xiaoying
    Tang, Dong
    Liao, Yuhan
    Li, Pidong
    Zhang, Yu
    Wang, Minxia
    Liang, Fan
    Wang, Xiao
    Gao, Yun
    Wen, Lu
    Wang, Depeng
    Wang, Yang
    Tang, Fuchou
    PLOS BIOLOGY, 2020, 18 (12)
  • [22] Transcriptome Analysis of Murine Olfactory Sensory Neurons during Development Using Single Cell RNA-Seq
    Scholz, Paul
    Kalbe, Benjamin
    Jansen, Fabian
    Altmueller, Janine
    Becker, Christian
    Mohrhardt, Julia
    Schreiner, Benjamin
    Gisselmann, Guenter
    Hatt, Hanns
    Osterloh, Sabrina
    CHEMICAL SENSES, 2016, 41 (04) : 313 - 323
  • [23] RNA-seq analysis of the C. briggsae transcriptome
    Uyar, Bora
    Chu, Jeffrey S. C.
    Vergara, Ismael A.
    Chua, Shu Yi
    Jones, Martin R.
    Wong, Tammy
    Baillie, David L.
    Chen, Nansheng
    GENOME RESEARCH, 2012, 22 (08) : 1567 - 1580
  • [24] Transcriptome analysis of wheat grain using RNA-Seq
    Liu WEI
    Zhihui WU
    Yufeng ZHANG
    Dandan GUO
    Yuzhou XU
    Weixia CHEN
    Haiying ZHOU
    Mingshan YOU
    Baoyun LI
    Frontiers of Agricultural Science and Engineering, 2014, 1 (03) : 214 - 222
  • [25] Computational Cell Cycle Analysis of Single Cell RNA-Seq Data
    Moussa, Marmar
    Mandoiu, Ion I.
    COMPUTATIONAL ADVANCES IN BIO AND MEDICAL SCIENCES, 2021, 12686 : 71 - 87
  • [26] Transcriptome analysis of wheat grain using RNA-Seq
    Wei, Liu
    Wu, Zhihui
    Zhang, Yufeng
    Guo, Dandan
    Xu, Yuzhou
    Chen, Weixia
    Zhou, Haiying
    You, Mingshan
    Li, Baoyun
    FRONTIERS OF AGRICULTURAL SCIENCE AND ENGINEERING, 2014, 1 (03) : 214 - 222
  • [27] Quantitative RNA-seq analysis of the Campylobacter jejuni transcriptome
    Chaudhuri, Roy R.
    Yu, Lu
    Kanji, Alpa
    Perkins, Timothy T.
    Gardner, Paul P.
    Choudhary, Jyoti
    Maskell, Duncan J.
    Grant, Andrew J.
    MICROBIOLOGY-SGM, 2011, 157 : 2922 - 2932
  • [28] Practical Compass of Single-Cell RNA-Seq Analysis
    Okada, Hiroyuki
    Chung, Ung-il
    Hojo, Hironori
    CURRENT OSTEOPOROSIS REPORTS, 2024, 22 (05) : 433 - 440
  • [29] Embracing the dropouts in single-cell RNA-seq analysis
    Peng Qiu
    Nature Communications, 11
  • [30] Single Cell RNA-Seq Analysis of Human Red Cells
    Jain, Vaibhav
    Yang, Wen-Hsuan
    Wu, Jianli
    Roback, John D.
    Gregory, Simon G.
    Chi, Jen-Tsan
    FRONTIERS IN PHYSIOLOGY, 2022, 13