Transcriptome Analysis of Cattle Embryos Based on Single Cell RNA-Seq

被引:0
|
作者
Wang, Jie [1 ]
Fang, Di [1 ]
Zhao, Jianqing [1 ]
Huang, Fei [2 ]
Liu, Bo [2 ]
Tao, Weikun [2 ]
Cui, Baoshan [2 ]
Gao, Qinghua [1 ,2 ,3 ]
机构
[1] Tarim Univ, Coll Life Sci, Alar 843300, Xinjiang, Peoples R China
[2] Tarim Univ, Coll Anim Sci, Alar 843300, Xinjiang, Peoples R China
[3] Xinjiang Prod & Construct Corps, Key Lab Tarim Anim Husb Sci & Technol, Alar 843300, Xinjiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Cattle; Early embryo; XX; XY embryo; RNA-Seq; Differential expression; IN-VITRO; GENE-EXPRESSION; GROWTH; STAGE;
D O I
10.17582/journal.pjz/20211016091046
中图分类号
Q95 [动物学];
学科分类号
071002 ;
摘要
Over the past few years, transcriptome sequencing has been applied to livestock and poultry, helping to select and investigate candidate genes associated with important traits. Yet, so far, only a few studies have reported differences in single-cell transcriptome between bovine embryos of different genders. In this study, we performed transcriptome analysis of cattle embryos based on a single Cell RNA-Seq. Bovine sex-controlled semen for artificial insemination were used to obtain different stage embryos: bovine 8 cell XX embryo, 8 cell XY embryo, 16 cell XX embryo, 16 cell XY embryo, morula XX embryo, morula XY embryo, blastocyst XX embryo, and blastocyst XY embryo. A sequencing library was constructed by the Smart-Seq2 amplification. The transcriptome was sequenced by Illumina HiSeqXten high-throughput sequence technology, and effective sequences were analyzed by functional annotation and related bioinformatics analysis. We found that Q30 percentage range of eight samples was 91.79-92.37%. The filtration sequence was 44106250-54234844. Compared with the reference genome by TopHat software, the net reading ratio of the bovine reference gene at each stage was 93.17-9 4.23%, the ratio of sequence numbers to multiple sites of the genome was 2.99-4.89. The DEG was identified by using the fold change & GE;2 and FDR <0.01 as cut-off values. There were 525 differentially expressed genes. GO and KEGG analysis showed that "cell part", "organelle", and organelle part" were significantly enriched in cell composition categories. As for molecular functional categories, DEGs were significantly enriched in cellular process "," biological regulation "and metabolic process" during biological processes. Moreover, KEGG analysis showed that the most abundant pathways were oxidative phosphorylation and Wnt signaling pathway","MAPK signaling pathway "," Regulation of actin cytoskeleton "and VEGF signaling pathway". To conclude, these RNA-Seq results confirmed the differential expression of several genes in embryos of different genders during embryonic development. These DEGs partcipate in the transcriptional regulation of bovine embryonic development of different genders.
引用
收藏
页码:1865 / 1872
页数:8
相关论文
共 50 条
  • [31] RNA-seq analysis of single bovine blastocysts
    James L Chitwood
    Gonzalo Rincon
    German G Kaiser
    Juan F Medrano
    Pablo J Ross
    BMC Genomics, 14
  • [32] A RNA-Seq Analysis to Describe the Boar Sperm Transcriptome and Its Seasonal Changes
    Godia, Marta
    Estill, Molly
    Castello, Anna
    Balasch, Sam
    Rodriguez-Gill, Joan E.
    Krawetz, Stephen A.
    Sanchez, Armand
    Clop, Alex
    FRONTIERS IN GENETICS, 2019, 10
  • [33] Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq
    Yang, Chaoyun
    Han, Liyun
    Li, Peng
    Ding, Yanling
    Zhu, Yun
    Huang, Zengwen
    Dan, Xingang
    Shi, Yuangang
    Kang, Xiaolong
    FRONTIERS IN GENETICS, 2021, 12
  • [34] A comparison of mRNA sequencing (RNA-Seq) library preparation methods for transcriptome analysis
    Hiroki Ura
    Sumihito Togi
    Yo Niida
    BMC Genomics, 23
  • [35] Whole transcriptome RNA-seq analysis: tumorigenesis and metastasis of melanoma
    Zhao, Hua
    Li, Yongjun
    Wang, Shaobin
    Yang, Yadong
    Wang, Junyun
    Ruan, Xiuyan
    Yang, Yaran
    Cai, Kan
    Zhang, Bing
    Cui, Peng
    Yan, Jiangwei
    Zhao, Yongliang
    Wakeland, Edward K.
    Li, Quanzhen
    Hu, Songnian
    Fang, Xiangdong
    GENE, 2014, 548 (02) : 234 - 243
  • [36] Analysis Considerations for Utilizing RNA-Seq to Characterize the Brain Transcriptome
    Zheng, Christina L.
    Kawane, Sunita
    Bottomly, Daniel
    Wilmot, Beth
    BRAIN TRANSCRIPTOME, 2014, 116 : 21 - 54
  • [37] A comparison of mRNA sequencing (RNA-Seq) library preparation methods for transcriptome analysis
    Ura, Hiroki
    Togi, Sumihito
    Niida, Yo
    BMC GENOMICS, 2022, 23 (01)
  • [38] RNA-Seq as an Emerging Tool for Marine Dinoflagellate Transcriptome Analysis: Process and Challenges
    Akbar, Muhamad Afiq
    Ahmad, Asmat
    Usup, Gires
    Bunawan, Hamidun
    PROCESSES, 2018, 6 (01)
  • [39] SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
    Guo, Minzhe
    Wang, Hui
    Potter, S. Steven
    Whitsett, Jeffrey A.
    Xu, Yan
    PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (11)
  • [40] RNA-Seq transcriptome analysis of Spirodela dormancy without reproduction
    Wenqin Wang
    Yongrui Wu
    Joachim Messing
    BMC Genomics, 15