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] 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
  • [32] FBA: feature barcoding analysis for single cell RNA-Seq
    Duan, Jialei
    Hon, Gary C.
    BIOINFORMATICS, 2021, 37 (22) : 4266 - 4268
  • [33] A systematic evaluation of single cell RNA-seq analysis pipelines
    Vieth, Beate
    Parekh, Swati
    Ziegenhain, Christoph
    Enard, Wolfgang
    Hellmann, Ines
    NATURE COMMUNICATIONS, 2019, 10 (1)
  • [34] RNA-Seq Analysis of the Arabidopsis Transcriptome in Pluripotent Calli
    Lee, Kyounghee
    Park, Ok-Sun
    Seo, Pil Joon
    MOLECULES AND CELLS, 2016, 39 (06) : 484 - 494
  • [35] Transcriptome analysis of rice root heterosis by RNA-Seq
    Zhai, Rongrong
    Feng, Yue
    Wang, Huimin
    Zhan, Xiaodeng
    Shen, Xihong
    Wu, Weiming
    Zhang, Yingxin
    Chen, Daibo
    Dai, Gaoxing
    Yang, Zhanlie
    Cao, Liyong
    Cheng, Shihua
    BMC GENOMICS, 2013, 14
  • [36] Embracing the dropouts in single-cell RNA-seq analysis
    Qiu, Peng
    NATURE COMMUNICATIONS, 2020, 11 (01)
  • [37] A systematic evaluation of single cell RNA-seq analysis pipelines
    Beate Vieth
    Swati Parekh
    Christoph Ziegenhain
    Wolfgang Enard
    Ines Hellmann
    Nature Communications, 10
  • [38] 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, (03) : 214 - 222
  • [39] Computational cell cycle analysis of single cell RNA-seq data
    Moussa, Marmar
    2018 IEEE 8TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL ADVANCES IN BIO AND MEDICAL SCIENCES (ICCABS), 2018,
  • [40] Transcriptome analysis of rice root heterosis by RNA-Seq
    Rongrong Zhai
    Yue Feng
    Huimin Wang
    Xiaodeng Zhan
    Xihong Shen
    Weiming Wu
    Yingxin Zhang
    Daibo Chen
    Gaoxing Dai
    Zhanlie Yang
    Liyong Cao
    Shihua Cheng
    BMC Genomics, 14