Short-read and long-read RNA sequencing of mouse hematopoietic stem cells at bulk and single-cell levels

被引:0
|
作者
Xiuran Zheng
Dan Zhang
Mengying Xu
Wanqin Zeng
Ran Zhou
Yiming Zhang
Chao Tang
Li Chen
Lu Chen
Jing-wen Lin
机构
[1] Sichuan University,Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, Department of Laboratory Medicine, State Key Laboratory of Biotherapy, West China Second University Hospital
[2] Sichuan University,Biosafety Laboratory of West China Hospital
来源
Scientific Data | / 8卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Hematopoietic stem cells (HSCs) lie at the top of the differentiation hierarchy. Although HSC and their immediate downstream, multipotent progenitors (MPP) have full multilineage differentiation capacity, only long-term (LT-) HSC has the capacity of long-term self-renewal. The heterogeneity within the HSC population is gradually acknowledged with the development of single-cell RNA sequencing and lineage tracing technologies. Transcriptional and post-transcriptional regulations play important roles in controlling the differentiation and self-renewal capacity within HSC population. Here we report a dataset comprising short- and long-read RNA sequencing for mouse long- and short-term HSC and MPP at bulk and single-cell levels. We demonstrate that integrating short- and long-read sequencing can facilitate the identification and quantification of known and unannotated isoforms. Thus, this dataset provides a groundwork for comprehensive and comparative studies on transcriptional diversity and heterogeneity within different HSC cell types.
引用
收藏
相关论文
共 50 条
  • [1] Short-read and long-read RNA sequencing of mouse hematopoietic stem cells at bulk and single-cell levels
    Zheng, Xiuran
    Zhang, Dan
    Xu, Mengying
    Zeng, Wanqin
    Zhou, Ran
    Zhang, Yiming
    Tang, Chao
    Chen, Li
    Chen, Lu
    Lin, Jing-Wen
    SCIENTIFIC DATA, 2021, 8 (01)
  • [2] Identification of cell type specific transcript isoforms by integration of bulk short-read, long-read and single cell RNA-seq
    Yamamoto, Ryo
    Zaitlen, Noah
    Xiao, Xinshu
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2024, 32 : 1641 - 1641
  • [3] Comprehensive characterization of single-cell isoform in mouse retina with long-read RNA sequencing
    Wang, Meng
    Oh, Soo
    Li, Yumei
    Cheng, Xuesen
    Wang, Jun
    Chen, Rui
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2023, 64 (08)
  • [4] The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools
    Dong, Xueyi
    Tian, Luyi
    Gouil, Quentin
    Kariyawasam, Hasaru
    Su, Shian
    De Paoli-Iseppi, Ricardo
    Prawer, Yair David Joseph
    Clark, Michael B.
    Breslin, Kelsey
    Iminitoff, Megan
    Blewitt, Marnie E.
    Law, Charity W.
    Ritchie, Matthew E.
    NAR GENOMICS AND BIOINFORMATICS, 2021, 3 (02)
  • [5] Startups use short-read data to expand long-read sequencing market
    Eisenstein, Michael
    NATURE BIOTECHNOLOGY, 2015, 33 (05) : 433 - 435
  • [6] Advances in single-cell long-read sequencing technologies
    Gupta, Pallavi
    ONeill, Hannah
    Wolvetang, Ernst J.
    Chatterjee, Aniruddha
    Gupta, Ishaan
    NAR GENOMICS AND BIOINFORMATICS, 2024, 6 (02)
  • [7] Startups use short-read data to expand long-read sequencing market
    Michael Eisenstein
    Nature Biotechnology, 2015, 33 : 433 - 435
  • [8] Long-read single-cell sequencing of liver cancer
    Luo, Jian-Hua
    Liu, Silvia
    Ren, Bao-Guo
    Yu, Yan-Ping
    CANCER RESEARCH, 2023, 83 (08)
  • [9] Short-read and long-read full-length transcriptome of mouse neural stem cells across neurodevelopmental stages
    Chaoqiong Ding
    Xiang Yan
    Mengying Xu
    Ran Zhou
    Yuancun Zhao
    Dan Zhang
    Zongyao Huang
    Zhenzhong Pan
    Peng Xiao
    Huifang Li
    Lu Chen
    Yuan Wang
    Scientific Data, 9
  • [10] Short-read and long-read full-length transcriptome of mouse neural stem cells across neurodevelopmental stages
    Ding, Chaoqiong
    Yan, Xiang
    Xu, Mengying
    Zhou, Ran
    Zhao, Yuancun
    Zhang, Dan
    Huang, Zongyao
    Pan, Zhenzhong
    Xiao, Peng
    Li, Huifang
    Chen, Lu
    Wang, Yuan
    SCIENTIFIC DATA, 2022, 9 (01)