Laboratory for Bioinformatics Research

Unit Leader

Itoshi NikaidoPh.D.

  • Location:Kobe / Developmental Biology Buildings, Wako / Information Science Bldg.
  • E-mail:itoshi.nikaido[at]riken.jpPlease replace [at] with @.
  • Lab Website

Research Summary

A multicellular organism is orchestrated by cell growth, death, differentiation, and communication at the single-cell level. To understand various crucial biological phenomena, we should massively perturb and measure transcriptomes and epigenomes at the single-cell level.

Our group will develop novel methods of comprehensive analysis and perturbation of transcriptome and epigenome at the single-cell level, in particular, by applying massively parallel DNA sequencing, genome editing, microfluidics, and machine learning. We focus on the development of methods for quantifying and controlling cell function, fate, and cell-cell communication at the single-cell level.

We have already reported novel single-cell RNA-sequencing methods, such as Quartz-Seq and RamDA-seq,which are highly reproducible and sensitive methods of quantifying single-cell transcriptome. Our unit will not develop new techniques but also collaborate with various life scientists within and outside of RIKEN using our new sequencing technologies.

With these techniques, our unit seeks to promote the social well-being by contributing insights into how humans can achieve health and longevity.

We are located at the RIKEN Center for Computing and Communication near Tokyo and the RIKEN Center for Developmental Biology in Kobe.

Quartz-Seq2: a high throughput single-cell RNA-sequencing method

Overview of RT-RamDA and single-cell RamDA-seq for detection of full-length total RNAs

Research Theme

  • Development of novel single-cell omics techniques
  • Collaboration with various biologists to apply novel single-cell omics technologies

Main Publications List

  • Hayashi T, Ozaki H, Sasagawa Y, et al.
    Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs.
    Nature Communications 9. 619 (2018) doi: 10.1038/s41467-018-02866-0
  • Matsumoto H, Kiryu H, Furusawa C, et al.
    SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.
    Bioinformatics 33(15). 2314–2321 (2017) doi: 10.1093/bioinformatics/btx194
  • Tsuyuzaki K, Morota G, Ishii M, et al.
    MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.
    BMC Bioinformatics 16. 45 (2015) doi: 10.1186/s12859-015-0453-z
  • Sasagawa Y, Nikaido I, Hayashi T, et al.
    Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.
    Genome Biology 14. 3097 (2013) doi: 10.1186/gb-2013-14-4-r31
  • Adachi K, Nikaido I, Ohta H, et al.
    Context-dependent wiring of Sox2 regulatory networks for self-renewal of embryonic and trophoblast stem cells.
    Molecular Cell 52. 380–392 (2013) doi: 10.1016/j.molcel.2013.09.002
  • Sasagawa Y, Danno H, Takada H et al.
    Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.
    Genome Biology 19. 29 (2018) doi: 10.1186/s13059-018-1407-3

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