Team Leader
Itoshi Nikaido Ph.D.

Laboratory for Bioinformatics Research

Location: Kobe / Developmental Biology Buildings, Wako / Information Science Bldg.

E-mailitoshi.nikaido [at] riken.jp

Please replace [at] with @.

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 team 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 team will not only develop new techniques but also collaborate with various life scientists within and outside of RIKEN using our new sequencing technologies.

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

Research Theme

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

Selected Publications

Lin CW, Septyaningtrias DE, Chao HW, et al.
A common epigenetic mechanism across different cellular origins underlies systemic immune dysregulation in an idiopathic autism mouse model.
Molecular Psychiatry 27(8), 3343-3354 Mon Aug 01 00:00:00 JST 2022 doi: 10.1038/s41380-022-01566-y

Ochiai H, Hayashi T, Umeda M, et al.
Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells.
Science Advances 6(25), eaaz6699 Tue Dec 01 00:00:00 JST 2020 doi: 10.1126/sciadv.aaz6699

Ozaki H, Hayashi T, Umeda M, Nikaido I.
Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets.
BMC Genomics 21, 177 Thu Oct 01 00:00:00 JST 2020 doi: 10.1186/s12864-020-6542-z

Tsuyuzaki K, Sato H, Sato K, Nikaido I.
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.
Genome Biology 21, 9 Tue Sep 01 00:00:00 JST 2020 doi: 10.1186/s13059-019-1900-3

Mereu E, Lafzi A, Moutinho C, et al.
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.
Nature biotechnology 38(6), 747-755 Mon Jun 01 00:00:00 JST 2020 doi: 10.1038/s41587-020-0469-4

Sato K, Tsuyuzaki K, Shimizu K, Nikaido I.
CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing.
Genome Biology 20, 31 Sun Dec 01 00:00:00 JST 2019 doi: 10.1186/s13059-019-1639-x

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 Sat Dec 01 00:00:00 JST 2018 doi: 10.1186/s13059-018-1407-3

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 Sat Dec 01 00:00:00 JST 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 Fri Dec 01 00:00:00 JST 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 Tue Dec 01 00:00:00 JST 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 Sun Dec 01 00:00:00 JST 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 Fri Nov 01 00:00:00 JST 2013 doi: 10.1016/j.molcel.2013.09.002