Elucidating the working principles of the genome
The genome plays a central role in the cell. Many biological functions, including cellular differentiation and the immune response, are achieved by expressing various genes in a programmed fashion that depends not only on the sequence of genes, but also on physiological conditions. Recent advances in genome science have made it possible to predict what amino acid sequences will be produced, but it is still difficult to predict how the amounts of proteins that are expressed proteins are programmed by the genome.
Our laboratory aims to understand the working principles underlying this property of the genome by investigating its molecular-level architecture from the standpoints of chemistry and physics.
The Hi-CO method
We recently developed the Hi-CO method to clarify the 3D molecular structure of the genome (Ohno et al., Cell, 2019). This method combines measurements of genomic structural information with next-generation genome sequencing and the simulation of molecular dynamics using a supercomputer to analyze the 3D arrangement and orientation of every nucleosome over the entire genome. It was already known that the genome wraps around histone octamer proteins at every 160 to 200 base pairs to form a unitary structure called the nucleosome. The Hi-CO method has expanded on this knowledge and revealed that the nucleosome folding structure is significantly altered at every genomic locus according to gene regulation status. This work has been prominently featured on the cover of Cell. For more details, please refer to https://www.riken.jp/en/news_pubs/research_news/rr/20190705_FY20190014/.
Derivation of 3D nucleosome folding structure by simulated annealing molecular dynamics simulation (Ohno et al., Cell, 2019)
The BHi-Cect method
The genome is comprised of macromolecules formed by very long series of nucleosomes with a hierarchically organized structure within the nucleus of the cell. The genome is known to form domain substructures at the multiple gene locus level, and to form loop substructures around transcription initiation sites. This complex structure exists in two structural states, a highly condensed state and a dispersed state, which are involved in gene inactivation and activation, respectively. In our laboratory, we have developed an algorithm, the BHi-Cect method, that identifies characteristic structural elements from the genome using machine learning (Kumar et al., Nucleic Acids Research, 2020). Using this method, we have discovered novel structural elements (“enclaves”) and demonstrated that they are tightly related to gene activity. For more information, please see https://www.riken.jp/en/news_pubs/research_news/rr/20200501_1/index.html.
Understanding the constitutional principles of cellular systems
Various types of proteins are expressed within the cell, and these proteins may act as triggers in the production of other proteins. These mutual interactions may generate an intracellular molecular environment suitable for various physiological states, including disease and differentiation. Logically explaining and predicting how the expression level of each protein is controlled as a system is an essential problem in biology and medicine. Our laboratory is developing techniques to quantify all intracellular proteins (the proteome) under various physiological conditions, which will allow us to determine the control logic of the proteome.
Imaging single-molecule gene expression in single cells
It is known that every single cell, even if it is genetically identical to other cells, exists in its own unique state. These unique states are achieved by the expression of different quantities of proteins. To illuminate the logic of intracellular gene regulation, we must be able to examine single-cell dynamics with high accuracy. Our laboratory developed a method to analyze single cell gene expression dynamics with single-molecule resolution for a large number of genes (Taniguchi et al., Science, 2010). Our technique involves a high-throughput platform that combines automated single-molecule fluorescence microscopy and a cell library of chromosomal fluorescent protein fusion. Using this technique, we discovered that there is a general lack of correlation between the quantity of mRNA and the quantity of proteins in a single cell.
In general, single-molecule fluorescence microscopes, such as the evanescent microscope, can image only near the surface of the cover glass (i.e. with a depth of a few hundreds of nanometers). Our laboratory has overcome this limitation through the invention of the PISA microscope. The PISA microscope permits single-molecule observations at depths several hundred micrometers from the coverslip (Taniguchi, Nishimura: Patent JP6086366, US9880378, EP2983029). PISA microscopy allows for single-molecule observation over the entire depth of not only human cells (typically several tens of micrometers), but also human tissues. A microscope containing this technology has now been commercialized world-wide.
New principles and methods in disease diagnosis
Our laboratory is pursuing various technological approaches aimed at accurately capturing and manipulating the physiological states of living organisms, including humans. We are developing methods for quantifying gene expression at a large scale, reconstructing genomic interactions, and integrating large amounts of data to extract rules. We pay close attention to the latest technologies in numerous academic fields as we take on this challenge.
Sensitive proteome analysis
Although mass spectrometry is a standard method for proteome analysis, it presents issues, such as low sensitivity, low throughput, and high equipment cost that make it less than ideal for the diagnosis of disease. Our laboratory seeks methodological solutions to these issues. For example, we are developing the use of single molecule microscopy to improve sensitivity (Leclerc et al., Bioconj. Chem., 2018; Taniguchi, Leclerc: Patent Application JP2017-177070; Taniguchi, Ohno: PCT/JP2018/048329). We expect that this technique will eventually enable the high-throughput analysis of small amounts of samples (even single cells) via an automated system.
Atomic genome structure simulator
While the development of the Hi-CO method has made it possible to determine genomic structure with nucleosome resolution, we want to increase the resolution to the atomic level. We are meeting this challenge by applying atomic-scale simulations of molecular dynamics to structural models of the genome, taking the laws of physics and chemistry into account.