The research goal of this laboratory is to understand the pathogenesis of diseases such as chronic kidney disease by integrating and analyzing various genomic big data. Using large-scale data, continuous efforts have been made to find biomarkers and therapeutic target genes for complex diseases through approaches such as precision medicine. However, since all organs are composed of several types of cells, and complicated changes are involved in the disease process, it is essential to approach this at a single-cell level. For this purpose, we are using single-cell analysis, our lab’s core technology, to overcome existing limitations and research various disease models. We plan to research and validate the underlying molecular mechanism of genetic targets identified from single-cell analysis through animal model research and other methods such as epigenetic modification.
Schematics of single-cell transcriptome analysis
Single-cell pseudotime trajectory analysis can reveal critical regulatory genes in cell differentiation of disease development. By ordering cells according to pseudotime, cells in intermediate states of disease development can be identified, and genes that change as a function of pseudotime can be found. Trajectory analysis helps our understanding of disease development at the cellular level.
Cell identity and function are determined by the precise regulation of gene transcription. Many studies have shown different levels of gene regulation by each cell state. Transcription is carried out by RNA polymerase enzymes and regulated by epigenetic features such as chromatin conformation, histone modifications, transcription factor (TF) availability, and regulatory elements. Although epigenetic changes are necessary for normal development and health, they can also contribute to disease conditions. For example, more than 90% of Single Nucleotide polymorphisms (SNPs) are located in the non-coding region and affect TF binding affinity, histone modification, etc. Epigenetic changes by SNPs are known to have a high correlation with gene expression and cause diseases, various phenotypes. Consequently, Epigenomics is essential for understanding overall gene regulation and elucidating specific regulatory mechanisms to each cell state.
To understand the gene regulation mechanisms by cell type-specific and disease conditions, our laboratory performs epigenetic profiling at the single-nucleus level using Single-nucleus ATAC sequencing (snATAC-seq). That is possible to classify cell types based on statistical tools in the complex cell population. This advantage can detect the differential accessible regions (DARs) classified according to cell types and disease conditions. Through an analysis based on DARs, it is possible to define TFs that bind DARs and identify de novo functional regions on the genome such as an enhancer, silencer. Moreover, through the development of single-cell sequencing technology and statistical analysis, we can simultaneously analyze gene expression and chromatin in the same cell at the single-cell level to determine the relationship between epigenetic changes and actual gene expression changes. Furthermore, our laboratory performs the integrated analysis with other epigenetic profiling methods to validate cell type-specific epigenetic features.
A proposed protocol for cost-effective high-throughput full-length RNA sequencing using long-read sequencing
Lineage information of cancer cells encoded in the protein-coding genes can be characterized by single-cell RNA sequencing
Studying transcriptomes at single-cell resolution has been revolutionizing many fields in biological sciences, unmasking previously unknown cellular heterogeneity in various tissues. There have been increasing efforts to detect mutations and alternative splicing at single-cell resolution through single-cell transcriptome profiling. The necessity of the cDNA fragmentation step for sequencing on Illumina and other short-read sequencers prevents the characterization of somatic mutations and splicing events located far from 5' or 3' ends of cDNA at single-cell resolution in a high-throughput manner using short-read sequencing. Instead, the intermediate full-length cDNA library from a single-cell RNA sequencing experiment can be sequenced without being fragmented using third-generation sequencing techniques (i.e. long-read sequencing), allowing genotyping and analysis of full-length transcriptome at single-cell resolution.
Characterizing somatic mutations in coding regions in individual cancer cells through single-cell RNA sequencing will enable identification and tracking of subclonal structures in cancer cells while also capturing transcriptome at single-cell resolution. Also, analyzing a complete structure of full-length transcriptome at a single cell resolution will enable an analysis of cell-type specific alternative splicing (AS), alternative promoter usage (APU), and alternative polyadenylation (APA) patterns, which will benefit the ongoing efforts to dissect cellular heterogeneity in complex tissues.
Our lab focuses on developing single-cell sequencing techniques for in vivo systems. We are particularly interested in several zebrafish tissues derived from various differentiated cell lineages. Although the zebrafish model has been extensively used for studying early tissue development, the genetic tool for investigating the cellular heterogeneity of tissue development is lacking. To fully understand the developmental processes of cell development for tissue formation, it is essential to develop techniques to trace distinct cell lineages at a single-cell level and analyze how and when these cell lineages acquire various fates during tissue development.
