BioLab
GIST 생명과학부 Functional Genomics Laboratory(기능 유전체학 연구실)
박지환 교수
연구실 소개
연구실 홈페이지The 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
연구분야
Single-cell RNA-seq in disease
Tissues consist of numerous cell types, each with specific properties and functions. Single-cell RNA-seq allows discriminating multiple cell types in tissues, including rare cell types, based on gene expression patterns. Single-cell RNA-seq enables analysis of cell types that are difficult to isolate from tissues and the comparison of cell type compositions between samples under different conditions. Through this, cell types detected only in disease samples or in which disease-related genes are specifically expressed can be identified. It is also possible to identify the change in gene expression according to the disease in each cell type.
In heterogeneous diseases, disease progression and response to treatment vary from patient to patient. Moreover, normal cells, diseased cells, and the tumor environment have complex interactions within the tissue of an individual, making tumor cell-specific treatment difficult. Single-cell resolution enables the identification of mutation, copy number alteration, or viral genomes in individual cells, allowing detailed comparisons of gene expression between normal and diseased cells in the same sample. By analyzing this, it is possible to find genes overexpressed in diseased cells compared to normal cells or to find master transcription factors in diseased cells. In addition, cell-cell interaction analysis can confirm activated or inhibited receptor-ligand interactions between normal cells, diseased cells, and the microenvironment.
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.
Epigenomics
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.
Single-cell full-length RNA-seq
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.
Development of single-cell sequencing techniques for in vivo model (zebrafish)
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.
We are particularly interested in studying multipotent cells, such as neural crest cells and hematopoietic stem cells, and bipotent tissues, such as zebrafish gonads, to understand the transcriptional regulation of cell fate determination. To fully investigate how these cells or tissues acquire different cell fates, we will adapt the CRISPR/Cas9 knockout system for uncovering the role of several essential genes that influence the cell fates and tracking the transcriptional changes of genetically perturbed cells at a single-cell level.
We are also interested in constructing the transcriptional trajectories from zebrafish embryos. We will design a novel barcode methodology to trace single-cell transcriptomes of zebrafish embryos. While the analysis of previous single-cell barcoding and sequencing relied on reading short fragments of transcriptomes, we will detect full-length transcriptomes at a single-cell resolution and cell-type-specific alternative splicing patterns using Nanopore sequencing.
연구성과
Publication
An H, Eun M, Yi J, et al. CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins. Briefings in Bioinformatics 2022. https://doi.org/10.1093/bib/bbac056
Lee, D., Yoon, C.-H., Choi, S. Y., Kim, J.-E., Cho, Y.-K., Choi, B.-S., &Park, J. (2021). Transcriptome Analysis Identifies Altered Biological Processes and Novel Markers in Human Immunodeficiency Virus-1 Long-Term Non-Progressors. Infection & Chemotherapy. Korean Society of Infectious Diseases and Korean Society for Chemotherapy. https://doi.org/10.3947/ic.2021.0031
Luo, H., Xia, X., Kim, G. D., Liu, Y., Xue, Z., Zhang, L., … Xu, H. (2021, July 30). Characterizing dedifferentiation of thyroid cancer by integrated analysis. Science Advances. American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/sciadv.abf3657
Kim, J., & Park, J. (2021, May 1). Single-cell transcriptomics: a novel precision medicine technique in nephrology. The Korean Journal of Internal Medicine. Korean Association of Internal Medicine. https://doi.org/10.3904/kjim.2020.415
Dhillon P, Park J* et al. (2020) The Nuclear Receptor ESRRA Protects from Kidney Disease by Coupling Metabolism and Differentiation. Cell Metab. 2020 Dec. S1550-4131(20)30606-9. (Co-corresponding author)
Park, J., Liu, C. (L., Kim, J., & Susztak, K. (2019). Understanding the kidney one cell at a time. Kidney International, 96(4), 862-870. doi: 10.1016/j.kint.2019.03.035
Park, J., Guan, Y., Sheng, X., Gluck, C., Seasock, M. J., Hakimi, A., … Susztak, K. (2019). Functional methylome analysis of human diabetic kidney disease. JCI Insight, 4(11). doi: 10.1172/jci.insight.128886
Li, S.-Y., Park, J., Guan, Y., Chung, K., Shrestha, R., Palmer, M. B., & Susztak, K. (2019). DNMT1 in Six2 Progenitor Cells Is Essential for Transposable Element Silencing and Kidney Development. Journal of the American Society of Nephrology, 30(4), 594-609. doi: 10.1681/asn.2018070687
Giri A, Hellwege JN, Keaton JM, Park J & et al. (2019) Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat Genet. Jan;51(1):51-62.
Park, J., Shrestha, R., Qiu, C., Kondo, A., Huang, S., Werth, M., … Susztak, K. (2018). Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science, 360(6390), 758-763. doi: 10.1126/science.aar2131
Park, J., Kwon, Y.-W., Ham, S., Hong, C.-P., Seo, S., Choe, M. K., … Roh, T.-Y. (2017). Identification of the early and late responder genes during the generation of induced pluripotent stem cells from mouse fibroblasts. Plos One, 12(2).doi: 10.1371/journal.pone.0171300
Park, J., Lim, C. H., Ham, S., Kim, S. S., Choi, B.-S., & Roh, T.-Y. (2014). Genome-wide analysis of histone modifications in latently HIV-1 infected T cells. Aids, 28(12), 1719-1728. doi: 10.1097/qad.0000000000000309
연구실 구성원
지도교수: 박지환
Students: 김지수, 김경대, 이다연, 안현수, 김동근, 은민호, 이현지, 임채민, 조준우, 김하영, 안준우
Researchers: 신소이, 최신영, 정수웅, 윤바울, 이자운, 배서경
Contact : jihwan.park@gist.ac.kr
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