Research Contents

1. Epigenomic Analysis of T Cells
Another focus of our laboratory is to understand the molecular mechanisms underlying T cell activation and differentiation by analyzing their epigenomic landscapes. Epigenetic modifications are known to be closely associated with the onset and progression of immune-related diseases, and through this research, we aim to uncover novel insights into immune regulation.

On the experimental side, we utilize epigenomic techniques such as ChIP-seq and ATAC-seq, and incorporate single-cell level approaches to gain high-resolution insight into chromatin dynamics. These experiments generate complex and large-scale datasets, which we analyze using statistical and computational methods to build a multidimensional analysis framework.

Furthermore, our laboratory integrates machine learning and artificial intelligence (AI) technologies to advance our understanding of the immune system through epigenomic regulation in T cells, and to explore its potential applications in the broader field of immunology.

2. Exploring Disease Mechanisms Through Large-Scale Molecular Data

Our laboratory is engaged in the development of novel molecular analysis technologies aimed at elucidating the mechanisms of biological phenomena and applying these insights to disease prediction and classification. In particular, we focus on building comprehensive omics-based analysis methods capable of sensitively and accurately capturing dynamic molecular changes, even in trace amounts, alongside a robust bioinformatics infrastructure to support such analyses.

We combine large-scale data obtained through next-generation sequencing (NGS) with statistical analysis, machine learning, and other data science approaches to construct original analytical pipelines that allow us to extract disease-specific molecular patterns and changes associated with disease progression.

Working in collaboration with both clinical and basic science researchers, we aim to apply our findings across a wide range of diseases, including neurodegenerative, inflammatory, and oncological disorders. Our work includes identifying molecular characteristics unique to these conditions and developing indicators that may assist in disease classification, progression assessment, and diagnostic support.

Through these efforts, we seek to build a foundation for next-generation molecular research that bridges basic science and clinical medicine, while generating new insights through the integration of biomedical and data sciences.