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Simultaneous Decoding of Genetic Maps Inside Cells... A Game Changer for Understanding Complex Human Diseases < (Clockwise from top left) Professor Inkyung Jung (KAIST), Dr. Dongchan Yang (KAIST), Dr. Kyukwang Kim (KAIST), Dr. Yueyuan Xu (Duke University), Dr. Xiaolin Wei (Duke University), Professor Yarui Diao (Duke University) > The origin of many diseases begins at the cellular level and involves multiple molecular interactions. However, previous methods have struggled to accurately observe changes in individual cells. Analyzing average values across thousands of cells made it challenging to detect the early signals of disease. Our university's research team has pioneered groundbreaking technology that decodes the genetic blueprint within a cell in 3D, akin to zooming in on Earth using Google Earth. This innovation is poised to transform research into complex diseases such as cancer, dementia, and Parkinson's disease. KAIST announced on March 4th that Professor Inkyung Jung's research team from the Department of Biological Sciences, in collaboration with Professor Yarui Diao's team at Duke University, has developed scHiCAR (single-cell Hi-C with assay for transposase-accessible chromatin and RNA sequencing). This is the world’s first ultra-high throughput & precise molecular map decoding technology that simultaneously analyzes gene expression (transcriptome), the epigenome, and the 3D genome structure within a single cell. The key to determining a cell's state lies in how its genes operate. Genes are not simply switches that turn on and off. The destiny of a cell is determined by which genes are actually active (transcriptome), why they are active (epigenome), and within what spatial structure they operate (3D genome structure). Existing technologies required obtaining this information from different cells separately and then matching them afterward, which could lead to the distortion or omission of subtle changes. The research team introduced ‘Trimodal Multi-omics’ technology, an integrated precision analysis method that concurrently examines these three types of genetic information within a single cell. By incorporating Artificial Intelligence (AI) analysis, they significantly enhanced accuracy and reproducibility, culminating in a unified platform that reads internal cellular genetic information akin to a ‘single 3D map.’ <Ultra-precision Single-cell Molecular Map> Notably, the team succeeded in lowering the analysis cost to approximately $0.04 (approx. 50 KRW) per cell. Using this, they constructed a high-resolution molecular map of 1.6 million cells in mouse brain tissue. This means it is now possible to precisely identify when, where, and within what structure disease genes are turned on or off at the cellular level. The research team applied this technology to brain tissue and the muscle regeneration process, revealing distinct gene operation principles across 22 major cell types. Notably, they successfully tracked in real-time how the 3D structure of genes dynamically changes to influence cell fate during muscle stem cell regeneration. This advancement is expected to lay a crucial foundation for developing treatment strategies for aging and incurable diseases. <Research Result Image (AI-generated)> Professor Inkyung Jung remarked, ‘This research transcends mere observation of cells; it opens the door to precisely reading and controlling the genomic blueprints within them. It represents a significant turning point in elucidating the developmental mechanisms of complex diseases like Parkinson's and cancer, as well as identifying target points for patient-specific new drugs.’ The study was published on February 19th in the international academic journal Nature Biotechnology (IF=46.9). Paper Title: Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR DOI: 10.1038/s41587-026-03013-7 Meanwhile, this research was conducted with support from the Suh Kyungbae Foundation, the Samsung Science and Technology Foundation, and the Basic Research Program and Bio-Medical Technology Development Program of the National Research Foundation of Korea (Ministry of Science and ICT).
2026.03.06 View 1192