[초청강연] 현대생물학 콜로퀴움 - Digital Immune Processing for Next Generation Healthcare

사람의 면역시스템의 시시각각 변하는 상황을 NGS, Single cell genomics등의 기술을 활용하여 영상화하는 방식으로 진단과 제약의 혁신을 목적하는 Digital Immune Processing프로젝트와 이와 관련된 다양한 연구주제를 선정하는 과정에 대한 스토리를 공유한다.

Digital Immune Processing for Next Generation Healthcare
Prof. Sunghoon Kwon (School of Electrical Engineering, SNU) 

Human immune system is most advanced (and also personalized) defense system for diseases. It continuously fights with various diseases by sensing them and producing molecules to cure them. Deciphering the time varying immune responses of individuals opens up big potential for early diagnostics and effective therapeutic developments. We recently introduce Digital Immune Processing (DIP) technology, which produces digitalized “immune images” of the human immune system, and envision its potential to become a main stream of next generation health checkup tools in the future just as iconic X-ray imaging of today. In this talk, I will first present some of past research projects such as sepsis diagnostics and single cell genomics that leads me into this exciting immune imaging projects. Three core technologies will be presented: Digital Immune Omics (DIO) for high quality immune data generation, Digital Immune Profiling and Imaging (DIPI) for analysis, and Digital Immune Storage (DIS) for large scale molecular banking. Then I will share the recent results in relation to cancer and COVID-19 diagnostics and therapeutics. We believe this will facilitate defining the baseline for the human immunity in healthy and diseased status, further contributing to scientific discoveries related to the human immune system.

참고논문
"Stereotypic neutralizing VH antibodies against SARS-CoV-2 spike protein receptor binding domain in COVID-19 patients and healthy individuals", Science Translational Medicine, 2021
"PHLI-seq: constructing and visualizing cancer genomic maps in 3D by phenotype-based high-throughput laser-aided isolation and sequencing", A rapid antimicrobial susceptibility test based on single-cell morphological analysis", Science Translational Medicine, 2014