한빛사논문
Jinhyun Kim 1, Sungsik Kim 2, Huiran Yeom 3, Seo Woo Song 4, Kyoungseob Shin1, Sangwook Bae 5, Han Suk Ryu 6,7, Ji Young Kim 8, Ahyoun Choi 2, Sumin Lee 1,15, Taehoon Ryu 9, Yeongjae Choi 10, Hamin Kim 2, Okju Kim 9, Yushin Jung 9, Namphil Kim 1, Wonshik Han 6,8,11, Han-Byoel Lee 6,8,11,*, Amos C. Lee 12,15,* & Sunghoon Kwon 1,2,8,12,13,14,*
1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea.
2Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea.
3Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong 18323, Republic of Korea.
4Basic Science and Engineering Initiative, Children’s Heart Center, Stanford University, Stanford, CA, USA.
5Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
6Cancer Research Institute, Seoul National University, Seoul 03080, Republic of Korea.
7Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
8Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Republic of Korea.
9ATG Lifetech Inc., Seoul 08507, Republic of Korea.
10School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
11Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
12Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea.
13Inter-University Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
14Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul 08826, Republic of Korea.
15Present address: Meteor Biotech, Co. Ltd., Seoul 08826, Republic of Korea.
*Corresponding author: correspondence to Han-Byoel Lee, Amos C. Lee or Sunghoon Kwon
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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