Affiliation: | 1 BGI-Shenzhen, Shenzhen, 518083, China 2 State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China 3 School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China 4 College of Life Sciences, Wuhan University, Wuhan 430072, China 5 CAS-Max Planck Junior Research Group, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences (CAS), Kunming, Yunnan 650223, China 6 Graduate School of Chinese Academy of Sciences, Beijing 100049, China 7 Department of Hematology, Peking University First Hospital, Beijing 100034, China 8 Pfizer Inc., San Diego, CA 92121, USA 9 Department of Biology, University of Copenhagen, DK-1165 Copenhagen, Denmark 10 The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, DK-1165 Copenhagen, Denmark |
Abstract: | Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients. |