Efficient reconstruction of cell lineage trees for cell ancestry and cancer

Author:

Jang Yeongjun1ORCID,Fasching Liana2,Bae Taejeong1,Tomasini Livia2,Schreiner Jeremy2,Szekely Anna3,Fernandez Thomas V24,Leckman James F2,Vaccarino Flora M256,Abyzov Alexej1

Affiliation:

1. Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic , Rochester , MN  55905, USA

2. Child Study Center, Yale University , New Haven , CT  06520, USA

3. Department of Neurology, Yale University , New Haven , CT  06520, USA

4. Department of Psychiatry, Yale University , New Haven , CT  06511, USA

5. Department of Neuroscience, Yale University , New Haven , CT  06520, USA

6. Yale Kavli Institute for Neuroscience , New Haven , CT  06520, USA

Abstract

Abstract Mosaic mutations can be used to track cell ancestries and reconstruct high-resolution lineage trees during cancer progression and during development, starting from the first cell divisions of the zygote. However, this approach requires sampling and analyzing the genomes of multiple cells, which can be redundant in lineage representation, limiting the scalability of the approach. We describe a strategy for cost- and time-efficient lineage reconstruction using clonal induced pluripotent stem cell lines from human skin fibroblasts. The approach leverages shallow sequencing coverage to assess the clonality of the lines, clusters redundant lines and sums their coverage to accurately discover mutations in the corresponding lineages. Only a fraction of lines needs to be sequenced to high coverage. We demonstrate the effectiveness of this approach for reconstructing lineage trees during development and in hematologic malignancies. We discuss and propose an optimal experimental design for reconstructing lineage trees.

Funder

National Institute of Mental Health

Simons Foundation

National Research Foundation of Korea

Publisher

Oxford University Press (OUP)

Subject

Genetics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3