Single-cell and single-chromosome genomics : technologies and applications
Abstract/Contents
- Abstract
- Technology has played a critical role in the advancement of biology throughout history. Most recently, technological breakthroughs in the form of high-throughput genetic analyses have opened up the field of genomics, and enabled studies at an unprecedented scale. This dissertation focuses on developing novel technologies for the next era of genomics, in order to address new challenges that have emerged after the Human Genome Project (HGP). Specifically, two general strategies guide the general direction of my technology development. I will demonstrate single-cell strategies that address the challenge of dissecting heterogeneous tissues, and single-chromosome methods that enable organization of massive genetic variations into haplotypes. The first part of this dissertation demonstrates the translation of recent achievements in basic research into the clinic. An automatic cell micromanipulation system was developed and used to select single cells with specific surface marker combinations for gene expression analysis. The application of this cell picking system on a colonoscopy biopsy sample identified distinct subpopulations in the colon tissue, including putative colon stem cells that were previously elusive. The system may also be useful for clinical prediction of cancer therapy outcomes, after we detected drug resistant DNA mutations in single cancer cells. This dissertation then focuses on the genetic variation within single cells. Haplotype information is a fundamental feature of the human genome that was largely missing from most prior genomics studies. A deterministic haplotyping technique was invented that enables the resolution of the two somatic haploid genomes from an individual. This dissertation further demonstrates the direct mapping of meiotic recombination in single sperm cells from an individual, using a method that combines haplotyping and single-sperm cell genome analysis. Such a personal recombination map revealed significant differences from pedigree data. Deep sequencing of eight single cells enabled systematic measurements of gene conversion details. This work represents the first ever high-resolution genome-wide study of personal recombination, and provides new insights into the evolution of recombination patterns and haplotype block structures. High-accuracy DNA sequencing confirmed the low error rate of single-cell MDA, and enabled the detection of de novo point mutations from single primary sperm cells. Multiple cells from the same individual displayed consistent mutation rates with distinct molecular characters. The last part of this dissertation tackles the ambitious task of genome assembly from single chromosomes. Using single-chromosome amplification, two haploid major compatibility complex (MHC) regions were sequenced independently. The haploid samples have greatly reduced heterogeneity, which improved assembly of this highly polymorphic region. The techniques and applications mentioned herein illustrate efforts towards high-resolution and high-accuracy genomics, for providing increasingly accurate and complete pictures of the human genome.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wang, Jianbin |
|---|---|
| Associated with | Stanford University, Department of Bioengineering. |
| Primary advisor | Quake, Stephen Ronald |
| Thesis advisor | Quake, Stephen Ronald |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Bustamante, Carlos |
| Advisor | Altman, Russ |
| Advisor | Bustamante, Carlos |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jianbin Wang. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Ph.D. Stanford University 2013 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Jianbin Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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