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JANG-DISSERTATION-2017.Pdf (6.114Mb) Investigation of the Gene Expression Dynamics of Early Mammalian Germ Layer Differentiation The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:39987998 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Investigation of the gene expression dynamics of early mammalian germ layer differentiation A dissertation presented by Sumin Jang to The Committee on Higher Degrees in Molecular and Cellular Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biology Harvard University Cambridge, Massachusetts September 2017 © 2017 Sumin Jang All rights reserved Dissertation Advisor: Prof. Sharad Ramanathan Sumin Jang Investigation of the gene expression dynamics of early mammalian germ layer differentiation Abstract The mechanisms regulating the timing of developmental processes are poorly understood. To systematically investigate the timing of development and its underlying mechanisms, the dynamics of development must first be characterized. Although the dynamics of developmental growth and morphogenesis have been well characterized for many species, the continuous dynamics of cell differentiation that leads to the diversity of cell types that arise during development is lacking. In this dissertation supervised by Sharad Ramanathan, I, along with Sandeep Choubey and Leon Furchtgott, characterize the continuous gene expression dynamics of early mouse germ layer differentiation, inferred from single-cell RNA-seq data. We further validate our results by using the inferred gene expression dynamics to model and experimentally test a gene regulatory network. Working with Adele Doyle, we also develop a method for extracting intact RNA from fixed, immunostained and sorted mammalian cells, which was adapted by Thomsen et al. to characterize human radial glial cells, a rare subpopulation of the developing brain. Finally, I discuss some preliminary findings that suggest programmed cell death may be a key factor in the temporal coordination of growth and early differentiation. iii Table of Contents Abstract .............................................................................................................................. iii List of Figures ................................................................................................................... vii List of Tables ...................................................................................................................... x Acknowledgements ............................................................................................................ xi Chapter 1. Introduction ..................................................................................................... 1 1.1. Open questions: the timing of developmental processes ....................................... 1 1.2. Phenotypic observations on temporal coordination during development .............. 2 1.3. Genetic factors controlling developmental timing ................................................ 3 1.4. Insights from chimeras .......................................................................................... 6 1.5. Mapping differentiation ......................................................................................... 7 1.6. References ............................................................................................................. 9 Chapter 2. Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states. ......................... 13 Abstract ......................................................................................................................... 13 2.1. Introduction ......................................................................................................... 14 2.2. Results ................................................................................................................. 17 2.2.1. Acquiring single-cell transcriptomics data during early differentiation ....... 17 2.2.2. Bayesian statistical approach discovers appropriate coordinate systems to infer cell states and state transitions .......................................................................... 21 iv 2.2.3. Correspondence of cell states discovered ab initio from single-cell data to known in vivo cell types ............................................................................................ 38 2.2.4. Differentiation occurs through a series of discrete cell state transitions ....... 45 2.2.5. A probabilistic model that replicates the observed discrete cell states predicts state-dependent interpretation of perturbations ......................................................... 49 2.2.6. Interpretation of Sox2, Snai1, and LIF+BMP are cell state dependent ........ 62 2.3. Discussion ............................................................................................................ 67 2.4. Materials and Methods ........................................................................................ 70 2.4.1. Clustering and re-clustering using Seurat ..................................................... 70 2.4.2. Convergence of clustering configurations from different seed configurations 70 2.4.3. Framework for quantitative modeling of germ layer differentiation ............ 71 2.4.4. ES-Cell Culture ............................................................................................. 78 2.4.5. ES Cell differentiation ................................................................................... 79 2.4.6. Single-Cell RNA-Seq .................................................................................... 80 2.4.7. Immunofluorescence ..................................................................................... 81 2.4.8. Live-Cell Microscopy ................................................................................... 82 2.4.9. Plasmid Transfection ..................................................................................... 83 2.4.10. Fluorescence-Activated Cell Sorting .......................................................... 84 2.4.11. Generation of mOTX2-Citrine reporter cell line ........................................ 84 v 2.4.12. Software ...................................................................................................... 85 2.5. References ........................................................................................................... 86 Chapter 3. Extraction of intact RNA from fixed, immunostained and FAC sorted cells 92 3.1. Introduction ......................................................................................................... 93 3.2. Results ................................................................................................................. 96 3.2.1. Development of FRISCR. ............................................................................. 96 3.2.2. FRISCR profiling of primary RG diversity. ............................................... 102 3.2.3. New RG molecular markers distinguish vRG and oRG cells. .................... 106 3.3. Discussion .......................................................................................................... 109 3.4. Materials and Methods ...................................................................................... 112 3.4.1. Cell isolation from fetal cortex ................................................................... 112 3.4.2. Cell isolation from culture .......................................................................... 113 3.4.3. FRISCR ....................................................................................................... 114 3.4.4. RNA extraction from populations of cells .................................................. 116 3.4.5. SmartSeq2 ................................................................................................... 117 3.4.6. RNA-Seq data analysis ............................................................................... 117 3.4.7. Computational analysis ............................................................................... 118 3.4.8. Tissue immunocytochemistry ................................................................... 120 3.4.9. Statistics ...................................................................................................... 122 3.5. References ......................................................................................................... 124 vi Chapter 4. Discussion ................................................................................................... 129 4.1. Significance of the characterization of gene expression dynamics ................... 129 4.2. Timing of differentiation and population size in vitro ....................................... 130 4.3. Cell death and coordination of differentiation and growth ................................ 135 4.4. Future Directions ............................................................................................... 139 4.5. References ......................................................................................................... 140 vii
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