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Signature Redacted Harvard-MIT Program in Health Sciences and 'Kchnology September 29, 2015 A single-cell perspective on infection by Nathan Scott Haseley B.S. Rochester Institute of Technology (2009) Submitted to the Harvard-MIT Program In Health Sciences and Technology in partial fulfillnent of the requirements for the degree of Doctor of Philosophy in Bioinformatics at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY Februrary 2016 Massachusetts Institute of Technology 2016. All rights reserved. Author .................................... Signature redacted Harvard-MIT Program In Health Sciences and 'kchnology September 29, 2015 Signature redacted C ertified by .......... ................... ............. \ Deborah T. Hung MD, PhD, Associate Professor Thesis Supervisor Signature redacted A ccepted by ........ .. .. .. .. ... .. ... .. ... .. .. ... .. .. Emery N. Brown MD, PhD/Director, Harvard T Program in Health Sciences and Technology/Professor of Computational Neuroscience and Health Sciences and Technology MASSACHUSES INSTITUTE OF TECHN0LOGY MAR 142016 LIBRARI S '2 A single-cell perspective on infection by Nathan Scott Haseley Submitted to the Harvard-MIT Program In Health Sciences and Technology on September 29, 2015, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Abstract The clinical course of infection is ultimately determined by a series of cellular interactions between invading pathogens and host immune cells. It has long been understood that these interactions, even when they occur in tissue culture models, give rise to a wide variety of different outcomes, some beneficial to the host, others to the pathogen. These cellular interactions, however, are typically studied at a bulk level; masking this cell-to-cell variation, losing important information about the full range of possible host-pathogen interactions, and leaving the mechanistic basis for these different outcomes largely unexplored. Here, we present a system that combines single-cell RNA sequencing with fluorescent markers of infection outcome to directly correlate host transcription signatures with infection outcome at the single cell level. Applying this system to the well-characterized model of Salmonella enterica infection of mouse macrophages, we found: 1) Unique transcription signatures associated with bacterial exposure and bacterial infection, 2) Sustained high levels of heterogene- ity in immune pathways in infected macrophages, and 3) A novel subpopulation of macrophages characterized by high expression of the Type I Interferon response after infection. Upon further investigation we found that this heterogeneity in the host Type I Interferon response was the result of heterogeneity in the population of infecting bacteria, namely in the extent of PhoPQ-mediated LPS modifications. This work highlights the importance of heterogeneity as a characteristic of bac- terial populations that can influence the host immune response. It also demonstrates the benefits of examining infection with single-cell resolution. Thesis Supervisor: Deborah T. Hung Title: MD, PhD, Associate Professor 3 4 Acknowledgements This thesis is a product of all of the support, care, and instruction that I have received over many years. There are many people to thank that have been instrumental in my graduate career and in getting me to this point. I am sure that I did not mention everyone here but nevertheless, I would like to say thank you. First, I would like to thank my wife, Psalm, for all her support and encouragement throughout graduate school and for making Boston into a home for both of us. I would like to thank my parents for their support and wisdom throughout the years. Also the numerous friends that I've had in Boston. I owe a debt to the entire community at CityLife Presbyterian Church for their support and prayers, and particularly to my community group. Thanks as well to Sam, Whitney, Trevor, and Megan for numerous game nights and other times of hanging out that were essential for sanity. I would like to thank my adviser Deb, for teaching me how to think like a scientist and all current and past members of the Hung lab for helpful discussions, encouragement when things weren't working, and for making the Broad Institute a great place to work. I would also like to thank all of our collaborators in the Regev and Xavier labs. I would additionally like to thank my previous mentor Dr. Ferran, for getting me started in science and old members of the Ferran lab for their still-constant friendship. Finally, I would like to thank all the members of my committee, all of the HST staff for their help through this process, and my classmates, particularly all of my fellow HST BIG students for feedback, friendship, and keeping me interested in science outside my own project. 5 6 Contents 1 Introduction 15 1.1 O verview ....... ............... ............... ..... 15 1.2 S. enterica pathophysiology .. .......... .......... ......... 17 1.3 Extracellular host-pathogen interactions ... ....... ........ ....... 18 1.3.1 The role of diverse inicroenvironments in promoting heterogeneity .... .. 18 1.3.2 Heterogeneity as a mechanism to control the host inflammatory response . 20 1.4 Intracellular host-pathogen interactions: A battle between two highly adaptable cells 21 1.5 Heterogeneity in macrophage and bacterial populations ........ ......... 25 1.6 The need for a new approach to study infection ......... ......... ... 26 2 Examining S. enterica-Macrophage Interactions at the Single-Cell Level 29 2.1 A ttributions .... ........ ....... ....... ........ ....... 29 2.2 Introduction ... ....... ....... ........ ....... ........ 29 2.3 Results . ....... .... .......... ......... ....... 30 2.3.1 Heterogeneous outcomes of S. enterica-macrophage encounters ........ 30 2.3.2 Single-cell RNA-Seq accurately captures host transcriptional states after bac- terial exposure . ........ ......... ........ ......... 31 2.3.3 Single-cell RNA-Seq identifies transcriptional changes associated with extra- cellular and intracellular bacterial detection .......... ......... 35 2.3.4 Bimodal induction of Type I IFN response genes in infected macrophages .. 37 2.3.5 Infected macrophages display high cell-to-cell variation in genes from immune response pathways .. ......... ........ ......... ..... 40 2.4 D iscussion .......... ............... ............... .. 43 7 2.4.1 A general approach to characterize the transcriptional underpinnings of phe- notypic heterogeneity in host-pathogen encounters ........ ..... 43 2.4.2 . A single-cell resolution map of S. typhimuriumr-inacrophageinteractions . 43 2.5 Materials and Methods ....... .... ... ... ... ... 4 4 2.5.1 Mice, cell lines and bacterial strains . ..... .... ... .. ... ... 44 2.5.2 Cell and bacterial cultures, single-cell sorting, and analysis . ... ... ... 45 2.5.3 Imaging assay protocol .. ...... ....... ...... .. .. .. .. 45 2.5.4 Image acquisition and analysis .... ....... ..... ... .. .. 45 2.5.5 Single-cell expression profiling ....... ...... .... .. .. .. .. 46 2.5.6 cDNA synthesis, amplification and library construction . .. .. .. .. .. 46 2.5.7 Transcript quantification . ...... ....... ...... ... ... .. 47 2.5.8 Quality filters and statistics . ..... ..... .... ... .. ... ... 47 2.5.9 Comparison of methods for differential expression analysis . .. .. .. .. 48 2.5.10 Differential expression analysis ...... ...... .... ... ... ... 48 2.5.11 P C A ... ...... ....... ...... ...... ... .. .. .. .. 49 2.5.12 Identifying genes responding to extracellular or intracellular ba(teria (Clusters I and II) ..... ...... ...... .. ..... 49 2.5.13 Identification of co-regulated gene clusters (Clusters III, IV, and V) . .... 50 2.5.14 Correlation plots ... ..... .... ..... .... .... ..... ... 51 2.5.15 H eatm aps .. ..... ...... ..... ..... ...... ..... ... 51 2.5.16 Single-molecule RNA-flow FISH ......... ......... ....... 51 2.5.17 Estimating gene variance scores . ..... ..... ...... ..... ... 51 2.5.18 Identification of low and high variance pathways in infected inacrophages . 52 2.5.19 Comparison of variance scores between exposure- and infection-induced gene ...... .............. ........ sets ..... ..... .... 52 2.5.20 Cell expression density plQts . ..... ..... ...... ..... ..... 52 2.5.21 D atasets ... ..... ...... ..... ..... ...... ..... ... 53 3 The Mechanisms Behind Heterogeneity in the Type I IFN Response 55 3.1 A ttributions .. ..... .... .... ..... .... ..... .... ..... .. 55 3.2 Introduction .. ...... ....... ....... ...... ....... ..... 55 3.3 R esu lts .. ......... ......... ......... ......... ..... 56 8 3.3.1 Intracellular TLR4 signaling through TRIF and IRF3 determines the expres- sion of the Type I IFN response in infected macrophages ...... ...... 56 3.3.2 Live bacteria, but not LPS-coated beads, elicit a variable Type I IFN response in infected macrophages ............. ............. .... 58 3.3.3 Variation in the host Type I IFN response is driven by bimodal activity of the bacterial PhoPQ two-component system in infecting bacteria ....... 59 3.3.4 Intracellular recognition of PhoPQ-mediated LPS modifications results in in- duction of the Type I IFN response ........................ 62 3.3.5 PhoPQ-mediated LPS modifications impact the in vivo Type I IFN response and infection outcome ............................... 64 3.4 D iscussion ...... ............................. ....... 67 3.4.1 Heterogeneity of pathogen populations as a mechanism to shape the host im mune response .................................. 67 3.4.2 Studies of the immune response in the context of heterogeneous
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