A single-cell perspective on infection
Name
952429310-MIT.pdf
Description
Full printable version
Size
11.8 MB
Format
Adobe PDF
Checksum (MD5)
64c0744f9592c40a23f295afe3f16e9d
Author(s)
Haseley, Nathan Scott
Advisor(s)
Deborah T. Hung.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
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 heterogeneity 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 bacterial populations that can influence the host immune response. It also demonstrates the benefits of examining infection with single-cell resolution.
Description
Thesis: Ph. D. in Bioinformatics, Harvard-MIT Program in Health Sciences and Technology, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-94).
Subjects
Harvard--MIT Program in Health Sciences and Technology.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
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