Characterizing Macrophage States in Immune Mediated Diseases Using Computational Sequencing Methods
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CitationRoberts, Kela. 2019. Characterizing Macrophage States in Immune Mediated Diseases Using Computational Sequencing Methods. Master's thesis, Harvard Medical School.
AbstractThe understanding of the heterogeneity of macrophages states and their tissue resident immune function has long been incomplete, yet, pertinent for therapeutic targets in a variety of immune mediated diseases. In vivo, tissue resident macrophages are known to have definitive changes when exposed to a broad range of stimuli whose integration eventually determines a continuum of distinct transcriptional and functional outputs. These outputs were not studied for many years because systems immunology research had focused on monitoring global molecular profiles of cells and tissues in response to perturbations or in the context of disease. Only now do the tools exist to evaluate changes on both an individual cell and system-wide scale. The discovery of genes that mark a particular immune cell population by single cell RNA sequencing (scRNAseq) can generate new opportunities for therapeutic targeting and manipulation of a particular cell type. The emergence of single cell RNA-seq has enabled de novo discovery of immune cell types and states, and the development of new mechanistic hypotheses.
Here, we investigate changes in macrophage states as a result of chronic disease and in response to treatment. Investigating these response states enables us to test the hypothesis that there are unique expression profile characteristics in macrophages related specifically to disease state.This multiphase project is unique in that we are analyzing macrophages in different immunemediated diseases. This work focuses on characterizing macrophage states in chronic hepatitis C virus (HCV) infection and how this characterization relates to other systems of disease, ie melanoma. The main result of this work demonstrates quantitative and differential expression changes between the transcription profiles of patients with chronic Hepatitis C Virus (HCV) infection, before and after treatment; the changes in macrophages during HCV disease have been illustrated. By extracting, sorting and sequencing, filtering, aligning, quantifying, and clustering the single cell transcriptome data, we can gain insight about macrophage activity in chronically infected HCV patients.
Using computational analysis and technology, the regulation of macrophage states in disease can expand and provide more targeted pharmaceutical treatments.
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