Investigating the functional consequences of heterogeneity within immune cell populations

Abstract

A well-functioning immune system is the result of the combined efforts of a multitude of different cell types, performing separate but coordinated tasks. Diversity within these historically categorized “cell types” has functional consequences, and deeper characterization of cell heterogeneity within immune cell populations is crucial for a better understanding of immune functionality. The main objectives of this research were to investigate heterogeneity in immune cell populations by 1) leveraging emerging microfluidics techniques for transcriptomic and phenotypic analysis, 2) developing systems-level computational models, and 3) interrogating cells using dynamic immunological stimuli. Through parallelized microfluidics-based gene expression assays on primary neutrophils and T cells, we collected information on a wide range of gene targets, including not traditionally assayed surface markers, key cytokine receptors, intracellular signaling components and metabolic enzymes. Through the comparison of data pre-processing and analysis methods, we identified novel subgroups within the immune cell populations, as defined by single cell gene expression. We observed that the prevalence of these subgroups differs between individual donors. Noting the wide range of expression values associated with the IL-2 receptor, we subsequently investigated the functional consequences of diversity in cytokine receptors on an individual cell basis. We coupled time-lapse fluorescent microscopy and tightly controlled delivery of fluctuating extracellular IL-2 cytokine concentrations in a microfluidic cell trap developed for the capture and immobilization of non-adherent cells. A computational model of IL-2 signaling established experimental timescales of interest for delivery of oscillatory stimulus. The combined computational modeling and microfluidics technologies allowed us to investigate features of T cell responses to IL-2 under physiologically relevant conditions. The computational model established the importance of specific receptor subunit expression on the dynamic responses to extracellular IL-2 ligand. The question of how to correctly process and analyze single cell data is as of yet unanswered, despite the fact that such analysis is crucial to correct interpretation of the biological information that such data can reveal. In this work, detailed single cell analyses at the transcriptomic and phenotypic levels are described for enhancing our understanding of population behaviors.