Deconvolution of Heterogeneous Tissue Samples into Relative Presence of Macrophage Phenotype

Thursday, May 12, 2016

1:00 PM-3:00 PM

BIOMED Master's Thesis Defense

Title:
Deconvolution of Heterogeneous Tissue Samples into Relative Presence of Macrophage Phenotype Based on Gene Expression

Speaker:
Nicole Ferraro, Master’s Candidate, School of Biomedical Engineering, Science and Health Systems

Advisor:
Kara Spiller, PhD, Assistant Professor, School of Biomedical Engineering, Science and Health Systems

Abstract:
Macrophages, as a primary cell type of the innate immune system, have a variety of phenotypes that correspond to various functions. The dysregulation of the appearance of these phenotypes can lead to symptoms seen in many diseases. Specifically, macrophage phenotype has been implicated as a potential source of sustained inflammation that prevents healing in chronic wounds. In order to design effective treatments, an understanding of the relative presence of macrophage phenotypes in tissue is necessary. Inferring the relative phenotype composition is currently challenging due to the heterogeneous nature, not only of macrophage behavior, but also of tissue samples. They contain many different cell types, which express many of the same genes.

We present here a proposed method to deconvolute heterogeneous tissue samples into the composition of two main macrophage phenotypes, M1, or classically activated macrophages, and M2, or alternatively activated macrophages. The final model uses gene expression from gene signatures for each phenotype as input to a predictive model that infers the macrophage composition of the sample, and generates predictions that strongly correlate with known composition values in test datasets. Finally, we apply the model to characterize macrophage behavior over time in gene expression data from heterogeneous samples of wound tissues.

Contact Information

Ken Barbee
215-895-1335
barbee@drexel.edu