Collaborative Research: Statistical Methods for RNA-seq Based Transcriptomic Analysis of Macrophage Function in Spinal Cord Injury

Florida State University, Tallahassee FL

Investigators

Abstract

Spinal cord injury causes functional impairment via the primary mechanical injury, followed by subsequent secondary injury mechanisms at cellular levels. Most research has focused on understanding the mechanisms of secondary injury since it corresponds to functional deficits. Inflammation is a principal mediator of the secondary injury cascade, with macrophages contributing profoundly to the secondary injury. Macrophages, one of the most important type of immune cells, migrate towards the injured spinal cord and engulf myelin debris that is generated at injury onset to form myelin-laden macrophages. In this project, the investigators will utilize RNA sequencing, a powerful method for analyzing global gene expression levels, and develop new statistical approaches to address biological questions related to how myelin-laden macrophages amplify inflammatory response and promote secondary injury. These studies will help researchers understand molecular mechanisms for spinal cord injury and shed new light on the treatment by targeting myelin-laden macrophages. This project aims to answer important questions regarding the mechanisms of secondary injury through the integration of new statistical methods and cutting-edge biological techniques. The central hypothesis is that myelin-laden macrophages in the injured spinal cord promote a local pathologic process and distal organ dysfunction. Specifically, the investigators will determine their molecular patterns and functions, study whether myelin-laden macrophages released extracellular vesicles (exosomes) carry microRNAs from myelin debris to recipient cells and then regulate their functions, and investigate whether these exosomes can enter the bloodstream and contribute to systemic inflammatory response syndrome and distal organ dysfunction. New statistical tools will be developed to analyze large datasets of microRNA sequencing and mRNA sequencing from multiple cell types and time points. In particular, novel robust statistical techniques are proposed to answer the following important questions: how to uniformly and robustly estimate large-scale gene expressions; how to compare them reliably across multiple phenotypes and measured across different platforms; how to accurately identify microRNAs' mRNA targets via sparse regression; how to use quantitative tools for studies of molecular mechanisms and comparisons of gene expression networks; how to control false discovery rate under general dependence; how to perform robust variable selection; how to estimate the size of spurious correlations. The proposed methods will be applied to newly collected, as well as existing data to answer the biological questions related to spinal cord injury. The project will integrate research and education by involving undergraduates, graduate students and postdoctoral fellows, creating new datasets, and developing publicly available software. Students from underrepresented groups will be trained as part of this project. The results will be disseminated broadly through presentations at seminars, conferences, and professional association meetings.