DMS/NIGMS 2: Collaborative Research: Developing Statistical Learning Methods for Revealing the Molecular Signatures of Microvascular Changes in Neural Injury

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Spinal cord injury (SCI) is a traumatic and detrimental condition that can result in temporary or permanent paralysis. SCI also causes various paralysis-related disorders that can become debilitating and often life-threatening. It can also lead to functional impairment via the primary mechanical injury followed by subsequent secondary injury mechanisms at cellular levels, including cell death and spinal blood vessel damage. Disruption of the blood-spinal cord barrier (BSCB), the structure regulating molecular exchange between blood and spinal cord, is one of the most detrimental factors to functional recovery. The BSCB is composed of at least three types of cell-to-cell functions. Understanding the molecular characteristics and functions of these cells in response to injury is a major interest of SCI research. The PIs will use high throughput single cell RNA sequencing (scRNA-seq), a powerful technique for the dissection of gene expression at single-cell resolution. They will also develop novel modern statistical approaches to elucidate the molecular characterizations of principal cell types of BSCB in the injured spinal cord. The study will help us understand the complexity of blood vessels in response to SCI, generate novel therapeutic targets for SCI treatment, and create new statistical machine learning tools for big data analysis. The PIs plan to integrate research with education and outreach activities and disseminate the results, data, and software broadly to the public. This project aims to develop statistical machine learning theory and methods to answer important questions regarding the identification of new subpopulations of microvessels that have disease-relevant functions in SCI, as well as microvessel crosstalk with and regulation by infiltrating immune cells. The novelty is to combine advanced scRNA-seq technologies with robust high-dimensional statistical methods for three biological aims: a) define single-cell profiling of microvascular cells; b) determine the mechanisms of the alteration of subpopulations of microvascular cells in the injured spinal cord; and c) identify the crosstalk patterns between microvascular cells and infiltrating immune cells and their roles in neuroinflammation. The large quantities of complex data generated from the study will prompt new challenges in developing scalable robust and reliable statistics tools for efficient analysis of big scRNA-seq data. In particular, PIs plan to develop (a) large-scale cell subpopulation learning tools including multi-scale clustering and cell marker hunting algorithms and unsupervised feature screening and selection; (b) efficient and robust methods for identifying cell markers and differently expressed genes by developing scalable Hodges-Lehmann's method with false discovery rate controls; (c) more efficient factor-adjusted learning methods that take advantages of co-expression of genes in subpopulation learning, cell marker hunting, differently expressed genes selection, as well as cell-cell interaction by identifying important ligand-receptor pairs. These newly developed methods will be applied to the large-scale scRNA-seq data to answer the biological questions for SCI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.