Abdulaziz Alkhalisi
Research Mentor(s): Megan Killian
Department or Program: Orthopaedic Surgery; Community College UROP
Authors: Abdulaziz Alkhalisi, Nicole Migotsky, PhD, Adam Abraham, PhD, Megan L. Killian, PhD
Session: Session 1 12:00-12:50 p.m. Hussey Room
Poster:
Abstract
Connective tissues, such as tendons and ligaments, are essential for vertebrate movement but often heal poorly due to their avascular nature necessitating the development of new treatment modalities. A key component in the structure and mechanical function of connective tissues is the alignment and uniformity of collagen fibers. Identifying individual factors that impact collagen fiber alignment and uniformity during the developmental and healing processes can lead to new treatments that improve patient care.
It is challenging to measure and visualize collagen alignment and uniformity with the naked eye; however, these measurements can be quantified and analyzed using quantitative polar light imaging (qPLI). This technique allows us to measure the degree of alignment and the level of uniformity among fibers, by recording a degree of linear polarization (DoLP) and the variation (standard deviation) of the angle of polarization (SDAoP), respectively. These measurements help us better understand tissue health by comparing collagen alignment between samples during tissue development, injury, and healing.
We utilized qPLI imaging to compare collagen organization in two distinct projects: (1) understanding the role of discoidin domain receptor 2 (DDR2), a receptor tyrosine kinase involved in collagen matrix remodeling during dental socket regeneration, and (2) examining tendon healing with the overexpression of hypoxia-inducible factor-1A (HIF1A), a transcription factor that responds to low oxygen levels by regulating genes involved in angiogenesis, metabolism, and survival. Histological slides of mouse (1) sockets following tooth extraction and (2) Achilles tendons after complete transection were stained with Picrosirius Red and imaged using a light microscope equipped with a circular polarizer and a polarized light camera/sensor. The images were analyzed using open-source software (FIJI) to identify regions of interest (ROI) from histology. A custom Python script then quantified DoLP and SDAoP for each ROI. Intra-user reliability was assessed by comparing DoLP and SDAoP within samples, and differences between treatments were analyzed independently via statistical analysis using GraphPad Prism version 10.
Our findings allow us to identify if and how DDR2 and HIF1A are contributors to tissue healing and regeneration. Our next steps are to improve our techniques with higher magnification imaging to increase signal to noise ratio and use a rotating stage to improve reproducibility of alignment for all samples.
