Methods for investigating forces in cortical neurons with FRET-based molecular tension probes
Creators: name: Ayad, Marina Adel Aziz
Publisher: name: No Publisher Supplied
Year: 2023

description: Cell adhesion forces play a critical role in neuronal function and development. Understanding the mechanical cues between neurons and their surrounding extracellular matrix can assist in developing therapeutic methods for pathological conditions such as traumatic brain injury or medial prefrontal cortex atrophy in post-traumatic stress disorder, reported to involve alterations in adhesion protein complexes. Vinculin, the focus of this thesis, plays a key role in stabilizing focal adhesions in cells. Unlike non-neuronal cells, in which adhesions appear as strike-like structures towards the edges of the cell, in neurons, vinculin punctate clusters have been reported in growth cones, branching points, and neurites with varying locations depending on day-in-vitro (DIV) and neuronal cell type. To study adhesions in neurons, in this thesis, methods were developed for fluorescence imaging of molecular tension probes. In this work, the previously designed FRET vinculin tension sensor (VinTS), which had only been used in non-neuronal cells, is used to investigate changes in vinculin tension in neurons. First, rat primary cortical neuronal growth cones plated on glass substrates were investigated. In growth cones imaged at DIV 5-8, the difference between the mean FRET efficiencies of VinTS and the negative control VinTL (Tail-less), which cannot bind actin, was small (1.2% lower in VinTS) but statistically significant. This suggests that vinculin is under low tension in cortical growth cones. Since vinculin clusters were reportedly found along neurites and in branching points, an investigation beyond the growth cones was necessary. Also, since alterations in the stiffness of the extracellular matrix are likely to transduce mechanical cues that affect neuronal cellular tension, neurons were plated on polyacrylamide (PA) gels with different elastic moduli (0.5 kPa, 1 kPa or 2 kPa). In a limited number of samples, the FRET efficiency of VinTS expressed at branching points increased by 1.8% with increased PA gel stiffness from 0.5 to 2 kPa when all the branching points (primary, secondary and beyond) are considered. This increase was limited to PA gels; FRET efficiency of VinTS expressed at branching points plated on glass had the same FRET efficiency as when plated on 0.5 kPa. By comparison, the results showed no significant changes in VinTL, the negative control, across the different substrate conditions when all branching points are considered. However, the FRET efficiency of VinTL, showed a significant change with stiffness (3.4% lower on glass compared with 0.5 kPa, 0.93% lower on glass compared with 2 kPa) when only primary branching points are considered. Additionally, a significant difference was observed between VinTS and VinTL expressed at the primary branching points only in the case of neurons plated on 2 kPa gels, where VinTS FRET efficiency was found to be 3% higher than VinTL FRET. Therefore, further investigation is required to elucidate the changes in vinculin tension at branching points in response to changes in substrate stiffness. A significant portion of this thesis was also devoted to developing an automated segmentation method for whole neuron segmentation. This segmentation eliminates background pixels and includes features with different morphologies and intensities in the neuron such as larger objects; soma, along with smaller features, such as growth cones, branching points, up to thinner neurites and weak signal filopodia. A novel deep-learning-based fusion architecture was developed and successfully achieved a segmentation of all features with high accuracy. Features extracted from Scale and Curvature Invariant Ridge Detector for Thin Structures (SCIRD-TS) filters were fused with the conventional encoder-decoder convolutional neural network (U-Net) and found to have a significantly higher dice value in comparison to several state-of-the-art networks. The segmentation obtained from the fused deep learning network was used to calculate the FRET efficiency of the whole neuron and used as a preprocessing step for FRET efficiency analysis in sub-regions of interest within the whole neuron. In all, the measurement methods developed in this thesis may serve as a basis for further study of molecular tension probes in neurons. To address the limitations of the experimental studies, additional work may include the use of larger datasets to minimize the uncertainty in FRET efficiency measurements. Furthermore, experiments may be undertaken with a lower and uniform exposure time to avoid bias due to photobleaching. Future work may focus on dynamic changes in FRET efficiency, investigating other neuronal types, imaging at different DIVs, and quantifying the FRET efficiency of isolated punctate clusters.