Interaction of micropatterned topographical and biochemical cues to direct neurite growth from spiral ganglion neurons

The geometry of photopolymerized topography influences neurite pathfinding by directing growth cone morphology and migration

Objective. Cochlear implants provide auditory perception to those with severe to profound sensorineural hearing loss: however, the quality of sound perceived by users does not approximate natural hearing. This limitation is due in part to the large physical gap between the stimulating electrodes and their target neurons. Therefore, directing the controlled outgrowth of processes from spiral ganglion neurons (SGNs) into close proximity to the electrode array could provide significantly increased hearing function.Approach.For this objective to be properly designed and implemented, the ability and limits of SGN neurites to be guided must first be determined. In this work, we engineer precise topographical microfeatures with angle turn challenges of various geometries to study SGN pathfinding and use live imaging to better understand how neurite growth is guided by these cues.Main Results.We find that the geometry of the angled microfeatures determines the ability of neurites to navigate the angled microfeature turns. SGN neurite pathfinding fidelity is increased by 20%-70% through minor increases in microfeature amplitude (depth) and by 25% if the angle of the patterned turn is made obtuse. Further, we see that dorsal root ganglion neuron growth cones change their morphology and migration to become more elongated within microfeatures. Our observations also indicate complexities in studying neurite turning. First, as the growth cone pathfinds in response to the various cues, the associated neurite often reorients across the angle topographical microfeatures. Additionally, neurite branching is observed in response to topographical guidance cues, most frequently when turning decisions are most uncertain.Significance.Overall, the multi-angle channel micropatterned substrate is a versatile and efficient system to assess neurite turning and pathfinding in response to topographical cues. These findings represent fundamental principles of neurite pathfinding that will be essential to consider for the design of 3D systems aiming to guide neurite growthin vivo.

Current advances in biomaterials for inner ear cell regeneration

Inner ear cell regeneration from stem/progenitor cells provides potential therapeutic strategies for the restoration of sensorineural hearing loss (SNHL), however, the efficiency of regeneration is low and the functions of differentiated cells are not yet mature. Biomaterials have been used in inner ear cell regeneration to construct a more physiologically relevant 3D culture system which mimics the stem cell microenvironment and facilitates cellular interactions. Currently, these biomaterials include hydrogel, conductive materials, magneto-responsive materials, photo-responsive materials, etc. We analyzed the characteristics and described the advantages and limitations of these materials. Furthermore, we reviewed the mechanisms by which biomaterials with different physicochemical properties act on the inner ear cell regeneration and depicted the current status of the material selection based on their characteristics to achieve the reconstruction of the auditory circuits. The application of biomaterials in inner ear cell regeneration offers promising opportunities for the reconstruction of the auditory circuits and the restoration of hearing, yet biomaterials should be strategically explored and combined according to the obstacles to be solved in the inner ear cell regeneration research.

The cochlear matrisome: Importance in hearing and deafness

The extracellular matrix (ECM) consists in a complex meshwork of collagens, glycoproteins, and proteoglycans, which serves a scaffolding function and provides viscoelastic properties to the tissues. ECM acts as a biomechanical support, and actively participates in cell signaling to induce tissular changes in response to environmental forces and soluble cues. Given the remarkable complexity of the inner ear architecture, its exquisite structure-function relationship, and the importance of vibration-induced stimulation of its sensory cells, ECM is instrumental to hearing. Many factors of the matrisome are involved in cochlea development, function and maintenance, as evidenced by the variety of ECM proteins associated with hereditary deafness. This review describes the structural and functional ECM components in the auditory organ and how they are modulated over time and following injury.

The geometry of photopolymerized topography influences neurite pathfinding by directing growth cone morphology and migration

Cochlear implants (CIs) provide auditory perception to those with profound sensorineural hearing loss: however, the quality of sound perceived by a CI user does not approximate natural hearing. This limitation is due in part to the large physical gap between the stimulating electrodes and their target neurons. Therefore, directing the controlled outgrowth of processes from spiral ganglion neurons (SGNs) into close proximity to the electrode array could provide significantly increased hearing function. For this objective to be properly designed and implemented, the ability and limits of SGN neurites to be guided must first be determined. In this work, we engineered precise topographical microfeatures with angle turn challenges of various geometries to study SGN pathfinding. Additionally, we analyze sensory neurite growth in response to topographically patterned substrates and use live imaging to better understand how neurite growth is guided by these cues. In assessing the ability of neurites to sense and turn in response to topographical cues, we find that the geometry of the angled microfeatures determines the ability of neurites to navigate the angled microfeature turns. SGN neurite pathfinding fidelity can be increased by 20-70% through minor increases in microfeature amplitude (depth) and by 25% if the angle of the patterned turn is made more obtuse. Further, by using engineered topographies and live imaging of dorsal root ganglion neurons (DRGNs), we see that DRGN growth cones change their morphology and migration to become more elongated within microfeatures. However, our observations also indicate complexities in studying neurite turning. First, as the growth cone pathfinds in response to the various cues, the associated neurite often reorients across the angle topographical microfeatures. This reorientation is likely related to the tension the neurite shaft experiences when the growth cone elongates in the microfeature around a turn. Additionally, neurite branching is observed in response to topographical guidance cues, most frequently when turning decisions are most uncertain. Overall, the multi-angle channel micropatterned substrate is a versatile and efficient system to assess SGN neurite turning and pathfinding in response to topographical cues. These findings represent fundamental principles of neurite pathfinding that will be essential to consider for the design of 3D systems aiming to guide neurite growth in vivo.

Sensitivity of CNN image analysis to multifaceted measurements of neurite growth

Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)-powerful models capable of learning complex tasks without the biases of hand-crafted models. Nevertheless, their complexity often relegates them to functioning as 'black boxes.' Therefore, research in the field of explainable AI is imperative to comprehend the relationship between CNN image analysis output and predefined morphological parameters of neurite growth in order to assess the applicability of these machine learning approaches. In this study, drawing inspiration from the field of automated feature selection, we investigate the correlation between quantified metrics of neurite morphology and the image analysis results from NeuriteNet-a CNN developed to analyze neurite growth. NeuriteNet accurately distinguishes images of neurite growth based on different treatment groups within two separate experimental systems. These systems differentiate between neurons cultured on different substrate conditions and neurons subjected to drug treatment inhibiting neurite outgrowth. By examining the model's function and patterns of activation underlying its classification decisions, we discover that NeuriteNet focuses on aspects of neuron morphology that represent quantifiable metrics distinguishing these groups. Additionally, it incorporates factors that are not encompassed by neuron morphology tracing analyses. NeuriteNet presents a novel tool ideally suited for screening morphological differences in heterogeneous neuron groups while also providing impetus for targeted follow-up studies.