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Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease is characterized by extracellular beta-amyloid plaques, intraneuronal tau neurofibrillary tangles and excessive neurodegeneration. The mechanisms of neuron degeneration and the potential of these neurons to form new nerve fibers for compensation remain elusive. The present study aimed to evaluate the impact of beta-amyloid and tau on new formations of nerve fibers from mouse organotypic brain slices connected to collagen-based microcontact prints. Methods: Organotypic brain slices of postnatal day 8–10 wild-type mice were connected to established collagen-based microcontact prints loaded with polyornithine to enhance nerve fiber outgrowth. Human beta-amyloid(42) or P301S mutated aggregated tau was co-loaded to the prints. Nerve fibers were immunohistochemically stained with neurofilament antibodies. The physiological activity of outgrown neurites was tested with neurotracer MiniRuby, voltage-sensitive dye FluoVolt, and calcium-sensitive dye Rhod-4. Results: Immunohistochemical staining revealed newly formed nerve fibers extending along the prints derived from the brain slices. While collagen-only microcontact prints stimulated nerve fiber growth, those loaded with polyornithine significantly enhanced nerve fiber outgrowth. Beta-amyloid(42) significantly increased the neurofilament-positive nerve fibers, while tau had only a weak effect. MiniRuby crystals, retrogradely transported along these newly formed nerve fibers, reached the hippocampus, while FluoVolt and Rhod-4 monitored electrical activity in newly formed nerve fibers. Conclusions: Our data provide evidence that intact nerve fibers can form along collagen-based microcontact prints from mouse brain slices. The Alzheimer’s peptide beta-amyloid(42) stimulates this growth, hinting at a neuroprotective function when physiologically active. This “brain-on-chip” model may offer a platform for screening bioactive factors or testing drug effects on nerve fiber growth.

Cochlear hair cell damage and spiral ganglion neuron (SGN) degeneration are the main causes of sensory neural hearing loss. Cochlear implants (CIs) can replace the function of the hair cells and stimulate the SGNs electrically. The condition of the SGNs and their spatial distance to the CI are key factors for CI-functionality. For a better performance, a high number of neurons and a closer contact to the electrode are intended. Neurotrophic factors are able to enhance SGN survival and neurite outgrowth, and thereby might optimize the electrode-nerve interaction. This would require chronic factor treatment, which is not yet established for the inner ear. Investigations on chronic drug delivery to SGNs could benefit from an appropriate in vitro model. Thus, an inner ear inspired Neurite Outgrowth Chamber (NOC), which allows the incorporation of a mini-osmotic pump for long-term drug delivery, was designed and three-dimensionally printed. The NOC's function was validated using spiral ganglion explants treated with ciliary neurotrophic factor, neurotrophin-3, or control fluid released via pumps over two weeks. The NOC proved to be suitable for explant cultivation and observation of pump-based drug delivery over the examined period, with neurotrophin-3 significantly increasing neurite outgrowth compared to the other groups. [ABSTRACT FROM AUTHOR]

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 growth in vivo ., (Creative Commons Attribution license.)

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 .

Neural networks have been cultured in vitro to investigate brain functions and diseases, clinical treatments for brain damage, and device development. However, it remains challenging to form complex neural network structures with desired orientations and connections in vitro . Here, we introduce a dynamic strategy by using diphenylalanine (FF) nanotubes for controlling physical patterns on a substrate to regulate neurite-growth orientation in cultivating neural networks. Parallel FF nanotube patterns guide neurons to develop neurites through the unidirectional FF nanotubes while restricting their polarization direction. Subsequently, the FF nanotubes disassemble and the restriction of neurites disappear, and secondary neurite development of the neural network occurs in other direction. Experiments were conducted that use the hippocampal neurons, and the results demonstrated that the cultured neural networks by using the proposed dynamic approach can form a significant cross-connected structure with substantially more lateral neural connections than static substrates. The proposed dynamic approach for neurite outgrowing enables the construction of oriented innervation and cross-connected neural networks in vitro and may explore the way for the bio-fabrication of highly complex structures in tissue engineering., (© 2022 IOP Publishing Ltd.)