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Front. Bioeng. Biotechnol., 11 December 2018 | https://doi.org/10.3389/fbioe.2018.00194

Challenges and Future Prospects on 3D in-vitro Modeling of the Neuromuscular Circuit

  • 1Institute for Bioengineering of Catalonia-Barcelona Institute of Science and Technology, Barcelona, Spain
  • 2Department of Electronics and Biomedical Engineering, Faculty of Physics, Universitat de Barcelona, Barcelona, Spain
  • 3Department of Cell Biology, Physiology and Immunology, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
  • 4Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
  • 5Institut de Neurociències de la Universitat de Barcelona, Barcelona, Spain
  • 6Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain

Movement of skeletal-muscle fibers is generated by the coordinated action of several cells taking part within the locomotion circuit (motoneurons, sensory-neurons, Schwann cells, astrocytes, microglia, and muscle-cells). Failures in any part of this circuit could impede or hinder coordinated muscle movement and cause a neuromuscular disease (NMD) or determine its severity. Studying fragments of the circuit cannot provide a comprehensive and complete view of the pathological process. We trace the historic developments of studies focused on in-vitro modeling of the spinal-locomotion circuit and how bioengineered innovative technologies show advantages for an accurate mimicking of physiological conditions of spinal-locomotion circuit. New developments on compartmentalized microfluidic culture systems (cμFCS), the use of human induced pluripotent stem cells (hiPSCs) and 3D cell-cultures are analyzed. We finally address limitations of current study models and three main challenges on neuromuscular studies: (i) mimic the whole spinal-locomotion circuit including all cell-types involved and the evaluation of independent and interdependent roles of each one; (ii) mimic the neurodegenerative response of mature neurons in-vitro as it occurs in-vivo; and (iii) develop, tune, implement, and combine cμFCS, hiPSC, and 3D-culture technologies to ultimately create patient-specific complete, translational, and reliable NMD in-vitro model. Overcoming these challenges would significantly facilitate understanding the events taking place in NMDs and accelerate the process of finding new therapies.

Introduction

From the physiological and anatomical points of view, the mechanosensory-motor circuit is complex, involving several cell-types with specific natural environments. Traditionally, it has been studied coculturing different cell-types on the same platform from animal origin in 2D (Vilmont et al., 2016; Charoensook et al., 2017; Happe et al., 2017) and 3D (Morimoto et al., 2013; Martin et al., 2015; Smith et al., 2016), or from human origin (Guo et al., 2011; Demestre et al., 2015), or mixed species (Yoshida et al., 2015; Prpar Mihevc et al., 2017). These models provide valuable information in understanding some of the mechanisms underlying the system; but to the date they have not been able to replicate the exact human complexity of physiological functional-units formed by the connection of different cell-types, arising from separated microenvironments. Compartmentalized microfluidic culture systems (cμFCS) (Bhatia and Ingber, 2014) represent an alternative to overcome those problems and, combined with 3D-culture techniques and the use of human induced pluripotent stem cells, they could help recreating neuromuscular physiology of humans in-vitro.

This review aims to: (i) provide basic insights about the locomotion circuit and neuromuscular diseases required for its in-vitro modeling; (ii) review the breakthrough of bioengineered technologies for neuromuscular-systems; (iii) discuss the limitations and challenges of current study models and future prospects.

Locomotion Circuit and Neuromuscular Diseases (NMDs)

Locomotion circuit, also known as mechanosensory-motor circuit or reflex-arc circuit, is responsible for executing voluntary, and reflex skeletal-muscle movement, alternating flexion, and extension of the muscle (McCrea, 2001; Purves et al., 2004; Kiehn and Dougherty, 2013). The coordinated-action of cells taking part within is what generates movement: (i) motor-neurons (MN) are in charge of carrying information from the central nervous system to the muscle (Kandel et al., 2013); (ii) sensory-neurons (SN) carry information from the periphery of the body (the muscle in this case) to the central nervous system (Kandel et al., 2013); (iii) interneurons innervate motoneurons and are linked to their pattern of sensory input (Côté et al., 2018); (iv) Schwann cells are small cells that form a myelin-sheath around MN and SN axons that insulates them and enhances signal conduction (Kandel et al., 2013); (v) astrocytes maintain synapses, modulate the transmission of the signal, regulate blood flow, and availability of oxygen, nutrients, and survival factors onto neurons (Rindt et al., 2015); (vi) microglia are phagocytic and immunocompetent cells within the central nervous system, able to induce MN cell-death (Sargsyan et al., 2005; Frakes et al., 2014); (vii) skeletal-muscle cells are multinucleated and elongated cells, with sarcomeric striations that form muscle-fibers distributed in fascicle fashion and are the last executors of voluntary and reflex skeletal-muscle movement (Marieb, 2015; Tortora and Derrickson, 2017).

The events that take part within the neuromuscular-circuit to guide the movement in mammals could be resumed as follows. Once the brain takes the decision of initiating a movement, the signal is transmitted from neocortical projecting neurons through the spinal-cord. Then the spinal-locomotion circuit takes part of guiding the voluntary and reflex skeletal-muscle movement (Purves et al., 2004; Kandel et al., 2013; Tortora and Derrickson, 2017): (1) somatic α-motoneurons (MNs) arising from the ventral-horns of spinal-cord, send the input to the synaptic end-bulbs, triggering calcium flows inwards, and the release of the neurotransmitter acetylcholine (ACh) in the neuromuscular junction (NMJ) between the motoneuron and the motor-end plate of extrafusal muscle-fibers; (2) ACh binds specifically to the skeletal-muscle motor-end plates' ACh-receptors (AChR), inducing contraction of sarcolemma, releasing calcium into the sarcoplasm, that binds to troponin on the thin filaments, facilitating myosin-actin binding, and triggering muscle contraction; (3) intrafusal muscle fibers, located interspersed parallel to extrafusal fibers, change in length as the whole muscle changes; (4) sensory-neurons (SN) sense muscle fiber elongation through muscle-spindle—formed by SN nerve endings wrapped around central areas of intrafusal fibers—, and contraction through Golgi tendon organ—formed by encapsulated structures of collagen fibers located at the joint between muscle fibers and tendons that compress innervating a single SN axon—propagating an impulse signal back to the spinal-cord where is modulated by local interneurons and; (5) γ-motoneurons modulate excitatory input adjusting the contractibility of the muscle-spindle by stimulating intrafusal fibers adapting them to an appropriate length; (6) the integration of both afferent signals from the muscle-spindle and the Golgi tendon organ travels through the spinal-cord to the brain to have awareness of the position of the muscle and movement (muscle extension-flexion state), coordinating movements.

Failures in any part of this circuit can hamper coordinated muscle movement and be the cause of neuromuscular diseases (NMDs) or be the consequence that defines their severity (Gogliotti et al., 2012). The term of NMD comprises several diseases with different origins and affectations (such as muscular dystrophy, amyotrophic lateral sclerosis, myasthenia gravis, or spinal muscular atrophy). The effects of NMDs are reflected in the mechanosensory-motor circuit at different cellular levels—including sensory and motor neurons (Jablonka et al., 2006; Gogliotti et al., 2012), Schwann cells (Hunter et al., 2016; Vilmont et al., 2016; Santosa et al., 2018), astrocytes (Rindt et al., 2015), microglia (Frakes et al., 2014; Cooper-Knock et al., 2017), muscle (Martínez-Hernández et al., 2014; Maimon et al., 2018)—, as well as in the connections among them—NMJ (Uzel et al., 2016b; Maimon et al., 2018; Santhanam et al., 2018), muscle spindle (Rumsey et al., 2010; Guo et al., 2017)—, or intraspinal circuits. However, they all share symptoms such as: peripheral hypotonia, muscle weakness, and orthopedic deformities, among others (Bhatt, 2016; Morrison, 2016; Mary et al., 2018). These symptoms impoverishes patient's life-quality (Mary et al., 2018). There is still no treatment for them. Current study models are far from mimicking physiology and therefore are limited on helping to find cures. The technologies here reviewed (Figure 1) aim to help on that direction.

FIGURE 1
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Figure 1. Future prospects on in-vitro neuromuscular disease modeling. The implementation and combination of later innovative technologies (hiPSCs, cμFCS, 3D cell-culture) would facilitate the recreation of patient's physiological conditions with his own disease-carrying cells mimicking the mechanosensory-motor circuit in 3D inside a microfluidic compartmentalized device. And finally, it would serve to find specific treatments for each NMD.

Mimicking in-vitro Human Spinal-locomotion Circuit with hiPSCs in 3D

NMD and NMJ in-vitro models have gone through a long evolution history (Thomson et al., 2012): the use of primary cells, cell lines and stem cells; animal-animal cocultures, human-human cocultures, xeno-cocultures; and disease-specific studies. But as reported, most of the research has been done until now by coculturing healthy and diseased cells; primary cells or cell lines with stem cells; and mostly on rodent models or xeno-cultures (human-animal models). However, rodent models offer limited benefit translated into clinic as they do not carry human genetic background. Therefore, personalized medicine needs patient-specific isogenic disease models. In this regard, human induced pluripotent stem cells (hiPSCs) offer the possibility of obtaining different isogenic cell-types from patient's somatic cells, by overexpressing some transcription factors (Takahashi and Yamanaka, 2006), later reviewed (Amabile and Meissner, 2009). They can serve both for creating study models that mimic the physiopathology of the patient, with the further development of future therapeutic transplantation strategies (Su et al., 2013).

The use of induced pluripotent stem cells for disease-modeling, drug-screening, and regenerative therapies of NMDs has widely evolved in the last years (Selvaraj and Perlingeiro, 2018). Nevertheless, few studies have cultured hiPSC-derived motoneurons with hiPSC-derived skeletal-muscle cells in-vitro (Demestre et al., 2015; Puttonen et al., 2015; Maffioletti et al., 2018; Osaki et al., 2018). Puttonen et al. (2015) reported a method for the simultaneous differentiation of motoneurons and myotubes from patient-specific hiPSCs, obtaining neuronal differentiation, multinucleated spontaneously contracting myotubes, and functional NMJs on a 2D monolayer. In contrast, Demestre et al. used hiPSCs from healthy donors differentiated separately to motoneurons and myotubes, and subsequently cocultured in 2D. They observed AChR formation in the muscle and neurites outgrowing from motoneurons within the first weeks; but AChR aggregation, maturation of muscle cells, and NMJ formation was not detected until 3 weeks of monolayer cocultures (Demestre et al., 2015).

The advantages and disadvantages between 2D and 3D hiPSC cultures for neurodegenerative disease studies were recently reviewed (Centeno et al., 2018). Briefly, the main outstanding contributions of 3D cultures on NMDs are that: (i) it has been proved that 2D monolayers present in some cases altered gene expression whereas 3D cultures display a genotype more relevant to in-vivo (Smith et al., 2012; Centeno et al., 2018); (ii) cells cultured in 3D acquire more in-vivo like phenotype (lower proliferation rate, areas with different levels of oxygen distribution, higher cell-to-cell, and cell-extracellular matrix interactions, increased viability, proliferation, differentiation, and response to stimuli of other cells) (LaPlaca et al., 2010; Antoni et al., 2015; Centeno et al., 2018); (iii) some higher-order processes, such as angiogenesis, occur inherently in 3D (Baker and Chen, 2012; Centeno et al., 2018). But so far, only two studies have been able to mimic the NMJ in 3D using hiPSC-derived motoneurons and skeletal-muscle cells in-vitro (Maffioletti et al., 2018; Osaki et al., 2018). Maffioletti et al. (2018) used somatic cells from muscular dystrophy patients to create hiPSC-derived isogenic multilineages (skeletal-muscle cells, vascular endothelial cells, pericytes, and motoneurons), subsequently cocultured embedded in fibrin hydrogels. Osaki et al. (2018) created an ALS microphysiological 3D model culturing ALS-hiPSC-derived neural stem cells with hiPSC-derived skeletal-myoblasts embedded in collagen-Matrigel composites.

However, there are still no studies utilizing hiPSC-derived sensory-neurons and hiPSC-derived muscle-cells to mimic the sensory pathway of the spinal reflex-arc circuit. The latest advances on this respect were published by the group of J.J. Hickman using sensory-neurons derived from human neural progenitor cells and intrafusal fibers generated from human skeletal-muscle stem cells, cocultured on a 2D monolayer (Guo et al., 2017).

Compartmentalized Microfluidic Culture Systems (cμFCS) for Spinal-locomotion Circuit in-vitro

Traditional coculture methods do not consider: (i) the different microenvironment requirements of muscle, nerves, and neurons; (ii) the distal connections as they are physically separated in-vivo. Furthermore, finding a medium composition compatible for the long-term coculture of both cell-types could be challenging, as several medium components ideal for MN are incompatible with long-term maintenance of skeletal-muscle cells (Thomson et al., 2012; Tong et al., 2014). Hence, a coculture system between neurons and muscle offers limited benefits on mimicking the pathophysiology of NMDs.

The use of compartmentalized microfluidic culture systems (cμFCS) is increasingly growing for neurobiology studies due to the advantages offered compared to classical coculture systems, reviewed in Box 1.

Box 1. Top 10 advantages of compartmentalized microfluidic culture systems (cμFCS) compared to traditional coculture systems for mimicking spinal-locomotion circuit in-vitro.

1. Fluidic control. Compartmentalization enables to control de fluidic environment and provide each cell-type required nutrients to efficiently mature, facilitating survival, functionality, and long-term coculture (Park et al., 2006; Tong et al., 2014).

2. Experiment feasibility. Ease to study, enhance, control, and monitor some processes (cell proliferation, differentiation, directional growth, migration, and media diffusion from one compartment to the other) (Kamm and Bashir, 2014; Esch et al., 2015).

3. Microenvironment spatiotemporal control and monitoring. Independent manipulation of each compartment cells or extracellular matrix hydrogels could be a critical point to assess some processes. Compartmentalized platforms make possible to dissect molecular and cellular events occurring in somal vs. axonal compartments (effect of compounds, drug-sensitivitiy test, etc.) (Yang et al., 2009; Hur et al., 2011; Zahavi et al., 2015); to track individual axons through microchannels (Hosmane et al., 2011); and to assess axon-specific molecules through immunostaining, protein lisate isolation, and mRNA isolation (Saal et al., 2014; Zahavi et al., 2015).

4. Customisability of cμFCS designs. Control over microchannel geometry and dimensions (and therefore sifting of cells or compounds that can pass from one compartment to the other), number of compartments (and therefore number or different microenvironments, if required), compartment division tool (micochannels, microgrooves, membranes), distance between compartments, possibility to include reservoirs, covered, or opened compartments, scalability, and device size (Park et al., 2009; Yang et al., 2009; Hosmane et al., 2010; Uzel et al., 2016b).

5. Customisable engineering features. Possibility to integrate and take control over parameters: shear-stress flows (Joanne Wang et al., 2008; Shin, 2009); mechanical (Hosmane et al., 2011), optical (Renault et al., 2015; Jang et al., 2016), and electrical stimuli (Hallfors et al., 2013); topographical cues or micropatterns (Hoffman-Kim et al., 2010); chemical gradients (Uzel et al., 2016a); and sensory systems (Shen et al., 2004; Jeong et al., 2018).

6. Cost-effectiveness. Low volume of cells and reagents are required (Millet et al., 2007).

7. Control over the polarity of neural development. Culturing somas on one compartment makes possible to take control over axon and dendrite polarity during the development, and hence, to mimic axon injuries and study post-injury regeneration easily (Peyrin et al., 2011; Tong et al., 2015; Renault et al., 2016).

8. Possibility to mimic neural distal connections. Neurons growing on one compartment can extend their axons to interact with the other compartment cells, mimicking distal connections (Yang et al., 2009; Zahavi et al., 2015; Maimon et al., 2018).

9. Possibility to integrate control over the polarity of myocyte differentiation. Skeletal-muscle cells usually adopt randomized distribution in-vitro, whereas the presence of aligned micropatterns for 2D culture, cantilevers for 3D culture, or some stimuli methods (mechanical, electrical, or optical) integrated on the cμFCS can enhance its appropriate differentiation and functionality (Tourovskaia et al., 2008; Hume et al., 2012; Uzel et al., 2014, 2016b).

10. Better mimicking of physiological conditions and possibility to connect with other microfluidic platforms. This facilitates the study interactions between different physiological functional-units (Maschmeyer et al., 2015), as well as to integrate blood-flow effects (Maoz et al., 2018), or in a future, to mimic a full human-on-a-chip (Williamson et al., 2013; Kamm and Bashir, 2014), enhancing the development of therapies or diagnostic tools (Esch et al., 2014, 2015; Kamm and Bashir, 2014).

Most relevantly, they enable independent culture conditions for neurons and muscle cells, each supplied by its own microenvironment requirements in different interconnected but fluidically isolated compartments, whilst axons can still go through microgroves connecting cells of both compartments.

First compartmentalized microfluidic culture system (cμFCS) for neurobiology studies on neurotrophic effects of dorsal rood ganglion cells (Campenot, 1977) used Teflon-made open compartments, silicone-glue, and microchannels shattered in glass. Since then, and after soft-lithography fabrication improvements, cμFCS have widely evolved in the last 10 years for spinal-locomotion circuit studies (Supplementary Table 1). Most compartmentalized cocultures are nowadays performed onto polydimethylsiloxane (PDMS)-based platforms with two cellular compartments separated through microchannels or, as described in the only two publications found using a 3D cell coculture, through a gel region (Uzel et al., 2016b; Osaki et al., 2018). Different cell sources are used for MN (predominantly mouse embryonic primary cells) and muscle-cells (using equally rodent hind limb primary skeletal-muscle cells and C2C12 cell-line).

Besides, there are progressively more commercial cμFCS on the market (Zhang and Radisic, 2017), available for neuromuscular and other neurobiology studies in 2D or 3D, connecting two compartments through microchannels, microposts, or membranes, from the following companies: (i) Xona neuron devices (Xona Microfluidics, California), used in some neuromuscular studies (Southam et al., 2013; Blizzard et al., 2015); (ii) ANANDA Neuro-Device and Coculture-Device (Advanced Nano Design Applications Devices, Canada) employed by Magdesian et al. (2016) combined with AFM measurements to study neuronal growth; (iii) OrganoPlates for 3D culture (Mimetas BV, Netherlands) used to differentiate stem cells into neurons in 3D (Moreno et al., 2015) or for high-throughput evaluation of compounds in glia and neuronal 3D culture (Wevers et al., 2016); (iv) AIM 3D culture chips (AimBiotech, Singapore), employed mostly in cancer-research (Jenkins et al., 2018), although with a great potential to be used in neuromuscular studies, as performed by Uzel et al. (2016b) with a similar custom made device; (v) Neural Diode (MicroBrain Biotech, France) to reconstruct oriented neural network monolayer cultures (Peyrin et al., 2011; Deleglise et al., 2014); (vi) Idealized coculture chips (Synvivo, USA), with different options of radial slits or pillars utilized in many cases to mimic the BBB (Prabhakarpandian et al., 2013), or linear slits for compartmentalization purposes.

However, and despite the advantages offered by both hiPSC and cμFCS technologies, at the moment only two studies have attempted to mimic the spinal-locomotion circuit combining both technologies (Osaki et al., 2018; Santhanam et al., 2018). Santhanam et al. (2018) seed healthy donor's hiPSC-derived MN and human skeletal-muscle fibers in 2D, for dose-evaluation study of toxins affecting the NMJ. Osaki et al. create an ALS microphysiological 3D in-vitro study-model and compare it with a healthy model (muscle contraction, recovery, and response to drugs administered via endothelial cell barrier).

Limitations of Current Study Models and Main Challenges

Combination of hiPSCs, cμFCS, and 3D-Culture Technologies

Uzel et al. (2016b) combine cμFCS with 3D cell-culture techniques for neuromuscular studies. Additionally, Santhanam et al. (2018) have attempted to mimic the spinal-locomotion circuit combining hiPSCs and cμFCS technologies. And Maffioletti et al. (2018) perform 3D culture of hiPSCs for NMD studies. But thus far, there is one single study combining 3D culture, hiPSCs and cμFCS as NMD models (Osaki et al., 2018). The three innovative technologies offer advantages (Figure 1), but the novelty itself comes with the challenge of developing, tuning, and implementing them together in a unique and biologically reproducible functional platform.

Consideration of Main Actors and Roles From the Spinal-Locomotion Circuit

The co-culture of main cell-types participating in the spinal-locomotion circuit is mandatory to provide a native microenvironment, including the inherent release of growth-factors, as well as to support the viability and maturation of both muscle and neurons, and axon elongation of MNs (Gingras et al., 2008). For instance, spinal MNs cannot achieve proper maturation even after long-term maintenance, unless cultured with muscle-cells, and Schwann cells, as previously reviewed (Bucchia et al., 2018).

Besides, both SN and MN could be altered in particular NMDs (Jablonka et al., 2006; Rumsey et al., 2010; Guo et al., 2017), but not being many available studies focused on the muscle spindle (Taylor et al., 2005; Dagberg and Alstermark, 2006; Rumsey et al., 2010; Bewick and Banks, 2015; Matthews, 2015; Guo et al., 2017) challenges the task of mimicking and characterizing the mechanosensory spinal-locomotion circuit. Additionally, glial cells are also affected and involved in several neuromuscular pathologies (Lobsiger and Cleveland, 2007; Vilmont et al., 2016; Bucchia et al., 2018). Yet, most publications do not consider them.

Most in-vitro NMD studies are focused on the α-MN-muscle connection (Supplementary Table 1), very few on the γ-MN-muscle connection (Colón et al., 2017), and some on the SN-muscle connection (Taylor et al., 2005; Dagberg and Alstermark, 2006; Rumsey et al., 2010; Bewick and Banks, 2015; Matthews, 2015; Guo et al., 2017; Levin et al., 2017) and fewer on the internal SN-MN connection (Schwab and Ebert, 2014). But there is still very little known about what happens beyond those connections, to what extent cell-strategies for synaptic-specificity contribute on the formation of a functional connection (Fukuhara et al., 2013; Maimon et al., 2018).

Studying parts of the circuit cannot provide a comprehensive and complete view of the pathological process, and functional alterations occurring within it. The big challenge that remains out there on NMD studies is the modeling of the whole spinal-locomotion circuit with all cell-types involved, and the evaluation of independent and interdependent roles of each part of the circuit on the development of a particular NMD.

Modeling Neurodegeneration in NMDs

In a broad sense, neurodegeneration is a process characterized by the progressive functional loss of a population of neurons by intrinsic cell-death or the loss of support cells (i.e., oligodendrocytes or astrocytes). Most NMDs are characterized by this devastating phenomenon (i.e., motoneuron diseases: amyotrophic lateral sclerosis, or spinal muscular atrophy, etc.).

Indeed, studying only functional changes in MNs cannot give a comprehensive and complete picture of the process as it is also regulated by non-neuronal cells (Lobsiger and Cleveland, 2007; Bucchia et al., 2018; Maimon et al., 2018). For instance, a recent study performed by Maimon et al. (2018) demonstrated that axon degeneration only occurred with both MN and muscle cells carrying the genetic mutation indicative of the disorder. Furthermore, the morphology of spinal MN axons in-vitro differs from the one presented in-vivo: contrary to in-vivo, axonal terminals in-vitro manifest growth-cones, and are prone to regenerate and lengthen in response to neurotrophic factors required for the in-vitro maintenance of the culture (Bucchia et al., 2018). On top of that, another caveat is the fact that extrapolating effects of short-term studies to longer-term disease processes is often not correlated. Therefore, mimicking neurodegeneration response in-vitro as it occurs in-vivo endures to this day as a challenge. And consequently, there is no effective treatment for neurodegenerative NMDs to promote axonal regeneration yet.

Conclusions and Future Prospects

Finding causes and treatment for NMD requires an accurate modeling of the microphysiological conditions that the patient is suffering. But reproducing the complete spinal-locomotion (reflex-arc) circuit in-vitro is very complex. Later progresses in neuromuscular-mimicking in-vitro systems, have been achieved incorporating increasingly evolving technologies of hiPSCs, cμFCS, and 3D cell-culture techniques here reviewed. The combination of novel technologies in the proper manner has proved to result in the acquisition of more reliable results (Uzel et al., 2016b; Maffioletti et al., 2018; Osaki et al., 2018; Santhanam et al., 2018). But there is still room for improvement. Future studies should focus on addressing unsolved questions related to: mimicking the whole spinal-locomotion circuit (including all cell-types involved, as well as evaluating the independent and interdependent roles of each one), defining the specific role of the factors that determine the NMD and their severity; mimicking neurodegeneration processes; and above all, finding treatments for NMD.

Author Contributions

MB-M wrote the manuscript with contributions from AH, JdR, and JS.

Funding

This work was supported by Networking Biomedical Research Center (CIBER), Spain. CIBER is an initiative funded by the VI National R&D Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions, and the Instituto de Salud Carlos III, with the support of the European Regional Development Fund. This work was funded by the CERCA Programme and by the Commission for Universities and Research of the Department of Innovation, Universities, and Enterprise of the Generalitat de Catalunya (2017 SGR 1079). This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through the projects MINDS (Proyectos I+D Excelencia + FEDER): TEC2015-70104-P and BIOBOT (Programa Explora Ciencia/Tecnología): TEC2015-72718-EXP. This research was supported by grants from the Spanish Ministry of Economy, Industry and Competitiveness (MEICO / FEDER) (BFU2015-67777-R), the Spanish Prion Network (Prionet Spain, AGL2015-71764-REDT and AGL2017-90665-REDT), the Generalitat de Catalunya (SGR2017-648), CIBERNED (PRY-2016-2, MFDEND), CERCA Programme/Generalitat de Catalunya and La Marató de TV3 to JADR. AH was supported by a fellowship from the Juan de la Cierva program from MINECO.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2018.00194/full#supplementary-material

References

Amabile, G., and Meissner, A. (2009). Induced pluripotent stem cells: current progress and potential for regenerative medicine. Trends Mol. Med. 15, 59–68. doi: 10.1016/j.molmed.2008.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Antoni, D., Burckel, H., Josset, E., and Noel, G. (2015). Three-dimensional cell culture: a breakthrough in vivo. Int. J. Mol. Sci. 16, 5517–5527. doi: 10.3390/ijms16035517

PubMed Abstract | CrossRef Full Text | Google Scholar

Baker, B. M., and Chen, C. S. (2012). Deconstructing the third dimension – how 3D culture microenvironments alter cellular cues. J. Cell Sci. 125, 3015–3024. doi: 10.1242/jcs.079509

PubMed Abstract | CrossRef Full Text | Google Scholar

Bewick, G. S., and Banks, R. W. (2015). Mechanotransduction in the muscle spindle. Pflügers Arch. Eur. J. Physiol. 467, 175–190. doi: 10.1007/s00424-014-1536-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhatia, S. N., and Ingber, D. E. (2014). Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772. doi: 10.1038/nbt.2989

PubMed Abstract | CrossRef Full Text | Google Scholar

Bhatt, J. M. (2016). The epidemiology of neuromuscular diseases. Neurol. Clin. 34, 999–1021. doi: 10.1016/j.ncl.2016.06.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Blizzard, C. A., Southam, K. A., Dawkins, E., Lewis, K. E., King, A. E., Clark, J. A., et al. (2015). Identifying the primary site of pathogenesis in amyotrophic lateral sclerosis - vulnerability of lower motor neurons to proximal excitotoxicity. Dis. Model. Mech. 8, 215–224. doi: 10.1242/dmm.018606

PubMed Abstract | CrossRef Full Text | Google Scholar

Bucchia, M., Merwin, S. J., Re, D. B., and Kariya, S. (2018). Limitations and challenges in modeling diseases involving spinal motor neuron degeneration in vitro. Front. Cell. Neurosci. 12:61. doi: 10.3389/fncel.2018.00061

PubMed Abstract | CrossRef Full Text | Google Scholar

Côté, M. P., Murray, L. M., and Knikou, M. (2018). Spinal control of locomotion: individual neurons, their circuits and functions. Front. Physiol. 9:784. doi: 10.3389/fphys.2018.00784

PubMed Abstract | CrossRef Full Text | Google Scholar

Campenot, R. B. (1977). Local control of neurite development by nerve growth factor (chemotaxis/culture methods/retrograde transport/sympathetic ganglia). Cell Biol. 74, 4516–4519.

Google Scholar

Centeno, E. G. Z., Cimarosti, H., and Bithell, A. (2018). 2D versus 3D human induced pluripotent stem cell-derived cultures for neurodegenerative disease modelling. Mol. Neurodegener. 13:27. doi: 10.1186/s13024-018-0258-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Charoensook, S. N., Williams, D. J., Chakraborty, S., Leong, K. W., and Vunjak-Novakovic, G. (2017). Bioreactor model of neuromuscular junction with electrical stimulation for pharmacological potency testing. Integr. Biol. 9, 956–967. doi: 10.1039/C7IB00144D

PubMed Abstract | CrossRef Full Text | Google Scholar

Colón, A., Guo, X., Akanda, N., Cai, Y., and Hickman, J. J. (2017). Functional analysis of human intrafusal fiber innervation by human γ-motoneurons. Sci. Rep. 7:17202. doi: 10.1038/s41598-017-17382-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Cooper-Knock, J., Green, C., Altschuler, G., Wei, W., Bury, J. J., Heath, P. R., et al. (2017). A data-driven approach links microglia to pathology and prognosis in amyotrophic lateral sclerosis. Acta Neuropathol. Commun. 5:23. doi: 10.1186/s40478-017-0424-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Dagberg, B., and Alstermark, B. (2006). Improved organotypic cell culture model for analysis of the neuronal circuit involved in the monosynaptic stretch reflex. J. Neurosci. Res. 84, 460–469. doi: 10.1002/jnr.20888

PubMed Abstract | CrossRef Full Text | Google Scholar

Deleglise, B., Magnifico, S., Duplus, E., Vaur, P., Soubeyre, V., Belle, M., et al. (2014). β-amyloid induces a dying-back process and remote trans-synaptic alterations in a microfluidic-based reconstructed neuronal network. Acta Neuropathol. Commun. 2:145. doi: 10.1186/s40478-014-0145-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Demestre, M., Orth, M., Föhr, K. J., Achberger, K., Ludolph, A. C., Liebau, S., et al. (2015). Formation and characterisation of neuromuscular junctions between hiPSC derived motoneurons and myotubes. Stem Cell Res. 15, 328–336. doi: 10.1016/j.scr.2015.07.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Esch, E. W., Bahinski, A., and Huh, D. (2015). Organs-on-chips at the frontiers of drug discovery. Nat. Rev. Drug Discov. 14, 248–260. doi: 10.1038/nrd4539

PubMed Abstract | CrossRef Full Text | Google Scholar

Esch, M. B., Smith, A. S., Prot, J. M., Oleaga, C., Hickman, J. J., and Shuler, M. L. (2014). How multi-organ microdevices can help foster drug development. Adv. Drug Deliv. Rev. 69–70, 158–169. doi: 10.1016/j.addr.2013.12.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Frakes, A. E., Ferraiuolo, L., Haidet-Phillips, A. M., Schmelzer, L., Braun, L., Miranda, C. J., et al. (2014). Microglia induce motor neuron death via the classical NF-κB pathway in amyotrophic lateral sclerosis. Neuron 81, 1009–1023. doi: 10.1016/j.neuron.2014.01.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Fukuhara, K., Imai, F., Ladle, D. R., Katayama, K. I., Leslie, J. R., Arber, S., et al. (2013). Specificity of monosynaptic sensory-motor connections imposed by repellent Sema3E-PlexinD1 signaling. Cell Rep. 5, 748–758. doi: 10.1016/j.celrep.2013.10.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Gingras, M., Beaulieu, M. M., Gagnon, V., Durham, H. D., and Berthod, F. (2008). In vitro study of axonal migration and myelination of motor neurons in a three-dimensional tissue-engineered model. Glia 56, 354–364. doi: 10.1002/glia.20617

PubMed Abstract | CrossRef Full Text | Google Scholar

Gogliotti, R. G., Quinlan, K. A., Barlow, C. B., Heier, C. R., Heckman, C. J., and DiDonato, C. J. (2012). Motor neuron rescue in spinal muscular atrophy mice demonstrates that sensory-motor defects are a consequence, not a cause, of motor neuron dysfunction. J. Neurosci. 32, 3818–3829. doi: 10.1523/JNEUROSCI.5775-11.2012

CrossRef Full Text | Google Scholar

Guo, X., Colon, A., Akanda, N., Spradling, S., Stancescu, M., Martin, C., et al. (2017). Tissue engineering the mechanosensory circuit of the stretch reflex arc with human stem cells: sensory neuron innervation of intrafusal muscle fibers. Biomaterials 122, 179–187. doi: 10.1016/j.biomaterials.2017.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, X., Gonzalez, M., Stancescu, M., Vandenburgh, H. H., and Hickman, J. J. (2011). Neuromuscular junction formation between human stem cell-derived motoneurons and human skeletal muscle in a defined system. Biomaterials 32, 9602–9611. doi: 10.1016/j.biomaterials.2011.09.014

PubMed Abstract | CrossRef Full Text | Google Scholar

Hallfors, N., Khan, A., Dickey, M. D., and Taylor, A. M. (2013). Integration of pre-aligned liquid metal electrodes for neural stimulation within a user-friendly microfluidic platform. Lab Chip 13, 522–526. doi: 10.1039/C2LC40954B

PubMed Abstract | CrossRef Full Text | Google Scholar

Happe, C. L., Tenerelli, K. P., Gromova, A. K., Kolb, F., and Engler, A. J. (2017). Mechanically patterned neuromuscular junctions-in-a-dish have improved functional maturation. Mol. Biol. Cell 28, 1950–1958. doi: 10.1091/mbc.e17-01-0046

PubMed Abstract | CrossRef Full Text | Google Scholar

Hoffman-Kim, D., Mitchel, J. A., and Bellamkonda, R. V. (2010). Topography, cell response, and nerve regeneration. Annu. Rev. Biomed. Eng. 12, 203–231. doi: 10.1146/annurev-bioeng-070909-105351

PubMed Abstract | CrossRef Full Text | Google Scholar

Hosmane, S., Fournier, A., Wright, R., Rajbhandari, L., Siddique, R., Yang, I. H., et al. (2011). Valve-based microfluidic compression platform: single axon injury and regrowth. Lab Chip 11, 3888–3895. doi: 10.1039/c1lc20549h

PubMed Abstract | CrossRef Full Text | Google Scholar

Hosmane, S., Yang, I. H., Ruffin, A., Thakor, N., and Venkatesan, A. (2010). Circular compartmentalized microfluidic platform: study of axon–glia interactions. Lab Chip 10, 741–747. doi: 10.1039/b918640a

PubMed Abstract | CrossRef Full Text | Google Scholar

Hume, S. L., Hoyt, S. M., Walker, J. S., Sridhar, B. V., Ashley, J. F., Bowman, C. N., et al. (2012). Alignment of multi-layered muscle cells within three-dimensional hydrogel macrochannels. Acta Biomater. 8, 2193–2202. doi: 10.1016/j.actbio.2012.02.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Hunter, G., Powis, R. A., Jones, R. A., Groen, E. J., Shorrock, H. K., Lane, F. M., et al. (2016). Restoration of SMN in schwann cells reverses myelination defects and improves neuromuscular function in spinal muscular atrophy. Hum. Mol. Genet. 25, 2853–2861. doi: 10.1093/hmg/ddw141

PubMed Abstract | CrossRef Full Text | Google Scholar

Hur, E. M., Yang, I. H., Kim, D. H., Byun, J., Saijilafu, X.U. W. L., et al. (2011). Engineering neuronal growth cones to promote axon regeneration over inhibitory molecules. Proc. Natl. Acad. Sci.U.S.A. 108, 5057–5062. doi: 10.1073/pnas.1011258108

PubMed Abstract | CrossRef Full Text | Google Scholar

Hyun Sung, P, Su, L., McDonald, J., Thakor, N., and In Hong, Y (2013). “Neuromuscular junction in a microfluidic device,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE), 2833–2835.

Google Scholar

Ionescu, A., Zahavi, E. E., Gradus, T., Ben-Yaakov, K., and Perlson, E. (2016). Compartmental microfluidic system for studying muscle–neuron communication and neuromuscular junction maintenance. Eur. J. Cell Biol. 95, 69–88. doi: 10.1016/j.ejcb.2015.11.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Jablonka, S., Karle, K., Sandner, B., Andreassi, C., von Au, K., and Sendtner, M. (2006). Distinct and overlapping alterations in motor and sensory neurons in a mouse model of spinal muscular atrophy. Hum. Mol. Genet. 15, 511–518. doi: 10.1093/hmg/ddi467

PubMed Abstract | CrossRef Full Text | Google Scholar

Jang, J. M., Lee, J., Kim, H., Jeon, N. L., and Jung, W. (2016). One-photon and two-photon stimulation of neurons in a microfluidic culture system. Lab Chip 16, 1684–1690. doi: 10.1039/C6LC00065G

PubMed Abstract | CrossRef Full Text | Google Scholar

Jenkins, R. W., Aref, A. R., Lizotte, P. H., Ivanova, E., Stinson, S., Zhou, C. W., et al. (2018). Ex vivo profiling of PD-1 blockade using organotypic tumor spheroids. Cancer Discov. 8, 196–215. doi: 10.1158/2159-8290.CD-17-0833

PubMed Abstract | CrossRef Full Text | Google Scholar

Jeong, S., Kim, S., Buonocore, J., Park, J., Welsh, C. J., Li, J., et al. (2018). A three-dimensional arrayed microfluidic blood–brain barrier model with integrated electrical sensor array. IEEE Trans. Biomed. Eng. 65, 431–439. doi: 10.1109/TBME.2017.2773463

PubMed Abstract | CrossRef Full Text | Google Scholar

Joanne Wang, C., Li, X., Lin, B., Shim, S., Ming, G., and Levchenko, A. (2008). A microfluidics-based turning assay reveals complex growth cone responses to integrated gradients of substrate-bound ECM molecules and diffusible guidance cues. Lab Chip 8, 227–237. doi: 10.1039/b713945d

PubMed Abstract | CrossRef Full Text | Google Scholar

Kamm, R. D., and Bashir, R. (2014). Creating living cellular machines. Ann. Biomed. Eng. 42, 445–459. doi: 10.1007/s10439-013-0902-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Kandel, E. R., Schwartz, J. H., Jessel, T. M., Siegelbaum, S. A., and Hudspeth, A. J. (eds.). (2013). Principles of Neural Science, 5th Edn. New York, NY: Mc Graw Hill Medical.

Google Scholar

Kiehn, O., and Dougherty, K. (2013). “Locomotion: circuits and physiology,” in Neuroscience in the 21st Century, eds D. W. Pfaff (New York, NY: Springer), 1209–1236. doi: 10.1007/978-1-4614-1997-6_42

CrossRef Full Text | Google Scholar

LaPlaca, M. C., Vernekar, V. N., Shoemaker, J. T., and Cullen, D. K. (2010). “Three-Dimensional Neuronal Cultures,” in Methods in Bioengineering: 3D Tissue Engineering, eds F. Berthiaume and J. R. Morgan (Norwood, OH: Artech House), 187–204.

Google Scholar

Levin, E., Andreadaki, A., Gobrecht, P., Bosse, F., and Fischer, D. (2017). Nociceptive DRG neurons express muscle lim protein upon axonal injury. Sci. Rep. 7:643. doi: 10.1038/s41598-017-00590-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Lobsiger, C. S., and Cleveland, D. W. (2007). Glial cells as intrinsic components of non-cell-autonomous neurodegenerative disease. Nat. Neurosci. 10, 1355–1360. doi: 10.1038/nn1988

PubMed Abstract | CrossRef Full Text | Google Scholar

Maffioletti, S. M., Sarcar, S., Henderson, A. B. H., Mannhardt, I., Pinton, L., Moyle, L. A., et al. (2018). Three-dimensional human iPSC-derived artificial skeletal muscles model muscular dystrophies and enable multilineage tissue engineering. Cell Rep. 23, 899–908. doi: 10.1016/j.celrep.2018.03.091

PubMed Abstract | CrossRef Full Text | Google Scholar

Magdesian, M. H., Lopez-Ayon, G. M., Mori, M., Boudreau, D., Goulet-Hanssens, A., Sanz, R., et al. (2016). Rapid mechanically controlled rewiring of neuronal circuits. J. Neurosci. 36, 979–987. doi: 10.1523/JNEUROSCI.1667-15.2016

PubMed Abstract | CrossRef Full Text | Google Scholar

Maimon, R., Ionescu, A., Bonnie, A., Sweetat, S., Wald-Altman, S., Inbar, S., et al. (2018). miR126-5p down-regulation facilitates axon degeneration and NMJ disruption via a non-cell-autonomous mechanism in ALS. J. Neurosci. 38, 5478–5494. doi: 10.1523/JNEUROSCI.3037-17.2018

CrossRef Full Text | Google Scholar

Maoz, B. M., Herland, A., FitzGerald, E. A., Grevesse, T., Vidoudez, C., Pacheco, A. R., et al. (2018). A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. Nat. Biotechnol. 36, 865–874. doi: 10.1038/nbt.4226

PubMed Abstract | CrossRef Full Text | Google Scholar

Marieb, E. N. (ed.). (2015). Essentials of Human Anatomy & Physiology, 11th Edn. Harlow: Pearson Education.

Google Scholar

Martin, N. R., Passey, S. L., Player, D. J., Mudera, V., Baar, K., Greensmith, L., et al. (2015). Neuromuscular junction formation in tissue-engineered skeletal muscle augments contractile function and improves cytoskeletal organization. Tissue Eng. Part A 21, 2595–2604. doi: 10.1089/ten.tea.2015.0146

PubMed Abstract | CrossRef Full Text | Google Scholar

Martínez-Hernández, R., Bernal, S., Alias, L., and Tizzano, E. F. (2014). Abnormalities in early markers of muscle involvement support a delay in myogenesis in spinal muscular atrophy. J. Neuropathol. Exp. Neurol. 73, 559–567. doi: 10.1097/NEN.0000000000000078

PubMed Abstract | CrossRef Full Text | Google Scholar

Mary, P., Servais, L., and Vialle, R. (2018). Neuromuscular diseases: diagnosis and management. Orthop. Traumatol. Surg. Res. 104, S89–S95. doi: 10.1016/j.otsr.2017.04.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Maschmeyer, I., Lorenz, A. K., Schimek, K., Hasenberg, T., Ramme, A. P., Hübner, J., et al. (2015). A four-organ-chip for interconnected long-term co-culture of human intestine, liver, skin and kidney equivalents. Lab Chip 15, 2688–2699. doi: 10.1039/C5LC00392J

PubMed Abstract | CrossRef Full Text | Google Scholar

Matthews, P. B. (2015). Where anatomy led, physiology followed: a survey of our developing understanding of the muscle spindle, what it does and how it works. J. Anat. 227, 104–114. doi: 10.1111/joa.12345

PubMed Abstract | CrossRef Full Text | Google Scholar

McCrea, D. A. (2001). Spinal circuitry of sensorimotor control of locomotion. J. Physiol. 533, 41–50. doi: 10.1111/j.1469-7793.2001.0041b.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Millet, L. J., Stewart, M. E., Sweedler, J. V., Nuzzo, R. G., and Gillette, M. U. (2007). Microfluidic devices for culturing primary mammalian neurons at low densities. Lab Chip 7, 987–994. doi: 10.1039/b705266a

PubMed Abstract | CrossRef Full Text | Google Scholar

Moreno, E. L., Hachi, S., Hemmer, K., Trietsch, S. J., Baumuratov, A. S., Hankemeier, T., et al. (2015). Differentiation of neuroepithelial stem cells into functional dopaminergic neurons in 3D microfluidic cell culture. Lab Chip 15, 2419–2428. doi: 10.1039/C5LC00180C

PubMed Abstract | CrossRef Full Text | Google Scholar

Morimoto, Y., Kato-Negishi, M., Onoe, H., and Takeuchi, S. (2013). Three-dimensional neuron–muscle constructs with neuromuscular junctions. Biomaterials 34, 9413–9419. doi: 10.1016/j.biomaterials.2013.08.062

PubMed Abstract | CrossRef Full Text | Google Scholar

Morrison, B. (2016). Neuromuscular diseases. Semin. Neurol. 36, 409–418. doi: 10.1055/s-0036-1586263

PubMed Abstract | CrossRef Full Text | Google Scholar

Osaki, T., Uzel, S. G. M., and Kamm, R. D. (2018). Microphysiological 3D model of amyotrophic lateral sclerosis (ALS) from human iPS-derived muscle cells and optogenetic motor neurons. Sci. Adv. 4:eaat5847. doi: 10.1126/sciadv.aat5847

PubMed Abstract | CrossRef Full Text | Google Scholar

Park, J., Koito, H., Li, J., and Han, A. (2009). Microfluidic compartmentalized co-culture platform for CNS axon myelination research. Biomed. Microdev. 11, 1145–1153. doi: 10.1007/s10544-009-9331-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Park, J. W., Vahidi, B., Taylor, A. M., Rhee, S. W., and Jeon, N. L. (2006). Microfluidic culture platform for neuroscience research. Nat. Protoc. 1, 2128–2136. doi: 10.1038/nprot.2006.316

PubMed Abstract | CrossRef Full Text | Google Scholar

Peyrin, J. M., Deleglise, B., Saias, L., Vignes, M., Gougis, P., Magnifico, S., et al. (2011). Axon diodes for the reconstruction of oriented neuronal networks in microfluidic chambers. Lab Chip 11, 3663–3673. doi: 10.1039/c1lc20014c

PubMed Abstract | CrossRef Full Text | Google Scholar

Prabhakarpandian, B., Shen, M. C., Nichols, J. B., Mills, I. R., Sidoryk-Wegrzynowicz, M., Aschner, M., et al. (2013). SyM-BBB: a microfluidic blood brain barrier model. Lab Chip 13, 1093–1101. doi: 10.1039/c2lc41208j

PubMed Abstract | CrossRef Full Text | Google Scholar

Prpar Mihevc, S., Pavlin, M., Darovic, S., Živin, M., Podbregar, M., Rogelj, B., et al. (2017). Modelling FUS mislocalisation in an in vitro model of innervated human muscle. J. Mol. Neurosci. 62, 318–328. doi: 10.1007/s12031-017-0940-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Purves, D., Augustine, G. J., Hall, W. C., LaMantia, A. S., McNamara, J. O., and Williams, S. M. (eds) (2004). Neuroscience, 3rd Edn. Sunderland: Sinauer Associates, Inc.

Google Scholar

Puttonen, K. A., Ruponen, M., Naumenko, N., Hovatta, O. H., Tavi, P., and Koistinaho, J. (2015). Generation of functional neuromuscular junctions from human pluripotent stem cell lines. Front. Cell. Neurosci. 9:473. doi: 10.3389/fncel.2015.00473

PubMed Abstract | CrossRef Full Text | Google Scholar

Renault, R., Durand, J. B., Viovy, J. L., and Villard, C. (2016). Asymmetric axonal edge guidance: a new paradigm for building oriented neuronal networks. Lab Chip 16, 2188–2191. doi: 10.1039/C6LC00479B

PubMed Abstract | CrossRef Full Text | Google Scholar

Renault, R., Sukenik, N., Descroix, S., Malaquin, L., Viovy, J. L., Peyrin, J. M., et al. (2015). Combining microfluidics, optogenetics and calcium imaging to study neuronal communication in vitro. PLoS ONE 10:e0120680. doi: 10.1371/journal.pone.0120680

PubMed Abstract | CrossRef Full Text | Google Scholar

Rindt, H., Feng, Z., Mazzasette, C., Glascock, J. J., Valdivia, D., Pyles, N., et al. (2015). Astrocytes influence the severity of spinal muscular atrophy. Hum. Mol. Genet. 24, 4094–4102. doi: 10.1093/hmg/ddv148

PubMed Abstract | CrossRef Full Text | Google Scholar

Rumsey, J. W., Das, M., Bhalkikar, A., Stancescu, M., and Hickman, J. J. (2010). Tissue engineering the mechanosensory circuit of the stretch reflex arc: sensory neuron innervation of intrafusal muscle fibers. Biomaterials 31, 8218–8227. doi: 10.1016/j.biomaterials.2010.07.027

PubMed Abstract | CrossRef Full Text | Google Scholar

Saal, L., Briese, M., Kneitz, S., Glinka, M., and Sendtner, M. (2014). Subcellular transcriptome alterations in a cell culture model of spinal muscular atrophy point to widespread defects in axonal growth and presynaptic differentiation. RNA 20, 1789–1802. doi: 10.1261/rna.047373.114

PubMed Abstract | CrossRef Full Text | Google Scholar

Santhanam, N., Kumanchik, L., Guo, X., Sommerhage, F., Cai, Y., Jackson, M., et al. (2018). Stem cell derived phenotypic human neuromuscular junction model for dose response evaluation of therapeutics. Biomaterials 166, 64–78. doi: 10.1016/j.biomaterials.2018.02.047

PubMed Abstract | CrossRef Full Text | Google Scholar

Santosa, K. B., Keane, A. M., Jablonka-Shariff, A., Vannucci, B., and Snyder-Warwick, A. K. (2018). Clinical relevance of terminal Schwann cells: an overlooked component of the neuromuscular junction. J. Neurosci. Res. 96, 1125–1135. doi: 10.1002/jnr.24231

PubMed Abstract | CrossRef Full Text | Google Scholar

Sargsyan, S. A., Monk, P. N., and Shaw, P. J. (2005). Microglia as potential contributors to motor neuron injury in amyotrophic lateral sclerosis. Glia 51, 241–253. doi: 10.1002/glia.20210

PubMed Abstract | CrossRef Full Text | Google Scholar

Schwab, A. J., and Ebert, A. D. (2014). Sensory neurons do not induce motor neuron loss in a human stem cell model of spinal muscular atrophy. PLoS ONE 9:e103112. doi: 10.1371/journal.pone.0103112

CrossRef Full Text | Google Scholar

Selvaraj, S., and Perlingeiro, R. C. R. (2018). “Induced pluripotent stem cells for neuromuscular diseases: potential for disease modeling, drug screening, and regenerative medicine,” in Reference Module in Biomedical Sciences, eds C. A. McQueen, W. F. Boron, E. L. Boulpaep, A. Bradshaw, Ralph, D. B. Bylund, S. J. Enna, et al. (Elsevier). doi: 10.1016/B978-0-12-801238-3.65504-6

CrossRef Full Text | Google Scholar

Shen, N. Y., Liu, Z., Jacquot, B. C., Minch, B. A., and Kan, E. C. (2004). Integration of chemical sensing and electrowetting actuation on chemoreceptive neuron MOS (CνMOS) transistors. Sens. Actuators B Chem. 102, 35–43. doi: 10.1016/j.snb.2003.10.013

CrossRef Full Text | Google Scholar

Shin, H. S. (2009). Shear stress effect on transfection of neurons cultured in microfluidic devices. J. Nanosci. Nanotechnol. 9, 7330–7335. doi: 10.1166/jnn.2009.1769

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, A. S., Passey, S. L., Martin, N. R. W., Player, D. J., Mudera, V., Greensmith, L., et al. (2016). Creating interactions between tissue-engineered skeletal muscle and the peripheral nervous system. Cells. Tissues. Organs 202, 143–158. doi: 10.1159/000443634

PubMed Abstract | CrossRef Full Text | Google Scholar

Smith, S. J., Wilson, M., Ward, J. H., Rahman, C. V., Peet, A. C., Macarthur, D. C., et al. (2012). Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition. PLoS ONE 7:e52335. doi: 10.1371/journal.pone.0052335

PubMed Abstract | CrossRef Full Text | Google Scholar

Southam, K. A., King, A. E., Blizzard, C. A., McCormack, G. H., and Dickson, T. C. (2013). Microfluidic primary culture model of the lower motor neuron–neuromuscular junction circuit. J. Neurosci. Methods 218, 164–169. doi: 10.1016/j.jneumeth.2013.06.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Su, H., Wang, L., Cai, J., Yuan, Q., Yang, X., Yao, X., et al. (2013). Transplanted motoneurons derived from human induced pluripotent stem cells form functional connections with target muscle. Stem Cell Res. 11, 529–539. doi: 10.1016/j.scr.2013.02.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Takahashi, K., and Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676. doi: 10.1016/j.cell.2006.07.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Taylor, M. D., Holdeman, A. S., Weltmer, S. G., Ryals, J. M., and Wright, D. E. (2005). Modulation of muscle spindle innervation by neurotrophin-3 following nerve injury. Exp. Neurol. 191, 211–222. doi: 10.1016/j.expneurol.2004.09.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Thomson, S. R., Wishart, T. M., Patani, R., Chandran, S., and Gillingwater, T. H. (2012). Using induced pluripotent stem cells (iPSC) to model human neuromuscular connectivity: promise or reality? J. Anat. 220, 122–130. doi: 10.1111/j.1469-7580.2011.01459.x

CrossRef Full Text | Google Scholar

Tong, Z., Segura-Feliu, M., Seira, O., Homs-Corbera, A., Del Río, J. A., and Samitier, J. (2015). A microfluidic neuronal platform for neuron axotomy and controlled regenerative studies. RSC Adv. 5, 73457–73466. doi: 10.1039/C5RA11522A

CrossRef Full Text | Google Scholar

Tong, Z., Seira, O., Casas, C., Reginensi, D., Homs-Corbera, A., Samitier, J., et al. (2014). Engineering a functional neuro-muscular junction model in a chip. RSC Adv. 4, 54788–54797. doi: 10.1039/C4RA10219C

CrossRef Full Text | Google Scholar

Tortora, G. J., and Derrickson, B. (eds.). (2017). Principles of Anatomy and Physiology, 15th Edn. Danvers, MA: John Wiley & Sons, Inc.

Google Scholar

Tourovskaia, A., Li, N., and Folch, A. (2008). Localized acetylcholine receptor clustering dynamics in response to microfluidic focal stimulation with agrin. Biophys. J. 95, 3009–3016. doi: 10.1529/biophysj.107.128173

PubMed Abstract | CrossRef Full Text | Google Scholar

Uzel, S. G., Platt, R. J., Subramanian, V., Pearl, T. M., Rowlands, C. J., Chan, V., et al. (2016b). Microfluidic device for the formation of optically excitable, three-dimensional, compartmentalized motor units. Sci. Adv. 2:e1501429–e1501429. doi: 10.1126/sciadv.1501429

PubMed Abstract | CrossRef Full Text | Google Scholar

Uzel, S. G. M., Amadi, O. C., Pearl, T. M., Lee, R. T., So, P. T. C., and Kamm, R. D. (2016a). Simultaneous or sequential orthogonal gradient formation in a 3D cell culture microfluidic platform. Small 12, 612–622. doi: 10.1002/smll.201501905

PubMed Abstract | CrossRef Full Text | Google Scholar

Uzel, S. G. M., Pavesi, A., and Kamm, R. D. (2014). Microfabrication and microfluidics for muscle tissue models. Prog. Biophys. Mol. Biol. 115, 279–293. doi: 10.1016/j.pbiomolbio.2014.08.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Vilmont, V., Cadot, B., Ouanounou, G., and Gomes, E. R. (2016). A system for studying mechanisms of neuromuscular junction development and maintenance. Development 143, 2464–2477. doi: 10.1242/dev.130278

PubMed Abstract | CrossRef Full Text | Google Scholar

Wevers, N. R., van Vught, R., Wilschut, K. J., Nicolas, A., Chiang, C., Lanz, H. L., et al. (2016). High-throughput compound evaluation on 3D networks of neurons and glia in a microfluidic platform. Sci. Rep. 6:38856. doi: 10.1038/srep38856

PubMed Abstract | CrossRef Full Text | Google Scholar

Williamson, A., Singh, S., Fernekorn, U., and Schober, A. (2013). The future of the patient-specific body-on-a-chip. Lab Chip 13, 3471–3480. doi: 10.1039/c3lc50237f

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, I. H., Siddique, R., Hosmane, S., Thakor, N., and Höke, A. (2009). Compartmentalized microfluidic culture platform to study mechanism of paclitaxel-induced axonal degeneration. Exp. Neurol. 218, 124–128. doi: 10.1016/j.expneurol.2009.04.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Yoshida, M., Kitaoka, S., Egawa, N., Yamane, M., Ikeda, R., Tsukita, K., et al. (2015). Modeling the early phenotype at the neuromuscular junction of spinal muscular atrophy using patient-derived iPSCs. Stem Cell Rep. 4, 561–568. doi: 10.1016/j.stemcr.2015.02.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Zahavi, E. E., Ionescu, A., Gluska, S., Gradus, T., Ben-Yaakov, K., and Perlson, E. (2015). A compartmentalized microfluidic neuromuscular co-culture system reveals spatial aspects of GDNF functions. J. Cell Sci. 128, 1241–1252. doi: 10.1242/jcs.167544

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, B., and Radisic, M. (2017). Organ-on-a-chip devices advance to market. Lab Chip 17, 2395–2420. doi: 10.1039/C6LC01554A

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Keywords: neuromuscular circuit, compartmentalized microfluidic culture systems (cμFCS), hiPSC, 3D-culture, in-vitro models

Citation: Badiola-Mateos M, Hervera A, del Río JA and Samitier J (2018) Challenges and Future Prospects on 3D in-vitro Modeling of the Neuromuscular Circuit. Front. Bioeng. Biotechnol. 6:194. doi: 10.3389/fbioe.2018.00194

Received: 31 August 2018; Accepted: 27 November 2018;
Published: 11 December 2018.

Edited by:

Massimo Alberti, Agency for Science, Technology and Research (A*STAR), Singapore

Reviewed by:

Michela Pozzobon, Università degli Studi di Padova, Italy
Abhigyan Satyam, Harvard Medical School, United States

Copyright © 2018 Badiola-Mateos, Hervera, del Río and Samitier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Josep Samitier, jsamitier@ibecbarcelona.eu