Introduction

Societal Impacts of Neurological Diseases and Disorders

Neurological diseases, disorders, and injuries (NDDIs) are significant causes of mortality and quality of life losses worldwide [1]. For example, the World Health Organization has recently determined that 8 of 10 disorders in the highest disability class are neurological disorders [2]. According to the American Academy of Neurology, genetic and infectious diseases and disorders of the nervous system currently affect over 6.4 million people in the United States (US) alone. In fact, stroke and Alzheimer’s disease are currently the third and sixth leading cause of death in the US, respectively [3, 4]. Furthermore, the number of Americans diagnosed with Alzheimer’s disease is rising and expected to reach 16 million by 2050 [5]. Neurological disorders resulting from injury also pose critical problems. For example, according to the Centers for Disease Control and Prevention each year over 1.7 million people experience a traumatic brain injury (TBI) in the US. As a result, 3.3–5.3 million people are currently living with a TBI [6]. Several recent studies have also estimated that ca. 20,000 new cases of spinal cord injury occur annually in the US with ca. 270,000 currently living survivors [6]. In addition to TBIs and spinal cord injuries, it has been estimated that over 200,000 peripheral nerve repair procedures are performed annually in the US to treat NDDIs that affect the peripheral nervous system [7].

Importantly, NDDIs also have a significant impact on the economy and national security. For example, the direct healthcare costs associated with stroke and TBI alone in the US have been estimated to be greater than $46 billion US dollars (USD) per year with indirect costs associated with work and productivity loss estimated near $91 billion USD per year [6]. Additionally, the high cost and time associated with developing drugs for the prevention and treatment of NDDIs also contributes to both direct and indirect healthcare costs [8]. High incidence of neurological disorders and injuries among the warfighter and veterans is also of great concern and has far reaching implications toward national security. For example, TBI and posttraumatic stress disorder (PTSD) have been recognized as the most common problem requiring medical intervention [9]. Ultimately, considering the likelihood of continued civil conflict worldwide [10] and the trend toward an aging population [11], the prevalence of NDDIs is projected to further increase. Thus, there is a critical need for novel cost-effective molecular, cellular, and device-based medicines and therapies for the prevention and treatment of NDDIs [12].

Limitations of State-of-the-Art Models for Neurological Diseases, Disorders, and Injuries

Historically, medicines and therapies for NDDIs have typically been developed by extending discoveries made using tissue culture and small animal models to clinical application. For example, two-dimensional (2D) monolayer cultures remain as standard models for many applications, such as high throughput drug screening. However, such ‘reductionist approaches’ often fail to replicate higher order features and trajectories of the human nervous system, and although incorporating more cell types or use of human cells can somewhat improve the realism of such models, this approach sacrifices robustness [13, 14]. This tradeoff between robustness and realism, as well as overall limitations in achievable complexity, currently impede the use of reductionist approaches for modeling higher order trajectories of the human nervous system.

Presently, three-dimensional (3D) histotypic and organotypic slice cultures and small animal models are gold standards for modeling higher order features and trajectories of the nervous system [13, 14]. However, such models are typically not compatible with human cells, which can limit the translational impact of associated results. Furthermore, ethical concerns and high cost associated with animal testing also drive the need for alternative approaches. Driven by these limitations, ‘microphysiological neural systems’ (MPNS) appear poised to advance our fundamental understanding of higher order structure, function, and pathophysiology of the human nervous system, therein providing novel platforms for developing next-generation medicines and therapies for NDDIs.

Microphysiological Neural Systems-on-a-Chip

MPNS are defined as biological constructs that reproduce higher order features, parameters, and trajectories of the nervous system. Importantly, MPNS design and manufacturing processes are compatible with human cells, endowing them with significant translational value. In general, microphysiological systems (MPS) can be broadly categorized as: 1) scaffold-free or 2) scaffold-based [15]. A discussion of scaffold-free MPS is beyond the scope of this review; various excellent reviews can be found elsewhere [16, 17]. Alternatively, scaffold-based MPS are versatile platforms based on a ‘tissue chip’ concept. Tissue chips are micro- or bio-fabricated devices that mimic higher order physiological or pathophysiological responses. Tissue chips typically integrate scaffolding, mechanical cues, biochemical cues, and topographical cues to recreate physiological conditions. Recently, chip-based MPS have been applied to a number of tissue and organ systems and now show significant promise in modeling the brain and nervous system.

Chip-based MPNS, hereinafter referred to as neural systems-on-a-chip (NSCs), are scaffold-based 2D or 3D culture systems that possess higher order structure or functionality of the nervous system. In addition to compatibility with human cells, the concept of constructing biological architecture on a versatile functional substrate (i.e. a chip) provides flexibility, robustness, and efficiency in controlling and monitoring system parameters [18,19,20]. Furthermore, NSCs are also highly attractive from a design and manufacturing perspective. For example, NSCs can be constructed using state-of-the-art computer-aided design and robotic-assisted biofabrication approaches. Researchers have recently demonstrated that novel NSCs can be created using 3D printing techniques [21]. Given such desirable characteristics, highly biomimetic NSCs are now emerging to model higher order features and trajectories of the human nervous system. Here, we provide a critical review of NSCs. Neural system-on-a-chip (NSC) designs, manufacturing approaches, and applications to modeling of NDDIs are comprehensively reviewed. We also highlight emerging trends and techniques, technical challenges, and future directions.

Bio-Inspired Design of Neural Systems-on-a-Chip for Structure, Function and Disease

Design and engineering of NSCs is non-trivial as reproducing higher order neuro-physiology or -pathophysiology requires realistic modeling of human neural anatomy, circuitry, and microenvironmental parameters. Furthermore, the hierarchal structure and function of the nervous system imposes additional design challenges. As a result, NSCs are designed using a ‘structure-function-disease’ heuristic. The typical bio-inspired design approach is multi-step process consisting of identifying the required anatomical and functional features needed for controlling system parameters toward a desired higher order trajectory.

Hierarchical Design toward Higher Order Neural Trajectories

NSCs typically possess one or more higher order anatomical features. As shown in Fig. 1a, such features include: 1) cellular heterogeneity; 2) clustering of multiple cell types; 3) spatial alignment of cell bodies; 4) spatial alignment of neurites; 5) controlled distribution of extracellular matrix (ECM); and 6) three-dimensionality. These features form the basis of complex anatomical systems found in NSCs. As shown in Fig. 1b, such systems include: 1) circuits of neuronal cells (e.g. used in Parkinson’s or Alzheimer’s disease modeling); 2) ensembles of neuronal and glial cells (e.g. used in brain tumor modeling); 3) ensembles of neuronal and other non-neuronal cells (e.g. used in neuromuscular junction modeling); and 4) ensembles of glial and other non-neuronal cells (e.g. used in models of the blood-brain barrier (BBB)).

Fig. 1
figure 1

Hierarchical design of neural systems-on-a-chip toward assembly of higher order functional neural anatomical systems. Anatomical features (a) are assembled into anatomical systems (b) which ultimately contain functional features (c) that control microenvironmental parameters

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In addition to higher order anatomical features and systems, NSCs typically possess one or more higher order functional or augmented features. As shown in Fig. 1c, such features include: 1) fluidic channels; 2) controlled drug release systems; and 3) electroactive components. These features serve to program and control higher order microenvironmental parameters including: 1) mass transport of solutes; 2) static and dynamic mechanical stresses; and 3) spatiotemporal distributions of biochemical cues. Such features also enable the stimulation and monitoring of biology. Ultimately, as shown in Fig. 2, the microenvironmental parameters established by functional or augmented features govern the higher order trajectories of realistic anatomical systems. Typical trajectories include: 1) cell and neurite outgrowth; 2) cell migration; 3) cell signaling; 4) circuit mapping; 5) phenotypic outcomes; and 6) gene expression. Thus, NSCs are a disruptive cell culture platform for the study of human NDDIs. However, the extent and flexibility by which the structure-function-disease design heuristic can be implemented depends on both the scaffold design and the manufacturing approach.

Fig. 2
figure 2

Hierarchical design of neural systems-on-a-chip toward control of neural microenvironmental parameters and modeling of higher order trajectories. Control over microenvironmental parameters, such as transport of diluted species, mechanical stimulation, electrical stimulation, and spatiotemporal distribution of biochemical cues, through functional and augmented features, allows NSCs to model higher order trajectories of the human nervous system

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Manufacturing Approaches for Neural Systems-on-a-Chip

Although design widely varies, NSCs are derived from the following components: 1) microchannels; 2) microchambers; 3) functionalized microdomains; 4) ECM; and 5) cells (see Fig. 3). Microchannels have diverse application-dependent function. For example, microchannels are most commonly used to guide neurite outgrowth [22, 23]. In some cases, they also serve as scaffolds for assembling non-neuronal cells [21]. In addition, microchannels are commonly used for fluid handling to provide perfusion [24], gradients of biochemical cues [25], and mechanical actuation [26]. Microchambers are commonly used to spatially-isolate different cell types. Thus, they are useful components for guiding the formation of heterogeneous tissues [27]. Functionalized microdomains are commonly used to spatially-control biochemical cue distribution, such as the patterning of ECM or growth factors. As a result, they are effective for controlling cell seeding and neurite outgrowth [28].

Fig. 3
figure 3

The fundamental building blocks of neural systems-on-a-chip include microchannels (a), microchambers (b), functionalized microdomains (c), extracellular matrix (d), and cells (e)

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The ability to both make and assemble these building blocks is highly dependent on the manufacturing approach used. Thus, since these building blocks serve as the basis for constructing higher order anatomical and functional features, the manufacturing approach has a direct impact on the type of microenvironmental parameters that can be controlled and the resultant trajectories that can be modeled. Overall, NSC manufacturing consists of three steps: design, fabrication, and integration with biology (i.e. surface functionalization and cell seeding). However, these steps widely differ depending on the manufacturing process used and may include: 1) manual techniques; 2) computer-aided design (CAD); 3) medical imaging; and 4) computer-aided manufacturing (CAM). As a result, the manufacturing process influences the robustness of the resultant NSC platform as discussed in greater detail in the following sections.

Manufacturing Processes

As shown in both Fig. 4 and Table 1, five primary manufacturing processes are used to construct NSCs: 1) photolithography; 2) soft lithography; 3) contact printing; 4) laser patterning; and 5) 3D printing. The following techniques are briefly reviewed below.

Fig. 4
figure 4

Schematics of the five commonly used neural system-on-a-chip manufacturing techniques: photolithography (a); soft lithography (b); contact printing (c); laser patterning (d); and 3D printing (e)

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Table 1 Summary of neural systems-on-a-chip
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Photolithography

Photolithography is a core process of microfabrication [29]. The base material for photolithography is typically silicon. As shown in Fig. 4a, the first step involves oxidizing the wafer’s top surface followed by coating with a thin photoresist layer. A laser or photomask is subsequently used to selectively expose specific locations of the wafer to UV light, which initiates a photochemical reaction in the photoresist. The photoresist can then be selectively removed to expose specific locations of the wafer to subsequent chemical etchants. For example, hydrofluoric acid is often used to remove silicon oxide. The final step involves removing, also referred to as ‘stripping’, the remainder of photoresist from the wafer by exposure to chemical solutions, such as sulfuric acid [29]. We note that many variations to the process have been developed and reviewed elsewhere [29, 30]. Advantages: High precision lasers and photomasks designed with CAD software allow highly precise designs. Subsequent etching or material deposition steps yield complex electronic systems. Disadvantages: Photoresist curing and material removal steps typically require high temperature, extreme pH, and exposure to radiation. As a result, the manufacturing process does not support simultaneous integration with biology during platform fabrication.

Soft Lithography

Soft lithography is a type of polymer casting process and has been extensively used for the fabrication of microfluidic devices [31]. The process begins with creation of a rigid mold, commonly referred to as a ‘master’, using photolithography. This process transfers the geometric pattern of the photomask to the master. As shown in Fig. 4b, an elastomeric polymer, commonly polydimethylsiloxane (PDMS), is then cast onto the master and solidified via crosslinking. The solidified elastomeric material, which contains an imprint of the master, is then released from the mold. Typical features include open microchannels, chambers, pads, and pillars. To form microfluidic devices, the solid elastomeric molds are bonded to rigid substrates, such as glass or plastic, thereby forming sealed channels. Advantages: Soft lithography is a high precision manufacturing process due to the use of photolithography for creating the master. Thus, it enables precision manufacturing of NSC building blocks, such as microchannels. The photomask creation step is also compatible with CAD resources. Disadvantages: Given soft lithography requires photolithography for master creation, the disadvantages of photolithography apply to soft lithography. Elastomer crosslinking also typically requires elevated temperatures and is impeded by moisture. As a result, similar to photolithography, the manufacturing process does not support simultaneous integration with biology during platform fabrication.

Contact Printing

Contact printing is a material deposition process and has been used extensively for functionalizing substrates for cell culture applications. The process begins with creation of an elastomeric stamp using soft lithography. As shown in Fig. 4c, the stamp is then coated with the desired adsorbate through exposure to an analyte-containing solution. Subsequently, the stamp is brought into mechanical contact with the substrate, thereby transferring the adsorbate from the stamp to the substrate. After transfer, the stamp is removed, resulting in a substrate functionalization pattern that matches the geometric pattern of the stamp. Advantages: Contact printing has similar advantages to soft lithography (e.g. precision and compatibility with CAD). Contact printing is also compatible with a wide range of analytes as the transfer mechanism is based on adsorption [28, 32]. Contact printing can also create periodic functionalized microdomains across macroscopic length scales [32]. Disadvantages: Repeated contact printing on the same substrate is challenging as the mechanical contact step is typically done manually. Contact printing can only be used to deposit small molecules and biomacromolecules, but faces challenges with depositing larger biologics, such as animal cells. Contact printing can only facilitate deposition of thin material layers.

Laser Patterning

Laser patterning in the scope of this review is a technique used to selectively bind biomolecules to a hydrogel in 3D [33, 34]. Although varying mechanisms exist, all processes involve the laser-triggered reaction of photolabile groups within a hydrogel (see Fig. 4d). The most common technique is to bind mono- or di-acrylated peptides to polyethylene glycol diacrylate (PEGDA) hydrogels, given PEGDA hydrogels inherently contain unreacted acrylate groups from the curing step [33, 35]. Another approach involves the laser-triggered cleavage of photolabile bonds, which exposes new reactive groups for selectively bonding biomolecules to the hydrogel [34]. Advantages: The ability to spatially control 3D hydrogel chemistry offers unique advantages for directing cell growth in hydrogel NSCs [34]. Disadvantages: Only hydrogels and biomolecules functionalized with photoreactive groups can be used, which significantly limits material availability.

3D Printing

3D printing is a biomanufacturing process and has been used extensively in tissue engineering applications. Various types of 3D printing processes exist, including stereolithography, inkjet printing, micro-extrusion printing, and laser-assisted bioprinting. A detailed description of the individual processes can be found elsewhere [36, 37]. While multiple types of 3D printing processes exist, they differ in terms of material deposition mechanism, process physics, material compatibility, multi-material printing capability, manufacturing speed, and precision. As shown in Fig. 4e, the first step to 3D printing is constructing the path information that describes the motion and triggering of the printing tool (e.g. a laser or extruder) from a 3D digital model. The next step in 3D printing is the formulation of a printable material, commonly referred to as an “ink”. Subsequently, the ink is loaded into a dispensing tool or holding reservoir and the printing process is initiated. This enables the conversion of the 3D digital model to a physical object. Advantages: Digital models, and thus printer path information, can be derived from medical imaging data. Unlike contact printing, 3D printing is a CAM process. This aspect affords repeatability and robustness in multi-layer and -material assembly. Certain types of 3D printing, such as micro-extrusion printing, are compatible with a diverse materials set including thermosets, thermoplastics, composites, hydrogels, and solutions [37]. 3D printing offers a one-pot biomanufacturing approach for directly interweaving biology with scaffold and functional materials. Disadvantages: 3D printing is currently a serial processing technique. Thus, throughput can be limited for large parts that contain intricate path geometries. However, we note that the development of advanced dies that accommodate the simultaneous printing of multiple parts can address this limitation similar to prior advancements in injection molding processes.

Classes of Neural Systems-on-a-Chip

NSCs are scaffold-based architectures. Thus, they require cell seeding either on the surface or within the bulk of an exogenous material. As a result, NSCs may differ with respect to the degrees of freedom for resultant trajectories, such as growth and migration. Importantly, the desired features and the manufacturing approach influence the scaffold design. In general, NSCs fall into one of two categories: 1) surface-based designs or 2) bulk-based designs. Surface-based designs are those that seed cells on the surface of an exogenous material. Thus, mono- or multi-layer cell growth, cell migration, cell-cell interactions, and cell-matrix interactions occur in 2D at the solid-liquid interface between the exogenous material and the growth medium. Microfluidic and compartmentalized NSCs are the most common type of surface-based designs. In contrast, bulk-based designs seed cells within the bulk of an exogenous material. Thus, cell growth, cell migration, cell-cell interactions, and cell-matrix interactions occur in 3D within the bulk of the growth medium-infused exogenous material. Hydrogel NSCs are the most common type of bulk-based designs.

Microfluidic Neural Systems-on-a-Chip

Microfluidic NSCs are surface-based designs based on isolated or interacting microfluidic channels (see Fig. 5). The microfluidic channels are typically rectangular and range 1–500 μm in width and 2–25 mm in length. The microfluidic channels typically have three walls that arise from the bulk polymer material (e.g. PDMS) and one wall (the bottom wall) that arises from the substrate (e.g. glass or tissue culture plastic). In some cases, the bottom wall consists of a porous membrane that enables the cells in two adjacent microchannels to chemically interact. For example, one common design involves two co-directional microchannels separated by a polycarbonate transwell membrane [38,39,40]. Advantages: The ability to use transparent materials for microfluidic construction provides ease in optical characterization and stimulation techniques. Microfluidic NSCs also support fluid handling, which has various advantages including establishing: 1) convective flow of nutrients and biochemical cues [41]; 2) diffusive profiles of biochemical cues [25]; and 3) mechanical cues [42], such as shear stress or dynamic scaffold deformation. Additionally, the ability to utilize a wide range of substrates, such as conductive materials [43], offers the ability to establish unique functional and augmented features for stimulation and monitoring. Limitations: Given microfluidic NSCs are surface-based designs, the degrees of freedom for cell growth and migration are limited relative to bulk-based designs (i.e. to 2D instead of 3D). Thus, higher order trajectories related to or derived from 3D cell growth, cell migration, cell-cell interactions, and cell-matrix interactions are difficult to model using microfluidic NSCs. Further, microfluidic NSCs typically utilize simplistic surface functionalization approaches, such as surface coating with adsorbed ECM components. Thus, it is relatively challenging to replicate native cell-matrix interactions using microfluidic NSCs.

Fig. 5
figure 5

Highlights of microfluidic neural systems-on-a-chip (NSCs). a) Microfluidic NSC for studying differentiation of neural progenitor cells under the influence of chemical gradients. i) Schematic of a Shh/FGF8 or Shh/BMP4 gradient microfluidic device; ii) visualization of gradient; and iii) immunoassay of TuJ1 to quantify cell clusters and neurite bundles in the device. Reprinted with permission [55]. Copyright John Wiley & Sons 2009. b) Microfluidic NSC for Alzheimer’s disease studies. i) Schematic of β-amyloid gradient device; ii) live/dead assays of sections 1–5 with intensity plots and slopes shown on the right; and iii) imaging of synapsin-ii distribution. Reprinted with permission [84]. Copyright Nature Publishing Group 2013. c) Microfluidic NSC for modeling the blood-brain barrier (BBB). i) Schematic of the BBB; ii) live/dead stain of endothelial cells; iii) immunoassay of tight junction ZO-1 in endothelial cells; iv) immunoassay of GFAP in astrocytes; and v) environmental scanning electron micrograph of astrocytes. Reprinted with permission [38]. Copyright Royal Society of Chemistry 2012

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Manufacturing of Microfluidic Neural Systems-on-a-Chip

Microfluidic NSCs consist of a microchannel-containing elastomeric cast bonded to a rigid substrate. Microfluidic NSCs are typically manufactured using a combination of photolithography and soft lithography (see “Photolithography” and “Soft lithography” sections, respectively) [44, 45]. However, we also note that 3D printing has recently emerged as a technique for manufacturing microfluidic devices and NSCs (see “3D printing” section) [21, 46,47,48,49]. In cases of microfluidic NSCs that contain fluid flow, microneedles are inserted into the inlet and outlet ports to interface with fluid handling components and instrumentation (e.g. tubing and pumps).

Microfluidic NSCs are typically functionalized by first exposing the fluidic channels to ECM-containing solutions in a static or flow mode. Subsequently, the coated fluidic channels are exposed to cell suspensions in a static or flow mode to seed cells on the coated surfaces, thereby resulting in a cell mono-layer. Cellular growth strongly follows topographical cues. If required, the process may be repeated to form multi-layers in the same microchannel (e.g. to establish a feeder layer).

Hierarchical Design of Microfluidic Neural Systems-on-a-Chip

Given the ease of designing and fabricating microchannels via soft lithography, active fluid handling capability, and flexibility with using alternative substrate materials, various higher order anatomical features of native neural systems can be reproduced using microfluidic NSCs (see Table 2). For example, microfluidic NSCs have been used to affect the: 1) clustering of different cell types; 2) spatial alignment of cell bodies; and 3) spatial alignment of neurites. Microfluidic NSCs have also been designed to reproduce higher order functional and augmented features given the ability to integrate: 1) fluidic channels, and 2) electroactive components [24, 43, 50,51,52,53]. For example, Bianco et al. developed a microfluidic system in which neuroinflammation could be simultaneously monitored by microscopy and electrophysiological recordings [54]. As a result, microfluidic NSCs offer programing and control of various higher order microenvironmental parameters such as: 1) convective mass transport of solutes; 2) actuation of static and dynamic mechanical stresses; 3) spatiotemporal distributions of biochemical cues; and 4) stimulation and monitoring of cells. For example, Griep et al. have demonstrated that shear stress (5.8 × 10−1 Pa) is an important parameter for endothelial cell function through tight junction formation in BBB models [39]. As shown in Fig. 5a, Park et al. used a microfluidic device to control spatiotemporal distributions of three different cytokines (Shh, FGF8, and BMP4) ranging from 0 to 500 ng/ml to affect the differentiation of neural progenitor cells [55]. Various studies have also used microfluidics to support the perfusion of tissues for long term studies [41, 56, 57]. Thus, microfluidic NSCs are useful platforms for modeling higher order trajectories of native neural systems such as: 1) neurite outgrowth; 2) cell migration; 3) cell signaling; and 4) gene expression. Chung et al. have used a microfluidic device to direct the proliferation and differentiation of human neural stem cells via gradients of epidermal growth factor, fibroblast growth factor 2, and platelet-derived growth factor established by laminar flow and diffusive mixing [25]. As shown in Fig. 5b, Booth et al. developed a BBB model that showed endothelial cells began to express tight junction in flowing media at 2.6 μL/min after three days via zonula occludens-1 (ZO-1) imaging [38]. Deosarkar et al. also showed that endothelial cells exhibited tight junction formation, as measured by the expression of ZO-1 in microfluidic BBB models, and allowed endfeet-like neonatal astrocyte-endothelial cell interactions through a porous interface [58]. Although beyond the scope of this review, we note that microfluidic tissue chips have been applied toward a number of organ systems [18, 19, 59]. As a result, the microfluidic-based tissue chip design is commonly referred to as an ‘organ-on-a-chip’; however, we caution the reader that the organ-on-a-chip concept does not strictly apply to microfluidic designs as alternative design and manufacturing approaches now exist for constructing tissue chips (e.g. 3D printing [21] and molding [60]).

Table 2 Summary of microfluidic neural systems-on-a-chip
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Compartmentalized Neural Systems-on-a-Chip

Compartmentalized NSCs are surface-based designs based on interconnected culture chambers (see Fig. 6). The design supports the manual addition of media to individual chambers for studies in static fluid or under gravity-perfusion. Compartmentalized NSCs typically have channels or chambers ranging from 1 to 5 mm in width and 5–20 mm in length for compartmentalizing cell bodies, and microchannels ranging from 1 to 5 μm in width and 100–1000 μm in length for directing axonal growth or creating diffusion gradients [22, 23]. For example, the most commonly used design consists of two compartmentalization channels separated by microchannels, as shown in Fig. 6a [22, 61,62,63,64,65]. In contrast to microchannels found in microfluidic NSCs, microchannels in compartmentalized NSCs may not contain a top wall, such as in Campenot chamber designs. Similar to microfluidic NSCs the bottom wall is formed by the substrate (e.g. a 35 mm dish). Importantly, the primary distinction between microfluidic and compartmentalized NSCs is the absence of active fluid handling in the latter design. Compartmentalized NSCs also offer the ability to incorporate controlled release systems within the cell compartments, such as loaded hydrogels or microparticles, as the millimeter-sized chambers accommodate the manual incorporation of controlled release systems [61, 66, 67]. Compartmentalized NSCs are maintained using conventional manual cell culture techniques. Advantages: The separation of cell chambers via microchannels enables the co-culture of multiple cell types as well as the use of multiple media types and biochemical cues in the same platform. This affords the ability to study the interaction between cells that require significantly different biochemical cues as well as to restrict cellular interaction to neurite-based signaling and transport [28, 68, 69]. Further, this feature enables the selective stimulation and inoculation of cells in a single chamber, which has importance for fundamental circuit mapping studies and modeling a wide range of NDDIs. Limitations: Cell-cell interactions are relatively restricted to those that occur through neurites. Similar to microfluidic NSCs, the fact that compartmentalized NSCs are surface-based designs makes higher order trajectories related to or derived from 3D cell growth, cell migration, cell-cell interactions and cell-matrix interactions difficult to model.

Fig. 6
figure 6

Highlights of compartmentalized neural systems-on-a-chip (NSCs). a) Compartmentalized NSC for central nervous system axonal injury, regeneration, and transport. i) Prismatic and cross-sectional views of a somal – axonal compartmentalized NSC; ii) demonstration of fluidic isolation with Texas Red dye, scale bar is 100 μm; and iii) application of Green Cell Tracker to the axonal side with backtracked identification of neurons in the somal chamber. Reprinted with permission [22]. Copyright Nature Publishing Group 2005. b) Compartmentalized NSC for the neuromuscular junction (NMJ). i) Schematic of a compartmentalized NSC for development of NMJs; ii) spinal cord motoneurons plated in the proximal channel extend axons into the distal channel to contact myotubes. Reprinted with permission [70]. Copyright Elsevier 2016

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Manufacturing of Compartmentalized Neural Systems-on-a-Chip

Compartmentalized NSCs consist of a multi-chamber bonded to a rigid substrate. Depending on the design, the multi-chamber is typically composed of either PDMS or Teflon (e.g. Campenot chamber designs), but can also be made of soft materials, such as hydrogels. Thus, the microchannels and microchambers are typically fabricated via molding processes, as PDMS and Teflon can be molded at relatively low temperatures using soft lithography (see “Soft lithography” section), extrusion, pressing, or injection molding. It was also recently demonstrated that compartmentalized NSCs can be fabricated using 3D printing, which enables the rapid prototyping of microchannel and microchamber design and geometry [21]. Although the chamber-substrate bonding process is typically done manually, 3D printing approaches have recently emerged as a one-pot bottom-up biomanufacturing process for compartmentalized NSCs [21].

Compartmentalized NSCs are typically functionalized prior to chamber-substrate bonding using standard manual surface coating approaches (i.e. exposure to solutions containing soluble ECM components). Thus, contact printing can also be used to create patterned functionalized microdomains prior to chamber-substrate bonding (see “Contact printing” section). Cell seeding is accomplished by conventional manual plating techniques (i.e. pipetting of cell suspensions into the cell compartments). Similar to microfluidic NSCs, cellular growth strongly follows topographical cues.

Hierarchical Design of Compartmentalized Neural Systems-on-a-Chip

Given the ability to co-culture multiple cell types and program cell-cell interactions via guided neurite outgrowth, various higher order anatomical features of native neural systems can be reproduced using compartmentalized NSCs (see Table 3). For example, compartmentalized NSCs have been used to affect the: 1) formation of heterogeneous tissues; 2) clustering of multiple cell types; 3) spatial alignment of cell bodies; and 4) spatial alignment of neurites. For example, as shown in Fig. 6b, Ionescu et al. developed a compartmentalized NSC for study of the neuromuscular junction, which enabled the monitoring of muscle-neuron communication [70]. Berdichevsky et al. used a compartmentalized NSC to co-culture cortical and hippocampal neurons separated by microgrooves to monitor the development of neurite pathways [71]. A compartmentalized NSC containing a cell-laden hydrogel component was used by Shin et al. to co-culture endothelial and neural progenitor cells in a 3D environment allowing them to study the effect of vasculature on neural progenitor cell differentiation [72]. Compartmentalized NSCs have also been designed to reproduce higher order functional and augmented features given the ability to integrate: 1) controlled drug release systems, and 2) electroactive components. For example, although not a controlled drug release system, Millet et al. demonstrated a microfluidic-based surface functionalization approach for compartmentalized NSCs to enable the study of laminin and poly-L-lysine gradients on neurite outgrowth [28]. Johnson et al. demonstrated that 3D printing could be used to guide neurite outgrowth in compartmentalized NSCs onto conductive grids to support transmission electron microscopy measurements of single axons [21]. As a result, compartmentalized NSCs offer programing and control of various higher order microenvironmental parameters such as: 1) spatiotemporal distributions of biochemical cues; and 2) stimulation and monitoring of cells. For example, Johnson et al. developed a 3D printed compartmentalized NSC with spatially segregated distributions of nerve growth factor and Schwann cell media to study Schwann cell-axon self-assembly and axon-to-cell viral spread [21]. Ch’ng et al. used a compartmentalized NSC to study the spread of viral infection between neuronal and epithelial cells via axonal transport [73]. Thus, compartmentalized NSCs are useful platforms for modeling higher order trajectories of native neural systems such as: 1) neurite outgrowth and tissue self-assembly; 2) cell migration; 3) cell signaling; 4) circuit mapping, and 5) gene expression profile. For example, one of the most common uses of compartmentalized NSCs is to establish aligned neurite outgrowth. This is a useful technique for both studying neurite physiology and pathophysiology, such as structure, transport, degradation, regeneration, and depolarization, as well as establishing neural circuits [74]. Southam et al. directed neurite outgrowth from a neuronal and glial compartment into a chamber containing skeletal muscle cells to form neuromuscular junctions [27]. Liu et al. used a compartmentalized NSC to track pseudorabies virus transport between cell bodies and axons [68]. A similar compartmentalized NSC was used by Taylor et al. to track viral spread from infected neurons along neurites through size exclusive barriers into a separate epithelial cell-containing compartment using fluorescence microscopy [75]. Bérangère et al. have shown that compartmentalized NSCs can also be used to study the effect of biochemical cues on neurite pathophysiology, such as the dying back of axons exposed to β-amyloid (Aβ) [76]. Compartmentalized NSCs have also provided useful platforms for investigating the effect of mechanical injury on neural system components. Siddique et al. developed a compartmentalized NSC that supports the co-culture of spinal cord and peripheral nerves for studying the effect of growth factors on axonal regeneration following mechanical injury [77]. Koyuncu et al. showed although pseudorabies virus induces protein synthesis to enable retrograde virus transport, damaging axons prior to infection decreases virion transport, suggesting that virus particles and damage signals compete for retrograde transport [78]. Shin et al. used gene expression analysis to study the effect of neural progenitor-endothelial cell interactions on neural progenitor cell morphology and differentiation [72].

Table 3 Summary of compartmentalized neural systems-on-a-chip
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Hydrogel Neural Systems-on-a-Chip

Hydrogel NSCs are bulk-based designs involving the growth of suspended cells within a 3D hydrogel scaffold (see Fig. 7). Hydrogel NSCs are typically a few hundred micrometers in thickness and may extend millimeters to centimeters in width. As shown in Table 1, hydrogel NSCs currently make up less than 10% of research efforts; however, this percentage is likely to increase with the emergence of biomanufacturing approaches, such as 3D printing. Hydrogels are formed from either synthetic or natural polymers. This feature enables the selection and design of hydrogels to mimic the native ECM of neural tissue. For example, many naturally derived hydrogels used for NSCs are based on collagen, hyaluronic acid, or matrigel [49, 79, 80]. Primary cells and cell lines are used to construct hydrogel NSCs. Hydrogel NSCs are also compatible with spheroids, which expands the design space of biomimetic cell-cell interactions. Advantages: Hydrogel NSCs are 3D architectures. Thus, they enable the study of higher order trajectories related to or derived from 3D cell growth, cell migration, cell-cell interactions and cell-matrix interactions [34]. An important distinguishing feature of hydrogel NSCs is the flexibility to design heterogeneous tissues through: 1) the suspension of multiple cell types within a single hydrogel phase; 2) the adjacent crosslinking of cell-laden hydrogels that contain different cell types; or 3) the adjacent crosslinking of hydrogels that contain different ECM compositions. Such features enable hydrogel NSCs to examine the effect of multiple higher order parameters, such as ECM composition and biochemical cue distributions, on the growth of heterogeneous neural tissues. Given hydrogel NSCs consist of 3D architectures and are derived from extrudable cell- and biochemical cue-laden hydrogels, 3D printing approaches can be leveraged to model novel higher order trajectories of the human nervous system. Limitations: The physical dimensions of hydrogel NSCs are constrained by the ratio of the bioconversion rate to the diffusion rate, which can be described by the Damköhler number. For hydrogel NSCs, the effective diffusivity of the rate-limiting substrate in the cell-laden hydrogel limits the maximal thickness to a couple hundred micrometers. Thus, hydrogel NSCs that exceed this value will develop an internal necrotic zone extending from the center of the hydrogel to a certain critical distance over which the concentration of the limiting substrate is below the threshold to sustain cell viability. To overcome this barrier, efforts are now ongoing to vascularize cell-laden hydrogel matrices [81].

Fig. 7
figure 7

Examples of hydrogel neural systems-on-a-chip (NSCs). a) Hydrogel NSC containing photolabile properties for study of 3D cellular migration. Top down (i) and prismatic (ii) images of fluorescently labeled oligopeptide channels within a 3D hydrogel – scale bars are 200 μm; and iii) primary rat dorsal ganglia growing exclusively within a GRGDS peptide modified column – scale bar is 100 μm. Reprinted with permission [34]. Copyright Nature Publishing Group 2004. b) Hydrogel NSC for modeling cortical neuron outgrowth in brain-like environments. i) 3D printed layered brain-like structure; ii) confocal image of neurons after 5 days – scale bar is 100 μm; and iii) magnified image of area inside square showing axonal projection into the cell-free gel – scale bar is 100 μm. Reprinted with permission [49]. Copyright Elsevier 2015. c) Hydrogel NSC for study of neural progenitor cell differentiation. i) Neural progenitor cell-laden 3D printed porous hydrogel structure; ii) live/dead assay of hydrogel construct; and iii) scanning electron micrograph of a neuron in the 3D structure with the arrows indicating the soma and axon, respectively. Reprinted with permission [80]. Copyright John Wiley & Sons 2016

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Manufacturing of Hydrogel Neural Systems-on-a-Chip

Hydrogel NSCs are composed one or more compositionally-unique 3D hydrogel domains. Cells are first propagated to reach high cell density and subsequently suspended in a nutrient-rich uncrosslinked hydrogel. Given uncrosslinked hydrogels are typically incapable of maintaining free-standing 3D structures, molding approaches are commonly used. For example, an uncrosslinked cell-laden hydrogel is first added to the mold cavity, subsequently crosslinked, and then removed. In addition to molding, 3D printing can also be used to additively assemble 3D hydrogel structures (see “3D printing” section). 3D printed hydrogel NSCs are first constructed by developing printable hydrogel ‘bio-inks’ that contain the desired cell type and biochemical cues. Subsequently, the bio-inks are loaded into dispensing tools, such as cartridges and syringes, or cast on energy absorbing plates for 3D printing. 3D printing provides a one-pot manufacturing process for hydrogel NSCs as it is possible to print both support materials and bio-inks using the same printing system [37].

In contrast to microfluidic and compartmentalized NSCs, which require post-processing steps to integrate biology through functionalization and cell seeding steps, hydrogel NSCs do not require such post-processing. This unique aspect of hydrogel NSCs arises due to bio-compatible manufacturing (i.e. biomanufacturing) processes, such as 3D printing.

Hierarchical Design of Hydrogel Neural Systems-on-a-Chip

Given the ability to additively assemble hydrogels via 3D printing, use hydrogels as carriers for multiple components (e.g. cells and biochemical cues), and assemble adjacent hydrogel systems of unique composition, various higher order anatomical features of native neural systems can be reproduced using hydrogel NSCs (see Table 4). For example, hydrogel NSCs have been used to affect the: 1) formation of heterogeneous tissues; 2) clustering of multiple cell types; 3) controlled distribution of extracellular matrix; and 4) construction of three-dimensional systems. Hydrogel NSCs also contain higher order functional and augmented features given the ability to integrate: 1) fluidic channels, and 2) controlled drug release systems. For example, Lee et al. combined a microfluidic flow chamber with a hydrogel NSC to model gliomas [24]. These functional features enable the programing and control of various higher order microenvironmental parameters such as: 1) mass transport of solutes (e.g. gases and biomolecules), and 2) spatiotemporal distributions of biochemical cues (e.g. rate and profile). For example, in that same study, Lee et al. leveraged fluidic flow through hydrogels to control the mass transport of fresh media and growth factors to glioma cells [24]. As shown in Fig. 7a, Luo et al. have shown that hydrogel NSCs can be created with controlled distributions of biochemical cues for directing cell growth [34]. Thus, hydrogel NSCs are useful platforms for modeling higher order trajectories of native neural systems, such as: 1) tissue self-assembly; 2) neurite outgrowth, 3) cell migration; 4) phenotypic outcomes, and 5) gene expression. For example, as shown in Fig. 7b, Lozano et al. demonstrated hydrogel NSCs composed of biomimetic layered brain-like structures could be used to examine the neurite outgrowth from cortical neurons between adjacent hydrogels [49]. Gu et al. demonstrated 3D printed hydrogel NSCs could be used to examine the differentiation of neural progenitor cells into neurons and glia using gene expression analysis as shown in Fig. 7c [80].

Table 4 Summary of hydrogel neural systems-on-a-chip
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Applications of Neural Systems-on-a-Chip

NSCs have various applications given their ability to model a variety of higher order physiological and pathophysiological phenotypes and trajectories of the human nervous system. The applications of NSCs can be broadly classified as: fundamental research (e.g. modeling of complex neural systems or disease phenotypes), drug discovery (e.g. creating biomimetic models of NDDIs derived from human cells as high throughput drug screening platforms), or personalized medicine (e.g. creating patient-specific disease models as personalized drug screening platforms). As a result, NSCs have a significant impact on basic, translational (i.e. applied), and clinical research in neuroscience, neurology, and neural engineering. Below, we highlight the application of NSCs to various disease, disorder, and injury models.

Alzheimer’s Disease

Alzheimer’s disease (AD) is a prevalent and serious neurological disorder and is currently the sixth leading cause of death in the US [5]. Our current understanding of AD suggests it is driven by extracellular deposition of Aβ and intracellular accumulation of tau proteins [82]. As a result, the presence of Aβ plaques, tau tangles, oxidative stress, and brain inflammation are hallmark characteristics of AD pathology [82]. Researchers are now using NSCs to create platforms to study higher order pathophysiological trajectories associated with AD. As shown in Fig. 8a for example, Song et al. used a compartmentalized NSC to show that Aβ is transmitted through neural connections in an effort to improve our understanding of the mechanism by which Aβ plaques lead to loss of synapsis [64]. Stoothoff et al. used a compartmentalized NSC to show that differences in tau levels change the mitochondrial distribution within a cell and affect axon transport dynamics [67]. Kunze et al. developed a microfluidic NSC that allowed them to spatially control the concentration of hyperphosphorylated tau proteins throughout a cell population, thereby creating an AD model with co-cultured “healthy” and “diseased” tissues [83]. Choi et al. used a microfluidic NSC to determine that Aβ fibrils had little neurotoxic effect, but oligomeric Aβ assemblies resulted in atrophy [84]. A microfluidic NSC designed by Park et al. based on applying interstitial flow to 3D neurospheroids showed that Aβ was significantly more destructive under flowing conditions than static conditions [42].

Fig. 8
figure 8

Applications highlights of neural systems-on-a-chip (NSCs) for modeling neurological diseases, disorders, and injuries. a) A compartmentalized NSC for studying long-distance transport of β-amyloid for better understanding Alzheimer’s disease, with a schematic showing three compartments and fluorescent images of the transport of fluorescein isothiocyanate – tagged β-amyloid monomer across all three compartments. Reprinted with permission [64]. Copyright John Wiley & Sons 2014. b) A compartmentalized NSC for studying peripheral nerve repair. i) Schematic of a compartmentalized NSC for manipulating, injuring, or treating isolated neurites; ii) regeneration of an untreated axonal injury; and iii) degeneration following the same axonal injury with the application of Nocodazole. Scale bar is 0.2 mm. Reprinted with permission [77]. Copyright Elsevier 2014

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Parkinson’s Disease

Parkinson’s disease (PD) affects over 10 million people worldwide [85]. Our current understanding of PD suggests it is driven by the progressive impairment and deterioration of dopaminergic neurons in the substantia nigra [86]. As a result, the presence of abnormal protein aggregates, known as Lewy bodies, are hallmark characteristics of PD pathology [86]. Although still emerging, researchers are now using NSCs to create platforms to study higher order pathophysiological trajectories associated with PD. For example, Lu et al. fabricated a compartmentalized NSC to study 6-hydroxydopamine-mediated axonal degradation, which proceeds the degeneration of dopaminergic neurons in PD [87]. This NSC also allowed researchers to study mitochondrial transport dynamics in conditions replicating PD [87].

Traumatic Brain or Nerve Injury

TBI, spinal cord injuries, and peripheral nerve injuries affect millions of people annually in the US [1]. Further, nerve injuries are especially challenging to model and treat given the wide variations in anatomy, injury mechanism, and regenerative pathways among the brain, spinal cord, and peripheral nerves. Importantly, NSCs are useful platforms for studying brain, spinal cord, and peripheral nerve injuries because they allow researchers to induce injuries to higher order neural anatomies and directly monitor trajectories associated with both injury and regeneration. For example, Hosie et al. used a compartmentalized NSC to isolate soma and axons toward the study of site-directed glutamate excitotoxicity in TBIs [62]. As shown in Fig. 8b, Siddique et al. have developed a compartmentalized NSC that allows researchers to manually induce nerve injuries and administer isolated treatments to axonal components in a biomimetic 3D environment [77]. Ghannad-Rezaie et al. created a microfluidic NSC that enabled live imaging of the neural response to peripheral axonal injury in Drosophila larvae [88]. Yin et al. developed a microfluidic NSC to study and optimize drug candidate dosages for nerve regeneration toward the goal of minimizing harmful side effects, such as tumors [89].

Emerging Areas and Future Directions

3D Printing

3D printing has enabled developments across a wide range of disciplines, including electronics, materials science, and tissue engineering, and is now poised to reconceptualize the design and engineering of NSCs. For example, although soft lithography is the gold standard for creating microfluidics and microchannels, 3D printing now offers the ability to construct microfluidic networks of complex 3D geometry within a wide range of materials [46, 47]. As shown in Fig. 7c, Gu et al. demonstrated that 3D printed neural progenitor cells in hydrogel bio-inks can be differentiated in situ to synapse-forming predominantly gamma-aminobutyric acid (GABA)-expressing neurons [80]. Johnson et al. used micro-extrusion 3D printing to construct multi-material compartmentalized NSCs containing microfluidic channels of complex geometry and embedded electroactive components, see Fig. 9a [21]. In that study, they also demonstrated viable printing of a wide range of cell types including primary embryonic neurons [21]. Another advantage of 3D printing is the ability to use medical imaging techniques, such as computed tomography (CT) [90], magnetic resonance imaging (MRI) [90], or structured-light scanning (SLS) [91], to reverse engineer anatomical geometry that would be otherwise be difficult to design and manufacture using traditional approaches. Of all 3D printing techniques, micro-extrusion 3D printing in particular lends itself to developing NSCs, due to its compatibility with processing the most expansive materials set, including solutions, cell suspensions, cell-laden hydrogels, thermoplastics, thermosets, elastomers, and composites [37]. 3D printing is also able to create 3D heterogeneous biomimetic neural tissues containing distributed ECM proteins and growth factors through the ability to control the composition of individually printed bio-inks [37]. For example, the ability to spatially distribute bio-inks containing different formulations of biochemical cues in 3D scaffolds via 3D printing was recently shown to selectively direct the growth of sensory and motor nerves [91].

Fig. 9
figure 9

Highlights of emerging areas and future directions for neural systems-on-a-chip (NSCs). a) 3D printed NSCs. i) Schematic of 3D printing process for a compartmentalized NSC; ii) three parallel microchannels with neurons and axons shown in the first chamber; iii) axons from the first chamber associated with self-assembled Schwann cells within the second chamber; and iv) axon termini from the first and second chamber interacting with epithelial cells in the third chamber. Reprinted with permission [21]. Copyright Royal Society of Chemistry 2015. b) Hydrogel NSC for modeling brain tumors. i) Prismatic and side view schematics of a microfluidic chip coupled with a glioma-laden hydrogel; ii) structure of the biomimetic hydrogel; and iii) a SEM image of the electrospun fiber separating the microfluidic channel from the hydrogel. Reprinted with permission [24]. Copyright John Wiley & Sons 2014

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Electronic Augmentation

Creating next-generation NSCs will require the seamless integration of electroactive components with neural tissue for enhanced stimulation and monitoring functionality. To date, efforts toward this goal have been achieved by integrating neural tissue with microelectrode arrays (MEAs). For example, Kanagasabapathi et al. have created MEA-coupled compartmentalized NSCs for monitoring cortical and thalamic cell connectivity [52, 53]. Various studies have also used MEA-coupled microfluidic NSCs to study the effect of biochemical cues on neural networks [43, 50, 51]. Smith et al. have created a cantilever-based NSC that measures muscle contraction following the stimulation of a motoneuron [92].

Human Cells

A major advantage of NSCs is their compatibility with human cells. Although the majority of NSCs to date have been constructed using non-human cells, human NSCs (i.e. NSCs constructed with human cells) are beginning to be explored. For example, Griep et al. used a human brain endothelial cell line (hCMEC/D3) for BBB applilcations [39]. Lee et al. implemented a human glioma cell line (A-172) into their microfluidic/hydrogel NSC to study the migration of glioma cells in 3D [24]. Stoothoff et al. used H4 human neuroglioma cells to study the mitochondrial axonal transport for AD applications [67]. Yeon et al. cultured primary human umbilical vein endothelial cells (HUVEC) and human astrocytes for BBB drug permeability studies [93]. The ability to use human cells provides unique opportunities for preclinical drug testing, such as target identification, target validation, target-based screening, phenotypic screening, pharmacodynamics, pharmacokinetics, absorption-distribution-metabolism-excretion (ADME) studies, and toxicology testing. Thus, human NSCs provide novel platforms that could reduce the cost and time associated with drug discovery for NDDIs. For a detailed discussion, we refer the reader elsewhere to comprehensive reviews on the application of tissue chips to drug discovery [19]. The ability to utilize human stem cells is also an emerging area, which we discuss in greater detail in the following sections.

Personalized Medicine

Tissue chips have been suggested to enable future paradigms of personalized medicine and pharmacology [19]. Likewise, the ability to construct NSCs from patient-derived cells now potentially enables the ‘personalized’ treatment of NDDIs via target-based or phenotypic screening conducted using patient-specific NSC disease models. For example, the ability to model higher order pathophysiological phenotypes and trajectories of the human nervous system could establish highly effective treatments [94]. The continued evolution of computer-aided biomanufacturing processes, such as 3D printing, also provides novel opportunities for customization and prototyping of patient-specific NSC disease models. Ultimately, given the sustained demand for personalized medicine [94, 95], NSCs are expected to play an integral role in the future personalized treatment of NDDIs. The ability to utilize human stem cells is also critical for developing patient-specific NSCs, which we discuss in greater detail in the following sections.

Biomimicry

Human neural systems consist of cells growing in soft 3D ECM in the presence of both immobilized and diffusive spatiotemporal distributions of biochemical cues. Cells also interact with 3D multi-scale topographical cues. Native neural systems are also influenced by mechanical factors, such as pulsatile fluid flow as well as vasodilation and vasoconstriction effects. Further, native neural tissue has highly controlled mechanical property matching. Lee et al. have approached this challenge by coupling a glioma-laden hydrogel with a microfluidic device, as shown in Fig. 9b [24]. Biomimetic NSCs should strive to possess each of the above features. Unfortunately, the vast majority of NSCs developed to date contain only one, or at most a few, of the aforementioned features. Thus, achieving realistic and balanced biomimicry of the nervous system in NSCs must be improved to achieve the most successful translational and clinical outcomes.

Stem Cells

The ability to construct NSCs from stem cells offers unique opportunities for studying the development and regeneration of neural systems, developing biomimetic models of human NDDIs, and creating personalized NSCs. Specifically, induced pluripotent stem cells (iPSCs) [96] provide useful tools for such applications. For example, the ‘holy grail’ in biomedical research is to generate a transgenic mouse model of the human illness. The MECP2 mutation in Rett syndrome or the mutant gene causing Huntington’s disease are excellent examples where animal models reproduce salient features of the disease. Unfortunately, the majority of human neurological and neuropsychiatric illnesses are believed to be poly-genetic, consisting of multiple, and often unknown, gene alterations, making the generation of transgenic animals highly challenging. However, iPSCs are beginning to come to the rescue. For example, fibroblasts can be readily harvested from patients and induced to form neurons or glial cells in vitro to study their molecular changes. This approach has recently been used to reveal unexpected changes in neuron complexity in Costello syndrome [97], a rare developmental disorder with autism traits, suggesting that NSCs derived from human iPSCs provide novel opportunities for modeling rare and complex human NDDIs. Given iPSCs are also patient-derived, NSCs constructed using iPSCs may also serve as personalized drug screening platforms where libraries of drugs can be examined regarding their ability to correct a protein or signaling pathway deranged by disease. Finally, NSCs constructed from iPSCs may eventually serve as platforms for differentiating and programming cells for neural regeneration and other cell-based therapies. For example, such cells could be differentiated into the cell of interest and corrected to express the right complement of genes in NSCs, and subsequently be collected for implantation. Since the iPSCs were initially harvested from the patient, the resultant autologous graft or cell-therapy will not elicit an immune response (e.g. immune rejection). In addition, the ability to construct NSCs from stem cells offers unique opportunities for understanding the development and regeneration of the nervous system. For example, NSCs constructed using neural progenitor cells derived from human embryonic stem cells have been used to study the effect of biochemical cues on the differentiation and formation of complex neurite networks [55].

Technical Hurdles, Remaining Challenges, and Opportunities

Although NSCs have progressed significantly over the past decade, there are still major technical hurdles and remaining challenges to overcome. It is established that cells exhibit different trajectories in 2D vs. 3D environments [98,99,100]. However, we still face significant manufacturing challenges associated with embedding functional and augmented features, such as fluidic channels and electroactive components in 3D. Another technical hurdle is to simultaneously program and control multiple microenvironmental parameters toward mimicking or reproducing signaling cascades. This is a critical requirement for modeling higher order trajectories associated with developing nervous systems and NDDIs. The use of NSCs for drug discovery and personalized medicine applications also involves regulatory considerations. For example, the future use of NSCs as alternatives to small animal models for preclinical drug testing may require steps equivalent to Animal Model Qualification, which is required by the FDA to rely on the evidence from animal studies regarding drug effectiveness. Alternatively, the use of NSCs as patient-specific disease models intended for the diagnosis or treatment of NDDIs would subject NSCs to the regulatory requirements of biomedical devices [101]. Ultimately, both the translational impact and the regulatory barriers of NSCs are tied to the challenge of creating highly robust and reproducible NSCs. However, as noted in “Manufacturing approaches for neural systems-on-a-chip” and “Classes of neural systems-on-a-chip” sections, the vast majority of NSCs, with the exception of 3D printed NSCs, involve manual assembly, functionalization, or seeding steps. Thus, the creation of robust NSCs hinges on eliminating manual processing steps toward fully automated biomanufacturing processes. As discussed in the “Emerging areas and future directions ” section, realistic biomimicry is a major driving force for NSC design. Specifically, mimicry of vascularized neural tissue remains a critical challenge. Additionally, the coupling of NSCs to hemodynamic processes, such as hemoglobin-based oxygen transport or lipoprotein-based lipid uptake, is required to advance NSCs.

Conclusions

NSCs appear poised to shift the paradigm for modeling human NDDIs. The ability to model higher order anatomical features, functional and augmented features, microenvironmental parameters, and ultimately, trajectories of the human nervous system is highly dependent on the NSC design (e.g. microfluidic, compartmentalized, or hydrogel NSCs). Emerging biomanufacturing processes, such as 3D printing, are now enabling the design and manufacturing of robust novel NSCs. The field of NSCs is currently in a developmental stage heading toward increased biomimicry, functional-augmentation, and personalized medicine and pharmacology. Opportunities exist in terms of addressing various technical and regulatory hurdles that remain toward NSC application to drug discovery and personalized medicine, including achieving more realistic biomimicry of the human nervous system and robustness in NSC manufacturing approaches.