Neurodegenerative diseases affect over 55 million individuals annually in North America, creating a multi-billion dollar burden on the health-care industry due to the costs associated with treatment, and rehabilitation therapy (Institute for Neurodegenerative Diseases, 2017). Often selective cell loss in the central nervous system (CNS) leads to these neurodegenerative diseases. Cell therapy can potentially treat neurodegenerative disease by replacing damaged tissues or augmenting remaining cell function (Levy et al., 2016). The basis of cell therapy is that living human cells can be injected into a damaged region of the body to instigate healing (Dove, 2002). Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, multiple sclerosis, and amyotrophic lateral sclerosis, as well as neurodegenerative disorders, such as traumatic brain injury, serve as potential candidates for cell therapy as they result in neuronal death in targeted areas of the brain (Vila and Przedborksi, 2003). Neuronal cells possess a low regenerative capacity as they do not proliferate after maturation (Tam et al., 2014). Thus, cell therapy can replace damaged neuronal and support cells, or work indirectly by secreting soluble factors to facilitate the repair process (Tsintou et al., 2015).

While current treatments for these diseases mainly focus on alleviating symptoms and physical rehabilitation, cell therapy can potentially promote cellular repair and remodeling, resulting in improved function. Several issues must be addressed before cell therapy can be widely implemented. These issues include ensuring that the proper number and type of cell are being generated, especially when using stem cells as they can become multiple types of cells. Large quantities of cells are often required for cell therapies to treat neurodegenerative disorders and thus, high-throughput methods for generating these cells must be developed (Rossi and Cattaneo, 2002; Cooke et al., 2010). Direct transplantation of cells in the damaged CNS is possible, but often these cells fail to properly integrate into the brain (Rossi and Cattaneo, 2002). Bioprinting, the use of 3-D printing technology with biocompatible materials that can be seeded with living cells to create tissue constructs, can potentially produce carefully controlled human neural tissue in a consistent rapid manner. The biomaterial scaffolds used in the 3-D printing process are often referred to as bioinks (Skardal and Atala, 2015). Engineered biomaterial microenvironments can help overcome low cell survival rates after transplantation in the damaged CNS and limit migration of cells away from the implantation site while providing a controlled environment for cell growth and differentiation (Cooke et al., 2010; Struzyna et al., 2014). These printable cell scaffolds degrade as the cells develop, either through hydrolysis, or through enzymatic degradation by byproduct proteases, leaving a biologically accurate tissue construct as the result (Freed et al., 1994).

Different types of stem cells have been evaluated in vitro and in vivo for neural regeneration. These cells include human embryonic stem cells (hESCs), which are pluripotent stem cells derived from a human embryo; mesenchymal stem cells (MSCs), which are multipotent stromal cells that can differentiate into osteoblasts, chondrocytes, myocytes, and adipocytes; neural stem/progenitor stem cells, which are multipotent and can differentiate into neurons, astrocytes, and oligodendrocytes; and human induced pluripotent stem cells (hiPSCs), which are adult cells taken back to a pluripotent state (Mothe and Tator, 2012). Both hESCs and hiPSCs are pluripotent, meaning they can differentiate into any cell type in the body (Itskovitz-Eldor et al., 2000). However, hESCs pose the risk of immune rejection after transplantation and remain ethically controversial because the blastocyst from which they are isolated does not survive the derivation process (Bobbert, 2006). hiPSCs are adult somatic cells reprogrammed into a pluripotent state using transcription factors (Takahashi et al., 2007). They offer the opportunity to replace cells lost while minimizing the risk of immune rejection as these cells lines can be derived directly from a patient’s own cells (Kamao et al., 2014). Neurodegenerative diseases can be modeled using hiPSCs by reprogramming adult cells taken from patients into neural cells, which then display disease hallmarks (Durnaoglu et al., 2011).

Any cell line chosen for bioprinting must have the ability to expand to sufficient numbers to be printable (Murphy and Atala, 2014). Many primary cell types cannot self-renew while being difficult to isolate, making pluripotent stem cells a more attractive option when bioprinting (Murphy and Atala, 2014). Recent advancements such as clustered regularly interspaced short palindromic repeats (CRISPER/Cas9) make it possible to correct gene mutations found in cell lines, enhancing the potential of hiPSCs for use in cell replacement therapies for treatment of neurodegenerative disease (McMahon et al., 2012). Scaffold-based strategies provide an attractive approach for culturing, expanding, and delivering cells because they offer structural support for growing cells and axons and can be loaded with chemical factors to encourage differentiation and integration with existing cell culture. 3-D bioprinting can control the spatial distribution of these factors to control cell differentiation. Biomaterial scaffolds that have supported neural cell scaffolds culture in vitro in mouse and rat trials include polyethylene glycol (PEG) (Freudenberg et al., 2008), modified peptide gels such as RADA16-YIGSR (Cui et al., 2016), hyaluronan (Gardin et al., 2011), fibrin (Gardin et al., 2011), and alginate (Perez et al., 2016). Many studies use extracellular matrix molecules to provide structural support such as collagen, fibrin, fibronectin, and laminin (Itosaka et al., 2009; Tate et al., 2009; Johnson et al., 2010; Elias and Spector, 2012; Lu et al., 2012; Wilems et al., 2015) and polymers such as poly(lactic-co-glycolic acid), N-(2-Hydroxypropyl) methacrylamide, and poly(a-hydroxy-acids) (Sykova et al., 2006).

In addition to cell therapy applications, 3-D bioprinted neural tissues can be used to model diseases and for drug discovery. Several groups have grown functional neural tissue in small tissue constructs, but these methods require long and labor-intensive culture protocols (Hopkins et al., 2015). Often the function of the resulting tissues is not fully developed, lacking fully mature neural cells and their associated function as assessed by electrophysiology (Hopkins et al., 2015). Bioprinting could create accurate, reproducible tissue constructs in a high-throughput manner, allowing for large sample sizes for evaluating electrophysiological function over time.

Cell therapy can repair damaged tissues by supplying growth factors to the injury site (Kim, 2004). To produce brain tissue constructs for drug screening, or disease modeling, the current bioprinting technologies must be changed to incorporate nutrient flow throughout the cell construct. Replacing brain tissue remains a futuristic goal, but finding a way to accurately produce neural tissue that mimics the mechanical and biochemical conditions found in vivo. These properties include reproducing the calcium and potassium gated voltage response for neuronal signaling (Kohler et al., 1996), displaying an elastic modulus of less than 1,000 Pa, similar to brain tissue (Georges et al., 2006), and supporting a mixed cell population to better represent the native population of neuronal and support cells. Such properties must be achieved without inducing inflammation or unexpected cellular responses. Engineering biologically accurate neural tissue requires a platform with complex controls with regards to sterilization and culture conditions as well as cell and scaffold placement.

Bioprinting enables significant control over the arrangement of cells and bioactive nanomaterials in defined-scaffold geometries in comparison with other tissue-engineering techniques (O’Brien et al., 2015). Printing cell scaffolds means more effective composition with less effort, achieving biomimetic constructs with ECM feature size and composition, chemical gradients, varied mechanical properties, and specific morphologies that were not previously accessible (Chia and Wu, 2015). 3-D printing has been widely investigated for industrial rapid prototyping and additive manufacturing protocols (Gross et al., 2014). 3-D printing neural tissue requires creating a computer-aided design (CAD) model of the desired tissue structure including cell type and elastic moduli, input your starting materials, and letting the program associated with the 3-D printer run. The program parses the solid object into a stack of cross-sections and then prints the desired structure upwards from the bottom along the Z-axis (O’Brien et al., 2015).

Fabricating tissues in a controlled environment outside of a living organism requires reproducing the chemical, mechanical, and morphological properties found in vivo (Ahmad and Makoto, 2017). Several key components when bioprinting must be optimized to achieve in vivo mimicry, including the most important component—the bioink. Many natural polymers, such as fibrin, laminin, gelatin, and collagen, can be crosslinked under mild conditions into a cytocompatible hydrogel scaffold suitable for 3-D bioprinting (O’Brien et al., 2015). Many synthetic scaffold materials require complex reactions for functionalization, which hinders their ability to be bioprinted (Carrow et al., 2015). Mechanical restrictions also influence the choice of bioink when 3-D printing. Inkjet and laser-based bioprinting methods require a low-viscosity liquid, while extrusion printing requires a higher viscosity, indicating that different formulations are necessary depending on the printing method (Ahmad and Makoto, 2017). Supplements such as alginate are often added to the bioink to improve gelation speed and mechanical strength and maintain a good printing environment (Ahmad and Makoto, 2017). Another important consideration for bioink preparation is the printability, which depends on several rheological factors, including viscosity, surface tension, and thixotropy (O’Brien et al., 2015). Bioprinting requires the ability to eject the bioink, deposit, and solidify the bioink while retaining spatial resolution of the material to control and generate desired high-quality 3-D construct with accurate geometry. Thus, bioink viscosity plays a vital role in determining the flexibility of freestanding constructs and preserving their structural integrity during and after the printing process. Cells and biomolecules experience shear stress, local rheologic forces, or other external physical forces during printing process, which influences cell response (O’Brien et al., 2015). Thus, understanding how the parameters of bioprinting affect cellular processes throughout the printing process ensures the ability to obtain a viable construct (Ahmad and Makoto, 2017). Physiochemical properties (such as viscosity, elastic moduli, yield strength, reactivity, and degradation products) and cytocompatibility for the chosen cell line for printing serve as the two most important factors when designing a bioink (Ahmad and Makoto, 2017). Neuronal lineage cells derived from any source tend to be delicate and easily disrupted, presenting a major challenge when bioprinting (Potter and DeMarse, 2001). Controlling neural cell differentiation often uses defined culture conditions to ensure lineage (Ahmad and Makoto, 2017). The cell scaffold introduces a new set of proteins and biomolecules which cells will encounter during growth. The scaffold presents a 3-D microenvironment for controlling cell behavior through biophysical and biochemical cues (Ahmad and Makoto, 2017).

The following sections introduce several methods of bioprinting (Figure 1). These printing technologies can be improved by developing more sophisticated nozzles, cartridges that allow for automated loading, and speed and accuracy of the printing process. High resolution cell distribution remains an issue despite being improved in the last decade (Ahmad and Makoto, 2017).

Several groups have bioprinted neural tissue using various cell types with varying levels of success (Table 1). In 2006, Xu et al. inkjet printed primary embryonic hippocampal and cortical neurons suspended in phosphate-buffered saline onto collagen-based biopaper (Xu et al., 2006). Circular single-layer constructs were printed and maintained in Dulbecco’s Modified Eagle Media with 10% fetal bovine serum and 5% retinoic acid. After 8 days, cell viability was 74.2 ± 6.3%, and after 15 days, cells stained positive for the neuronal marker MAP2. Electrophysiological measurements at 15 days indicated neurons had developed voltage-gated potassium and sodium channels. The same study alternating printing a layer of cells with a layer of fibrin hydrogels (Xu et al., 2006). Initially, fibrinogen was printed in a thin layer and then thrombin was printed on top. The addition of thrombin polymerized the scaffold. A single layer or neurons was then printed on top using direct cell printing. Constructs were printed 50–70 µm thick resulting in a 3-D neural sheet 25 mm × 5 mm × 1 mm. The resulting samples stained positive for DAPI, and the cells spread and exhibited neurite outgrowth after 12 days in culture.

In 2014, Lorber et al. inkjet printed retinal glial cells and disassociated retinal cells, resulting in 57% cell death in glial cells and 33% cell death in retinal cells compared with controls of unprinted cells grown on tissue culture plates (Lorber et al., 2014). No differences in neurite outgrowth or survival were observed after 5 days compared with control cultures. The high levels of cell death suggest the need for optimization of nozzle technology to reduce cell stress and deformation to improve viability post-printing.

Suri et al. (2011) photopatterned glycidyl methacrylate modified hyaluronic acid containing laminin using a digital micromirror device before seeding Schwann cells upon the resulting construct. Scaffolds were printed in various geometries including circles, hexagons, and squares with different pore characteristics (Figure 2). Adhered cells maintained viability after 36 h. The researchers also showed this method could be used to create gradients of fluorescent microparticles as a model for growth factor gradients, which have been shown to guide developing neurites.

Curley et al. (2011) also used a micromirror array to polymerize PEG into various geometries. The voids in the PEG gel were then filled with a puramatrix/agarose cell suspension. It was shown that cells retained their viability and grew only in the cell permissive (puramatrix or agarose) region of the scaffold (Figure 3).

Lee et al. (2017) combined stereolithography and electrospinning techniques to create PCL microfibers. Scaffolds with fibers improved neural stem cell adhesion, increased neurite length, and directed neurite extensions along the length of the fiber. Zhu et al. (2017) used stereolithography to cure GelMA and PEGDA and then seeded NSCs on top of the scaffold. These constructs showed comparable viability to plated cells. Low-level light stimulation increased cell proliferation and expression of the neural marker TUJ1 (Zhu et al., 2017).

Gu et al. (2016) extruded a bioink made up of alginate, carboxymethyl chitosan (CMC) and agarose seeded with frontal cortical human NSCs. The CMC concentration influenced the cell viability. Immediately after printing 25% of seeded cells died, and cell proliferation peaked on day 11. After 3 weeks, samples stained positive for DAPI and vimentin, but had little SOX2 expression, indicating mature neurons (Figure 4).

Similarly, Lozano et al. (2015) extruded a peptide modified gellan gum seeded with primary cortical neurons. Cells remained viable and exhibited neuronal cell morphology after 5 days of culture and stained positive for the neuronal marker TUJ1 (Figure 5). A comparable study using FDM to print polyurethane seeded with murine NSCs by Hsieh et al. (2015) observed cell proliferation 72 h after printing. After 3 days, printed NSCs expressed more neurotrophic factor genes than NSCs cultured on tissue culture plates. The corresponding in vivo study implanted 3-D printed constructs into cerebellum-lesioned zebrafish. Treated fish showed increased spontaneous coiling contraction and increased hatching rate compared with lesioned untreated fish, indicating cellular restoration.

Lee et al. (2010) used microextrusion to print collagen and fibrin as well as fibrin loaded with VEGF seeded with murine neural stem cells. Constructs were printed layer-by-layer into a cylindrical shape on a tissue culture dish. Printed cells showed no difference in viability compared with manually plating cells. Cells located up to 1 mm from the fibrin border migrated toward the VEGF-containing fibrin gel, indicating that cells will migrate toward a more permissive region.

These studies differ greatly in the number of cells lost due to the stress of the printing process. Cell viability allows the user to seed at the correct cell density. However, some studies do not report cell death while others report up to 57% cell death during the printing (Lorber et al., 2014). Cell death during printing can be due to small nozzle size, polymerization or solidification reactions, or bioink composition (Zhu et al., 2017). Optimizing the bioink makeup is key to reducing the immediate loss of cell viability post-printing.

Current work indicates that a wide variety of bioink materials may be suitable for 3-D printing neural tissue. However, more research needs to be done comparing the printability of each of these materials in terms of efficiency and ease-of-use, both which become important when scaling up production. This review has covered multiple methods of 3-D printing neural constructs. Inkjet bioprinting is the most well documented but is limited in both bioink material and geometries. Microfluidic extrusion has recently seen success in printing complex shapes with various neural cell types and remains an option of interest that needs further research in creating ideal bioink compositions. Other possibilities, such as stereolithography and SLS, remain underused for neural tissue applications.

What remains to be done is finding a cohesive unit of bioink and bioprinting method which results in a high cell viability post-printing and is adaptable enough to print multiple different neural cell types with a bioink which has controllable elastic properties and porosity and can be loaded with factors to further control differentiation.

In addition, most studies lack a hands-off manner of controlling bioprinting. Incorporating CAD and microtechnology into printing projects would help fully realize the high-throughput nature of 3-D bioprinting tissue, as the field is still largely limited by human-controlled systems. The use of CAD would further assist in increasing cell resolution within printed constructs. Advancing the resolution of bioprinting could also allow the printing of vascular networks within a designed tissue, something which would allow neural models to be scaled-up beyond a maximum achieved size of mm. This development would allow more physiologically relevant constructs to be printed for disease modeling and drug discovery.

Bioprinting can change how neural tissue are engineered, moving it from a time consuming, hands-on process that can vary from lab-to-lab to a sterile, high-throughput process that can rapidly produce physiologically accurate brain constructs for applications in cell therapy and drug screening. The low throughout methods for engineering brain tissue limit their applicability for drug screening. Cell therapy has had limited success for the same reason: the number of cells required for injection requires lengthy culture time in addition to the difficulty controlling cell diffusion and differentiation. For bioprinting to succeed as the new standard for engineering neural tissue more bioinks must be done to accurately control brain region development, and the issue of vascularization must be solved to print accurate constructs suitable for long-term culture. However, such bioprinted neural tissues hold great promise for applications in both cell therapy and for drug screening.

MT and SW both contributed to the authorship. SW proposed the topic and provided feedback through the writing process. MT wrote the initial draft and completed revisions based on feedback provided.

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.