Aug 28, 2016 - ... N. Johnson received his BS in Chemical Engineering and certificate in ... Graduate Student Mentoring
Beyond the Cell
From print to patient: 3D-printed personalized nerve regeneration Blake N. Johnson (Virginia Tech, USA) and Michael C. McAlpine (University of Minnesota, USA)
3D printing is revolutionizing regenerative medicine and accelerating the pace of biological discovery via its ability to interweave materials and components of disparate properties, guided by anatomical digital templates. These capabilities have led to a breakthrough in the customization and personalization of complex biological systems and devices ranging from platform technologies such as organs-on-a-chip, to implantable devices, such as patient-specific scaffolds. Yet, understanding and regenerating the nervous system has historically provided a challenging benchmark for drug therapy, surgical methods and bioengineering strategies. The question we pose is can: 3D printing be utilized to address these scientific standards? In principle, extrusion-based 3D printing should offer the ability to flexibly interweave multiple materials, over various length scales, while incorporating diverse functionalities. This may allow the ability to expand biological design paradigms and develop them into novel personalized device architectures. Indeed, 3D printing appears poised to offer an exciting future in the realization of personalized anatomical nerve pathways and platforms for point-of-care opportunities from print to patient.
3D printing for personalized medicine The development of personalized tissue engineering and regeneration strategies offers a potential remedy for currently untreatable injuries and diseases via the development of personalized screening approaches, tissue scaffolds and advanced biomedical devices1,2. Such strategies are a fundamental requirement for regenerating tissues, which contain complex geometry and heterogeneity in composition. Particularly useful would be novel biomanufacturing approaches capable of generating geometrically and functionally programmable architectures augmented with biomimetic physical, biochemical and cellular components. Extrusion-based 3D printing, a form of additive manufacturing, has emerged as a promising approach for the fabrication of emerging biotechnologies, including mini-tissues and organs1, 3D microfluidics3, biosensors4, biomedical devices5, bionics2, 6 and organs-on-a-chip7. Its ‘on-the-spot’ capability of converting design to device renders 3D printing a particularly promising manufacturing approach for personalized biomedical applications, enabled via the coupling of 3D printing approaches with 3D imaging technologies. Further, its emerging ability to incorporate a multivariate material set (e.g. metals, synthetic polymers, biomaterials and nanomaterials)2, could make 3D printing a particularly
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expansive tool in next-generation biomanufacturing initiatives. Here we highlight a recent ‘print to patient’ paradigm via the union of 3D scanning and 3D printing to realize nervous systems on a chip7 and anatomical nerve regeneration pathways5. Modern technologies for neural engineering and regeneration consist of advanced scaffolds for guiding the regeneration of neural tissues and brain–machine interfaces for irreparable injuries. Such platforms, biomedical devices and therapies are vital as peripheral nerve injuries result in over 200,000 annual nerve repair procedures5, and approximately $150 billion US dollars spent on annual healthcare in the USA alone8. However, the realization of future advanced nerve repair technologies will require the embedded programming of biomimetic designs, precise placement of cellular components and developmental cues, and precision in anatomical design. These three key needs are challenging to achieve in aggregate using traditional manufacturing approaches, such as wet chemistries and microfabrication techniques. We believe that compact and portable 3D printing platforms that are capable of processing 3D scan data and assembling biological, biochemical and structural components into multi-scale neurological systems constitute a powerful technology for the neurosciences (see Figure 1).
Beyond the Cell 3D printing neural circuits The combination of 3D scanning and extrusion-based 3D printing suggests great promise for catalysing the ‘print to patient’ biomanufacturing transformation. The nervous system is composed of a complex interconnected network of neurons, supporting cells (e.g. Schwann cells) and terminal cells as shown schematically in Figure 2. Additionally, the properties of biomaterials and cells which comprise the nervous system vary in terms of their material properties. As a result, the ability to model neurological phenomena such as cell signalling, communication, infection, regeneration and degradation, requires advanced in vitro models capable of capturing such complexity9. Microfluidics, chamber-based technologies and 3D cell culture models have been used extensively for neuroscience applications. However, such approaches are typically non-programmable and require biologically incompatible processing conditions, limiting their ability to rapidly prototype an intricate biological landscape. Microchannel-infused chamber-based architectures provide a valuable template for the controlled development of neural networks. Our approach to manufacturing a 3D-printed nervous system on a chip involves printing microchannels and superior tri-chamber structures on protein-coated Petri dishes. As shown in Figure 3, 3D printing can be used to create precision microchannels which span the full length of the tri-chamber. A 3D-printed grease layer in the same geometry as the tri-chamber seals the printed trichamber to the dish, thereby restricting inter-chamber fluid flow. Thus, communication between the chambers occurs only through axonal pathways that originate from seeded neurons, whose cell bodies remain restricted to a single chamber (the left chamber). Axonal chamber penetration was facilitated via a thin methylcellulose interface deposited between the grease and microchannels. Our ability to 3D print this microchannel-infused, chamber-based scaffold architecture allows the directed growth and assembly of microscale neural features over centimetre length scales. To demonstrate this, the 3D-printed device can be seeded with cells based on the nervous system model shown schematically in Figure 2. Neurons from the superior cervical ganglia are seeded in the left chamber, Schwann cells are seeded in the middle chamber and epithelial cells are seeded in the right chamber in order to construct a biomimetic model illustrated by Figure 4a. As shown in Figure 4b–d, the 3D-printed microchannels direct the growth and assembly of axonal pathways across all chambers. This leads to isolated axonal pathways in the left chamber, Schwann cell-associated axonal pathways in the middle chamber and axon termini-epithelial cell junctions in the right chamber. Growth in individual 3D-printed microchannels, which span the full tri-chamber, is shown in Figures 4e–g. Overall, these proof-of-concept
Figure 1. From multi-scale biomimicry to prototype devices via 3D printing. Reproduced from Johnson, B.N. et al. (2016) Lab on a Chip, 16, 1393–1400 and Johnson, B.N. et al. (2015) Advanced Functional Materials, 25, 6205–6217 with permission from the publishers.
Figure 2. Structure and function of the peripheral nervous system (PNS). Schematic of the PNS comprising interconnected PNS neurons, nerve supporting cells (Schwann cells) and terminal cells. PNS neurons serve to relay signals between the central nervous system (CNS) and terminal cells. Reproduced from Johnson, B.N. et al. (2016) Lab on a Chip, 16, 1393–1400 with permission from the publisher.
Figure 3. 3D-printed multi-scale in vitro model of the peripheral nervous system. Reproduced from Johnson, B.N. et al. (2016) Lab on a Chip, 16, 1393–1400 with permission from the publisher.
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Beyond the Cell results suggest that extrusion-based 3D printing provides a valuable customized biomanufacturing approach for realizing complex and biomimetic in vitro models for neuroscience studies.
3D printing personalized nerve pathways Nerve damage can originate from a variety of sources, including traumatic injuries, such as automobile accidents and battlefield wounds, or degenerative diseases such as diabetes. As a result, the majority of nerve injuries are patient specific and the appropriate repair technologies and procedures are highly variable10. Often, such nerve injuries occur in regions of bifurcating nerve corridors (see Figure 5a), resulting in complex nerve gap geometries such as shown in Figure 5b. These are highly challenging to effectively regenerate using conventional nerve guidance channel approaches due to both geometric complexity and challenges in guiding the regeneration of mixed nerves comprising both sensory and motor nerve types. Thus, there is a demand for personalized nerve regeneration technologies that are anatomically designed to match the native nerve tissue. As mentioned, 3D printing is particularly well suited for direct-demand scanto-print applications, via coupling with contact-free imaging technologies, including magnetic resonance imaging (MRI) and computed tomography (CT). Another useful, compact technology is 3D scanning, which uses the principle of object-light interactions to reconstruct a digital template of the scanned object for manufacturing or analysis. As shown in Figure 5c, 3D scanning shows promise for creating 3D printable digital templates of geometrically complex nerve pathways via reverse engineering of native nerve models.
Figure 4. Multi-scale biological features of the 3D-printed in vitro model showing axongenerating neurons, Schwann cells interacting with axons, axon termini and terminal cells. (a) Schematic of the in vitro model constructed using the 3D-printed microchannel-based tri-chamber scaffold. (b) Fluorescence micrograph from the left chamber showing isolated neurons and axonal pathways, (c) middle chamber showing isolated Schwann cell-associated axonal pathways and (d) right chamber showing isolated axon termini and epithelial cells within three printed microchannels. (e–g) Fluorescence micrographs showing single microchannel isolation from the left (e), middle (f ) and right (g) chambers. Reproduced from Johnson, B.N. et al. (2016) Lab on a Chip, 16, 1393–1400 with permission from the publisher.
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In addition to anatomical accuracy, a significant advantage of 3D printing is its ability to customize a scaffold’s external and internal physical and biochemical properties. As shown in Figure 6a, the 3D-scanned digital template and tunable extrusion physics were exploited to construct a bifurcating scaffold which contained: 1) a physical cue for guiding axonal growth longitudinally across the nerve gap and 2) a multi-component, path-specific gradient of biochemical cues intended to selectively guide the regeneration of sensory and motor nerves into their respective channels. As shown in Figure 6b, 3D-printed biochemical cue-containing hydrogel micro-droplets are deposited into channels according to a longitudinal
Figure 5. a) Photograph of the tibial bifurcation of the sciatic nerve in a small animal model. b) Photograph of the complex mixed nerve gap model resulting from transection of the tibial bifurcation. c) Reverse engineered complex nerve pathway via 3D scanning. Reproduced from Johnson, B.N. et al. (2015) Advanced Functional Materials, 25, 6205–6217 with permission from the publisher.
Beyond the Cell gradient pattern to provide controlled release of selective growth factors for sensory and motor nerve regeneration. These result in gradients of the cues which develop over the course of the clinically relevant time interval (1–3 months). The 3D-printed anatomical nerve regeneration pathways were validated in a proof-of-concept small animal model (see Figure 6c), suggesting promise for both regenerating complex injuries and enhancing the functional outcomes of regenerated nerve tissue5.
The future of neuro-manufacturing In summary, 3D printing offers a valuable opportunity for potential patient-specific advanced manufacturing capabilities at the point-of-care and in clinical settings, as well as providing personalized biomedical devices and ‘on-the-spot/off-grid’ therapies. Not only can 3D printing be used to generate biomedical devices and platforms in real time, which match the patients’ anatomies, but its multi-material processing capabilities also offer unprecedented opportunities to interweave biomimetic replicas of native biology within multi-scale scaffold architectures. Future improvements in the precision, accuracy and repeatability of this biomanufacturing tool, coupled with software advances, will continue to enable novel applications in neuroscience and the broader medical community. This will make these possibilities for personalized medical treatment accessible to a wide spectrum of users who may lack technical training in control and automation. Indeed, this combination of 3D scanning and printing technologies forecasts a promising future for a new biomedical design and manufacturing paradigm, ‘body biomanufacturing’.
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Blake N. Johnson received his BS in Chemical Engineering and certificate in Chemistry from the University of Wisconsin-Madison (2008), and PhD in Chemical Engineering from Drexel University (2013). His PhD research on electromechanical biosensors received the Outstanding Dissertation Award. His postdoctoral research in the Department of Mechanical and Aerospace Engineering at Princeton University focused on the development of advanced manufacturing technologies for neural and bioengineering applications. Currently, his research as an Assistant Professor in the Department of Industrial and Systems Engineering at Virginia Tech is focused on additive and advanced biomanufacturing and biosensing. He is a member of the American Institute of Chemical Engineers, the American Chemical Society and the Institute of Industrial Engineers. Email:
[email protected] Michael C. McAlpine is the Benjamin Mayhugh Associate Professor of Mechanical Engineering at the University of Minnesota. Previously, he was an Assistant Professor of Mechanical and Aerospace Engineering at Princeton University (2008–2015). He received a BS in Chemistry with honours from Brown University (2000) and a PhD in Chemistry from Harvard University (2006). His research is focused on 3D printing functional materials and devices, including the 3D interweaving of biological and electronic materials using 3D printing. He has received a number of awards, most prominently the NIH Director’s New Innovator Award, a TR35 Young Innovator Award, an Air Force Young Investigator Award, an Intelligence Community Young Investigator Award, a DuPont Young Investigator Award, a DARPA Young Faculty Award, an American Asthma Foundation Early Excellence Award, a Graduate Student Mentoring Award, the Extreme Mechanics Letters Young Lecturer and an invitation to the National Academy of Engineering Frontiers in Engineering. Email:
[email protected]
References Figure 6. a) Schematic of the 3D-printed anatomical nerve regeneration pathway showing the longitudinal physical cue for guiding the regenerating peripheral nerve, and the channelspecific 3D-printed gradient of growth factors. b) Customization of the scaffold biochemical landscape intended for targeted regeneration of specific nerve pathways. c) 3D-printed anatomical nerve regeneration pathways with personalized geometry, physical cue and biochemical cue, fit to the transected tibial bifurcation of the sciatic nerve. GDNF: glial cellderived neurotrophic factor (GDNF); NGF: nerve growth factor. Reproduced from Johnson, B.N. et al. (2015) Advanced Functional Materials, 25, 6205–6217 with permission from the publisher.
1. Murphy, S.V. and Atala, A. (2014) Nat. Biotech. 32, 773–785 2. Kong, Y.L., Gupta, M.K., Johnson, B.N. and McAlpine, M.C. (2016) Nano Today, doi: 10.1016/j.nantod.2016.1004.1007 3. Bhattacharjee, N., Urrios, A., Kang, S. and Folch, A. (2016) Lab Chip 16, 1720–1742 4. Krejcova, L., Nejdl, L., Rodrigo, M.A.M. et al. (2014) Biosens. Bioelectron. 54, 421–427 5. Johnson, B.N., Lancaster, K.Z., Zhen, G. et al. (2015) Adv. Funct. Mater. 25, 6205–6217 6. Mannoor, M.S., Jiang, Z., James, T. et al. (2013) Nano Lett. 13, 2634–2639 7. Johnson, B., Lancaster, K., Hogue, I.B. et al. (2016) Lab Chip 16, 1393–1400 8. Grinsell, D. and Keating, C.P. (2014) BioMed Res. Int. 2014, 1–13 9. Huh, D., Matthews, B.D., Mammoto, A., Montoya-Zavala, M., Hsin, H.Y. and Ingber, D.E. (2010) Science 328, 1662–1668 10. Schmidt, C.E. and Leach, J.B. (2003) Annu. Rev. Biomed. Eng. 5, 293–347
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