Chapter 4

The Power of CAD/CAM Laser Bioprinting at the Single-Cell Level: Evolution of Printing

S.C. Sklare¹

Theresa B. Phamduy²

J. Lowry Curly²

Yong Huang³

Douglas B. Chrisey^1
1    Department of Physics and Engineering Physics, Tulane University, New Orleans, LA, USA
²    Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
³    Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, USA

Abstract

The ability to deposit single cells in nonrestrictive two-dimensional and three-dimensional environments with high accuracy and reproducibility has proven invaluable for innovative cell and tissue research, and is now entering a new stage of maturity through systems engineering and next-generation designs. Coupling computer-aided design and computer-aided machining with laser direct write systems enables researchers to create reproducible biological models with micron-level precision. In particular, matrix-assisted pulsed-laser evaporation direct write system, and variations, has demonstrated this enhanced bottom-up approach to fabricating single-cell level constructs. This technology showcases the advantages of laser-assisted, nozzle-free, and contact-free printing that avoid probabilistic printing methods. In this chapter, we illustrate the importance of single-cell deposition and introduce laser-assisted bioprinting technology capable of micron-resolution and accuracy. This chapter will also review the post-deposition cell viability and mechanistics of laser-assisted transfer of biological materials.

Keywords

CAD/CAM bioprinting

cell array laser

induced forward transfer

MAPLE-DW

single-cell printing

4.1. Introduction

Single-cell deposition onto homogeneous two-dimensional (2D) and into three-dimensional (3D) environments with high accuracy and reproducibility is currently entering a new stage of maturity by way of systems engineering, based on a critical mass of technological developments. Coupling computer-aided design (CAD) and manufacturing (CAM) principles with the benefits of laser systems enables researchers to create reproducible or iteratively varying biological constructs with single-cell precision. This chapter illustrates the importance of single-cell deposition and introduces laser-assisted bioprinting as a viable method of printing individual cells. Specifically, the following topics will be discussed: advantages of laser-assisted bioprinting systems; examples of laser systems adapted for cell printing; the technology and basic physics behind these systems; mechanistic modeling of laser-assisted cell transfer; and several case studies illustrating the importance of printing on an individual cell basis. In particular, we showcase matrix-assisted pulsed-laser evaporation direct write (MAPLE-DW) as the premier laser-assisted, nozzle-free, and contactless printing method. We focus on recent enhancements in MAPLE-DW: potential scalability; single-cell deposition; ease-of-use; and environmental controls (e.g. temperature and humidity control).

4.1.1. Direct-Contact vs. Direct Write for Single-Cell Printing

Laser-assisted bioprinting possesses inherent advantages for single-cell applications, but it is not the only method to demonstrate single-cell resolution deposition. Four of the principal categories of bioprinting methods, (1) micropatterning, (2) ink-jet printing, (3) nanocontact bioprinting, and (4) laser deposition, can be further classified as direct-contact (DC) or direct-write (DW) mechanisms. DC methods, such as microcontact patterning, load cells suspended in media onto a “stamp” surface and then bring that “inked” surface in contact with the desired substrate surface. This prints an entire pattern at once, as opposed to being built over time. Stamps are a priori patterns that require new molds for new patterns. After the solution is applied to the stamp, there is no way to select certain cells or groups of cells for printing. Thus, the nature of cell distribution in suspension determines the probabilistic cell number per print area. DW methods, such as ink-jet printing and laser-assisted transfer, use “bottom-up techniques” that generate whole constructs on a subunit-by-subunit basis. DW methods can be combined with CAD/CAM techniques to facilitate the controlled transfer of biological “voxels” (volume pixels) containing desired cells. By designing blueprints and depositing biological voxels accordingly, researchers can generate 2D arrays or layered 2D patterns to create 3D constructs. However, laser-assisted cell transfer is distinct from other DW methods for single-cell printing applications.

Unambiguous scientific conclusions rely on reproducibility of experiments to test theories and prove statistical significance. Single-cell printing reduces sample-to-sample variability and permits clear quantification in biological studies by repeating addressable units (voxels) to create cell or tissue constructs. Inkjet methods that rely on nozzle-ejected droplets and blind laser-assisted methods that depend on laser-material interactions for droplet formation are capable of printing one cell at a time on average. This average and reproducibility depend on the probabilistic localization of only a single cell within the ejection volume (Liberski et al., 2011; Barron et al., 2005). Caution should be taken, however, as voxel-to-voxel reproducibility necessarily varies due to inherent cell-to-cell differences. In addition, the ability to select cells during DW procedures produces less variance compared to blind DW methods simply by enabling the end-user to visibly target cells. Moreover, the impact of scientific conclusions increases inversely with the number of cells written with single-cell deposition being paramount.

Rather than rely on distribution statistics, MAPLE-DW and other select DW techniques provide in situ, real-time optical monitoring and recording to enable users to select cells for printing and confirm deposition onto the substrate. This distinguishing feature provides feedback to determine precision in cell placement, relative to target location. MAPLE-DW spatial resolution is ±5 μm (Schiele, 2010). By using a camera-equipped, laser-assisted DW system, one is able to reduce voxel-to-voxel variation, monitor deposition in real time, and achieve tight control in spatial printing. This platform for cell printing enables researchers to explore numerous biological systems, including stem niches, cancer invasion, and neuron manipulation/functionalization using functional testing platforms, such as isolated-node single-cell arrays, network-level single- cell arrays, integrated single cells, and 3D cellular invasion models.

4.2. Basics of Laser-Assisted Printing: Overview of Systems and Critical Ancillary Materials

4.2.1. Laser-Assisted Cell Transfer System Components

Variations and generational iterations of laser-based cell transfer bioprinting systems are based on similar principles to those of laser-induced forward transfer (LIFT) for inorganic electronic materials (Piqué et al., 1999). The four most common laser bioprinting systems are LIFT, absorbing film-assisted LIFT (AFA-LIFT), biological laser processing (BioLP), and MAPLE-DW. They all utilize optically-transparent “ribbon” disks, biopolymer-coated receiving substrates, beam delivery optics, and imaging and pulsed laser system, but contain variations in ribbon processing and laser wavelength for optimal laser-material interaction (Figure 4.1). Auxiliary components include in situ imaging devices, laser beam energy meters, and up to three computer-controlled stages. A complete survey of laser-assisted printing systems is provided by Phamduy et al. (2010). Two specific system schemes, AFA-LIFT and MAPLE-DW, are highlighted here.

Figure 4.1 General schematic for laser-assisted bioprinting.

The laser-transparent disks, termed “print ribbons,” are analogous to ink ribbons historically utilized in typewriters. By analogy, ribbons are loaded on one side with ink, as the optically transparent print ribbon disks are coated on one side with cells. Cells either adhere to a biopolymer coating on the disk ribbon or are suspended in a sacrificial matrix layer, such as cell culture media or low viscosity biopolymer (e.g. hydrogel). In AFA-LIFT, there may be an additional metallic layer between the ribbon surface and cell-embedded medium. This sacrificial layer acts as an absorption medium intended to reduce the effects of laser radiation on cells and facilitate the conversion of light energy to mechanical energy. In systems with UV or near-UV lasers this is a laser-absorbing biopolymer layer. However, in high-power (longer wavelengths with longer pulse widths) laser systems, this layer is often metallic, gold, or titanium. Potential cytotoxic effects exist from volatilization of the sacrificial metal layer, which leads to potential nanoparticulate inclusion during droplet ejection (Smausz et al., 2006; Lewinski et al., 2008). In addition, the metal layer blocks visualization of cells on the ribbon.

MAPLE-DW positions the print ribbon on planar motorized stages in conjunction with a coordinated imaging system, which allows researchers to traverse the ribbon and select individual cells for printing in real time. For single-cell transfer applications, print ribbons must have enough intercellular spatial separation so that only one cell is situated within the transfer area. The ability to visually target individual cells by way of coordinated imaging and a low cell density on print ribbon removes the element of volumetric probability, reducing droplet-to-droplet variation.

In MAPLE-DW, the receiving substrate is mounted on a triaxial motorized stage positioned below the ribbon, separated by a small gap approximately 700–2000 μm wide (Patz et al., 2006). There is a critical medium coating on the receiving face of the substrate (whichever side faces the ribbon). This coating is usually a low-viscosity biopolymer (e.g. hydrogel) or gelatin mixture containing cell culture medium. The substrate coatings are carefully chosen to cushion cell impact, maintain a moist environment for cells during printing at ambient conditions, and promote cell adhesion when appropriate. Additional requirements of substrate coatings are discussed later in this section.

Single laser beam pulses are focused at the print ribbon-cell suspension medium (or intermediate absorption layer) interface to achieve noncontact transfer. Each laser pulse causes localized, rapid evaporation of the support medium and the formation of a vapor bubble. The expanding bubble forces a volume of material to be ejected from the cellular suspension layer. The mechanism of bubble formation and material ejection are detailed later in this chapter and a schematic representation is given in Figure 4.2. In general, systems that use metallic absorption layers (e.g. LIFT, AFA-LIFT, and BioLP) also utilize high-power lasers (typically KrF). Low-power, high-energy excimer UV lasers (typically ArF) are used in MAPLE-DW. In general, higher laser pulse energies lead to increased impact force during cell deposition onto the receiving substrate (Patz et al., 2006). When coupled with a viscous receiving medium, this method can be used to create 3D constructs with adherent cells initially deposited at variable depths. As cells may be damaged due to impact and DNA irradiation from laser radiation, the variation in laser energy must be minimized.

Figure 4.2 Droplet formation and expanding bubble schematic (Wang et al., 2009).

4.2.2. AFA-LIFT

AFA-LIFT is a modified version of LIFT, which incorporates a thicker (50–100 nm) metal film to protect cells from laser-related damage rather than a dynamic release layer as used in traditional LIFT. This metallic layer is deposited by vacuum evaporation and interacts with the laser at the print ribbon interface. As KrF excimer lasers (λ = 248 nm) used for AFA-LIFT have a penetration depth of 10–20 nm, the thick metal layer protects cells from UV irradiation effects. Depending on the laser wavelength, the pulse width at half max pulsing frequency can range from 500 fs–30 ns. Femtosecond lasers impose more unfavorable mechanical force on the cells. In AFA-LIFT, the cell suspension medium, 140–160 μm thick, is spread onto the metal layer. Distance between the ribbon and receiving substrate is typically between 600–1000 μm. Spot size and fluence vary, but the apparatus described by Hopp et al. (2010), uses 250–300 μm diameter spot size and 200–350 mJ/cm² fluence. Thermal expansion of the metallic layer expels a volume of cell suspension to the receiving substrate rather than force generation via vaporization of the cell suspension medium. Some AFA-LIFT systems use charge couple device cameras to observe and record the transfer process, and motorized stages to enhance laser focusing and cell placement (Hopp et al., 2010).

4.2.3. MAPLE-DW

MAPLE-DW facilitates forward transfer of biological materials in a controlled environment by utilizing two 3D motorized computer-controlled stages and automated features. Additionally, MAPLE-DW is an all-optical imaging and printing system, with environmental controls in the current version. These are among the features that distinguish it from AFA-LIFT and other laser-assisted transfer systems. The current system is the culmination of improvements from previous generations; the first-generation MAPLE-DW system had no motorized stages, the second generation added motorized stages and incorporated CAD/CAM features, and the contemporary third-generation system has subsequently added environmental control. Previously, during printing, it has been difficult to keep small volumes hydrated and trypsinized cells alive for attachment. Environmental control, which includes control and monitoring of temperature and relative humidity, mollifies this without disrupting the print pattern or changing other print parameters.

Screen captures from the graphical user interface (GUI) of the third-generation MAPLE-DW system is shown in Figure 4.3. This GUI enables researchers to control ribbon and substrate stage positioning, laser fluence, pulse trigger, local temperature, and relative humidity. Live video feed of the print ribbon allows researchers to inspect and select cell groups for printing. The live video feed is shown on the right side of the first screen in Figure 4.3. Geometric arrays, 2D patterns and 3D constructs can be printed in a semi-autonomous way thanks to incorporated CAD and CAM tools. Cell selection, the rate-limiting step, still needs to be done manually, significantly decreasing the process speed. In order to automate single-cell printing, “machine vision” (MV) capability was incorporated into the latest generation of MAPLE-DW. The MV image analysis algorithms identify and locate individual cells for transfer into user-defined patterns, thereby increasing the fabrication rate and achieving fully automated printing.

MAPLE-DW print ribbons are coated with a thick (∼30 μm) sacrificial biopolymer layer, frequently Matrigel® or gelatin, to absorb laser energy instead of the metal layer used in AFA-LIFT. Trypsinized cells are partially encapsulated into the hydrogel layer, leaving a sacrificial zone between the ribbon-biopolymer interface and cell layer. MAPLE-DW uses low-powered lasers in UV or near-UV range, typical pulse duration is 8 ns. Single pulses focused at the quartz ribbon-absorption layer interface cause volatile bubble formation that enables transfer. Bubble formation and the beginning of material transfer are schematically represented in Figure 4.2. Shallow penetration depth associated with low-power UV lasers prevents direct effects and interaction between the cells and laser, preventing adverse effects on the cells (Riggs et al., 2011).

4.2.4. Ancillary Materials

Materials chosen for the print ribbon and receiving substrate can maintain cell viability before printing and promote cell proliferation, movement, and differentiation after printing. Poor choices for these mediums can lead to cell damage and death. In general, hydrogel biopolymers are used for print ribbons and substrates, but the amounts and biopolymer materials used should be carefully considered.

Cells are embedded in biopolymer hydrogel precursors on the print ribbon. The print ribbon requires a biopolymer that allows cell adherence and admits cell ejection with a single laser pulse. For nonfilm assisted transfer methods, such as MAPLE-DW, this layer acts as a volatile sacrificial medium. Shear thinning and controllable viscosity are in new biopolymers for print applications, because these qualities permit adjustments to compensate for altered printing parameters and while maintaining single pulse ejection. Less viscous biopolymers (e.g. hydrogels) reduce the propagation and magnitude of the stress wave generated, and thus subsequent cell damage, during printing (Wang et al., 2009).

Gelatin has been an effective biopolymer, used for both film-assisted and non-film-assisted methods (Hopp et al., 2005; Ringeisen, Kim, et al., 2004). Gelatin is amenable to thermal manipulation and can be partially cross-linked using heat, which reduces pressure on the embedded cells during printing. Thickness of this coating affects cell viability and varies based on the system and cells of interest. Ringeisen, Kim, et al. (2004) found that cell viability increased from 50% to more than 95% when their Matrigel® biopolymer coating increased from 20 μm to 40 μm for pluripotent embryonal carcinoma cells transferred using the MAPLE-DW platform.

Biopolymers with various growth factors and/or extracellular matrix (ECM) keep cells moist and promote cell adherence. However, growth factors and ECM can introduce additional variability and complications. ECM causes cells to bind firmly to the coated print ribbon, and thus requires more power to achieve cell ejection. This in turn causes cells to be exposed to more irradiation and suffer greater eventual impact damage. Matrigel®, one such biopolymer, is often a good choice of biopolymer and has been extensively utilized by laser DW researchers, even though it has been shown to have unintended effects on cells, such as unintended stem cell differentiation (Riggs et al., 2011; Vukicevic et al., 1992).

The receiving substrate serves to cushion impact-induced stress, maintain moisture, and provide the appropriate growth environment for post-transfer cells. Gelatin and Matrigel® are both used as receiving substrate biopolymers. Impact-induced stress and modeling is discussed in the mechanistics section of this chapter.

4.3. MAPLE-DW Mechanics

The cell transfer process in MAPLE-DW occurs in three sequential events: cellular droplet formation, cellular droplet travel, and cellular droplet landing. Analytical modeling of the two main events—cellular droplet formation and landing—and their effect on postdeposition cell viability are discussed in detail next.

4.3.1. Modeling Cellular Droplet Formation

External pressure and internal stresses are exerted on cells during rapid evaporation (normal boiling and phase explosion) and thermoelastic expansion of viscous droplets. The transformation of a superheated liquid to an equilibrium state of mixed phases is called a “phase explosion,” which eventually leads to a pressure pulse. The pressure pulses are systematically exploited by MAPLE-DW to generate printable droplets on demand. Although necessary for the formation of cellular droplets, the pressure pulse and thermoelastic expansion can injure printed cells, reducing viability after deposition. As such, both bubble formation and thermoelastic stress should be investigated via computational modeling to understand and minimize possible sources of damage to printed cells.

4.3.1.1. Modeling Bubble-Formation-Induced Process Information

Figures 4.3 and 4.4 are schematic representations of the laser-induced bubble formation and expansion process in a typical MAPLE-DW setup. While the MAPLE-DW scheme is shown here, the proposed modeling approach is applicable to other laser-based printing methods that utilize a sacrificial energy-absorbing layer. The modeling assumes that the energy conversion thickness (< 100 nm) at the ribbon-hydrogel interface is negligible.

Figure 4.3 Dual screen GUI for MAPLE-DW. GUI Screen 1: (Left side top to bottom) Ribbon and substrate X and Y stage position feedback and motion controls; temperature and humidity control; laser fire type controls. (Right side top to bottom) Tabs to select display below (live feed, original image, processed image, humidity and temperature graph, system parameters); system shutdown; display window (live feed pictured here). GUI Screen 2: Counter-clockwise from top left: energy meter; ribbon and substrate Z stage position feedback, motion controls, and focus control; automated iris control; laser control software.

Figure 4.4 Modeling domain for the bubble expansion-induced cell deformation (Wang et al., 2009).

During the bubble expansion process, a high-pressure pulse is generated, which ejects a droplet volume containing the cells. The bubble expansion process can be modeled using a computational domain as shown in Figure 4.4. The materials involved consist of (1) vaporized gas bubble, (2) air, (3) hydrogel (used here as a coating material), and (4) the cell. Typically, the cell is modeled as a solid type material and applied a Lagrangian mesh for simplicity. The bubble, coating material, and air are modeled using the Eulerian mesh to avoid any extreme element distortion of these materials during ejection. The cell/hydrogel interaction is modeled using the appropriate Euler/Lagrange coupling to capture the effect of viscosity at the cell boundary layer. In addition, the interaction among the hydrogel, bubble gas, and air is modeled by defining the borders in a multimaterial grouping.

The pressure pulse accelerates resting cells from the ribbon until the droplet is ejected at a critical ejection velocity. Ejection velocity largely determines the initial velocity at which the cell droplet encounters the receiving substrate, and should be controlled to minimize cell injury during landing. Figure 4.5 shows the cell-center velocity evolution during the ejection process. It can be seen that the cell velocity oscillates initially and then gradually smoothes out to a constant ejection velocity, in this case 107 m/s. This velocity oscillation is attributed to the elasticity of the cell, implying a negative acceleration. Due to the compressibility of hydrogel, there is a delay in the velocity response to the bubble expansion as seen from Figure 4.5. After approximately 2 μs, the cell droplet has a very weak connection with the coating material and starts to separate from the coating material with a constant velocity.

Figure 4.5 Cell center velocity during printing process (Wang et al., 2009).

It has been found that the cell can initially accelerate as high as 10⁹ m/s² at the beginning period of bubble expansion and then quickly approach zero in an oscillatory manner. Fortunately, this period of high acceleration is very brief, only lasting about 0.1 μs. The pressure that the cell experiences can also be very high at the beginning period of bubble expansion, but quickly decreases to zero in an oscillatory manner, as seen from the cell acceleration evolution. The top surface region of the cell usually experiences the highest pressure level, followed by the bottom surface and then middle regions (Wang et al., 2009).

4.3.1.2. Modeling Laser–Matter Interaction Induced Thermoelastic Stress

In general, and during cell deposition, localized heating and thermal expansion of a material cause thermoelastic stress. Two confinement conditions are necessary for the prominent generation of the thermoelastic stress: (1) the laser pulse duration should be much shorter than the characteristic thermal relaxation/diffusion time, and (2) the laser pulse duration should also be shorter than the characteristic acoustic relaxation time to achieve a high-amplitude thermoelastic stress wave. If the laser beam size is taken as finite (laser spot diameter is comparable to the optical penetration depth), the wave generation becomes 3D, which can be solved analytically using Green’s function. Unfortunately, however, this approach usually assumes the wave propagation is within a homogenous infinite medium. The image source method has also been explored to model this wave propagation challenge when one of the boundaries is rigid (Paltauf et al., 1998). However, the coating layer during MAPLE-DW is usually very thin. Consequently, this layer cannot be treated as an infinite medium as in a 2D case and the wave is reflected at the free surface. To better understand the effect of thermoelastic stress on the cell injury, the thermoelastic stress wave propagation is modeled here by considering the unique boundary conditions which are different from other previous studies, such as Paltauf et al. (1998).

As shown in Figure 4.6, the computational domain used to simulate the thermoelastic stress generation is treated as 2D. This domain is due to the axisymmetric characteristic of the laser bioprinting process under a typical round laser spot. For the stress wave governing equation, the second order central difference scheme is used to approximate spatial derivatives, and the backward difference scheme is used for the time derivative computation. The Crank-Nicolson method, which has second-order time accuracy, is used to solve the stress wave governing equation. For a finite thickness coating, the pressure wave reflection at the coating-air and the coating-glass interfaces has to be considered. Pressure reflection occurs at the coating-air and the coating-glass interfaces due to their acoustic impedances. The interface reflectivity is equal to −1 for a free surface (interface) and 1 for a rigid surface (interface). Thus the reflected stress at the rigid transparent support does not change the sign of stress due to the very high acoustic impedance in the rigid support, while it does change its sign due to the reflection at the free surface. The stress wave may be canceled by the reflected stress wave in the near vicinity of the coating-air free surface since the reflected stress wave has an opposite sign.

Figure 4.6 Schematic of the computational domain (Wang et al., 2009).

Figure 4.7 shows the pressure profile at a fixed location, 50 μm below the laser spot center, for the first 140 ns (Wang et al., 2011). It is found that a bipolar pressure pulse is developed. A bipolar pulse such as this was also observed in the study of the acoustic wave field generated in front of a submerged fiber tip by Paltauf et al. (1998). At about 33 ns after laser radiation, a positive compressive pressure arrives at this fixed location, immediately followed by a negative tensile pressure. The first pressure peak (13.9 MPa magnitude) originates from the compressive pressure of a plane wave, and the subsequent tensile pressure (−14.4 MPa magnitude) emits from the edge of the laser spot. Both compressive and tensile components physically coexist for the sake of the momentum conservation (Vogel and Venugopalan, 2003). They are experienced 4.6 ns apart on the order of 10 MPa at this fixed location, 50 μm below the center of the laser spot. At approximately 66 ns, the compressive pressure wave reaches the free surface and is reflected back into the coating medium as a tensile stress wave. Then, at approximately 100 ns, the first reflected wave reaches the fixed location with a peak magnitude of −6.4 MPa, and another compressive wave is observed with an even higher peak magnitude of 10.3 MPa; that is, a negative tensile pressure is followed by a larger positive compressive pressure. The second pressure pair is formed due to pressure reflection at the coating-air free surface, which changes the sign of pressure upon reflection. Since the wave energy is transmitted into the surrounding coating during propagation, both components of the second pressure pair decrease in magnitudes, as seen in Figure 4.7.

Figure 4.7 A representative pressure profile below the laser spot center (z = 50 μm) (Wang et al., 2011).

4.3.2. Modeling of Droplet Landing Process

During landing, cell droplets undergo significant deceleration and impact(s), surviving a much higher external force than they are capable of surviving under steady state conditions. This landing process and its induced impact can be modeled using the mass, momentum, and energy conservation equations (Wang et al., 2007, 2008). These equations hold true for cells, hydrogel in the droplet, and the substrate coating. In addition to boundary and initial conditions, proper material models, which include the equation of state, constitutive model, and failure criteria, are also indispensable in solving these equations. The equation of state is used to define the functional relationship between pressure, density, and internal energy. The constitutive model defines the stress dependence of related strain, strain rate, and temperature. Generally, a material model also includes a failure criterion to determine whether and when the material fails and loses its ability to support certain stress/strain.

A representative result of simulated landing is presented in Figures 4.8 and 4.9, when a cell droplet with a velocity of 50 m/s hits a rigid substrate coated with a 30 μm thick layer of hydrogel. A cell droplet with a cell in the center is modeled using a mesh-free smooth particle hydrodynamic (SPH) method. It can be seen that there are two different impacts during the process under the specified conditions. The first impact is between the cell droplet and the hydrogel coating, and the second impact is between the cell and the rigid substrate after the cell passes through the coating after the first impact. As the landing progresses, the hydrogel-enclosed cell droplet gradually merges into the substrate coating. After the cell is immersed in the coating (Figure 4.8), the outside hydrogel enclosure and the coating bear relatively less stress even though the cell experiences more stress.

Figure 4.8 Landing process at 2.4865 μs.

Figure 4.9 Particle von Mises stress information (coating thickness = 30 μm and V0 = 50 m/s) (Wang et al., 2008).

To study the von Mises stress and shear strain information during the landing process, three particles, the top particle 19139, the inner particle 19144 (one of the four center particles), and the bottom particle 19150, are selected as the representative positions to better understand the overall cell response during the landing process. The simulation is performed using a coating thickness of 30 μm and initial velocity (V₀ ) of 50 m/s. The particles’ von Mises stress responses are shown in Figure 4.9. The stress profiles show that two different impacts occur during the process under the specified conditions. First impact happens at the computation start time, and second impact happens at approximately 2.2 μs. During the process, the peripheral particles 19139 (top) and 19150 (bottom) are subject to a higher stress level than the inner particle 19144, which indicates that the cell membrane has a higher impact-induced mechanical stress during laser bioprinting. Also, the bottom particle 19150 undergoes a higher stress than the top particle 19139. Figure 4.9 also shows that the second impact has a negligible effect on the particles 19139 (top) and 19144 (inner). However, the bottom particle 19150 experiences higher stresses during the second impact compared to the first impact (1.33 MPa vs. 0.96 MPa), which means that it is important to study the stress information of the bottom particles during both impacts. In this simulation, the bottom particle 19150 experiences the first impact-induced stress peak at 0.2 μs and the second peak at approximately 2.6 μs. The probability of experiencing a second impact is highest for the bottom peripheral particles, lowest for the inner particles with the top peripheral particles falling somewhere in between.

Through modeling studies (Wang et al., 2007, 2008), it has been found that cell peripheral regions, especially the bottom peripheral region, usually experience a higher stress level than the inner regions. This indicates that the cell membrane can be adversely affected by the impact-induced mechanical injury during laser bioprinting. Additionally, the cell mechanical loading profile and the post-transfer cell viability depend on the cell droplet initial velocity and the substrate coating thickness. Generally, a larger initial velocity poses a higher probability of cell injury, and substrate coating can significantly relieve the cell mechanical injury severity. Furthermore, two important impact processes occur during the cell droplet landing process: first impact between the cell droplet and the substrate coating, and second impact between the cell and the substrate. It is assumed that impact-induced cell injury depends on the magnitudes of stress, acceleration, and/or shear strain, and also the cell loading history. In fact, over the entire impact duration, the collective cell momentum change, rather than peak stress, acceleration and/or strain, appears to be critical in determining the cell viability during laser bioprinting and deposition.

4.4. Cell Viability

Post-deposition cell viability has been demonstrated for laser-assisted transfer techniques in terms of cell survival, adhesion, mobility, proliferation, and differentiation. Damage to the cells can be due to mechanical, thermal, or irradiative sources. Modeling of mechanical forces is detailed earlier. Due to the properties of the sacrificial hydrogel layer, thermal and irradiative injury is considered negligible. For example, in MAPLE-DW, for a print ribbon coating of 100 μm, approximately 5 μm depth would be affected by UV laser irradiation. In addition, thermal cell injury does not occur due to disparate time scales. During a typical 8–12 ns laser pulse, cell ejection from the print ribbon will occur within a few μs (as previously shown) and heat conduction does not permeate through the first biopolymer layer to cause cell damage. As explained earlier in the discussion of ancillary materials, the choice of medium for both ribbon and substrate coating are crucial to cell viability (Schiele et al., 2010; Lin et al., 2010; Riggs et al., 2011).

Similarly, in a study on the transfer of “vulnerable cell types” via AFA-LIFT, Hopp et al. (2005) demonstrated successful transfer of rat Schwann and astroglial cells as well as pig lens epithelial cells. Two weeks after transfer, the cells survived, proliferated, differentiated, and regained their original phenotypes. The three cell types were each isolated and then cultured in HEPES-buffered Dulbecco’s modified Eagle’s medium (HDMEM). Cells were then harvested from culture and prepared for AFA-LIFT printing. Cells were printed onto gelatin using an LLG two-pass amplification KrF excimer laser. Each target was irradiated with a single 30 ns pulse from the laser. Cell viability was inspected using trypan blue dye, before and after printing, showed that 98–99% of each cell type was alive before printing and 80–85% of each type of the transferred cells remained alive after printing. In addition, the cells retained the ability to proliferate and differentiate 2 weeks after initial printing.

Greater survival rates have been demonstrated with MAPLE-DW. Ringeisen et al. (2004) demonstrated 95% cell survival rate when printing with pluripotent embryonic carcinoma cells. This MAPLE-DW array used an ArF excimer laser from Lambda Physik, model LPX 305, wavelength of 193 nm, and 30 ns full width at half maximum. The target spot on the print ribbon was 100 × 125 μm² and incident laser fluence was in a controlled range from 100 to 500 mJ/cm². P19 (pluripotent embryonal carcinoma) cells were cultured and differentiated to neural and muscle cell phenotypes. Cells were prepared on the gelatin-coated ribbon. Printing was done in a humidity-controlled environment, as evaporation of droplet volume reduces cell survivability.

Cell viability was tested 6 h following transfer using live/dead visibility kit. Cells printed onto a 40 μm layer of hydrogel resulted in greater than 95% post-transfer viability. Furthermore, the P19 cells retained their ability to differentiate in induction media.

This study also investigated DNA damage due to ultraviolet light exposure. Comet assays were performed to detect single- and double-strand breaks. MAPLE-DW was used to perform noncontact cell transfer from the quartz ribbon directly into α-MEM. After comparison with control groups, there was no statistically significant damage to the cell DNA as a result of the ultraviolet laser interaction with the cell. This mitigation is due to the energy absorption by the sacrificial biopolymer on the quartz print ribbon.

With laser-assisted cell transfer methods, researchers benefit from soft cell deposition into programmable pattern positions to study cell behavior.

4.5. Case Studies and Applications Illustrating the Importance of Single-Cell Deposition

High-throughput, parallel analysis of single cell behavior within the context of biological screening tools or structured cell–cell interaction interfaces necessitates the ability to isolate and position individual cells into engineered arrays. The rationale for intentional cell isolation is to identify significant singularities that may be lost when averaging response across an entire population of heterogeneous cells (Birtwistle et al., 2012). Resolving single-cell characteristics allows researchers to identify cells with the greatest differentiation potential—for example, pluripotent stem cells or cancer stem cells (Shackleton et al., 2006). Similarly, as tissues are composed of various cell types, the dynamics of cells from an explant or whole organism can drown out the finer nuances of the cell–cell interaction of interest. Reintegrating individual cells into a spatially controlled, construct-containing discrete voxel of single cells simplifies the cellular cross-talk. In this section, we explore case studies of single-cell patterns, organized by general application.

4.5.1. Isolated-Node, Single-Cell Arrays

The simplest single-cell arrays are ones wherein each cell is a standalone node for independent analysis. Neighboring cells, isolated by physical or spatial means, cause negligible effects on an individual cell’s response to presented external cues. These types of arrays are particularly useful for studying cell plasticity in response to external stimuli or as screening tools to identify unique cells in a heterogeneous population. In the following cases, cell isolation and immobilization may be done in parallel. The ability to perform subsequent analysis in a high-throughput manner is critical. By arranging cells into organized arrays, high-throughput analysis can be done in an efficient manner.

In cases where arrays are populated by clonally similar cells, studies focus on individual cell response to combinatorial external stimuli (e.g. extracellular matrix, growth factors, and therapeutics). For example, correlation of cell-matrix interactions to biomechanical transduction events caused by in situ stresses localized at the cellular boundaries benefits from single-cell resolving technologies. These fundamental studies utilize chemical micropatterning of surfaces to dually generate attachment islands of various geometries and present combinatorial extracellular matrix cues for assays that profile cell adhesion, morphology, and cytoskeletal stresses (Kuschel et al., 2006; Gallant et al., 2005; Théry 2010). In general, these differential-surface assays contain islands 2–20 μm in diameter to bind single cells. Recent work by Ma et al. (2013) constrained cells in 3D volumes by laser-guided micropatterning to study contractile functions of differentiated mesenchymal stem cells. Subsequent analysis, ranging from fluorescence image-based analysis of mechanical stress fibers to AFM-tip force probing, identified distinctive extracellular matrix components that may serve therapeutic targets in disease states.

Although static adhesion islands are suitable for cell-matrix adhesion assays, additional complexity is required for studies that investigate dynamic cell interaction with soluble cues in an external microenvironment, such as growth factors or therapeutic drugs. Microfluidic device-based assays provide the means to organize individual cells and directly interrogate them with a combinatorial library of soluble factors. High-throughput, single-cell resolving is achieved by physically catching cells in sized chambers against the flow stream. Because microfluidic platforms utilize flow rates in the laminar region (Reynolds number < 1000), gradual mixing allows for continuous presentation of defined chemical gradients to cell arrays (Carlo et al., 2006; ChunHong Zheng et al., 2012). To date, single-cell arrays have been used to monitor cellular-level response to growth, cytokines signaling, migratory, and metabolite factors (Grecco et al., 2010; Köstler et al., 2013; Cheng et al., 2010). As a natural extension and spin-off, such devices have also been used for drug screening and resistance studies (Kuss et al., 2013).

In cases where heterogeneous cell populations must be sorted, single-cell arrays provide ideal platforms for identification and subsequent processing. For example, adult stem and cancer stem cell populations consist of heterogeneous constituent cells. Some cells differentiate into new tissues while others are more likely to differentiate into metastatic lesions. Fractionation of such heterogeneous populations down to the single-cell level elucidates the internal cellular mechanism that gives cells their unique phenotype. Nontrivial isolation and identification of pluripotent stem cells requires high-throughput genetic analysis to process clonally unique cells. Generally, microfluidic devices are used to capture single stem or cancer cells and allow for in situ lysing for genetic analysis (Wilson et al., 2014; Liang et al., 2013; Wood et al., 2010; Czy et al., 2014; Liu et al., 2013).

4.5.2. Network-Level, Single-Cell Arrays

Homotypical and heterotypical cell–cell communication dictates events that regulate tissue function, and disruption of this cellular signaling mechanisms leads to disease states (Pirlo et al., 2011). However, interrogation of desired cell–cell interactions may be hampered by background noise produced by extraneous cellular regulating events. Omission of these irrelevant processes and deposition of desired cells into a pattern drastically simplifies the nature of studied cross-talk into well-defined, intentional reciprocal interactions. Unlike single-cell arrays in which each node is independent contact with the external environment (previous section), in network-level single-cell arrays, cells interface with each other and produce interdependent responses that cumulate in network-level behavior. Thus the two most important parameters are: (1) identity of the neighboring cells, as the different cells elicit different interactions, and (2) distance between individual cells, as distance defines the mode of cellular communication (direct contact vs. paracrine signaling). Two research areas that utilize single-cell arrays are cancer invasion and neural networks.

Tumor invasion is a critical step in cancer progression to lethal metastatic disease, and understanding the cancer-stroma interface will provide clues to new therapeutic targets. Stroma is connective and supportive tissue. Fabricating artificial tissue interfaces at the single-cell level allows for the study of high-resolution cell–cell interactions at the micrometer length scale. Microfluidic and micropatterned devices have been fabricated to study both paracrine signaling (Hong et al., 2012) and direct contact events (Frimat et al., 2011; Zhang et al., 2014; Nikkhah et al., 2011), but are limited to isolated pair-wise interactions. Direct-write tools, such as laser-based bioprinters, are advantageous in depositing cells over a homogeneous area to study network-level behavior. Printing cells onto homogeneous substrates ensures that the development of the construct is influenced solely by guidance cues from neighboring cells, rather than limited by physical chambers or adhesion islands. In addition, there has been a push toward 3D tissue models to better study the in vivo cancer-stroma cross-talk. Several researchers have demonstrated the patterning of single hydrogel microbeads containing cancer cells into small-scale patterns (Dolatshahi-Pirouz et al., 2014; Phamduy et al., 2012). Larger patterns have also been generated by coprinting 3D hydrogel biomaterials and cells (Gruene et al., 2011; Kingsley et al., 2013).

In monoculture neural networks, guiding spontaneous synaptic connectivity requires defined spacing and pathways between individual cells. Several schemes have been developed toward this end. Dinh et al. (2013) generated high-density interconnected circuits of compartmentalized neurons in separate microfluidic chambers, with a biomaterials-guided outgrowth path between pairs of cells. Sanjana and Fuller (2004) and Macis et al. (2007) printed adhesion islands onto nonadherent substrates to allow for both cell attachment and directed outgrowth. In such examples, cell bodies are stationary and outgrowth occurs due to physical guides. Difato et al. (2011) utilized a laser-based optical tweezers technique to generate neural network patterns on homogeneous surfaces, which allows for cell migration in addition to synaptic development due to soluble guidance cues. Extracellular recording of action potential stimulation reveals that increasing internodal distance accelerates signal propagation (Wu et al., 2012), but decreases signal propagation efficiency (James et al., 2004). Single-neuron patterns with defined synaptic networks open up new avenues for fundamental biology studies and neural sensor/actuator applications.

4.5.3. Next-Generation Single-Cell Arrays: Integrated, Computation-Driven Analysis

As single-cell arrays are generally compared against a combinatorial library of soluble cues or first-neighbor cells, computational tools could be used to predict or validate empirical data in next-generation platforms. Standalone technology for high-throughput single-cell analysis currently exists. However, integration of existing technologies with single-cell arrays, especially in the context of network arrays, would produce a synergistic platform that is greater than the sum of its parts. Such improved platforms would provide in situ, multiplexed analysis of cell behavior, including morphology, biochemical state (Burguera et al., 2010), biomechanical stresses, genetic (Vanneste et al., 2012), metabolic, and migration (Chunhong Zheng et al., 2012). Information gleaned from arrays can then be compared to in vivo observations by computation analysis simply by compiling a database of single-cell array responses. This is the type of unambiguous testing that drives research scientists to push the boundaries of precision bioprinting technologies.

4.5.4. Example Single-Cell Array via MAPLE-DW

Figure 4.10 is a 4 × 4 array pattern (800 μm center-to-center spacing) generated by MAPLE-DW and populated by single MDA-MB-231 human breast cancer cells (Phamduy, in press). The phase contrast image was taken immediately after the printing process, so that the transferred gelatin droplets appear raised on the gelatin-coated substrate. In addition, each droplet contains single cells in the trypsinized state that will eventually attach to the underlying substrate surface.

Figure 4.10 Single-cell array via LDW.

4.5.5. Laser Direct Write for Neurons

The nervous system represents the most complicated organ in the body, controlling all higher- order faculties. As Tim Berners-Lee stated in 1999, “All that we know, all that we are, comes from the way our neurons are connected.” Its fascinating complexity makes it one of the most difficult systems to study. Many advances have been made in studying population level events in the brain (e.g. fMRI, MEG, and PET), but mechanistic approaches to ascertaining fundamental neurobiology at the cellular level are limited, preventing a complete understanding of the governing order.

As previously mentioned, the ability to study neuronal cell behavior in a separated manner has value to understanding both environmental and cell–cell interactions that regulate mammalian systems. Interest in such biological mechanisms spans the breadth of subjects from developmental neuroscience to functioning electrophysiology, with the added benefit of understanding and treating pathological disease states. Not until researchers can tease apart each component will we be able to truly appreciate how all of the nervous system’s complex pieces fit together. Currently, to examine neurons in vitro, the most commonly available methods involve organotypic slices or dissociated cell cultures. Studying individual neuronal characteristics within the context of a defined slice of living brain circuitry has led to some incredible insights, but the inability to parse out individual influences undermines the capacity to examine distinct mechanistic components (Gahwiler et al., 1997; Shankar et al., 2007; Yamaguchi et al., 2003). Dissociated cell cultures, on the other hand, offer more control over cell types and the influences of extracellular factors (Potter and DeMarse, 2001; Shahaf and Marom, 2001), while inherently negating the complex spatial organization present in vivo. While some control of cell-level interactions can be modulated through seeding density, the random nature of dissociated cultures limits a researcher’s ability to define specific cell–cell interactions in a contextual manner.

Some of the techniques to engineer both isolated node and network-level single-cell arrays were briefly described earlier. These technologies involve either “trapping” cells through patterned adhesion molecules or microfluidics, or “printing” cells through extrusion or ink-jet deposition. Again, limitations currently exist on spatial accuracy and cell specificity, with study of nuanced interactions hinging on the ability to control the manner in which distinct cell types interact. To that end, organized patterns of both 2D and 3D printed neuronal cells using laser direct write has been demonstrated, with cells maintaining the ability to sprout axons (Patz et al., 2006). Laser direct write addresses advantages for both shortcomings in a manner that has so far been underutilized for neural applications, despite the apparent need.

4.5.1.1. Neural Development

Early development of the nervous system is a highly organized and multifaceted process in which environmental and extracellular inputs, including biochemical and mechanical cues, govern the emergence of increasingly differentiated cell types. The first experiment to show that cells influence the fate of other cells was performed in 1924 using a crude method to separate and grow developing neuronal cells (Spemann and Mangold, 2001). Since then, the use of bioprinting for precise control of cell-environment interaction has facilitated studies of neural stem cell differentiation into neurons, astrocytes, and oligodendrocytes. lkhanizadeh et al. demonstrated that guidance of neural stem cell fate was possible through ink-jet printing (Ilkhanizadeh et al., 2007). Additionally, 3D printed microarrays of embryonic stem cells were able to characterize factors influencing neural commitment in a high-throughput capacity (Fernandes et al., 2010). Lastly, laser micromachining of growth substrates was shown to grow, proliferate, and differentiate neuroblasts in reproducible and controllable patterns (Doraiswamy et al., 2006). The combination of environmental manipulation with single-cell printing would allow combinatorial studies of cell–cell and cell-environment interactions on differentiation pathways.

Similarly, targeted guidance of neuronal processes to their proper functional targets is an amazingly precise and intricate process governed by attractive and repulsive signals. The sprouting axon of each developing neuron samples its surroundings and makes directional decisions through its growth cone. By engineering growth substrates, an increased understanding of how axons interact with the extracellular environment has begun to develop, including how individual molecular guidance cues affect guidance (McCormick and Leipzig, 2012; Yu et al., 2008). Where single-cell printing can add value is recapitulating the numerous external cellular “guideposts” each axon encounters along its journey, to study the multitude of cellular interactions that coalesce during development.

4.5.1.2. Engineered Circuits

The ability to fabricate highly specific patterns of neuronal cells has the potential to lend insight into many of the functional units of the brain and beyond. Xu et al. were the first to demonstrate that printed neuronal cells retained healthy electrophysiological characteristics (Xu et al., 2006). The subsequent ability to engineer neuronal circuits at the single-cell level was demonstrated by Edwards et al. with hippocampal neurons (Edwards et al., 2013). In a similar vein, techniques have been demonstrated to control individual synaptic connectivity of larger homogenous neuronal populations, enabling specific control over network and circuit properties (Staii et al., 2009; Vogt et al., 2005).

These methods do not allow for incorporation of multiple cell types, severely limiting their ability to recapitulate critical heterogeneous cell synapses. The advantages of laser bioprinting to control the precise location and association of multiple cell types, in combination with hydrogel and biomolecular-based models for controlled axonal outgrowth, would allow the creation of mono and polysynaptic circuits between heterogeneous neuronal populations (Curley and Moore, 2011; Horn-Ranney et al., 2013). Laser direct write represents the only technology currently available to accomplish this complicated feat, and the ability to recreate neural tracts of virtually any system at the single- and multicell level would allow for unprecedented experiments into the function of higher-order activities such as memory, learning, cognition, locomotion, and pain.

Highly specific brain regions are known to integrate and communicate through transient synapses, facilitating the signaling that encodes and dictates all activity. Further complicating matters, these neuronal connections are constantly being strengthened, weakened, or lost based on external outputs. For example, within the hippocampus, multiple cell types are known to control memory and spatial navigation (Dickerson and Eichenbaum, 2010; Turrigiano, 2012). Similarly, thalamocortical circuits govern cognition, behavior, and consciousness, with clinical implications in autism, schizophrenia, and attention deficit disorder (Buonanno, 2010; Sudhof, 2008). Sensorimotor neuron integration is another wide ranging pathway that influences motor control and proprioception as well as all of the five classic senses (Goulding et al., 2002). The ability to understand and engineer highly specific sensorimotor circuits also has implications in advancing prosthetic design to interface with native tissue and restore near-natural function (Guo et al. 2012; Raspopovic et al., 2014). Another system with implication in limb prostheses is the neuromuscular junction. Das et al. have defined and explored in vitro models for synapse formation, though to date only in heterogeneous dissociated cultures (Das et al., 2010, 2007). In both cases, precisely how synaptic plasticity influences information processing is not yet understood. All of these systems are highly complex and divergent across applications, but laser direct write could be used to manipulate long-term studies of cellular, genetic, and molecular influences on individual synapse formation and maintenance. Due to the stochastic nature of these systems, the high-throughput potential of laser direct write will be critical to increasing our understanding of normal function and will inform treatment of pathological states.

4.5.1.3. Non-neuronal Interactions

The consequences of studying engineered interactions between neuronal and non-neuronal cells also have application outside of synaptic function. Though the number is still contested, there are at least as many non-neuronal as neuronal cells in the brain (Azevedo et al., 2009). Clearly supportive glial cells are critical to nervous system function, but the intricacies of their interactions are not well characterized. Schwann cell and oligodendrocyte myelination of axons is required for physiologically relevant conduction of nerve impulses, and astrocyte–neuron communication influences both neuronal differentiation and synapse formation (Sherman and Brophy, 2005; Sofroniew and Vinters, 2010). Mirroring the sentiments of previous applications, glia cells are both necessary for natural neuronal function and responsible for many pathogenic states. Implications in Alzheimer’s, Parkinson’s, multiple sclerosis, neuropathy, and chronic pain have been tied to aberrant glial function (Antony, 2014; Block et al., 2007; Luongo et al., 2014; McMahon and Malcangio, 2009). In order to study such interactions, Park et al. constructed a microfluidic device to isolate axonal processes and enable coculture with both oligodendrocyte precursor and astrocyte cells (Park et al., 2012). They then saw evidence of healthy oligodendrocyte differentiation and myelination, alongside induced astrocytic and biomolecular damage. Goudriaan et al. also demonstrated the reciprocal nature of genetic disturbances in astrocyte–neuron interactions through a novel cellular isolation method (Goudriaan et al., 2014). Both tools represent powerful examples of potentially high-throughput studies in neuron–glia interaction. However, the speed and spatial resolution of laser direct write and CAD/CAM technology would allow for the rapid manipulation of multiple-cell juxtaposition to define mechanisms for axon–glia communication and interaction. The combination of throughput, flexibility, and accuracy would enable experiments to define and explore contact- and signaling-related influences toward understanding both healthy and pathological states.

4.5.1.4. Outlook

Through the examples given, it is clear that advances in technology have fundamentally increased our understanding of the nervous system. By incrementally deconstructing the intricate milieu of factors which govern the organization of the brain, both from a developmental and functional standpoint, researchers are beginning to recognize and understand the consequences of particular biological mechanisms. Engineered models utilizing laser direct write techniques would enable the manipulation of molecular, cellular, and synaptic contributions from a single-cell level, representing the next advancement required to decipher implication on emergent behavior and inform potential treatment strategies.

4.6. Conclusion

Laser bioprinting has become a proven method for biomaterial transfer and it is the only bioprinting approach having single-cell resolution. Because of the unique combination of optical imaging and CAD/CAM control, researchers have created a platform capable of fabricating complex constructs from the bottom up with the potential for high-throughput biological construct fabrication, for example, reproducible tissue constructs to the single-cell-level resolution to test different drugs with iterative composition and/or spacing manipulations allowing unambiguous conclusions about the influence of the cellular microenvironment. The MAPLE-DW platform, especially, has demonstrated greater than 5 μm spatial precision with post-transfer cell viability consistently greater than 90%. Researchers have achieved viable transfers with all types of cells enabling an even greater number of applications. Moreover, the short time required to make constructs coupled with conventional cell culture protocol and ease of printing make laser bioprinting with MAPLE-DW an efficient, feasible, and reliable way to isolate and study cellular interactions and behaviors. Research models created by laser direct-write for cancer, stem cell, and neural studies show promise for the future, and are a disruptive technology in tissue engineering.

Some of the major challenges facing bioprinting and tissue engineering, which must be addressed in the next generation of bioprinting systems, include optimizing the protocol, scale up, and imaging. Laser-assisted single-cell printing enables more reproducible studies, but also hinders large-scale tissue engineering until comprehensive pattern recognition and automation are realized. Next-generation laser-assisted bioprinters will have single-cell print resolution and demonstrate parallelization, to show that scaling up these systems is possible and eventually can be used for large-scale tissue engineering as well as clonal studies.

Improved imaging techniques will not only help researchers monitor and analyze deposition in situ, but also will be incorporated into automated data mining systems that will track deposition successes and failures, and associate each transfer attempt with printing metadata. Bioprinting metadata will include print conditions (e.g. temperature and humidity) and parameters (e.g. laser fluence and pulse duration) and be stored in a database with information on cell phenotypes, transfer precision, and morphological changes. Using data analysis, mining, and visualization techniques, users will be able to learn from each cell transfer, accelerate system improvements, and incorporate embedded system and construct statistics in subsequent studies with the cell constructs.

Next-generation laser bioprinting systems need to build on current success and continue to look to other disciplines for inspiration. This approach will help push single-cell laser-assisted bioprinting to a point of enhanced functionality, reproducibility, and parallelization. Single-cell printing is crucial but needs to be done with a systems engineering approach that has an eye turned toward future applications and problems, such as vascularization in tissue engineering. The MAPLE-DW platform advances this effort beyond other currently available technologies and demonstrates how generations of laser bioprinting systems evolve and improve to enact cutting-edge research technology.

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