Article Cite This: ACS Biomater. Sci. Eng. 2019, 5, 3499−3510
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An Image-Guided Intrascaffold Cell Assembly Technique for Accurate Printing of Heterogeneous Tissue Constructs Kevin F. Firouzian,†,‡,§ Ting Zhang,*,†,‡,§ Hefeng Zhang,†,‡ Yu Song,†,‡,§ Xiaolei Su,†,‡,§ and Feng Lin†,‡,§ †
Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China § 111 “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
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‡
S Supporting Information *
ABSTRACT: For tissue engineering and regenerative medicine, creating thick and heterogeneous scaffold-based tissue constructs requires deep and precise multicellular deposition. Traditional cell seeding strategies lack the ability to create multicellular tissue constructs with high cell penetration and distribution, while emerging strategies aim to simultaneously combine cell-laden tissue segments with scaffold fabrication. Here we describe a technique that allows for three-dimensional (3D) intrascaffold cell assembly in which scaffolds are prefabricated and pretreated, followed by accurate cell distribution within the scaffold using an image-guided technique. This two-step process yields less limitation in scaffold material choice as well as additional treatments, provides accurate cell distribution, and has less potential to harm cells. The image processing technique captures a 2D geometric image of the scaffold, followed by a series of processes, mainly including grayscale transformation, threshold segmentation, and boundary extraction, to ultimately locate scaffold macropore centroids. Coupled with camera calibration data, accurate 3D cell assembly pathway plans can be made. Intrascaffold assembly parameter optimization and complex intrascaffold gradient, multidirectional, and vascular structure assembly were studied. Demonstration was also made with path planning and cell assembly experiments using NIH3T3-cell-laden hydrogels and collagen-coated poly(lactic-co-glycolic acid) (PLGA) scaffolds. Experiments with CellTracker fluorescent monitoring, live/dead staining, and phalloidin−F-actin/DAPI immunostaining and comparison with two control groups (bioink manual injection and cell suspension static surface pipetting) showed accurate cell distribution and positioning and high cell viability (>93%). The PrestoBlue assay showed obvious cell proliferation over seven culture days in vitro. This technique provides an accurate method to aid simple and complex cell colonization with variant depth within 3D-scaffold-based constructs using multiple cells. The modular method can be used with any existing printing platform and shows potential in facilitating direct spatial organization and hierarchal 3D assembly of multiple cells and/or drugs within scaffolds for further tissue engineering studies and clinical applications. KEYWORDS: intrascaffold cell assembly, image processing, 3D bioprinting, heterogeneous tissue constructs, tissue engineering repair and regeneration.3,4 Directing cells in appropriate 3D arrangements within scaffolds and creating complex heterogeneous tissue constructs that better resemble those of desired tissues still remain larger challenges requiring advanced manufacturing techniques that can precisely position multiple cells in 3D space and promote effective organization of cells to create functional tissues with features that resemble those in vivo.5−7
1. INTRODUCTION The aim of tissue engineering has long been to create tissue and organ substitutes for maintenance, restoration, and/or augmentation of functions in vivo.1,2 Tissue engineering scaffolds play an important role in providing both the physical and chemical cues to guide cell growth, differentiation, and cell assembly to generate three-dimensional (3D) tissue constructs. It can be argued that the engineering concept of scaffold-based tissue engineering was first introduced in the mid-1980s by Langer and Vacanti to design scaffolds for cell delivery. Today’s concepts, however, utilize scaffolds with a combination of cells and/or biomolecules in order to promote tissue © 2019 American Chemical Society
Received: March 5, 2019 Accepted: June 4, 2019 Published: June 4, 2019 3499
DOI: 10.1021/acsbiomaterials.9b00318 ACS Biomater. Sci. Eng. 2019, 5, 3499−3510
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ACS Biomaterials Science & Engineering
Figure 1. Illustration of the image-guided intrascaffold cell assembly system. (a) The system is composed of a dual-nozzle 3D cell printing platform and an image capture and processing module. The scaffold image is processed via the camera calibration and image processing algorithms to extract pore centroid coordinates. Coordinates are used to create suitable 3D assembly pathway designs for seeding multiple cell materials inside the scaffold. (b) The camera system is first calibrated to compute intrinsic and extrinsic parameters. The image processing algorithm is composed of three main stages: image preprocessing, image enhancement, and coordinate extraction. All three steps are vital to obtain scaffold pore centroids efficiently. The two algorithms are used together to obtain and perform coordinate conversion for assembly pathway design.
tissue repair in vivo that mimics complex cellular and extracellular matrix (ECM) structure.20−23 The development of techniques such as programmed selfassembly and 3D bioprinting have given researchers the ability to generate complex biological structures with multiple cells or ECM types at high spatial resolution.24 The 3D bioprinting technique alone has offered great versatility in cell and biomolecule manipulation with precise composition control and spatial resolution.25−28 Current and emerging scaffoldbased bioassembly and 3D tissue construct manufacturing techniques include 3D cell printing within synthetic polymer frameworks with or without sacrificial layer printing using multinozzle printers.29−31 Research has also been done on a scaffold-based bottom-up 3D bioassembly technique allowing for automated assembly of scaffold and cell-laden material concurrently or subsequently. In this case, cell deposition locations (pore areas) are deduced from scaffold printing GCODE data.32,33 All of the aforementioned techniques allow
In tissue engineering methodology, scaffold architecture aside, the choice of cell seeding technique is vital, as this highly influences cell distribution and positioning. The importance of the cell seeding technique becomes more apparent when dealing with applications where spatial, heterogeneous, and multicellular cell seeding are required. Traditional scaffoldbased seeding strategies include static and dynamic cell seeding methods, in which cells are added and populated on and/or within premade scaffolds.8−10 Examples include, but are not limited to, static surface seeding,11,12 cell suspension injection into scaffolds,13,14 cell seeding using an orbital shaker,15 cell seeding using a centrifuge,16 low-pressure cell seeding,17,18 and cell seeding using magnetic force.19 Such strategies have been developed substantially since their emergence but still struggle in creating heterogeneous constructs where multiple cells are accurately populated and distributed within scaffolds. Moreover, cells need to be efficiently guided to yield high-quality 3500
DOI: 10.1021/acsbiomaterials.9b00318 ACS Biomater. Sci. Eng. 2019, 5, 3499−3510
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where s is an arbitrary scale factor, [R t] represents the extrinsic parameters (rotation and translation) that relate the world coordinate system to the camera coordinate system, and A is the camera intrinsic matrix, in which u0 and v0 are the principal (center) point coordinates, α and β are the scale factors (focal lengths) in image axes u and v, and γ is the skewness parameter of the two image axes. As the model plane is on Z = 0 of the world coordinate system, the equation can be written as follows: ÄÅ ÉÑ ÅÅ X ÑÑ ÄÅ ÉÑ ÄÅ u ÉÑ ÅÅ ÑÑ ÅÅ X ÑÑ ÅÅ Y ÑÑ ÅÅÅ ÑÑÑ ÅÅ ÑÑ Å Ñ Å Ñ Å Ñ Å Ñ v Å Ñ sÅÅ ÑÑ = A[r1 r2 r3 t ]ÅÅ ÑÑ = A[r1 r2 t ]ÅÅÅÅ Y ÑÑÑÑ ÅÅ ÑÑ ÅÅ Z ÑÑ ÅÅÅ1 ÑÑÑ ÅÇÅ 1 ÑÖÑ ÅÅ ÑÑ ÅÇ ÑÖ ÅÅ 1 ÑÑ (2) ÅÇ ÑÖ
for complex layer-by-layer strategies that alternate between deposition of scaffold and cell-laden material. However, there are limitations on what scaffold materials can be extruded and/ or printed alongside cell-laden material as well as on the ability to perform cell printing preprocesses (heating, coating, sterilizing). This article proposes a novel technique that enables direct printing of cells within tissue engineering scaffolds using an accurate image-guided cell assembly technique via image processing. Accurate positioning of scaffold pore centroids is realized by a camera system coupled with camera calibration and image processing algorithms. Once scaffold pore centroids have been located, adequate 3D assembly path plans can be designed. The technique is modular and enables separation between scaffold fabrication and cell printing while still allowing for accurate cell deposition and distribution within scaffolds. Hence, there are fewer limitations on the materials preparation and scaffold fabrication processes as well as fewer potential user-linked errors arising from, for example, manual seeding operation. Many of these processes do require the use of harsh chemicals and/or varying temperature gradients that would prove harmful to cells if scaffold materials and cells were to be printed simultaneously. It is our belief that this proposed technique can be used as a valuable tool to aid biomanufacturing of spatial and heterogeneous cell assembly within 3D scaffolds that is suitable for tissue engineering and regenerative medicine studies.
Camera calibration and script generation were performed using the Camera Calibrator App, part of the Computer Vision System Toolbox in MATLAB. A checkerboard calibration plate was purchased from Dongguan Hongcheng Optical Products Co., Ltd. (Dongguan, China) (49 × 50 squares, 1 × 1 mm square size, 1−2 μm design precision). A CMOS camera (Basler AG, Ahrensburg, Germany) was used alongside a Dino-Lite Edge digital microscope (Wuxi Vidy Precision Equipment Co., Ltd., Wuxi, China) to capture images and aid with sample/machine setups. Calibration images were taken from different orientations and angles, and mean reprojection errors were computed. 2.2.2. Image Preprocessing. After conversion of the input images to grayscale, a piecewise-linear transform is used to determine a range of the image to be highlighted using contrast stretching.35 Unwanted image noise is reduced by a spatial-domain-filtering Gaussian smoothing operation, which is a neighborhood processing method. Each pixel value is changed by a function of the intensities of the neighboring pixels into the mean value of the neighborhood.36,37 2.2.3. Image Enhancement. A threshold segmentation operation based on the Otsu method is used that classifies foreground and background pixels by minimizing intraclass variance while maximizing interclass variance.38 A global image threshold value G is selected, and the gray value of each image point is compared to said value. If the gray value (x) is lower than the threshold value G, it is given the set value 0, and if it is over the threshold value, it is given the value 255:36,37,39
2. MATERIALS AND METHODS 2.1. System Design. The image-guided intrascaffold cell assembly system, which comprises a dual-nozzle 3D cell printing platform and an image capture and processing module, is illustrated in Figure 1a. A CMOS camera system is used to capture the porous structure and positioning of the scaffold. Additional cameras can be used for additional depth analysis and imaging from other orientations. The camera calibration and image processing algorithms are illustrated in Figure 1b. Scaffold images are analyzed using our custom image processing algorithm to locate pore centroid positions. Images are subjected to various image preprocessing, image enhancement, and coordinate extraction processes to clearly distinguish between scaffold areas and void areas. Ultimately, the pore centroid positions are located, and the image pixel coordinate positions are converted to world coordinates using the camera calibration algorithm. Afterward, the user can design suitable assembly pathway designs for cell assembly 2.2. Camera Calibration and Image Processing. 2.2.1. Camera Calibration. Camera calibration is used to compute the correct extrinsic and intrinsic parameters to be used to accurately convert pixel coordinates into world coordinates. The algorithm and calculations are based on a flexible camera calibration method by viewing a plane from unknown orientations.34 This method consists of a closed-form solution followed by a nonlinear refinement based on a maximum likelihood criterion. The camera is fixed, and a planar pattern (checkerboard) is photographed at different orientations; the motion need not be known, and radial and tangential lens distortions are modeled. A 2D point is denoted by m = [u v]T, and a 3D point is denoted by M = [X Y Z]T. We use x̃ to denote the augmented vectors with 1 as the last element: m̃ = [u v 1]T and M̃ = [X Y Z 1]T. The camera is modeled by a pinhole model, and the relationship between a 3D point M and its image projection m is given by
s m̃ = A[R t]M̃
with
ÄÅ α γ u ÉÑ ÅÅ 0Ñ ÑÑ ÅÅ ÑÑ ÅÅ A = ÅÅÅ 0 β v0 ÑÑÑÑ ÅÅ Ñ ÅÅ 0 0 1 ÑÑÑ ÅÇ ÑÖ
l x93% cell viability. Scale bars 500 μm. (d) Day 7 Alexa Fluor 568 phalloidin−F-actin/DAPI immunostaining (40× magnification) with accumulation of healthy and elongated fibroblast cells assembled inside the scaffold. Scale bar 50 μm. (e) PrestoBlue results showing favorable increase in cell proliferation over seven culture days for the image-guided assembly sample.
Figure 7d shows phalloidin−F-actin/DAPI immunostaining results for Day 7 of the image-guided cell assembly sample at 40× magnification. The image shows an accumulation of healthy and elongated fibroblast cells inside the scaffold. Lastly, the PrestoBlue results in Figure 7e show a favorable and stable increase in cell proliferation over seven culture days for the image-guided assembly sample.
required side and reimaging for new assembly pathway design paths. Additional four-layer sectional images of all three samples are shown in Figure S2. 3.5. Intrascaffold Cell Assembly and Evaluation. Figure 6 shows experimental results for NIH3T3-cell-laden hydrogels assembled inside collagen-coated casted PLGA scaffolds with interconnected pores and a spatial grid structure. The two colors represent two separate stained bioinks (CellTracker Red and Green) that were prepared with the same cell concentration. Figure 6a−d shows Day 1 and Day 3 results with image cross sections perpendicular and parallel to the print direction for the image-guided cell assembly samples. Figure 6e−h shows the corresponding images for the manual injection control samples, and Figure 6i−l shows the corresponding images for the static cell seeding (surface pipetting) control samples. The image-guided cell assembly samples show that the cells were successfully assembled in the scaffold’s center nine axial pores with no diffusion into neighboring pores and observably retained their shape well during three culture days. Manually injected samples for Day 1 and Day 3 also show assembly of cells inside pores, albeit without an evenly controlled distribution, as some areas visibly have more cells than others. Furthermore, obvious diffusion into neighboring pores is observed. For the static cell seeding control samples, most of the cells were populated randomly on the scaffold surface only, as expected, and there was some migration into the scaffold with attachment to the pore walls both for Day 1 and Day 3. In all of the samples there was obvious cell proliferation over three culture days. Figure 7a−c shows live/dead cell staining results for Day 1 of the image-guided cell assembly, manual injection control, and static cell seeding control samples. For the image-guided cell assembly samples, >93% cell viability was established.
4. DISCUSSION Creating heterogeneous tissue constructs with multicell structure remains a larger design problem for existing cell seeding techniques. Emerging bioassembly techniques involve either printing scaffold material and cell-laden material concurrently layer by layer or printing the scaffold first and later depositing the cell-laden material. However, these techniques lack flexibility in pore position localization for and cell distribution in premade scaffolds. Furthermore, assembling scaffold and cell-laden material using the same platform at the same time adds additional limitations, including less choice in scaffold materials and not allowing for any additional processes (heating, coating, sterilization) prior to cell seeding. Many typical scaffold fabrication methods in fact do involve the use of harsh chemicals and high/low temperatures that could potentially be harmful to cells. Additionally, creating heterogeneous tissue constructs requires high precision. For scaffolds a major challenge is precisely locating pores. The difficulty increases further if the scaffold and pore designs, shapes, and sizes are not uniform or the scaffold has a convoluted structure, gradient pore structure, and/or tortuous pore channels. Some examples of image processing and imaging analysis being used in the tissue engineering field include cell counting tools,46 3D cell morphometry analysis and cell imaging,47−49 3506
DOI: 10.1021/acsbiomaterials.9b00318 ACS Biomater. Sci. Eng. 2019, 5, 3499−3510
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ACS Biomaterials Science & Engineering cell alignment quantification,50 analysis of astrocyte scarring and reconstruction,50 porous scaffold characterization (volume porosity, pore interconnectivity, pore size distribution, morphology),51−57 scaffold fiber characterization (diameter, pore size, orientation),58 and image-guided design and manufacturing of patient-specific devices and anatomic implants.59−61 The method proposed in this paper provides a very unique image processing tool with the potential to create high-quality tissue engineering constructs with a high degree of multicell distribution and deposition accuracy within certain premade scaffold designs. Separation between the scaffold fabrication and cell seeding processes provides a flexible method of creating heterogeneous tissue constructs with fewer limitations and less potential harm to cells. The algorithm can locate the centroid of any object with a clear boundary regardless of its boundary shape. Essentially, if we can visualize the scaffold surface pores, the pores have a homogeneous axial pore channel structure, we not only can deduce the pore size and centroid location but also deposit cells at various depths within the scaffold. Furthermore, the ability to locate pore boundaries with uneven geometries down to ∼200 μm adds to the method’s flexibility. 4.1. Scaffold Characteristics and Image Processing. Scaffolds have several features that need to be considered when image processing is performed. Examples include color, surface texture and imperfections, pore size, and design complexity. The image processing technique needs to consider these issues and be flexible enough when dealing with varying pore size and pore density. In this study, from images of scaffold surfaces we managed to locate macropores and their boundaries and establish what is and is not classified as scaffold material. Nonscaffold areas were defined as pores as well as background. Image noise was considered to be rough surface texture, imperfections, surface damages, and even irregular light diffraction that can influence and vary contrast levels in different pores (Figure 3b−i). The algorithm efficiently minimized these issues by performing a Gaussian smoothing operation to reduce the surface texture and an image closing operation to remove connected components that would otherwise be considered small pores, and it established clear boundary information via threshold segmentation, image binarization, and image dilation. It should be noted that minor connected components occasionally did appear after conversion of the image to binary. In many cases, however, these minor imperfections added little influence to the boundary of the main macropores. Additionally, minor errors in the scaffold boundary also occurred in some instances and was in most cases due to high scaffold surface texture differences. However, as long as pore structures, their dimensions, and centroid locations could be accurately acquired, these data were deemed to be good enough. The MATLAB region analysis function was able to find centroids of any 2D boundary shape. The function measures properties of image regions and returns properties for each 8-connected component and contiguous region with black boundaries. Hence, the efficiency of the algorithm also depends on the image clarity and resolution. 4.2. Camera Calibration. The camera calibration script generated using the Camera Calibrator App in MATLAB was adapted and combined with our imaging processing algorithm to efficiently perform pixel-to-world coordinate transforms. A library of camera calibration data at different sample-to-lens distances was created to help with accessible data suitable for
various scaffold thicknesses. The Camera Calibrator App in MATLAB provided a relatively fast way of automatically reading and processing the images. The generated generic calibration scripts were easy to amend and combine with other algorithm scripts for quick and seamless coordinate transforms. 4.3. Intrascaffold Assembly Parameter Optimization and Choice of Materials. Printing in a vertical manner does have its own set of difficulties. Suitably adapting extrusion speeds and z-scan speeds is vital to accurately print cell-laden hydrogels. A vertical cylindrical deposition shape with roughly the same diameter as the pore is desired. Material has to be deposited from and to desired scaffold depths without diffusion of excess material into the interconnecting pores. Also, the axially designed pore walls should ideally provide support for the cylindrical shape as the nozzle moves upward. Furthermore, for intrascaffold assembly to work, a balance between extrusion speed and z-scan speed for a range of pore sizes with their own suitable nozzle sizes is required. This allows for suitable deposition of material with less risk of lack of material deposition or overflow. Therefore, the assembly parameter experiments and evaluations shown in Figure 4 were deemed to be very important. The pairing of nozzle gauge size to the pore diameter size was also important, although the preference is based on the errors, drift, and similar errors experienced using our extrusion-based printer. However, it should be noted that this technique is not solely aimed toward extrusion-based printing but can in fact be used with any heterogeneous cell assembly platform, which will pose its own set of assembly parameter difficulties and requirements. With regard to the choice of materials, gelatin and alginate are the most commonly used biomaterials for extrusion-based temperature-sensitive 3D bioprinting,43 and their combined use has long been reported. This is especially due to their high biocompatibility with mammalian cells and their ease of gelation.44 4.4. Scaffold Positioning. A minor but key difficulty in image-guided intrascaffold cell assembly is the positioning of the scaffold itself. The slightest of movements would render the attained coordinate data useless. Without a way to hold the scaffold in place, the main objective of providing precise deposition control would be very difficult to attain. Hence, scaffolds were gently attached to Petri dishes using temperature-controlled gelatin solution. At room temperature the scaffolds were held firmly enough for both imaging and assembly. 4.5. Key Advantages and Limitations of the Technique. The experimental results show the proof of concept of image-guided intrascaffold cell assembly as a feasible technique to aid with the manufacturing of heterogeneous tissue engineering constructs within certain premade scaffolds. Scaffolds can first be made using whatever manufacturing processes and secondary processes are needed without potentially harming cells. By the use of image guidance, cells can be accurately populated/distributed on and within the scaffold. An image-guided technique like this to aid scaffold cell seeding has not been attempted before. It is believed that this novel technique can be useful in any area of application of scaffold-based tissue engineering where precise and controlled deposition of cells and/or drugs is required. The technique is modular in design and allows for the camera system to be a stand-alone system or directly implemented with the cell assembly system. 3507
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heterogeneous tissue engineering constructs with accurate cell deposition and controlled distribution. Furthermore, the modular technique can be used with any existing heterogeneous cell assembly platforms and techniques.
However, there are several limitations. The camera system can only take 2D images and would ideally require a flat scaffold surface. Visualization and deduction of pore shape, boundary, and depth would be more difficult if the scaffold surface is oval and/or the pores are not axial. If the sample can be tilted, perhaps this might help to accurately deduce pores and insert materials. We successfully attempted a multidirectional assembly approach by assembling complex structures from both the vertical and horizontal direction (Figure 5) by tilting the scaffold by 90°. However, any rotation or tilting for nozzle insertion from other sides/angles does require additional image analysis that can become quite tedious and timeconsuming. Ideally the method favors axial pore channels and homogeneous pore structure. The method would clearly struggle with overly complex porosities or pore channels displaying higher-level tortuosity. Gradient pore structures, however, might be able to work if they consist of parallel axial pore channels or gradient structures where diameters incrementally get smaller or larger with variant depth and the gradient structure is visible and accessible from the side of the scaffold. In such cases, a multinozzle approach could potentially work. It should be noted, however, that the scope of this technique emphasizes intrascaffold assembly of cell-laden and/or non-cell-laden materials. The aim is more to create complex tissue constructs as opposed to deducing porosity and assembly pathway plans for complex scaffolds. Future works include testing the technique with even smaller scaffold and pore structures together with multicell arrangements. Intrascaffold assembly parameter optimization is required for smaller pore diameters to better control and study deposition of cell-laden material. Other improvements and testing may also include combining multinozzle and multidirectional cell assembly, if so required, for future applications and studies.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsbiomaterials.9b00318. Scaffold fabrication processes; PVA negative mold casting; PDMS stamp casting; complex intrascaffold assembly; sectional views of complex structures (PDF)
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AUTHOR INFORMATION
Corresponding Author
*Phone: +86-10-62782988. E-mail:
[email protected]. ORCID
Ting Zhang: 0000-0002-3619-2492 Author Contributions
This manuscript was written with contributions from all of the authors, who have approved the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors acknowledge funding support from the National Natural Science Foundation of China (81571420 and 31771108). The authors also acknowledge Technician Yue Sun from the Center of Biomedical Analysis at Tsinghua University and Ph.D. student Jiaju Lu from the State Key Laboratory of New Ceramics and Fine Processing (School of Material Science) at Tsinghua University for valuable insight and help with immunostaining analysis and cell imaging.
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5. CONCLUSIONS In this study, a novel and modular image-guided technique was developed for intrascaffold cell assembly. Image processing and camera calibration algorithms were used to locate and convert scaffold pore centroid image coordinates to world coordinates, allowing the creation of accurate 3D cell assembly pathway designs. Intrascaffold assembly parameter experiments established suitable extrusion speeds and z-scan speeds for optimal printability within pores of varying size. The proof of this concept was demonstrated by the successful assembly of complex intrascaffold structures within transparent PLA scaffolds with 500 μm pore sizes. Casted PLGA scaffolds with axial pore diameters as small as ∼200 μm were also fabricated and used for testing. Additionally, NIH3T3-cellladed gelatin/alginate hydrogels were successfully deposited within collagen-coated PLGA scaffolds with 500 μm pore size. Comparisons were made with manual injection seeding and static seeding (surface pipetting) as control groups. Cell distribution analysis using CellTracker showed high cell positioning and distribution control when the proposed image-guided cell assembly technique was used. Live/dead staining assay displayed high cell viability (>93%), and PrestoBlue analysis displayed a favorable increase in cell proliferation over seven culture days, while phalloidin/DAPI immunostaining on Day 7 displayed healthy and elongated cells within the scaffold structure. The technique shows potential to be used as a tool to aid manufacturing of
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