Oct 24, 2021 Recent innovations for creating 3D printed tissues have made it possible for the large-scale production of tissue products from regenerative medicine to alternative meat applications (Mekhileri et al 2018). To realize such production levels, there are several considerations related to the automation of fabrication processes. This will include fabrication that is highly repeatable, the utilization of appropriate quality controls, integration with multiple types of processes, and the implementation of artificial intelligence (AI). The following will delve deeper into these concepts and their importance towards enabling the automation of 3D tissue fabrication.
Repeatability
Whether a 3D tissue will be used as a tissue replacement therapy, a test model for novel therapeutics, or a food product, the ability to reliably generate tissues is important for turning the optimized fabrication process into an automated workflow. This reliability comes from the repeatability of the process. One way to achieve repeatability is with technology that is inherently repeatable when conducting fabrication steps or procedures, such as robotic arms. Fabrication with robotic arm technology can generate tissue environments and scaffolds with high levels of precision such that the starting point of each experiment is the same across wells or across time points. Another way to ensure that each fabrication step is highly repeatable is through testing the consistency of the result of these steps, which is done through quality control assessment.
Quality control
To ensure 3D tissue products meet the appropriate standards for their associated applications, they will need to undergo a series of tests qualifying the tissue that is being fabricated. Due to the dynamic nature of tissues, it is important to assess how a tissue is growing/maturing so that there is not a major loss of expensive materials or manufacturing time whilst maintaining product consistency.
Quality control processes come in different forms based on the application. For example, tissue validation can come in the form of histological analysis, to ensure that the structural components of the tissue appropriately match with the end-product tissue of interest. Tensile and compression testing determines the structural integrity of a 3D construct which can play major roles in the phenotype of the tissue or can drastically affect the texture of the meat product, thereby impacting whether it closely mimics the desired feel of the target meat tissue. For microphysiological systems or in vitro environments, phenotypic analysis can be done in the form of proteomics or transcriptome analysis from media supplements to assess the status of the culture system based on what has been secreted within their exosomes. Whether the quality control data comes in the form of tissue compression or exosome composition, such analytics can provide decision points throughout a fabrication process, where there is a definition (provided by the engineer) of whether to proceed to the next stage of the process or adjust aspects of the tissue environment as necessary. These adjustments can range from adding a particular compound or growth factor to a culture environment, altering the flow conditions of a microfluidic based platform, or even providing additional scaffold-based supports. To trust the results of a quality control assessment, the environment must also be controlled so as to not to not affect the fabrication process. Environmental sterility is also important for tissue products that will either be ingested or used directly as a therapeutic. A review of environmental considerations for tissue fabrication processes is referenced here (Whitford and Sanders 2021). Technology that can both conduct live imaging, tissue construct probing, as well as media aspirating without the requirement of human intervention as well as 3D tissue fabrication, will help realize the automation of an entire process and workflow.
Process integration
The automation of tissue fabrication involves integration between multiple and varied processes. Ideally, these processes are conducted by a single platform that can interchange between both sets of tools, which can range from those that enable pipetting and aspirating fluids as well as picking up multi-well plates, all in addition to tissue fabrication. A workflow that exemplifies such integration begins with a multi-well plate being moved onto a print stage, followed by the tissue fabrication or bioprinting. Media components are then added to the cultures, and the plate is then picked up and moved to an incubation and/or conditioning system. After a predetermined amount of time, the plate can then be placed into an imaging platform for histological analysis (Figure 1).
Artificial Intelligence
Process integration can then be further optimized through the latest innovations in artificial intelligence and machine learning. The assessment of tissue quality can inform an AI system on the optimal way to fabricate a product of interest. Therefore, the automation of new lines of products within the same vein of solutions can be streamlined by learned decision points during different stages of fabrication.
“If everyone is moving forward together, then success takes care of itself.”
-Henry Ford
The concepts listed above help lay the groundwork for automatic production of tissue products. Relevant solutions entail, what can appear to be disparate fields in engineering and biology such as robotics, to software engineering, as well as molecular biology. Expertise in tissue biology will also be imperative in successful tissue product assembly. The automation of tissue fabrication inherently brings together expertise from across these fields. This intersection will enable new levels of innovation and the results are only beginning to be realized.
References:
N.V. Mekhileri et al 2018 Biofabrication 10, 024103
W.G. Whitford and L. Sanders 2021 GEN 41, 7
About the Author:
Dr. Lehanna Sanders, Business Development Manager, Advanced Solutions Life Sciences, is a cellular and molecular biologist and has done research within the field of regenerative medicine for 8 years. She received her Bachelor’s degree in Cellular, Molecular, and Development Biology from Purdue University where she completed an honors thesis project in Biomedical Engineering. She then completed her PhD at Vanderbilt University where she published work in the area of molecular repair processes following acute cardiac injury. During her time at Vanderbilt, she served on the Skills Development Committee for the NHLBI Progenitor Cell Biology Consortium, as well as a member of the Board of the Directors for the Life Science Tennessee-Academic Alliance. She now works in Business Development for Advanced Solutions Life Sciences, where she is continuing to grow the field of regenerative biology through working closely with scientists and engineers to advance innovations in 3D bioprinting and biofabrication.
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