Optimizing Bioprinting Hydrogel using Machine Learning, Modified or Decellularized ECM

Category: Blog,From Academia
blank blank Feb 10, 2021

 Bioprinting organs cannot succeed without the right biomaterials. In this issue, three articles focus on different ways to optimize bioprinting hydrogel. The first article focuses on using machine learning with various parameters to optimize FRESH technique with alginate. This is one of few articles exploring the future scaling of automated biofabrication and tissue engineering. The second study explores an application using 3D bioprinting optimized methacrylated HA (MeHA), a modified major component of extracellular matrix (ECM) to create in vitro testbeds for studying neural repair. The final article goes a step further by using 3D printed esophageal derived decellularized ECM hydrogel-loaded stent to treat radiation esophagitis in the animal models. “From Academia” features recent, relevant, close to commercialization academic publications. Subjects include but not limited to healthcare 3D printing, 3D bioprinting, and related emerging technologies.

Email: Rance Tino (tino.rance@gmail.com) if you want to share relevant academic publications with us.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers

Authored by Jennifer M. Bone, Christopher M. Childs, Aditya Menon, Barnabás Póczos, Adam W. Feinberg, Philip R. LeDuc, and Newell R. Washburn. ACS Biomaterials Science & Engineering. 20 November 2020

HML model of the FRESH printing process represented by a tiered structure. The bottom layer consists of system variables (predictors) that are directly controlled in the laboratory. The middle layer is a set of physical variables chosen to describe the print system and is parameterized by the bottom-layer predictors. Statistical inference in the form of LASSO is used to determine the system response (print score for lines and corners). The print score is related to print fidelity by prints that have less than 10% error compared to the CAD file (or a score of at least 90%) in randomly assessed ROI on the print. Scale bars are 250 μm. Copyright ACS Biomaterials Science & Engineering
HML model of the FRESH printing process represented by a tiered structure. The bottom layer consists of system variables (predictors) that are directly controlled in the laboratory. The middle layer is a set of physical variables chosen to describe the print system and is parameterized by the bottom-layer predictors. Statistical inference in the form of LASSO is used to determine the system response (print score for lines and corners). The print score is related to print fidelity by prints that have less than 10% error compared to the CAD file (or a score of at least 90%) in randomly assessed ROI on the print. Scale bars are 250 μm. Copyright ACS Biomaterials Science & Engineering

Abstract: 

A hierarchical machine learning (HML) framework is presented that uses a small dataset to learn and predict the dominant build parameters necessary to print high-fidelity 3D features of alginate hydrogels.

We examine the 3D printing of soft hydrogel forms printed with the freeform reversible embedding of the suspended hydrogel (FRESH) method based on a CAD file that isolated the single-strand diameter and shapes fidelity of printed alginate.

Combinations of system variables ranging from print speed, flow rate, ink concentration to nozzle diameter were systematically varied to generate a small dataset of 48 prints. Prints were imaged and scored according to their dimensional similarity to the CAD file, and high print fidelity was defined as prints with less than 10% error from the CAD file.

As a part of the HML framework, the statistical inference was performed, using the least absolute shrinkage and selection operator to find the dominant variables that drive the error in the final prints. The model fit between the system parameters and print score was elucidated and improved by a parameterized middle layer of variable relationships which showed good performance between the predicted and observed data (R2 = 0.643).

Optimization allowed for the prediction of build parameters that gave rise to high-fidelity prints of the measured features. A trade-off was identified when optimizing for the fidelity of different features printed within the same construct, showing the need for complex predictive design tools.

A combination of known and discovered relationships was used to generate process maps for the 3D bioprinting designer that show error minimums based on the chosen input variables. Our approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.

Tradeoff between optimized features showing that conditions for optimizing corners were less well-suited for lines (left), and conversely, optimization of line morphology reduced the print fidelity for corners (right). Copyright ACS Biomaterials Science & Engineering
Tradeoff between optimized features showing that conditions for optimizing corners were less well-suited for lines (left), and conversely, optimization of line morphology reduced the print fidelity for corners (right). Copyright ACS Biomaterials Science & Engineering

Three-Dimensional Bioprinted Hyaluronic Acid Hydrogel Test Beds for Assessing Neural Cell Responses to Competitive Growth Stimuli

Authored by Tran B. Ngo, Benjamin S. Spearman, Nora Hlavac, and Christine E. Schmidt, ACS Biomaterials Science & Engineering. 1 December 2020 

A schematic of the 3D bioprinted in vitro test bed design consisting of a base printing with the main bioink and two chambers that can be loaded with different growth factors. A trimmed DRG is seeded in Col-I glue at the center of the design. Copyright ACS Biomaterials Science & Engineering
A schematic of the 3D bioprinted in vitro test bed design consisting of a base printing with the main bioink and two chambers that can be loaded with different growth factors. A trimmed DRG is seeded in Col-I glue at the center of the design. Copyright ACS Biomaterials Science & Engineering

Abstract: 

Hyaluronic acid (HA) is an abundant extracellular matrix (ECM) component in soft tissues throughout the body and has found wide adoption in tissue engineering.

This study focuses on the optimization of methacrylated HA (MeHA) for three-dimensional (3D) bioprinting to create in vitro testbeds that incorporate regeneration-promoting growth factors in neural repair processes.

To evaluate MeHA as a potential bioink, rheological studies were performed with PC-12 cells to demonstrate shear thinning properties maintained when printing with and without cells.

Next, an extrusion-based Cellink BIO X 3D printer was used to bioprint various MeHA solutions combined with collagen-I to determine which formulation was the most optimal for creating 3D features. Results indicated that MeHA (10 mg/mL) with collagen-I (3 mg/mL) was most suitable.

As Schwann cells (SCs) are a critical component of neural repair and regeneration, SC adhesion assessment via integrin β1 immunostaining indicated that the bioink candidate adequately supported SC adhesion and migration when compared to Col-I, a highly cell-adhesive ECM component. MeHA/collagen-I bioink was adapted for neural-specific applications by printing with the neural growth factor (NGF) and glial cell line-derived neurotrophic factor (GDNF). These testbeds were conducive for SC infiltration and presented differential migration responses. Finally, a two-chamber in vitro testbed design was created to study competitive biochemical cues. Dorsal root ganglia were seeded in test beds and demonstrated directional neurite extension (measured by β-III tubulin and GAP43 immunostaining) in response to NGF and GDNF.

Overall, the selected MeHA/collagen-I bioink was bioprintable, improved cell viability compared to molded controls, and was conducive for cell adhesion, growth factor sequestration, and neural cell infiltration. MeHA is a suitable bioink candidate for extrusion-based bioprinting and will be useful in the future development of spatially complex testbeds to advance in vitro models as an alternative to common in vivo tests for neural repair applications. 

SC migration into bioprinted hydrogels containing no GF, GDNF or NGF after 7 days in culture. (A) Representative images of SC migration into printed hydrogels. (B) Quantificaiton of distance traveled into hydrogel showed that SCs tended to migrate farther in scaffolds bioprinted with GDNF (n=5). **p ≤ 0.01. Copyright ACS Biomaterials Science & Engineering
SC migration into bioprinted hydrogels containing no GF, GDNF or NGF after 7 days in culture. (A) Representative images of SC migration into printed hydrogels. (B) Quantificaiton of distance traveled into hydrogel showed that SCs tended to migrate farther in scaffolds bioprinted with GDNF (n=5). **p ≤ 0.01. Copyright ACS Biomaterials Science & Engineering

Therapeutic effect of decellularized extracellular matrix-based hydrogel for radiation esophagitis by 3D printed esophageal stent

Authored by Dong-HeonHaab, Suhun Chaea, Jae Yeon Lee, Jae Yun Kim, Jung bin Yoon, Tugce Sen, Sung-Woo Lee, Hak Jae Kim, Jae Ho Cho, Dong-Woo Cho. ACS Applied Materials & Interfaces. 20 January 2021

Schematic illustration of the current research. The esophagus-derived decellularized extracellular matrix (EdECM) hydrogel-loaded stent was fabricated using a rotating rod combined 3D printing system (2RPS). Polycaprolactone (PCL) was extruded onto the rotating rod layer-by-layer to fabricating stent framework with a reservoir for holding EdECM hydrogel. An EdECM hydrogel was then deposited into this reservoir frame. Simultaneously, a radiation esophagitis rat model was established. The fabricated stent was implanted into the esophagus of the animal model using custom catheter. Copyright. Biomaterials
Schematic illustration of the current research. The esophagus-derived decellularized extracellular matrix (EdECM) hydrogel-loaded stent was fabricated using a rotating rod combined 3D printing system (2RPS). Polycaprolactone (PCL) was extruded onto the rotating rod layer-by-layer to fabricating stent framework with a reservoir for holding EdECM hydrogel. An EdECM hydrogel was then deposited into this reservoir frame. Simultaneously, a radiation esophagitis rat model was established. The fabricated stent was implanted into the esophagus of the animal model using custom catheter. Copyright. Biomaterials

Abstract: 

Radiation esophagitis, the most common acute adverse effect of radiation therapy, leads to unwanted consequences including discomfort, pain, an even death. However, no direct cure exists for patients suffering from this condition, with the harmful effect of ingestion and acid reflux on the damaged esophageal mucosa remaining an unresolved problem.

Through the delivery of the hydrogel with a stent platform, we aimed to evaluate the regenerative capacity of a tissue-specific decellularized extracellular matrix (dECM) hydrogel on damaged tissues. For this, an esophagus-derived dECM (EdECM) was developed and shown to have superior bio functionality and rheological properties, as well as physical stability, potentially providing a better microenvironment for tissue development.

An EdECM hydrogel-loaded stent was sequentially fabricated using a rotating rod combined 3D printing system that showed structural stability and protected a loaded hydrogel during delivery. Finally, following stent implantation, the therapeutic effect of EdECM was examined in a radiation esophagitis rat model. Our findings demonstrate that EdECM hydrogel delivery via a stent platform can rapidly resolve an inflammatory response, thus promoting a pro-regenerative microenvironment. The results suggest a promising therapeutic strategy for the treatment of radiation esophagitis.

(A) 3D printed esophageal stent at various sizes (i) - reservoir, (ii) - bridge, and (iii) - dumbbell-like shape. (B) EdECM loaded-esophageal stent for implantation. (C) Outer and inner parts of the catheter. (D) Assembled catheter with esophageal stent. Copyright. Biomaterials
(A) 3D printed esophageal stent at various sizes (i) – reservoir, (ii) – bridge, and (iii) – dumbbell-like shape. (B) EdECM loaded-esophageal stent for implantation. (C) Outer and inner parts of the catheter. (D) Assembled catheter with esophageal stent. Copyright. Biomaterials

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