Design-encoded dual shape-morphing and shape-memory in 4D printed polymer parts toward cellularized vascular grafts.

Current additive manufacturing technologies wherein as-printed simple two-dimensional (2D) structures morph into complex tissue mimetic three-dimensional (3D) shapes are limited to multi-material hydrogel systems, which necessitates multiple fabrication steps and specific materials. This work utilizes a single shape memory thermoplastic polymer (SMP), PLMC (polylactide-co-trimethylene carbonate), to achieve programmable shape deformation through anisotropic design and infill angles encoded during 3D printing. The shape changes were first computationally predicted through finite element analysis (FEA) simulations and then experimentally validated through quantitative correlation. Rectangular 2D sheets could self-roll into complete hollow tubes of specific diameters (ranging from ≈6 mm to ≈10 mm) and lengths (as long as 40 mm), as quantitatively predicted from FEA simulations within one minute at relatively lower temperatures (≈80 °C). Furthermore, shape memory properties were demonstrated post-shape change to exhibit dual shape morphing at temperatures close to physiological levels. The tubes (retained as the permanent shape) were deformed into flat sheets (temporary shape), seeded with endothelial cells (at T < Tg), and thereafter triggered at ≈37 °C back into tubes (permanent shape), utilizing the shape memory properties to yield bioresorbable tubes with cellularized lumens for potential use as vascular grafts with improved long-term patency. Additionally, out-of-plane bending and twisting deformation were demonstrated in complex structures by careful control of infill angles that can unprecedently expand the scope of cellularized biomimetic 3D shapes. This work demonstrates the potential of the combination of shape morphing and SMP behaviors at physiological temperatures to yield next-generation smart implants with precise control over dimensions for tissue repair and regeneration.

4D Printed Programmable Shape-Morphing Hydrogels as Intraoperative Self-Folding Nerve Conduits for Sutureless Neurorrhaphy.

There are only a few reports of implantable 4D printed biomaterials, most of which exhibit slow deformations rendering them unsuitable for in situ surgical deployment. In this study, a hydrogel system is engineered with defined swelling behaviors, which demonstrated excellent printability in extrusion-based 3D printing and programmed shape deformations post-printing. Shape deformations of the spatially patterned hydrogels with defined infill angles are computationally predicted for a variety of 3D printed structures, which are subsequently validated experimentally. The gels are coated with gelatin-rich nanofibers to augment cell growth. 3D-printed hydrogel sheets with pre-programmed infill patterns rapidly self-rolled into tubes in vivo to serve as nerve-guiding conduits for repairing sciatic nerve defects in a rat model. These 4D-printed hydrogels minimized the complexity of surgeries by tightly clamping the resected ends of the nerves to assist in the healing of peripheral nerve damage, as revealed by histological evaluation and functional assessments for up to 45 days. This work demonstrates that 3D-printed hydrogels can be designed for programmed shape changes by swelling in vivo to yield 4D-printed tissue constructs for the repair of peripheral nerve damage with the potential to be extended in other areas of regenerative medicine.

Analytical modelling of spatial deformation pathways in planar and spatial shallow bistable arches.

We analyse spatial bistable arches and present an analytical model incorporating axial, two transverse bending and torsion energy components. We extend the St. Venant and Michell relationship used in flexural-torsional buckling of planar arches and use it in modelling spatial arches. We study deformation pathways in spatial arches and their effect on critical characteristics of bistability such as back and forth switching forces, and the distance travelled by a point of the arch. We show that not considering spatial deformation leads to incorrect inferences concerning the bistability of planar arches too. Thus, this model serves as a generalized framework for the existing analysis on planar arches since they belong to a subset of spatial arches. Additionally, the effects of eccentric loading on spatial deformations are explored for arches with a range of as-fabricated shapes and boundary conditions, and the results are validated with finite-element analysis.

A Nondimensional Model Reveals Alterations in Nuclear Mechanics upon Hepatitis C Virus Replication.

Morphology of the nucleus is an important regulator of gene expression. Nuclear morphology is in turn a function of the forces acting on it and the mechanical properties of the nuclear envelope. Here, we present a two-parameter, nondimensional mechanical model of the nucleus that reveals a relationship among nuclear shape parameters, such as projected area, surface area, and volume. Our model fits the morphology of individual nuclei and predicts the ratio between forces and modulus in each nucleus. We analyzed the changes in nuclear morphology of liver cells due to hepatitis C virus (HCV) infection using this model. The model predicted a decrease in the elastic modulus of the nuclear envelope and an increase in the pre-tension in cortical actin as the causes for the change in nuclear morphology. These predictions were validated biomechanically by showing that liver cells expressing HCV proteins possessed enhanced cellular stiffness and reduced nuclear stiffness. Concomitantly, cells expressing HCV proteins showed downregulation of lamin-A,C and upregulation of ß-actin, corroborating the predictions of the model. Our modeling assumptions are broadly applicable to adherent, monolayer cell cultures, making the model amenable to investigate changes in nuclear mechanics due to other stimuli by merely measuring nuclear morphology. Toward this, we present two techniques, graphical and numerical, to use our model for predicting physical changes in the nucleus.

Single-photon-multi-layer-interference lithography for high-aspect-ratio and three-dimensional SU-8 micro-/nanostructures.

We report microstructures of SU-8 photo-sensitive polymer with high-aspect-ratio, which is defined as the ratio of height to in-plane feature size. The highest aspect ratio achieved in this work exceeds 250. A multi-layer and single-photon lithography approach is used in this work to expose SU-8 photoresist of thickness up to 100 µm. Here, multi-layer and time-lapsed writing is the key concept that enables nanometer localised controlled photo-induced polymerisation. We use a converging monochromatic laser beam of 405 nm wavelength with a controllable aperture. The reflection of the converging optics from the silicon substrate underneath is responsible for a trapezoidal edge profile of SU-8 microstructure. The reflection induced interfered point-spread-function and multi-layer-single-photon exposure helps to achieve sub-wavelength feature sizes. We obtained a 75 nm tip diameter on a pyramid shaped microstructure. The converging beam profile determines the number of multiple optical focal planes along the depth of field. These focal planes are scanned and exposed non-concurrently with varying energy dosage. It is notable that an un-automated height axis control is sufficient for this method. All of these contribute to realising super-high-aspect-ratio and 3D micro-/nanostructures using SU-8. Finally, we also address the critical problems of photoresist-based micro-/nanofabrication and their solutions.

A Scalable Perfusion Culture System with Miniature Peristaltic Pumps for Live-Cell Imaging Assays with Provision for Microfabricated Scaffolds.

We present a perfusion culture system with miniature bioreactors and peristaltic pumps. The bioreactors are designed for perfusion, live-cell imaging studies, easy incorporation of microfabricated scaffolds, and convenience of operation in standard cell culture techniques. By combining with miniature peristaltic pumps-one for each bioreactor to avoid cross-contamination and to maintain desired flow rate in each-we have made a culture system that facilitates perfusion culture inside standard incubators. This scalable system can support multiple parallel perfusion experiments. The major components are fabricated by three-dimensional printing using VeroWhite, which we show to be amenable to ex vivo cell culture. Furthermore, the components of the system can be reused, thus making it economical. We validate the system and illustrate its versatility by culturing primary rat hepatocytes, live imaging the growth of mouse fibroblasts (NIH 3T3) on microfabricated ring-scaffolds inserted into the bioreactor, performing perfusion culture of breast cancer cells (MCF7), and high-magnification imaging of hepatocarcinoma cells (HuH7).

A novel approach for large-scale polypeptide folding based on elastic networks using continuous optimization.

We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa-Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation.

A search for energy minimized sequences of proteins.

In this paper, we present numerical evidence that supports the notion of minimization in the sequence space of proteins for a target conformation. We use the conformations of the real proteins in the Protein Data Bank (PDB) and present computationally efficient methods to identify the sequences with minimum energy. We use edge-weighted connectivity graph for ranking the residue sites with reduced amino acid alphabet and then use continuous optimization to obtain the energy-minimizing sequences. Our methods enable the computation of a lower bound as well as a tight upper bound for the energy of a given conformation. We validate our results by using three different inter-residue energy matrices for five proteins from protein data bank (PDB), and by comparing our energy-minimizing sequences with 80 million diverse sequences that are generated based on different considerations in each case. When we submitted some of our chosen energy-minimizing sequences to Basic Local Alignment Search Tool (BLAST), we obtained some sequences from non-redundant protein sequence database that are similar to ours with an E-value of the order of 10(-7). In summary, we conclude that proteins show a trend towards minimizing energy in the sequence space but do not seem to adopt the global energy-minimizing sequence. The reason for this could be either that the existing energy matrices are not able to accurately represent the inter-residue interactions in the context of the protein environment or that Nature does not push the optimization in the sequence space, once it is able to perform the function.

An amino acid map of inter-residue contact energies using metric multi-dimensional scaling.

We present an amino map based on their inter-residue contact energies using the Miyazawa-Jernigan matrix. This work is based on the method of metric multi-dimensional scaling (MMDS). The MMDS map shows, among other things, that the MJ contact energies imply the hydrophobic-hydrophilic nature of the amino acid residues. With the help of the map we are able to compare and draw inferences from uncorrelated data sets such as BLOSUM and PAM with MJ methods. We also use a hierarchical clustering method on our MMDS distance matrix to group the amino acids and arrive at an optimum number of groups for simplifying the amino acid set.

A method for computing the inter-residue interaction potentials for reduced amino acid alphabet.

Inter-residue potentials are extensively used in the design and evaluation of protein structures. However,dealing with all (20 x 20) interactions becomes computationally difficult in extensive investigations. Hence, it is desirable to reduce the alphabet of 20 amino acids to a smaller number. Currently, several methods of reducing the residue types exist; however a critical assessment of these methods is not available. Towards this goal,here we review and evaluate different methods by comparing with the complete (20 x 20) matrix of Miyazawa-Jernigan potential, including a method of grouping adopted by us, based on multi dimensional scaling (MDS). The second goal of this paper is the computation of inter-residue interaction energies for the reduced amino acid alphabet, which has not been explicitly addressed in the literature until now. By using a least squares technique, we present a systematic method of obtaining the interaction energy values for any type of grouping scheme that reduces the amino acid alphabet. This can be valuable in designing the protein structures.

Protein sequence design based on the topology of the native state structure.

Computational design of sequences for a given structure is generally studied by exhaustively enumerating the sequence space or by searching in such a large space, which is prohibitively expensive. However, we point out that the protein topology has a wealth of information, which can be exploited to design sequences for a chosen structure. In this paper, we present a computationally efficient method for ranking the residue sites in a given native-state structure, which enables us to design sequences for a chosen structure. The premise for the method is that the topology of the graph representing the energetically interacting neighbours in a protein plays an important role in the inverse-folding problem. While our previous work (which was also based on topology) used eigenspectral analysis of the adjacency matrix of interactions for ranking the residue sites in a given chain, here we use a simple but effective way of assigning weights to the nodes on the basis of secondary connections, along with primary connections. This indirectly accounts for the edge weight in the graph and removes degeneracy in the degree. The new scheme needs only a few multiplications and additions to compute the preferred ranking of the residue sites even for structures of real proteins of sizes of a few hundred amino acid residues. We use HP lattice model examples (for which exhaustive enumeration of sequences is practical) to validate our ranking approach in obtaining sequences of lowest energy for any H-P residue composition for a given native-state structure. Some examples of native structures of real proteins are also included. Quantitative comparison of the efficacy of the new scheme with the earlier schemes is made. The new scheme consistently performs better and with much lower computational cost. An optimization procedure is added to work with the new scheme in a few rare cases wherein the new scheme fails to provide the best sequence, an optimization procedure is added to work with the new scheme.