Neural regeneration devices interface with the nervous system and can provide flexibility in material choice, implantation without the need for additional surgeries, and the ability to serve as guides augmented with physical, biological (e.g., cellular), and biochemical functionalities. Given the complexity and challenges associated with neural regeneration, a 3D printing approach to the design and manufacturing of neural devices can provide next‐generation opportunities for advanced neural regeneration via the production of anatomically accurate geometries, spatial distributions of cellular components, and incorporation of therapeutic biomolecules. A 3D printing‐based approach offers compatibility with 3D scanning, computer modeling, choice of input material, and increasing control over hierarchical integration. Therefore, a 3D printed implantable platform can ultimately be used to prepare novel biomimetic scaffolds and model complex tissue architectures for clinical implants in order to treat neurological diseases and injuries. Further, the flexibility and specificity offered by 3D printed in vitro platforms have the potential to be a significant foundational breakthrough with broad research implications in cell signaling and drug screening for personalized healthcare. This progress report examines recent advances in 3D printing strategies for neural regeneration as well as insight into how these approaches can be improved in future studies.
Synthetic biological systems are used for a myriad of applications, including tissue engineered constructs for in vivo use and microengineered devices for in vitro testing. Recent advances in engineering complex biological systems have been fueled by opportunities arising from the combination of bioinspired materials with biological and computational tools. Driven by the availability of large datasets in the “omics” era of biology, the design of the next generation of tissue equivalents will have to integrate information from single‐cell behavior to whole organ architecture. Herein, recent trends in combining multiscale processes to enable the design of the next generation of biomaterials are discussed. Any successful microprocessing pipeline must be able to integrate hierarchical sets of information to capture key aspects of functional tissue equivalents. Micro‐ and biofabrication techniques that facilitate hierarchical control as well as emerging polymer candidates used in these technologies are also reviewed.
more » « less- Award ID(s):
- 1647837
- NSF-PAR ID:
- 10091109
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 31
- Issue:
- 26
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Tissue engineered grafts show great potential as regenerative implants for diseased or injured tissues within the human body. However, these grafts suffer from poor nutrient perfusion and waste transport, thus decreasing their viability post‐transplantation. Graft vascularization is therefore a major area of focus within tissue engineering because biologically relevant conduits for nutrient and oxygen perfusion can improve viability post‐implantation. Many researchers used microphysiological systems as testing platforms for potential grafts owing to an ability to integrate vascular networks as well as biological characteristics such as fluid perfusion, 3D architecture, compartmentalization of tissue‐specific materials, and biophysical and biochemical cues. Although many methods of vascularizing these systems exist, microvascular self‐assembly has great potential for bench‐to‐clinic translation as it relies on naturally occurring physiological events. In this review, the past decade of literature is highlighted, and the most important and tunable components yielding a self‐assembled vascular network on chip are critically discussed: endothelial cell source, tissue‐specific supporting cells, biomaterial scaffolds, biochemical cues, and biophysical forces. This paper discusses the bioengineered systems of angiogenesis, vasculogenesis, and lymphangiogenesis and includes a brief overview of multicellular systems. It concludes with future avenues of research to guide the next generation of vascularized microfluidic models.
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Gorodkin, Jan (Ed.)Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.more » « less
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2938 Using a Human Liver Tissue Equivalent (hLTE) Platform to Define the Functional Impact of Liver-Directed AAV Gene Therapy 63rd ASH Annual Meeting and Exposition, December 11-14, 2021, Georgia World Congress Center, Atlanta, GA Program: Oral and Poster Abstracts Session: 801. Gene Therapies: Poster II Hematology Disease Topics & Pathways: Bleeding and Clotting, Biological, Translational Research, Hemophilia, Genetic Disorders, Clinically Relevant, Diseases, Gene Therapy, Therapies Sunday, December 12, 2021, 6:00 PM-8:00 PM Ritu M Ramamurthy1*, Wen Ting Zheng2*, Sunil George, PhD1*, Meimei Wan1*, Yu Zhou, PhD1*, Baisong Lu, PhD1*, Colin E Bishop, PhD1*, Anthony Atala, M.D.1*, Christopher D Porada, PhD1* and M. Graca Almeida-Porada, MD3 1Fetal Research and Therapy Program, Wake Forest Institute for Regenerative Medicine, Winston Salem, NC 2Massachusetts Institute of Technology, Cambridge, MA 3Fetal Research and Therapy Program, Wake Forest Institute For Regenerative Medicine, Winston-Salem, NC Clinical trials employing AAV vectors for hemophilia A have been hindered by unanticipated immunological and/or inflammatory responses in some of the patients. Also, these trials have often yielded lower levels of transgene expression than were expected based upon preclinical studies, highlighting the poor correlation between the transduction efficiency observed in traditional 2D cultures of primary cells in vitro, and that observed in those same cell types in vivo. It has been also recognized that there are marked species-specific differences in AAV-vector tropism, raising the critical question of the accuracy with which various animal models will likely predict tropism/vector transduction efficiency, and eventual treatment success in humans. Human liver tissue equivalents (hLTEs) are comprised of major cell types in the liver in physiologically relevant frequencies and possess the ability to recapitulate the biology and function of native human liver. Here, we hypothesize that hLTEs can be used as a better model to predict the efficacy and safety of AAV gene therapy in humans. We fabricated hLTEs using 75% hepatocytes, 10% stellate cells, 10% Kupffer cells, and 5% liver sinusoid-derived endothelial cells in 96-well Elplasia plates with 79 microwells per well. hLTEs were transduced at an MOI of 105vg/cell, on the day of fabrication, with the clinically relevant serotypes AAV5 (hLTE-5) or AAV3b (hLTE-3b), both encoding a GFP reporter. After 4 days of self-aggregation, live/dead assay was performed to confirm viability. Non-transduced hLTEs served as negative controls (hLTE(-)), and hLTEs exposed to 20 mM acetaminophen were used as positive controls for liver inflammation/damage. Incucyte® Live-Cell Imaging system was used to track the aggregation and GFP expression of hLTEs. Over the course of the next 5 days, media was collected to determine hepatic functionality, RNA was isolated to assess dysregulation of genes involved in inflammation and fibrosis, DNA was isolated to determine whether AAV vectors integrate into the genome of human hepatocytes and, if so, to define the frequency at which this occurs and the genomic loci of integration, and hLTEs were fixed and processed at appropriate times for histological analyses and transmission electron microscopy (TEM). TEM analysis revealed that all groups exhibited microvilli and bile-canaliculus-like structures, demonstrating the formation of a rudimentary biliary system and, more importantly, proving that hLTEs resemble native liver structure. Incucyte® imaging showed that AAV5 and AAV3b transduction impaired formation of hLTEs (57.57 ± 2.42 and 24.57 ± 4.01 spheroids/well, respectively) in comparison with hLTE(-) (74.86 ± 3.8 spheroids/well). Quantification of GFP expression demonstrated that AAV5 yielded the most efficient transduction of hLTEs (fold change in GFP expression compared to control: 2.73 ± 0.09 and 1.19 ± 0.03 for hLTE-5 and hLTE-3b, respectively). Chromogenic assays showed decreased urea production in cell culture supernatants of AAV transduced groups compared to the non-transduced hLTEs on days 6 and 10 of culture, demonstrating decreased hepatocyte functionality. However, ALT and AST levels were similar in all groups. 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Summary Next‐generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini‐review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static‐, temporal‐, sample‐specific‐ and differential‐networks. The intention of this mini‐review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)‐unsupervised data‐driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.
