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.
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
More Like this
-
more » « less
Abstract -
more » « less
Abstract 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.
-
more » « less
Abstract Direct laser writing via two‐photon polymerization (2PP) is an emerging micro‐ and nanofabrication technique to prepare predetermined and architecturally precise hydrogel scaffolds with high resolution and spatial complexity. As such, these scaffolds are increasingly being evaluated for cell and tissue engineering applications. This article first discusses the basic principles and photoresists employed in 2PP fabrication of hydrogels, followed by an in‐depth introduction of various mechanical and biological characterization techniques used to assess the fabricated structures. The design requirements for cell and tissue related applications are then described to guide the engineering, physicochemical, and biological efforts. Three case studies in bone, cancer, and cardiac tissues are presented that illustrate the need for structured materials in the next generation of clinical applications. This paper concludes by summarizing the progress to date, identifying additional opportunities for 2PP hydrogel scaffolds, and discussing future directions for 2PP research.
-
more » « less
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.
-
more » « less
In recent years, significant work has been devoted to the use of angle‐resolved elastic scattering for the extraction of nuclear morphology in tissue. By treating the nucleus as a Mie scattering object, techniques such as angle‐resolved low‐coherence interferometry (a/LCI) have demonstrated substantial success in identifying nuclear alterations associated with dysplasia. Because optical biopsies are inherently noninvasive, only a small, discretized portion of the 4π scattering field can be collected from tissue, limiting the amount of information available for diagnostic purposes. In this work, we comprehensively characterize the diagnostic impact of variations in angular sampling, range and noise for inverse light scattering analysis of nuclear morphology, using a previously reported dataset from 40 patients undergoing a/LCI optical biopsy for cervical dysplasia. The results from this analysis are applied to a benchtop scanning a/LCI system which compromises angular range for wide‐area scanning capability. This work will inform the design of next‐generation optical biopsy probes by directing optical design towards parameters which offer the most diagnostic utility.
