Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities

Polymeric Materials, Advances and Applications in Tissue Engineering: A Review

Tissue Engineering (TE) is an interdisciplinary field that encompasses materials science in combination with biological and engineering sciences. In recent years, an increase in the demand for therapeutic strategies for improving quality of life has necessitated innovative approaches to designing intelligent biomaterials aimed at the regeneration of tissues and organs. Polymeric porous scaffolds play a critical role in TE strategies for providing a favorable environment for tissue restoration and establishing the interaction of the biomaterial with cells and inducing substances. This article reviewed the various polymeric scaffold materials and their production techniques, as well as the basic elements and principles of TE. Several interesting strategies in eight main TE application areas of epithelial, bone, uterine, vascular, nerve, cartilaginous, cardiac, and urinary tissue were included with the aim of learning about current approaches in TE. Different polymer-based medical devices approved for use in clinical trials and a wide variety of polymeric biomaterials are currently available as commercial products. However, there still are obstacles that limit the clinical translation of TE implants for use wide in humans, and much research work is still needed in the field of regenerative medicine.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art

Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficient computer-aided and mathematical model-based methods are required for efficient and knowledge-driven characterization, optimization, and routine manufacturing of tissue culture systems. As an alternative to purely experimental-driven research, the usage of comprehensive mathematical models as a virtual in silico representation of the tissue culture, namely a digital twin, can be advantageous. Digital twins include the mechanistic of the biological system in the form of diverse mathematical models, which describe the interaction between tissue culture techniques and cell growth, metabolism, and the quality of the tissue. In this review, current concepts, expectations, and the state of the art of digital twins for tissue culture concepts will be highlighted. In general, DT’s can be applied along the full process chain and along the product life cycle. Due to the complexity, the focus of this review will be especially on the design, characterization, and operation of the tissue culture techniques.

Guidelines for establishing a 3-D printing biofabrication laboratory

Advanced manufacturing and 3D printing are transformative technologies currently undergoing rapid adoption in healthcare, a traditionally non-manufacturing sector. Recent development in this field, largely enabled by merging different disciplines, has led to important clinical applications from anatomical models to regenerative bioscaffolding and devices. Although much research to-date has focussed on materials, designs, processes, and products, little attention has been given to the design and requirements of facilities for enabling clinically relevant biofabrication solutions. These facilities are critical to overcoming the major hurdles to clinical translation, including solving important issues such as reproducibility, quality control, regulations, and commercialization. To improve process uniformity and ensure consistent development and production, large-scale manufacturing of engineered tissues and organs will require standardized facilities, equipment, qualification processes, automation, and information systems. This review presents current and forward-thinking guidelines to help design biofabrication laboratories engaged in engineering model and tissue constructs for therapeutic and non-therapeutic applications.

Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering.

Recent Advances in Enabling Technologies in 3D Printing for Precision Medicine

Advances in areas such as data analytics, genomics, and imaging have revealed individual patient complexities and exposed the inherent limitations of generic therapies for patient treatment. These observations have also fueled the development of precision medicine approaches, where therapies are tailored for the individual rather than the broad patient population. 3D printing is a field that intersects with precision medicine through the design of precision implants with patient-directed shapes, structures, and materials or for the development of patient-specific in vitro models that can be used for screening precision therapeutics. Toward their success, advances in 3D printing and biofabrication technologies are needed with enhanced resolution, complexity, reproducibility, and speed and that encompass a broad range of cells and materials. The overall goal of this progress report is to highlight recent advances in 3D printing technologies that are helping to enable advances important in precision medicine.

Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration

Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.

Emerging Technologies for Tissue Engineering: From Gene Editing to Personalized Medicine

IMPACT STATEMENT

History has shown us how tissue engineering can be advanced by embracing technological innovation. In this perspective, we highlight some of the most promising emerging technologies and discuss how they can be integrated into existing tissue engineering protocols. The proposed technologies offer the opportunity to reshape how we currently design, engineer, and characterize tissue grafts for improved in vivo regeneration.

Integrating Organs-on-Chips: Multiplexing, Scaling, Vascularization, and Innervation

Organs-on-chips (OoCs) have attracted significant attention because they can be designed to mimic in vivo environments. Beyond constructing a single OoC, recent efforts have tried to integrate multiple OoCs to broaden potential applications such as disease modeling and drug discoveries. However, various challenges remain for integrating OoCs towards in vivo-like operation, such as incorporating various connections for integrating multiple OoCs. We review multiplexed OoCs and challenges they face: scaling, vascularization, and innervation. In our opinion, future OoCs will be constructed to have increased predictive power for in vivo phenomena and will ultimately become a mainstream tool for high quality biomedical and pharmaceutical research.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

Engineering Precision Medicine

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs-on-chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential.

iPhemap: an atlas of phenotype to genotype relationships of human iPSC models of neurological diseases

Disease modeling with induced pluripotent stem cells (iPSCs) is creating an abundance of phenotypic information that has become difficult to follow and interpret. Here, we report a systematic analysis of research practices and reporting bias in neurological disease models from 93 published articles. We find heterogeneity in current research practices and a reporting bias toward certain diseases. Moreover, we identified 663 CNS cell-derived phenotypes from 243 patients and 214 controls, which varied by mutation type and developmental stage in vitro We clustered these phenotypes into a taxonomy and characterized these phenotype-genotype relationships to generate a phenogenetic map that revealed novel correlations among previously unrelated genes. We also find that alterations in patient-derived molecular profiles associated with cellular phenotypes, and dysregulated genes show predominant expression in brain regions with pathology. Last, we developed the iPS cell phenogenetic map project atlas (iPhemap), an open submission, online database to continually catalog disease phenotypes. Overall, our findings offer new insights into the phenogenetics of iPSC-derived models while our web tool provides a platform for researchers to query and deposit phenotypic information of neurological diseases.

Emerging functional markers for cancer stem cell-based therapies: Understanding signaling networks for targeting metastasis

Metastasis is one of the most challenging issues in cancer patient management, and effective therapies to specifically target disease progression are missing, emphasizing the urgent need for developing novel anti-metastatic therapeutics. Cancer stem cells (CSCs) gained fast attention as a minor population of highly malignant cells within liquid and solid tumors that are responsible for tumor onset, self-renewal, resistance to radio- and chemotherapies, and evasion of immune surveillance accelerating recurrence and metastasis. Recent progress in the identification of their phenotypic and molecular characteristics and interactions with the tumor microenvironment provides great potential for the development of CSC-based targeted therapies and radical improvement in metastasis prevention and cancer patient prognosis. Here, we report on newly uncovered signaling mechanisms controlling CSC's aggressiveness and treatment resistance, and CSC-specific agents and molecular therapeutics, some of which are currently under investigation in clinical trials, gearing towards decisive functional CSC intrinsic or surface markers. One special research focus rests upon subverted regulatory pathways such as insulin-like growth factor 1 receptor signaling and its interactors in metastasis-initiating cell populations directly related to the gain of stem cell- and EMT-associated properties, as well as key components of the E2F transcription factor network regulating metastatic progression, microenvironmental changes, and chemoresistance. In addition, the study provides insight into systems biology tools to establish complex molecular relationships behind the emergence of aggressive phenotypes from high-throughput data that rely on network-based analysis and their use to investigate immune escape mechanisms or predict clinical outcome-relevant CSC receptor signaling signatures. We further propose that customized vector technologies could drastically enhance systemic drug delivery to target sites, and summarize recent progress and remaining challenges. This review integrates available knowledge on CSC biology, computational modeling approaches, molecular targeting strategies, and delivery techniques to envision future clinical therapies designed to conquer metastasis-initiating cells.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

Advances in cancer stem cell targeting: How to strike the evil at its root

Cancer progression to metastatic stages is still unmanageable and the promise of effective anti-metastatic therapy remains largely unmet, emphasizing the need to develop novel therapeutics. The special focus here is on cancer stem cells (CSC) as the seed of tumor initiation, epithelial-mesenchymal transition, chemoresistance and, as a consequence, drivers of metastatic dissemination. We report on targeted therapies gearing towards the CSC's internal and membrane-anchored markers using agents such as antibody derivatives, nucleic therapeutics, small molecules and genetic payloads. Another emphasis lies on novel proceedings envisaged to deliver current and prospective therapies to the target sites using newest viral and non-viral vector technologies. In this review, we summarize recent progress and remaining challenges in therapeutic strategies to combat CSC.