Translational Biomedical Research Research Articles

Specimen science-guided construction of biobanks

<p indent="0mm">Biological samples are known to be essential for clinical diagnosis of various diseases. They are also extremely valuable for subsequent scientific studies to unravel the mystery of illnesses. Translational medicine is an interdisciplinary field to discover new diagnostic tools and treatments by combining clinical science with basic biomedical research. Biological studies in translational medicine are increasingly dependent on large numbers of relevant specimens. Biobanks are established to collect, store, and deliver such complex specimens to meet clinical and research needs. Biobanks are biorepository facilities capable of storing large numbers of specimens. Most domestic and foreign biobanks utilize different degrees of standardization and automation to index and organize the large numbers of biological samples. It remains a challenge to accurately store various biological samples while maintaining their original biological characteristics to meet the demands of precision medicine and basic scientific research. This article focuses on current efforts in the construction and operation of biobanks to maintain accuracy and serve the complex requirements of clinical diagnosis and basic research. Firstly, the importance of sample collection, transportation, processing, storage, and information management are discussed. We emphasize the integration of clinical information and scientific research data as an essential part of biobanks. Secondly, we summarize how to maintain the biological integrity of various biological samples such as arterial and venous blood and various tissue samples. More specifically, (1) peripheral blood samples are important sources of material for liquid biopsies, while blood samples during surgeries are often collected from arteries of patients. In addition, there are significant differences in both cellular and non-cellular components between blood from arteries and veins. Therefore, it is important to differentiate these two blood samples. (2) Tissue samples are notoriously heterogenic, in particular for tumors and symmetrical organs. Thirdly, we discuss biological activities of specimens to support chimeric antigen receptor T cell immunotherapies, organoid establishment, exosome functional studies, and patient-derived tumor xenograft (PDX) animal models. Finally, we try to explore a multi-dimensional utilization of biological samples and aim to achieve “One specimen for multiple purposes” and “Multiple specimens for one purpose” management models as an important part of the new discipline of sample science and cryobiology. The ultimate goal is to utilize biobanks in an accurate and precise manner to aid the advancement of precision medicine and basic biological research efforts. The large number of human biological specimens managed by biobanks is poised to be an integral part of future translational medicine and fundamental biomedical research.

Emerging Roles of 1D Vertical Nanostructures in Orchestrating Immune Cell Functions.

Engineered nano–bio cellular interfaces driven by 1D vertical nanostructures (1D‐VNS) are set to prompt radical progress in modulating cellular processes at the nanoscale. Here, tuneable cell–VNS interfacial interactions are probed and assessed, highlighting the use of 1D‐VNS in immunomodulation, and intracellular delivery into immune cells—both crucial in fundamental and translational biomedical research. With programmable topography and adaptable surface functionalization, 1D‐VNS provide unique biophysical and biochemical cues to orchestrate innate and adaptive immunity, both ex vivo and in vivo. The intimate nanoscale cell–VNS interface leads to membrane penetration and cellular deformation, facilitating efficient intracellular delivery of diverse bioactive cargoes into hard‐to‐transfect immune cells. The unsettled interfacial mechanisms reported to be involved in VNS‐mediated intracellular delivery are discussed. By identifying up‐to‐date progress and fundamental challenges of current 1D‐VNS technology in immune‐cell manipulation, it is hoped that this report gives timely insights for further advances in developing 1D‐VNS as a safe, universal, and highly scalable platform for cell engineering and enrichment in advanced cancer immunotherapy such as chimeric antigen receptor‐T therapy.

HEnRY: a DZIF LIMS tool for the collection and documentation of biomaterials in multicentre studies

BackgroundWell-characterized biomaterials of high quality have great potential for acceleration and quality improvement in translational biomedical research. To improve accessibility of local sample collections, efforts have been made to create central biomaterial banks and catalogues. Available technical solutions for creating professional local sample catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Therefore, the Translational Thematic Unit HIV of the German Center for Infection Research (DZIF) developed a Laboratory Information and Management System (LIMS) called HIV Engaged Research Technology (HEnRY) for implementation into the Translational Platform HIV (TP-HIV) at the DZIF and other research networks.ResultsHEnRY is developed at the University Hospital of Cologne. It is an advanced LIMS to manage processing and storage of samples and aliquots of different sample types. Features include:monitoring of stored samples and associated informationdata selection via query tools or Structured Query Language (SQL)preparation of summary documents, including scannable search listscentralized management of the practical laboratory part of multicentre studies (e.g. import of drawing schemes and sample processing steps),preparation of aliquot shipments, including associated documents to be added to shipmentsunique and secure identification of aliquots through use of customizable Quick Response (QR) code labels directly from HEnRYsupport of aliquot data transmission to central registries.In summary, HEnRY offers all features necessary for a LIMS software. In addition, the structure of HEnRY provides sufficient flexibility to allow the implementation in other research areas.ConclusionHEnRY is a free biobanking tool published under the MIT license. While it was developed to support HIV research in Germany, the feature set and language options, allow much broader applications and make this a powerful free research tool.

Research goes wild

Immunology Like many fields of biology, immunology research often relies on the use of a limited set of animal models for discovery and biomedical research. However, accumulating examples point to the importance of diversifying immunology research to consider numerous animals and environments. In a Perspective, Flies et al. discuss the use of diverse animals to study immunology, as well as “wild” or “dirty” environments to more closely model immune development and responses. Expanding the reach of immunology studies is expected to improve conservation efforts, understanding of emerging infectious diseases, and translational biomedical research. Science , this issue p. [37][1] [1]: /lookup/doi/10.1126/science.abb8664

Assessing the Organizational Climate for Translational Research with a New Survey Tool

Promoting translational research as a means to overcoming chasms in the translation of knowledge through successive fields of research from basic science to public health impacts and back is a central challenge for research managers and policymakers. Organizational leaders need to assess baseline conditions, identify areas needing improvement, and to judge the impact of specific initiatives to sustain or improve translational research practices at their institutions. Currently, there is a lack of such an assessment tool addressing the specific context of translational biomedical research. To close this gap, we have developed a new survey for assessing the organizational climate for translational research. This self-assessment tool measures employees’ perceptions of translational research climate and underlying research practices in organizational environments and builds on the established Survey of Organizational Research Climate, assessing research integrity. Using this tool, we show that scientists at a large university hospital (Charité Berlin) perceive translation as a central and important component of their work. Importantly, local resources and direct support are main contributing factors for the practical implementation of translation into their own research practice. We identify and discuss potential leverage points for an improvement of research climate to foster successful translational research.

The Application of Deep Learning in Cancer Prognosis Prediction.

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Genetically Engineered Pigs to Study Cancer.

Recent decades have seen groundbreaking advances in cancer research. Genetically engineered animal models, mainly in mice, have contributed to a better understanding of the underlying mechanisms involved in cancer. However, mice are not ideal for translating basic research into studies closer to the clinic. There is a need for complementary information provided by non-rodent species. Pigs are well suited for translational biomedical research as they share many similarities with humans such as body and organ size, aspects of anatomy, physiology and pathophysiology and can provide valuable means of developing and testing novel diagnostic and therapeutic procedures. Porcine oncology is a new field, but it is clear that replication of key oncogenic mutation in pigs can usefully mimic several human cancers. This review briefly outlines the technology used to generate genetically modified pigs, provides an overview of existing cancer models, their applications and how the field may develop in the near future.

Microfluidic lumen-based systems for advancing tubular organ modeling.

Microfluidic lumen-based systems are microscale models that recapitulate the anatomy and physiology of tubular organs. These technologies can mimic human pathophysiology and predict drug response, having profound implications for drug discovery and development. Herein, we review progress in the development of microfluidic lumen-based models from the 2000s to the present. The core of the review discusses models for mimicking blood vessels, the respiratory tract, the gastrointestinal tract, renal tubules, and liver sinusoids, and their application to modeling organ-specific diseases. We also highlight emerging application areas, such as the lymphatic system, and close the review discussing potential future directions.

Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research.

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources.Materials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.ResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.DiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication.ConclusionsLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts.

High-Quality Biobanks: Pivotal Assets for Reproducibility of OMICS-Data in Biomedical Translational Research.

Human biospecimen samples (HBS) and associated data stored in biobanks (also called "biotrusts," "biorepositories," or "biodistributors") are very critical resources for translational research. As HBS quality is decisive to the reproducibility of research results, biobanks are also key assets for new developments in precision medicine. Biobanks are more than infrastructures providing HBS and associated data. Biobanks have pioneered in identifying and standardizing sources of preanalytical variations in HBS, thus paving the way for the current biospecimen science. To achieve this milestone, biobankers have successively assumed the role of "detective," and then "architect," to identify new detrimental impact of preanalytical variables on the tissue integrity. While standardized methods in omics are required to be practiced throughout research communities, the accepted best practices and standards on biospecimen handling are generally not known nor applied by researchers. Therefore, it is mandatory to raise the awareness within omics communities regarding not only the basic concepts of collecting, storing, and utilizing HBS today, but also to suggest insights on biobanking in the cancer omics context.

Development of an informatics system for accelerating biomedical research.

Biomedical translational research can benefit from informatics system that support the confidentiality, integrity and accessibility of data. Such systems require functional capabilities for researchers to securely submit data to designated biomedical repositories. Reusability of data is enhanced by the availability functional capabilities that ensure confidentiality, integrity and access of data. A biomedical research system was developed by combining common data element methodology with a service-oriented architecture to support multiple disease focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules - Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Repository Manager and globally unique identifier (GUID) facilitate the management of research protocols, submitting and curating data (clinical, imaging, and derived genomics) within the associated data repositories. No personally identifiable information is stored within the repositories. Data is made findable by use of digital object identifiers that are associated with the research studies. Reuse of data is possible by searching through volumes of aggregated research data across multiple studies. The application of common data element(s) methodology for development of content-based repositories leads to increase in data interoperability that can further hypothesis-based biomedical research.

Quality Assessment of the Preanalytical Workflow in Liquid Biobanking: Taurine as a Serum-Specific Quality Indicator for Preanalytical Process Variations.

The scientific impact of translational biomedical research largely depends on the availability of high-quality biomaterials. However, evidence-based and robust quality indicators (QIs) covering the most relevant preanalytical variations are still lacking. The aim of this study was to identify and validate a QI suitable for assessing time-to-centrifugation (TTC) delays in human liquid biospecimens originating from both healthy and diseased individuals. Serum and plasma samples with varying TTCs were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in a pilot cohort of healthy individuals to identify a suitable QI candidate. Taurine (TAU), as a TTC QI candidate, was validated in healthy individuals and patients with rheumatologic and cardiologic diseases, considering the (1) preanalytical handling temperature, (2) platelet count, and (3) postcentrifugation delay. For discrimination of high TTC (TTC >60 minutes) from low TTC serum specimens, a probability calculation tool was developed (Triple-T-cutoff-model). TTC-dependent changes in healthy individuals were observed for amino acids, particularly TAU. Validation of the TAU levels in an independent cohort of healthy individuals revealed a time-dependent increase in serum, but not in plasma, for a TTC delay of 30-240 minutes. TAU increases were dependent on the handling temperature and platelet count and volume. By contrast, no changes in TAU concentrations were observed for additional postcentrifugation delays. Validation of TAU and the Triple-T-cutoff-model, in rheumatologic/cardiologic patient collectives, allowed the discrimination of samples with TTC ≤60 min/>60 min with estimated AUROC (area under the receiver operating characteristic curve) values of 89% [78%-100%]/86% [71%-100%] and 91% [79%-100%]/84% [68%-100%], respectively. Considering the preanalytical handling temperature and platelet count and volume, TAU and the Triple-T-cutoff-model represent reliable QIs for TTC >60 minutes in serum samples from healthy individuals and selected rheumatologic/cardiologic patients. However, further studies in larger patient collectives with various diseases are needed to assess the robustness and potential of the QIs presented in this article as biobanking quality assurance/quality control tools to support high-quality biomedical research.

Facile and Label-Free Electrochemical Biosensors for MicroRNA Detection Based on DNA Origami Nanostructures.

MicroRNAs (miRNAs) have emerged as the promising molecular biomarkers for early diagnosis and enhanced understanding of the molecular pathogenesis of cancers as well as certain diseases. Here, a facile, label-free, and amplification-free electrochemical biosensor was developed to detect miRNA by using DNA origami nanostructure-supported DNA probes, with methylene blue (MB) serving as the hybridization redox indicator, for the first time. Specifically, the use of cross-shaped DNA origami nanostructures containing multiple single-stranded DNA probes at preselected locations on each DNA nanostructure could increase the accessibility and the recognition efficiency of the probes (due to the rational controlled density of DNA probes). The successful immobilization of DNA origami probes and their hybridization with targeted miRNA-21 molecules was confirmed by electrochemical impedance spectroscopy and cyclic voltammetry methods. A differential pulse voltammetry technique was employed to record the oxidation peak current of MB before and after target hybridization. The linear detection range of this biosensor was from 0.1 pM to 10.0 nM, with a lower detection limit of 79.8 fM. The selectivity of the miRNA biosensor was also studied by observing the discrimination ability of single-base mismatched sequences. Because of the larger surface area and unprecedented customizability of DNA origami nanostructures, this strategy demonstrated great potential for sensitive, selective, and label-free determination of miRNA for translational biomedical research and clinical applications.

Rationalized Development of a Campus-Wide Cell Line Dataset for Implementation in the Biobank LIMS System at Bioresource Center Ghent.

The Bioresource center Ghent is the central hospital-integrated biobank of Ghent University Hospital. Our mission is to facilitate translational biomedical research by collecting, storing and providing high quality biospecimens to researchers. Several of our biobank partners store large amounts of cell lines. As cell lines are highly important both in basic research and preclinical screening phases, good annotation, authentication, and quality of these cell lines is pivotal in translational biomedical science. A Biobank Information Management System (BIMS) was implemented as sample and data management system for human bodily material. The samples are annotated by the use of defined datasets, based on the BRISQ (Biospecimen Reporting for Improved Study Quality) and Minimum Information About Biobank data Sharing (MIABIS) guidelines completed with SPREC (Standard PREanalytical Coding) information. However, the defined dataset for human bodily material is not ideal to capture the specific cell line data. Therefore, we set out to develop a rationalized cell line dataset. Through comparison of different datasets of online cell banks (human, animal, and stem cell), we established an extended cell line dataset of 156 data fields that was further analyzed until a smaller dataset—the survey dataset of 54 data fields—was obtained. The survey dataset was spread throughout our campus to all cell line users to rationalize the fields of the dataset and their potential use. Analysis of the survey data revealed only small differences in preferences in data fields between human, animal, and stem cell lines. Hence, one essential dataset for human, animal and stem cell lines was compiled consisting of 33 data fields. The essential dataset was prepared for implementation in our BIMS system. Good Clinical Data Management Practices formed the basis of our decisions in the implementation phase. Known standards, reference lists and ontologies (such as ICD-10-CM, animal taxonomy, cell line ontology…) were considered. The semantics of the data fields were clearly defined, enhancing the data quality of the stored cell lines. Therefore, we created an essential cell line dataset with defined data fields, useable for multiple cell line users.

A macroeconomic assessment of the impact of medical research expenditure: A case study of NIHR Biomedical Research Centres.

Quantifying the value of investment in medical research can inform decision-making on the prioritisation of research programmes. Existing methodologies to estimate the rate of return of medical research are inappropriate for early-phase translational research due to censoring of health benefits and time lags. A strategy to improve the process of translational research for patient benefit has been initiated as part of the UK National Institute for Health Research (NIHR) investment in Biomedical Research Centres (BRCs) in England. By providing a platform for partnership between universities, NHS trusts and industry, successful BRCs should reduce time lags within translational research whilst also providing an impetus for local economic growth through industry collaboration. We present a novel contribution in the assessment of early-phase biomedical research by estimating the impact of the Oxford Biomedical Research Centre (OxBRC) on income and job creation following the initial NIHR investment. We adopt a macroeconomic assessment approach using Input-Output Analysis to estimate the value of medical research in terms of income and job creation during the early pathway towards translational biomedical research. Inter-industry linkages are assessed by building a model economy for the South East England region to estimate the return on investment of the OxBRC. The results from the input-output model estimate that the return on investment in biomedical research within the OxBRC is 46%. Each £1 invested in the OxBRC generates an additional £0.46 through income and job creation alone. Multiplicative employment effects following a marginal investment in the OxBRC of £98m during the period 2007-2017 result in an estimated additional 196 full time equivalent positions being created within the local economy on top of direct employment within OxBRC. Results from input-output analyses can be used to inform the prioritisation of biomedical research programmes when compared against national minimum thresholds of investment.

Case-Based Learning in Translational Biomedical Research Education: Providing Realistic and Adaptive Skills for Early-Career Scientists.

Case-based learning is an established means of educating students in law, business, and medicine; however, this methodology is not often applied to educating translational biomedical researchers. The application of case-based learning to translational biomedical research education allows scholars to actively engage with real-world material and apply their newfound knowledge as it is acquired. Through the Mayo Clinic Center for Clinical and Translational Science (CCaTS), three courses were delivered in 2009-2017 which emphasized case-based learning in clinical and translational science, entrepreneurship, and individualized medicine. Quantitative measures collected in student course reviews upon course completion were analyzed. Additionally, products arising from each course were identified, including publications and startups pitched. Analyses demonstrate that case-based learning techniques are well suited to graduate biomedical research education. Furthermore, case studies can be employed throughout the entire clinical and translational spectrum, from basic and preclinical work through to clinical and population-based learning. Within CCaTS, next steps include creating case-based courses in regulatory and team science to continue to allow scholars to learn and apply these critical skills to real-world material. The goal is to continue to provide immersive training opportunities in areas of clinical and translational science that cannot be readily learned in a traditional lecture-based class setting.

Visualization capabilities of experimental oncological models in small laboratory animals

For a long time non-invasive imaging methods have been inaccessible in preclinical practice; their introduction lately has broadened the boundaries of relevant studies and felicitated new approaches to solving fundamental problems. Up-to-date imaging methods constitute an essential component of preclinical and translational biomedical research allowing quick and non-invasive extended representation of structural organization and functional characteristics of pathological processes in vivo. Methods of radiation diagnosis and nuclear magnetic resonance allow to assess the state of bones, soft tissues, internal organs, blood vessels and peripheral nerve fibers in various animals, not only mammals, but also fish, amphibians, reptiles and insects. Multiparametric studies can uniquely localize any anatomical structure or pathological process. However, not all existing techniques are applicable to various oncological models of small laboratory animals.

Humanized Mice Are Instrumental to the Study of Plasmodium falciparum Infection.

Research using humanized mice has advanced our knowledge and understanding of human haematopoiesis, non-adaptive and adaptive immunity, autoimmunity, infectious disease, cancer biology, and regenerative medicine. Challenges posed by the human-malaria parasite Plasmodium falciparum include its complex life cycle, the evolution of drug resistance against anti-malarials, poor diagnosis, and a lack of effective vaccines. Advancements in genetically engineered and immunodeficient mouse strains, have allowed for studies of the asexual blood stage, exoerythrocytic stage and the transition from liver-to-blood stage infection, in a single vertebrate host. This review discusses the process of “humanization” of various immunodeficient/transgenic strains and their contribution to translational biomedical research. Our work reviews the strategies employed to overcome the remaining-limitations of the developed human-mouse chimera(s).

An Introduction to Hardware, Software, and Other Information Technology Needs of Biomedical Biobanks.

Biobanks support medical research by facilitating access to biospecimens. Biospecimens that are linked to clinical and molecular information are particularly useful for translational biomedical research. Tracking and managing the biospecimens and their associated data are therefore crucial tasks in the functioning of a biobank. Adequate computing hardware, efficient and comprehensive biobanking software, and cost-effective data storage are needed for proper management of biospecimens. As biobanks build up extensive stores of specimens and patient data, ethical considerations also inevitably arise. Herein, we describe some basic considerations for establishing a biobanking information technology infrastructure that a beginning biobanker needs. Finally, we also discuss trends and future needs in biobanking informatics.

The pig as an animal model in biomedical research: A review

The advances in translational biomedical research, especially in genetic engineering, created new opportunities to trace the courses of human diseases and develop effective therapeutic methods. There remains, however, a growing demand for appropriate animal models for the precise evaluation of the efficacy and safety of new drugs or therapeutic concepts. Thus far, rodent models have been most widely used in translational research; however, since they do not perfectly reflect the human disease phenotype, transgenic pigs are increasingly being utilized as animal models. Thanks to the anatomical and physiological similarities between pigs and humans, swine are considered to be one of the most valuable animal models used in preclinical studies, including nutritional, metabolic and cardiovascular research. The resemblances involve the gastrointestinal, cardiovascular, urinary, respiratory, skeletal muscle and immune systems, as wells as body size, body composition and the omnivorous food choice. In addition, pigs are characterized by high fertility and fecundity, as well as the ease of use and low maintenance costs. Importantly, the existing efficient genetic engineering techniques enable relatively easy generation of tailored porcine models of human disease. One should be aware, however, of some physiological differences between humans and pigs to correctly interpret induced toxicological changes. The article provides an overview of current techniques for genetic modification of pigs, as well as the use of swine models in translational research exemplified by xenotransplantation, metabolic and coronary heart disease, and the gastrointestinal motility studies.