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The term "biobanking" is often misapplied to any collection of human biological materials (biospecimens) regardless of requirements related to ethical and legal issues or the standardization of different processes involved in tissue collection. A proper definition of biobanks is large collections of biospecimens linked to relevant personal and health information (health records, family history, lifestyle, genetic information) that are held predominantly for use in health and medical research. In addition, the International Organization for Standardization, in illustrating the requirements for biobanking (ISO 20387:2018), stresses the concept of biobanks being legal entities driving the process of acquisition and storage together with some or all of the activities related to collection, preparation, preservation, testing, analysing and distributing defined biological material as well as related information and data. In this review article, we aim to discuss the basic principles of biobanking, spanning from definitions to classification systems, standardization processes and documents, sustainability and ethical and legal requirements. We also deal with emerging specimens that are currently being generated and shaping the so-called next-generation biobanking, and we provide pragmatic examples of cancer-associated biobanking by discussing the process behind the construction of a biobank and the infrastructures supporting the implementation of biobanking in scientific research., (© 2021. The Author(s).)

Objective: This study performed individual-centric, data-driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD)., Methods: A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD., Results: The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD., Conclusions: The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individual's metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and consequently enable optimization of treatment., (© 2019 AMRA Medical AB. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).)

Biobanks are one of the most important biomedical research resources and contribute to the development of biomarker detection, molecular diagnosis, translational medicine, and multidisciplinary disease research, as well as studies of interactions between genetic and environmental or lifestyle factors. Aiming for the wide clinical application of biobanks, biobanking efforts have recently switched from a focus on accumulating samples to both formalizing and sustaining collections in light of the rapid progress in the fields of personalized medicine and bioinformatics analysis. With the emergence of novel molecular diagnostic technologies, although the bioinformatics platform of biobanks ensures reliable bioinformatics analysis of patient samples, there are a series of challenges facing biobanks in terms of the overall harmonization of policies, integrated processes, and local informatics solutions across the network. Further, there is a controversy regarding the increased role of ethical boards, governance, and accreditation bodies in ensuring that collected samples have sufficient informatics capabilities to be used in biobanks. In this volume, we present a selection of current issues on the inevitable challenges of the clinical application of biobanks in informatics.

In 2011, Watson and Barnes proposed a schema for classifying biobanks into 3 groups (mono-, oligo-, and poly-user), primarily based upon biospecimen access policies. We used results from a recent comprehensive survey of cancer biobanks in New South Wales, Australia to assess the applicability of this biobank classification schema in an Australian setting. Cancer biobanks were identified using publically available data, and by consulting with research managers. A comprehensive survey was developed and administered through a face-to-face setting. Data were analyzed using Microsoft Excel™ 2010 and IBM SPSS Statistics™ version 21.0. The cancer biobank cohort (n=23) represented 5 mono-user biobanks, 7 oligo-user biobanks, and 11 poly-user biobanks, and was analyzed as two groups (mono-/oligo- versus poly-user biobanks). Poly-user biobanks employed significantly more full-time equivalent staff, and were significantly more likely to have a website, share staff between biobanks, access governance support, utilize quality control measures, be aware of biobanking best practice documents, and offer staff training. Mono-/oligo-user biobanks were significantly more likely to seek advice from other biobanks. Our results further delineate a biobank classification system that is primarily based on access policy, and demonstrate its relevance in an Australian setting.

Background: Biosample collections and biobank information systems have become a key enabler for medical research. Therefore it is important to identify potentially relevant ontologies to semantically enrich information related to the biobanking domain., Objectives: We present a three-stage semi-automated evaluation approach which allows identifying relevant ontologies for the biobanking domain based on competency questions., Methods: After identifying candidate biobanking ontologies (Stage 1) and competency questions (Stage 2), a six-step lexical evaluation approach, which assesses the coverage of concepts, properties or instances defined by competency questions is suggested and described (Stage 3)., Results: We were able to perform a proof-of-concept evaluation of the OMIABIS ontology using our proposed three-stage approach together with a sample competency question., Conclusion: Our evaluation approach allows a swift evaluation of candidate ontology entities based on a search for higher hierarchy key terms that exist in comprehensive medical vocabularies in order to state the usability of specific ontologies for the biobanking domain.

Animal models have historically informed veterinary and human pathophysiology. Next-generation genomic sequencing and molecular analyses using analytes derived from tissue require integrative approaches to determine macroanalyte integrity as well as morphology for imaging algorithms that can extend translational applications. The field of biospecimen science and biobanking will play critical roles in tissue sample collection and processing to ensure the integrity of macromolecules, aid experimental design, and provide more accurate and reproducible downstream genomic data. Herein, we employ animal experiments to combine protein expression analysis by microscopy with RNA integrity number and quantitative measures of morphologic changes of autolysis. These analyses can be used to predict the effect of preanalytic variables and provide the basis for standardized methods in tissue sample collection and processing. We also discuss the application of digital imaging with quantitative RNA and tissue-based protein measurements to show that genomic methods augment traditional in vivo imaging to support biospecimen science. To make these observations, we have established a time course experiment of murine kidney tissues that predicts conventional measures of RNA integrity by RIN analysis and provides reliable and accurate measures of biospecimen integrity and fitness, in particular for time points less than 3 hours post-tissue resection.

The term "biobank" first appeared in the scientific literature in 1996 and for the next five years was used mainly to describe human population-based biobanks. In recent years, the term has been used in a more general sense and there are currently many different definitions to be found in reports, guidelines and regulatory documents. Some definitions are general, including all types of biological sample collection facilities. Others are specific and limited to collections of human samples, sometimes just to population-based collections. In order to help resolve the confusion on this matter, we conducted a survey of the opinions of people involved in managing sample collections of all types. This survey was conducted using an online questionnaire that attracted 303 responses. The results show that there is consensus that the term biobank may be applied to biological collections of human, animal, plant or microbial samples; and that the term biobank should only be applied to sample collections with associated sample data, and to collections that are managed according to professional standards. There was no consensus on whether a collection's purpose, size or level of access should determine whether it is called a biobank. Putting these findings into perspective, we argue that a general, broad definition of biobank is here to stay, and that attention should now focus on the need for a universally-accepted, systematic classification of the different biobank types.

Background: Many biobanks have struggled to deliver on the high expectations and claims made for them because of insufficient samples, inadequate infrastructure, cost of establishing and maintaining a large enough resource over the long term, and satisfying legal, ethics and governance requirements. Increasingly, networks have formed to help with the collection, processing, storage, advertising, and distribution of samples. However, there are also challenges to establishing and maintaining biobank networks., Aim: To classify biobanks in order to better understand the problems faced by biobank networking., Methods: Interviews were conducted with principal investigators and/or managers responsible for 33 biobanks in 9 countries., Results: Biobanks were classified into the following categories: 'storage', 'bring-and-share', 'catalogue', 'partnership', 'contribution' and 'expertise'., Conclusion: It was possible to allocate all of the biobanks visited to one of the network categories although some fitted better than others. Thus, the classification may not be mutually exclusive nor encompass all types of biobanks. Many of the governance and operational problems associated with the biobanks visited were due to networking functions: either intra- or inter-biobank networks. Thus, this proposed classification system should help better understand these issues and identify solutions.