Fay Betsou

The FIND Integrated Biobank (FIB) network was established to address long-standing barriers in translational research, diagnostic development, and pandemic preparedness, particularly in low- and middle-income countries (LMICs). This article presents the FIB network as an innovative, contract-based model for equitable biobanking partnerships that support rapid, quality-assured access to clinical biospecimens for translational research and diagnostic evaluations. The FIB model offers a replicable and scalable approach to global diagnostic research and preparedness, anchored in equitable, legally binding partnerships. It advances the goals of the WHO Pandemic Agreement and provides a practical pathway for integrating LMIC institutions into global translational research ecosystems.

Introduction: Biobanks of specimens of human origin have accumulated millions of specimens. Their storage is costly, while many of them may not be useful and should be culled. Objectives: Our objective was to develop, pilot test, and evaluate a quantitative culling tool. Methods: We developed a culling tool based on a series of parameters with a quantitative score attributed to each. The parameters of the culling tool correspond to different aspects of the value of collections, such as the richness of the associated data, the types of samples, their conservation mode, and regulatory constraints. Results: The culling tool was adapted and independently applied by the Foundation for Innovative New Diagnostics and the Biological Resource Center of Institut Pasteur biobanks. The cumulative final score supported evidence-based and standardized decision-making. A “diagnostic” threshold could be established for the “diagnosis” of collections of low value. Conclusion: The culling tool is an algorithm developed to assess the value of legacy collections of biological resources of human origin and help establish culling plans. Biobanks can use this culling tool when they periodically assess the value of stored collections and need to decide or advise to cull them, and also when deciding whether to accept requests to host new collections previously stored elsewhere.

A target specimen profile (TSP) corresponds to the required characteristics of the specimen panels needed to demonstrate that a diagnostic kit meets the target product profile (TPP). TSPs can guide biobanks in the prospective collection of sample panels to support the development and validation of diagnostics.

Parkinson’s disease is an age‐related progressive neurodegenerative disorder. A large majority of Parkinson’s disease patients have an unknown etiology, which is classified as idiopathic Parkinson’s disease. Generating disease models directly from idiopathic Parkinson’s disease patients may improve the understanding of the disease pathology. Both neuroprotective and neurodegenerative roles have been suggested for cystatin C in neurodegenerative disease. In Parkinson’s disease, investigations assessing cystatin C levels in different types of biospecimens such as blood, cerebrospinal fluid, and in vitro models have had conflicting results. We present a study assessing cystatin C levels in different biospecimen types originating from the same subjects. Using a sandwich ELISA, we compared cystatin C concentration in blood derivatives (plasma and serum) and culture media of derived models (stem cells, neuroepithelial stem cells, and midbrain organoids) of three idiopathic Parkinson’s disease and three age‐matched healthy control subjects with the same CST3 genotype. Genotyping analyses confirmed that all subjects were homozygous AA. The identity of the derived models was confirmed using immunohistochemical staining. Secreted cystatin C concentration was measured in each biospecimen tested. No statistically significant differences in cystatin C concentrations were found between the subjects in any of the biospecimen types. As a personalized marker, a higher cystatin C concentration was shown for some individual patients in the culture medium of midbrain organoids, suggesting a potential involvement in Parkinson’s disease physiopathology. This proof of concept demonstrates that cystatin C is secreted by various idiopathic Parkinson’s disease cell models, including midbrain organoids, which in turn suggests that cystatin C secretion by midbrain organoids might be pertinent in neurodegenerative disease research.

While the FAIR (Findable, Accessible, Interoperable, and Reusable) principles are primarily concerned with data, samples can also be considered a distinct category of data. In light of these considerations, the FAIR principles represent a major challenge for biobanks, as discussed in detail in two recently published studies. We invited seven experts with diverse backgrounds to share their views on these studies and the FAIR principles in general. The contributions are written from different perspectives, including those from human biobanks operating globally, located in low- or middle-income countries or in high-income countries, as well as those from industrial or environmental biobanks. The last two contributions focused on technical feasibility and the necessary incentives. All authors agreed that while the FAIR principles present a challenge for biobanks, they also offer opportunities. Various useful instruments already exist, and more will follow. The key is to provide meaningful incentives.

Early detection of PDAC remains challenging due to the lack of early symptoms and the absence of reliable biomarkers. The aim of the present project was to identify miRNA and proteomics signatures discriminating PDAC patients with DM from nondiabetic PDAC patients. Proteomics analysis and miRNA array were used for protein and miRNA screening. We used Western blotting and Real-Time Quantitative Reverse Transcription polymerase chain reaction (qRT-PCR) for protein and miRNA validation. Comparisons between experimental groups with normal distributions were performed using one-way ANOVA followed by Tukey’s post hoc test, and pairwise tests were performed using t-tests. p ≤ 0.05 was considered statistically significant. Protein clusters of differentiation 166 (CD166), glycoprotein CD63 (CD63), S100 calcium-binding protein A13 (S100A13), and tumor necrosis factor-β (TNF-β) were detected in the proteomics screening. The miRNA assay revealed a differential miRNA 1285 regulation. Previously described target proteins of miR-1285 cadherin-1 (CDH-1), cellular Jun (c-Jun), p53, mothers against decapentaplegic homolog 4 (Smad4), human transglutaminase 2 (TGM2) and yes-associated protein (YAP), were validated via Western blotting. miR-1285-3p was successfully validated as differentially regulated in PDAC + DM via qRT-PCR. Overall, our data suggest miRNA1285-3p, TGM2, CDH-1, CD166, and S100A13 as potential meaningful biomarker candidates to characterize patients with PDAC + DM. Data are available via ProteomeXchange with the identifier PXD053169.

Early detection of PDAC remains challenging due to the lack of early symptoms and absence of reliable biomarkers. The aim of the present project was to identify miRNA and proteomics sig-natures discriminating PDAC patients with DM from nondiabetic PDAC patients. Proteomics analysis and miRNA array were used for protein and miRNA screening. We used western blot-ting and Real-Time Quantitative Reverse Transcription polymerase chain reaction (qRT-PCR) for protein and miRNA validation. Comparisons between experimental groups with normal dis-tributions were performed using one-way ANOVA followed by Tukey’s post hoc test and pair-wise tests were performed using t tests. P≤0.05 was considered statistically significant. Protein cluster of differentiation 166 (CD166), glycoprotein CD63 (CD63), S100 calcium binding protein A13 (S100A13), and tumor necrosis factor-β (TNF-β) were detected in the proteomics screening. The miRNA assay revealed a differential miRNA 1285 regulation. Previously described in the literature target proteins of miR-1285 cadherin-1 (CDH-1), cellular Jun (c-Jun), p53, mothers against decapentaplegic homolog 4 (Smad4), human transglutaminase 2 (TGM2) and yes-associated protein (YAP), were validated via Western blotting. miR-1285-3p was successfully validated as differentially regulated in PDAC + DM via qRT-PCR. Overall, our data suggest miRNA1285-3p, TGM2, CDH-1, CD166 and S100A13 as potential meaningful biomarker candi-dates to characterize patients with PDAC + DM. Data are available via ProteomeXchange with identifier PXD053169.

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Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre‐analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross‐organizational documentation, traceability, and non‐repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine‐actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.