The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.
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http://dx.doi.org/10.3390/ani14111541 | DOI Listing |
Expert Rev Neurother
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Department of Neurology, Loyola University Stritch School of Medicine, Loyola University Health System, Maywood, IL, USA.
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View Article and Find Full Text PDFBiomaterials
May 2025
Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore, 18 Science Drive 4, Singapore, 117559, Singapore. Electronic address:
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View Article and Find Full Text PDFEar Hear
December 2024
Institut national de la santé et de la recherche médicale, U1028, Centre National de Recherche Scientifique, UMR5292, Lyon Neuroscience Research Center, Integrative Multisensory Perception and ACTion Team, Lyon, France.
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View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR.
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