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Standard operating procedures (SOPs) optimization for nucleic acid extraction from stored samples is of crucial importance in a biological repository, considering the large number of collected samples and their future downstream molecular and biological applications. However, the validity of molecular studies using stored specimens depends not only on the integrity of the biological samples, but also on the procedures that ensure the traceability of the same sample, certifying its uniqueness, and ensuring the identification of potential sample contaminations. With this aim, we have developed a rapid, reliable, low-cost, and simple DNA fingerprinting tool for a routine use in quality control of biorepositories samples. The method consists of a double ALU insertion/deletion genotyping panel suitable for uniqueness, identification of sample contaminations, and gender validation. Preliminary data suggest that this easy-to-use DNA fingerprinting protocol could routinely provide assurances of DNA identity and quality in a biorepository setting.

Biobanks provide stored material to basic, translational, and epidemiological research and this material should be transferred without institute-dependent intrinsic bias. The ISBER Biospecimen Science Working Group has released a "Standard PREanalytical Code" (SPREC), which is a proposal for a standard coding of the preanalytical options that have been adopted in order to track and make explicit the preanalytical variations in the collection, preparation, and storage of specimens. In this paper we address 2 issues arising in any biobank or biolaboratory aiming at adopting SPREC: (i) reducing the burden required to adopt this standard coding, and (ii) maximize the immediate benefits of this adoption by providing a free, dedicated software tool. We propose SPRECware, a vision encompassing tools and solutions for the best exploitation of SPREC based on information technology (www.sprecware.org). As a first step, we make available SPRECbase, a software tool useful for generating, storing, managing, and exchanging SPREC-related information associated to specimens. Adopting SPREC is useful both for internal purposes (such as finding the samples having some given preanalytical features), and for exchanging the preanalytical information associated to biological samples between Laboratory Information Systems. In case of a common adoption of this coding, it would be easy to find out whether and where, among the participating Biological Resource Centers, the specimens for a given study are available in order to carry out a planned experiment.

Background: Biospecimen quality is crucial for clinical and translational research and its loss is one of the main obstacles to experimental activities. Beside the quality of samples, preanalytical variations render the results derived from specimens of different biobanks or even within the same biobank incomparable. Specimens collected along the years should be managed with a heterogeneous life cycle. Hence, we propose to collect detailed data concerning the whole life cycle of stored samples employing radio-frequency identification (RFID) technology.?, Methods: We describe the processing chain of blood biosamples that is operative at the biobank of IRCSS San Raffaele, Rome, Italy (BioBIM). We focus on the problem of tracing the stages following automated preanalytical processing: we collected the time stamps of all events that could affect the biological quality of the specimens by means of RFID tags and readers.?, Results: We developed a pilot study on a fragment of the life cycle, namely the storage between the end of the preanalytics and the beginning of the analytics, which is usually not traced by automated tools because it typically includes manual handling. By adopting RFID devices we identified the possible critical time delays. At 1, 3 and 6 months RFID-tagged specimens cryopreserved at -80°C were successfully read.?, Conclusions: We were able to record detailed information about the storage phases and a fully documented specimen life cycle. This will allow us to promote and tune up the best practices in biobanking because i) it will be possible to classify sample features with a sharper resolution, which allows future utilization of stored material; ii) cost-effective policies can be adopted in processing, storing and selecting specimens; iii) after using each aliquot, we can study the life cycle of the specimen with a possible feedback on the procedures.