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.
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.
Authors: Jorge Muriel Fernandez; María José Sánchez Ledesma; Manuel López Millan; María Begoña García Cenador Journal: J Med Syst Date: 2017-03-27 Impact factor: 4.460
Authors: Hua Zhao; Jie Shen; Qiang Hu; Warren Davis; Leonardo Medico; Dan Wang; Li Yan; Yuqing Guo; Biao Liu; Maochun Qin; Mary Nesline; Qianqian Zhu; Song Yao; Christine B Ambrosone; Song Liu Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-12 Impact factor: 4.254