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Open Access Full Text Article
Sample PReanalytical Code for labeling of
biospecimens: an analysis of specimen
labeling protocols
Silvia Riondino 1,*
Umberto Nanni 2,*
Fay Betsou 3
Luisa Rossetti 2
Corrado Campisi 4
Riccardo Fiorentino 1
Raffaele Palmirotta 1
Patrizia Ferroni 1
Mario Roselli 5
Fiorella Guadagni 1
Abstract: The suitability of samples for a given research depends on many variables, among
which, the best preanalytical conditions for the type of test that should be carried out. Thus,
standardization and harmonization of processing conditions for samples entering a particular
study should be highly pursued to facilitate research collaborations among different institutions and biological banks, allowing specimen comparison both for sample sharing and for the
development of large-scale clinical trials. In this study, we review some issues and scenarios
related to the adoption of the Sample PREanalytical Code, which deserve attention for a proper
management of the samples and, ultimately, for maximizing the efficiency of the consistent
investments required to set up biobanking resources.
Keywords: biobanking, preanalytical variables, sample labeling
1
Introduction
BioBiM-interinstitutional
Multidisciplinary BioBank, iRCCS
San Raffaele Pisana – Research
Center, Rome, italy; 2Department of
Computer, Control and Management
engineering “Antonio Ruberti”,
“Sapienza” University, Rome, italy;
3
integrated Biobank of Luxembourg,
Luxembourg City, Luxembourg;
4
LC Campisi Laboratory Ltd, Avola
(SR), italy; 5Department of Systems
Medicine, University of Rome
“Tor vergata”, Rome, italy
*These authors contributed equally to
this work
Correspondence: Fiorella Guadagni
BioBiM-interinstitutional Multidisciplinary
BioBank, iRCCS San Raffaele
Pisana – Research Center, via di val
Cannuta 247, 00166, Rome, italy
Tel +39 06 5225 3733
Fax +39 06 5225 5668
email fiorella.guadagni@sanraffaele.it
15
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http://dx.doi.org/10.2147/JBSAM.S68062
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Biobanks are defined as operational units that provide a service for the storage and
management of biological material (organs, tissues, blood, cells, and fluids having an
amount of DNA or RNA that allows genetic analysis) and associated personal data,
in accordance with the codes of good practice, privacy law, and ethics guidelines as
defined by the European Oviedo Convention.1
The increasing frequency of collaboration between research institutions and
biobanks renders the knowledge of a biosample “quality” mandatory. The availability
of biological samples has, in fact, assumed a crucial role in the field of biomedical
research, from a basic to translational one, to assess predisposition to complex diseases, and identification and validation of new diagnostic biomarkers, drug targets, or
the improvement of monitoring strategies. All these tasks critically depend upon the
availability of a large number of standardized biological specimens, and many research
activities are seriously invalidated by the different methodological approaches employed
during sample preparation.2 At present, in fact, the requirements for a specific intended
use of the stored biomaterials are often lacking, as well as information on origin and
preparation (preanalytical phase), and there are no standardized methods that can be
used in comparative approaches between different institutions.3
In this review, we will consider some issues and scenarios related to the adoption of
the Sample PREanalytical Code (SPREC), which deserve attention for a proper management of biospecimens and, ultimately, for maximizing the efficiency of the consistent
investments required to set up biobanking resources. Moreover, we will describe some
information technology (IT) solutions developed by joint efforts within our institutions,
aimed at facilitating the adoption of SPREC by all the organizations for managing
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Riondino et al
biosamples. These functionalities enable interested biological
resource centers to establish shared searchable collections of
SPRECs among a network of biobanking facilities.
A systematic literature review was performed by searching in PubMed, Scopus, and Web of Science. The inclusion
criteria regarded relevant research studies published in
English. Furthermore, we extended our search on health care
systems due to their similarity with the subject reported. For
the selection of the search terms, we referred to previous
literature reviews and the keywords of leading papers on the
topic of biobanking as provided by the International Society
for Biological and Environmental Repositories (ISBER)
guidelines.
Sample quality and methodological
pitfalls
The National Cancer Institute (NCI)/Office of Biorepositories
and Biospecimen Research (OBBR) guidelines4 represent,
together with the Biospecimen Research Network Symposium that took place in 2009,5 the first evidence of the urge
to address the issue of biosample quality and of standardized procedures for biospecimen collection, processing,
and storage. Members of the previous working groups participated in Biospecimen Reporting for the Improved Study
Quality (BRISQ) in 2011, which released recommendations
intended for the reporting of data elements of human biospecimens (solid tissues and bodily fluids), running through
the whole life cycle of a sample (from collection to analysis).6
The recommendations gave birth to the BRISQ reporting item
list, documenting sample preanalytical variables aimed at
harmonizing the methods to trace information and facilitate
effective inter- and intralaboratory specimen sharing/use.
Furthermore, BRISQ efforts were focused on the standardization and improvement of procedures for tissue processing
to assess quality and suitability for research use, to identify
quality control tools for tissues stored in biorepositories,2,7–9
and to interpret and validate data from archived specimens.
Tissue processing is of the utmost importance for the preservation of both molecular and morphological integrity,10 and
several preanalytical factors have been shown to significantly
affect sample quality (eg, postmortem interval; cold ischemia
time; specimen size; fixative buffer, delivery method, temperature, and duration; and section thickness and storage)
(for an extensive review see10).
Concomitant to the development of BRISQ recommendation, the International Society for Biological and
Environmental Repositories Biospecimen Science Working
Group endorsing the need of a deeper knowledge of the
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preanalytical conditions of specimens used for research
activities generated the SPREC.11 The SPREC identifies the
main preanalytical factors of clinical fluid and solid biospecimens and their simple derivatives, which, together with its
accompanying data, represent an essential part of its clinical and scientific value.2 Accordingly, quality of specimens
becomes a multidimensional concept that determines the
scientific value and exchangeability potential for health care
research. Recording information about a specimen and its processing (eg, defining a set of features that are to be explicitly
traced), in fact, gives the opportunity of choosing the right
options for the right specimens and, at the same time, the right
specimens for the target study (“fitness for purpose”).3 This
concept finds its highest applications in the field of oncology,
and applies both for sera and tissue samples.12
Thereby, biospecimen processing methods are recognized
as a critical issue whose inappropriate application causes
detrimental effects which are widely acknowledged.2
Among the many examples of methodological pitfalls,
one may cite the important confounders in the interpretation
of soluble CD40 ligand (sCD40L) level measurements due to
preanalytical and analytical interferences.13 Indeed, platelet
preactivation during the course of improper sample handling
may substantially increase sCD40L levels,13,14 as well as keeping samples at room temperature for prolonged periods before
processing or freezing.15 Measurement of serum sCD40L
levels is sensitive to all storage-associated preanalytic conditions to such an extent that it has been proposed not only
as a decay marker but also for the establishment of standard
operating procedures in biobanking.13,15
Other methodological examples come from studies on
vascular endothelial growth factor (VEGF), whose levels
might be concealed in the presence of thrombocytopenia.
To overcome this bias, VEGF levels should always be normalized by platelet counts.16 Another interesting issue is
represented by the use of ethylenediaminetetraacetic acid
anticoagulated samples for mean platelet volume measurement, which are biased by ethylenediaminetetraacetic acidinduced platelet changes over time.17 This effect must be
controlled by standardizing the duration between sampling
and analysis, thus ensuring a relative homogeneity among the
samples used and minimizing the differences in analyses.18
Furthermore, particular attention should be paid to the
choice of samples to be analyzed for the research of lupus
anticoagulant in studies on coagulation alterations occurring
in conditions of acquired thrombophilia.19,20
Numerous sources of variability have been described for
the nonobservance of the basic requirements for an optimal
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sample, such as the contamination of the plasma with platelets, the presence of activated platelets, or the concentration
of calcium ions.21 It is evident that even a nonoptimal pretreatment (as might occur during the normal steps of plasma
preparation), rather than a real error, would render the sample
not fit for the purpose of the study.
Finally, several authors emphasize the importance of
knowing the status of the subject who has agreed to provide
the sample for the study, as some conditions can significantly
interfere with the use of certain methods. For example, inconsistent results may be obtained with the use of functional tests
of coagulation in carriers of the mutation R506Q FV Leiden
and in case with the presence of lupus anticoagulant, increase
of FVIII, or presence of factor VIIa.22
However, beside properly defined errors, the suitability of
samples for a given research also depends greatly on the type
of test that should be carried out. Thus, standardization and
harmonization of processing conditions for samples entering a particular study should be highly pursued to facilitate
research collaborations among different laboratories, both
allowing specimen comparison for sample sharing, and for
the development of large-scale clinical trials. In this context,
comprehensive guidelines for the best practices for managing
biosamples in repositories have been provided by the ISBER
guidance on best practices for collection, storage retrieval,
and distribution of biological materials for research.23
Proper sample labeling
The urge to use “talking” codes rather than simple numeric
ones stems from the need to mitigate errors in sample identifiers during biosample mislabeling in research laboratories or
biorepositories.24–26 The biospecimen publication guidelines
of the NCI/OBBR4 first envisaged the need for more detailed
information used for research activities.
As a possible support for this issue, ISBER has proposed
and refined the SPREC, a labeling methodology which
provides detailed information on the preanalytical conditions that a single stored specimen has encountered during
its manipulation.11,27 SPREC is a seven-element-long code,
where each element corresponds to a punctual preanalytical
variable and contains a string of letters (different for fluid
or solid samples).11 This code can be integrated into the
local institutional IT system (such as Biobank Information
System [BIS]) and tracking databases so that the SPREC
can share the same record and the information contained
can be applied to all aliquots of the parent sample. The
stand-alone application SPRECbase ensures SPREC
output-coding and input-decoding of each specimen. Basic
Journal of Biorepository Science for Applied Medicine 2015:3
SPReC labeling in biobanking
information concerning preanalytical data can be selected
from SPREC tables by drop-down menus; the resulting
SPREC will be stored in the local database, and can be
easily retrieved.28,29 Also, 2D barcodes or quick response
codes can provide plain text information combined with
multiple links to online content (videos, photos, hypertexts).
This last coding system, although highly envisaged, would
require the purchase of specialized software/application,
which might represent a substantial investment both in time
and resources.30 Such implementation of the SPREC system
is already under study in our institutions, and is going to
be delivered together with its package and validated in a
biological bank environment.
SPREC can be applied both to primary samples (samples
directly collected from the donor) and to their derivative
tubes, and provides different information on sample withdrawal, processing, and storage for tissues and bodily fluids,
all summarized in one single string on a sample label.11
Obviously, this will be of particular help in the case of
samples resulting from extensive technical manipulations
that introduce preanalytical variables related to each laboratory’s standard operating procedures and operators. Indeed,
the prevalence of errors in the selection of a sample by a
laboratory that processes specimens manually is difficult
to estimate. On the other hand, if the biological samples
are SPREC-labeled by means of a barcode (as in the case
of SPRECbase), any personnel handling the sample (even
nontrained operators) can easily and explicitly obtain all the
information associated with that preanalytical encoding by
means of any reading device.
Since not all research laboratories are equipped with
a management system shared with the central “core lab”,
an application has been developed (SPRECware), and a
server has been set up (SPRECbase), allowing for bar code
(or quick response code) reading by means of any codescanning device: this provides the immediate decoding of
the SPREC into the corresponding preanalytical information
(Figure 1).28
In this scenario, checking whether a biological sample
has characteristics that prevent its inclusion in a given study
can be done by reading/scanning the SPREC. Moreover, if
the information related to the nature of a biological sample
is paired with the exact location within the biological bank,
the construction of new studies and national and international
collaborations is facilitated, with a remarkable saving of time
otherwise spent for manual sample searching, and there is a
significant increase of availability in terms of numbers and
quality of samples as well as data related to them.
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Riondino et al
Figure 1 Generating sample Sample PReanalytical Code, with the corresponding barcode and quick response code.
Besides preanalytical variables, the whole biospecimen
chain of custody, ie, the sample life cycle, should also be carefully monitored and checked. Indeed, this should occur even
when minimizing the amount of time during which samples
of a given batch undergo a drastic change in temperature
(ie, when the batch is taken from the freezers at –80°C to
insert/pick up a cryovial). Tracking temperature observations
can undoubtedly help to rapidly identify artefacts during
downstream sample assay.23
All samples (eg, sera, plasma, whole blood, urine, tissues)
are stored in freezers at −80°C equipped with systems that
allow the temporal marking of the stages of insertion/withdrawal of the samples themselves. These systems, which may
include radio-frequency identification (RFID)31 or bluechiip®
technology (a passive wireless technology based on microelectromechanical systems),32 can overcome the possible
problems stemming from the need to maintain the cold chain.
RFID is a technology that uses communication via radio
waves to exchange data between a reader (interrogator) and an
electronic tag attached to an object (label), for the purpose of
identification and tracking.31 The cryotag uses a transmission
contactless passive type and can be placed on the cryovial,
on the wall of plates, on cryoboxes, and on the freezer door.
Electronic information is then written on a portable remote
writer/reader device that allows multiple and contemporary
readings of RFID supports, and can associate a time record
to every operation performed. Moreover, if samples are
also mapped by software, this will allow their immediate
localization and recognition, and will minimize the sample
temperature-related alterations. Indeed, as an example of the
detrimental effects of repeated freeze–thaw cycles,33,34 it has
been reported that even one freeze–thaw cycle is capable of
leading to substantial changes in the concentrations of serum
analytes.35 The use of a RIFD-implemented SPREC will
allow the operators to find and collect in the shortest possible
time the required samples that might otherwise be exposed
to sudden changes in temperature and consequent decay. It
is obvious that this could ultimately lead to the acquisition
of erroneous results due to no-longer-suitable samples. This
is the case of plasma samples which, once frozen at −80°C,
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should be rapidly thawed at 37°C to prevent denaturating
fibrinogen.36 The analysis of a sample not properly thawed
would provide unreliable results.21
In general, the availability of samples of different quality
levels in a biobank – if known and properly managed – can
result in the optimization of costs and in the maximization
of the outcome. Several procedures in sample preparation
and maintenance have an intrinsic relevant cost. A crucial
point is to avoid expensive procedures for specimens, which,
at some point in their early life cycle, have been managed
out of the best practices. In other words, the resulting final
quality of the sample usually does not only depend on the
last step, but rather on the “worst stage” over the whole
life cycle.
In this scenario, a SPREC label which allows one to
easily obtain most of the information associated with that
preanalytical encoding can be very useful for:
• avoiding degradation of high-quality specimens due to a
poor management;
• avoiding expensive handling procedures for specimens
that do not fulfil the required quality criteria;
• selecting specimens which are suitable for inclusion in
specific biomarker discovery projects.
For an easier selection of the suitable specimens, once
the inclusion criteria have been defined, sample searching
can be performed by partial “pattern-matching”. In other
words, the user may specify only some of the fields (eg, type
of sample, pre-centrifugation conditions, or elapsed time
from sample acquisition), and the SPRECbase application
would filter and list all the samples complying to such partial
specification.28
Transfer of SPREC-labeled samples
between laboratories/institutions
As part of the collaboration between research institutions, it
is very frequent that a laboratory should send a large amount
of samples to another laboratory. This may be the case in
retrospective studies involving the analysis of a large segment
of the population with special characteristics or belonging
to a temporal situation or to a specific geographic area,37
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SPReC labeling in biobanking
or of multicenter studies designed to determine the incidence
and clinical relevance of a given condition.38
In the event of such large enlistments, it is not sufficient
that the biological samples are provided with identifiers and
paper documents containing the demographic information,
medical history, and methodological description, since the
amounts of data having to be consulted would be so elevated
that it would expose the personnel of the “receiving” laboratory to manual mistakes during the compilation of the database with sample-related information.
Conversely, if the laboratory that sends samples uses
SPREC coding, then the laboratory which receives the
samples can easily decode the SPREC of the sample batch
and subsequently store this code on the local installation
of SPRECbase.28 In this way, the transfer of samples and
that of an integrated database with all the necessary preanalytics information can be achieved simultaneously, and
the event of accidental mistakes due to manual handling of
the information can be minimized. It is also very important
to emphasize that the information related to a particular
batch of samples will always be available so that the sample
data can be retrieved at any time, facilitating the use of the
samples for future scientific collaborations and allowing the
update of the existing ones according to the new results.
If the facility receiving the SPREC-labeled samples
does not adopt SPREC for internal purposes, it can still get
interesting information about the samples by scanning the
SPREC labels and decoding them, restoring the original
preanalytical information in its own laboratory information
system (Figure 2).
SPREC-based sample retrieval
within a network of laboratories
This scenario applies well to all those trials that require the
involvement of several research institutes due to either the
objective difficulties of recruitment or the need for a multicenter approach for external data validation. Both situations,
requiring the exchange of large amounts of samples, allow
for the translation of relevant findings into clinical practice.
For this reason, it is desirable to establish a network of institutions sharing a labeling coding system in which context
the availability and location of even few samples with given
characteristics could be easily identified, thanks to the ability
to consult a privileged common database (Figure 3).
The purpose of this desirable scenario would be the establishment of a SPREC database shared among a network of
biorepositories. We can assume that SPREC-labeled samples
are imported from every laboratory pertaining to the network
where biological samples are made available to the entitled
operators. This would allow for certain advantages in the
planning of clinical trials: to avoid unnecessary duplication
of research, to not wasting precious samples, to foster collaboration between researchers, and to optimize the distribution of research funds.
Future perspectives
The SPREC-coding system can be applied at various levels
from handwritten to “high-tech” facilities to summarize
both preanalytical and relevant post-analytical information.
Moreover, SPREC has infinite applications, and in many
Receiving BRC
Physical samples
Laboratory
information system
Sample ID
SPREC
SPRECbase
Sample ID
Preanalytics
summary
Figure 2 importing SPReC-coded samples in a biorepository.
Abbreviations: BRC, biological resource center; ID, identification; SPREC, Sample
PReanalytical Code.
Journal of Biorepository Science for Applied Medicine 2015:3
Figure 3 Searching SPReC-coded samples in a network of biorepositories.
Abbreviation: SPReC, Sample PReanalytical Code.
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Riondino et al
different, not strictly clinical, environments. Presently, it has
been successfully applied to algal culture collections, proving optional coding from the moment of sample collection to
post-cryostorage manipulations.39 Future applications include
its exploitation for a microbial culture bank within the Multidisciplinary Interinstitutional Biobank (BioBIM) facilities.
Thus, the “SPRECware architecture” previously described
and here reported, does not represent an end point, but is susceptible to further implementation to increase its easiness to
use and widespread diffusion among researchers. Preliminary
results have been obtained in a pivotal study specifically
designed to facilitate the exchangeability of information using
a web-based platform that does not require any software installation, and tests are being performed between the BioBIM
of the IRCCS San Raffaele Pisana in Rome (the “sender”
laboratory) and the LC Campisi Laboratory Ltd in Sicily (the
“receiver” laboratory). This has been made possible by the
establishment of a permanent centralized web server, which
is being implemented with the following features:
• SPREC-coding and -decoding servers with no installation
requirements;
• SPREC-sharing server allowing enabled users to formulate queries with search/filtering capabilities;
• development of a downloadable version of SPRECbase.
These novel functionalities will ultimately provide the
users with easy-to-use solutions for upload/download capabilities to/from shared SPREC collections on a centralized
server. Implementation with novel IT tools will also allow
adjustments and timely updates to keep the SPREC’s optimal
use in line with future technological developments.
Author contributions
All authors contributed toward data analysis, drafting and
critically revising the paper and agree to be accountable for
all aspects of the work.
Acknowledgments
This study was partially supported by research funding
from the European Regional Development Fund PO FESR
2007/2013 Linea di Intervento 4.1.1.1 – SIASOP (CUP
G83F11000290004), the European Social Fund, under the
Italian Ministry of Education, University and Research
PON03PE_00146_1/10 BIBIOFAR(CUP B88F12000730005),
and Lazio Region, with financial resources from the Italian
Ministry of Economic Development, Decree May 7, 2010.
Disclosure
The authors report no conflicts of interest in this work.
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