In search of an evidence-based strategy for quality assessment of human tissue samples: report of the tissue Biospecimen Research Working Group of the Spanish Biobank Network

  • Margalida Esteva-Socias
  • María-Jesús Artiga
  • Olga Bahamonde
  • Oihana Belar
  • Raquel Bermudo
  • Erika Castro
  • Teresa Escámez
  • Máximo Fraga
  • Laura Jauregui-Mosquera
  • Isabel Novoa
  • Lorena Peiró-Chova
  • Juan-David Rejón
  • María Ruiz-Miró
  • Paula Vieiro-Balo
  • Virginia Villar-Campo
  • Sandra Zazo
  • Alberto Rábano
  • Cristina VillenaEmail author
Open Access
Part of the following topical collections:
  1. Cancer microenvironment


The purpose of the present work is to underline the importance of obtaining a standardized procedure to ensure and evaluate both clinical and research usability of human tissue samples. The study, which was carried out by the Biospecimen Science Working Group of the Spanish Biobank Network, is based on a general overview of the current situation about quality assurance in human tissue biospecimens. It was conducted an exhaustive review of the analytical techniques used to evaluate the quality of human tissue samples over the past 30 years, as well as their reference values if they were published, and classified them according to the biomolecules evaluated: (i) DNA, (ii) RNA, and (iii) soluble or/and fixed proteins for immunochemistry. More than 130 publications released between 1989 and 2019 were analysed, most of them reporting results focused on the analysis of tumour and biopsy samples. A quality assessment proposal with an algorithm has been developed for both frozen tissue samples and formalin-fixed paraffin-embedded (FFPE) samples, according to the expected quality of sample based on the available pre-analytical information and the experience of the participants in the Working Group. The high heterogeneity of human tissue samples and the wide number of pre-analytic factors associated to quality of samples makes it very difficult to harmonize the quality criteria. However, the proposed method to assess human tissue sample integrity and antigenicity will not only help to evaluate whether stored human tissue samples fit for the purpose of biomarker development, but will also allow to perform further studies, such as assessing the impact of different pre-analytical factors on very well characterized samples or evaluating the readjustment of tissue sample collection, processing and storing procedures. By ensuring the quality of the samples used on research, the reproducibility of scientific results will be guaranteed.


Quality Pre-analytical variables Biobank Tissue Biospecimen science 



Automated Quantitative Analysis


European Research Infrastructure for Biobanking and Biomolecular Resources


Biospecimen Reporting for Improved Study Quality


cold ischemia time


fluorescence detector-based denaturing high-performance liquid chromatography


formalin-fixed paraffin-embedded




liquid chromatography


mass spectrometry


non-available data


Next Generation Sequencing


research and development


RNA integrity number


reverse phase


reverse phase protein array


reverse transcription quantitative polymerase chain reaction


Spanish Biobank Network


sodium dodecyl sulphate polyacrylamide gel electrophoresis


standardized operation procedures


Standard PREanalytical Code


tissue quality index


Human tissue samples obtained from biopsies, surgical specimens, organ transplants and autopsies are a great resource to find potential targets to aid clinical decisions such as diagnosis and treatment of diseases. Over the last decades, the use of human biospecimens has heavily increased in biomedical research in order to evaluate the outcome, survival, and new therapies for patients, and also to test new hypotheses related to the genetic and molecular basis of diseases. Besides, the constant technology advances for biomarker discovery have led to an increasing demand of large sets of human biospecimens and for new formats for the preservation of biospecimens suitable for these technologies, promoting also the creation of new human biorepositories [1].

Research on disease biomarkers is one of the main requirements for the progress of personalized medicine and its use for targeted therapies [2, 3, 4, 5]. This clinical approach, particularly in Oncology, allows a great number of patients to access more efficient and safer therapeutic protocols, which have been selected according to molecular findings in tissue samples obtained from patients for diagnostic or therapeutic purposes. Indeed, many studies report the sustained discovery of different clinical biomarkers with potential application to personalized medicine [6]. However, most of them cannot be applied to clinical practice due to a lack of high sensitivity or/and specificity, compromising its reproducibility and its successful clinical implementation [7]. In this context, the recruitment of subjects, as well as the selection and management of tissues, is critical in biomarker research [8, 9].

Specifically, it is well known that the handling of human biospecimens during their collection, processing and storage can alter their characteristics and influence their quality, integrity and/or molecular composition [10]. These variations are considered as a bias in biomarker discovery, hindering the development of new targeted therapies.

As a result, there is a crucial need for the standardization of collection, processing and storage procedures to improve the quality of biospecimens, in order to enhance the reproducibility of biomarker development. Consequently, in recent years, a large number of strategies have been described to standardize and improve the quality control of human samples for their use in biomedical research, such as the “Standard PREanalytical Code” (SPREC) version 2.0, a method developed and agreed by the International Society for Biological and Environmental Repositories Biospecimen Science Working Group, which allows controlling the main pre-analytical factors that may have an impact on the integrity of the biological sample during its collection, processing and storage. SPREC assigns to each sample a code of 7 elements based on its pre-analytical characterization, helping to standardize the quality of the set of samples to be used [11].

There are also some guidelines to guarantee collection of clinical and pre-analytical data, such as BRISQ (Biospecimen Reporting for Improved Study Quality [12]. The aim of BRISQ is to ensure the registration of human samples data, including the preanalytical factors which could influence the integrity, quality or molecular composition such as (a) type of pathology, (b) clinical status and features of the patient and (c) handling and preservation conditions (for example: stabilization, shipping and storage conditions).

However, despite the efforts made in the last years, the scientific community still lacks a standardized approach to ensure and evaluate the clinical and research usability of human tissue samples. For these reasons, we have undertaken to summarize and give a general overview of the current situation concerning quality assurance in human tissue biospecimens.

Literature review

The Spanish Biobank Network (SBN), formed by 39 biobanks, provides mainly samples to the scientific community to support biomedical research, as well as technical, ethical and legal advice, and other services related to human biological samples. One of its most developed areas is the Biospecimen Science, where mainly biologists and pathologists from 13 biobanks of the SBN, participate cooperatively in a working group focused on innovation in human tissues handling, (i) for improving standards on tissue collection, processing and storage, and (ii) for setting a global quality assessment method of human tissues for biomedical research.

Firstly, the SBN Biospecimen Science working group conducted an exhaustive literature review of the analytical techniques used to evaluate the quality of human tissue samples over the past 30 years, as well as their reference values if they were published, and classified them according to the biomolecules evaluated: (i) DNA, (ii) RNA, and (iii) soluble or/and fixed proteins for immunochemistry. The group focused on publications where colon, breast, kidney, lung, ovary or brain tissues were used, since these organs are the main source of samples collected by the participating biobanks.

Secondly, based on results of the literature review and the expertise of the participating biobanks, a proposal for quality assessment of tissues based on the type of preservation method and biomolecule of interest was led. The algorithm was purposed to classify the solid tissue samples according their expected quality, taking into account the type of analytical technique required for the research project.

For the design of the algorithm, the Group made a prioritization of pre-analytical factors defined by SPREC v.2.0 [11] and BRISQ [12] with the highest expected impact on the integrity of tissue samples according to the literature. So, the Working Group classified in three categories (optimal or non-effect, moderate or unknown effect and suboptimal quality) the expected quality of the nucleic acids integrity and fixed proteins for immunochemistry for each factor, both in snap-frozen and in formalin fixed-paraffin embedded (FFPE) tissue samples.

Identification of techniques for tissue quality assessment

More than 130 publications released between 1989 and 2019 were analysed, most of them reporting results based on the analysis of tumour and samples from biopsy procedures. Tables 1, 2, 3, 4 show a summary of the analytical techniques used to evaluate tissue quality, according to the analysed biomolecule (RNA, DNA, soluble proteins and antigenicity, respectively). They describe (i) the measurement method of the biomolecule, (ii) the analytical technique used, (iii) the parameters of the evaluated biomolecule, (iv) the threshold values and (v) the anatomical organ analysed.
Table 1

Summary of publications evaluating quality of RNA samples

Measurement method

Analytical technique

Evaluated parameter





UV spectroscopy (A260/280) NanoDrop

Quantity and purity

Around 2

Human trabecular bone

[13, 14, 15, 16, 17, 18]

> 1.8 excellent

Colon, articular cartilage and subchondral bone, brain

1.8–1.6 adequate

< 1.6 inadequate

UV spectroscopy (A260/230) NanoDrop

Quantity and purity

> 2 non contaminated RNA

Articular cartilage and subchondral bone


< 2 contaminated RNA


RIN, RIS, or equivalent


≥ 7 high-integrity RNA

Colon, kidney, placenta, articular cartilage and subchondral bone, trabecular bone, pancreas

[13, 14, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27]

6–7 adequate-integrity RNA

Trabecular bone, pancreatic, stomach, liver, colon, brain

[13, 26, 28, 29, 30, 31, 32, 33]

5–6 low integrity

Pancreas, breast, thyroid, stomach, lung, colon

[26, 34, 35]

3–5 partially degraded

Breast, thyroid, stomach, lung, colon, kidney, pancreas

[14, 26, 33, 34, 35]

1–3 totally degraded

Trabecular bone, breast, thyroid, stomach, lung, colon, brain, placental

[13, 17, 27, 34, 36]



> 70% high quality

Brain and other tissue types

[37, 38]

50–70% medium quality

30–50% low quality

< 30% too degraded

28S:18S peak ratio


Around 2

Stomach, pancreas, liver, colorectal


Electrophoretic profile


2 bands 2000 nt (18S), 4000 nt (28S) → (Non-degraded RNA)

Pancreatic tissue

[26, 27]

Diffuse banding indicative of degraded RNA

Pancreatic tissue

[26, 27]


3′:5′ ratio


1–5 perfectly intact mRNA



> 5 suggests degradation

≥ 10 denatured mRNA

Ct values


Increasing Ct values of ABL1, FOSB and JUN genes suggest RNA degradation



Table 2

Summary of publications evaluating quality of DNA samples

Measurement method

Analytical technique

Evaluated parameter

Quality stratification threshold




UV spectroscopy (A260) NanoDrop


Pancreas, spleen, duodem, liver


Fluorochrome binding and fluorometer (Qubit)


Pancreas, spleen, duodem, liver, sarcoma, breast, gastric, colorectal, prostate, lung adenocarcinoma

[41, 42, 43, 44, 45]

UV spectroscopy (A260/280) NanoDrop


1.8–2.1 optimal, < 1.8 or > 2.1 contamination with RNA proteins or others

Lung adenocarcinoma, prostate

[44, 45]

UV spectroscopy (A260/230) NanoDrop


2–2.2 optimal, lower ratios may indicate presence of contaminants




Pulsed field gel electrophoresis


Size distribution between 12 and 300 kb


Agarose gel, and capillary electrophoresis (DNA Integrity Number, DIN)



Pancreas, spleen, duodenum, liver, sarcoma

[41, 47, 48]


Multiplex PCR and dHPLC/multiplex PCR and gel electrophoresis


Presence of the 300- to 400-bp amplicon indicates optimal quality, amplicon sizes ranging from 102 to 300 bp

Brain, colon and prostate

[44, 49]

Multiplex PCR and gel electrophoresis


Threshold not defined (amplicons between 268 and 1327 bp), optimal samples with amplification of 200 bp fragment or larger

Colon, uterine, myometrium and liver, breast

[50, 51]

Multiplex PCR and microfluidic analysis


A QC ratio above 0.20 indicates optimal quality, ratios below 0.20 suggests moderate or poor quality



Multiplex digital PCR (dPCR)


Validation needed to establish stratification thresholds






Increasing qPCR ratio between frozen and FFPE tissue samples, 93 bp human GAPDH qPCR, detection of 18S5 rRNA by qPCR (CT-value < 38), qPCR using FFPE QC kit and PreSeq QC assay, Q-ratio (with a value between 0 and 1), in which 41 bp and 129 bp targets were amplified by qPCR (KAPA human genomic DNA quantification and QC Kit-KAPA Biosystems). High Q-ratio: less fragmentation and vice versa

Liver, breast, tongue, prostate, sarcoma, lung adenocarcinoma, breast, gastric, colorectal

[43, 45, 47, 54, 55]

Multiplex qPCR


percentage of functional templates (QFI, ranging from 0.03 to 24.5%), optimal > 3% to 6%

Different sources


MF (somagen diagnostics) is a mixture of methanol and polyethylene glycol (90% and 10%, respectively)

Table 3

Summary of publications of quality control tools used in proteomics for evaluating the impact of pre-analytical factors

Measurement method

Analytical technique

Evaluated parameter

Pre-analytical factor





DC protein assay

Concentration determined based on standard curve



Colon, kidney

[57, 58]

BCA protein assay


Western blot

PCNA detection




Comparative evaluation of reactivity of fresh and FFPE using antibodies against GAPDH, tropomyosin, vinculin and myosin

Sheep tissue from skeletal muscle, liver, human hyperplastic thyroid tissue


SDS-PAGE and silver staining

Size distribution

Sample age

High quality proteins are feasible to extract from 14 years samples



N-cadherin and phospo-ERK detection


Comparison of 2D-PAGE gel protein profiles

Time to freeze

30 min




P-p27 detection

Cell culture


Mass spectrometry

LC–MS/MS analysis

Comparative analysis of peptide hits between fresh-frozen and FFPE samples




Protein overlap between fresh and FFPE tissue sections




Capillary isoelectric focusing coupled with RP LC–MS/MS

Storage time

From 7 years fewer distinct peptides and proteins were identified but the normalised expression values of actin, desmin and progesterone receptor were consistent until 12 years



Protein microarray


Evaluation of increase and decrease percentage of phosphoproteins

Time to fixation

20 min

Uterus, colon, lung, ovary, breast, lymph node


Table 4

Summary of publications evaluating antigenicity quality

Analytical technique

Evaluated parameter

Pre-analytical factor




Quantitative IF (AQUA score)

ER, HER2, Ki-67, CK

Storage time

IF signal decreases 10% in 4–8 years depending on the marker



Increased marker: 95th percentile of slope for n = M is higher than 0


Labile and loss of antigenicity within 1–2 h of CIT



Decreased marker: 95th percentile of slope for n = M is lower than 0

No changes in marker: 95% CI for the slope with both n = M and n = 10 × M Including the zero slope

Trend up/trend down: 95% CI for the slope with n = 10 × M not including it

Cytokeratin, pERK1/2 and pHSP-27 expression


Negative TQI values (as indicator of loss of tissue quality) for increasing CIT








ER and PgR

Fixation, slicing, storage of slides

Samples for ER and PgR testing are fixed in 10% NBF for 6 to 72 h. CIT < 1 h. Samples should be sliced at 5-mm intervals. Storage of slides for more than 6 weeks before analysis is not recommended





SNRPA and SnRNP70 H-score


H-score < 60 as a cut off for positive signal




Fixation, slicing and storage

Decrease of MAP2 immunoreactivity in unfixed and in delayed-fixed

Rat brain


Actin, desmin and progesterone receptor staining

Storage time

Consistent staining over 18 years



It should be mentioned that the Group found little information focused on quality control of soluble proteins (Table 3) and antigenicity, including objective threshold values and analytical techniques used (Table 4). For this reason, the Working Group decided to include the most relevant publications regarding pre-analytical factors and its consequent effect on them. Consequently, Tables 3 and 4 include information regarding the pre-analytical factor under study for each cited reference and, if known, the threshold established to determine the effect of the pre-analytical factor on the sample.

Consensus on an integrated algorithm for quality assessment

With the aim of systematizing the classification of human tissue samples according to their expected quality, a categorization proposal has been drawn up in the present study, based on SPREC and BRISQ tools as reference. Becker et al. [73] has already eloquently discussed in a review paper the importance of these pre-analytical factors for the meaningful translation of proteomic methods and findings to clinical practice. Next, in order to verify the functionality of the proposed categories and to establish reference ranges of analytical values, an algorithm was designed for decision-making based on the different biomolecules with different susceptibility profiles and on the type of sample preservation.

A quality assessment proposal for frozen tissue samples

Because of the increasing use of human frozen tissue specimens as a gold-standard for molecular analysis, a testing approach was designed for frozen tissue samples based on RNA evaluation (Fig. 1). As a first step, purity and concentration assessment of total RNA through spectrophotometry is recommended, since it is a quick and relatively simple method to evaluate (1) great deteriorations according to SPREC variables suffered during storage or analysis, or (2) a low cellular content related to its anatomical origin.
Fig. 1

Procedures proposed to evaluate molecular integrity in order to classify the suitability of samples for expected applications

In case that an adequate concentration of total RNA is obtained and it is necessary to evaluate the suitability of the sample to perform gene expression studies, it would be advisable to evaluate the potential effect of pre-analytical factors (SPREC and BRISQ) on RNA integrity (Table 5) to decide if further optional analyses are required to determine whether a sample is suitable to the research purpose.
Table 5

Expected quality for frozen tissue samples based on RNA quality assessment according to pre-analytical factors prioritized following SPREC and BRISQ recommendations

Type of codification


Optimal expected quality

Moderate expected quality

Sub-optimal expected quality


Anatomical site

Colon, lung and liver




Neuronal [74, 75, 76]


Body temperature

4 °C (post-mortem) [77]

RT 18–28 °C (post-mortem) [77]


37 °C (alive)


Type of sample





Type of collection



A24, A48, A72 [77]



Warm ischemia time

A, B, C, D, N


F, X


Cold ischemia time

A, B, C, D [20, 78]

E, N

F, X [30, 66, 79]


Fixation/stabilization type

OCT, PXT [80, 81]


Others (ACA, ALD, FOR, HST, NAA, NBF, XXX, ZZZ) [83]

RNL [32, 82]

SNP [24, 80, 81]



Fixation/stabilization time

D, E (PXT) [84]

A, B, C


F (ALL, RNL) [85]

D, E (ALL, RNL) [85]


F, G (PTX) [84]


G (ALL, RNL) [85]




Long-term storage

A, J, N Q, S, W [86]

B, V, C, D, E, F, G, H, I, K, T, X

P [27, 87]



Storage duration

< 5 years [88, 89]

5–20 years [89]

> 20 years

If predicted RNA quality is optimal according to pre-analytical factors, it is suggested to perform an integrity analysis of the total RNA through its visualization in an agarose gel and/or the calculation of the 28S:18S ratio using the RNA Integrity Number (RIN). According to recent publications, three ranges of RIN values have been set up as indicators of molecular integrity. A value greater than or equal to 7 is considered a non-degraded RNA, and therefore, it is assumed to be a high quality sample valid to carry out high-performance gene expression techniques (arrays, miRNA microarrays, RNA-Seq), and to be used in in Next Generation Sequencing (NGS) of small RNA. In contrast, RIN values between 5 and 7 are indicative of RNA slight degradation and, finally, values below 4 indicate a high level of RNA degradation. The use of samples with RIN values included in the latter two groups is not valid for high-throughput technologies for gene expression analysis. However, they may be suitable for strategies whose main objective is to detect present or absence of a particular marker, such as Endpoint PCR o miRNA detection [19, 20, 21, 90, 91].

In contrast, if a moderate RNA quality level is estimated according to pre-analytical variables, more economic analytical techniques than RIN can be performed to evaluate sample quality. A good choice could be studying transcript degradation of a housekeeping genes set by RT-qPCR (GAPDH, ACTB, B2M, 18S, ATP5E, TUBB, for example) and evaluate the 3′/5′ ratio, as an indirect indicator of degradation and functionality [39, 92]. In most cases, RNA degradation is initiated by a gradual shortening of the poly(A) tail [93], which modifies the proportion of amplicons of the 3′ and 5′ region. This means that values close to 1.0 would indicate no degradation, while values further from 1.0 would indicate degradation and loss of functionality [94]. Samples with optimal quality to perform gene expression assays should present a rate of approximately 1.0 for most genes studied. Otherwise, if samples with a ratio significantly different from 1.0 are detected, they should not be considered for high performance analysis [92, 95].

Finally, if a sub-optimal quality is predicted (RIN values below 5), RIN determination itself is not a reliable measure of sample usefulness for RT-PCR or other applications, and accordingly other parameters should be taken into account in “fitness for purpose” decisions [96]. On those cases, it would be recommendable to perform endpoint PCR analyses, amplifying different fragments of several housekeeping genes, such as G6PD, TBP, HPRT, ACTB, GAPDH and then determine amplicon sizes by electrophoresis, loading the PCR product in an agarose gel, to start the quality control analysis. For samples showing differential size amplicons, it is assumed that whole RNA has enough quality for RT-qPCR assays. If only small amplicons are visible, it is considered that RNA has been degraded and it is only suitable for miRNAs analysis. If no amplicons are visible, the RNA quality is not enough for any gene expression study.

In summary, the expected quality of a sample and its pre-analytical variables should lead us to starting the process of quality assessment with a specific analytical technique or even a combination of them depending on the subsequent application (Fig. 1).

A quality assessment proposal for formalin-fixed paraffin-embedded samples

For the FFPE samples, according to the expected quality of the sample based on a first basic immunochemistry of CD31 and/or vimentin, a decision tree is proposed for the immunohistochemical process to be carried out (Fig. 2) in order to evaluate the antigenicity tissue quality. The antibodies selected for quality assessment were proposed based on the following criteria: (1) since they are widely used in Diagnostic Pathology routine, they could lead to an easier and rapid implementation of the quality control strategy and no changes would be necessary in work routines. Moreover, these antibodies are economically affordable and available from many reagent suppliers; (2) these antibodies are included in the Quality Assurance Program of the Spanish Society of Pathology (in Spanish: Sociedad Española de Anatomía Patológica, SEAP). This fact ensures that they are considered as antibodies used for current immunohistochemical diagnosis; (3) they hybridize with targets present in most human tissues, both healthy and pathological, which allows the quality control system to be robust.
Fig. 2

Procedures proposed to evaluate antigenicity tissue quality, in order to classify the suitability of the sample for the expected application

Taking into account the above criteria, Ki-67 and TTF-1 were selected as nuclear markers, Vimentin and Cytokeratin AE1–AE3 as cytoplasmic markers; and CD31 and Beta-catenin as membrane markers. The selection of antibodies of different localizations inside cells also could help to understand how cellular location of a specific antigen can influence on its antigenicity preservation, which is currently a controversial concept.

So, in order to perform quality control on FFPE tissues a process based on two consecutives stages differentiated both by the implementation or not of an antigenic reconstitution procedure is recommended (Fig. 2). Antigenic retrieval allows recovering the antigenicity lost by the epitopes during the fixation process with formaldehyde preventing antibody recognition. The antigen retrieval process is considered as a key process for antigenicity preservation. It is advisable to use it in those samples where the concentration of the antigen to be identified is very low and in samples that have undergone prolonged periods of fixation.

We propose to carry out a first staining process with Vimentin and CD31 antibodies without antigenic reconstitution. Ki-67 is not included in this first step because it is well known that it has a low proportion of antigen and, therefore, for its proper function an antigen recovery process must be carried out. Those samples presenting high signal with Vimentin and CD31 stain, both in number of stained cells and in average intensity, would be considered as samples with optimal quality for carrying out immunohistochemistry (IHC) experiments. On the contrary, slides with low or no signal are recommended to be considered as samples of unknown quality.

Meanwhile, to evaluate the quality of those samples with unknown standards, it is proposed to carry out the second phase of the process but with a previous step of antigen retrieval. The procedure involves new staining processes, identical to the one carried out previously, but also including Ki-67 antibody. Those samples presenting a high and positive stain should be considered as samples with moderate quality to use IHC. The loss of signal between stage 1 (without antigen retrieval) and stage 2 (with antigen retrieval) would be related to pre-analytical factors affecting stability and sensitivity of epitope binding and recognition. Samples presenting no signal for the antibodies tested would have to be considered as samples of sub-optimal quality to perform IHC analysis.

Discussion and conclusions

Human biological samples from the most prevalent chronic and rare diseases are nowadays essential for advanced biomedical research. In the case of rare diseases, only collaborative approaches make it possible to collect a relevant number of samples with high quality associated clinical data [97, 98], while it is essential for any collection that the quality of samples remains homogenous. However, the emerging lack of reproducibility of scientific results is a relevant international problem, especially in the development of clinical biomarkers for the diagnosis, treatment and follow-up of a large number of diseases [99]. Regarding tissue samples, the availability of analytical techniques to assess their quality is important and necessary to ensure reproducibility of scientific results. Fortunately, the identification of pre-analytical factors affecting integrity of samples has been very well developed in international initiatives, as SPREC, BRISQ, MIABIS, etc. Nevertheless, a standardized and extensive method to determine the usability of a sample for a particular analytical technique, or even for general tissue samples quality evaluation, has not been developed in detail. The availability of these methods, as proposed in the present work, would reduce the bias posed by a specific group of samples selected for a study. In addition, these methods would allow the identification of threshold values to determine the impact of each pre-analytical factor on the quality, integrity and functionality of tissue samples, allowing the optimization of handling, preservation and storage procedures.

Recent developments in national and international regulations on human biospecimens for research present biobanks as organizations aimed at supplying biological material with the highest quality requirements to support biomedical research [100]. In Spain, biobanks have a specific national legal regulation and normally operate under quality management systems and standardized operation procedures (SOPs) to guarantee the minimum bias among preserved tissue samples. Biobanking staff is increasingly aware of the impact that pre-analytical factors may have on the handling of tissue samples and, moreover, of the importance of having analytical tools available for taking fundamental and strategic decisions in biobanks.

In 2009, with the aim of promoting the biomedical research in Spain, a solid network of biobanks, the SBN was created to improve the overall quality of samples for research use. At present, 39 biobanks are members of the network, including regional networks of biobanks, population biobanks, disease-specific biobanks and neurological biobanks, among others. Despite being a numerous, complex and heterogeneous network, three common objectives have been established: (i) to promote the biomedical research by supplying samples with the maximum guarantee of quality; (ii) to collaborate in order to achieve the best service for the researchers; and (iii) to improve the knowledge in Biospecimen Science, in order to help on strategic decisions such as the implementation of a national quality program in biobanks. The entire network operates under a strategic plan 2018–2020, and the executive part is configured by 5 programs focused on (1) engagement of researchers and recruitment of collaborative scientific groups, (2) visibility and accessibility of the available collections and services, (3) R&D in biobanking, (4) internal and external communication, (5) specific training in biobanking procedures and network coordination. All the activity is supported by an internal structure formed by a Coordination Office, a Quality Committee, an Advisory Events Committee and an Advisory Ethical-legal Committee, headed by a coordinator advised by the Steering Committee following the recommendations of an Advisory External Scientific Committee. Similar initiatives on quality issues are faced in Europe, solved in part with the establishment of the European Research Infrastructure for Biobanking and Biomolecular Resources (BBMRI-ERIC), formed by national biobank networks, dedicated to providing researchers with the support they need to find new treatments. In all these networks, a particular concern for global quality of samples and the implementation of specific quality tests are addressed in order to improve the homogenization and standardization, and in consequence, the reproducibility of the scientific results worldwide.

To help on that issue, our Working Group has conducted thorough review of the literature and has shared common expertise between its members on a wide range of preanalytical factors and analytical tests. As a result, we have designed, two algorithms for the classification of biobank tissue samples according to their expected level of performance in various analytical procedures. Both algorithms are based on (1) a selection of preanalytical data that are relevant for the final quality of samples; and (2) on a multi-step evaluation of samples by selected analytical methods that allow a final classification in terms of expected sample quality. One of the algorithms is aimed at defining sample quality for frozen tissue samples, while a second algorithm is directed to FFPE samples.

However, the great heterogeneity of human tissue samples and the large number of pre-analytical factors associated with the quality of samples makes it very difficult to harmonize the quality criteria. Nonetheless, assessing the integrity of the tissue itself and derived biomolecules, such as its antigenicity, as the method we propose, will help to evaluate if stored human tissue samples fit for the purpose for which they were collected, as well as if they are suitable for other unspecified uses not considered previously.

To conclude, the analytical strategies and techniques that are presented here constitute a first step to evaluate the real impact of pre-analytical factors. The implementation of such analytical methods will allow the periodical evaluation of the need to perform readjustments in collection, processing and storing processes to ensure the availability of well characterized human tissue samples for their use in biomedical research.



We are grateful to the Spanish Biobank Network for supporting project.

Authors’ contributions

MES drafted the manuscript and all authors contributed drafting part of the manuscript, including tables and figures. AR and CV coordinated the draft and provided critical review of the manuscript. All authors also contribted with literature search. All authors read and approved the final manuscript.


This work was funded by the Ministerio de Ciencia, Innovación y Universidades of Spain and Instituto de Salud Carlos III (PI16/00528, PI16/00946, PI16/01207 and PI16/01276), co-funded by the Spanish Biobank Network (PT13/0010/0030, PT17/0015/0001, PT17/0015/0021, PT17/0015/0049, PT17/0015/0018, PT17/0015/0002, PT17/0015/0016, PT17/0015/0038, PT17/0015/0027, PT17/0015/0004, PT17/0015/0047, PT17/0015/0014, PT17/0015/0041, and PT17/0015/0006), European Regional Development Fund (FEDER) “A way to make Europe” and granted by Conselleria d’Innovació, Recerca i Turisme del Govern de les Illes Balears (TEC/002/2017).

Availability of data and materials

The data supporting the conclusions of this article are included within the article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.


  1. 1.
    Hughes SE, Barnes RO, Watson PH. Biospecimen use in cancer research over two decades. Biopreserv Biobank. 2010;8:89–97.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353:1673–84. Scholar
  4. 4.
    Marchetti A, Martella C, Felicioni L, Barassi F, Salvatore S, Chella A, et al. EGFR mutations in non-small-cell lung cancer: analysis of a large series of cases and development of a rapid and sensitive method for diagnostic screening with potential implications on pharmacologic treatment. J Clin Oncol. 2005;23:857–65.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363:809–19.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Drucker E, Krapfenbauer K. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA J. 2013;4:7.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Taube SE, Clark GM, Dancey JE, McShane LM, Sigman CC, Gutman SI. A perspective on challenges and issues in biomarker development and drug and biomarker codevelopment. J Natl Cancer Inst. 2009;101:1453–63. Scholar
  8. 8.
    Prudkin L, Nuciforo P. Obstacles to precision oncology: confronting current factors affecting the successful introduction of biomarkers to the clinic. Cell Oncol. 2015;38:39–48.CrossRefGoogle Scholar
  9. 9.
    Ransohoff DF, Gourlay ML. Sources of bias in specimens for research about molecular markers for cancer. J Clin Oncol. 2010;28:698–704.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ellervik C, Vaught J. Preanalytical variables affecting the integrity of human biospecimens in biobanking. Clin Chem. 2015;61:914–34.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Lehmann S, Guadagni F, Moore H, Ashton G, Barnes M, Benson E, et al. Standard preanalytical coding for biospecimens: review and implementation of the sample preanalytical code (SPREC). Biopreserv Biobank. 2012;10:366–74.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Moore HM, Kelly AB, Jewell SD, McShane LM, Clark DP, Greenspan R, et al. Biospecimen reporting for improved study quality (BRISQ). Cancer Cytopathol. 2011;119:92–102. Scholar
  13. 13.
    Cepollaro S, Della Bella E, de Biase D, Visani M, Fini M. Evaluation of RNA from human trabecular bone and identification of stable reference genes. J Cell Physiol. 2018;233:4401–7. Scholar
  14. 14.
    Hong SH, Baek HA, Jang KY, Chung MJ, Moon WS, Kang MJ, et al. Effects of delay in the snap freezing of colorectal cancer tissues on the quality of DNA and RNA. J Korean Soc Coloproctol. 2010;26:316. Scholar
  15. 15.
    Manchester KL. Use of UV methods for measurement of protein and nucleic acid concentrations. Biotechniques. 1996;20:968–70.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Le Bleu HK, Kamal FA, Kelly M, Ketz JP, Zuscik MJ, Elbarbary RA. Extraction of high-quality RNA from human articular cartilage. Anal Biochem. 2017;518:134–8.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Haynes HR, Killick-Cole CL, Hares KM, Redondo J, Kemp KC, Moutasim KA, et al. Evaluation of the quality of RNA extracted from archival FFPE glioblastoma and epilepsy surgical samples for gene expression assays. J Clin Pathol. 2018;71:695–701.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Sambrook J, Fritsch EF, Maniatis T. Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory Press; 1989.
  19. 19.
    Botling J, Edlund K, Segersten U, Tahmasebpoor S, Engström M, Sundström M, et al. Impact of thawing on RNA integrity and gene expression analysis in fresh frozen tissue. Diagn Mol Pathol. 2009;18:44–52.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Freidin MB, Bhudia N, Lim E, Nicholson AG, Cookson WO, Moffatt MF. Impact of collection and storage of lung tumor tissue on whole genome expression profiling. J Mol Diagn. 2012;14:140–8. Scholar
  21. 21.
    Liu NW, Sanford T, Srinivasan R, Liu JL, Khurana K, Aprelikova O, et al. Impact of ischemia and procurement conditions on gene expression in renal cell carcinoma. Clin Cancer Res. 2013;19:42–9.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Thompson KL, Pine PS, Rosenzweig BA, Turpaz Y, Retief J. Characterization of the effect of sample quality on high density oligonucleotide microarray data using progressively degraded rat liver RNA. BMC Biotechnol. 2007;7:57. Scholar
  23. 23.
    Song SY, Jun J, Park M, Park SK, Choi W, Park K, et al. Biobanking of fresh-frozen cancer tissue: RNA is stable independent of tissue type with less than 1 hour of cold ischemia. Biopreserv Biobank. 2018;16:28–35.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Bao W-G, Zhang X, Zhang J-G, Zhou W-J, Bi T-N, Wang J-C, et al. Biobanking of fresh-frozen human colon tissues: impact of tissue ex-vivo ischemia times and storage periods on RNA quality. Ann Surg Oncol. 2013;20:1737–44.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Wolfe LM, Thiagarajan RD, Boscolo F, Taché V, Coleman RL, Kim J, et al. Banking placental tissue: an optimized collection procedure for genome-wide analysis of nucleic acids. Placenta. 2014;35:645–54.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Jun E, Oh J, Lee S, Jun H-R, Seo EH, Jang J-Y, et al. Method optimization for extracting high-quality RNA from the human pancreas tissue. Transl Oncol. 2018;11:800–7.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kashofer K, Viertler C, Pichler M, Zatloukal K. Quality control of RNA preservation and extraction from paraffin-embedded tissue: implications for RT-PCR and microarray analysis. PLoS ONE. 2013;8:e70714.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Betsou F, Bulla A, Cho SY, Clements J, Chuaqui R, Coppola D, et al. Assays for qualification and quality stratification of clinical biospecimens used in research: a technical report from the ISBER Biospecimen Science Working Group. Biopreserv Biobank. 2016;14:398–409.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Hu Y, Han H, Wang Y, Song L, Cheng X, Xing X, et al. Influence of freeze–thaw cycles on RNA integrity of gastrointestinal cancer and matched adjacent tissues. Biopreserv Biobank. 2017;15:241–7.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Lalmahomed ZS, van den Braak RRJC, Oomen MHA, Arshad SP, Riegman PHJ, IJzermans JNM. Multicenter fresh frozen tissue sampling in colorectal cancer: does the quality meet the standards for state of the art biomarker research? Cell Tissue Bank. 2017;18(3):425–31.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    White K, Yang P, Li L, Farshori A, Medina AE, Zielke HR. Effect of postmortem interval and years in storage on RNA quality of tissue at a repository of the NIH NeuroBioBank. Biopreserv Biobank. 2018;16:148–57.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Aktas B, Sun H, Yao H, Shi W, Hubbard R, Zhang Y, et al. Global gene expression changes induced by prolonged cold ischemic stress and preservation method of breast cancer tissue. Mol Oncol. 2014;8:717–27.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Philips T, Kusmartseva I, Gerling IC, Campbell-Thompson M, Wasserfall C, Pugliese A, et al. Factors that influence the quality of RNA from the pancreas of organ donors. Pancreas. 2017;46:252–9.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Viana CR, Neto CS, Kerr LM, Palmero EI, Marques MMC, Colaiacovo T, et al. The interference of cold ischemia time in the quality of total RNA from frozen tumor samples. Cell Tissue Bank. 2013;14:167–73. Scholar
  35. 35.
    Galissier T, Schneider C, Nasri S, Kanagaratnam L, Fichel C, Coquelet C, et al. Biobanking of fresh-frozen human adenocarcinomatous and normal colon tissues: which parameters influence RNA quality ? PLoS ONE. 2016;11:1–17.CrossRefGoogle Scholar
  36. 36.
    Reiman M, Laan M, Rull K, Sõber S. Effects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples. FASEB J. 2017;31:3298–308.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Wimmer I, Tröscher AR, Brunner F, Rubino SJ, Bien CG, Weiner HL, et al. Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples. Sci Rep. 2018;8:1–17.CrossRefGoogle Scholar
  38. 38.
    Illumina. Evaluating RNA Quality from FFPE samples. 2014.
  39. 39.
    Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1:1559–82.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Musella V, Verderio P, Reid JF, Pizzamiglio S, Gariboldi M, Callari M, et al. Effects of warm ischemic time on gene expression profiling in colorectal cancer tissues and normal mucosa. PLoS ONE. 2013;8:e53406.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Simbolo M, Gottardi M, Corbo V, Fassan M, Mafficini A, Malpeli G, et al. DNA Qualification workflow for next generation sequencing of histopathological samples. PLoS ONE. 2013;8:e62692. Scholar
  42. 42.
    Kresse SH, Namløs HM, Lorenz S, Berner JM, Myklebost O, Bjerkehagen B, et al. Evaluation of commercial DNA and RNA extraction methods for high-throughput sequencing of FFPE samples. PLoS ONE. 2018;13:1–12.CrossRefGoogle Scholar
  43. 43.
    Nagahashi M, Shimada Y, Ichikawa H, Nakagawa S, Sato N, Kaneko K, et al. Formalin-fixed paraffin-embedded sample conditions for deep next generation sequencing. J Surg Res. 2017;220:125–32.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Patel PG, Selvarajah S, Guérard K-P, Bartlett JMS, Lapointe J, Berman DM, et al. Reliability and performance of commercial RNA and DNA extraction kits for FFPE tissue cores. PLoS ONE. 2017;12:e0179732. Scholar
  45. 45.
    Watanabe M, Hashida S, Yamamoto H, Matsubara T, Ohtsuka T, Suzawa K, et al. Estimation of age-related DNA degradation from formalin-fixed and paraffin-embedded tissue according to the extraction methods. Exp Ther Med. 2017;14:2683–8.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Shao W, Khin S, Kopp WC. Characterization of effect of repeated freeze and thaw cycles on stability of genomic DNA using pulsed field gel electrophoresis. Biopreserv Biobank. 2012;10:4–11.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Kresse SH, Namløs HM, Lorenz S, Berner J-M, Myklebost O, Bjerkehagen B, et al. Evaluation of commercial DNA and RNA extraction methods for high-throughput sequencing of FFPE samples. PLoS ONE. 2018;13:e0197456. Scholar
  48. 48.
    Gassmann M, Mchoull B. DNA integrity number (DIN) with the agilent 2200 TapeStation system and the agilent genomic DNA ScreenTape assay technical overview.
  49. 49.
    Wang F, Wang L, Briggs C, Sicinska E, Gaston SM, Mamon H, et al. DNA degradation test predicts success in whole-genome amplification from diverse clinical samples. J Mol Diagn. 2007;9:441–51.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Turashvili G, Yang W, McKinney S, Kalloger S, Gale N, Ng Y, et al. Nucleic acid quantity and quality from paraffin blocks: defining optimal fixation, processing and DNA/RNA extraction techniques. Exp Mol Pathol. 2012;92:33–43.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    van Beers EH, Joosse SA, Ligtenberg MJ, Fles R, Hogervorst FBL, Verhoef S, et al. A multiplex PCR predictor for aCGH success of FFPE samples. Br J Cancer. 2006;94:333–7.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Araujo LH, Timmers C, Shilo K, Zhao W, Zhang J, Yu L, et al. Impact of pre-analytical variables on cancer targeted gene sequencing efficiency. PLoS ONE. 2015;10:1–15.Google Scholar
  53. 53.
    Didelot A, Kotsopoulos SK, Lupo A, Pekin D, Li X, Atochin I, et al. Multiplex picoliter-droplet digital PCR for quantitative assessment of DNA integrity in clinical samples. Clin Chem. 2013. Scholar
  54. 54.
    Einaga N, Yoshida A, Noda H, Suemitsu M, Nakayama Y, Sakurada A, et al. Assessment of the quality of DNA from various formalin-fixed paraffin-embedded (FFPE) tissues and the use of this DNA for next-generation sequencing (NGS) with no artifactual mutation. PLoS ONE. 2017;12:e0176280.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Carlsson J, Davidsson S, Fridfeldt J, Giunchi F, Fiano V, Grasso C, et al. Quantity and quality of nucleic acids extracted from archival formalin fixed paraffin embedded prostate biopsies. BMC Med Res Methodol. 2018;18:161.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Sah S, Chen L, Houghton J, Kemppainen J, Marko AC, Zeigler R, et al. Functional DNA quantification guides accurate next-generation sequencing mutation detection in formalin-fixed, paraffin-embedded tumor biopsies. Genome Med. 2013;5:77. Scholar
  57. 57.
    Ikeda K, Monden T, Kanoh T, Tsujie M, Izawa H, Haba A, et al. Extraction and analysis of diagnostically useful proteins from formalin-fixed, paraffin-embedded tissue sections. J Histochem Cytochem. 1998;46:397–403.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Shi SR, Liu C, Taylor CR. Standardization of immunohistochemistry for formalin-fixed, paraffin-embedded tissue sections based on the antigen-retrieval technique: from experiments to hypothesis. J Histochem Cytochem. 2007;55:105–9.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Addis MF, Tanca A, Pagnozzi D, Crobu S, Fanciulli G, Cossu-Rocca P, et al. Generation of high-quality protein extracts from formalin-fixed, paraffin-embedded tissues. Proteomics. 2009;9:3815–23.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Kroll J, Becker KF, Kuphal S, Hein R, Hofstädter F, Bosserhoff AK. Isolation of high quality protein samples from punches of formalin fixed and paraffin embedded tissue blocks. Histol Histopathol. 2008;23:391–5.PubMedPubMedCentralGoogle Scholar
  61. 61.
    Sarto C, Valsecchi C, Mocarelli P. Renal cell carcinoma: handling and treatment. Proteomics. 2002;2:1627–9. Scholar
  62. 62.
    Surjit M, Lal SK. Glycogen synthase kinase-3 phosphorylates and regulates the stability of p27kip1 protein. Cell Cycle. 2007;6:580–8. Scholar
  63. 63.
    Balgley BM, Guo T, Zhao K, Fang X, Tavassoli FA, Lee CS. Evaluation of archival time on shotgun proteomics of formalin-fixed and paraffin-embedded tissues. J Proteome Res. 2009;8:917–25.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Espina V, Edmiston KH, Heiby M, Pierobon M, Sciro M, Merritt B, et al. A portrait of tissue phosphoprotein stability in the clinical tissue procurement process. Mol Cell Proteom. 2008;7:1998–2018.CrossRefGoogle Scholar
  65. 65.
    Combs SE, Han G, Mani N, Beruti S, Nerenberg M, Rimm DL. Loss of antigenicity with tissue age in breast cancer. Lab Investig. 2016;96:264–9.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Vassilakopoulou M, Parisi F, Siddiqui S, England AM, Zarella ER, Anagnostou V, et al. Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time. Lab Investig. 2014;95:334–41.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Neumeister VM, Parisi F, England AM, Siddiqui S, Anagnostou V, Zarrella E, et al. A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue. Lab Investig. 2014;94:467–74.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Battifora H. Assessment of antigen damage in immunohistochemistry: the vimentin internal control. Am J Clin Pathol. 1991;96:669–71.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28:2784–95.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Betsou F, Barnes R, Burke T, Coppola D, DeSouza Y, Eliason J, et al. Human biospecimen research: experimental protocol and quality control tools. Cancer Epidemiol Prev Biomark. 2009;18:1017–25.CrossRefGoogle Scholar
  71. 71.
    Pierceall WE, Wolfe M, Suschak J, Chang H, Chen Y, Sprott KM, et al. Strategies for H-score normalization of preanalytical technical variables with potential utility to immunohistochemical-based biomarker quantitation in therapeutic response diagnostics. Anal Cell Pathol. 2011;34:159–68.CrossRefGoogle Scholar
  72. 72.
    D’Andrea M, Howanski R, Saller C. MAP2 IHC detection: a marker of antigenicity in CNS tissues. Biotech Histochem. 2017;92:363–73.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Becker KF. Using tissue samples for proteomic studies—critical considerations. Proteom Clin Appl. 2015. Scholar
  74. 74.
    Grizzle WE, Sexton KC, Bell WC. Quality assurance in tissue resources supporting biomedical research. Cell Preserv Technol. 2008;6:113–8.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
  76. 76.
    Kap M, Oomen M, Arshad S, de Jong B, Riegman P. Fit for purpose frozen tissue collections by RNA integrity number-based quality control assurance at the Erasmus MC tissue bank. Biopreserv Biobank. 2014;12:81–90.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Ferrer I, Santpere G, Arzberger T, Bell J, Blanco R, Boluda S, et al. Brain protein preservation largely depends on the postmortem storage temperature: implications for study of proteins in human neurologic diseases and management of brain banks: a BrainNet Europe Study. J Neuropathol Exp Neurol. 2007;66:35–46.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Caboux E, Paciencia M, Durand G, Robinot N, Wozniak MB, Galateau-Salle F, et al. Impact of delay to cryopreservation on RNA integrity and genome-wide expression profiles in resected tumor samples. PLoS ONE. 2013;8:e79826.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Guerrera F, Tabbo F, Bessone L, Maletta F, Gaudiano M, Ercole E, et al. The influence of tissue ischemia time on RNA integrity and patient-derived xenografts (PDX) engraftment rate in a non-small cell lung cancer (NSCLC) Biobank. PLoS ONE. 2016;11:1–15.CrossRefGoogle Scholar
  80. 80.
    Viertler C, Groelz D, Gündisch S, Kashofer K, Reischauer B, Riegman PHJ, et al. A new technology for stabilization of biomolecules in tissues for combined histological and molecular analyses. J Mol Diagn. 2012;14:458–66.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Groelz D, Sobin L, Branton P, Compton C, Wyrich R, Rainen L. Non-formalin fixative versus formalin-fixed tissue: a comparison of histology and RNA quality. Exp Mol Pathol. 2013;94:188–94.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Salehi Z, Najafi M. RNA preservation and stabilization. Biochem Physiol. 2014;3:2.Google Scholar
  83. 83.
    Howat WJ, Wilson BA. Tissue fixation and the effect of molecular fixatives on downstream staining procedures. Methods. 2014;70:12–9.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Gmbh P. Tissue container product circular for fixation and stabilization of tissue specimens. 2012.Google Scholar
  85. 85.
    Florell SR, Coffin CM, Holden JA, Zimmermann JW, Gerwels JW, Summers BK, et al. Preservation of RNA for functional genomic studies: a multidisciplinary tumor bank protocol. Mod Pathol. 2001;14:116–28.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Andreasson A, Kiss NB, Juhlin CC, Ho A. Long-term storage of endocrine tissues at −80 °C does not adversely affect rna quality or overall histomorphology. Biopreserv Biobank. 2013;11:366–70.CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Peskoe SB, Barber JR, Zheng Q, Meeker AK, De Marzo AM, Platz EA, et al. Differential long-term stability of microRNAs and RNU6B snRNA in 12–20 year old archived formalin-fixed paraffin-embedded specimens. BMC Cancer. 2017;17:32.CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Shabihkhani M, Lucey GM, Wei B, et al. The procurement, storage, and quality assurance of frozen blood and tissue biospecimens in pathology, biorepository, and biobank settings. Clin Biochem. 2014;47:258–66.CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Stewart GD, Baird J, Rae F, Harrison DJ. Utilizing mRNA extracted from small, archival formalin-fixed paraffin-embedded prostate samples for translational research: assessment of the effect of increasing sample age and storage temperature. Int Urol Nephrol. 2011;43:961–7.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Butler AE, Matveyenko AV, Kirakossian D, Park J, Gurlo T, Butler PC. Recovery of high-quality RNA from laser capture microdissected human and rodent pancreas. J Histotechnol. 2016;39:59–65.CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Sun H, Sun R, Hao M, Wang Y, Zhang X, Liu Y, et al. Effect of duration of ex vivo ischemia time and storage period on RNA quality in biobanked human renal cell carcinoma tissue. Ann Surg Oncol. 2016;23:297–304.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Hatzis C, Sun H, Yao H, Hubbard RE, Meric-Bernstam F, Babiera GV, et al. Effects of tissue handling on RNA integrity and microarray measurements from resected breast cancers. J Natl Cancer Inst. 2011;103:1871–83.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Beelman CA, Parker R. Degradation of mRNA in eukaryotes. Cell. 1995;81:179–83.CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Die JV, Obrero Á, González-Verdejo CI, Román B. Characterization of the 3′:5′ ratio for reliable determination of RNA quality. Anal Biochem. 2011;419:336–8.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Roberts L, Bowers J, Sensinger K, Lisowski A, Getts R, Anderson MG. Identification of methods for use of formalin-fixed, paraffin-embedded tissue samples in RNA expression profiling. Genomics. 2009;94:341–8.CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Mathieson W, Marcon N, Antunes L, Ashford DA, Betsou F, Frasquilho SG, et al. A critical evaluation of the PAXgene tissue fixation system. Am J Clin Pathol. 2016;146:25–40.CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Garcia M, Downs J, Russell A, Wang W. Impact of biobanks on research outcomes in rare diseases: a systematic review. Orphanet J Rare Dis. 2018;13:202. Scholar
  98. 98.
    Monaco L, Crimi M, Wang CM. The challenge for a European network of biobanks for rare diseases taken up by RD-connect. Pathobiology. 2014;81:231–6.CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Baker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533:452–4.CrossRefGoogle Scholar
  100. 100.
    Kinkorová J, Topolčan O. Biobanks in Horizon 2020: sustainability and attractive perspectives. EPMA J. 2018;9:345–53.CrossRefPubMedPubMedCentralGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Margalida Esteva-Socias
    • 1
    • 2
  • María-Jesús Artiga
    • 3
  • Olga Bahamonde
    • 4
  • Oihana Belar
    • 5
  • Raquel Bermudo
    • 6
  • Erika Castro
    • 5
  • Teresa Escámez
    • 7
  • Máximo Fraga
    • 8
    • 9
  • Laura Jauregui-Mosquera
    • 10
  • Isabel Novoa
    • 11
  • Lorena Peiró-Chova
    • 4
  • Juan-David Rejón
    • 12
  • María Ruiz-Miró
    • 13
  • Paula Vieiro-Balo
    • 9
  • Virginia Villar-Campo
    • 10
  • Sandra Zazo
    • 14
  • Alberto Rábano
    • 15
  • Cristina Villena
    • 1
    • 2
    Email author
  1. 1.Centro de Investigación Biomédica en Red Respiratory Diseases (CIBERES), Plataforma Biobanco Pulmonar CIBERESHospital Universitari Son EspasesPalmaSpain
  2. 2.Grupo de Inflamación, reparación y cáncer en enfermedades respiratorias, Institut d’Investigació Sanitària de les Illes Balears (IdISBa)Hospital Universitari Son EspasesPalmaSpain
  3. 3.CNIO BiobankSpanish National Cancer Centre (CNIO)MadridSpain
  4. 4.INCLIVA BiobankValenciaSpain
  5. 5.Basque Foundation for Health Innovation and Research, Basque BiobankBarakaldoSpain
  6. 6.Hospital Clínic-IDIBAPS BiobankInstitut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
  7. 7.IMIB BiobankInstituto Murciano de Investigación BiosanitariaMurciaSpain
  8. 8.Depto. de Ciencias Forenses, Anatomía Patolóxica, Xinecología e Obstetricia, e Pediatría, Facultade de MedicinaUniversidade de Santiago de Compostela (USC)SantiagoSpain
  9. 9.Biobanco Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), SERGASSantiagoSpain
  10. 10.University of Navarra’s Biobank-IdiSNAPamplonaSpain
  11. 11.Vall d’Hebron University Hospital BiobankVall d’Hebron Hospital Research InstituteBarcelonaSpain
  12. 12.Biobanco del Sistema Sanitario Público de AndalucíaGranadaSpain
  13. 13.IRBLleida BiobankInstituto de Investigaciones Biomédica de Lleida-Fundación Dr. PifarreLéridaSpain
  14. 14.Department of PathologyIIS-Fundación Jiménez DíazMadridSpain
  15. 15.Banco de Tejidos, Fundación CIENInstituto de Salud Carlos IIIMadridSpain

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