Influence of Next-Generation Sequencing and Storage Conditions on

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Analytical Chemistry

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The influence of Next-Generation Sequencing and storage conditions on miRNA patterns generated from PAXgene blood Christina Backes1, Petra Leidinger2, Gabriela Altmann3, Maximilian Wuerstle3, Benjamin Meder4, 1 1 3 3 2 Valentina Galata , Sabine C. Mueller , Daniel Sickert , Cord Stähler , Eckart Meese , Andreas 1,* Keller

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Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany Institute of Human Genetics, Saarland University, Homburg, Germany Siemens AG, Germany Internal Medicine II, University Hospital Heidelberg, Heidelberg, Germany

* corresponding author: Andreas Keller, [email protected], Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Phone: +49 681 302 68611

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ACS Paragon Plus Environment

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Abstract

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Whole blood derived miRNA signatures determined by Next-Generation Sequencing (NGS)

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offer themselves as future minimally invasive biomarkers for various human diseases. The

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PAXgene system is a commonly used blood storage system for miRNA analysis. Central to

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all miRNA analyses that aim to identify disease specific miRNA signatures, is the question of

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stability and variability of the miRNA profiles that are generated by NGS.

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We characterized the influence of five different conditions on the genome wide miRNA

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expression pattern of human blood isolated in PAXgene RNA tubes. In detail, we analyzed

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15 miRNomes from three individuals. The blood was subjected to different numbers of

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freeze / thaw cycles and analyzed for the influence of storage at -80 °C or 8 °C. We also

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determined the influence of blood collection and NGS preparations on the miRNA pattern

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isolated from a single individual, which has been sequenced 10 times. Here, five PAXGene

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tubes were consecutively collected that have been split in two replicates, representing two

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experimental batches. All samples were analyzed by Illumina NGS. For each sample,

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approx. 20 million NGS reads have been generated. Hierarchical clustering and Principal

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Component Analysis (PCA) showed an influence of the different conditions on the miRNA

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patterns. The effects of the different conditions on miRNA abundance are, however, smaller

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than the differences that are due to inter-individual variability. We also found evidence for an

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influence of the NGS measurement on the miRNA pattern. Specifically, hsa-miR-1271-5p

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and hsa-miR-182-5p showed coefficients of variation above 100% indicating a strong

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influence of the NGS protocol on the abundance of these miRNAs.

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Keywords: miRNA; PAXgene, Next-Generation Sequencing, technical evaluation


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Introduction

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For the identification of biomarkers and even more for the translation from basic research to

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clinical routine, it is crucial to understand how markers vary depending on different storage

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conditions and technical analysis. Especially for complex marker profiles like miRNA

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signatures, a systematic bias will compromise their diagnostic and prognostic values. While

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tissue based miRNA profiles have first been in the focus of research, there are increasing

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efforts to identify miRNA signatures as non- or minimally invasive markers in body fluids

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such as blood, serum or urine. Besides Heparin and EDTA blood tubes, PAXgene blood

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RNA tubes have frequently been used to collect patients’ blood. Examples of PAXgene

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blood RNA pattern include biomarkers for myocardial infarction1, lung cancer2,3, multiple

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sclerosis4,5, melanoma6, ovarian cancer7, chronic obstructive pulmonary disease8,

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glioblastoma9 and Alzheimer´s disease10. More recently, miRNA profiles of single blood cell

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types or exosomes have been accomplished11,12.

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To obtain profiles of miRNAs, different technologies have been applied. In the early stages

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of miRNA profiling, microarrays have been widely used to generate miRNA patterns. High-

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throughput qRT-PCR platforms also enable the parallel measurement of hundreds of

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miRNAs. As the most recent technology, Next-Generation Sequencing (NGS) generates

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millions of short reads that can be aligned to known miRNAs annotated in the miRBase13.

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Likewise, new miRNA candidates can be predicted by aligning the fragments to the target

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genome. To facilitate clinical applications, other methods such as immunoassays are

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currently developed14. Technical stability of the profiles for reliable biomarker discovery is of

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high impact, independent of the applied screening technique. Since NGS is increasingly

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applied to generate patient based miRNA signatures we investigated NGS-related variability

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and stability of miRNA pattern that are derived from PAXgene samples. In detail, we

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investigate three main question: First, markers are frequently discovered in retroperspective

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studies. Often, samples that are stored in biobanks are thawed and a part of the sample is

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used for measurement. We asked whether additional freeze / thaw cylces have a significant

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influence of miRNAs. Second, we asked how storage at 8 degree Celsius relates to the

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samples stored at -80 degree Celsius. The time was thereby restricted to four days. Third,

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we also investigated the influence of NGS on the variability / stability of miRNA patterns by

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performing two NGS batches echa containing technical replicates of five PAXgene blood

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tubes, which were all taken from the same individual.

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Materials and Methods

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Study set-up and miRNA profiling

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In this study we focused on the influence of freeze / thaw cycles and short time storage of

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PAXGene blood samples. We performed 25 miRNA measurements from 4 individuals. To

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minimize the influence of pathogenic processes healthy individuals without known diseases

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affection were investigated. All blood donors participating in this study gave their informed

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consent. For each of the first three individuals we collected 5 PAXgene Blood RNA tubes.

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The first tube has been stored at -80 °C for 4 days, while the second tube has been frozen at

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-80 °C and was subsequently subjected to one additional freeze / thaw cycle on the first day

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and finally stored again at -80 °C for the remaining days. The third tube has been subjected

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to an additional freeze / thaw cycle on the first day, frozen again at -80 °C, subjected to a

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second additional freeze /thaw cycle on the second day and stored at -80 °C for the

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remaining days. The fourth tube has been subjected to one freeze / thaw cycle on the first

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day, a second freeze / thaw cycle on the second day, a third additional freeze / thaw cycle

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on the third day and stored at -80 °C for the fourth day. The fifth tube has been stored at 8

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°C for four days. The study set-up is sketched in Figure 1A. For all samples independent

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miRNA isolation using the PAXgene Blood miRNA Kit and individual library preps using the

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Illumina TruSeq small RNA Library Prep Kit have been generated according to

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manufacturer’s instruction.

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To determine the influence of NGS on the miRNA pattern, NGS was performed on miRNAs

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isolated from a single individual. We have taken five PAXgene tubes from this donor

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(storage at -80 °C), isolated the RNA and performed two batches of library preps and NGS

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runs on these five RNA eluates (10 miRNomes). An overview of the study set-up is

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illustrated in Figure 1B.

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For each of the 25 libraries, Illumina HiSeq2500 runs have been carried out according to

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manufacturer’s instruction. For all samples around 20 Million raw sequencing reads have

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been generated. All miRNA extractions and sequencing runs have been carried out by

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CeGaT GmbH (Tübingen, Germany).

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Bioinformatics analysis

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We preprocessed the raw sequencing data as described previously10. In brief, the reads

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were mapped against the current miRBase v21 sequences by using the miRDeep2

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pipeline15. The raw miRBase counts for all samples were summarized in an expression

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matrix.


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In order to carry out hierarchical clustering and calculate heat maps source code from the

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heat map.2 function, provided as part of the “gplots” CRAN package (version 2.12.1) has

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been used. In more detail, hierarchical clustering relying on the Euclidian distance has been

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carried out on quantile normalized data (normalization has been done by the

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“preprocessCore” package using standard parameters). As alpha level, 0.05 has been used

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through the manuscript. If not stated explicitly, p-values have been adjusted for multiple

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testing using the Benjamini-Hochberg approach16. Analysis of variance has been calculated

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by using the “anova” package.

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Results & Discussion

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In total, we generated 25 miRNomes from four individuals by NGS. 15 miRNomes have

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been derived from three individuals to determine the influence of different storage conditions

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and freeze / thaw cycles (see Figure 1A) and from the fourth individual 10 NGS runs have

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been performed (5 consecutive blood drawings, two technical replicates that have been

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processed in batches, see Figure 1B).

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The expression values of blood miRNAs showed a high dynamic range of approximately 7

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orders of magnitude. Out of 2,588 human miRNAs annotated in miRBase 21, we found

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1,252 different miRNAs in at least one of the 25 samples analyzed. 1,060 miRNAs remained

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after markers covered by just a single read were removed. Regarding the distribution of NGS

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reads to miRNAs we observed significant variations. 90.3% of all reads matched to hsa-miR-

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486-5p and 5% to hsa-miR-92a-3p. The remaining 4.7% reads (approx. 20 million reads),

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matched to 1,250 miRNAs. To minimize a potential bias introduced by very low abundant

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miRNAs, we empirically determined a threshold of 50 counts and continued the analysis with

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a remaining set of 455 miRNAs. All miRNAs with absolute read count and percentage of all

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reads mapping to this miRNA are summarized in Supplemental Table 1.

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Influence of storage conditions and additional freeze / thaw cycles on miRNA patterns

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from human blood

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In order to determine the effect of different storage conditions and additional freeze / thaw

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cycles on the miRNA pattern, we first employed cluster analysis and Principal Component

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Analysis (PCA) as two commonly applied statistical approaches, on the set of 455 miRNAs.

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In detail, we performed a complete linkage hierarchical clustering using the Euclidian

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distance as distance measure. The results for all miRNAs measured under five different

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conditions for three individuals, are summarized in Figure 2 as heat map with dendrograms

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on top for the storage conditions and on the left side for the miRNAs. The heat map indicates

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clustering of samples that have been stored throughout the experiment at -80 °C without

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additional freeze / thaw cycles (indicated by an orange color in the heat map). Likewise, the

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samples that have been stored at 8 °C for four days without changing the storage condition

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(indicated with green color in the heat map), cluster together as well. In addition, samples

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that have been stored at -80 °C without thawing cluster together with samples that have

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been stored at -80 °C with only one additional freeze / thaw cycle (indicated by an light blue


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color in the heat map). We also observed a clustering of samples that are stored at -80 °C

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with three additional freeze / thaw cycles (indicated by a dark blue color in the heat map)

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with samples that have been stored solely at 8 °C. The results may be indicative of an

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overall influence of the time period during which a sample is stored either at -80 °C or at 8

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°C. The PCA, which has been used to generate a 2-dimensional mapping of the high-

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dimensional miRNA profiles, largely confirmed the clustering results.

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After having investigated the systematic effects on the miRNA patterns, we analyzed

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whether single miRNAs show differences under the tested storage conditions. Therefor an

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analysis of variance (ANOVA) as well as the coefficient of variation (CV) have been applied.

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ANOVA identified 41 markers that were significant according to raw ANOVA p-values and an

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alpha level of 0.05. Since multiple markers were measured, the p-values had to be adjusted

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for multiple testing, resulting in 5 miRNAs being still significant, including hsa-miR-320b

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(p=0.0002), hsa-miR-320a (p=0.001), hsa-miR-16-5p (0.018), hsa-miR-18b-5p (0.037) and

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hsa-miR-375 (0.0375). As one example of a significant miRNA, has-miR-375 is presented as

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boxplot in Figure 3A. For miRNA-375 we found a significant difference between samples that

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have been stored at 8 °C for four days and samples that have been stored at -80 °C. We did

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not find a significant influence of the freeze / thaw cycles. All miRNAs along with the raw and

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adjusted p-values are presented in Supplemental Table 2.

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Finally, we addressed the question of the importance of the miRNA changes observed under

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the different storage conditions. To this end, we compared the differences between the three

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donors to the differences between the different storage conditions. An analysis of the

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coefficient of variation highlighted 37 miRNAs with the standard deviations exceeding the

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mean value (CV > 1). The mean value, standard deviation and CV for all miRNAs are

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presented in Supplemental Table 3. Largest CV was calculated for miR-1291 (mean of 24.6,

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standard deviation of 40.7, CV of 1.7). One example is provided in Figure 3B, where the log

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of normalized read counts of the five different conditions for the three individuals is

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presented for miR-99a-5p. For this miRNA, the mean value is 508, the standard deviation

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624 and the CV 1.23. The variation of the five measurements for each of the individuals is

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substantially smaller than the deviation between individuals 1 and 2 compared to individual

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3, which had high read counts for this miRNA. In general, we observed that the variability of

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miRNA abundance between different donors was higher than the miRNA variability under

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different storage conditions.

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We conclude that there is a general systematic influence of the storage conditions on miRNA

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patterns. Largest variability was observed between storage at 8 degrees Celsius and

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samples stored at -80 degree Celsius without additional freeze / thaw cycles. The specific


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effect of the storage conditions has to be verified for each single miRNAs separately.

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Importantly, the effects of storage conditions on miRNA abundance are generally smaller

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than the differences due to inter donor variability.

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While we investigated a short time period, the long time storage of samples has also been

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investigated. Seelenfreund and co-workers reported that miRNA from PAXGene tubes can

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be recovered even after periods of up to 4 years, if samples are frozen at -80 degrees

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Celsius17. In this study, a subset of all known miRNAs analyzed by qRT-PCR has been

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included. Viprey et al. considered even longer time periods of up to 5 years18. In detail they

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evaluated the reliability of expression for a subset of 377 miRNAs by qRT-PCR. The authors

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did not observe a correlation of miRNA abundance with storage time. As most stable

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reference miRNAs, miR-26a, miR-28-5p, and miR-24 were identified. These miRNAs were

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also not affected significantly in our study with respect to freeze / thaw cycles, indeed

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rendering them as reasonable and stable reference markers.

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Influence of NGS preparation on miRNA patterns from human blood

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As mentioned above, two NGS runs with different library prep were performed with each on

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miRNAs isolated from five PAXgene tubes from a single individual and thus resulting in 10

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miRNomes. The five samples of the first NGS run have been processed together, and

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likewise the samples of the second NGS run with a slightly changed preprocessing step as

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mentioned in material & methods. The results of the analysis are summarized as heat map

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in Figure 4. The dendrogram on top of the heat map show the clustering of different blood

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tubes and sequencing procedures, while the dendrogram on the left side for the miRNAs.

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The heat map indicates no clustering between the five different blood sample tubes

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(indicated by five shadings of red). However, a strong clustering between the two different

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NGS preparations is shown (indicated by an orange color for the first NGS run and a blue

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color for the second NGS run). These results show a significant influence of the NGS

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preparation on the miRNA pattern identified in human blood. The findings from this cluster

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analysis were confirmed by a PCA.

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We next addressed the question of importance miRNA changes observed for the NGS

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preparations by analyzing the coefficient of variation. The analysis showed lower CV values

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for the 10 miRNomes measured by two NGS preparations than for the miRNome obtained

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under different storage conditions. The standard deviation exceeded the mean for only 21


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miRNAs the majority of which were low abundant. Only two miRNAs with expression levels

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(normalized read counts >5) showed average CV values >1 including hsa-miR-182-5p with

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average value of 2,824 and standard deviation of 2,960 resulting in a CV of 1.05 and hsa-

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miR-1271-5p with average read count of 5.7 and standard deviation of 6.2 resulting in a CV

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of 1.07. As indicated by the barplot of logarithmized normalized read counts in Figure 5A, the

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second batch of NGS library preparation (indicated in blue) showed for hsa-miR-182-5p

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significantly higher expression level as the first NGS library preparation (indicated in orange).

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Notably, an analysis of raw read counts revealed the same behavior indicating that the

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differences between the two NGS batches are not due to the normalization process (Figure

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5B). The same values are presented in Figure 5C and 5D for miR-1271-5p. This miRNAs

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was almost not present in the second NGS batch while substantially expressed in the first

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batch. The CV values, mean and standard deviations for all miRNAs are presented in

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Supplemental Table 4.

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In sum, we found evidence for an influence of the NGS measurement on specific miRNAs’

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profiles including the library preparation.

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Conclusion

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In this study we systematically explored the influence of different conditions and freeze /

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thaw cycles on miRNA profiles generated by using NGS. Furthermore, we investigated the

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stability and reproducibility of the respective miRNA patterns by carrying out 10 replicated

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measurements of the same individual.

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For selected miRNAs, we found an influence with respect to up to three additional freeze

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thaw cycles. Directly processed samples showed overall closest proximity to samples

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undergoing one freeze / thaw cycle. Interpreting the replicated measurements of the same

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donor also revealed a certain degree of variability. Specifically, we observed that the

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influence of the NGS procedure of miRNAs seems to be partially exceed the variability of the

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the blood drawing and miRNA extraction. These results were determined by considering all

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miRNAs for the analysis, with techniques such as clustering and PCA as well as considering

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the coefficient of variation. However, the variability was only observed for a subset of all

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miRNAs. It is therefore essential to be aware of the potential pitfalls of sample storage and

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NGS preparation that can contribute to the variability of miRNAs. If specific case control

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studies present these miRNAs as candidates to detect a disease, further in-depth validation

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is required.

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Competing interest The study has been funded in parts by Siemens Healthcare.

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Author’s contribution

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CB, PL, VG, SM contributed in data analysis, GA contributed in study set up, coordinated

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experiments, evaluated data, MW contributed in manuscript preparation and writing, DS

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contributed in study design, CS contributed in study design and set up, contributed in

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manuscript preparation, EM wrote the manuscript, AK wrote the manuscript and contributed

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in data analysis.

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Acknowledgement

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This work has been funded in parts by internal funds of Saarland University, by Siemens

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Healthcare and by the EU FP7 Project BestAgeing.

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References (1) Meder, B.; Keller, A.; Vogel, B.; Haas, J.; Sedaghat-Hamedani, F.; Kayvanpour, E.; Just, S.; Borries, A.; Rudloff, J.; Leidinger, P.; Meese, E.; Katus, H. A.; Rottbauer, W. Basic Res. Cardiol. 2011, 106, 1323. (2) Keller, A.; Leidinger, P.; Borries, A.; Wendschlag, A.; Wucherpfennig, F.; Scheffler, M.; Huwer, H.; Lenhof, H. P.; Meese, E. BMC cancer 2009, 9, 353. (3) Leidinger, P.; Backes, C.; Blatt, M.; Keller, A.; Huwer, H.; Lepper, P.; Bals, R.; Meese, E. Mol. Cancer 2014, 13, 202. (4) Keller, A.; Leidinger, P.; Lange, J.; Borries, A.; Schroers, H.; Scheffler, M.; Lenhof, H. P.; Ruprecht, K.; Meese, E. PloS one 2009, 4, e7440. (5) Keller, A.; Leidinger, P.; Steinmeyer, F.; Stahler, C.; Franke, A.; Hemmrich-Stanisak, G.; Kappel, A.; Wright, I.; Dorr, J.; Paul, F.; Diem, R.; Tocariu-Krick, B.; Meder, B.; Backes, C.; Meese, E.; Ruprecht, K. Mult Scler 2013. (6) Leidinger, P.; Keller, A.; Borries, A.; Reichrath, J.; Rass, K.; Jager, S. U.; Lenhof, H. P.; Meese, E. BMC cancer 2010, 10, 262. (7) Hausler, S. F.; Keller, A.; Chandran, P. A.; Ziegler, K.; Zipp, K.; Heuer, S.; Krockenberger, M.; Engel, J. B.; Honig, A.; Scheffler, M.; Dietl, J.; Wischhusen, J. Br. J. Cancer 2010, 103, 693-700. (8) Leidinger, P.; Keller, A.; Borries, A.; Huwer, H.; Rohling, M.; Huebers, J.; Lenhof, H. P.; Meese, E. Lung cancer 2011, 74, 41-47. (9) Roth, P.; Wischhusen, J.; Happold, C.; Chandran, P. A.; Hofer, S.; Eisele, G.; Weller, M.; Keller, A. Journal of neurochemistry 2011, 118, 449-457. (10) Leidinger, P.; Backes, C.; Deutscher, S.; Schmitt, K.; Muller, S. C.; Frese, K.; Haas, J.; Ruprecht, K.; Paul, F.; Stahler, C.; Lang, C. J.; Meder, B.; Bartfai, T.; Meese, E.; Keller, A. Genome biology 2013, 14, R78. (11) Leidinger, P.; Backes, C.; Dahmke, I. N.; Galata, V.; Huwer, H.; Stehle, I.; Bals, R.; Keller, A.; Meese, E. What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls, 2014. (12) Leidinger, P.; Backes, C.; Meder, B.; Meese, E.; Keller, A. BMC Genomics 2014, 15, 474. (13) Kozomara, A.; Griffiths-Jones, S. Nucleic Acids Res. 2011, 39, D152-157. (14) Kappel, A.; Backes, C.; Huang, Y.; Zafari, S.; Leidinger, P.; Meder, B.; Schwarz, H.; Gumbrecht, W.; Meese, E.; Staehler, C. F.; Keller, A. Clin. Chem. 2015, 61, 600-607. (15) Friedländer, M. R.; Mackowiak, S. D.; Li, N.; Chen, W.; Rajewsky, N. Nucleic Acids Res. 2011. (16) Benjamini, Y.; Hochberg, Y. Journal of the Royal Statistical Society. Series B. Methodological 1995, 57, 289-300. (17) Seelenfreund, E.; Robinson, S. E.; Amato, C. M.; Bemis, L. T.; Robinson, W. A. Biopreserv Biobank 2011, 9, 29-33. (18) Viprey, V. F.; Corrias, M. V.; Burchill, S. A. Anal. Biochem. 2012, 421, 566-572.

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Figure Legends Figure 1: Study set up: 4 donors without known disease affection were included. For the first three donors 5 Blood Tubes were extracted and processed over the next four days (Panel A). For the fourth donor 5 blood tubes were extracted and handled in duplicates (Panel B). All patients are labeled by X-Y, where X is the patient number and Y the condition. Figure 2: Cluster heat map with dendrogram on top and left. Color scale for the clustering heat map is provided in the upper left corner. The samples are colored as follows: The three different cycles are shown in three different blue shadings above the heat map, orange samples have been directly handled and the four-day refrigerator samples are colored in green. The three different cycles that are shown in three different blue shadings above the heat map mix between the orange samples that have been directly handled (clustering on the left side) and the four-day fridge control samples that are colored in green (clustering on the right side). This left cluster also contains the samples that have been frozen and thawed once while the right cluster contains the samples with three freeze / thaw cycles. PCA, which has been used to generate a 2-dimensional mapping of the high-dimensional miRNA profiles, confirmed these clustering results

Figure 3A: Boxplot resulting from the ANOVA for conditions 1-5 and donors 1-3. For miR375 signals increase for samples stored for four days at the refrigerator. The color scheme corresponds to Figure 2.

Figure 3B: log of normalized read counts for conditions 1-5 and donors 1-3 for miR-99a-5p. The overall high variability is due to the overall higher expression of that miRNA in donor 3 as compared to the first two donors. Figure 4: Cluster heat map with dendrogram on top. The samples are colored with respect to the blood drawing (first row on top of the heat map) and according to the analytical NGS batch (second row on top of the heat map).

Figure 5: Normalized (Panel A) and raw (Panel B) read counts for hsa-miR-182-5p. Shown are logarithmized reads for five replicated blood drawings (denoted 4 1, 4 2, 4 3, 4 4 and 4 5) in two NGS batches. The first batch is highlighted in orange the second in blue. Panels C and D present the same analysis for the second variable miRNA, has-miR-1271-5p (please note the absolute scale in contrast to the logarithmic for Panel A and B).


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15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 20

375 376 377


16

Page 17 of 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


378 379 380 381 382


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 20

383 384


18

Page 19 of 20

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385


19

Analytical ChemistryPage 20 of 20

50000 40000 30000 20000

10000 ACS 0

stored at 8°C

60000

3 freeze-‐thaw

70000

2 freeze-‐thaw

80000

1 freeze-‐thaw

NGS read count

1 2 3 4 5 6 7 8 9 10

stored at -‐80°C

90000

Paragon Plus Environment

Influence of Next-Generation Sequencing and Storage Conditions on

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