Effects of Cold Ischemia on RNA Stability and Quality of Lung Tissues Based on Standard PREanalytical Code Categorization

Abstract

Introduction

Recently, Grizzle et al. reported that many studies have concluded that RNA stability and quality vary with respect to different cold ischemia times.¹ However, the number of samples analyzed in each study is relatively small, and the effects of cold ischemia time on sample stability and quality are still matters under discussion. In this report, using 40 nonmalignant lung tissues we investigated the correlation between RNA stability and quality—which are assessed by electrophoresis (RINe or RNA integrity number equivalent) and/or real time reverse transcription-PCR (RT-PCR) method (Ct and ΔCt)^2,3—and actual cold ischemia time were recorded under the standard operating procedure. In addition, we also analyzed the correlations based on Standard PREanalytical Code (SPREC) 2.0,^4–6 which was developed to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens, to investigate its resolution for RINe, Ct, and ΔCt.

Materials and Methods

Sample collection

The study protocol was approved by the Institutional Review Board/Ethical Committee of Okayama University, Okayama, Japan (ref. no. K1509-030). All the patients signed a written informed consent. Forty nonmalignant human lung specimens were obtained at Okayama University Hospital immediately after removal from the body, distal to the tumor site when the surgical specimen was large enough to include both tumor and normal lung margins. Dissected tissues were brought from the hospital to the biobank in the same building at room temperature (transport time ranging from 8 to 108 min.). After arriving at the biobank, each sample was divided into several pieces and one of the divided tissue pieces was fixed using RNAlater (Life Technologies, Carlsbad, CA) (processing time ranging from 13 to 63 min.). Samples without the supernatant were incubated overnight at 4°C and then moved to a −80°C freezer according to the manufacturer's instructions.

RNA extraction and RINe

RNA was extracted according to manufacturer's instructions using an RNeasy Mini Kit (QIAGEN). RNA yields were determined using a LabChip DS Spectrophotometer (PerkinElmer). Electropherograms were obtained from Agilent 2200 TapeStation (Agilent) using High Sensitivity RNA ScreenTape to calculate the RINe.

One step real time RT-PCR

Each sample was subjected as a template to one step real time RT-PCR using One Step SYBR PrimeScript PLUS RT-PCR Kit (TAKARA-Bio). PCR with human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was performed according to the manufacturer's instructions. The forward primer was 5′-CCACATCGCTCAGACACCAT-3′. The reverse primer for 71 bp was 5′-ACCAGGCGCCCAATACG-3′ and that for 277 bp was 5′-ACTTGATTTTGGAGGGATCT-3′.² All samples and controls were analyzed in triplicate on the StepOnePlus Real Time PCR System and StepOne software version 2.3 (Applied Biosystems).

Statistical analysis

We performed analysis of variance (ANOVA) using aov function of R3.3.2 to investigate the correlation between SPREC categorization and indicators of RNA stability and quality assessed by RINe, Ct, and ΔCt as already described.

Results

Effects of cold ischemia time on RNA stability and quality

In the case of solid samples obtained from surgical specimens, cold ischemia time consists of transport and processing time (Fig. 1a). We investigated the effects of cold ischemia time in addition to individual effects of transport and processing time, respectively, using RINe, Ct, and ΔCt values as indicators of RNA stability and quality (RNA indicators). A statistically significant correlation was not observed for this study's period of transport time (ranging from 8 to 108 minutes), although a sample with >100 minutes of transport time showed relatively lower values of ΔCt (Fig. 1b). Similarly, a significant correlation was not noticeable between processing time and RNA indicators (ranging from 13 to 63 minutes) (Fig. 1c). As a consequence, cold ischemia time was not statistically correlated significantly with RNA indicators at least in this period of cold ischemia time (ranging from 25 to 123 minutes) (Fig. 1d).

FIG. 1.

Correlation between cold ischemia time and RNA stability and quality. (a) The scheme of cold ischemia time. (b) The correlation between transport time and RNA indicators. (c) The correlation between processing time and RNA indicators. (d) The correlation between cold ischemia time and RNA indicators. (e) The correlation between short–long tetrameric categorized cold ischemia time and RNA indicators. (f) The correlation between cold ischemia time categorized by SPREC and RNA indicators. Sample number in each category is as follows; D (n = 3), E (n = 29), and F (n = 8). Cross bar in each panel shows average. RINe, RNA integrity number equivalent; SPREC, Standard PREanalytical Code.

Correlation between transport time and processing time

Next, we subdivided the samples into four categories: (1) short transport time and short processing time (n = 7), (2) short transport time and long processing time (n = 12), (3) long transport time and short processing time (n = 13), and (4) long transport time and long processing time (n = 8), to make it easy to link and compare the two factors (transport time and processing time) together for each sample (Fig. 1e). The effect of processing time on samples with short transport time could be evaluated by comparing the samples with categories (1) and (2). We performed ANOVA for RNA indicators and none of them showed statistical significance, suggesting that there were no significant correlations between the two factors, at least in this period of cold ischemia time based on the short–long tetrameric categorization.

Resolution of SPREC on RNA quality

To evaluate the resolution of SPREC of cold ischemia time in terms of correlation with RNA indicators, we categorized the 40 samples based on SPREC of cold ischemia time, resulting in 3 for D (20–30 minutes), 29 for E (30–60 minutes), and 8 for F (>60 minutes) (Fig. 1f). We performed ANOVA for RNA indicators. However, none of them showed statistical significance, suggesting that there were no differences in the RNA stability and quality, at least in this period of cold ischemia time based on SPREC categorization.

Discussion

We investigated the correlation between cold ischemia time and RNA stability and quality, treating the time from sample resection to storage start as cold ischemia time for convenience, although tissues were transported at room temperature. Forty samples were a relatively large number of samples used in one study, compared with the previously published studies.³ Furthermore, we used only nonmalignant lung tissue to exclude the effects of organ and phenotype difference. Our results showed that there were no significant correlations between two factors (transport time and processing time) based on the short–long tetrameric categorization, at least in this period of cold ischemia time ranging from 25 to 123 minutes. In addition, our results showed that there were no significant differences in the RNA indicators among D, E, and F of SPREC, at least in this period of cold ischemia time using the actual biobank specimens. These results corresponded well to the recently published study, showing that there were no statistical differences between samples collected within 1 hour and those collected in 1–2 hours, although the authors pointed out the effect of prolonged ischemia times of >10 hours.⁷ Various factors, not only duration time but also the patient (specimen) background, affected the RNA stability and quality, and further investigation is needed to identify which factors correctly predict the specimen quality.⁸

Conclusion

In conclusion, the identification of appropriate predictive markers for RNA stability and quality is still needed; however, our study reveals that RNA stability and quality, which are assessed by RINe, Ct, and ΔCt, are unchanged during cold ischemia times within 3 hours in the nonmalignant lung tissues.

Footnotes

Acknowledgments

The authors thank Hiroko Hanafusa, Yayoi Kubota, Hiromi Emi, Natsumi Hibino, and Ruriko Ogawa for technical support. This research was partially supported by The Translational Research program, Strategic PRomotion for practical application of INnovative medical Technology (TR-SPRINT), and by the program for an Integrated Database of Clinical and Genomic Information from Japan Agency for Medical Research and Development (AMED).

Author Disclosure Statement

No conflicting financial interests exist.

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