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Cracking it - successful mRNA extraction for digital gene expression analysis from decalcified, formalin-fixed and paraffin-embedded bone tissue

['Alireza Saraji', 'Pathology Of The University Hospital Schleswig-Holstein', 'Campus Luebeck', 'Luebeck', 'Anne Offermann', 'Janine Stegmann-Frehse', 'Katharina Hempel', 'Duan Kang', 'Rosemarie Krupar', 'Research Center Borstel']

Date: 2021-10

Abstract With the advance of precision medicine, the availability of tumor tissue for molecular analysis has become a limiting factor. This is particularly the case for bone metastases which are frequently occurring in cancer types such as prostate cancer. Due to the necessary decalcification process it was long thought that transcriptome analysis will not be feasible from decalcified formalin-fixed, paraffin-embedded (DFFPE) in a large manner. Here we demonstrate that mRNA extraction from DFFPE is feasible, quick, robust and reproducible and that decalcification does not hamper subsequent gene expression analysis. This might assist in implementing transcriptome analysis from DFFPE into every day practice.

Citation: Saraji A, Offermann A, Stegmann-Frehse J, Hempel K, Kang D, Krupar R, et al. (2021) Cracking it - successful mRNA extraction for digital gene expression analysis from decalcified, formalin-fixed and paraffin-embedded bone tissue. PLoS ONE 16(9): e0257416. https://doi.org/10.1371/journal.pone.0257416 Editor: Vincenzo L’Imperio, Universita degli Studi di Milano-Bicocca, ITALY Received: June 23, 2021; Accepted: August 31, 2021; Published: September 16, 2021 Copyright: © 2021 Saraji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript. Funding: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. List of abbreviation: PCa, Prostate cancer; PM, Precision medicine; PCaBM, Prostate cancer bone metastases; DFFPE, Decalcified formalin-fixed, and paraffin-embedded; EDTA, Ethylenediaminetetraacetic acid; FFPE, Formalin-fixed paraffin-embedded; DEG, Differentially expressed genes; DSP, Digital spatial profiling; MM, Plasma cell myeloma; RFU, Relative fluorescence unit; IQ, Integrity and quality; QC, Quality control; RCC, Report code count; FOV, Field of view; IHC, Immunohistochemistry; NGS, Next generation sequencing; H&E, Haematoxylin and Eosin

Introduction Prostate cancer (PCa) is the most frequent non-cutaneous cancer among men [1]. Although the prognosis of PCa has continually been improving during the last twenty years [2, 3], a significant number of patients will experience tumor progression with metastatic disease and cancer-related death. For example, in 2019, more than 30,000 deaths were caused by metastatic PCa in the United States [1]. The most common site for metastatic spread of PCa is the skeletal system [4, 5]. Bone metastases cause high morbidity with pain and skeletal-related events such as pathological fractures [6]. In the context of precision medicine (PM) recent advances were made in understanding the molecular biology and pathology of cancer by implementing high throughput gene sequencing methods and integrative molecular analyses [7]. The molecular landscape of metastatic prostate cancer differs significantly from primary PCa, therefore molecular analysis from metastases rather than primary tumors might provide the most useful information to guide clinical management [8]. However, availability of metastatic tissue for molecular analyses is considered a major limiting factor, particularly in the setting of tissue obtained from bone metastases [9]. Several biobanking protocols for fresh tissue from PCa bone metastases (PCaBM) have been developed. However, the majority of PCaBM are obtained by routine surgery followed by decalcification [10, 11]. In order to prepare a proper paraffin section from bone tissue, the tissue is generally treated either with acids (formic acids) or with an organic chelating agent such as ethylenediaminetetraacetic acid (EDTA) to soften the bone tissue by reacting with calcium in a process called “decalcification” [12]. Decalcification can result in severe degradation of RNA [13]. The quality of archival FFPE tumor tissue is further affected by several factors including pre-fixation time and process, fixation processing, temperature and sample size. In addition, the quality and quantity of FFPE-extracted RNA is influenced by fragmentation, degradation, and cross-linking with proteins [14, 15]. The resulting low quality and quantity in particular of RNA is thought to hamper further analysis [16]. In a comprehensive study, Bohmann et al. compared different RNA extraction methods and could show that the fully automated bead-based method provided the overall best yield and reproducibility for high-throughput RNA expression analysis [11]. Most RNA extraction methods from FFPE (mainly using PCR) were performed from non-bone tissue rather than decalcified bone tissue [17]. Traditionally, quantification of RNA yield analyses was measured by RT-PCR. In contrast, performing digital expression profiling by NanoString™ technology enables RNA quality control without any amplification or enzymatic reactions methods [18–20]. We aimed to show that mRNA extraction from DFFPE bone samples is feasible in a quick, robust and reproducible manner. In addition, subsequent successful digital gene expression (DEG) analyses opens new opportunities to carry out molecular analyses from DFFPE bone samples.

Materials and methods Ethic statement Our study was approved by the Ethics Committee of the University of Luebeck (project code 18–053, date of approval: March 2nd 2018, date of amendment: June 17th 2020). DFFPE samples and cohort description This study included 36 DFFPE blocks from PCaBM (12), plasma cell myeloma (MM) (12) and normal bone tissue (12) (Table 1). The latter two were used as control tissue. All samples have been collected from the archive of the Institute of Pathology, University Hospital Schleswig-Holstein (UKSH) Luebeck, Germany. All samples have been decalcified using an EDTA based method with a fixation time of 48 to 72 hours. Histopathological evaluation and annotation of tumor areas for macrodissection was performed by Verena Sailer and Anne Offermann. PPT PowerPoint slide

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TIFF original image Download: Table 1. Data set reference and cohorts (source of data). https://doi.org/10.1371/journal.pone.0257416.t001 Study cohort included 36 DFFPE randomly selected blocks from prostate cancer bone metastases (PCaBM) (n = 12), plasma cell myeloma (MM) (n = 12) and normal bone tissue (n = 12). RNA extraction from decalcified FFPE blocks DFFPE blocks from PCaBM, MM and normal bone tissue were sectioned into 8 μm-thick cuts. Two tissue sections were placed on each slide. The sections were compared with the annotated HE and only the marked tumor tissue was scraped off with a scalpel. RNA was isolated using the automatic bead-based Maxwell® RSC RNA FFPE Kit (Cat. No: AS1440, Promega). According to the manufacturer’s manual, the scraped tissue was transferred into a RNase-free tube with 300μl mineral oil and vortexed for 10 seconds. Then the sample was heated at 80°C and incubated at room temperature for a while. Furthermore, 250 μl of master mix including lysis buffer, proteinase K and blue dye was added to the sample and followed by 20 second centrifuging. The sample was later heated at 56°C and 80°C. After an incubation time a DNase cocktail was added and the sample was transferred to the Maxwell® FFPE cartridge yielding purified RNA. The isolated RNA was eluted in 50μl of nuclease-free water and then measured with NanoDrop® or QubitTM. For long-time storage, the RNA samples were divided into 7 μl aliquots and stored at -80°C. mRNA quantity and purity assay The quality and quantity of the extracted mRNA were analyzed by two independent methods to enable inter-operator variability comparison, namely by QubitTM 2.0 fluorometer (Thermo Fisher Scientific Inc.) and NanoDrop® (Thermo Fisher Scientific Inc.). NanoDrop® performs nucleic acid purity and quantification assay by measuring the ratio of absorbance at 260/280 nm. QubitTM 2.0 utilizes fluorescent dyes to measure the concentration of nucleic acids by determining the emission of relative fluorescence emission (RFU) value for each sample automatically. Since no difference in inter-operator variability was observed between these two methods we continued using the QubitTM 2.0 fluorometer. Digital quality control (QC) NanoStringTM analysis for mRNA expression Digital quality control (QC) analysis for mRNA was performed using the NanoStringTM PanCancer Progression Panel. The samples were loaded (10–35 ng RNA in a total of 30 μl loading mixture) on a cartridge and proceeded by utilizing a fully automated Prep Station following manufacturer’s recommendations (NanoStringTM Inc.). The proceeded cartridge was then sent for digital analysis with the nCounter® Sprint Profiler system (performed at the Institute of pathology, Hannover Medical School, Hannover, Germany). Data were exported as reporter code count (RCC) files and then imported to NanoString nSolver™ analysis software v4.0 for further analysis. Automatic quality control of mRNA was performed according to the software’s instructions. Statistical analyses All the sample sizes are mentioned in each figure. For comparison of two samples student’s two-tailed t-test was used. For comparison of more than two samples 1- way-ANOVA with Tukey post hoc test was used. A p<0.05 was considered as statistically significant. Data are shown as means ± SD. For statistical analysis and data presentation the following software systems were applied; QubitTM 2.0 IQ Analyzer, NanoString nSolver™ analysis software v4.0 and Prism 6 (GraphPad Software Inc., San Diego, USA).

Discussion In this study we were able to demonstrate that mRNA extraction and subsequent digital gene expression analysis using the NanoString™ method from decalcified formalin-fixed and paraffin-embedded bone samples is feasible and produces robust results. Despite well-known RNA degradation in FFPE [22, 23], we have shown that mRNA from DFFPE using an automated bead-based extraction method was sufficient both in quantity and quality to pass the digital QC as provided by NanoString™ technology. Thus, decalcification by EDTA does not hamper subsequent gene expression analysis. In particular, PCaBM showed less mRNA fragmentation and more density equities. Importantly, we did not alter regular mRNA extraction protocols thus confirming that mRNA extraction form DFFPE can be employed in daily routine. We initially started with utilizing tissue from PCaBM for gene expression analysis to study the complex landscape of metastatic prostate cancer. Even though we employed archival DFFPE that was several years old we successfully performed NanoString™ analyses. This opens up new opportunities for gene expression analysis in the daily management of patients e.g., for patients with hematological diseases who usually undergo routine bone biopsy at the time of first diagnosis and often during therapy as well. The turn-around time from macrodissection to data analysis is around 6–10 working days. This short timespan might further assist implementing gene expression analysis from DFFPE in clinical management. Furthermore, the vast archives of pathology laboratories worldwide [24] provide a valuable, as yet largely untapped resource for studying bone metastases, benign bone diseases and the bone microenvironment. Our work was limited by the sample’s range and number. We are confident that this initial study warrants performing large scale transcriptomic analyses from bone samples.

Conclusions In conclusion we have shown that DFFPE can be utilized for gene expression analysis thus assisting to integrate transcriptome data into everyday patient care to improve the prognosis and prediction.

Acknowledgments We thank Eva Dreyer for her technical assistance.

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