- Research article
- Open Access
mRNA-seq whole transcriptome profiling of fresh frozen versus archived fixed tissues
© The Author(s). 2018
- Received: 5 December 2017
- Accepted: 4 May 2018
- Published: 30 May 2018
The main bottleneck for genomic studies of tumors is the limited availability of fresh frozen (FF) samples collected from patients, coupled with comprehensive long-term clinical follow-up. This shortage could be alleviated by using existing large archives of routinely obtained and stored Formalin-Fixed Paraffin-Embedded (FFPE) tissues. However, since these samples are partially degraded, their RNA sequencing is technically challenging.
In an effort to establish a reliable and practical procedure, we compared three protocols for RNA sequencing using pairs of FF and FFPE samples, both taken from the same breast tumor. In contrast to previous studies, we compared the expression profiles obtained from the two matched sample types, using the same protocol for both. Three protocols were tested on low initial amounts of RNA, as little as 100 ng, to represent the possibly limited availability of clinical samples. For two of the three protocols tested, poly(A) selection (mRNA-seq) and ribosomal-depletion, the total gene expression profiles of matched FF and FFPE pairs were highly correlated. For both protocols, differential gene expression between two FFPE samples was in agreement with their matched FF samples. Notably, although expression levels of FFPE samples by mRNA-seq were mainly represented by the 3′-end of the transcript, they yielded very similar results to those obtained by ribosomal-depletion protocol, which produces uniform coverage across the transcript. Further, focusing on clinically relevant genes, we showed that the high correlation between expression levels persists at higher resolutions.
Using the poly(A) protocol for FFPE exhibited, unexpectedly, similar efficiency to the ribosomal-depletion protocol, with the latter requiring much higher (2–3 fold) sequencing depth to compensate for the relative low fraction of reads mapped to the transcriptome. The results indicate that standard poly(A)-based RNA sequencing of archived FFPE samples is a reliable and cost-effective alternative for measuring mRNA-seq on FF samples. Expression profiling of FFPE samples by mRNA-seq can facilitate much needed extensive retrospective clinical genomic studies.
- RNA sequencing
- Poly-a capturing
- Ribosomal depletion
- Breast cancer
Gene expression profiling of tumor samples is a powerful technique for identifying prognostic and predictive biomarkers. To date, all large scale transcriptomic profiling of cancer were performed on frozen tissue samples and required international efforts; nevertheless, analysis of the results was limited by availability of the numbers of frozen tumor samples that were obtained from patients with long-term clinical follow up [1, 2]. On the other hand, there exist large diagnostic repositories of archived tissues, with matched clinical records on disease progression and outcome, which could potentially comprise invaluable resources for comprehensive genomic studies of cancer. Exploiting archived reservoirs could allow unprecedented large-scale retrospective investigations of longitudinal samples, focusing on specific subtypes or ethnic groups of interest, without the need for long-term prospective collection of samples. Such informative datasets could facilitate biomarker discovery and personalized drug development.
Archived tissues are preserved as formalin-fixed paraffin-embedded (FFPE) blocks, which is the standard preparation format for pathological diagnosis. The quality of FFPE-extracted RNA is highly variable due to fragmentation of RNA transcripts, chemical modifications and cross-linking of nucleic acids and proteins [3, 4] as well as variability in tissue handling and processing . These factors are influenced by various methods for tissue fixation and by archiving time of tissue samples. Hence, due to the relative low quality of FFPE-derived RNA, applying standard protocols for whole transcriptome analysis is a challenging task.
In recent years the field has moved from microarray based profiling to RNA-sequencing (RNA-seq), an unbiased method which provides greater analytical depth and increased dynamic range for gene expression measurement [6, 7]. RNA-seq protocols commonly include steps for enrichment of exonic RNA sequences and removal of ribosomal RNA. The conventional protocol, known as mRNA-Seq, is based on capturing polyadenylated (poly(A)) RNA transcripts with oligo(dT) primers, thereby depleting the highly abundant ribosomal RNA (rRNA) and all other non-poly(A) fragments . The concern in utilizing mRNA-Seq for FFPE samples is that degraded or modified poly(A) tail of transcripts will not perfectly anneal to oligo(dT) and the captured short transcripts will not comprise an unbiased, accurate representation of the transcriptome. Other protocols eliminate rRNA by capturing these highly abundant transcripts and removing them by either magnetic beads (i.e. RiboZero by Illumina) or by enzymatic digestion, (i.e., RNAse H or Ovation by NuGEN) [9–12].
Several previous studies evaluated the feasibility of RNA-seq to reliably profile gene expression comparing matched FF and FFPE samples. However, most studies used the gold-standard poly(A) mRNA-seq protocol for FF samples compared to ribosomal-depletion protocol for FFPE [10, 12–14], out of the assumption that mRNA-seq will not optimally capture degraded mRNAs. Such comparisons mix effects of the different sample types with those of the protocol used. In the current study we perform an unbiased evaluation of RNA-seq of archived tumor tissues by comparing the same library preparation methods for both FF and FFPE matched tumor samples and for small amounts of total RNA starting material. In addition to comparing the coverage and mapping parameters of FF and FFPE, we addressed the question of the reproducibility characteristics of the sample types and the methods, when trying to estimate differential gene expression, with particular focus on quantifying expression of clinically relevant genes, derived from the METABRIC dataset .
Three pairs of matched FF/FFPE tumor samples were collected, with a moderate archival time of about 4–5 years (T1-T3). To compare the efficiency of the various RNA-seq protocols we used the same library preparation protocol for both FF and FFPE samples. Three protocols were tested: illumina Truseq RNA after poly(A) selection (mRNA-seq); Truseq after ribosomal depletion (RiboZero); and Nugen Ovation with Ribosomal depletion. As sometimes archived biopsies are limited in size, we also compared 2 different amounts of starting material, 500 and 100 ng of RNA, to assess the lower starting material limitations of the protocols. In addition, to evaluate the limitation of the mRNA-seq method for highly degraded RNA, we analyzed additional 3 FFPE tumor samples archived for more than 10 years (with no matching FF samples; T4-T6). RNA integrity was high for the FF samples (RIN number 6–7) and low for the FFPE samples (1.8 or below detection limit) (see Additional file 1: Table S1).
Transcriptome mapping and coverage
Mapping percentages for the different RNA-seq methods and samples
n = 6
n = 5
n = 3
n = 3
n = 3
n = 3
n = 6
To estimate the total number of reads one needs in order to get expression data for a satisfactory number of genes from each protocol, we compared the fraction of exonic reads out of total reads (Fig. 1b). Again, for all protocols the percentages of reads mapping to exons (out of the total number of reads) were lower in FFPE RNA libraries compared to FF (about 1/2). However, focusing on FFPE samples reveals that while the exonic fraction is around 30% for the mRNA-seq protocol, only about 10% of the total reads remain for analysis in the RiboZero protocol (Fig. 1b). This key point is further demonstrated when calculating the estimated total number of reads required to detect approximately 11,000 genes (threshold is set according to the saturation of the curves in Fig. 1c). While with mRNA-seq the total number of reads needed to reach this endpoint is 26–42 million for FFPE samples, depending on their quality and age (range between ~ 4 to ~ 10 years old), the RiboZero protocol requires at least 70 million reads for the ~ 4 years old samples (Fig. 1c).
As the mRNA-seq protocol captures transcripts based on their poly(A) tail, a 3′-end bias is expected, predominantly for degraded transcripts. To check the extent to which the library preparation protocol generates a 3′-bias, we plotted the coverage along the normalized transcript length (Fig. 1d). A small 3′-end bias is evident for FF samples in both protocols. In the RiboZero protocol, no difference was observed between FF and FFPE. For mRNA-seq libraries, the 3′-end bias is much more evident for FFPE samples, indicating that with this protocol fragmented RNA transcripts are mainly represented at their 3′-end.
Total gene expression correlation
Differential gene expression
Biological and clinical validity of gene expression
Additional biological relevance of the expression data is demonstrated by comparing the immunohistochemistry score of estrogen receptor to its expression level (Fig. 4c). There was a very good agreement between the ESR1 expression level and its pathological score, even for the older FFPE samples from ~ 10 years ago (T4-T6; marked by stars in Fig. 4c).
Importantly, the correlation between the mRNA-seq and the RiboZero method is very high despite the difference in distribution of the reads across the genes. A representative plot of the reads obtained for each method is illustrated at the chromosomal map of the ESR1 gene (Fig. 4d). It is clear that the number of reads at the 3′-end of the transcript is much higher for the mRNA-seq protocol than the RiboZero protocol and this is much more evident in the FFPE sample. The RiboZero library is distributed across the entire exome as well as at intergenic regions. Nevertheless, as shown in Fig. 4a-c, expression levels are very similar despite the difference in distribution of reads across the gene.
Genome profiling datasets of many tumors types are the basis for a large number of studies, in both basic research and clinically relevant diagnostic developments. Most studies are performed on fresh frozen biopsies, assuming that the much more readily available FFPE samples are less suitable for large-scale genomic profiling. The availability of FFPE tumors at pathological archives enables researchers to expand the current expression databases to specific tumor subtypes, to perform extensive retrospective analyses and to execute longitudinal studies on individual patients over the course of disease. In this study, we demonstrate that standard mRNA-seq of FFPE samples is a consistent and efficient method for basic transcriptome profiling.
RNA-seq is increasingly gaining ground as the preferred unbiased method for genome profiling. Indeed, several previous studies have already showed high correlation between matched FF and FFPE samples, indicating feasibility of RNA-seq for the latter [10, 12–14, 16–18]. However, most studies compared the standard poly(A) mRNA-seq protocol for FF samples to ribosomal depletion, used for FFPE samples [10, 12–14]. Alternatively, FF samples were compared to matched FFPE using ribosomal depletion methods for both sample types [16–18] (Additional file 3: Table S3). When FF samples are compared to matched FFPE, using different protocols for the two sample types, it is difficult to interpret the results since difference can be caused by both factors – different samples and different protocols. Nevertheless, the “ideal” comparison, of results obtained for matched samples of the two types, using mRNA-seq for both, was not yet performed. The reason is that it was assumed that oligo(dT) primers would not reliably capture the partially degraded and chemically modified transcripts, and therefore gene expression will be underestimated in an unknown transcript-dependent manner. These studies concluded that RiboZero is a satisfactory method for RNA-seq of FFPE, compared to the gold standard mRNA-seq of matched FF. But the ribosomal-depletion methods frequently resulted in lower fraction of exonic transcripts out of total reads (10–24%, depending on the initial starting material, Additional file 3: Table S3), requiring significantly higher numbers of reads (55 M–65 M reads) ([10, 12–14, 16–19]). A large fraction of reads obtained by the ribosomal-depletion methods map to intronic regions and to unspliced mRNA .
Here we compared matched FF and FFPE samples using both the standard protocol for mRNA-seq (Truseq) and the RiboZero method, used for both tissue types. The results indicate that gene expression measurement of FFPE tissues using the standard poly(A) protocol is feasible and represents similar differential expression as obtained for FF tissues. Our results are consistent with a study by Beck et al., showing that their customized 3’end sequencing is an effective technique for expression profiling of FFPE samples .
Although we observed a 3′-end bias in the mRNA-seq libraries of FFPE compared to the RiboZero libraries, this bias was not reflected in the expression levels. Gene quantification was highly equivalent for a wide range of expression levels. Estimation of expression fold changes between FF and FFPE samples from both protocols was also very consistent, resulting in high overlap, in differentially expressed genes and their fold changes, between the two methods and sample types.
The main advantage of the mRNA-seq protocol over the RiboZero protocol is the higher percentage of exonic reads out of the total number of reads, which enables to quantify gene expression with much lower coverage, and hence it is a cost-effective approach. In addition, in the protocol we select the desired genomic regions (i.e. exons, mRNA), as opposed to the RiboZero protocol, in which we deplete some of the undesired regions (i.e. the rRNA). Thus, mRNA-seq requires less preliminary calibrations (e.g. calibration of the beads required for efficient depletion of rRNA in the RiboZero protocol).
Our results suggest that for standard transcriptome profiling, mRNA-seq of FFPE is a reliable and cost-effective method. With this said, it is important to keep in mind the limitations of the mRNA-seq methods. Since this method sequences mainly the 3’end of the transcript, it excludes transcripts that do not have poly(A) tails. Notably, for the non-coding lincRNAs that are mostly polyadenylated we observed a very good correlation between the RiboZero and mRNAseq libraries of the FFPE samples (Additional file 6: Figure S3A). Both methods, however, are not ideal for short non-coding RNAs, such as miRNAs, that require size selection steps, yet the very few detected miRNAs were similarly represented in the two methods (Additional file 6: Figure S3B).
In the case of highly degraded samples, mRNA-seq will not be ideal for accurate sequence-based discoveries such as identification of novel transcripts, alternative splicing or of single nucleotide polymorphisms. Low quality samples can be related to archival time as well as to preservation methods. All the FFPE samples in this study were of low quality, as measured by the RNA integrity number. For a relatively moderate time of archiving, starting material of 100 ng of RNA is sufficient and comparable to 500 ng of RNA. For highly degraded samples, such as very old archived samples, the minimal amount of RNA is 500 ng or higher.
In summary, when considering methods for transcriptome profiling, practice and cost are important factors. Ribosomal depletion protocols have higher cost per sample and require 2–3 times more reads to compensate for higher fraction of intergenic reads. mRNA-seq method has lower library as well as sequencing costs due to higher fraction of exonic reads. Our results demonstrate that conventional transcriptome profiling of FFPE tissues is feasible as an alternative for frozen samples. Although mRNA-seq libraries are mainly represented by the 3′-end relative to the uniform distribution of reads by the RiboZero method, the resulting gene expression levels are highly comparable and obtained at much lower total numbers of reads.
FF breast cancer tumor samples, collected for the Sheba Medical Center institutional tumor bank at time of surgery were included (n = 3). Matched FFPE blocks were obtained from the same resected tumors, archived at the Sheba Pathology Institute (preservation time of 4–5 years). All patients had estrogen receptor positive tumors. Three additional FFPE tumor samples, with longer preservation times (~ 10 years), were included. Tissue slides were examined by expert breast pathologists to include a minimum of 70% cancer cells. The study was approved by the Institutional Review Board (IRB).
FFPE tumor samples were sectioned to 5 μm slices and deparafinized at 90 °C for 5 min. Total RNA was extracted using nucleic acid isolation kit (AllPrep® Qiagen) according to the protocol instructions, with the following modifications: DNAse treatment was at 37° for 20 min and the column was incubated for 1 min before each washing step with buffer RPE. Samples were eluted in 30 μL purified water. RNA from FF tumor samples was extracted using Trizol. RNA concentrations were determined by Qubit™ fluorometer (Thermofisher scientific) and the RNA quality was determined by measuring the RNA integrity number (RIN) using Agilent TapeStation.
Truseq RNA Sample preparation kit v2 (illumina) (cat# RS-122-2002) for mRNA-seq libraries. Briefly, polyA fraction (mRNA) was purified from 500 ng or 100 ng of total RNA following by fragmentation and generation of double stranded cDNA. Then, end repair, A base addition, adapter ligation and PCR amplification steps were performed.
Truseq Stranded total RNA with Ribo-Zero Gold. Library Protocol: TruSeq® Stranded Total RNA (Illumina) (Cat # RS-122-2301, RS-122-2302) for ribosomal-depletion libraries. Briefly, After rRNA depletion from 500 ng total RNA with rRNA Ribo-Zero Gold removal mix, cDNA was performed followed by second strand synthesis with dUTP instead of dTTP. Then, A base addition, adapter ligation, UDG treatment and PCR amplification steps were performed.
Ovation Human FFPE RNA-Seq Library (Cat # 0340–32, NuGEN) for ribosomal-depletion libraries. Starting material for the libraries was: 100 ng for fresh frozen samples whereas 100 and 250 ng for FFPE samples.
Libraries concentrations were evaluated by Qubit (Thermofisher scientific) and their size was evaluated by Agilent Tapestation. Primer dimers were eliminated using 1× Agencourt RNAClean XP Beads after library preparation. Sequencing libraries were constructed with TruSeq SBS Kit using barcodes to allow multiplexing of few samples in one lane with a read length of 60 bp single-end run in Illumina HiSeq V4 instrument.
Diagnostic slides were immunostained for Estrogen receptor (ER) on the Ventana Discovery autostainer (Ventana) using commercial ER antibody. ER levels were determined by a dedicated breast pathologist in accordance with the clinical guidelines by the American Society of Clinical Oncology (ASCO) and the College of American Pathology (CAP).
Processing and alignment
Fragments were mapped to the human genome (hg19) using TopHat version V2.0.5 . Only uniquely mapped fragments to the genome were considered and exonic and intronic signals were calculated using HTSeq . The signals of the same sample from all lanes were summed. The number of fragments obtained for each gene in each sample was normalized to the total number of fragments obtained from this sample, and comparisons were made between the expression levels of a gene across all samples and not between different genes. The minimum expression level threshold was set to 5 (log2 scale) to reduce noise (based on data distribution).
Coverage along the genomic region
The average coverage along the genomic region from 5′ transcription start site to 3′ transcription end site was calculated for each sample using ngs.plot .
Estimation of total number of reads required for each protocol and sample type
The number of uniquely mapped reads to exonic regions varied significantly between samples and protocols (from 1 to 20 million). For each library (total of 29 libraries from the different sample types and protocols), we counted the number of exonic reads and the number of detected genes; the resulting 29 points lied, to a good approximation, on a smooth monotonic curve (Additional file 4: Figure S1). For each protocol and sample type we estimated the percentage of exonic reads out of the total number of reads. Using the curve and this percentage, we extrapolated, for each protocol and sample type, the corresponding curve of the number of detected genes for a given total number of reads (see Fig. 1c).
Fold change agreement between FF and FFPE samples
We estimated the level of agreement between the differential expression (Fold Change, FC) of a gene, as measured in two FFPE samples, and the FC obtained from their matched FF samples. This was done for all genes with FC of at least 2 in the two FFPE samples (up- or down-regulated), to calculate how many of these genes exhibited FC in the same direction in the matched FF samples. To control the dependence of this measure of the FC agreement, the same procedure was repeated for FC thresholds varying between 1 to 10.
The METABRIC dataset  contains mRNA expression data for ~ 2000 breast tumors and 126 normal breast samples. Two-samples t-test was performed on all expressed genes to compare their expression levels in normal versus tumor samples. Genes with 0.1% FDR were defined as differentially expressed between normal and tumor samples.
We thank the Cancer Research Center at Sheba Medical Center for the support. We thank Goni Hut-Siloni for helping with the tissue bank samples. We acknowledge Gilgi Freidlander for her assistance with the bioinformatics.
Availability of data and materials
The data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are available through GEO accession number GSE113976 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113976).
This research was supported by a grant from the Israel Ministry of Science (# 3–11175) and funds for Research and Development of the Nancy and Stephen Grand Israel National Center for Personalized Medicine. ED and NBB were supported by the Leir Charitable Foundation.
MD, NBB and SG designed the study. NBB performed the bioinformatics and data analysis. SG and SB carried out the library preparation and the RNA sequencing. GP, AP and SH collected the samples, and were responsible for their macrodissection and extraction of RNA. NBL and AY are the pathologists that scored the samples. BM contributed to bioinformatics analysis. BK and IB participated in the study coordination. MD, NBB, ED, SG and ENG participated in paper writing and editing. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study was approved by the Sheba Institutional Review Board (IRB). Full exemption for consent form for anonymized samples was approved by the Sheba IRB.
The authors declare that they have no competing interests.
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