Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Summary

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Abstract

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

Introduction

Use of next-generation sequencing approaches has enabled us to glean a wealth of information from various types of samples. However, old and poorly preserved samples remain unworkable for the commonly used methods of generating sequence data and often require modifications to well-established protocols. FFPE tissues represent such a sample type that has been widely utilized for clinical specimens^1,2,3. While FFPE preservation maintains tissue morphology, the nucleic acids in FFPE tissues usually exhibit a wide range of damage and degradation, making it difficult to retrieve the genomic information that may lead to important insights about molecular mechanisms underlying various disorders.

Gene expression data generated by RNA sequencing is often instrumental in studying disease and resistance mechanisms and complements DNA mutation analysis. However, RNA is more susceptible to degradation, making it more challenging to generate accurate gene expression data from FFPE tissues. Furthermore, because the wide availability and affordability of sequencing is relatively recent, older specimens were often not stored in conditions required to preserve RNA integrity. Some of the issues for FFPE samples include degradation of RNA due to embedding in paraffin, chemical modification of RNA leading to fragmentation or refractoriness to enzymatic processes required for sequencing, and loss of the poly-A tails, limiting the applicability of oligo-dT as a primer for reverse transcriptase⁴. Another challenge is the handling/storage of FFPE samples under suboptimal conditions, which may lead to further degradation of labile molecules such as RNA in the tissues⁵. This is especially relevant for older samples that may have been collected at a time when gene expression analysis by RNA sequencing was not anticipated for the samples. All these lead to decreased quality and quantity of the extracted RNA available for generating useful sequence data. The low probability of success, combined with the high cost of sequencing, has dissuaded many researchers from trying to generate and analyze gene expression data from potentially useful FFPE samples. Some studies in recent years have demonstrated the usability of FFPE tissues for gene expression analysis^2,6,7,8,9, albeit for fewer and/or more recent samples.

As a feasibility study, we used RNA extracted from FFPE tumor tissue specimens from three Residual Tissue Repositories from Surveillance, Epidemiology, and End Results (SEER) cancer registries for RNA sequencing and gene expression analysis¹⁰. Procured from clinical pathology labs, the FFPE tissues from high-grade ovarian serous adenocarcinomas were stored from 7–32 years under varying conditions before RNA extraction. Because in most cases these blocks had been stored in different sites for years without the expectation of any sensitive genetic analysis in the future, not much care had been taken to preserve the nucleic acids. Thus, most of the samples exhibited poor quality RNA, with a large proportion of samples contaminated with bacteria. Nevertheless, we were able to perform gene quantification, measure the uniformity and continuity of gene coverage, and perform the Pearson correlation analysis among biological replicates to measure reproducibility. Based on a set of key signature gene panel, we compared the samples in our study with The Cancer Genome Atlas (TCGA) data and confirmed that approximately 60% of the samples had comparable gene expression profiles¹¹. Based on the correlation between various QC results and sample metadata, we identified key QC metrics that have good predictive value for identifying samples that are more likely to generate usable sequence data¹¹.

Here we describe the methodology used for FFPE-RNA quality assessment, generation of sequencing libraries starting from extracted RNA samples, and bioinformatic analysis of the sequencing data.

Protocol

1. RNA quantity and quality assessment

1. Select the FFPE samples according to predefined criteria and extract RNA using an appropriate method (e.g., FFPE-nuclei acid extraction kit, Table of Materials).
   NOTE: There are several different methods available for FFPE-RNA extraction, including the newer microdissection methods that can work with very little tissue and extract good quality RNA^12,13,14.
2. Utmost care should be taken to preserve the integrity of RNA at all stages. This includes working with RNase free deionised water, using RNase free plasticware, and cleaning all instruments that come in contact with the FFPE blocks with RNase decontamination reagents.
3. RNA should always be handled carefully and kept in ice unless otherwise specified to minimize degradation while handling.
4. If enough material is available, extract RNA from more than one region in the FFPE block to generate biological replicates from as many samples as possible. For some of the samples with ample RNA yield, divide the extracted RNA into two to process as technical replicates.
5. If possible, collect a small amount of sample separately after extraction for QC (i.e., a QC aliquot) to avoid repeated handling and freeze-thaw cycles of the sample that will likely lead to degradation of the RNA.
6. Check the quality of the RNA (preferably from the QC aliquot) by running it on an RNA QC system (e.g., Agilent Bioanalyzer system using an RNA Nano chip, Table of Materials) according to the manufacturer’s instructions.
7. Analyze the distribution of RNA fragments in the samples (e.g., using the Bioanalyzer 2100 Expert software) by calculating the DV₂₀₀ and DV₁₀₀ values as the percent of fragments larger than 200 nt (DV₂₀₀) or 100 nt (DV₁₀₀) in size.
8. Among DV₂₀₀ and DV₁₀₀, identify the metric that has a larger spread of values for the given sample set, and pick that for grouping the samples according to their degree of intactness.
   NOTE: For sample sets with more intact RNA molecules (i.e., high DV₂₀₀ values, all or most with DV₂₀₀ > 40%), DV₂₀₀ is likely to be a useful QC metric. However, for sample sets with more degraded transcripts (i.e., low DV₂₀₀ values, all or most with DV₂₀₀ < 40%), DV₁₀₀ is more likely to be useful.
9. Based on the QC metrics, identify the samples that have DV₁₀₀ < 40%. Because this degree of degradation is highly likely to not generate useful sequencing data¹¹, it is advisable to avoid processing such samples. If replacements for such samples are available, their quality should be checked to ideally only include samples with DV₁₀₀ > 50%.

2. Sequencing library preparation

1. Based on the quality of the samples as assessed in section 1, identify an appropriate method for generating the sequencing libraries.
   1. For sample sets with very low degradation and high DV₂₀₀ values, use mRNA sequencing (i.e., capture of polyadenylated transcripts), targeted RNA sequencing (i.e., use of capture probes for specific genes of interest), RNA exome sequencing (i.e., use of capture probes to enrich for the coding transcriptome), or total RNA sequencing (i.e., use of random primers for reverse transcription to sequence the entire RNA population after removing ribosomal RNA from the samples). However, it is important to note that the fixation process may introduce bias in the extracted RNA. Thus, the capture approaches may not work well in all cases, even with high DV₂₀₀ values.
   2. If the sample set includes samples with high degradation (DV₂₀₀ < 30%), use a total RNA library preparation method and not one that depends on the capture of specific regions of the transcripts, because those specific regions may be missing in degraded samples. The use of random primers for generation of cDNA leads to higher representation of usable RNA in the final library, and is, therefore, more suited for FFPE-RNA samples.
   3. For ribosomal RNA depletion for sample sets with high degradation, use RNaseH-based methods. These are methods where rRNA-specific DNA probes bind to rRNA, double-stranded molecules are digested by RNaseH, and leftover probes are cleaned up by DNase (e.g., NEBNext rRNA depletion kit, Table of Materials). These methods work better for degraded samples than some other methods⁸.
2. For generating sequencing libraries, use higher input amounts (if possible) for samples that have more degraded RNA (DV₁₀₀ < 60%). While samples with reasonably good quality RNA (DV₁₀₀ > 60%) may yield good sequence data even at lower input amounts (the lowest tested for this protocol with FFPE-RNA was ~20 ng), for more degraded RNA (DV₁₀₀ < 60%), it is better to start with higher input amounts (e.g., >100 ng).
   NOTE: If enough (e.g., >500 ng) sample is available, it is advisable to save at least half of the sample for repeating the library preparation, if needed. For low input samples (e.g., <100 ng), it is usually better to use the entire amount and generate a library of sufficient diversity.
3. After selecting a suitable library preparation kit for generating total RNA seq libraries from samples with high degradation (e.g., NEBNext Ultra II RNA Library Prep Kit for Illumina, see Table of Materials), follow the manufacturer’s instructions to generate the libraries.
   NOTE: During library preparation, it is important to skip the RNA fragmentation step for degraded samples and to ensure the use of random primers for first strand cDNA synthesis.
4. For improving the efficiency and speed, especially for the low-input samples, use appropriate magnetic racks with strong fixed magnets for bead-based purification and size-selection steps (see Table of Materials).
5. For PCR enrichment of adapter ligated DNA, adjust the number of amplification cycles based on the amount of input DNA to ensure maximum representation while avoiding unnecessary duplication of the library molecules. For low input FFPE-RNA samples (<100 ng), we recommend 16–18 amplification cycles, while the high input samples (1,000 ng) usually generate enough library amounts in 12–14 rounds of amplification.
6. Following PCR amplification and cleanup per the manufacturer’s instructions, assess the library quality by analyzing library concentration and molecule distribution on an appropriate platform (e.g., Agilent Bioanalyzer DNA Chip, see Table of Materials). For samples with primer peaks (~80 bp) or adapter-dimer peaks (~128 bp), repeat the cleanup to remove those peaks.
7. Calculate the average library size for each library (e.g., using the Bioanalyzer 2100 Expert software).

3. Sequencing library QC

1. Once it has been ascertained that the libraries are free of excess primer and adapter-dimers and have sufficient concentration for subsequent sequencing, quantitate further by qPCR.
   NOTE: Owing to the sensitivity of cluster generation towards library concentration, accurate quantification is vital to prevent costly sequencing runs from underperformance or overloading. Quantitative real-time PCR (qPCR) methods are useful for improving cluster density on Illumina platforms without resulting in overclustering. The qPCR method is more precise and more sensitive than the methods based on qualitative and/or quantitative analysis of all library molecules (e.g., Agilent Bioanalyzer), because it measures the templates that have both adapter sequences on either end that will form clusters on the flowcell. Library size must, however, be known in advance as a size correction must be applied to all samples so that results can be compared against a standard curve.
   CAUTION: Lab coats and gloves must always be worn when performing qPCR, and the procedure must be performed in a biosafety cabinet following the manufacturer’s instructions.
   1. Set up a 96 well plate with three replicates for each sample for error prevention using a suitable kit (e.g., KAPA SYBR FAST qPCR Master Mix for Illumina libraries, a part of Library Quantification kit, see Table of Materials), along with the standards, a positive control (e.g, PhiX control, see Table of Materials), and a no template control (NTC). The NTC is qPCR mix without DNA library. The positive control can be any library with known concentration and fragment size.
      1. Prepare a minimum of six dilutions of the standards following the vendor protocol.
   2. After adding all the components (i.e., qPCR master mix, libraries, standards), cover the plate with sealing film and use a squeegee to ensure the film makes even and secure contact with the plate.
   3. Vortex and spin down the plate at 1,500 rpm for at least 1 min. Visually inspect the plate to make sure there are no air bubbles at the bottom of the wells.
   4. Set up the plate on the thermal cycler (e .g. CFX96 Touch System, see Table of Materials) using the manufacturer's recommended settings.
   5. Save the run folder where it can be accessed for data analysis.
   6. During data analysis, check that the slope is in the -3.1 to -3.6 range, efficiency from 90% to 110% and the R² (coefficient of correlation obtained for the standard curve) no less than 0.98.
2. Pooling: Once the qPCR concentration of the sequencing ready libraries is obtained, pool equimolar amounts of each of the libraries, depending on the number of sequencing reads required per sample and the sequencing output of the instrument.
3. QC of the pools: Quantitate the library pools again by qPCR following the same protocol as described in step 3.1.

4. Sequencing

1. Depending on the run parameters, pull the sequencing reagent kits and thaw them following the user guide. Please check the Illumina website for the latest versions of all user guides for sequencing on Illumina instruments.
2. Make sure the reagents are completely thawed and place the reagents tray at 4 °C. The run should be started no later than 2 h after the reagents have been defrosted. Not doing that could affect quality of the run results.
3. Invert the cartridge 5x to mix reagents and gently tap on the bench to reduce air bubbles.
4. Set the unwrapped flow cell package aside at room temperature for 30 min.
5. Unwrap the flow cell package and clean the glass surface of the flow cell with a lint-free alcohol wipe. Dry the glass with a low-lint laboratory tissue.
6. Open the Illumina “Experiment Manager” application. Choose “Create Sample Sheet”, then choose the Sequencer and click “Next”.
7. Create and upload the sample sheet based on Illumina sequencer criteria (e.g., Illumina Experiment Manager, software guide).
8. At the prompts, scan in the reagent kit barcode and enter the run Set Up Parameters (e.g., for a single indexed PE 75 cycle run, enter 76-8-76).
9. Denature and dilute the library pool based on the sequencer user guide recommendation (e.g., NextSeq 500 System guide from Illumina, see Table of Materials).
10. Denature and dilute the control library PhiX (see Table of Materials) to the appropriate concentration (e.g., 1.8 pM for NextSeq).
11. Mix sample library and PhiX control to result in a 1% PhiX control volume ratio.
12. Load denatured and diluted sample into the reagent cartridge in the designated reservoir.
13. Load the flowcell, buffer cartridge, and the reagent cartridge.
14. Perform an automated check and review to ensure that the run parameters pass the system check.
15. When the automated check is complete, select Start to begin the sequencing run.

5. Data analysis and quality assessment

NOTE: A typical RNA-seq data analysis workflow (Figure 1) includes preprocessing and QC, alignment to genome and post alignment QC, gene and transcript quantification, sample correlation analysis, differential analysis between different sample groups, treatment conditions, and gene set enrichment and pathway analysis.

The RNA-seq data may have quality issues that can affect the accuracy of gene profiling and lead to erroneous conclusions. Therefore, initial QC checks for sequencing quality, contamination, sequencing coverage bias, and other sources of artifacts are very important. Applying an RNA-Seq QC pipeline similar to the workflow described here is recommended to detect artifacts and apply filtering or correction before downstream analysis.

1. Preprocessing
   NOTE: This includes demultiplexing, assessment of sequence read quality, GC content, presence of sequencing adapters, overrepresented k-mers, and PCR duplicated reads. This information helps to detect sequencing errors, PCR artifacts, or contamination.
   1. Demultiplex Illumina sequencing run using the Illumina software tool bcl2fastq2 to generate raw FASTQ files for each sample defined in the sample sheet. Allow one mismatch in the sample index barcodes to tolerate sequencing errors if there is no barcode collision.
   2. Run the FASTQC¹⁵ software tool to perform a quality check on raw FASTQ files to detect any poor quality or abnormalities in sequencing reads.
   3. For adapter and low-quality bases trimming, trim the sequencing adapters and low quality bases using Cutadapt¹⁶ or Trimmomatic¹⁷ software tools. Save the trimmed reads in the pair-end fastq files.
   4. Contamination screen
      1. Run FASTQ_screen¹⁸ to detect possible cross contamination with other species.
      2. Run miniKraken of Kraken2¹⁹ to identify the taxonomies of contaminating species.
2. Alignment to reference genome and post alignment QC
   1. The trimmed reads can be aligned to a reference genome sequence (GRCh Build hg19 or hg38) using STAR aligner²⁰. Apply the Gencode annotation GTF file to guide the spliced transcript alignment. It is recommended to run STAR 2-pass to increase sensitivity to novel splice junctions. In the second pass, all reads will be remapped using annotated gene and transcripts and novel junctions from the first pass.
   2. Perform post-alignment QC.
      1. Run Picard’s²¹ MarkDuplicates to evaluate the library complexity by determining the amount of unique or nonduplicated reads in the samples.
      2. Run Picard’s CollectRnaSeqMetrics program to collect mapping percentages on coding, intronic, intergenic, UTR regions, and gene body coverage.
      3. Run RSeQC²² to determine the read pair inner distance, read distribution among CDS exons, 5’UTR, 3’UTR, intron, TSS_up_1kb, TSS_up_5kb, TSS_up_10kb, TES_down_1kb, TES_down_5kb, TES_down_10kb, read GC content, junction saturation, and library strand information.
      4. Run multi-QC²³ to generate an aggregated report in HTML format.
3. Gene quantification and correction analysis
   1. Run RSEM²⁴ to get raw count as well as normalized read count on genes and transcripts. The read count measurement such as RPKM (reads per kilobase of exon model per million reads), FPKM (fragments per kilobase of exon model per million mapped reads), and TPM (transcripts per million) are the most often reported RNA-seq gene expression values. Genes expressed below a noised threshold (such as TPM < 1 or raw count <5) can be filtered.
   2. Perform transcript quantification to aggregate raw counts of mapped reads to each transcript sequences using programs such as HTSeq-count or featureCounts.
   3. Run Principal Components Analysis (PCA) using an R script to determine batch effects and assess a quality map of the given dataset²⁵. Sample correlation analysis can be carried out using the Pearson correlation between different metrics.
4. Differential gene expression analysis
   1. Perform gene differential analysis between sample conditions using the program edgeR^26,27 and/or limma-Voom²⁸ and use normalization methods including TPM, TMM, DESeq, or UpperQuartile.
   2. It is recommended to run at least two differential analysis software tools in order to call two set of DEGs lists for comparison and get the final DEGs to improve detection sensitivity and accuracy.
5. Gene set enrichment and pathway analysis
   1. Perform Gene Set Enrichment Analysis (GSEA)^29,30 based on ranking of transcripts according to a measurement of differentially expressed genes (DEGs) list to determine if the DEGs show statistically significant, concordant differences between biological conditions.
   2. Perform function analysis using resources such as Gene Ontology³¹, DAVID^32,33, or other available software tools.

Results

The methodology described above was applied to 67 FFPE samples that had been stored under a variety of different conditions for 7–32 years (the median sample storage time was 17.5 years). The dataset and analysis results presented here were previously described and published in Zhao et al.¹¹. On checking the sample quality as described earlier (i.e., example traces in Figure 2), DV₁₀₀ was found to be more useful than DV₂₀₀ because it is more sens...

Discussion

The method described here outlines the main steps required to obtain good sequence data from FFPE-RNA samples. The main points to consider with this method are: (1) Ensure that the RNA is preserved as best as possible after extraction by minimizing the sample handling and freezing and thawing cycles. Separate QC aliquots are very helpful. (2) Use a QC metric that is best for the given sample set. RIN values and DV₂₀₀ are often not useful for degraded samples, and DV₁₀₀ may be the metric of choice to...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
2100 Bioanalyzer	Agilent	G2939BA
Agilent DNA 7500 Kit	Agilent	5067-1506
Agilent High Sensitivity DNA Kit	Agilent	5067-4626
Agilent RNA 6000 Nano Kit	Agilent	5067-1511
AllPrep DNA/RNA FFPE Kit	Qiagen	80234
CFX96 Touch System	Bio-Rad	1855195
Library Quantification kit v2-Illumina	KapaBiosystems	KK4824
NEBNext Ultra II Directional RNA Library Prep Kit for Illumina	New England Biolabs	E7765S	https://www.neb.com/protocols/2017/02/07/protocol-for-use-with-ffpe-rna-nebnext-rrna-depletion-kit
NEBNext rRNA Depletion Kit (Human/Mouse/Rat)	New England Biolabs	E6310L
NextSeq 500 Sequencing System	Illumina	SY-415-1001	NextSeq 500 System guide: https://support.illumina.com/content/dam/illumina-support/documents/documentation/system_documentation/nextseq/nextseq-500-system-guide-15046563-06.pdf
NextSeq PhiX Control Kit	Illumina	FC-110-3002
NSQ 500/550 Hi Output KT v2.5 (150 CYS)	Illumina	20024907
10X Genomics Magnetic Separator	10X Genomics	120250
Rotator Multimixer	VWR	13916-822
C1000 Touch Thermal Cycler	Bio-Rad	1851197
Sequencing reagent kit	Illumina	20024907
Flow cell package	Illumina	20024907
Buffer cartridge and the reagent cartridge	Illumina	20024907
Sodium hydroxide solution (0.2N)	Millipore Sigma	SX0607D-6
TRIS-HCL Buffer 1.0M, pH 7.0	Fisher Scientific	50-151-871