Getting the best transcriptomics results out of your FFPE cohort requires expertise. GenomeScan offers you a comprehensive, robust, and reproducible solution for FFPE RNA analysis.
FFPE RNAseq
Reliable measurement of transcriptomics in formalin-fixed, paraffin-embedded material
Is FFPE RNA sequencing challenging?
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How to ensure FFPE RNAseq reliability
Extracting gene-expression profiles from Biobanks
Biobanks harbor a wealth of gene-expression data of patients with a certain disease. The effects of treatment are documented and can be followed over time on molecular level. These biobanks generally consist of Formalin-Fixed Paraffin-Embedded (FFPE) tissue. High degree of RNA degradation of such FFPE cohorts is often hampering analyses by, for instance, leading to exclusion of key samples and/or essential time points. Factors that affect RNA quality are e.g. age of the blocks, treatment of the material before fixation, storage conditions. Also, formalin fixation damages RNA, causing extensive shredding and modification of nucleotides.
GenomeScan’s solution for FFPE RNA analysis
Our scientists have thoroughly studied the effects of RNA degradation and RNA yield on the accuracy and reliability of the transcriptomics data.
In validation studies performed with the library preparation chemistry of New England Biolabs (NEB) combined with Illumina sequencing, we have determined the parameters that are best suitable for monitoring the validity of the data. The figure, below on this page, shows that the mapping of FFPE material remains consistent, even with less input. Thus, gene-expression analysis of FFPE RNA is robust and reproducible in the range of 5 to 200ng, for this cohort.
Service workflow
1. Initial meeting
When you consider using one of our services, our scientific team will help choosing the optimal project design. Each service can be customized by adding options. You will receive an overview of project budget and deliverables, so you can make an informed choice. Once you return your Purchase Order form, your project manager will contact you to discuss the next steps.
2. Sample delivery
Sample specifications for FFPE RNAseq are:
- Optimal total RNA amount for standard workflow: ≥ 250 ng up to 1000 ng
- Optimal total RNA amount for low input workflow: 1 ng up to 10 ng
- Degraded RNA fragments >200 bp (DV200 >50%)
Additional services:
- Isolation of RNA from FFPE blocks
- Tumor microdissection by pathologists from the Leiden University Medical Center (LUMC)
3. Sample entry QC
After sample arrival, Entry-QC is performed to determinate the sample quality and quantity.
A report with QC results will be provided and results and progression of the project can be discussed.
4. Library prep/QC and sequencing
The library resulting from sample preparation goes into a library QC step to ensure sufficient material per sample and the right library size. Sequencing on NovaSeq:
- rRNA depletion
- Stranded RNA library preparation
- Paired-end 150 bp (PE150)
- Reads: 60 million/sample
5. Data QC
The raw data that is generated by the sequencer must be divided to obtain one data file per sample. This is called demultiplexing.
Your project manager checks the data quality by analyzing the quality metrics of the run. Final inspection of the data takes place and the dataset is transferred onto a hard disk.
6. Reporting
You receive your Project Report together with your data. It contains an overview of the experimental methods used, sample specifications, and a summary of all technical information for publication. Deliverables:
- FastQ files via secure e-transfer or shipment on storage medium
- Count file table (expression levels (FPKM values) per gene for individual sample)
- Quality score Q30 of ≥80%
- Optional data analysis with comprehensive report
Frequently asked questions
How does FFPE compare to Fresh Frozen material?
Studies generally include a comparison between Fresh Frozen (FF) material and FFPE. The goal is to show that data derived from various sources, will generate similar results.
As elegantly shown by our partners of New England Biolabs (NEB), fixation will always lead to a certain change of the transcripts that you retrieve (see figure below). Factors that affect RNA quality are e.g. age of the blocks, treatment of the material before fixation, storage conditions. The GenomeScan entry QC will measure the extent of fragmentation of the RNA. The effect of other factors such as depurination will be measured in the small pilot study in which we perform the whole workflow. This will provide you direct and sample-specific information on the quality of your samples.
Figure: Effect of sample fixation on the read distribution. Figure adapted from the NEB rRNA depletion kit. Data generated using the Universal Human RNA Reference (UHR).
Service validation data
Gene-expression analysis on FFPE RNA is reproducible in a wide range of input amounts
Effect of the amount of FFPE RNA on mapping of the reads to exons, introns and intragenic regions. A high concordance between results is a measure of robustness and reliability. Note that the quality of FFPE material is generally diverse and results will vary depending on the extent and type of RNA damage. Sample: RNA was obtained from a pool of multiple archival FFPE blocks. Average insert size 318 ±22 bp. Isolation method: Qiagen AllPrep DNA/RNA FFPE.
Conclusion
In the figure, the number of detected genes and percentage of reads that are successfully mapped are shown. These parameters decline rapidly when the samples perform sub-optimal. Despite that the location to which the reads are mapped remain stable until the lower input range, these and other quality metrics show that sufficient input RNA is necessary to reach the highest level of reproducibility. Thus, especially with FFPE material, optimal results are obtained with higher amounts of RNA input. Generally, our lab experts observe optimal results with 100 to 200ng input, depending on the extent of degradation.
Gene expression analysis of FFPE RNA is robust and reproducible
Fragment Analyzer measurement of RNA in submitted samples. From left to right: good quality, intermediate and poor quality RNA (resp. RIN 8.8, 6.6, 2.3). Ribosomal RNA’s are clearly visible in samples with a high integrity score. Fixation damages RNA, resulting in degradation of the 16S and 18S peaks. In fixated tissue the RNA becomes highly fragmented (RIN 2,3). GenomeScan’s FFPE gene profiling service can handle even highly degraded RNA.
Conclusion
Every FFPE cohort is different. All new study starts with a pilot experiment to explore the quality of the your FFPE material. This will reduce the chance on suboptimal data quality and quantity. We will provide you data and guidance to make an informed decision before proceeding with the main study.
Over many projects, we have shown that our laboratory and data-analysis pipelines provide the optimal means for generating results that are biologically relevant and suitable for publication.
Gene-expression analysis with higher amounts of RNA input leads to higher quality data
Effect of FFPE RNA input on number of detected genes and percentage mapping.
Conclusion
Our scientists have thoroughly studied the effects of RNA degradation and RNA yield on the accuracy and reliability of the transcriptomics data.
In validation studies performed with the library preparation chemistry of New England Biolabs (NEB) combined with Illumina sequencing, GenomeScan determined the parameters that are best suitable for monitoring the validity of the data. The figure on the left shows that the mapping of FFPE material remains consistent, even with less input. For this cohort, gene-expression analysis of FFPE RNA is robust and reproducible in the range of 200 to 5ng, although below 10ng the number of presence genes are significantly deceasing.
Service specifications
We have summarized key information about our FFPE RNAseq service into a service specification sheet.
Let's get the conversation started for your next NGS project
Please either fill in this form or email us directly at info@genomescan.nl
and we will get in touch with you to discuss your requirements.
