Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
June 8th, 2020
11.8K views
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.
Overview
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. The protocol enables optimization of RNA quality assessment and downstream processing for enhanced sequencing data.
Key Study Components
Area of Science
- Neuroscience
- Biology
Background
- FFPE samples are often of suboptimal quality.
- Assessing RNA quality is crucial for reliable sequencing.
- Downstream processing can significantly affect data quality.
- Visual demonstrations can aid in understanding the methodology.
Purpose of Study
- To improve gene expression data from FFPE-derived RNA samples.
- To provide insights into functional elements of the genome.
- To enhance the characterization of transcripts in biological and clinical samples.
Methods Used
- Assessment of RNA quality within FFPE tissue samples.
- Optimization of library preparation techniques.
- Data analysis methods tailored for FFPE-RNA samples.
- Implementation of appropriate controls during the sequencing process.
Main Results
- Improved quality and quantity of sequencing data from FFPE samples.
- Enhanced understanding of transcript characteristics.
- Successful application of the method to poorly preserved samples.
- Visual aids effectively demonstrate critical steps in the process.
Conclusions
- The method provides a reliable approach to FFPE-RNA sequencing.
- Careful selection of methods and controls is essential.
- This technique can advance research in development and disease.
Frequently Asked Questions
What are FFPE samples?
Formalin-fixed paraffin-embedded samples are tissues preserved for histological analysis.
Why is RNA quality assessment important?
It ensures reliable sequencing results and accurate gene expression data.
How does this method improve sequencing data?
By optimizing RNA quality and library preparation techniques.
Can this method be used for poorly preserved samples?
Yes, it is specifically designed to enhance data from suboptimal samples.
What insights can be gained from this method?
It can provide insights into functional elements of the genome related to development and disease.
Executive Industry Relevance
Degraded FFPE-RNA samples present a major bottleneck in translational oncology and biomarker discovery, where archival tissue repositories hold critical clinical correlations. Optimizing RNA quality assessment and library preparation enables reliable gene expression profiling from historically unusable specimens, expanding cohort sizes for population-based studies. This approach enhances predictive confidence in target validation by reducing technical noise and improving reproducibility across biological replicates.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses using clinically archived FFPE samples with quantified RNA integrity metrics.
- Operational Value: Supports exclusion of low-quality samples (<40% fragments >100 nt) to prevent wasted sequencing resources and improve data reliability.
- Predictive Value: Increases confidence in differential expression calls by minimizing artifacts from RNA degradation and chemical modification.
Screening & Assay Development
- Scientific Value: Generates standardized, quantification-ready sequencing libraries compatible with downstream differential expression and mutation analysis pipelines.
- Operational Value: Enables consistent library preparation across variable FFPE sample quality through QC-guided method selection (e.g., total RNA library prep for highly degraded samples).
- Scalability: Facilitates large-scale NGS studies using diverse FFPE archives with differing storage conditions and durations.
Translational & Preclinical Research
- Translational Continuity: Bridges discovery and preclinical workflows by enabling molecular profiling of FFPE samples linked to rich clinical annotations (e.g., treatment response, resistance mechanisms).
- Biomarker Alignment: Supports reproducible gene expression profiling across biological replicates via PCA and Pearson correlation, aiding biomarker signature validation.
- Mechanistic De-risking: Reduces false positives in pathway analysis by filtering low-quality reads and assessing 3' bias through RSeQC and FastQC tools.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from sample qualification through data analysis, supporting lead identification and preclinical validation by improving input material quality for sequencing.
- Discovery Biology: Enables hypothesis testing on archived FFPE cohorts by providing QC metrics (e.g., % fragments >100 nt) to guide sample inclusion/exclusion decisions.
- Screening: Produces sequencing-ready libraries with improved yield and quality, directly enabling reliable compound screening in disease-relevant models.
- Analytics: Delivers quantitative outputs including uniquely mapped read percentages, gene coverage uniformity, and intergenic/intronic base proportions for data quality assessment.
- Translational Research: Supports continuity to preclinical work by allowing molecular characterization of FFPE samples with known clinical outcomes, enhancing biomarker-translational alignment.
- Enterprise Reuse: Establishes a standardized QC and library prep framework applicable across multiple projects and sites, reducing variability in FFPE-RNA handling.
Operational & Enterprise Impact
- Scientific Value: Improves target validation confidence by increasing accuracy and reproducibility of gene expression profiles from degraded clinical samples.
- Operational Value: Standardizes RNA QC, library preparation, and sequencing setup across sites, reducing batch effects and technical variability.
- Strategic Value: Increases go/no-go decision reliability by enabling larger, more representative FFPE-derived datasets for target prioritization.
- Portfolio Impact: Enhances risk-adjusted advancement by unlocking historical tissue archives for retrospective biomarker and resistance mechanism studies.
Implementation Considerations
- Requires expertise in RNA QC systems (e.g., Bioanalyzer, TapeStation) and fragment length analysis for accurate sample stratification.
- Needs access to sequencing platforms and reagent kits compatible with low-input, degraded RNA library preparation (e.g., total RNA-seq kits).
- Demands cross-team standardization on QC thresholds (e.g., 40% fragment >100 nt cutoff) and bioinformatics pipelines (MultiQC, STAR, RSeQC, PCA).
- Must account for sample-specific variables such as fixation duration, storage conditions, and tissue type when applying QC metrics.
- Practical limitation: Extreme degradation may still limit transcript coverage, necessitating method validation per sample set and tissue type.
Biopharma FAQs
Why does RNA fragment length >100 nt matter for FFPE sample QC?
The proportion of RNA fragments longer than 100 nucleotides is a more sensitive metric for highly degraded FFPE-RNA samples, enabling accurate assessment of usable material for library preparation. Samples with <40% fragments >100 nt are recommended for exclusion to avoid poor sequencing library yields and low-quality data. This threshold supports informed go/no-go decisions in sample processing workflows.
How does selecting the right library preparation method improve FFPE-RNA sequencing outcomes?
For highly degraded FFPE-RNA samples, a total RNA library preparation method is recommended over mRNA-selective approaches to maximize representation of fragmented transcripts. This method increases the quantity and quality of sequencing data by adapting to the fragmented RNA profile typical of FFPE samples. Appropriate method selection based on QC metrics directly improves library complexity and reduces bias.
What role does MultiQC play in assessing FFPE-RNA sequencing data quality?
MultiQC generates an aggregated HTML report that visualizes pre- and post-alignment QC metrics, enabling rapid evaluation of sequencing run performance across samples. It helps detect contamination (e.g., bacterial, mouse) via FastQC and summarizes key indicators like adapter content and base quality scores. This consolidated view supports cross-sample comparison and batch effect identification in FFPE-RNA studies.
Why is principal component analysis (PCA) used for FFPE-RNA sample correlation?
PCA determines the percent of total variation captured by principal components, enabling assessment of biological replicate consistency and identification of outliers in FFPE-RNA datasets. It complements Pearson correlation analysis by providing a multidimensional view of sample relationships based on global expression patterns. This supports quality assurance and reproducibility assessment in translational research workflows.
How do RSeQC and STAR alignment metrics inform FFPE-RNA data interpretation?
RSeQC evaluates the proportion of messenger, intronic, and intergenic bases in mapped reads, helping detect biases such as 3' enrichment common in degraded FFPE-RNA libraries. STAR alignment quantifies uniquely mapped reads, multi-mapping rates, and unmapped read proportions, providing insight into alignment efficiency and potential contamination. Together, these metrics enable filtering of low-quality reads and assessment of true biological signal versus technical artifacts.
