RNA Quality Control - Comparing Different RNA Quality Indicators
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This slide deck explains the most important factors in RNA QC and technologies for assessing sample quality
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RNA Quality Control - Comparing Different RNA Quality Indicators
- 1. Sample to Insight 1 Dr. Pierre-Henri Ferdinand Global Product Management Pierre-henri.ferdinand@qiagen.com Quality Control of RNA samples RNA quality control – comparing different RNA quality indicators
- 2. Sample to Insight Legal Disclaimer 2 QIAGEN products shown here are intended for molecular biology applications. These products are not intended for the diagnosis, prevention, or treatment of a disease. For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit handbook or user manual. QIAGEN kit handbooks and user manuals are available at www.QIAGEN.com or can be requested from QIAGEN Technical Services or your local distributor. Quality Control of RNA samples
- 3. Sample to Insight Outline Quality Control of RNA samples 3 Why is RNA quality control so important1 Main parameters for RNA QC2 Fast and reliable quality control3 Comparison of RIN and RIS4 Wrap up5
- 5. Sample to Insight 5 Non reproducibleReproducible biomedical research 50% • Culture of “publish or perish” • Inefficiencies in designing, conducting, and reporting of studies • Lack of standardization of sample quality for inter-lab reproducibility Some solutions for tackling the issue: • Implementing quality control procedures at key steps of laboratory workflows to help standardizing the parameters of the samples, and thus the quality of the data generated. • Combining lab automation with quality chemistries to enhance reproducibility of experiments, bringing confidence in data interpretation. Quality Control of RNA samples
- 6. Sample to Insight 6 QC emphasizes on gaining insight into a sample quality and assessing its suitability for downstream applications and its potential for generating reliable results. QC plays an important role when it comes to developing routine procedures or troubleshooting an experiment. µg ? Purity? Quantity? Integrity? Size? Are these parameters in the range of my experiment’s requirements for generating high-quality results? RNA Quality? What is RNA sample QC? Quality Control of RNA samples
- 7. Sample to Insight 7 Why is sample QC so important? Risk of failure Molecular biology workflows are complex, error- and failure-prone multi-step procedures A lot of things can potentially go wrong during workflows RNA can be unstable: sensitive to heat, radiations and nucleases Confidence in results At the end, only the quality of the final result matters The quality of the final results is highly influenced by quality of samples Cost aspects Downstream applications get more sensitive but not necessarily more robust to variations in samples quality Failure means waste of time and resources Price per sample Time qPCR 1.5 $US 4-5 hrs Sanger Sequencing 5-6 $US 4-6 hrs Next-Gen Sequencing >200 $US(5plex) 2-3 working days Affymetrix GeneChip Analysis 500-800 $US/sample > 2 working days Quality Control of RNA samples
- 8. Sample to Insight 8 Sample collection/ stabilization RNA purification, cDNA & cRNA synthesis Quantification, Normalization, Fragmentation Array Hybridization and scanning Data analysis & interpretation Gene expression Microarrays QC QC Sample collection/ stabilization RNA purification Amplification Analysis Data analysis & interpretation Gene expression RT-PCR QC Sample collection/ stabilization DNA/RNA purification NGS Library Prep Sequencing Data analysis & interpretation RNA-Seq QC QC RNA Quality Control is recommended in Gene expression workflows RNA samples Quality Control and standardization Quality Control of RNA samples
- 9. Sample to Insight Outline Quality Control of RNA samples 9 Why is RNA quality control so important1 Main parameters for RNA QC2 Fast and reliable quality control3 Comparison of RIN and RIS4 Wrap up5
- 10. Sample to Insight Six parameters of prime relevance for sample Quality Control Quality Control of RNA samples 10 QC Criteria UV spectro. Gels Dye-based spectro. CE / µ- fluidic UV/VIS spectro. Protein contaminants (A260/280) Salts & other contaminants (A260/230) Quantity of dsDNA vs. other NA Yield () Degradation/ Size distribution () Sizing There is no one-for-all solution – 2 technologies cover all sample QC needs purity quantity integrity
- 11. Sample to Insight Main RNA quality parameters Quality Control of RNA samples 11 Purity Quantity Integrity What is the yield of my extraction? How much RNA is in the tube and at what concentration? Will I have enough material? When the concentration of NA is overestimated, the input amount will be too low. This leads to weak amplification and weak signal strength. When the concentration of DNA is underestimated the input amount will be too high and can lead to overuse of precious samples, increased concentration of inhibitors, false priming, unfavorable ratios, etc. What’s really in my tube? Could contamination or impurities interfere with my assay? Phenol, ethanol, salts, etc. can jeopardize sensitivity and efficiency of downstream enzymatic reactions. Large amounts of unwanted ssDNA or dsDNA template lead to overestimation of the molecule of interest and interferes with downstream applications. Does my RNA sample have the correct size distribution? Has RNA been degraded? RNA samples of poor integrity can affect PCR or sequencing reactions and lead to errors in replication and/or lower yields e.g. irrelevant CT values. Degraded samples can produce false negative results.
- 12. Sample to Insight Quality Control of RNA samples 12 • RNA QC requirements vary depending on downstream applications: RT-PCR, qRT-PCR, Microarray or RNA-Seq • Small deviations in key sample parameters can have potential impact on final results Implementation of QC procedures with relevant pass-criteria and ensuring RNA samples quality parameters are within acceptable range are essential to maximize success of experiments, thus saving time, money and peace of mind Highest quality results are only achieved from quality samples Only quality results are worth sharing
- 13. Sample to Insight Key quality indicators Quality Control of RNA samples 13 QC Criteria UV spectro. Gels Dye-based spectro. CE / µ- fluidic UV/VIS spectro. Protein contaminants (A260/280) Salts & other contaminants (A260/230) Quantity of dsDNA vs. other NA Yield () Degradation/ Size distribution () Sizing There is no one-for-all solution purity quantity integrity
- 14. Sample to Insight Key quality indicators Quality Control of RNA samples 14 QC Criteria UV spectro. Gels Dye-based spectro. QIAxcel Advanced QIAxpert Protein contaminants (A260/280) Salts & other contaminants (A260/230) Quantity of dsDNA vs. other NA Yield () Degradation/ Size distribution () Sizing purity quantity integrity There is no one-for-all solution – QIAGEN covers all your needs for sample Quality Control!
- 15. Sample to Insight Outline Quality Control of RNA samples 15 Why is RNA quality control so important1 Main parameters for RNA QC2 Fast and reliable quality control3 Comparison of RIN and RIS4 Wrap up5
- 16. Sample to Insight Quality Control of RNA samples 16 Sample collection/ stabilization RNA purification Amplification/ NGS Library Prep Analysis Data analysis & interpretation QCQC Quantity & Purity Integrity & size distribution QIAxpert QIAxcel Advanced
- 17. Sample to Insight QIAxpert uses innovative microfluidic sample carrier 17 Key features: 1 2 3 4 5 1 2 3 4 5 Sample loading well Capillaries Microcuvette Overflow Vent hole for vacuum Only 2 µl of sample required DNA range: 1.5 ng/μl up to 2000 ng/μl dsDNA (A260) Fast analysis, up to 16 samples in less than 2 minutes Algorithm can unmixe sample’s spectra to differentiate contribution of different molecules. QIAxpert is a high-speed spectrophotometer for DNA, RNA and protein analysis. It is fast, accurate and sensitive. Quality Control of RNA samples
- 18. Sample to Insight Quality Control of RNA samples 18 Classic spectrophotometry A230: impurities A280: proteins A260: nucleic acids
- 19. Sample to Insight Quality Control of RNA samples 19 1. Absorbance measurement (and background correction) 2. Content profiling of the measured spectrum by fitting of reference spectra into • Specific DNA or RNA spectrum • Impurities spectrum • Residue spectrum 3. Quality control • Impurities spectrum • Residue spectrum • Backround spectrum • A260/A280 ratio for protein contamination QIAxpert – Unique spectral content profiling protocols Quantity of DNA or RNA you think you have Quantity of DNA or RNA you actually have
- 20. Sample to Insight With QIAxpert – tell RNA from DNA and other contaminants without a dye 20 High-speed microfluidics • No drop & clean action required, no cross contamination • Evaporation-safe for 2h • Flexible input & up to 16 samples in <2 min Comprehensive Export • Reports can be viewed on any browser • USB and Network data output • No extra software or computer required Easy interaction • Inbuilt touchscreen • Simple GUI • Variety of pre-installed protocols Spectral content profiling • Differentiation between molecules • Quantify and subtract identified impurities • Give best insight into samples’ quality Quality Control of RNA samples
- 21. Sample to Insight Quantity & Purity ! QIAxpert – Unique spectral content profiling protocols 21 Classic spectrophotometry Quantity Spectral Content Profiling For internal use only • DNA or RNA spectrum • Impurities spectrum • Residue spectrum Quality Control of RNA samples
- 22. Sample to Insight Quality Control of RNA samples 22 Sample collection/ stabilization RNA purification Amplification/ NGS Library Prep Analysis Data analysis & interpretation QCQC Quantity & Purity Integrity & size distribution QIAxpert QIAxcel Advanced
- 23. Sample to Insight QIAxcel Advanced allows separation, detection and analysis of NA 23 QIAxcel Advanced Fully automated DNA and RNA analysis Fast processing: 12 samples in 8 – 13 min Up to 96 samples per run Up to twenty 96-well plates with QIAxcel HT High resolution down to 3 – 5 bp up to 500 bp Safety and convenience with ready-to-use gel cartridges Digital data output QIAxcel Advanced is an automated capillary electrophoresis system for DNA and RNA analysis. QIAxcel Advanced allows analysis of gDNA, RNA and NGS libraries Quality Control of RNA samples
- 24. Sample to Insight QIAxcel Advanced capillary electrophoresis principle 24Quality Control of RNA samples • Reusable 12-capillary cartridges • Fast processing: 12 samples in 8-13 min • Detection limit of 0.1 ng/µl • Sample consumption < 0.1µl
- 25. Sample to Insight How does it work? Place the gel cartridge into the instrument Load samples (tube strips or 96-well plates), buffers, and markers Select the Process Profile of your choice... and GO! Look at the results in real-time on the screen and report data just a few minutes later Operating the QIAxcel Advanced system: Quality Control of RNA samples 25
- 26. Sample to Insight Total RNA quality is assessed by analysing the migration pattern 26 28S 18S 5S 18S/28S ratio Smears Objectivity of the visual ratio estimation? Ratio is not always correlated to integrity! Quality Control of RNA samples
- 27. Sample to Insight RNA Quality Control - RNA Integrity Score (RIS) 27 A1 RIS: 9.5 A7 RIS: 5.8 A11 RIS: 3.6 Superimposed view Lane Name RIS A1 rat_liver _1 9.5 A7 rat_liver _4 5.8 A11 rat_liver _6 3.6 Quality Control of RNA samples RIS 10 1 RNA Integrity
- 28. Sample to Insight 28 The gel images of QIAxcel and Agilent 2100 are comparable QIAxcelBioanalyzer Quality Control of RNA samples
- 29. Sample to Insight RIS and RIN can objectively assess RNA integrity 29 RIS : RNA Integrity Score, by QIAGEN RIN : RNA Integrity Number, by Agilent • Indicators reflecting RNA integrity • Intended to predict the validity of downstream qPCR Frame of reference of RIS and RIN: • Values range from 1 (highly degraded) to 10 (mostly intact) • Analyse several different electropherogram’s parameters, including 28S and 18S peaks analysis • Values between 7 and 10 (depending of what is achievable with samples) are indicators of RNA quality suitable for downstream applications • Allow comparison of sample, standardization and repeatability of experiments Quality Control of RNA samples
- 30. Sample to Insight Outline Quality Control of RNA samples 30 Why is RNA quality control so important1 Main parameters for RNA QC2 Fast and reliable quality control3 Comparison of RIN and RIS4 Wrap up5
- 31. Sample to Insight RIS is more robust than RIN for assessing RNA integrity 31 • Depending on the degradation mechanism (heat, nuclease or UV), total RNA samples have different electrophoretic behavior • Different RNA degradation methods result in different ranges of ΔΔCt values for identical RIS/RIN values • “Overall, RIS was more robust than RIN for assessing RNA integrity” Unger, C et al., 2015 http://www.ncbi.nlm.nih.gov/pubmed/25998866 www.researchgate.net/publication/277080839_Ultraviolet_C_radiation_influences_the_robustness_of_RNA_integrity_measurement Quality Control of RNA samples
- 32. Sample to Insight Methods 32 RNA extraction RNA degradation x3 independent experiments RNA integrity assessment measured in duplicate SYBR Green qRT-PCR RT2 RNA QC PCR Array, selective degraded RNA samples applied in triplicate Goal of the study: compare RIS and RIN to the mRNA integrity observed by qRT-PCR in order to evaluate their respective ability to assess RNA integrity Jurkat & HeLa S3 cells RNase III 0-7 min, 37°C Heat 0-120 min, 70°C UV light 0-220 min, 254 nm Agilent 2100 Bioanalyzer RIN QIAxcel Advanced RIS Jurkat ΔΔCt of actb and hprt1 Quality Control of RNA samples
- 33. Sample to Insight 33 Figure : Decreasing RIS and RIN values were obtained by increasing time of heat degradation of total RNA. RIS and RIN were determined of duplicates from partially degraded RNA. In three independent experiments. The mean of RIS and RIN obtained for each cell type is presented over time of heat degradation (A). The correlation of RIS and RIN calculated with a regression analysis using Minitab 16 presented for degraded RNA from Jurkat (B) and HeLa S3 cells (C). RIN and RIS have a high correlation for heat-degraded RNA Quality Control of RNA samples
- 34. Sample to Insight 34 Figure : Increasing degradation level of total RNA by RNase digestion resulted in decreasing RIS and RIN. RIS and RIN were determined for duplicates of degraded RNA samples from three independent experiments; the mean was plotted as function of time of incubation with RNase (A). A linear regression analysis was performed with Minitab 16 to determine correlation of RIS and RIN for degraded RNA from Jurkat (B) and HeLa S3 cells (C). RIN and RIS have a high correlation for RNase-digested RNA Quality Control of RNA samples
- 35. Sample to Insight 35 RIS and RIN values are dissimilar after 100 minutes of UV exposure Figure: RNA exposure to UV-irradiation resulted in decreasing RIS and RIN over time with high variances in lower RIN values. RIS and RIN were determined of duplicates for each sample from three independent experiments. The mean values were plotted as function of exposure time (A), the correlation between RIS and RIN was analyzed by a linear regression analysis with Minitab 16 for Jurkat (B) and HeLa S3 cells (C). Presented are also screen shots of the 2100 Expert software showing the electropherogram from duplicates of Jurkat RNA degraded for 185 min, resulting in RIN 2.6 (D) and 4.7 (E). Quality Control of RNA samples
- 36. Sample to Insight RIN algorithm struggles defining 28S peaks of highly UV-damaged RNA 36 Electropherograms of these duplicates look similar but 28S peaks are defined differently (Jurkat RNA degraded for 185 min) resulting in RIN 2.6 (D) and 4.7 (E). Quality Control of RNA samples
- 37. Sample to Insight 37 RIS>RIN RIS>RIN RIS>RIN RNase digestion Heat degradation UV-light exposure Correlation ΔΔCt and RIS values R²= 89,7% R²= 89,6% R²= 97,4% Correlation ΔΔCt and RIN values R²= 89,6% R²= 87,6% R²= 93,1% Figure: the correlation of ΔΔCt-values with RIS and RIN values Calculated by a linear regression analysis with Minitab 16, to study whether RIS and RIN could predict the outcome of a qPCR. Fitted line plots with regression line and adjusted R2 values of RIS and RIN against ΔΔCt-values of ACTB Quality Control of RNA samples
- 38. Sample to Insight Heat 38 RNase UV light 1) RNase, heat and UV light have different degradation mechanisms on RNA 2) Different RNA degradation methods result in different ranges of ΔΔCt values at similar RNA integrity values (1-log difference) 3) RIS is more robust than RIN to determine RNA integrity of RNA degraded by different methods and to evaluate suitability of RNA sample for qRT-PCR High correlation between RIS and RIN values for heat- and RNase III-degraded RNA Lower correlation between RIN and RIS values for UV-degraded RNA, due to high variation in RIN values, but similar at a decision level of RIN/RIS 7 Quality Control of RNA samples
- 39. Sample to Insight Outline Quality Control of RNA samples 39 Why is RNA quality control so important1 Main parameters for RNA QC2 Fast and reliable quality control3 Comparison of RIN and RIS4 Wrap up5
- 40. Sample to Insight Sample Insight Sample collection/ stabilization DNA/RNA purification Amplification/ NGS Library Prep Analysis Data analysis & interpretation Sample QC must be part of molecular biology workflows, not optional steps on top. QC brings value to workflow and can save cost, time and peace of mind QC QC Quality Control of RNA samples
- 41. Sample to Insight Key quality indicators Quality Control of RNA samples 41 QC Criteria UV spectro. Gels Dye-based spectro CE / µ- fluidic UV/VIS spectro. Protein contaminants (A260/280) Salts & other contaminants (A260/230) Quantity of dsDNA vs. other NA Yield () Degradation/ Size distribution () Sizing There is no one-for-all solution purity quantity integrity
- 42. Sample to Insight Key quality indicators Quality Control of RNA samples 42 QC Criteria UV spectro. Gels Dye-based spectro. QIAxcel Advanced QIAxpert Protein contaminants (A260/280) Salts & other contaminants (A260/230) Quantity of dsDNA vs. other NA Yield () Degradation/ Size distribution () Sizing purity quantity integrity There is no one-for-all solution – QIAGEN covers all your needs for sample Quality Control!
- 43. Sample to Insight Size distribution analysis Quantity & Purity QIAGEN covers all your needs for sample Quality Control 43 Spectral Content Profiling For internal use only Integrity QIAxpert QIAxcel Advanced Quality Control of RNA samples
- 44. Sample to Insight Sample QC can save you time, money, and peace of mind Quality Control of RNA samples 44 Application Price per sample Time Downstream application qPCR 1.5 $US 4-5 hrs Sanger Sequencing 5-6 $US 4-6 hrs Next-Gen Sequencing >200 $US(5plex) 2-3 working days Affymetrix GeneChip Analysis 500-800 $US/sample > 2 working days Sample Quality Control QIAxpert <1$US 90 seconds (16 samples in parallel) QIAxcel Advanced <1$US 3-10 minutes (12 samples in parallel) Purity & quantity Integrity QC gives insights into sample suitability for downstream application • Brings confidence in data analysis and interpretation, troubleshooting • Saves time, money, and peace of mind by excluding samples of poor quality “Save time, money, and peace of mind only dealing with the samples you deserve: the ones of highest quality”
- 45. Sample to Insight How does QIAGEN helps you achieving reliable QC? Quality Control of RNA samples 45 Quantity & Purity Integrity & size distribution QIAxpert QIAxcel Advanced Let lab automation work for you and do the job they are excellent at: Delivering reliable and reproducible results Perform accurate measurements, keep track of your sample quality, standardize your experiments and increase reproducibility of your results. Increase your lab productivity: automation frees your hands and your mind!
- 46. Sample to Insight Quality Control of RNA samples 46 Quantity & Purity Integrity & size distribution QIAxpert QIAxcel Advanced More info on: QIAGEN.com Discover how these instruments can help you improve the reproducibility of your experiments and achieve higher quality results with our interactive online demo tools and request a free demo in your lab!
- 47. Sample to Insight 47 Questions? Thank you for your attention! Pierre-Henri Ferdinand Global Product Management Pierre-henri.ferdinand@qiagen.com Learn more about nucleic acids QC on: qiagen.com/QCSolutions Quality Control of RNA samples
- 48. Sample to Insight Quality Control of RNA samples 48 Back-up slides
- 49. Sample to Insight Additional Methods & Materials Title, Location, Date 49 Determination of RNA integrity QIAxcel Advanced QIAxcel RNA QC Kit v2.0 Method: CL-RNA Software: QIAxcel ScreenGel Output: RNA Integrity Score (RIS) Agilent 2100 Bioanalyzer Agilent RNA 6000 Nano Kit Method: Eukaryote Total RNA Nano Software: 2100 Expert Output: RNA Integrity Number (RIN) RNA extraction Material: Jurkat and HeLa S3 cells Homogenization: TissueRuptor RNA extraction: RNeasy Mini kit, including DNase I digestion, on the QIAcube Determination of RNA concentration: Nanodrop 1000
- 50. Sample to Insight Title, Location, Date 50 RNA degradation Three independent experiments for each degradation method, comparing RNA of Jurkat and HeLa S3 cells. 1. RNA digestion with ShortCut® RNase III (NEB) 90 µl reaction volume with: 9 µg RNA 0.1x Reaction buffer 0.2 units RNase III 0.1x MnCl2 Incubation at 37 °C for 0.5 / 1.5 / 3 / 5 and 7 min, stop reaction with 5 mM EDTA Remove enzyme with RNeasy MinElute Cleanup kit 2. RNA degradation by heat (established by QIAGEN) 50 µl of 300 ng/µl RNA in RNase-free water Incubation in a heat block at 75 °C for 0 / 20 / 40 / 60 / 80 / 100 and 120 min 3. RNA degradation by UV-irradiation (established by IBBL) 50 µl of 320 ng/µl RNA in RNase-free water Exposure to UV-irradiation (254 nm) 10 time spans between 0 and 220 min
- 51. Sample to Insight Correlation between RIN & RIS and ΔΔCt values were comparable Title, Location, Date 51 RIN RIS ΔΔCtofactbΔΔCtofhprt1 Figure: For RNA degraded by RNase, the correlations between ΔΔCt-values and RIS or RIN were comparable, ΔΔCt values were between 0 and 2.5. Selected samples of RNA from Jurkat cells degraded by ShortCut® RNase III were analyzed by qPCR. A linear regression analysis was performed using Minitab 16 to study the correlation between RIN (left) or RIS (right) and ΔΔCt-values of the housekeeping genes actb (top) or hprt1 (bottom).
- 52. Sample to Insight ΔΔCt values showed slightly higher correlation with RIS than RIN Title, Location, Date 52 RIN RIS ΔΔCtofactbΔΔCtofhprt1 Figure: For heat degraded RNA, the correlation between ΔΔCt-values and RIS was slightly higher compared to RIN, ΔΔCt values were between 0 and 1.6. Selected samples of heat degraded RNA from Jurkat cells were analyzed by qPCR and a linear regression analysis was performed using Minitab 16 to study the correlation between ΔΔCt-values of the housekeeping genes actb (top) or hprt1 (bottom) and RIN (left) or RIS (right).
- 53. Sample to Insight ΔΔCt values showed higher correlation with RIS than with RIN Title, Location, Date 53 RIN RIS ΔΔCtofactbΔΔCtofhprt1 Figure: ΔΔCt values showed higher correlation with RIS than with RIN, ΔΔCt values were between 0 and 16. qRT-PCRs were run with selected samples of UV degraded RNA from Jurkat cells and a linear regression analysis was performed using Minitab 16 to study the correlation between ΔΔCt-values of ACTB (top) or HPRT1 (bottom) and RIN (left) or RIS (right).
- 54. Sample to Insight Total RNA and cRNA analysis QIAxcel Advanced – Pure Excellence Typical workflow within microarray analysis Quality control of unlabelled cRNA generated From yeast total RNA A gel view, B electropherogram view Quality control of total RNA purified from yeast A gel view, B electropherogram view Data kindly provided by D. van Leenen, Microarray Facility UMC Utrecht, NL
- 55. Sample to Insight QIAxpert – excellent measurement accuracy 55 QIAxpert: lowest %CV value Qubit: high mean variation 250 ng/µl reference RNA (Agilent Technologies) Accuracy qPCR Human Reference Total RNA (Agilent Technologies) was diluted to 250 ng/µL (dilution from original solution in H2O). A total of 40 replicates were measured, each on the QIAxpert (RNA260 app), on a Nanodrop 8000, and the Qubit system.
- 56. Sample to Insight QIAxpert – excellent linearity in RNA quantification 56 QIAxpert Nanodrop Qubit Excellent linearity Systematic overquantification Systematic underquantification Comparison of RNA linearity using different systems Linearity Human Reference RNA (Agilent) was diluted to 1000 ng/µl, 500 ng/µl, 100 ng/µl, 50 ng/µl, 10 ng/µl, 5 ng/µl, and 1.5 ng/µl. A total of 5 replicates of each dilution were measured using the QIAxpert system, a Nanodrop 8000, and the Qubit. Data shown for the QIAxpert reflects total NA measured with the RNeasy app.