Enhance your transcriptome analysis: tips for preparing optimal RNA
Get Started Designing Your Project
When planning an experiment, you first design your RNA-seq workflow and then choose an appropriate RNA extraction method for the workflow. To ensure the resulting data address the essential questions of your experiments, a thorough understanding of your needs and careful planning of your experiments are essential. Think about your objectives: What questions do you want to answer with your analysis? Your selection for the RNA-seq assay will depend on several parameters, most importantly the experimental objectives (Figure 1). For example, if you would like to analyze the whole transcriptome, you will need data for all the RNA in your sample. In contrast, to analyze protein-coding mRNA, you will need to perform 3’-mRNA sequencing. Also, if you work with small RNAs (miRNAs or siRNAs), you need to be sure to use an RNA extraction process that does not lose these small molecules.
Figure 1. Decision tree for selecting your RNA-seq assay1.
The number of samples in your experiment will depend on how many controls and replicates you decide to include. We advise the use of experimental controls for almost any sequencing experiment as they can indicate the degree of reproducibility or reveal experimental biases. To minimize batch effects and to control variance across samples, we suggest incorporating a predetermined amount of synthetic RNA as an internal reference2. Spike-in RNA controls can bioinformatically standardize the total number of sequenced reads between samples and offer a measure of sensitivity and specificity.
Choosing the appropriate number of biological replicates for an experiment involves balancing cost and precision. You might consider the following:
- If the biological sample has highly variable transcriptome expression, a high number of replicates is better. This improves the statistical power and reliability of differential expression analysis. The number of replicates needed may change based on the amount of biological variability associated with the samples of interest and should be empirically determined.
- The number of replicates will also depend on the kind of data analysis: more replicates for lower sequencing depth or fewer replicates for much higher sequencing depth.
- Sample size will also depend on the sample condition and, particularly, the RNA quality within the sample. When the sample is highly degraded, it is recommended to increase the number of replicates to reduce variability. Also consider that long-read sequencing is not appropriate for degraded RNA, e.g., from FFPE samples.
Reproducibility, accuracy, and assay costs increase with the number of replicates. In early RNA-seq experiments, technical replicates were commonly used. However, when coverage of at least 5 reads per nucleotide (5x coverage) is reached, it is known that biological variance is vastly more predominant than technical variation3.
Tips and Tricks to Optimize Your RNA Extraction
Anyone working with RNA soon realizes how susceptible it is to degradation by ubiquitous RNases. It always helps to get the basics right: Wear sterile disposable gloves when handling reagents and RNA samples. Be sure your bench is properly cleaned with RNase inhibitors and avoid speaking over opened tubes.
Know your sample requirements
There are specific challenges for RNA extraction that are associated with specific sample types.
Cells. When extracting RNA from cultured cells, it's crucial to start with a homogeneous cell population to minimize variability. Choose an appropriate lysis buffer to effectively disrupt cell membranes and release RNA while preserving its integrity. Homogenization methods such as pipetting or vortex mixing can help ensure uniform cell lysis without RNA degradation.
Tissues. RNA extraction from tissue samples requires careful handling to prevent RNA degradation and maintain sample integrity. Homogenize tissues quickly after collection with an effective mechanical disruption method such as bead-beating or tissue homogenizers. Liquid nitrogen can be used to freeze tissues before homogenization to preserve RNA integrity. As tissues contain endogenous RNases, you might use RNase inhibitors in buffers to prevent RNA degradation. TRIzol reagent, commonly used for tissue RNA extraction, efficiently solubilizes RNA while denaturing proteins and inactivating RNases. However, there is a risk of that TRIzol can be carried over in the extracted RNA and presents a hazard for the operator.
FFPE tissues. RNA extraction from FFPE tissues is challenging due to RNA degradation, cross-linking caused by formalin fixation, and contamination with genomic DNA (gDNA). As FFPE samples are commonly used to store valuable clinical and biobank samples, methods to work with these sample types are in high demand. When working with FFPE tissue, be sure to use an RNA extraction kit that is suitable for fragmented RNA and minimizes the carry-over of gDNA.
Blood. RNA extraction from blood samples requires special consideration due to the presence of RNases and other contaminants. Process blood samples promptly as delayed processing can lead to increased RNase activity and compromise the RNA quality. Each component of blood (red blood cells, buffy coat, plasma) has particular extraction requirements. Since blood cells often have a higher DNA content than RNA, gDNA carry-over is more common than for other eukaryotic cells. Furthermore, the high concentration of proteins and DNA can produce extremely viscous lysates; therefore, you should use smaller amounts of input for blood cells than for other cell types. Serum and plasma contain low concentrations of RNA, which can also be fragmented, coupled to proteins, or enclosed in extracellular vesicles, requiring a suitable kit for low RNA quantities and short fragments.
Plants. Extracting RNA from plant tissues presents unique challenges due to the presence of polysaccharides, polyphenols, and other secondary metabolites. Choose a kit specifically designed for plant RNA extraction, as they often include reagents optimized for removing contaminants while preserving RNA integrity. Minimize handling and processing time to prevent RNA degradation from plant samples.
When is DNase treatment necessary and when can it be excluded? Certain samples, such as FFPE tissues, that contain degraded RNA are prone to gDNA contamination. Also, we advise incubating blood samples with DNase, as blood cells contain more DNA than RNA. In addition, mechanical disruption during the lysis process can produce gDNA fragmentation and result in DNA carry-over. DNase treatment is not required for some RNA-seq projects, e.g., targeted sequencing experiments. Also, since the probes used for 3’-mRNA sequencing, are selected by poly(A) tail priming, the impact of DNA contamination is minimal in comparison with total RNA-seq4.
We hope we have convinced you that carefully thinking about your research question before starting the RNA extraction procedure will help you get the best results possible
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Determine the quality of your RNA
Before starting library preparation, you should assess RNA quantity and quality to ensure the success of your RNA sequencing. To estimate accurate RNA concentration, we recommend using fluorometry, such as with Qubit® or PicoGreen™. DNA and RNA both absorb at 260 nm, and DNA contamination can cause an overestimation with spectroscopic methods. To assess quality, certain forms of contamination can be detected using UV spectroscopy. For RNA, the expected ratios are 1.7 – 2.1 for A260/230 ratio and 2.0 – 2.2 for A260/280. RNA integrity can be measured using commercially available automated systems, such as the Agilent 2100 Bioanalyzer or TapeStation®. Many researchers aim for an RNA integrity number (RIN) value of 8 or above (depending on the sample type). DV200, another parameter to estimate RNA integrity, indicates the percentage of fragments approximately 200 nt in length. This evaluation is useful for samples with highly degraded RNA, such as FFPE tissues. Depending on the DV200 value, a range of sample inputs are recommended (Table 1). Another parameter for assessing RNA quality is the amplifiability in an RT-qPCR. This parameter is used as a technique for transcriptomic analysis and can also be used to qualify RNA for other downstream applications. Any deviation in Ct value may indicate the presence of PCR contaminants (such as DNA) or inhibitors that could affect other applications.
Table 1. Suggested RNA input according to the DV200 value5
Store your sample properly
Proper RNA sample storage is essential to prevent degradation: use RNase-free conditions, a buffer at pH 7, and appropriate temperatures (at –70°C to –80°C) to maintain RNA integrity. We advise preparing multiple RNA aliquots and reducing the freeze/thaw cycles to a minimum. For long-term storage, you can add additional chelators such as EDTA, reducing agents like DTT, or RNase inhibitors. Keep in mind that these additives should be included only after determining the concentration because they interfere with optical density measurements. If you want to review your RNA concentration later, you should include the additives in the blank buffer: so, do not forget to annotate the addition of such substances to your samples. Also, be sure to add a proper amount of such additives, too much can interfere with enzymes involved in the sequencing procedure.
Enhance your RNA for sequencing analysis
Obtaining high-quality RNA is essential for accurate transcriptomic analysis and sequencing studies. For all sample types, careful attention to sample handling, choice of extraction method, and optimization of extraction protocols are crucial for achieving reliable results. By following the tips and techniques outlined in this article, you can enhance the quality and integrity of RNA extracted from various sample types, ultimately improving the success of downstream molecular analyses.
Different RNA isolation methods can substantially affect relative transcript abundance. BioEcho offers a wide range of products for nucleic acid extraction, which can be adapted to different sample types. Our products help you ensure high-quality RNA samples for your sequencing applications, obtained from simple and shorter workflows, giving you more time to assess your library and sequencing data excellence.
References
Laura is a passionate scientific communicator, with an extensive experience as a researcher in several fields, including human genetics, biotechnology, and neuroscience. Since joining BioEcho in 2022, she enjoys creating appealing content and material for our customers and interested parties. Laura likes practicing yoga, cooking wholesome Mediterranean food, and playing the piano. She also loves spending weekends on the nature, rock climbing and hiking.