RNA sequencing (RNA-Seq) measures the full spectrum of gene expression in a biological sample. It converts unstable RNA molecules into stable complementary DNA (cDNA) fragments, which are then sequenced to provide a quantitative snapshot of the transcriptome. The success of this process depends heavily on the quality and quantity of the starting RNA material. Insufficient or degraded input leads to unreliable data, increased technical bias, and difficulty detecting lower abundance transcripts. Preparing for a successful RNA-Seq experiment requires a precise understanding of the minimum material requirements, which are dictated by the chosen laboratory protocol.
Establishing the Baseline RNA Input Requirement
The standard input requirement for a typical, bulk RNA-Seq experiment falls within a relatively narrow range, establishing a benchmark for most high-throughput sequencing facilities. For samples that yield high-quality total RNA, the common requirement for library preparation kits is often between 100 nanograms (ng) and 1 microgram (\(mu\)g) of material. This range is generally considered the gold standard for reliably generating a comprehensive transcriptome profile.
Many commercial library preparation kits, such as those used on Illumina sequencing platforms, specify a minimum of 100 ng of total RNA to ensure sufficient molecular complexity for library construction. Using less than the recommended amount risks “jackpotting,” where a small number of RNA molecules are over-amplified, leading to a loss of complexity and biased quantification. Conversely, using too much RNA can lead to inefficient adapter ligation during library construction, which compromises the final data quality.
The exact input quantity is primarily determined by the specific chemistry and manufacturer’s instructions of the chosen library preparation kit. Researchers must adhere to these precise guidelines. While 100 ng is a common starting point, many researchers prefer to start with 500 ng to 1 \(mu\)g of total RNA. This provides a buffer for potential material loss during the multiple enzymatic and clean-up steps of the protocol.
Factors Dictating RNA Input Variability
The baseline input requirement is not a fixed universal number, but fluctuates based on several factors, particularly the type of library preparation strategy employed. The two primary methods for enriching target RNA molecules are Poly-A selection and ribosomal RNA (rRNA) depletion. Poly-A selection targets the polyadenylated tails present on most mature messenger RNA (mRNA), effectively isolating the protein-coding fraction of the transcriptome.
Poly-A selection is generally more efficient and requires a lower initial RNA input, often performing well with high-quality samples in the 10 ng to 100 ng range. In contrast, rRNA depletion chemically removes the highly abundant ribosomal RNA, which can account for up to 90% of total RNA, leaving behind all other RNA species, including non-coding RNAs and pre-mRNAs. Because it targets a broader range of molecules, the rRNA depletion method typically requires a higher input of total RNA, often 500 ng to 1 \(mu\)g, to ensure enough material remains for sequencing.
The source and nature of the sample also influence the required quantity. Samples from robust sources like cell lines tend to have high-quality, intact RNA, making them suitable for lower input Poly-A selection. Challenging samples, such as archived formalin-fixed, paraffin-embedded (FFPE) tissue or clinical biopsies, often contain highly degraded and fragmented RNA. For these compromised samples, rRNA depletion is the only viable option and may require input exceeding 1 \(mu\)g to compensate for the poor starting material quality.
Strategies for Low-Input and Challenging Samples
When a sample cannot meet the standard 100 ng minimum, specialized ultra-low input methodologies are employed. These techniques are designed to work with extremely limited amounts of material. One such approach is Whole Transcriptome Amplification (WTA), which uses enzymatic reactions to uniformly increase the amount of cDNA synthesized from the limited RNA input.
WTA-based kits can successfully generate a sequencing library from total RNA inputs as low as 100 picograms (pg). This amplification step is performed before the final library preparation, providing sufficient material for subsequent sequencing steps while attempting to maintain the relative abundance of transcripts. These methods are valuable for rare cell populations or precious clinical samples where only a minute amount of tissue is available.
The most extreme example of low-input sequencing is single-cell RNA sequencing (scRNA-Seq), which captures the transcriptome from individual cells. Input is measured by the number of cells, not nanograms of RNA. Specialized scRNA-Seq platforms isolate a single cell and construct a library from the minute amount of RNA it contains, often 10 to 20 picograms per cell. These methods integrate cell isolation and library barcoding into a microfluidic system, allowing researchers to profile thousands of individual cells simultaneously.
Assessing RNA Purity and Integrity
While quantity establishes the minimum starting point, the quality of the RNA is equally important for a successful sequencing run. RNA is susceptible to degradation by RNases, meaning the sample’s integrity must be rigorously assessed before library preparation. The standard metric for this assessment is the RNA Integrity Number (RIN), a value generated by an automated electrophoresis system.
The RIN is scored on a scale of 1 (severely degraded) to 10 (completely intact). For most standard bulk RNA-Seq protocols, a RIN score of 8 or higher is ideal, though a score greater than 7 is often acceptable. Using a sample with a low RIN score results in a poor-quality library because fragmented RNA leads to a bias toward the 3’ end of transcripts and fails to accurately represent longer genes.
RNA purity is also measured using spectrophotometric absorbance ratios. The A260/280 ratio determines protein and phenol contamination, with an ideal value of approximately 2.0. The A260/230 ratio checks for contamination by residual salts and organic solvents, ideally ranging from 2.0 to 2.2. Fluorometric methods, like those using a Qubit system, are preferred over spectrophotometry for final quantification because they specifically measure only the RNA molecules, providing a more accurate input concentration.