RNA sequencing (RNA-seq) is a technique for investigating gene expression. It quantifies RNA molecules to provide insights into cellular processes and disease mechanisms. A key consideration for RNA-seq is the amount of RNA starting material. The quantity of RNA influences the quality and reliability of sequencing data.
The Importance of RNA Input Quantity
The amount of RNA introduced into an RNA-seq workflow impacts library preparation, the process of converting RNA into DNA fragments for sequencing. Sufficient RNA ensures optimal enzymatic reactions during library construction, leading to a robust and diverse library. Low RNA input can result in inefficient library generation, leading to biased transcriptome representation. Adequate input also helps achieve desired sequencing depth, generating enough reads to detect both abundant and low-expressed transcripts. The accuracy of gene expression results depends on starting with appropriate, high-quality RNA.
General RNA Input Guidelines
The specific quantity of RNA required for RNA-seq varies based on the type of sequencing and the particular library preparation protocol used. For bulk total RNA sequencing, where all RNA species are analyzed, input typically ranges from 100 nanograms (ng) to 1 microgram (µg).
For bulk messenger RNA (mRNA) sequencing, which enriches for polyadenylated RNA, approximately 1 µg of total RNA is usually needed, or as little as 50 ng if purified. Small RNA sequencing, targeting microRNAs and other short RNA molecules, generally requires around 1 µg of total RNA for selection, though some specialized kits can work with purified small RNA in the range of 10 to 50 ng. Single-cell RNA sequencing (scRNA-seq) typically requires picogram quantities of RNA. These figures serve as general guidelines; consult the specific recommendations provided by the library preparation kit manufacturer.
Factors Influencing RNA Requirements
Several variables can modify the optimal RNA input for RNA-seq, making it a dynamic rather than fixed requirement. The biological source of the RNA, or sample type, affects RNA quantity and quality. Fresh tissue or cell cultures often yield higher quality and quantity RNA than challenging samples like formalin-fixed paraffin-embedded (FFPE) tissues, which contain fragmented RNA. RNA integrity, assessed by metrics like the RNA Integrity Number (RIN), is another determinant; degraded RNA needs higher input to compensate for fragmentation and ensure adequate transcript representation.
The chosen library preparation method also plays a role, with different protocols having distinct starting material requirements. Protocols involving ribosomal RNA depletion, suitable for degraded samples, might have different input needs than those relying on poly(A) selection for mRNA enrichment. Some advanced low-input kits are designed to work with very small amounts, sometimes as little as 500 picograms of total RNA. Desired sequencing depth, or the number of reads per sample, impacts the initial RNA requirement; detecting low-abundance transcripts needs a more complex library from sufficient input RNA to achieve higher read counts. Research goals, such as identifying novel genes versus quantifying differential expression, also influence the required RNA input.
Implications of Suboptimal RNA Input
Insufficient quantity or poor quality RNA in RNA-seq can lead to negative outcomes. Insufficient RNA can result in low library yields or complete failure of library construction, preventing sequencing. Libraries prepared with limited input may exhibit low complexity, not accurately representing the full diversity of transcripts in the original sample. This can lead to biased data, where certain transcripts are over- or underrepresented, making accurate conclusions about gene expression difficult.
Data from low-input or degraded samples often contain increased technical noise, reducing reproducibility across replicates. This increased noise can obscure genuine biological signals and make identifying true changes in gene expression challenging. A reduced number of genes might be detected, particularly those expressed at low levels, limiting study comprehensiveness. Using suboptimal RNA input can lead to wasted resources, including expensive reagents, time, and costly sequencing runs, without yielding reliable data.
Assessing RNA Quantity and Quality
Accurate assessment of RNA quantity and quality is a preliminary step before RNA-seq. For quantifying RNA, fluorometric methods like Qubit are recommended due to their specificity and sensitivity, providing accurate measurements even with contaminants. Spectrophotometry, often using a NanoDrop, is another common method, but it measures all nucleic acids and substances absorbing at 260 nm, potentially overestimating RNA concentration and being less sensitive for dilute samples.
Evaluating RNA quality, or integrity, is important to ensure the RNA is not degraded. Gel electrophoresis offers a visual check of ribosomal RNA bands, though it provides a subjective assessment. Capillary electrophoresis systems (e.g., Agilent Bioanalyzer, TapeStation, Fragment Analyzer) provide a more objective and quantitative measure of RNA integrity.
These instruments generate an RNA Integrity Number (RIN) or similar metric (RQN, RINe) on a scale of 1 to 10; higher numbers indicate more intact RNA. A RIN score of 8 or higher is generally ideal for most RNA-seq applications, while lower scores may be acceptable for specific protocols like total RNA sequencing with ribosomal RNA depletion. A combination of these methods provides a comprehensive understanding of RNA suitability for downstream sequencing.