ERCC spike-ins are synthetic RNA molecules added to biological samples during RNA sequencing (RNA-seq) experiments. They serve as external controls, providing a standardized reference to evaluate and adjust for technical variations in the RNA-seq workflow. By including these known quantities, scientists gain a clearer understanding of gene expression levels, enhancing the reliability of genetic studies.
The Need for Controls in RNA Sequencing
RNA sequencing experiments, while powerful, inherently face various complexities and sources of variability that can obscure true biological signals. Differences in the initial amount of sample material, often due to variations in cell count or tissue size, can lead to different starting points for RNA extraction. The efficiency of RNA extraction itself can vary between samples, resulting in unequal yields of total RNA, which directly impacts downstream steps.
Library preparation, the process of converting RNA into a form suitable for sequencing, introduces further biases. Steps like reverse transcription, cDNA amplification, and adapter ligation can have varying efficiencies across samples. Even during the actual sequencing process, factors such as sequencing depth can differ significantly. These technical variations make it difficult to compare gene expression levels accurately, as observed differences might stem from experimental artifacts rather than genuine biological changes.
What Are ERCC Spike-Ins?
ERCC spike-ins are a standardized set of synthetic RNA molecules, developed by the External RNA Controls Consortium (ERCC), designed to address technical variability in gene expression studies. These molecules are not found naturally, ensuring their sequences do not interfere with or map to endogenous RNA. This allows them to be distinctly identified and quantified without confounding results.
The ERCC spike-in mixes, such as Mix 1 and Mix 2, contain 92 distinct RNA transcripts, each present at precisely known concentrations. These concentrations span a wide dynamic range, often up to a million-fold difference, allowing for the assessment of assay performance across various expression levels. The synthetic transcripts are engineered to possess diverse lengths (273 to 2,022 nucleotides) and varied GC content (5% to 51%). This broad range of characteristics helps mimic the natural diversity of messenger RNA (mRNA) molecules, providing a comprehensive control for technical biases.
How ERCC Spike-Ins Aid Data Analysis
ERCC spike-ins are integrated into the RNA sequencing workflow at the very beginning, typically by adding them to the RNA sample before any processing steps. This early introduction ensures that the spike-ins undergo the exact same experimental procedures as the endogenous RNA, including RNA extraction, library preparation, and sequencing. Their known input concentrations then serve as a reference during data analysis, allowing researchers to accurately account for technical variations introduced at each stage.
One primary application of ERCC spike-ins is in normalization. By comparing observed sequencing reads for each spike-in to its known input concentration, researchers can derive scaling factors. This adjusts for differences in sequencing depth and other technical biases between samples, enabling more accurate comparisons of gene expression levels.
ERCC spike-ins also contribute to quality control by providing insights into the efficiency and consistency of the experimental process. Researchers use spike-in data to assess the dynamic range of detection, lower limit of detection, and linearity of the assay. For example, plotting detected spike-in reads against known input concentrations generates a standard curve, revealing how well the platform quantifies RNA across different abundance levels. This helps identify potential issues like biases in capturing short or long transcripts, or inconsistencies in amplification efficiency. They can also help determine the actual number of RNA molecules in a sample, useful for applications like biomarker discovery.
Impact on Research Reliability
The application of ERCC spike-ins contributes to the robustness and reproducibility of RNA sequencing data, enhancing overall research reliability. By providing an independent, quantitative measure of technical variability, ERCCs allow researchers to distinguish between true biological changes in gene expression and those caused by experimental artifacts. This distinction is important for drawing accurate conclusions from gene expression studies and increasing confidence in research findings.
The consistent use of ERCC spike-ins facilitates better cross-study comparisons, even when experiments are conducted in different laboratories or with variations in protocols or equipment. The standardized nature of these controls offers a common benchmark, enabling researchers to objectively assess the performance of various gene expression platforms and compare results across diverse datasets. This capability helps overcome the challenges of reproducibility in high-throughput biological experiments, promoting greater transparency and verifiability of scientific discoveries. Ultimately, by improving the accuracy and comparability of RNA sequencing data, ERCC spike-ins accelerate the discovery of biologically meaningful gene expression changes.