How circular RNAs and other confounding transcripts are distorting gene expression data and what researchers can do about it
Imagine you're a detective examining a crime scene, but every time you search for fingerprints, you find that someone has subtly altered the evidence. This is precisely the challenge facing scientists using gene expression profiling to understand cellular behavior. While this powerful technology allows researchers to measure the activity of thousands of genes simultaneously, creating a global picture of cellular function, what if some of our fundamental measurements were being distorted by invisible factors we've largely overlooked? 3
Circular RNAs and other confounding elements have become the "unidentified variables" in countless experiments, potentially leading researchers to draw incorrect biological conclusions.
At the heart of this issue lies a fascinating cast of molecular characters: circular RNAs, truncated transcripts, and other confounding elements that resemble traditional messenger RNAs but behave quite differently. These elements have become the "unidentified variables" in countless experiments, potentially leading researchers to draw incorrect biological conclusions. As we'll discover, the very methods we use to profile gene expression contain hidden biases that can dramatically alter our interpretation of what happens inside cells. 1
The implications extend far beyond basic research. Gene expression profiling now informs personalized cancer treatments, disease diagnosis, and drug development—areas where inaccurate readings can have real-world consequences for patients. Understanding these confounding factors isn't just academic; it's essential for advancing reliable biomedical science. 2
Before diving into the confounding factors, let's establish what gene expression profiling entails. In simple terms, it's the process of determining which genes are active in a cell at a given moment and to what degree. If you think of DNA as the entire cookbook a cell could potentially use, gene expression represents the specific recipes the cell is actually following right now. 3
Measure relative activity of previously identified target genes by detecting their RNA transcripts.
Provides information on sequences of genes in addition to expression level, allowing discovery of new genes.
Highly sensitive method for quantifying individual candidate genes, often used to validate results.
These technologies have revolutionized biology by allowing scientists to see the "big picture" of cellular activity. However, each method comes with its own assumptions and limitations that can be exploited by confounding transcripts.
The central challenge in accurate gene expression profiling stems from the fact that not all RNA transcripts are created equal. While we tend to focus on traditional messenger RNAs (mRNAs), cells contain a diverse ecosystem of RNA molecules with different properties and functions. 1
These include most canonical mRNAs that undergo traditional processing including the addition of a poly-A tail, which is often used for their selective capture in experiments.
This diverse group includes various non-coding RNAs and the recently discovered circular RNAs that lack poly-A tails.
The problem arises because standard mRNA sequencing methods specifically target poly(A)+ transcripts, potentially missing or misrepresenting important biological players. Meanwhile, total RNA-seq approaches capture both populations but present their own interpretive challenges. 1
| Transcript Type | Key Features | Impact on Expression Profiling |
|---|---|---|
| Circular RNAs (circRNAs) | Covalently closed circles from back-splicing; lack poly-A tails; can be 1000x more abundant than linear mRNAs | Skews quantification of cognate genes; cell-type specific expression independent of linear mRNA |
| Truncated Transcripts | Early termination variants; alternative cleavage and polyadenylation within coding region | Present in ~20% of genes; exclusively in total RNA-seq; impacts transcriptome and proteome interpretation |
| Bimorphic RNAs | Exist in both polyadenylated and non-polyadenylated forms | Only poly(A)+ form detected in mRNA-seq; both forms in total RNA-seq |
| Histone Variants | Natural mRNAs lacking poly-A tails; use stem-loop structure instead | Abundant transcripts detected in total RNA-seq but absent from mRNA-seq |
CircRNAs are particularly problematic because they're not just rare oddities—some can be up to 1,000 times more abundant than their linear mRNA counterparts, and they're often cell-type specific and developmentally regulated independently of their parent genes. 1
Present in approximately 20% of genes, truncated transcripts can significantly impact both the transcriptome and proteome. Like circRNAs, they're indistinguishable from the mRNA sequence in standard analyses. 1
To understand how these confounding factors play out in real research, let's examine a crucial experiment that highlighted their impact. Researchers treated human umbilical vein endothelial cells (HUVECs) with transforming growth factor beta (TGFβ) over a 4-hour period, generating both mRNA-seq and total RNA-seq libraries from the same starting RNA. 1
HUVECs were treated with TGFβ over a time course (0, 60, 120, and 240 minutes) in biological duplicates to observe dynamic changes in gene expression.
From the same RNA samples, researchers prepared both:
The team then compared transcript abundances between the two methods from the same starting material. If both methods were comparable, all transcripts would distribute tightly along a parity line (where x=y). 1
A distinct population of RNAs was visible only within total RNA-seq libraries, completely absent from mRNA-seq data. This group predominantly consisted of poly(A)- RNAs—specifically long non-coding RNAs, early termination transcripts, and replication-dependent histone transcripts.
A pronounced broadening of the scatterplot for mRNA transcripts indicated significant discrepancies in abundance estimation between the two methods for many genes. 1
Most notably, the "mRNA-indistinguishable" transcripts—particularly circRNAs—were responsible for much of the observed discrepancy. Because circRNAs are excluded from mRNA-seq libraries (due to their lack of poly-A tails) but included in total RNA-seq libraries, they create an artificial impression of differential expression for their parent genes.
| Observation | Description | Biological Significance |
|---|---|---|
| Unique RNA Population | RNAs detected only in total RNA-seq, not mRNA-seq | Comprised of poly(A)- transcripts: lncRNAs, histone variants, early termination transcripts |
| Broad Scatter | Wider distribution of mRNA transcript abundances between methods | Suggests circRNAs and other confounding factors skew quantification in one method |
| circRNA Effects | "mRNA-indistinguishable" transcripts affecting quantification | Most problematic confounder; highly abundant and regulated independently of linear mRNAs |
"The 'mRNA-indistinguishable' transcripts—particularly circRNAs—were responsible for much of the observed discrepancy between mRNA-seq and total RNA-seq results." 1
Thankfully, researchers aren't helpless against these confounding factors. Several methodological and computational approaches can help increase the accuracy of gene expression quantification.
Newer library prep methods like the QuantSeq 3' mRNA-seq approach focus on the 3' end of transcripts, providing a more targeted profile that's less susceptible to circRNA contamination while maintaining strand specificity (>99.9%). These methods also enable unique molecular identifiers (UMIs) to correct for PCR amplification bias. 4
When designing experiments, researchers should consider whether their biological question requires detection of non-polyadenylated transcripts, use both poly(A)+ and total RNA approaches in pilot studies, and employ strand-specific protocols that can help distinguish overlapping transcripts.
Advanced statistical methods are increasingly important for untangling these effects. Gene set analysis approaches like Gene Set Enrichment Analysis (GSEA) and Generally Applicable Gene-set Enrichment (GAGE) test the significance of pre-defined gene groups rather than focusing solely on individual genes, potentially making them more robust to confounding by individual transcripts. 3
For time-course experiments, methods like maSigPro help identify significantly differential expression profiles across multiple time points and experimental groups, using a two-regression-step approach to study differences between groups and find statistically significant different profiles.
| Solution Type | Specific Approach | Function/Benefit |
|---|---|---|
| Library Prep Kits | QuantSeq 3' mRNA-seq | Targets 3' end; reduces circRNA interference; maintains strand specificity |
| Unique Identifiers | UMIs (Unique Molecular Identifiers) | Tags individual transcripts; identifies PCR duplicates; eliminates amplification bias |
| Experimental Design | Combined mRNA-seq + total RNA-seq | Reveals presence of confounding transcripts; assesses quantification discrepancies |
| Computational Tools | Gene Set Enrichment Analysis (GSEA) | Tests significance of pre-defined gene groups; more robust to individual transcript confounding |
| Statistical Methods | maSigPro for time-course data | Identifies differential expression across time points and experimental groups |
As gene expression profiling becomes increasingly integrated into clinical decision-making—from cancer subtyping to drug response prediction—addressing these confounding factors becomes ever more critical. 2 The field is moving toward approaches that offer greater precision and context.
New techniques like single-cell RNA sequencing are revealing astonishing heterogeneity in gene expression patterns, potentially including cell-specific differences in circRNA production and regulation. 8
Rather than relative measures, absolute quantification approaches—as demonstrated in yeast studies—are providing more precise insights into actual molecule counts, helping to distinguish true biological changes from methodological artifacts. 9
Spatial transcriptomics methods now allow researchers to profile gene expression while maintaining the spatial organization of tissues, adding another layer of biological context that could help interpret the functional significance of confounding transcripts. 8
Combining transcriptomic data with proteomic and metabolic measurements—as in studies examining growth rate effects on gene expression—provides a more comprehensive view that can identify when mRNA changes do or don't translate to functional consequences. 9
The journey toward completely accurate gene expression profiling will likely involve:
As the field addresses these challenges, we move closer to realizing the full potential of gene expression profiling to decode the complexities of biology and disease.
The discovery of circular RNAs and other confounding factors in gene expression profiling hasn't undermined the technology's value, but has instead refined our understanding of transcriptional complexity.
Much like early astronomers who gradually realized that planets move in elliptical rather than circular orbits, biologists are developing a more nuanced understanding of genetic regulation—one that acknowledges the surprising circularity of some transcripts.
By recognizing and accounting for these confounding factors, researchers can avoid "going in circles" in their data interpretation and instead uncover deeper biological truths. The solutions—both technical and computational—are increasingly within reach, promising a future where gene expression profiling provides an ever-clearer window into the intricate workings of life at the molecular level.
As the field continues to evolve, one thing remains clear: in the quest to understand gene expression, what we don't know—and account for—can be as important as what we measure.