Understanding gene expression at a detailed level is crucial for many applications, especially in drug discovery, target validation, and disease research. Two technologies that have transformed this space are RNA-sequencing (RNA-seq) (sometimes also referred to as bulk RNA-sequencing) and single-cell RNA-sequencing (scRNA-seq). While both allow researchers to study the transcriptome - the complete set of RNA transcripts - these methods provide very different views of gene expression.
In this post, we’ll break down the basics of RNA-seq and scRNA-seq, highlight the key differences, and explain how these technologies can be leveraged by for target discovery. Additionally, we’ll touch on the concept of pseudobulking in scRNA-seq and why it is useful for certain types of analysis.
RNA-seq
What is RNA-seq?
RNA-seq is a powerful technique that allows you to measure the gene expression of an entire population of cells. It works by converting the RNA in a sample into complementary DNA (cDNA), which is then sequenced to provide a snapshot of all the RNA molecules present at that moment in time.
In practical terms, RNA-seq provides a bulk measure of gene expression from a tissue or cell population. This means that when you analyze a sample, you're getting an average signal from all the cells within it. For instance, if you're studying a tumor biopsy, RNA-seq will tell you which genes are expressed across all the cancer cells (and any other cells in the tissue), but you won’t be able to distinguish between different cell types or their individual contributions.
Key features of RNA-seq:
- Average view of gene expression across all cells.
- Ideal for studying bulk tissues or cell populations.
- Highly accurate and quantitative for measuring overall gene expression.
scRNA-seq
What is scRNA-seq?
Single-cell RNA-seq (scRNA-seq) takes this one step further by allowing you to examine gene expression at the single-cell level. Rather than measuring an average expression profile from a mixed population, scRNA-seq enables you to profile the transcriptomes of individual cells within a sample. This is particularly useful in complex tissues, such as tumors, the immune system, or the brain, where the cellular composition is heterogeneous.
In scRNA-seq, each cell is isolated and its RNA is sequenced independently, giving you a rich, high-resolution dataset that reflects the unique gene expression profile of each individual cell. This can reveal rare cell types, identify cellular states, and uncover subtle changes in gene expression that bulk RNA-seq could miss.
Key features of scRNA-seq:
- High-resolution view of gene expression at the single-cell level.
- Enables the discovery of rare cell types and cellular heterogeneity.
- Allows for the study of cellular states, transitions, and developmental processes.
The smoothie vs. fruit salad analogy
To make sense of the difference between RNA-seq and scRNA-seq, let's use a simple analogy:
- RNA-seq is like making a smoothie. You start with a variety of fruits, blend them together, and end up with a single, uniform mixture. This smoothie represents the overall composition of your sample (the tissue or cell population) - you can taste the flavor of each fruit, but you can’t tell how much of each fruit was in the original mix.
- scRNA-seq, on the other hand, is like making a fruit salad. Instead of blending everything together, you keep each fruit piece separate, preserving the individuality of each component. In this case, you can examine each piece of fruit individually and understand exactly how much of each fruit is in the mix. This is how scRNA-seq works: it allows you to look at each individual cell’s gene expression, preserving the diversity of cell types and states within the sample.
Strengths of RNA-seq and scRNA-seq
RNA-seq strengths:
- Speed and scalability: RNA-seq is well-established and can be applied to large, bulk samples, making it ideal for studies where cell-level resolution isn’t required.
- Quantitative accuracy: Since the signal comes from a pooled population of cells, RNA-seq provides highly reliable quantitative information on overall gene expression.
- Ideal for bulk tissues: If your goal is to study the overall gene expression profile of a tissue (such as tumor, blood, or organ), RNA-seq is efficient and effective.
scRNA-seq strengths:
- Uncovering cellular diversity: scRNA-seq excels in dissecting the complexity of tissues and identifying rare or novel cell types, making it invaluable for studying heterogeneous samples like tumors or immune cell populations.
- Exploring cellular states: scRNA-seq provides a dynamic view of gene expression, capturing transitions between different cellular states or disease progression.
- Tissue deconstruction: In complex biological systems, scRNA-seq can help you dissect the contributions of individual cell types to disease, development, or response to treatment.
Application in Biotech and Pharma: Target Discovery
Both RNA-seq and scRNA-seq have powerful applications in target discovery:
- RNA-seq: Ideal for generating a broad gene expression profile of tissues or disease states. By comparing normal and disease tissues, or before and after drug treatment, RNA-seq can help identify differentially expressed genes that could be novel drug targets. It’s also useful for biomarker discovery, where you want to identify genes whose expression correlates with disease progression or therapeutic response.
- scRNA-seq: When studying diseases that involve a mix of different cell types (e.g., cancer, autoimmune disorders), scRNA-seq helps identify specific cell populations that are driving the disease. This is critical for discovering cell-specific targets or immune cell subsets that may respond to targeted therapies or immunotherapies. By profiling single cells, you can also track gene expression changes over time, offering insights into disease mechanisms or drug resistance at a more granular level.
Pseudobulking: What is it and why is it done?
One common question in scRNA-seq is about pseudobulking. Since scRNA-seq generates a lot of data on individual cells, it can sometimes be difficult to analyze every single cell in a sample, especially when dealing with large datasets or when cells are very rare.
Pseudobulking is a technique where you aggregate gene expression data across a set of individual cells to create a "pseudo-bulk" sample. For example, you might pool cells from a particular condition or cell type, and calculate the average gene expression across these cells, much like in traditional bulk RNA-seq. This approach can make data analysis more manageable and enable comparisons across conditions, such as comparing gene expression profiles in treated vs. untreated cells.
Why do this? Pseudobulking can help bridge the gap between bulk and single-cell analysis by allowing researchers to use standard bulk RNA-seq tools while still preserving some level of single-cell resolution. It’s especially useful in studies where there are not enough cells from each condition to perform full single-cell analysis but you still want to preserve the heterogeneity of individual cell contributions.
Conclusion
RNA-seq and scRNA-seq are both powerful technologies, each with its strengths and applications. RNA-seq gives you a broad, population-level overview of gene expression, making it great for studying bulk tissues and identifying potential biomarkers. scRNA-seq, on the other hand, provides a much higher resolution view, allowing you to uncover the complexity of tissues at the single-cell level, which is critical for understanding disease mechanisms and discovering new therapeutic targets.
By leveraging both RNA-seq and scRNA-seq, researchers can gain a deeper understanding of gene expression, uncover novel disease targets, and improve drug development strategies. And with the technique of pseudobulking, researchers can even combine the best of both worlds - smoothing out the complexity of single-cell data while retaining some of its richness for downstream analysis.
As these technologies continue to evolve, the potential for novel discoveries in biomedicine is boundless, offering exciting possibilities for the future of drug development and personalized medicine.
Are you working with RNA-seq or scRNA-seq data and wondering where to begin? Contact us at Pluto, and let us help you discover meaningful insights faster - with no coding or bioinformatics expertise required!