How scRNA-seq Became the New Hot Topic?

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What's the difference between a fruit salad and a fruit smoothie? You would probably taste some of the fruits in a smoothie, but it would be impossible to reconstruct the recipe just by drinking it. A fruit salad gives you way more detail on what ingredients were used. It tells you what exactly is happening in your dish.

Now, you might wonder why you’re reading about fruits and smoothies in a blog post about sequencing, but this is the most common analogy used to explain the difference between bulk RNA sequencing (bulk RNA-seq) and single-cell RNA sequencing (scRNA-seq).

Bulk RNA-seq vs. scRNA-seq

With the conventional bulk RNA-seq, you analyze cells together and get an average gene expression across the population of cells in a sample, just like a fruit smoothie. The average characteristics of cells can provide answers to some questions, such as the identification of biomarkers in cancer [1] or comparing the average changes in gene expression between patients with and without cancer.

However, this is not always the case, and the answers can be hidden in differences between cell types that are masked by the average representation. For example, if a sub-population of cells expresses a gene at very high level whilst another sub-population of cells expresses the same gene at a very low level, these differences cannot be detected using bulk sequencing.

Despite their similarities, cells can show unique characteristics when examined closely. A cell’s ability to function within the body depends on the right genes being switched on at the right time. Gene expression also varies according to the cell type and developmental stage. Looking at these details is impossible with bulk RNA-seq. However, scRNA-seq made it achievable!

scRNA-seq overview

scRNA-seq allows you to look at the gene expression of each individual cell in a sample, and see the differences in gene transcription in small groups of cells or thousands of individual cells that would have otherwise gone unnoticed (Figure 1). It enables a greater understanding of the different cell populations in your sample at a single cell level. The function of a cell or a group of cells can also be implied from scRNA-seq data. 

Learn more about the applications of single-cell RNA-seq at https://www.biomage.net/blog/top-5-most-revolutionary-uses-of-single-cell-rna-sequencing

Figure 1. What is scRNAseq? The figure shows two common visualization methods of single-cell data - a heatmap of single-cell feature expression on the right, and a Uniform Manifold Approximation and Projection (UMAP) cluster plot.

So, how does scRNA-seq work?

In short, the result of reading or transcribing a gene under a specific environmental stimulus is reflected in the messenger RNA (mRNA), which scientists can then measure. Detecting the mRNA of a gene indicates the gene is turned on and therefore has the potential to be translated and expressed. So, by studying the RNA transcriptome we can find out what proteins a cell makes and what it’s doing at a single cell level. 

At a practical level, single-cell sequencing requires four key steps. Single cells are captured from a population and the mRNA is extracted through cell lysis. RNA from each isolated cell is transcribed to complementary DNA (cDNA) using reverse transcriptases. Then amplification is carried out in a next-generation sequencing machine, and molecules get tagged with specific identifiers or barcodes, which allows the sequencing data to be mapped back to each individual cell [2]. 

Increasing popularity

Single-cell sequencing has been one of the most successful academic and clinical research approaches in recent years. The number of publications is growing at an exponential rate (Figure 2):

Figure 2. The number of publications utilizing scRNA-seq has increased tremendously from 2009 to 2021. Source: Pubmed, March 2022

As a result of the ability to study each individual cell, scRNA-seq is at the forefront of research resulting in rapid technological advancements. Since the establishment of scRNA-seq in 2009 on mouse primordial germ cells [3], we have seen the appearance of new single-cell applications and technologies [4, 5]. Novel technologies are used in omics to provide researchers with more information from a single cell, for instance, by increasing the sequencing depth.

Also, combining various omics techniques has allowed for the simultaneous collection of different data about a cell. Combining single-cell transcriptome and genome data, for example, can lead to a better understanding of how genomic changes affect gene transcription of target genes. So it’s now possible to study single-cell genomic, epigenomic, and transcriptomic environments at once. 

Commercial scRNA-seq platforms are also increasingly available. Researchers have developed microfluidic devices to carry out scRNA-seq protocols with smaller volumes. The whole reaction can take place in volumes as small as a pico- or nanoliter, thereby reducing the number of reagents required and lowering the costs. Specific protocols and technologies are available to optimize for cells of different sizes, from 5µm immune cells to 100µm neurons. The sequencing machines themselves are cheaper, faster, and more sensitive.

Although scRNA-seq technology has improved on the wet-lab side, the associated data analysis and bioinformatics still present a major challenge. In other words, single-cell data analysis is currently the bottleneck of scRNA-seq research. As a result, there is an increasing need for bioinformatics solutions. At Biomage, we host a community instance of Cellenics®, an open-source, cloud-based analytics tool for scRNA-seq data. It’s fast and user-friendly, enabling biologists to analyze single-cell data without the need to develop bioinformatics skills or have prior coding knowledge.

Start using the Biomage-hosted community instance of Cellenics® here: https://www.biomage.net/get-started

Summary

To wrap up, with single-cell technologies at the peak of their popularity, the field continues to rapidly evolve. Currently, scientists using scRNA-seq in their research are struggling with data analysis. So, biological insight continues to be locked away in sequencing data that is poorly mined. At Biomage, we continue to contribute new features to the cloud-based scRNA-seq software Cellenics® and make the Biomage-hosted community instance of Cellenics® available for free in order to address this bottleneck in the scientific community. We welcome the challenge of data sets growing in size and complexity. And we are excited about the many thrilling opportunities in the future of single-cell research. 

References

[1] Silva-Fisher, J.M., Dang, H.X., White, N.M. et al. Long non-coding RNA RAMS11 promotes metastatic colorectal cancer progression. Nat Commun 11, 2156 (2020). https://doi.org/10.1038/s41467-020-15547-8

[2] Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017). A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome medicine, 9(1), 75. https://doi.org/10.1186/s13073-017-0467-4

[3] Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., et al. (2009). mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382. doi: 10.1038/nmeth.1315

Read more about recent advances:

[4] Lu Wen, Fuchou Tang, Recent advances in single-cell sequencing technologies, Precision Clinical Medicine, Volume 5, Issue 1, March 2022, pbac002, https://doi.org/10.1093/pcmedi/pbac002

[5] Kashima, Y., Sakamoto, Y., Kaneko, K. et al. Single-cell sequencing techniques from individual to multiomics analyses. Exp Mol Med 52, 1419–1427 (2020). https://doi.org/10.1038/s12276-020-00499-2