Single-Cell RNA Sequencing vs Single-Nucleus RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are both efficient techniques for studying heterogeneity in the transcriptome of individual cells. While similar in principle, each technique has its own strengths that make it ideal for different applications. This article reviews the various aspects of using scRNA-seq and snRNA-seq while exploring critical factors for choosing the right method for your research.

What are the differences between scRNA-seq and snRNA-seq?

The primary difference between these techniques lies in their approach to sample preparation. In scRNA-seq workflows, whole cells are isolated, which allows for the capture and analysis of RNA from the entire cell. Conversely, snRNA-seq requires only the removal of nuclei, focusing on the capture and analysis of RNA within the nucleus. Alberim Kurtishi, Ph.D., Field Application Scientist at Bio-Techne, explained that despite their differences, scRNA-seq and snRNA-seq are comparable in the number of genes recovered during sequencing. However, he identified several instances where each approach offers certain benefits.

Advantages and limitations

snRNA-seq

Kurtishi stated that snRNA-seq is an efficient method to profile thousands of transcriptomes at high throughput from a chosen tissue. This technique offers researchers the flexibility to work with either fresh, frozen, or fixed tissue samples. Additionally, snRNA-seq accommodates a broader range of tissue types for dissociation while still providing robust performance in these complex scenarios. Studies have demonstrated the effectiveness of snRNA-seq for analyzing complex cell types, including kidney cells,¹ heart cells,² and neurons,³ among others. Another notable benefit lies in the stress response of the cells. With snRNA-seq, there is a lower incidence of dissociation-induced transcriptional stress response when compared to scRNA-seq. This can significantly improve the interpretation and utility of the data obtained from these sequencing methods.

Kurtishi also emphasized the effectiveness of snRNA-seq in profiling rare cells and diverse cell types that are traditionally challenging to analyze. This distinction is particularly important in sensitive research areas, such as tumor studies. He pointed out that while scRNA-seq can present challenges when trying to dissociate rare tumor types causing problematic recovery, snRNA-seq has been shown to capture a diverse range of cell types from tumors.

One notable limitation of snRNA-seq is its inability to sequence cytoplasmic RNA, as it exclusively collects and analyzes nuclear transcripts. Prior research has indicated that this limitation can make snRNA-seq unsuitable for investigating certain transcripts due to their lower abundance in the nucleus.⁴

scRNA-seq

With many technologies and workflows built around it, scRNA-seq remains the most popular tool for studying cellular heterogeneity. This method has the ability to sequence both cytoplasmic and nuclear transcripts, giving researchers a more comprehensive view of the transcriptome. Moreover, scRNA-seq has a higher throughput than snRNA-seq and is a more scalable approach to sequencing single cells. Kurtishi also noted that scRNA-seq is particularly effective for freshly dissociated tissues or cells in suspension.

While many tissue types are suitable for scRNA-seq workflows, certain tissues or their specific conditions may either be incompatible or require extensive preparation to ensure compatibility. Another shortcoming of scRNA-seq is that the cell dissociation process can be quite cumbersome and difficult, often involving complex protocols with long incubation times. This dissociation process can also introduce biases due to cellular stress. Furthermore, scRNA-seq has been observed to favor certain cell types, like immune cells,⁵ potentially skewing results.

Further considerations

Although the dissociation process in scRNA-seq workflows is typically more intricate, breaking down tissue into single cells or nuclei can present technical challenges in both scRNA-seq and snRNA-seq techniques. This process can be time-consuming and involves careful preparation of numerous reagents. Kurtishi advised that researchers manage the time that cells or nuclei spend on ice before proceeding with the single-cell library preparation, as prolonged exposure can negatively affect the quality of the results. He also recommended having sufficient tissue on hand, in case a second round of dissociation is required to obtain the desired sample quality and quantity.

When comparing the data quality and resolution between scRNA-seq and snRNA-seq, Kurtishi shared that both methods yield very similar results in terms of the number of cells recovered and the genes screened. However, a key difference emerges when these techniques are used on the same sample. They tend to capture different types of cells. This has been highlighted in various studies where the same tissue was analyzed using both methods, and each recovered a larger subset of different cells.^5,6

Recent developments

Both methods have seen significant advancements in recent years, which have greatly expanded their applications. Notably, Kurtishi explained that snRNA-seq has improved upon various methods that were challenging or less effective with scRNA-seq, offering more efficient processing of samples. In particular, he noted that neuronal studies have been improved due to the successful dissociation methods for post-mortem brain samples.

Another critical application that Kurtishi highlighted was a 2020 study where German scientists successfully used snRNA-seq to sequence an entire mammalian heart.² This task would have been difficult to perform using scRNA-seq due to challenges in dissociating heart tissue into single cells without causing damage.

Conclusion

Understanding the nuances between scRNA-seq and snRNA-seq is essential for selecting the right method for your research. scRNA-seq is effective for analyzing cells that are easily dissociable and resilient to stress. It provides a comprehensive view of cellular function by capturing the full transcriptome of individual cells.

In contrast, snRNA-seq is often the preferred method when dealing with hard-to-dissociate tissues like brain tissue or when working with archived or frozen samples. This technique is also efficient at preserving the integrity of difficult-to-isolate cells by minimizing cellular stress and degradation. Other considerations on method selection may depend on the practicalities of sample availability and handling. Ultimately, the choice between scRNA-seq and snRNA-seq depends on the most suitable technique for your samples as well as your specific research goals.

References

1. Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. Journal of the American Society of Nephrology [Internet]. 2019;30(1). 

2. Wolfien M, Galow AM, Müller P, Bartsch M, Brunner RM, Goldammer T, et al. Single-nucleus sequencing of an entire mammalian heart: Cell type composition and velocity. Cells [Internet]. 2020;9(2). 

3. Grindberg RV, Yee-Greenbaum JL, McConnell MJ, Novotny M, O’Shaughnessy AL, Lambert GM, et al. RNA-sequencing from single nuclei. Proceedings of the National Academy of Sciences [Internet]. 2013;110(49). 

4. Thrupp N, Frigerio S, Wolfs L, Skene NG, Fattorelli N, Poovathingal S, et al. Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans. Cell Reports [Internet]. 2020;32(13).

5. Andrews TS, Atif J, Liu JC, Perciani, Catia T, Ma X, Thoeni C, et al. Single‐cell, single‐nucleus, and spatial RNA sequencing of the human liver identifies cholangiocyte and mesenchymal heterogeneity. Hepatology Communications [Internet]. 2022;6(4). 

6. Wen F, Tang X, Xu L, Haixia Q. Comparison of single nucleus and single cell transcriptomes in hepatocellular carcinoma tissue. Molecular Medicine Reports [Internet]. 2022;26(5):339.