Modern Methods and Techniques in Single-Cell Sequencing

Single-cell sequencing has become a transformative tool in biological research by providing scientists with the ability to detect cellular heterogeneity. The evolution of this technology has led to its widespread adoption, resulting in a seemingly endless list of publications, each emphasizing insights gained from its application. In this article, we’ll briefly review several key advancements in single-cell sequencing that have led to its continual rise.

Isolation Methods
The isolation process is a critical step that sets this technology apart from traditional sequencing. Accurate cell separation is essential for generating data that truly represents individual cells. Although challenges like maintaining cell viability, avoiding biases, and achieving complete cell isolation were routine problems, refinements to isolation methods have mitigated many of these concerns.

Over the years, isolation methods have evolved from labor-intensive manual manipulations to encompass fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), and droplet-based microfluidics. Many of these methods have also been updated to seamlessly integrate with downstream processes, such as nucleic acid amplification and sequencing, facilitating a more streamlined workflow.

The rise in single-cell sequencing can also be attributed to newer approaches like the Evercode split-pool combinatorial barcoding technology that is employed by Parse Biosciences¹. “We're trying to take down any barriers that are preventing people from using single-cell,” stated Alex Rosenberg, CEO and Co-Founder of Parse Biosciences. Their innovative method utilizes a series of split-and-pool steps to uniquely label individual cells with combinatorial barcodes, allowing for simultaneous sequencing of many cells without needing to physically isolate each one. Rosenberg explained that eliminating the need for researchers to make an upfront instrument purchase has enhanced the accessibility of single-cell sequencing. Additionally, the inherent scalability of their technology allows researchers to expand their projects and address their questions more comprehensively.

In a relatively short time span, Parse has broadened this technology and launched products enabling custom gene capture, immune profiling, and single-cell CRISPR screens. We're enabling applications, Rosenberg noted, in which researchers need to scale up with more cells and more samples. This has been a broader trend in the single-cell market, with the average size of published studies almost doubling every year.

Amplification Strategies
Advancements in single-cell sequencing span the entire experimental workflow, including the DNA and RNA amplification stages. Given the limited nucleic acids in individual cells, obtaining the right concentration for an accurate representation in the sequencing data is challenging. Several primary amplification strategies exist and have already been extensively reviewed^2,3. Although this article won't detail all of them, it's essential to recognize that advancements in amplification methods have significantly contributed to the current progress of single-cell sequencing, enhancing accuracy, coverage, uniformity, and reducing bias.

Charles Gawad, Associate Professor in Pediatrics at Stanford University and Co-Founder of BioSkryb Genomics, pointed out that poor data quality from whole-genome amplification was formerly a bottleneck for researchers. This unreliability often prevented sequencing from being used as an actionable diagnostic tool. To address these shortcomings of whole-genome amplification, BioSkryb Genomics introduced their primary template-directed amplification (PTA) method⁴.

PTA offers enhanced coverage and is unique in its ability to reduce the amplification of amplicons with more of the amplification occurring from the primary template. In contrast, other single-cell whole-genome amplification methods often introduce significant biases from amplifying previously amplified products. Jay West, CTO and Co-Founder of BioSkryb Genomics, highlighted the benefits of PTA, emphasizing its capacity to produce high-quality single-cell data and obtain significantly more information compared to alternative techniques. Taking advantage of this method, BioSkryb developed their ResolveOME platform, offering a full multiomic workflow⁵. This comprehensive approach allows researchers to simultaneously study the complete transcriptome and genome of individual cells, which is essential for investigating complex diseases like cancer.

Expansion, Integration, and Analysis
The scientific community is continually developing new methodologies for single-cell research. We’ve come to the point where a short collection of letters preceding a “-seq” often reveals a specialized single-cell method in a Google search. Initially, this work predominantly focused on the transcriptome, but it’s no longer safe to assume that “single-cell sequencing” refers solely to RNA-seq. Numerous techniques now exist to investigate the transcriptome, genome, epigenome, proteome, and more.

While one article couldn’t begin to review the growth and highlight the impact of each of the different techniques, the integration of diverse datasets is another important advancement of single-cell research. Merging information from multiple modalities has been shown to give a more interconnected understanding of cellular systems. For instance, single-cell DNA sequencing can help identify mutations that drive tumor formations, but when paired with transcriptome or proteome data, it can provide deeper insights into tumor behavior and drug response.

Another major shift in single-cell sequencing includes the increased scalability of the analysis tools. The data produced from single-cell sequencing is notably more complex and demanding than those from bulk sequencing. To manage these intricate datasets, scientists have developed a growing list of specialized tools. Some of the most prevalent include tools such as Seurat^6,7 and Scanpy^8,9. However, these types of tools are primarily tailored for individuals who already possess a foundational understanding of coding and data analysis.

Recognizing the challenges this complexity presents, several companies involved in the single-cell workflow have implemented initiatives to simplify data analysis. Reagan Tully, CCO at BioSkryb Genomics, explained that not everyone is a bioinformatician or has access to a bioinformatics team, which can severely hinder research. In response, BioSkryb introduced their BaseJumper analysis platform. Built for researchers with any level of analysis experience, BaseJumper’s key features include cloud-based functionality, multi-modality, and platform-agnosticism. Furthermore, it was designed to produce customizable, publication-quality figures and to integrate datasets for a more comprehensive analysis. Tully emphasized, “We're not trying to differentiate our products by having a software platform; what we're trying to do is enable science.”

As these types of analysis tools become available, data interpretation becomes easier, leading to significant findings from various labs. Tackling these immense datasets becomes more manageable, and integration of other modalities further enhances our research capabilities. Multiomic analysis is set to become even more central to the single-cell workflow, and the proper analysis tools will be crucial for advancing our comprehension of cellular biology.

Recommended Reading and Resources

• Vandereyken K, Sifrim A, Thienpont B, et al. Methods and applications for single-cell and spatial multiomics. Nature Reviews Genetics. 2023;24(8):494-515. doi:https://doi.org/10.1038/s41576-023-00580-2
• Heumos L, Schaar AC, Lance C, et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics. 2023;24(8):550-572. doi:https://doi.org/10.1038/s41576-023-00586-w
• Flynn E, Almonte-Loya A, Fragiadakis GK. Single-Cell Multiomics. Annual Review of Biomedical Data Science. 2023;6(1):313-337. doi:https://doi.org/10.1146/annurev-biod...-020422-050645
• Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multiomics. Nature Reviews Molecular Cell Biology. 2023;24(10):695-713. doi:https://doi.org/10.1038/s41580-023-00615-w
• Growing list of software packages for single-cell sequencing analysis: https://github.com/seandavi/awesome-single-cell

References

1. Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176-182. doi:https://doi.org/10.1126/science.aam8999
2. Kashima Y, Sakamoto Y, Kaneko K, et al. Single-cell sequencing techniques from individual to multiomics analyses. Experimental & Molecular Medicine. 2020;52(9):1419-1427. doi:https://doi.org/10.1038/s12276-020-00499-2
3. Wen L, Tang F. Recent advances in single-cell sequencing technologies. Precision Clinical Medicine. 2022;5(1)bac002. doi:https://doi.org/10.1093/pcmedi/pbac002
4. Gonzalez-Pena V, Natarajan S, Xia Y, et al. Accurate genomic variant detection in single cells with primary template-directed amplification. Proceedings of the National Academy of Sciences. 2021;118(24):e2024176118. doi:https://doi.org/10.1073/pnas.2024176118
5. Marks JR, Zawistowski JS, Salas-González I, et al. Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms. bioRxiv. Published online January 1, 2023:2022.04.29.489440. doi:https://doi.org/10.1101/2022.04.29.489440
6. Satija R, Farrell JA, Gennert D, et al. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology. 2015;33(5):495-502. doi:https://doi.org/10.1038/nbt.3192
7. Hao Y, Stuart T, Kowalski MH, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology. Published online 2023. doi:https://doi.org/10.1038/s41587-023-01767-y
8. Alexander WF, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology. 2018;19(1):15. doi:https://doi.org/10.1186/s13059-017-1382-0
9. Virshup I, Bredikhin D, Heumos L, et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology. 2023;41(5):604-606. doi:https://doi.org/10.1038/s41587-023-01733-8