Single-Cell Analysis—Uncovering Diversity and Granularity in Molecular Biology

Learn about how molecular biology is fundamental to single-cell analysis and supports the rising interest in this approach and its varied applications.

Methods have evolved to be sensitive enough to allow for single-cell analysis. This approach to biological studies often utilizes omics-based methods, but considers the variations between individual cells. Studying these variations has provided deeper insights that could not have been discovered from bulk studies that take the average of a complex population of cells. Results from single-cell studies uncover heterogeneity that can be quite important, revealing potential biomarkers for disease states and responses.

The single-cell analysis approach has been utilized in fields as diverse as oncology, stem cell biology, immunology, infectious disease, and neuroscience. In this article, we explore how core molecular biology methods and techniques are fundamentally critical to enabling the success of single-cell analysis methods and applications research in these and other biological fields of study.

As a scientific community, our understanding of biology has improved and increased as we have developed techniques that are sensitive and selective enough to allow us to gain a finer line of sight. Molecular biology and other techniques have improved enough to provide this ultimate level of granularity in biological analysis.

Cell populations were initially considered homogeneous, but recent research suggests heterogeneity occurs even within small populations. Each cell contains an individual genome that may differ from its neighboring cell. Measurements of gene expression using homogenized cell populations may be misleading and ignore small but crucial changes within individual cells. In organs and tissues, cells within any given population can display dramatic differences in size, protein levels, and the expression of RNA transcripts among individual cells.

Considering this heterogeneity in cellular populations, examining individual cells has provided a more detailed picture of the processes occurring within biological systems. These cell-to-cell variations can answer questions that have previously eluded scientists in oncology, stem cell biology, immunology, infectious disease, and neuroscience. Therefore, in these various fields and others, single-cell research will help to better inform the diagnosis and treatment of diseases.

Due to the clear benefits of conducting single-cell analyses rather than relying on the averaged results from heterogeneous cellular populations, studies using single-cell approaches have grown steadily over the past ten years. These analysis methods are becoming more accessible and routine. As a result, single-cell approaches are likely to continue to become more prevalent in future research.

A fundamental aspect of many single-cell analysis methods is molecular biology. Methods that rely on basic molecular biology, such as genomics, transcriptomics, and epigenomics, help advance our understanding of this important cellular heterogeneity.

Though we have gained vast knowledge from bulk analysis of tissues or other biological samples, it is clear that single-cell analysis contributes increased value due to its precision and ability to discern differences between individual cells within a population. Cell analysis at the bulk level is often achieved through omics methods, using one or more of the following: genomics, transcriptomics, epigenomics, proteomics, or metabolomics. When multiple omics methods are combined for analysis, these studies are deemed multi-omics analyses. Going further, single-cell spatial multi-omics can help map out population heterogeneity within tissues and biological samples [1]. Molecular biology techniques and products are fundamental to several omics methods, where DNA and/or RNA are the molecules of interest to identify or differentiate the cells.

Now, like never before, we can see and assess the activities of cells at the individual level. The ability to investigate individual cells for single-cell analysis has been enabled by techniques and technologies that allow us to better isolate and analyze with sensitivity and selectivity. Single-cell isolation methods include FACS, microfluidic separators, and optical tweezers, but these are not the focus of this article.

The greater detail of single-cell analysis has been helpful and informative for cell subtyping, assessing cell response to stimuli, and determining the heterogeneity of cells within samples and tissues [2]. Therefore, it is now possible to discover rare cells and events in biological samples that previously remained hidden in population-averaged results. This technology also allows for work with scarce or precious samples to support applications relating to the detection of cancer from tissue samples, fetal diagnosis based on maternal blood, forensic and medical analysis from limited or archival tissue, drug library screening, and stem cell and developmental biology.

Fields with novel findings stemming from single-cell analysis include:

Oncology

• Numerous studies assessing single cells have led to a better understanding of tumor metastasis.[3]

Stem cell biology

• Stem cells are among the types of cells that can demonstrate important heterogeneity at the single cell level.[4]

Immunology

• Sars-CoV-2 immune cell responses differ significantly between moderate and severe cases.[5]

Infectious disease

• Genomic analysis of influenza-infected single cells helped improve our understanding of viral gene and immune response heterogeneity.[6]

Neuroscience

• Single-cell analysis of the brain during development provided a better understanding of the developmental changes occurring across cell types.[7]

Many of these studies have relied on fundamental molecular biology techniques like PCR and qPCR.[4–6]

Numerous technologies used for single-cell analyses are centered around the same nucleic acid detection and quantification methods conducted on bulk cell populations. With the need to probe individual cells within the population, these techniques become highly specialized and sensitive. Versions of these commonly used nucleic acid-based methods, probing DNA and RNA for genomics, epigenomics, and transcriptomics purposes rely on specialized enzymes that are highly sensitive and selective. As a result, molecular biology techniques and tools are foundational to single-cell analysis.

PCR is fundamental for single-cell analyses using genomics, epigenomics, and transcriptomics to deliver the sensitivity and selectivity to amplify DNA or RNA from a single cell. These next-generation sequencing (NGS) techniques—which offer discovery power as they are hypothesis-free techniques—rely on PCR methods including basic PCR, qPCR, and RT-PCR (Reverse Transcription-PCR). NGS library preparation and Whole Genome Analysis (WGA) rely on PCR. Omics methods are often used in tandem with RT-PCR. Furthermore, single-cell PCR, qPCR, and RT-PCR also provide viable alternatives to NGS as they are more accessible and affordable.

Genomic analysis relies on DNA analysis detection and quantification through PCR and qPCR. It requires the use of molecular biology tools such as DNA polymerases, dNTPs, master mixes, oligonucleotide primers, and more.

Epigenomics techniques, which involve DNA sequencing, require PCR and qPCR enzymes that are either methylation-sensitive or insensitive. These methods also require controls and standards which are created through the methylation of nucleic acids, so DNA polymerases and modifying enzymes, capable of methylation and demethylation (methyltransferase and epigenetic products), are necessary.

Transcriptomics employs RT-PCR and RT-qPCR for gene expression analysis and therefore requires reverse transcriptases for reverse transcription, followed by DNA polymerases and other PCR reagents.

With single-cell analyses, sensitivity and selectivity are paramount for PCR and RT-PCR. Off-target amplification, primer-dimer, and other non-target-specific amplification must be minimized when so few target copies are available at the start.

Table 1. Molecular biology techniques are crucial for omics approaches used for single-cell analysis

Single-cell analysis has been leveraged in biological studies for years, however recent technological advances have allowed us to analyze single cells with greater precision. Methods like FACS, microfluidic separators, and optical tweezers combined with omics approaches, such as genomics, epigenomics, and transcriptomics, have revealed deeper biological insights in fields such as oncology, stem cell biology, immunology, infectious disease, and neuroscience. Moreover, work in this space depends on fundamental molecular biology skills, techniques, and tools. PCR, DNA modification, and electrophoresis play roles in the multi-omics methods used in single-cell analyses. Therefore, molecular biology will remain a vital component in the success of single-cell analyses.

1. Zhuang X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat Methods. 2021;18(1):18-22. doi:10.1038/s41592-020-01037-8
2. Zucha D, Kubista M, Valihrach L. Tutorial: Guidelines for Single-Cell RT-qPCR. Cells. 2021;10(10). doi:10.3390/cells10102607
3. Han Y, Wang D, Peng L, et al. Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis. J Hematol Oncol. 2022;15(1):59. doi:10.1186/s13045-022-01280-w
4. Li Y, Ma L, Wu D, Chen G. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform. 2021;22(5). doi:10.1093/bib/bbab024
5. Liao M, Liu Y, Yuan J, et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med. 2020;26(6):842-844. doi:10.1038/s41591-020-0901-9
6. Russell AB, Elshina E, Kowalsky JR, Te Velthuis AJW, Bloom JD. Single-Cell Virus Sequencing of Influenza Infections That Trigger Innate Immunity. J Virol. 2019;93(14). doi:10.1128/JVI.00500-19
7. Eze UC, Bhaduri A, Haeussler M, Nowakowski TJ, Kriegstein AR. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia. Nat Neurosci. 2021;24(4):584-594. doi:10.1038/s41593-020-00794-1