Decoding Living Systems One Cell at a Time
Researchers can start to understand the diversity of cells within seemingly homogeneous populations with single-cell analysis technologies.
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How can a vast array of cell types, with distinct forms and functions, be produced from a single genome? With single-cell analysis technologies, researchers can start to understand the diversity of cells within seemingly homogeneous populations. This could involve anything from analyzing the genetic variation of single-celled organisms in the ocean, to exploring the impacts of mutations in a tumor.
Single-cell analysis offers a unique perspective of the genetic and molecular features of individual cells. It enables the characterization of rare cell types, cell states and cell-to-cell variability that cannot be discerned in bulk analyses.
In this article, two early adopters of single-cell analysis technologies highlight some of the latest advances and new applications of these technologies.
Iain Macaulay heads up the single-cell genomics team at the Earlham Institute in Norwich, UK, where he has set up a single-cell platform that is accessible to the UK bioscience community. His team optimize protocols for isolating and sequencing single cells from bacteria, plants and animals, as well as provide hands-on training courses for those wishing to start using single-cell technologies.
“Averaging data across many cells can mask critical differences,” he said. “Single-cell analysis gives a truer picture of how the genome is regulated, and how this regulation can produce complex systems that regulate the organism in health and dysregulate the system in disease.”
Joseph Powell, director of Translational Genomics at the Garvan Institute and director of the University of New South Wales (UNSW) Cellular Genomics Futures Institute in Sydney, Australia, uses single-cell analysis technologies to explore how genetic differences between people affect individual cell function in various diseases. The results of his research are informing clinical trials and the development of early-stage mRNA therapies.
“In almost every instance, disease arises at the level of cells, so to understand the underlying mechanisms and best treatment strategies, we need to determine the different molecular signatures that lead to it,” Powell said. Single-cell analysis offers better resolution and a more accurate measure of individual molecules compared with bulk analysis. In stem cells or cancer cells, for example, small differences can have huge differences on outcome for the organism.
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Over the past decade, Macaulay and Powell have witnessed remarkable technological advances in single-cell analysis firsthand. The possibility of isolating and analyzing hundreds of thousands of individual cells has led to ambitious projects such as The Human Cell Atlas, which aims to map every cell type in the human body.
“The vast majority of single-cell analyses focus on RNA sequencing, and over the last 10 years there has been a major drive towards scale in single-cell RNA-seq – experiments profiling 10s of thousands of cells in parallel are now commonplace, whereas 10 years ago this would have been 10s–100s of cells in total,” Macaulay said.
In addition to the scale at which experiments are performed, researchers are increasingly measuring more than just RNA. Single-cell technologies now span genomics, epigenomics and proteomics. “In some cases, these measurements are experimentally or computationally integrated, such that different aspects of the same cell can be measured in parallel, for example, parallel epigenetic and transcriptomic measurements,” he added.
Many studies have highlighted the advantages of multiomic single-cell analysis for understanding diseases such as cancer, COVID-19 and neuroinflammatory diseases.1, 2,3 However, working with multiomic data requires investing in expertise to handle it. “New users can be overwhelmed by the choice of methods,” Powell said.
He stressed the importance of using (and understanding) the methods that are most appropriate for the research question being asked.
“If you use the wrong method, you can end up drawing the wrong conclusions.”
Best practice guidelines and user-friendly tools can help researchers decide which methods are most suitable and understand the data that they are generating. “The single-cell community is very collegiate and has put a lot of effort into addressing reproducibility issues and standardising approaches,” Powell added. Cell atlasing projects are contributing to these goals by developing standardized protocols for sample collection, cell isolation, sequencing and data analysis and producing data sets that serve as benchmarks for the community.
Balancing depth and breadth
Because single-cell analysis involves making measurements on a very small scale, data are inevitably lost. In the transcriptome, genes that are expressed at low levels may not be detected, whereas in the genome or epigenome, some areas of interest may not be covered.
“The problem of missing data is compounded when working with multiomic data, and this can limit the resolution at which a biological problem can be studied,” Macaulay warned.
“Current high-throughput methods emphasize scale over depth,” he said. Enriching samples for cells of interest or using targeted approaches to look at particular genes in more focus can help researchers get more information out of their single-cell data.
He is very excited and committed to the application of long-read sequencing in single-cell transcriptomics, as this will enable the analysis of alternative splicing in almost any system. “Single-cell long-read sequencing will reveal new and complex biological dimensions.”
Indeed, a recently published map of the splicing patterns in mouse brain cells showed important changes across brain regions, cell subtypes and developmental time points.4 This type of study highlights the unappreciated degree of isoform variability and the importance of going beyond simple gene counts to obtain a fuller picture of single-cell biology.
Single-cell analysis in precision medicine
Population-scale single-cell genomic data is shedding new light on the mechanisms through which genetic risk factors contribute to disease at the cellular level. “By analyzing how individual genetic backgrounds influence disease pathology, we can identify specific drug targets and understand how different patients express these targets,” Powell explained. This approach is crucial to advance precision medicine and tailor treatments to patients’ unique genetic makeup.
Powell and colleagues have shown that single-cell transcriptomic data can be used to predict a patient’s likelihood of responding to a drug.
Clinical trials of drugs for inflammatory bowel disease (IBD) currently taking place across New South Wales are using this information to assess how patients with different genetic profiles respond to medication and, ultimately, improve therapeutic outcomes.
Looking forward, Powell plans to expand this research approach to other diseases and potentially transform treatment paradigms across various medical conditions. He believes single-cell technologies will revolutionize the treatment of diseases where cellular heterogeneity is linked to treatment resistance and disease progression. For instance, in cancer, understanding the genetic diversity within tumor cells can help identify resistant cells that might lead to relapse.
Macaulay agrees. “I think the study of the mutations we acquire with age may also generate clinical applications – for example, analyzing clonal mutations in the blood and stem cell populations, with single-cell resolution, could become a diagnostic or predictive tool for blood cancers,” he added.
Despite its potential, several challenges remain for the widespread adoption of single-cell genomics in clinical settings.5 These include the need for specialized equipment and training, and the integration of large-scale data analysis into clinical workflows. Although the cost of single-cell sequencing continues to decrease, further efforts are required to facilitate broader access. “By building and running research centres such as the UNSW Cellular Genomics Futures Institute, we are helping to advance and democratize the technology,” said Powell.
Further applications and outlook
Macaulay and colleagues at the Earlham Institute are applying single-cell analysis to other fields in which these technologies are likely to have a big impact, such as aging and the analysis of crime scene evidence.
Aging is characterized by the loss of cell function over time and because it affects different organs and cell types to varying degrees, single-cell analysis can provide a detailed molecular picture of age-related changes in individual cells. In the nervous system, aging has been shown to affect the composition and function of microglia, whereas in the hematopoietic system, aging causes a decline in the differentiation potential of stem cells.6,7 These types of studies could offer valuable insights for developing anti-aging therapies and tackling age-related diseases from an early stage, potentially preventing their development in the future.
In a new project called SCAnDi (single-cell and single-molecule analysis for DNA identification), Macaulay will use single-cell technologies to analyze forensic samples that contain cells from multiple individuals. “Techniques developed to isolate and analyze cells from complex mixtures could revolutionize the ability to identify different individuals from forensic samples,” he said. Integrating these techniques with current forensic practices could offer a new tool for the generation of DNA evidence used in criminal investigations and court cases.
Finally, another area that both Macaulay and Powell are keeping a keen eye on is spatially-resolved analysis approaches that enable researchers to explore cellular gene expression within the context of an organ or tissue. “Because spatial transcriptomics aligns well with histopathology workflows, it is likely to enter clinical settings more quickly than single-cell analysis,” Powell concluded.
About the interviewees:
Joseph Powell is director of Translational Genomics at the Garvan Institute and director of the University of New South Wales (UNSW) Cellular Genomics Futures Institute in Sydney, Australia. His team specialize in the use of single-cell sequencing technologies to understand the genomic mechanisms that contribute to complex human diseases.
Iain Macaulay is a Technical Development Group Leader at the Earlham Institute in Norwich, UK. His research focuses on the development of technologies for multiomic single-cell analysis and on applying these technologies to understand biological heterogeneity at the single-cell level.
References:
1. Pleasance E, Bohm A, Williamson LM, et al. Whole-genome and transcriptome analysis enhances precision cancer treatment options. Annals of Oncology. 2022;33(9):939–949. doi: 10.1016/j.annonc.2022.05.522
2. Naik N, Patel M, Sen R. Developmental impacts of epigenetics and metabolism in COVID-19. J Dev Biol. 2024;12(1):9. doi: 10.3390/jdb12010009
3. Ingelfinger F, Beltrán E, Gerdes LA, Becher B. Single-cell multiomics in neuroinflammation. Current Opinion in Immunology. 2022;76:102180. doi: 10.1016/j.coi.2022.102180
4. Joglekar A, Hu W, Zhang B, et al. Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain. Nat Neurosci. 2024;27:1051–1063. doi: 10.1038/s41593-024-01616-4
5. Lim J, Chin V, Fairfax K, et al. Transitioning single-cell genomics into the clinic. Nat Rev Genet. 2023;24:573–584. doi: 10.1038/s41576-023-00613-w
6. He X, Memczak S, Qu J, Belmonte JCI, Liu GH. Single-cell omics in ageing: a young and growing field. Nat Metabo. 2020;2:293–302. doi: 10.1038/s42255-020-0196-7
7. Mincarelli L, Uzun V, Wright D, et al. Single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells. Commun Biol. 2023;6(1):558. doi: 10.1038/s42003-023-04936-6
