Five Applications of Single-Cell Analysis Technologies

Five Applications of Single-Cell Analysis Technologies Joanna Owens, PhD Single-cell analysis methods are accelerating research in a range of areas, including immunology, cancer and neuroscience. In this listicle, we explore some of the methods being used and how they are enabling interrogation of complex biology in unprecedented detail. The single cell analysis toolkit There is a growing list of single-cell analysis technologies now being used to probe the inner workings of cells in health and disease. These include both the analysis methods themselves and associated computational tools that enable interpretation and reconstruction of single-cell data. Perhaps the most widely used method is singlecell RNA sequencing (scRNAseq), where single cells are isolated, mRNA is captured from each cell and then sequenced. Other single-cell technologies include single-cell DNA sequencing, single-cell immune cell receptor sequencing, single-cell CRISPR screening, and single-cell proteomics, epigenetics and multiomics technologies – and integrated versions of these.1 In addition, because these methods all remove the context of the surrounding cells, a parallel emerging field of spatial biology has developed to reconstruct the spatial context of single-cell analysis data. Here are five recent examples of how single-cell analysis methods are driving forward research in different areas. 1. Characterizing immune response to COVID-19 Single-cell analysis methods are transforming understanding of the immune system by allowing researchers to profile individual types of immune cells involved in various immunological processes – from fighting infections to the pathophysiology of disease. In many cases, scRNA-seq is used to identify which distinct subsets of immune cells are present or active in a disease. By contrast, sequencing the immune cell surface receptors of T cells and B cells can reveal their clonal origins, thus revealing insights into how the adaptive immune system responds to a particular antigen or disease-causing agent. The two approaches can be used together, as illustrated by a study into the role of innate immune cells in causing the severe dysfunctional response observed in some patients with COVID-19.2 Here, both scRNAseq and single-cell B-cell and T-cell receptor sequencing were used to analyze >895,000 peripheral blood mononuclear cells (PBMCs) from 73 patients with COVID-19 and 75 healthy controls. Being able to study immune cell dynamics at single-cell levels showed that there was a reduced transition from classical monocytes to non-classical monocytes (ncMono cells) in patients with COVID-19, and reduced expression of a cytokine called CXCL10 in ncMono cells in patients with severe disease, among other changes. FIVE APPLICATIONS OF SINGLE-CELL ANALYSIS TECHNOLOGIES 2 Listicle 2. Revealing immune targets in inflammatory arthritis Another single-cell method used to profile immune cells is single-cell mass cytometry, a fusion of flow cytometry and mass spectrometry that can measure more than 40 cell parameters at single-cell resolution.3 However, one of the drawbacks of mass cytometry is that it requires prior knowledge of the protein markers to be analyzed. By combining the approach with shotgun proteomics, where a complex mix of proteins is digested into peptides and analyzed by high-performance liquid chromatography and mass spectrometry, a team of researchers was able to resolve the immune landscape of ankylosing spondylitis (AS) – a form of inflammatory arthritis with no known cause.4 In the first step, a shotgun proteomics approach was used to identify changes in the proteome of PBMCs in AS, and this revealed key signaling molecules linked to inflammation. Subsequently, these signaling molecules were used as protein markers to determine the immunome of AS using single-cell mass cytometry. This singlecell analysis indicated that certain chemokines in specific subsets of both innate and adaptive immune cells were upregulated in AS, suggesting these may have value as therapeutic targets. 3. Tracking the complexity of cancer Until the advent of single-cell analysis technologies, it was challenging to obtain a comprehensive understanding of cancer growth and spread. Most studies relied on bulk cell analysis of biopsy tissue or blood, which did not include all the cells in the tumor microenvironment or allow for monitoring of cancer evolution and treatment resistance. Now, single-cell analysis is allowing researchers to track the evolution of tumor clones in response to treatment and use liquid biopsies to detect if cancer cells have spread into the bloodstream. A recent paper5 applied scRNAseq to the challenge of understanding malignant ascites – the excess abdominal fluid made when cancer spreads to the gut wall (peritoneum). Spread to the peritoneum is a leading cause of death from cancers affecting the gastrointestinal tract, but the ascites ecosystem is poorly understood. In this study, researchers used scRNAseq to characterize gene expression at the single-cell level from nearly 200,000 ascites cells in 35 gastric cancer patients with and without peritoneal metastasis. As the cancer progressed, they found an increase in a subset of immune cells that correlated with poor prognosis. They also identified a highly adaptable subset of gastric cancer cells that were more likely to multiply aggressively and had two hallmark genes involved in autophagy – revealing the mechanisms underlying the progression and spread of gastric cancer, and identifying key therapeutic targets. This identification and tracking of cancer and immune cell dynamics in disease progression would not have been possible without single-cell analysis methods. 4. Understanding aging As humans are living longer lives than ever, there is intense focus on understanding the aging process and how it is linked to the development of chronic diseases such as neurodegenerative disorders, cardiovascular disease and cancer. One group of cells now recognized to be crucial to maintaining a healthy brain as we age are the brain’s immune cells – the microglia.6 These cells perform a range of functions as sentinels signaling when injury occurs, housekeepers maintaining normal function and defense cells that respond to changes by protecting nerve cells, and they switch between nerve-protecting and nerve-damaging roles. It’s now known that aging alters microglial function in diseases such as multiple sclerosis,7 but it’s not understood what aging-related factors cause this. A recent study7 used scRNAseq and spatial RNA sequencing to study how the expression of microglial genes changes within the spinal cord in aging mice. It compared the single-cell gene expression of six-week-old and middle-aged (52-week-old) mice after injury caused by a neurotoxin, and identified several RNA transcripts that were increased in the older mice but not in the younger animals and correlated with neurodegeneration. One of the upregulated transcripts codes for a protein called osteopontin, and the authors were able to show that knocking this gene down in aging mice prevented nerve damage and inflammation mediated by microglia. This further illustrates how single-cell analysis has powerful potential for identifying key genes and proteins within complex pathophysiological processes that can be targeted therapeutically. FIVE APPLICATIONS OF SINGLE-CELL ANALYSIS TECHNOLOGIES 3 Listicle 5. Accelerating drug discovery and development As these examples show, scRNAseq and other single-cell analytical tools are enabling target identification and a deeper understanding of disease through cell subtyping. But these technologies are also playing a crucial role in translational and clinical research – from target validation, to choosing the most relevant preclinical model and ultimately monitoring patient response in clinical trials.1 For example, single-cell CRISPR screening combined with scRNAseq makes it possible to modulate a single target within a cell and then study all possible gene signatures and genetic interactions that follow, improving confidence that a specific target has its desired phenotypic effect.1 This approach was used to identify immune regulators that could be targeted with cancer immunotherapies,8 but it can also be adapted for use in vivo to allow target validation in a physiological context.1 Single-cell analysis also allows researchers to better characterize preclinical models and select those that are most representative of the pathology they want to reproduce. While at the clinical end of the drug development journey, single-cell sequencing is being explored for its potential to define more robust biomarkers – either molecules or subsets of cells – that can indicate prognosis (e.g., subsets of more aggressive gastric cells in the example above) or to measure tumor response, such as the presence of exhausted T cells following checkpoint inhibitor immunotherapy treatment for cancer. Summary In this listicle we have highlighted five different recent applications of single-cell analysis for understanding disease and identifying and developing new therapeutic options. As the capability of individual single-cell analysis methods evolves, so too does the potential for integrating these techniques into single-cell multiomic approaches and spatially resolved -omics approaches. The technologies are also now expanding beyond the analysis of RNA, DNA, protein and epigenetics, to other molecules such as metabolites, ribosomes, microRNAs or even higherorder 3D structures.1 Although each method has its limitations, by using these tools in combination, researchers now have access to a rapidly growing toolkit for probing the intricate mechanics and dynamics of individual cells.