Living the single life

27 May 2019

Scientists are increasingly taking advantage of microfluidic technology for single cell applications to understand individual cells and their role in health and immunity. Here we look at latest advances in the technology and some examples of microfluidic approaches to single cell research...

Understanding how single cells within an individual person, tissue or organ differ throughout development or as a result of disease has great potential to help advance personalised medicine.

Since high throughput single cell applications based on microfluidic droplet technology came to the fore in 2015,¹^,2 researchers have taken advantage of single cell RNA-seq (scRNA-seq) methods to increase their knowledge of cell development, immunology and immunotherapy, as well as tumour heterogeneity, lineage analysis and clonal evolution.^3,4,5,6

Individual immune cells can be profiled making it possible to identify tumour-associated macrophages that are crucial to determining tumour fate

The commercial availability of easy-to-use microfluidic instruments has helped drive the acceptance and widespread use of high throughput scRNA-seq, particularly in the materials science and chemistry sectors. Biologists were slower to embrace this technology, perhaps believing that it was necessary to become an expert in setting up microfluidic experiments. Everything changed with the advent of intuitive, application-orientated microfluidic instruments, enabling researchers to explore cell diversity and tissue heterogeneity, and quantify gene expression at the single cell level.

Many cells, few minutes… Using these systems, tens of thousands of single cells can be encapsulated in a matter of minutes, capturing the mRNA of each cell on a solitary uniquely barcoded oligo bead before reverse transcription and sequencing. As each sequencing read can be associated with its original cell, it is possible to perform transcriptome analysis of thousands of cells. Scientists are also starting to use individual nuclei as a proxy for whole cells, allowing the investigation of gene expression in cells that are difficult to isolate – such as brain cells – or cells obtained from archived tissue, which has potential applications for clinical studies.^7,8

Single cell techniques also have the potential to enable identification of the full range of cell types and states involved in immune responses, and to increase knowledge of immune cell development and differentiation. In the past, technical limitations – such as only being able to analyse a few RNA molecules at a time⁴ – have hindered immunology research, but these are being overcome by new scRNA-seq methods, offering improved sensitivity, reproducibility and throughput. Individual immune cells can be profiled with a higher degree of accuracy and in high numbers, making it possible to identify tumour-associated macrophages that are crucial to determining tumour fate. It is also possible to study cell states and cell activation to identify genes that act as drivers of immune responses.⁹

As well as transcriptomic analysis by scRNA-seq, other technologies are now available to aid the investigation of genomics, epigenetics and proteomics at the single cell level – including some commercial systems with standard protocols and applications for methods such as CITE-seq, CNV analysis or ATAC-seq – with more in the pipeline.^{10,11, 12}

Mapping the human cell atlas

Single cell technologies have advanced rapidly, revolutionising scientists’ understanding of individual cells and their role in health and immunity.

The Human Cell Atlas (HCA) project – an international collaboration launched in 2016 – is one such example, aiming to create a comprehensive reference map of all human cells and their function within the body. This will further researchers’ understanding of human health to help improve the diagnosis, monitoring and treatment of disease.

It is an ambitious target, but one that is becoming increasingly achievable with the easy-to-use tools and instruments for high throughput single cell research available today. Novel cell types and markers can be identified and used to guide the development of new drugs and treatments and, in the long term, personalised healthcare.

scRNA-seq in practice

Although single cell technologies are comparatively new, researchers are already reaping the benefits of this approach. At the Centre for Neural Circuits and Behaviour at the University of Oxford, scientists with an interest in learning and memory are using the Drop-Seq¹ technique and a single cell system to study cellular diversity in the Drosophila (fruit fly) midbrain.¹³ The midbrain of the fruit fly is ideal for single cell sequencing as it only contains 50,000 cells, allowing the whole brain to be analysed in one experiment. Different areas of the brain are then compared to identify and understand the neurons involved in memory and learning, typing them according to the neurotransmitter or neuromodulator that they produce.

The group is also performing scRNA-seq experiments aimed at discovering new genes that are important in the formation of these neurons, as well as identifying neuropeptides and exploring dopamine pathways.

Another notable project is underway in Denmark, where researchers at the Aarhus University Hospital are focusing on translational research to obtain a deeper understanding of bladder cancer, seeking to identify and validate molecular markers that could aid personalised medicine. In early stage bladder cancer, the main clinical challenge is to predict disease recurrence, aggressiveness and treatment response. Analysis of the normal tissue surrounding the tumour may allow the underlying disease mechanism forming the field cancerisation to be established, helping to predict disease outcomes.

In the past, the group has used next generation sequencing (NGS, DNA and RNA-seq) for its cancer genomics studies, but it is now moving into the single cell arena to delve further into cell heterogeneity, clonality and the tumour microenvironment. NGS-based analysis of bulk tumours identified three major molecular groups predicting different disease outcomes, and scRNA-seq technology is now being used to determine the specific cell type contribution of these molecular subgroups, investigating whether particular immune cells are over- or under-represented in the aggressive tumours. The characterisation of cell type composition in apparently normal bladder tissue may also contribute to the development of molecular tools and clinically-applicable pipelines to predict the likely aggressiveness of the disease and the frequency of recurrence.

The single cell market is young and fast moving. As new protocols and applications are developed, the widespread availability of flexible, automated and easy-to-use microfluidics-based droplet systems will be crucial to scientists exploring fresh avenues for biology research. In this regard, open systems have an advantage over locked platforms, as they can be reconfigured to support an ever-growing list of applications, enabling further insight into cell diversity throughout development or disease.

The future of scRNA-seq is exciting, and biologists, whether they have microfluidics expertise or are novices, are well-placed to exploit the technique and reap the benefits of single cell droplet encapsulation.

References

Macosko, E et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 2015, 161, 1202-1214.
Klein, A et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell, 2015, 161, 1187-1201.
Treutlein, B et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature, 2014, 509,
Stubbington, MJT et al. Single-cell transcriptomics to explore the immune system in health and disease. Science, 2017, 358, 58-63.
Patel, AP et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 2014, 344, 1396-1401.
Navin, N et al. Tumour evolution inferred by single-cell sequencing.Nature, 2011, 472, 90.
Habib, N et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Methods, 2017, 14(10), 955.
Haque, A et al. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome medicine,2017, 9, 75.
Proserpio, V and Mahata, B. Single?cell technologies to study the immune system. Immunology, 2016, 147, 133-140.
Stoeckius, M et al. Simultaneous epitope and transcriptome measurement in single cells. Nature methods, 2017, 14, 865.
Waldron, D. Technique: Single-cell CNV detection. Nature Reviews Genetics, 2016, 17, 128.
Pott, S and Lieb, JD. Single-cell ATAC-seq: strength in numbers. Genome biology, 2015, 16, 172.
Croset, V, Treiber, CD, Waddell, S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife 2018;7:e34550 doi: 7554/eLife.34550