The new genomics weapon in the armoury of biologists is ready for action - to seek out the true complexity and diversity of living things. Following his talk at the 'Festival of Genomics' in Boston, Dr Iain Macaulay explains why single-cell genomics is making life scientists stand to attention.
All living species are made of cells, and whether its individual bacteria in microbiome or different cells from a human tissue – especially within a tumour – there can be dramatic differences in the genomes of those cells, as well as the genes those cells are using to function.
Normal genomics approaches will take thousands, or millions, of cells and treat these as a single sample, losing all of their diversity. It’s only when we do single-cell genomics that we can tease apart the system and see the complex mixtures of cells that make up living things.
The great thing is, we can apply many of these new techniques to very diverse organisms, and so single-cell genomics can be thought of as a new weapon in the armoury of a broad range of biologists.
Scientists have been interested in single-cells for a long time; many stem cell biology discoveries were made by analysing the function of single-cells. So there has been a long-standing understanding that individual cells can be diverse in form and function. But advances in genomics, particularly in the isolation and molecular processing of small amounts of RNA and DNA for sequencing have enabled those focussing on cellular biology to embrace genomics.
Devices like Fluidigm’s C1 and the 10x Genomics Chromium have enabled many groups to easily apply single-cell genomics approaches to their cellular system of interest. Reductions in the cost of sequencing and increases in the number of samples that can be sequenced in parallel have helped too. Importantly, the field has attracted the interest of many bioinformaticians, which has seen a proliferation in the kinds of tools that are needed to investigate single-cell data.
... there are more cells in one human body than there are stars in the Milky Way, many of them doing very different jobs and all of them arising from one single-cell ...
Single-cell genomics has allowed some really exciting collaborations between cellular biologists – who might never have sequenced anything before – and genomics and bioinformatics scientists.
It’s an appealing approach; applicable to almost all living systems, so I can see it being adapted quite broadly – at the moment, much of the focus is on developmental and cancer biology, but I think studies of cellular responses to drugs or chemicals will also start to embrace single-cell approaches.
The technology is developing so fast; every week it seems there is a new method to or technology to think about – but everything is trending towards higher throughput so that thousands of cells can be analysed in parallel – or towards the integration of different datasets from the same single-cell. There is also a largely unmet desire to generate spatially resolved single-cell sequencing data – where the original positions of the cells within the organism is recorded.
In terms of public impact, single-cell genomics techniques can be applied in pre-implantation genetic diagnosis, which allows screening of IVF embryos to detect genetic problems before implantation.
There is also a lot of work ongoing in cancer biology to use single-cell genomics to track the life history of a tumour – mutations unique to subsets of the cells within a tumour can be used to reconstruct a ‘lineage tree’ of the cancer cells. By knowing how the cells are related, and the ways in which they are mutated, it might be possible for clinicians to offer enhanced personalised treatments.
Beyond the immediately practical, single-cell genomics has the potential to reveal some of the true complexity of living things – there are more cells in one human body than there are stars in the Milky Way, many of them doing very different jobs and all of them arising from one single-cell after fertilisation.
Even the populations of bacteria in our guts, or the cells in the root of a plant, are enormously diverse and complex. At the single-cell level, pretty much everything is amazing – and I’d like to think that by capturing some of this complexity and visualising it, single-cell genomics might capture some of the public’s imagination too.
We are really interested in developing new techniques and applying them to as broad a range of biological systems as possible. We are working with collaborators across the Norwich Research Park site and beyond to explore single-cell genomics in plants, animals and humans.
One particular area where single cell genomics can have real impact is in understanding how individual cells can develop into the multitude of cells within a tissue, organ or organism – and this is a theme we are following with various collaborators – looking at the single-cell genomics of tissues from diverse systems including developing chicken embryos (with Dr Andrea Münsterberg, UEA) and the reproductive systems of plants (with Dr Xiaoqi Feng, JIC). We’ll also be providing the work we do in developing these techniques at EI as services through our National Capability in Genomics.
Multi-cellular organisms like plants, mice and humans contain many billions – often trillions - of cells, all of which originate from a single cell, or zygote, after fertilisation. As the organism develops from the zygote, cells make changes to their gene expression profiles, a process termed differentiation, allowing them to fulfil diverse and unique tasks throughout the organism.
Next generation sequencing (NGS) typically analyses pools of thousands, or even millions of single cells – and much of the detail and complexity of the cellular populations within these pools is lost. Single cell genomics approaches allow us to apply NGS technologies to the tiny amounts of DNA (genome) and RNA (transcriptome) that can be found in a single cell.
My background is in the study how blood cells develop and function. Scientists in that field were among the first to really study how different single-cells – even from the same tissue – can act very differently.
But I’ve always liked working with new technology, especially genomics technology, and whenever I got the chance I would want to see how we could answer a biological question with a genomics approach. Single-cell genomics is right at that interface between the technology and the biology, and I think it’s for that reason that I was attracted to the field.
There are many different ways to get into the industry – either as a cellular biologist with a question that can only be addressed at the single-cell level; a molecular biologist who likes to develop new approaches for sequencing; or an informatician who likes working with complex datasets. It’s a very collaborative area and relies on a diverse range of expertise, and it is developing so quickly that there are plenty of opportunities to do exciting science.
My presentation at the Festival of Genomics in Boston explored the growing trend for single-cell ‘multi-omics’ approaches – where more than one component of a single-cell is analysed in parallel.
As an example, with Chris Ponting and Thierry Voet at the Sanger Institute, we recently developed a protocol which allowed us to sequence not just the genome of the single-cell, but it’s transcriptome as well. This kind of technique lets us look, in one cell, at the relationship between variation in the genome – e.g. mutations – and gene expression. These kind of multi-omic approaches are proliferating rapidly, linking not just genetic but epigenetic variation with expression – it’s a really exciting opportunity to learn as much about a cell as possible.
While there is a lot of interest in this area, these multi-omic techniques are still evolving, as are the analysis tools required to integrate all the data. The methods can also be costly and challenging to implement. I think the data we can now generate from a single-cell can be quite overwhelming, and the trick will be to use the right techniques and analysis to match the biological question. Some great data will come from these kinds of technologies, and people are keen either to collaborate or to get the right kind of training and advice to successfully implement them in their own labs.
