The mapping of the human genome back in 2003 was a genuinely important milestone in the history of human civilisation. But there’s been a second major revolution that started in 2009 that’s still ongoing – and which really picked up in 2016. This second revolution has been the ability to generate genomic data on individual cells, at scale. It means we are now able to resolve how things work at the biological unit of the body.
You can call this revolution single-cell sequencing. As the name suggests, it’s basically a set of particularly revolutionary technologies that are able to generate genomic data at the level of one cell, and to do that for very large numbers of cells in parallel. The reason this is important is because almost every biological process in any multicellular organism, like humans, starts at the level of an individual cell.
If you think about what any cell is, it’s a functional part of the body. It might be a cardiac muscle cell, it might be a neuron in your brain, it might be a rod or cone cell in the retina, turning light into vision. And they all have very specific functions. But even if you look at cardiac muscle cells, for example, you realise that how they’re working can be quite different. We call this heterogeneity – variation between the cells.
Almost every biological process in any multicellular organism, like humans, starts at the level of an individual cell.
The way in which diseases arise is that something is wrong in a particular subset of cells. By studying the resolution biology at this level, you can start inferring which cells are going wrong and contributing to disease, or alternatively, which cells are doing something which might be targetable by a particular therapy, and therefore improve drug efficacy.
This type of precision medicine will revolutionise drug treatment procedures. Cancer is probably the easiest example to translate: here’s something that starts literally at the point of an individual cell with a sort of somatic mutation; it does something it shouldn’t do, doesn’t die, starts proliferating, dividing, dividing, dividing, and as they divide, you get different new mutations within that cancer. You end up with a single cancer tumour that can have a series of clonal cell types, each with different genetic profiles.
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The treatment at the moment is to basically assume they’re all the same, and typically use a single treatment, and blast them. This can be really effective at killing perhaps 90% of the cells or maybe more, but there might be a set of cells there that will have a particular genomic profile that will be completely treatment resistant. Obviously, then you might have a recurrence a few months or years later – and that’s a common story you see in cancers. There’s some form of effective remission, but it comes back, and the reason is that we don’t target the individual diversity or makeup of cells and the clones of a particular patient.
This is the nut that is cracked with single-cell sequencing: you can clearly identify those cloner cells. And if there are therapies that can be used at what we call multi-line therapy, using multiple drugs or treatment options for the same patient, then we have an informed way to do that.
You’re sequencing hundreds of thousands of genomic pieces of information on one cell, and you do that for tens of thousands to millions of cells.
What often happens is we’ll find particular cells where we don’t know if it’s an effective treatment, but once we’ve got their profile we can start figuring out how we can develop new therapies to target this newly discovered cell type. This is a really active area of research for us, using this single-cell genetic information to rapidly inform new therapeutic development.
You generate vast amounts of data. You’re sequencing hundreds of thousands of genomic pieces of information on one cell, and you do that for tens of thousands to millions of cells. This is where there’s a strong intellectual contribution, a combination of thinking very deeply about the way genetic differences act between different people, and then using that intersection of maths and biology to identify the signals that you get from that sort of data.
I’d formed a deep interest in that intersection of mathematics and biology.
Growing up in a rural part of the UK, I was always very drawn to the natural world. I was pretty nerdy, I suppose – birdwatching and exploring. My initial interests focused largely around trying to understand the natural world, so I specialised in sciences at school and when I went to university it was initially to study zoology.
My interest then was field science – going to interesting places to observe animals. But during my undergrad, I became exposed to evolutionary theory – starting with the classic Victorian work from Darwin, and through to molecular evolutionary theory from Motoo Kimura and his contemporaries. And then I saw the way mathematics has been used to take the observations that were seen in the natural world and integrate theories about the way in which things were evolving. By the end of my undergraduate, I’d formed a deep interest in that intersection of mathematics and biology.
It’s bases of DNA, bases of code, that you boil down using maths to identify which particular parts are doing something that’s important, or doing something they shouldn’t, like causing disease.
My Masters at the University of Edinburgh is a famous course that has been running for a very long time, supervised by some very distinguished evolutionary geneticists. By the end of that Masters I was drawn to a PhD, understanding the way in which the genome works and how it applies to human disease.
A strong motivator in my PhD and subsequent work has been combining this natural curiosity of figuring new things out with something that will hopefully make a tangible difference to society and to people – which sounds possibly arrogant to assume, but that’s been the story arc running through my career. The type of mathematics I specialised in was also used in finance jobs, and when I finished my PhD I was offered a number of roles by some of the biggest banks and investment firms in the UK. I had a bit of a Sliding Doors moment where I had the options to go into finance or to stay as a postdoc – and the salary differential was enormous. But I had a chat with my Dad and he made a pretty short argument: when you get to the end of your life, would you feel that you’ve made more of a difference by working in finance or science? So that was a way-marker for me to stay on that track.
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In my world, the mathematics we use we just call “statistics”, but I guess “machine learning” is the most accurate description – it lends itself very well to genetics, because humans have this enormous genetic diversity between individuals. It’s bases of DNA, bases of code, that you boil down using maths to identify which particular parts are doing something that’s important, or doing something they shouldn’t, like causing disease.
We work in a very disease-agnostic way, but we’ve already made important steps forward across COVID severity, also neurological and neurodegenerative disorders like Alzheimer’s and Parkinson’s, and obviously a lot in cancer. I think in the next 10 years we’ll be able to use cellular genomics to dramatically increase the amount of drug discovery and quality of drug decision-making processes across most diseases.
It’s going to be impactful to have this ability to move the whole field of medicine across many disciplines and usher in the new era of genomic medicine. I don’t think it’s too grand to say that.