The NEXT genomics revolution
Single-cell sequencing is unlocking the secrets of disease – and drug therapy – one cell at a time. Joseph Powell tells us what its future holds.
The mapping of the human genome in 2003 was a genuinely important milestone in human history. But a second major revolution that started in 2009 is ongoing, and really picked up in 2016: 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. It’s basically a set of technologies able to generate genomic data at the level of one cell, and do that for very large numbers of cells in parallel. This is important because almost every biological process in multicellular organisms, 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. Diseases arise when 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 that might be targetable by a particular therapy, therefore improving 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 cells divide, you get 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.
The strategy at the moment is to assume they’re all the same, 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 that have a particular genomic profile that is completely treatment resistant. 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 make-up 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.
What often happens is we’ll find particular cells we don’t know an effective treatment for, but once we’ve got their profile we can start figuring out how we can target this new 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 mathematics and biology to identify the signals that you get from that sort of data.
Growing up in a rural part of the UK, I was always drawn to the natural world. I specialised in sciences at school and went to university to study zoology. My interest was field science, but during my undergrad I was exposed to evolutionary theory and how maths can be used to integrate theories about evolution in the natural world. By the end of my Masters, I was drawn to understanding how the genome works and how it applies to disease.
A strong motivator in my PHD and subsequent work has been combining a natural curiosity for figuring things out with making a difference. With the maths I specialised in, I had the option to stay as a postdoc or go into finance when I finished my PHD. The salary differential was enormous, but my dad made a short argument: at the end of your life, will you feel you made more of a difference by working in finance or science?
In my world, we call the maths we use statistics, but I guess machine learning is most accurate. It lends itself to genetics because humans have enormous genetic diversity. It’s bases of DNA, bases of code, that you boil down using maths to identify which particular parts are doing something important, or something they shouldn’t, like causing disease.
We’ve already taken important steps across COVID, cancer, and neurological and neurodegenerative disorders. Within 10 years we’ll be able to use cellular genomics to dramatically increase drug discovery and quality of drug decision-making processes.
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.
It’s a privilege to work as a physicist. We do what we find most interesting and are paid to conjure up hypotheses that sound like science fiction. I can’t ask for a more fulfilling career.
PROFESSOR JOSEPH POWELL is Director of Cellular Science at the Garvan Institute of Medical Research, and Deputy Director of the UNSW Cellular Genomics Futures Institute.