Spying on single cells
The human body is an enormous conglomeration of trillions of cells, of probably thousands of different types, all working together. Advances in technology now mean that we can spy on their activity and behaviour, right down to the level of a single cell. Kat Arney took a trip to the Wellcome Trust Sanger Institute to meet Stephan Lorenz, head of the single cell genomics core facility, to discover why scientists need to get up close and personal with single cells, and how on earth they manage it.
Stephan - Well, if you think about it, the cell is the fundamental unit of life. By looking at single cells, you can really study the basic fundamental unit of life.
Kat - What can you study in a single cell if a single cell is – it’s got a set of DNA and it’s got some stuff in it? What can you actually look at?
Stephan - It’s an interesting question. I would make the argument that the oldest and most important biological tool has been around for 150 years and is used to study single cells which is the microscope. So, we have looked at single cells for basically over a century.
These days now, when we look at a particular single cell, we can look at its genome, its transcriptome, its epigenome, and to some extent, also the proteome. What's new these days is that we are now able to sequence both the genome and the transcriptome of individual cells.
Kat - Let’s dig into that a little bit more. So the genome is the DNA, that’s looking at the cell’s instructions. What is the transcriptome?
Stephan - So the transcriptome is basically, you read the set of instructions that’s present in the genome and take smaller pieces of these 3 billion base pairs that we have, and use the transcriptome, which has individual transcripts or RNA molecules who then instruct the cellular machinery to make proteins which are the actual things that make a cell work.
Kat - So they're kind of the message between the genes and then the proteins, the stuff of the cell.
Stephan - Exactly.
Kat - How on Earth do you start thinking about how to study all these messages, these RNAs that are in the transcriptome? How do you get them out of a cell? How do you look at them?
Stephan - These days, we look at the transcriptome and these messages by sequencing them. Over the last couple of years, there has been an explosion of methods that allow us to take these tiny quantities of RNA that are present in a single cell which is typically less than 50 pico grams per cell.
Kat - Tiny, tiny!
Stephan - Tiny, tiny! It’s basically not visible and there's probably not that many balances in the world that could actually measure that. So we take this tiny amount of RNA which is still a couple of hundred thousand molecules really and we use methods that have been around for over 20 years but are now applied in a very particular fashion.
So, it’s still at its core, a very old school method. You reverse transcribe your RNA so you copy it over into DNA. And then we use PCR which has been around for a long time to just amplify this material to a point where we can actually sequence it.
Kat - So, it’s effectively the same kind of technique you'd use if you were sequencing the genome of a person or a bacteria or something like that, but you're getting all the RNA, you're turning it all into DNA, and then just reading that – seeing what's in there.
Stephan - Yes. We need to amplify it a lot by probably between 100,000 and a million fold to actually make it compatible with the current sequencing technology.
Kat - So you make loads of copies and then you read all of them.
Stephan - Yes. We make lots of copies which has its own issues because it’s getting to lose some bios and lots of noise which makes the analysis of the top of data very challenging, but at its core, that’s what we do.
Kat - And then you said the analysis. So you’ve got all these reads, all these sequences at the RNA, the messages that a single cell is making, which I guess tells you which of its genes are working. What sort of things can that information tell you?
Stephan - So by looking at transcripts of cells, you could argue that you can infer the function and even the identity of a cell if you think along cell types. All cells in one particular individual share more or less the same genome so they have full instruction set of doing whatever that genome encodes they could do. But as an embryo develops, they differentiate into different cell types and the transcripts reveals what cells are we looking at and which instruction set the cell is actually performing that is encoded in the genome.
So, you can by taking two cell types, mix them and then do single cell sequence, you could tell them perfectly apart. That’s very high level. What you can also see is if you have a better defined cell type, let’s say a T-cell, you could for example then see whether it’s an active or inactive state, whether it’s been exposed to some antigens and some host defence programme working in the cell or whether during the cell cycle of any cell type. You could see which genes are activated as a cell gets ready to divide.
Kat - I was going to say, so a T-cell is a type of immune cell. Why can't you just look at a bunch of immune cells or a bunch of liver cells? Why do you need to look at just one?
Stephan - We’re not just looking at one. We’re looking at thousands, but each cell individually might be in a different state at a point in time. What we used to do in the old days was look at 100,000 cells and average them. You only get an average response from the tissue of interest you're looking at.
So, what we see more and more is that these tissues are actually complex mixtures of cells with different function or states. So, that response really gets diluted if you just look at the average.
Kat - I guess it’s like doing an opinion poll and you could say, “Well, on average, everyone likes cake” but actually, no. Some people like cakes, some people like biscuits, some people want fruit.
Stephan - Yeah, if you want to go with the food analogy. You could ask people, “Do you like cake?” and they would say yes and “Do you like steak?” and they would also say yes. So your average response is everybody likes cake and steak although if you look at each individual response, you can actually say, “Oh, people that like cake are less likely to like steak.”
Kat - There's been huge advances in this technology over the past couple of years. Where do you think it’s going to go next? Are there more developments that could happen?
Stephan - Absolutely. I would make the argument that we are still in a kind of infant stage when it comes to single cell sequencing. So over the last 3 to 4 years, there has been a lot of attention when it comes to single cell transcriptome sequencing. But the interesting thing to note is that in Nature Methods, which is a journal, the Method of the Year was called Single cell genomics.
So actually, most people at the moment focus on the transcriptome and the functional state of the cell was just the genome of the single cell which we start to realise can actually be quite different across cells of the same individuals. That’s deeply linked to cancer for example.
Then we have epigenetics which are imprinting mechanisms active in cells. So, there will be a lot more attention in the coming years on understanding what information we could gather from looking at genomes of single cells, and at the epigenome.
In our institute, we’ve developed quite a few methods in collaboration with others that allow us to look at multiple things at the same time of the same cells. So we have now methods to look at the genome and the transcriptome so we can then see how certain in the genome actually change the biological programme of the cell. We have similar methods that allows to look at epigenetic markers and the transcriptome. And there's a lot of focus and effort on enabling more multi-omic techniques.
Ideally at the end of the day, what we would like to do is look at the genome, the epigenome, the transcriptome, and the proteome of cells so that we have the full cascade of the classical paradigm of biology.
Kat - The sort of the Ome-some.
Stephan - Yes.
Kat - Stephan Lorenz from the Wellcome Trust Sanger Institute.