At the Festival of Genomics and Biodata, we are lucky enough to be joined by some of the biggest names in the business. In January, we sat down with a few of our esteemed speakers to chat about their backgrounds, roles and the work of their organisations. In this interview, we speak with Fei Chen, (Core Faculty Member at the Broad Institute and Assistant Professor at Harvard) about his career, the future of spatial technologies, expansion microscopy and more.
Please note the transcript has been edited for brevity and clarity.
Interview originally conducted by Miyako Rogers.
FLG: Thank you so much for speaking at the Festival. Can you first tell us about your background and how you came to be a Core Faculty Member at the Broad Institute and Assistant Professor at Harvard?
I came from a background in engineering, I did electrical engineering at Caltech. At the time, I was actually very against biology! But I was doing a lot of computing and electrical generation – and I got fascinated with this idea of DNA computing, or using DNA to make circuits in synthetic biology, and so then that led me to do bioengineering.
I went on to do a PhD in bioengineering at MIT and we developed this new way to do microscopy, expansion microscopy. And that was very multidisciplinary, because it had some engineering in it, and a lot of chemistry in it, and it had a lot of microscopy in it. And doing that project made me realise two things. One is that there’s a lot of things going on in biology, it’s pretty hard. And one way to maybe understand what’s going on is to be able to measure more things at the same time. That could either be in spatial context, which is what expansion microscopy set out to do, increase the resolution of microscopes, but it can also be in molecular context.
At that time, I had been doing a lot of microscopy, but not very much genomics. And so that’s how I ended up at the Broad.
FLG: You touched on expansion microscopy, but could you explain the concept behind it and the process of developing it?
So, microscopes have this fundamental limit of how small the thing we can see is. That’s actually set by physics. The wavelength of light is a couple of hundred nanometers and, because of that wave nature, you can’t see things much smaller than the wavelength of light, under 200 nanometers. But if you think about what’s in your cells, like mitochondria or a synapse, they will be 200 nanometers.
Basically, you can’t make out anything like the structure inside of a synapse. And this was the case with microscopy for a long time. But people came up with some clever optical tricks for breaking that diffraction limit and folks won the Nobel Prize for that, recently. But those sorts of microscopes were challenging to use, they required very fancy machines, they required a very high sample quality. Those techniques were just being invented when I was starting my PhD. And so, we wanted a way to do this sort of super resolution microscopy, without having to use these fancy microscopes.
And there was a crazy idea, basically, instead of making your microscope better, if you just made your sample bigger, then you would be able to use your normal microscopes and see the same features. It’s a little bit out there because you might think, ‘Oh, well, you can’t really make your sample bigger in a way that preserves the relative structure of other things.’ But we realised that there are super swellable hydrogels that expand, like the same material that goes into diapers. And they have this quality, wherein they expand up to 100 times in volume.
But they also have this other quality in that they form this really dense network of polymers, even at the nanoscale, and what we’re doing in expansion microscopy is we’re linking up the dyes, or labels or proteins, to this polymer network. And then we’re cutting them up, digesting the structure of the tissue. But we’ve linked the molecules to this network. It’s almost like a cast, and you’re expanding the cast. And so that makes it very isotropic. And it’s very easy to do, because it’s a chemistry reaction. So, lots of folks are using it these days.
FLG: That’s fantastic. Can you describe the development of, and also how people are applying, SLIDE-seq?
When I moved to the Broad, there was this idea that expansion microscopy allows you to look at a couple of things with very high spatial resolution. But at that time, there were really no methods that allowed you to look at a lot of genes in tissues, at the same time, at high resolution. We developed SLIDE-seq, which allows you to capture RNA from your tissues with 10-micron resolution, and that was just RNA. And RNA is for gene expression, but for a lot of contexts, we might also care about the genome itself, right? Especially in the context of tumours, where you can accumulate mutations or chromosomal number alterations over time.
And so, what we did was we adapted the same arrays that we used for SLIDE-seq that captured only RNA. Now, we adapted them to capture DNA through some molecular biology tricks. And we can use that to study chromosomal number alterations at high resolution inside of tissue. So, you see small subclones of tumour cells forming and evolving. And the other thing that you can do is start exploring how the different clones’ genomic identity is related to its environment, or how its transcriptome identity is related to either its environment or its clonal nature.
FLG: How does SLIDE-seq compare to other technologies for spatial genomics?
Well, there’s not that many other approaches to do that high resolution capture of DNA, unlike RNA. And previously, what people had done was they used laser capture microdissection. But that’s a way where you have to know, a priori, what regions you want to sample versus the DNA-seq approach. You know, you have many beads in the array, and they’re each individual pixels, so you just put your tissue on and you capture the genomes in an unbiased way. And then I think there’s some imaging-based methods where if you know, a priori, the mutations that are being accumulated, you can go and detect those, but it’s not like unbiased DNA sequencing. So, there’s not too many methods out there.
FLG: What are some of the challenges you encountered while developing SLIDE-seq?
One challenge is that we’re making these arrays ourselves. It’s not something an academic lab normally does. It’s kind of like a manufacturing problem, right? Because you have to make these bar-coded beads. And then you have to sequence them in situ and there’s a matter of QC. So, getting this sort of operation set up is kind of challenging from an academic lab’s perspective. And we had some challenges like that in the beginning, and then lots of people were obviously interested in using it. And then it’s challenging to say, ‘how many collaborative efforts do we want to support with this limited bandwidth?’ And so, I think there are efforts that are trying to commercialise and manufacture these things at scale to share them with the community. I think that’s good.
And then I think there’s unexpected things that come along the way in any project. For example, we were developing SLIDE-seq, and we didn’t realise it, but the surface that you put the beads on really has a strong effect on how much RNA can be captured. And we were getting just horrible results for a while, not quite understanding what was going on. And then, not inadvertently, but just serendipitously, we changed the surface, and it dramatically changed the results. It was luck, basically.
FLG: Could you talk a little about the importance of preserving spatial context? Because that’s a real advantage that the spatial technologies have over single-cell, for example.
I think it’s very important. And obviously people are arriving at that conclusion. I think there’s a couple of areas where it’s very important. One is learning cell-cell interactions; you sequence the single cells, but you don’t quite know which ones are interacting with which. And, for example, those interactions are really quite important both in development or immunology, right? Immune cells need to physically interact. And so just understanding how cellular interactions change as a function of pathology and disease. And that’s just learning the mechanisms.
But I think, looking forward, you can see, ‘what interactions are predictive of response to therapy? What interactions should we be promoting or drugging?’ And that’s the cell-cell interactions, but one layer below that would be, what molecules are cells actually using to interact with? And by that, I mean, what are the receptors and ligands and communication networks? I think that’s something that we’re just starting to get the data to be able to explore and build models for, but I think it’s really important, because that’s how we’ll be able to learn relevant mechanisms that we can manipulate for therapy.
One example is PD-1 and PD-L1. That’s clearly a very important spatially proximal interaction for tumour immunology. There’s probably many, many circuit networks and tissue networks that are happening that support an immune response. And we could probably systematically discover which ones of those are important and how we might manipulate them to change the state of tissue therapy.
FLG: So, what do you see for the future of multi-omics and spatial multi-omics?
I think it’s an exciting area. There’s a lot of spatial multi-omics being done already. There’s a couple of aspects, one, I think, being able to sequence the genome and epigenome in conjunction with the transcriptome will be very important. The epigenome for looking at tracking plasticity of cell states, in priming and these sorts of things. And the genome, we’re tracking mutations, evolution, and that’s an area that we’re really excited about.
And then the second is, there’s a lot of technologies out there that people are commercialising that are trying to get access to joint protein and RNA measurements, I think that’s starting to get more mature. So, it’d be interesting to see what comes out of that.
FLG: Can you talk about any current or future developments in your lab that you’re particularly excited about and we should keep an eye out for?
Yeah, we’re doing some really cool single-cell spatial multi-omics, like epigenomics, ATAC-Seq. And we want to broaden that out to more things, stuff that we have never worked on yet. Like the metabolome or Hi-C or these sorts of things. But certainly, we’re doing some exciting new stuff in spatial and multi-omics, in particular.
FLG: Finally, thank you for coming to the Festival. What did you enjoy about the Festival and why did you choose to come?
I really liked a lot of the cancer genome evolution talks. They were awesome. I think the other thing that I notice, and I’ve always kind of known this, is the UK has such a vibrant genomics community. And it’s also very dense. I think that’s awesome. And it’s been cool to connect – that was one of the reasons I wanted to come, I wanted to connect with some of my UK colleagues in that space. So I appreciate that it was in person.