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“Houston, we have a question”: Adam Glaser on light-sheet microscopy, its obstacles, role in the research and the future
We continue our 'Houston, we have a question' series of interviews with scientists and experts from various industries. Today's guest is Adam Glaser - a renowned light-sheet microscopist who wears so many hats from his position at the University of Washington to being a co-founder at Lightspeed Microscopy, Inc.

Adam presented a great overview of light-sheet microscopy explaining how it differs from traditional methods, discussed its obstacles, role in the research, and the future direction. As always, at the end, we ask our interviewees what they see to be the theme for humanity or their field for the next few years.
How would you describe what light-sheet microscopy is and what is the benefit of it, to the person who doesn't know it?
I think the easiest way to explain light-sheet microscopy, just at a very general level, is that it allows you to see things at scale, that was not possible with previous microscopy techniques.

That is mainly due to the speed that light-sheet microscopy is able to provide. If we dig a layer deeper, how does light-sheet microscopy go so quickly? More traditional microscopy methods typically produced high-quality images point by point across the sample. Light-sheet microscopy is using a sheet of light to excite the sample and making that co-planned with the focal plane of the imaging objective. Basically, that means that you can collect whole 2d images now. What that means is: you're not capturing just one pixel at a time and moving through the sample, but you're actually capturing a whole plane of, say, 4 million pixels all at once. So that's really where the speed, the speed difference comes from.

I think the ideas of light-sheet microscopy have maybe been around for decades, but actually, the technology was not there to enable it. So once scientific CMOS cameras became more mainstream, more affordable, I think that really is when light-sheet microscopy was able to take off and explode.

In most recent light-sheet systems, the speed is in many cases, just limited by how fast can the camera take pictures. How fast can you snap those 2d images?

It's a tremendously exciting type of microscopy and it's broken up into two camps. One camp looks at live specimens. So in that case, the speed is very advantageous because you can do high-speed 3d imaging of living embryos, for example. And then the other camp, more where I have been, is if you have, a dead fixed tissue, how quickly can you just move your way through this large tissue? And there again, the speed is really helpful.

I'm sure new applications of light-sheet microscopy will continue to pop up and they are all really based on that speed factor, which it provides.
Is it true that light-sheet microscopy research becomes less fundamental and more focused on real case scenarios linked to drug development or cancer research, for instance? Is it the general trend or is it just one camp?
That is a great question. I think that there'll always be both sides. Within optics and microscopy, where I've been working for several years now, it's important to have the fundamental research into discovering new ways of doing microscopy.

In light-sheet microscopy, in particular, there is this kind of core technology. This core idea of how you set up a light-sheet system. But it's such a hot inactive field that every month, maybe even every other week, there are new tricks either on the hardware side, the software side, or both, that are really interesting.

So many of those papers just prove out the point that we can do this much faster, that we can see something this much smaller. But then on the applicational side, and I've been working more on the applicational side, you can kind of look at this tool belt of tricks that people have come up with and kind of pick what is going to be most useful to achieve your end goal for your application.

Several years ago, when I joined the University of Washington, I joined the lab of John Lou. He and a pathology resident had this idea: "Can we use some sort of microscopy, maybe not light-sheet, to address this clinical pathology question?". As we researched the methods that are out there, we determined that light-sheet microscopy was a great fit because of the speed.

Then our innovations were, or what we've tried to do in this field is to adapt light-sheet microscopy to be compatible with clinical pathology, with clinical workflows. And that kind of goes back to looking at that tool belt of tricks. How people set up these microscopes in a way that could be compatible, what resolution do we need, etc.

And then if there's a clinical need or a life science need, it can hopefully motivate a fundamental scientist to come up with a trick. I'd hope that it's this sort of cyclical feedback loop that drives innovation on both sides.
Do you think such innovation today is scalable, the real-life scenarios? For instance, we work with high-throughput screening and digital pathology applications too. And recently, together with a hospital in Europe, we have calculated that they have 5,000 cancer patients per year. But if they use AI in clinical cancer diagnostics and keep all the data properly- the hospital will be able to keep data of only 125 patients per year. So basically, the solutions which are available today are not yet scalable for real-life scenarios. Do you think it's linked to the light-sheet microscopy too? And what would be the biggest obstacle to overcome for such solutions to be applicable to hospital environments?
I can say yes, scaling it out to a clinical level. I'll back up and just say at the academic level, in our laboratory, just over the last few years, there's one elephant in the room, which has really been an issue. And that is the data. The data is this beast with light-sheet microscopy that you have to wrangle at all levels. The first stage is just how do you ingest the data off of these cameras? Light-sheet microscopy tends to run one or sometimes more of these cameras full blast for hours, even days.

So, the question is how do you move that data around? How do you store it? How do you process it? When trying to think about how to move light-sheet microscopy to clinical pathology, I think we're there on the hardware and say the specimen prep. Regulatory approval is maybe a different thing to tackle, but the data is where a lot of the work has to be done.

I think moving the data to the cloud is one thing we've been exploring within our startup company. That makes things a little bit more feasible, I'd say, but still the storage of the data and actually just moving the data to the cloud can actually become a bottleneck.

The sheer size of the data is something that needs to be addressed.
Do you think the cloud will be definitely the way forward?
I think at a clinical level the cloud would be the place to go. Although there could be a regulatory approval issue, so that would have to be worked out. But we are grappling with this on-prem versus in-the-cloud issue right now. Do we keep the data in our storage servers locally? Or do we actually just get out to the cloud? I think there, the cost analysis is what will determine what ends up being the most feasible path forward.

Not to get too detailed, but two really important things there are: can we make the data smaller so that it's cheaper to store? And then secondly, if it is made smaller, what is the necessary means to uncompress it in the same way we compress data. We've been doing something which requires a GPU that is, I would say, not as feasible in the cloud because it is extremely expensive to use GPUs.

Being able to move away from that, not to get too detailed, but I think that's very important.
You've been published in the Nature and many other great research magazines that a lot of scientists dream about. How can one maximize chances of interesting discovery? What elements need to be present there?
That's a very good question. I think, my experience at the University of Washington can really speak to that one. For me personally, with this whole project in light-sheet microscopy, that has been kind of born out of our team at the University of Washington. I think no one person has all of the expertise to do everything.

In order to maximize impact, you really need to have different key components in a team that allow things to be successful. And I think we've had exactly that at the University of Washington, which has allowed us, hopefully, start to really drive this application and this technology. That is involved me arriving as a postdoc, someone who had technical engineering skills and was looking for a project to work on, but really within the clinical space, having clinicians on board and working with you. Because the clinicians actually have the issues, the challenges, the problems that they want to be solved.

It's almost like the customer in some ways. We worked directly with Nick Reader. Who's actually was a resident at the time and had this idea of: “Hey, I want to transform pathology”. And then his supervisor, Dr. True. Having them both as key members was extremely important. And then, of course, John Lou, my advisor to advise the technical side of things.

I think for any application, to maximize the impact you need to have a very important team of people. Specifically, within clinical applications, you need to have clinicians on board.
It's similar to the startup world. That it's not just about the idea, but a lot about the team. Does it ring a bell for you?
I definitely think so. On their own, each team member may be brilliant, but if they cannot work well together, then projects can stall projects can veer in the, in the wrong direction.

So, I think, yes, the team is extremely important and there's synergy there, there's chemistry there. We've been really lucky to have that in our work over the last few years and hope that it will continue into the future. And I hope that for whatever future pursuits, I kind of bite off. I hope that there's that same sort of experience because it's been a blast these last few years working with everybody, with our team here.
Thank you! And a question we ask everyone at 'Houston, we have a problem': if there's one word that would define the society in the next few years, what would it be for you?
I would think that, maybe it's in my mind because of what we just mentioned, but I would say 'teamwork' or 'teamwork at a global level'. I mean, I think that COVID-19 has really highlighted this as an issue which faces society, which crosses borders, crosses cultures, crosses economic status.

So we have had to work together. Well, there are still issues around how COVID has been addressed at some of these different levels, but by large we have worked together as states, countries, humans to address this problem. And I think that we are making good progress and hopefully we are able to beat it.

I think we were lucky with COVID-19 and that it wasn't as severe as some of the scientists said that it could have been. But when we look to the future towards things like climate change... climate change has been something which needs the same sort of teamwork to really address it. And unfortunately, something like COVID is so in your face that it kind of demands an immediate response, whereas climate change does not. There are other global issues which we faced for a long time.

And I think, we just have not been serious about addressing them at a really rapid pace. In my mind defining society, moving forward is really these global issues. How can we work together as a team, as a planet to address them? That's extremely challenging, probably the most difficult scientific, and also just social issues to address. But I would like to think that those are the areas where we make rapid strides, especially learning after this COVID-19 experience.
Dotphoton
Swiss deep tech startup specialised on software solutions for raw image compression for critical applications. Dotphoton uses insights from quantum physics to provide high compression ratios whilst still preserving original quality and image fidelity. Dotphoton's products are already being used by biomedical camera manufacturers and laboratories, and the core technology has been validated by major Swiss research centres.
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