A biophysicist armed with coding expertise, Richard de Mets is deputy facility manager at the Microscopy Core at the Mechanobiology Institute (MBI) in Singapore. If you’ve ever wondered what your confocal image would look like under a super-expensive, super-resolution microscope, or you need suitable image processing codes for your research, de Mets has the right tools for the job.
Previously, de Mets worked in the Virgile Viasnoff lab at MBI for nearly four years, engaging in a wide variety of research projects and enjoying collaboration with scientists from diverse backgrounds and areas of expertise. But he knew from his early lab days in France that he did not want to be a principal investigator. The thought of being “stuck in the office to write grants and forms,” de Mets says, didn’t match his gregarious personality. Instead, he seized upon the chance to move to the Microscopy Core at MBI in September 2019.
“I want to help researchers understand their projects and teach them how to use microscopes,” de Mets says, “so they can be fully independent and, of course, bring new knowledge wherever they are.” Although he had no prior experience working in a core facility, the job responsibilities matched his outgoing personality and agile research interests.
Currently, de Mets and the rest of the team are working to develop new algorithms to help scientists with their image processing needs. Their first algorithm is currently under testing and uses a class of machine learning systems called GAN (Generative Adversarial Network).
“I was trying to develop something capable of image restoration and improving image quality at the same time,” de Mets says. “My idea was to use a GAN to be able to link a low signal-to-noise ratio (SNR) image to a high resolution, high SNR image.” In other words, the algorithm can predict how a confocal microscopy image might look if it were taken with a super-resolution microscope (see fig 1). This image processing approach is particularly suitable for thick samples that typically cannot be imaged by super-resolution microscopy and need to be protected from photobleaching. While a super-resolution microscope may be hard to come by for most scientists, de Mets’s algorithm may be an attractive alternative!
Together with his colleagues, de Mets is also developing and adapting a plethora of other image processing codes that utilize neural networks and deep learning approaches such as Unets, CARE, Noise2Void and Noise2Noise. One such code has been configured exclusively for classification and segmentation purposes, specifically to differentiate signals and reduce background noise in images (see fig 2). De Mets says the biggest challenges in developing such image analysis methods are understanding the nature of the scientific question, evaluating which microscopy and image processing approaches can help provide answers, and pinpointing how these approaches can be improved. He describes his team’s arsenal of image analysis tools as works in progress that he is open to sharing with interested researchers, to help test and develop them further.
“I am happy for people to contact me if they want to know more about our work and to discuss how we can help their research,” de Mets says.
De Mets looks back at the past year fondly, stating that he has found an ideal role where he can follow his passion for developing resources that cater to a multitude of research interests rather than a single project. He advises those looking to transition professionally from the bench to a scientific platform to keep their research interests broad, acquire basic knowledge in the platform of interest, and gain collaborative experience. In addition to technical skills, de Mets emphasizes that developing soft skills, such as effective communication and teamwork, is very important. At the end of the day, building trust in your platform’s capabilities and forging partnerships are key to working on exciting science.
Richard de Mets can be reached at mbirdm@nus.edu.sg and https://www.linkedin.com/in/richard-de-mets-1b1806103/
Image details
Fig 1
Sample PFA-fixed monolayer of primary rat hepatocytes cultured on glass bottom dishes coated with fibronectin. Cells are stained with Phalloidin-ATTO565.
Microscopy and imaging setup
Low SNR image: Spinning disk W1 unit at 60X magnification, Photometrics Prime 95b camera with low laser power and 30ms exposure time.
High SNR image: Spinning disk W1 unit at 60X magnification, Photometrics Prime 95b camera with high laser power and 500ms exposure time. Roper Scientific Live-SR module engaged to improve the resolution.
The first set of images exemplifies the use of a variant of the Generative Adversarial Network (GAN), called pix2pix network, dedicated to study the link between paired images for the purpose of image restoration and improvement. Both high and low signal-to-noise ratio (SNR) images were taken using the same sample and microscope. Upon training, the network was able to scan the low SNR image and provide a super-resolution image prediction that is comparable to a high SNR image taken independently.
Fig 2
Sample PFA-fixed monolayer of primary rat hepatocytes cultured on glass bottom dishes coated with fibronectin. Cells are stained with Phalloidin-ATTO565.
Microscopy and imaging setup
Spinning disk W1 unit at 60X magnification, Photometrics Prime 95b camera. Image taken 3 microns above the coverslip in order to see lumen formation between two hepatocytes.
The second set of images shows the use of an algorithm dedicated to classification and segmentation, based on the Unets convolutional neural network approach. The Z-stack images show lumen formation between adjacent cells. The goal of the algorithm is to be able to differentiate signals coming from a cell junction to those from the lumen, as this cannot be performed using classical thresholding methods with a single staining. To train the algorithm, de Mets first manually demarcated masks on ImageJ to differentiate nuclei (red), junctions (green) and lumen (blue) across 20 fields of view. Using these as targets, the network was able to predict masks with 94% accuracy after just 20 minutes of training. Moreover, while the network has been trained on 2D data, it can be used for 3D segmentation purposes too, for example to estimate nuclei volumes or lumen volume.
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Why did you leave academia?
Our interviewees describe their frustrations.
“I was on about my fourth temporary contract in academia, getting a bit frustrated. I would just get started with projects and then have to move on to the next job.”
—Dan Metcalf, microscopy sales manager
“After becoming a mother, I was thinking, ‘Do I really want to go back to research?’ And with two scientists at home—my husband is a researcher—I thought, it’s not possible.
—Gloria Fuentes, medical illustrator
“I realized that academic research wasn’t for me while trudging through days of failed experiments…I wanted to work on something that impacted society in my lifetime.
—Bani K. Suri, entrepreneur
“I realized I became a postdoc because it was the ‘default’ choice, rather than something I really wanted to do.”
—Anjana Narayanan, consultant and product manager
“We were doing non-applied science, basic research, and I had reached the point where I was like, ‘Why am I doing all of this?’ I decided to go to a company because I needed to have meaning in the work I’m doing.”
—Carla Pratt, organic farmer
On taking the leap…
Preparing, and then no turning back.
“When a recruiter approached me about selling Nikon products and training Nikon customers in superresolution microscopy, it seemed like a natural step.”
“I stumbled onto an opportunity to be part of a talent incubator. I learned a great deal about startups, thought processes, customer relations, doing market analysis and other ‘real-world’ skills.”
“I tried to learn as much as possible about management consulting. I read articles about top consulting firms, connected to consultants on LinkedIn, did informational chats, and signed up to a consulting newsletter.”
…and landing on your feet.
What skills did you need in your new position?
“Presentation skills are important in the medical liaison industry. Communication really underpins everything we do.”
—Sheri Hussain, medical liaison
“You need to know and communicate the science in order to put all these things into a visual that makes sense for the project. I find illustrating trickier than writing sometimes.”
“Being my own boss, I have to be strict enough to keep my own schedule and make sure I do all the work. ”
In a startup, you work long, intense hours on your own work while putting other processes in place at the same time—you roll up your sleeves and do what needs to be done! You learn a lot and wear many hats.”
The best part of your new career…
It’s still about science, many told us.
“Meeting with clinicians and talking to them is probably my favorite bit.”
“I really enjoy keeping up with the latest research. Even when I was on maternity leave, instead of watching Netflix I was reading scientific papers. Today, I’ve been burying myself in papers related to Covid-19 just like any other researcher.”
“When I was a consultant, I loved working on different cases, understanding business scenarios and problem solving.”
…and the worst.
Paperwork, managing people, and oh, the emails!
“Meeting tender deadlines is the most stressful. Filling out tender documents can be several days of work…You can miss a £300,000 sale by missing a tender deadline by just an hour.”
“The least favorite is probably admin. That’s something I work on; I try to make sure I make time for my admin first up.”
“Continuous human interaction, which is often lacking in a lab setup, is a major part of startup life that can sometimes overwhelm people from academia.”
“After two years as a consultant I realized that consulting is more than a job, it is a lifestyle. Constant travel and living out of a suitcase was hard.”
Advice for those who want to leave academia?
Words of warning and encouragement.
“As a scientist, you have many transferable skills that are highly valuable. The key is to make sure you showcase them in the right light.”
“For creative people, I think the field of medical illustration is here to stay—especially animation. Five years ago, you wouldn’t think someone working from home could do it. Now, you invest in a GPS card and you can make it.”
“My advice for biologists venturing into entrepreneurship is to carefully consider the type of incubator you wish to join and thoroughly assess their capability to support your specific ideas and startup style.”
“Employers are less interested in knowing which labs you worked in and for how long. Focus on things you have done and skills you have picked up at each stage of your professional journey.”
—Tony Cabrejos, recruiter
“It’s only as difficult as you make it in your head.”
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After years in neurobiology, Daniel Metcalf realized that he was interested in microscopes as much as cell biology research itself. He moved to sales manager for Scientifica’s EMEA region, overseeing six product specialists.
What drew you to this job, and can you briefly describe the work?
I’ve always liked instrumentation and microscopy. I like the high-value technical sales. It’s about building a relationship and understanding the research, and helping the customer solve their research questions.
Tell me about your sales team.
Scientifica is me plus six others in sales in EMEA; then there’s a U.S. sales team of about the same size. We have someone dedicated to China. My team is split between the key European countries and Israel primarily, for example UK, France, Germany, Benelux, Spain, Italy, and Scandinavia. For some countries, like Japan, Australia, and India, distributors sell on our behalf.
We have two product ranges. One is electrophysiology: the electrical recordings from neurons and brain samples. The other is multiphoton microscopes for imaging the brain and the nervous system. Almost all of our customers are academic research scientists in neuroscience.
Can you describe a typical day or week?
I have regular weekly catch-ups, helping the team with their sales; I run team meetings and help with customer calls and visits. Before the coronavirus we traveled quite a bit—my team might spend 30 to 50 percent of its time traveling.
We often do product demos. You have to work out what experiments will be important to your client. If it’s a pre-existing project, what are the problems with their current equipment and how can you do it better? It’s a back and forth, because they might not know what’s possible with the equipment.
Particularly in Europe, you’re often talking to a customer before they’ve secured funding to make the purchase. It could be six months before they get a decision on the funding scheme. And there are tender procedures on high-value equipment. Some of the multiphoton microscopes cost £300,000 or more, and those sales can last six months to a couple of years. We have further discussions after the money comes through—by then, they might have changed their research project, so we change the specifications, give them updated quotes, pricing, exchange rates. And then these tender procurement processes can take three to six months.
You moved from academia to industry quite a few years ago. Why? Was the transition intimidating?
I was on about my fourth temporary contract in academia, getting a bit frustrated. I would just get started with projects and then have to move on to the next job.
The transition to industry happened in two steps. I moved from doing a postdoc at the Cambridge Institute for Medical Research to being a research scientist at the National Physical Laboratory (NPL), which is still quite academically focused.
I made the decision to move away from researching neurodegenerative diseases using microscopes to actually putting the emphasis on microscope development itself. I realized I was much more interested in the use of microscopy and its application than in Cell Biology and neurodegeneration research.
When a recruiter approached me about selling Nikon products and training Nikon customers in superresolution microscopy, it seemed like a natural step—I was already doing that at NPL, just not with a commercial emphasis. I wasn’t worried, though I did know that once I did it, there probably wouldn’t be any going back to academia.
There was a bit of a transition in my first few years, finishing off some projects that I had started at NPL. But I haven’t done any publishing recently. I’ve helped with research projects, but not enough to qualify as an author. But there’s definitely still an interaction with researchers.
What are the career pathways at Scientifica?
A lot of people stay in sales; there is the management role like mine. That’s mostly the linear career path. But people can move from sales into the applications or technical side of things. Or, another path is sideways into things like product management or marketing. There’s occasionally movement back into academia, into something like microscopy core facility management.
What technological advances are coming in microscopy?
One area is three-photon imaging, as opposed to the typical two-photon. It allows you to go deeper. We’re starting to sell it.
There’s a new technology that a few companies are using involving spatial light modulators. Instead of just observing, researchers can target and stimulate individual cells, even individual synapses, and control that in three dimensions. One can simultaneously hit thirty cells with light and see how they react.
How is your relationship with researchers?
The key is building a relationship. Some people are very open to having conversations; others are more reserved or suspicious until you’ve built a level of trust. They’re cynical about sales or perhaps aren’t convinced that you know what you’re talking about and think they’re just going to get misled or badly advised.
So long as you don’t waste their time and you listen to them, rather than just talk about your product endlessly, you get a nice kind of rapport going. It doesn’t always translate into sales, but over six or seven years you get to know a customer really well. You know their research; they know your product.
How much work and time goes into a quote?
If it’s a big multiphoton system, you probably want at least an hour speaking with the scientists to understand what they’re trying to do. Then it takes maybe another hour to put together a complex quote, and then we prefer to talk it through with them—there could be fifty lines of itemized things with technical jargon. For more complex configurations, we have to contact our tech support or R&D team and it could take a day to answer their questions; we may then go back and forth with the researcher a few more times to refine the details and the specifications.
Because some procurement rules require three quotes, we often get people contacting us just to get a quote to submit to procurement or align with tender requirements—they have no intention of buying our product. For low-value stuff, a camera, say, it’s Ok, it’s only two minutes of our time. We might decline to provide more detailed quotes, but you can sometimes convince someone to consider your product if they’re willing to discuss their requirements.
Your products are often made of materials from other companies.
Yes. We use Nikon and Olympus objective lenses in our microscopes. And we don’t make our own cameras—we use Hamamatsu, Photometrics, or Andor. It’s true for almost all high-end scientific equipment made by small and medium sized companies: the optics are almost always from another company. All of our products are some kind of fusion of Scientifica manufactured products plus some of those other products from other companies to make up a complete system.
Do all these different companies’ components lead to sales conflicts?
It’s a funny thing in the industry. Andor sells spinning disk microscopes and cameras. At Nikon we used to buy the Andor cameras, but then we’d be competing against them on the spinning disks. At Scientifica, Nikon and Leica might buy our stages or manipulators but then we occasionally compete with them for microscope sales. But by and large it’s treated separately.
What are the most stressful things about your job?
Meeting tender deadlines. Filling out tender documents can be several days of work. You have to stay very organized. You can miss a £300,000 sale by missing a tender deadline by just an hour. Also, as a manager, losing good team members is very stressful because it takes a while to train people.
You can reach out to Daniel Metcalf via LinkedIn.
https://www.linkedin.com/in/daniel-metcalf-84575334/
The views and opinions expressed in this article are solely those of the author and not that of ImagenScience.
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