Perspectives on Microscopy

0
Olympus scanR 3.1 high-content screening station
Olympus has introduced the scanR 3.1 high-content screening station. According to the company, the scanR 3.1 embodies the “self-learning microscopy” concept and is capable of quickly gathering data from large live-cell populations. To accelerate setup, the system uses a quickly acquired set of images to generate “ground truth” data without requiring human annotations. It then uses convolutional neural networks to autonomously and rapidly analyze large sets of images.

GEN: Liquid lenses were poised to make a big impact for microscope objectives. Has that come to fruition or fizzled out?

Itai Hayut, PhD
Itai Hayut, PhD
CEO & Co-founder, Scopio Labs

Hayut: While liquid lenses have not yet come to fruition, they will have their place in specific niche markets. The potential for the product must be further explored with possibilities such as 3D imaging on fixed samples, which offers additional dimensions of information that have not been previously available. However, given the current quality limitations of liquid lenses, the product is not poised to make any further impact on microscopy unless it is combined with computational photography tools.

The integration of computational photography with liquid lenses will enable image correction and quality improvement on samples captured. Once this integration takes place, liquid lenses can have a vastly greater impact in the field of microscopy.

 

GEN: Cloud-based imaging software seems to be gaining ground and allowing more researchers access to sample analysis that they didn’t have previously. How do you see this evolving in the future?

Hayut: The future of microscopy lies in our ability to combine digital information with artificial intelligence (AI). Cloud-based imaging presents a significant advantage to the field of research and science by increasing collaboration in a way that has never before been possible. What is most exciting about its integration is it enables product developers to embed software tools into AI-powered devices.

To transform cloud-based imaging into a useful and scalable industry tool, development of AI tools must be accelerated to meet the growing needs of researchers. Such tools must be created for immediate deployment, seamlessly integrating with standard tools and offering a true end-to-end solution. Most importantly, AI models must also be built to meet clinical levels of validation.

 

GEN: What do you anticipate will be the biggest trend in microscopy over the next several years?

Hayut: The ability to immediately develop AI on top of data will enable seamless transformation of any hypothesis into a useful tool that can propel the industry forward. We are facing a future of new discoveries that have become possible with the integration of digital tools in the laboratory. With AI-powered products such as the Scopio Labs X100 Full Field Peripheral Blood Smear platform that captures high-res digital images, we can broaden the accessibility of microscopy data like never before, shattering the limitations of glass-based analysis.

With the digitization of microscopy files, researchers can easily store, upload, and share high-res data, integrate results with existing medical records, and develop big data sets to make diagnostics more meaningful.

 

GEN: Liquid lenses were poised to make a big impact for microscope objectives. Has that come to fruition or fizzled out?

Jimmy Fong
Jimmy Fong
Product Line Manager for Multiphoton Microscopes, Bruker

Fong: Liquid lenses have been intriguing because of their ability to vary their curvature and focal length, in contrast to standard glass lenses that have fixed focal lengths. In a microscope, the traditional method to image a different depth in the sample has been to move the objective lens up or down, which is a slow process using a motor or a piezoelectric stage. Liquid lenses promise faster volumetric imaging by being able to change focus quickly, all without moving the objective. Early implementations of the liquid lenses were not tightly integrated into optical systems, causing image quality issues, but manufacturers now have improved designs. In our new designs, we take advantage of the fast-focusing property and the fact that the objective lens is stationary to enable simultaneous imaging with 3D photostimulation, a technique that is difficult in microscopes with moving objective lenses.

 

GEN: Two-photon microscopy has been around for roughly two decades. Are there new innovations in two-photon technology or improvements to other types of microscopy to allow for visualization deep within tissues?

Fong: Advances in two-photon microscopy have allowed for faster lateral scanning with resonant galvanometers, faster axial scanning with piezoelectric stages and liquid lenses, as well as new techniques for using two-photon photomanipulation to stimulate changes in the sample during visualization. New tools like spatial light modulators allow for coupling two-photon imaging with 3D patterned activation of cells deep within a live specimen, which is critical for many research areas like optogenetics.

In addition, researchers now can pair the instruments with Gradient Index (GRIN) lenses that are implanted within an animal to extend the imaging depth even further than with two-photon imaging alone. Furthermore, improvements in the brightness of fluorescent probes increase the signal-to-noise ratio of imaging deep within the sample, as more of the photons are captured by detectors. Beyond two-photon imaging, three-photon imaging is becoming more widely adopted, which aims to penetrate even deeper by utilizing even further infrared excitation wavelengths.

 

GEN: What do you anticipate the biggest trend in microscopy to be over the next several years?

Fong: Microscopists have long desired to image faster, deeper, at higher resolutions, over larger areas, and for longer time periods. It is not likely that a perfect microscope will be developed that can excel at all these criteria for every specimen; therefore we are anticipating that there will be a growing trend to combine multiple microscopy modalities in the study of a biological question. This does not necessarily mean that all these modalities will be found on a single instrument, but access to microscopy instrumentation has expanded, and scientists can now approach investigations from different facets.

Researchers can study long-term developmental questions with a light-sheet microscope, probe the same structures in high detail with a super-resolution microscope, and furthermore, alter in-vivo function through photoactivation two-photon optogenetics. A combination of these new tools can illuminate our understanding and paint a more complete picture of the underlying biology.

 

GEN: Can you separate out the microscope facts from the fiction? For example, how easy is it really to crush an objective? Or, how bad is it really to get oil on the other (non-oil immersion) objectives?

Mark Clymer
Mark Clymer
Director of Marketing, ACCU-SCOPE

Clymer: Let’s address two common “No Nos” in microscopy.

True or False:

It is really bad to get immersion oil on a non-oil immersion objective (this includes dry, water, glycerin, and silicon oil immersion objectives). False. Although unfortunate, the non-oil immersion objective is not ruined. Simply follow the manufacturer’s procedure to clean the objective ASAP.

It is really bad to turn the nosepiece using the objectives. False, with some caveats.  Best practice is to use the knurled ring to rotate the nosepiece. For very high-quality objectives, optical distortions may result with repeated disregard for the knurled ring—you may also inadvertently change the position of a correction collar. For other objectives, the writing on the barrel of the objective can be worn off, effectively rendering it unreadable.

 

GEN: What is the one thing you wish people would do with their microscopes?

Clymer: Köhler illumination, Köhler alignment, or Köhler adjustment, whatever you choose to call it. This is the least performed procedure that can significantly improve microscope performance. In the first part of the procedure, Köhler illumination aligns the light path. The second part of the procedure optimizes the light delivery to the specimen and for the objectives being used. During the second part, the operator sets the numerical aperture (“NA”) of the condenser to approximate that of the objective. The objective’s NA is written on the barrel of the objective, next to the magnification. Many condensers have NA markings on them with an adjustable iris diaphragm. It only takes a few seconds to adjust the condenser NA after changing the objective, and the difference can be remarkable.

 

GEN: What’s the most important thing for long-term care of a microscope?

Clymer: Regardless of how sophisticated the microscope, every microscope is an investment that can pay dividends for decades, provided it is well maintained. Routine cleaning of the microscope is the most important thing that anyone can do to ensure performance. Wipe the stage regularly with 70% alcohol. Clean the objectives and eyepieces beginning with the least aggressive method (e.g., air puffer or compressed air to remove dust). As needed to remove debris and other substances, use lens tissue wrapped around a cotton swab and gently wipe in a circular motion from the inside of the lens to the outside (spiral). Add solvents in increasing strength (i.e., water, lens cleaner, 70% alcohol). If these steps fail, seek professional cleaning. For mechanical maintenance, contact a microscope service dealer.

 

GEN: Liquid lenses were poised to make a big impact for microscope objectives. Has that come to fruition or fizzled out?

Cheng-Hao Chien, PhD
Cheng-Hao Chien, PhD
Associate Product Manager, Life Science/Scientific Solutions Group, Olympus

Chien: Liquid lens technology is a recent innovation that enables rapid scanning in Z without the physical movement of the objective. This field of technology is continuing to evolve and impact the field of microscopy. To address some of the challenges that microscopists still face, Olympus recently developed the Inner Focus unit, an articulating nosepiece that employs movement-free rapid axial focusing technology. This technology is proving to be crucial for biological imaging applications that were not possible using conventional methods. For example, rapid focus adjustment enables researchers to record in parallel dynamic neuronal activity at multiple depth planes in an intact brain. This technique can be used to examine interactions between neurons at different depths or to simply sample more independent neurons at the same time.

 

GEN: Two-photon microscopy has been around for roughly two decades. Are there new innovations in two-photon technology or improvements to other types of microscopy to allow for visualization deep within tissues?

Chien: One of the challenges in deep optical imaging within tissues comes from the increase in light scattering that greatly compromises signal and image contrast. Recent developments in red-shifted fluorescent proteins, combined with convenient long-wavelength pulsed laser sources, have enabled researchers to extend the multiphoton excitation wavelength beyond 1000 nm and thus minimize the impact of light scattering in deep imaging. In addition to imaging with longer wavelengths, deep imaging can be improved by reducing spherical aberration. For example, Olympus TruResolution objectives use an automated correction collar to dynamically compensate for spherical aberration. For samples that produce heavy laser scattering, Deep Focus Mode in the FVMPE-RS Multiphoton Microscope can maximize fluorescence signal by adjusting the diameter of the laser beam so that more excitation light reaches depth, helping produce bright, high-contrast images.

 

GEN: Total internal reflection fluorescence microscopy (TIRFM) and light-sheet fluorescence microscopy (LSFM) are two relatively recent innovations in the microscopy field. How can a researcher know what is the right technology for a given application? If someone wants the best possible picture of a fixed or live tissue sample, how can they decide which one to use? What are the limitations of these new technologies?

Joanna Hawryluk
Joanna Hawryluk, PhD
Associate Product Manager, Microscopy, Scientific Solutions Group, Olympus Corporation of the Americas

Hawryluk: TIRFM and LSFM employ new ways to optically section your sample of interest to reduce photobleaching. The most important first step for researchers is to determine which model samples they intend to use and which objects of interest they are trying to measure/image with fluorescence microscopy. TIRF will allow one to observe single molecule detection (i.e., cell adhesion assays) within your sample of interest, but you are limited to less than a 200-nm section of your sample mounted on a coverslip. However, if you are interested in observing the overall three-dimensional structure (i.e., neural or vascular networks) of whole tissue sections or development of sample organisms such as zebrafish, LSFM will be or more interest.

Combined with a high-speed sCMOS camera for emission collection, LSFM provides greater depth-of-field, lower signal-to-noise, and greater imaging speeds for reduced phototoxicity as previously mentioned. LSFM also allows researchers to image in three-dimensions (3D) and at high speeds with reduced phototoxicity. This is ideal for sample types from whole model organisms, tissue explants, and cell cultures, while maintaining the physiological integrity of your sample. In other words, no more tissue sectioning.

 

GEN: What do you anticipate the biggest trend in microscopy to be over the next several years?

Mike Wördemann
Mike Wördemann, PhD
Product Manager, Olympus

Wördemann: The power of artificial intelligence (AI) is enabling groundbreaking analyses of living cells that previously seemed impossible. The highlight of deep-learning is the concept of self-learning microscopy. It simplifies the acquisition of data from large populations of living cells, enabling reliable, robust experimental results. In the observation and analysis of biological characteristics and processes, the labeling of cell with color markers, especially fluorescent markers, is invaluable. Deep learning-based approaches of AI can provide better access to information contained in transmitted light images, making the fluorescent markers that are currently used for staining cells or cell components unnecessary. The shorter exposure times in brightfield imaging and in analysis using low-light fluorescence conditions also reduce phototoxicity and contribute to time savings.

This site uses Akismet to reduce spam. Learn how your comment data is processed.