Microscopy101
How to Get Better Fluorescence Images with Your Widefield Microscope: A Methodology Review
Dr. Markus Sticker, Dr. Rebecca Elsässer, Dr. Markus Neumann, and Dr. Horst Wolff* Carl Zeiss Microscopy GmbH, Germany
*
horst.wolff@zeiss.com
Abstract: Today, researchers have a number of powerful image pro- cessing methods at hand that have the potential to make comparably simple and inexpensive widefield microscope systems more powerful and versatile. These techniques can enable new insights into biologi- cal samples and allow new discoveries, so long as their limitations and pitfalls are known.
Keywords: deconvolution, computational clearing, background sub- traction, unsharp masking, image improvement
Introduction For decades, fluorescence microscopy has been an
invaluable tool in life sciences research, with new variations and implementations emerging almost every year. Laser scanning microscopy, airyscanning, structured illumination, light field and light sheet microscopy are among the many methods that have been developed through the years to over- come the limitations of the original widefield (WF) micros- copy setups. Tese more recent techniques have brought new insights into biological samples and enabled new discoveries. However, we should not forget that even comparably simple and inexpensive WF microscopes can help to extract mean- ingful data from many samples. In this article we focus on some popular image processing methods. Tese techniques have the potential to make WF microscope systems more powerful and versatile so long as their limitations and pitfalls are known. Simple deblurring methods such as background subtraction, computational clearing, unsharp masking, and the like deliver a quick and clearer preview of the sample, while more accurate deconvolution models yield higher reso- lution, fewer artifacts, and more quantitative results.
Limitations of Widefield Microscopy Te most obvious limitation of WF fluorescence micros-
copy is the out-of-focus blur in the image that essentially limits contrast and prevents clear identification of structures and objects of interest. In a WF microscope, a beam of light simultaneously illuminates the whole field-of-view to excite all the fluorophores it contains. Te beauty of this approach is that all parts of the specimen are viewed simultaneously, and the image can be captured simply and rapidly with a camera. However, due to diffraction-limited optics and the projection
36 doi:10.1017/S155192952000156X
of out-of-focus light onto the camera-sensor, this usually results in images of low contrast, especially with thicker and more densely labeled specimens. Optical sectioning meth- ods, such as confocal laser scanning microscopy (CLSM) or structured illumination microscopy (SIM), exclude out-of- focus light from the image and thereby will typically show better contrast and reveal more details (Figure 1). Using a WF setup typically does the job for thin speci-
mens, for example, layers of single cells or isolated organ- elles. When the signal is sparse and the objects are thin, the resulting WF images can reveal as much information as those captured with costlier, more highly sophisticated equipment (Figure 2). On the other hand, most WF microscopes can- not image thick tissues, 3D cell culture, spheroids, or whole organisms with sufficient contrast. Tese samples signifi- cantly scatter the light, and multiple layers of fluorescent structures make it hard to distinguish details in a 3D volume.
Solutions for 3D Volumes Figure 1 shows the situation that occurs when only a single
image of the sample is recorded. Tere are multiple possibilities when a 3D volume is sought. Optical sectioning techniques can be used to generate a stack of images that can then be rendered into a 3D object (Figure 3). For WF instruments, a technique called deconvolution has been available in microscopy for more than 20 years. In contrast to the early-2000s, today’s deconvolu- tion programs and microscopes produce results almost instan- taneously, as GPU-based processing with the latest computer hardware accelerates the computation tremendously. Deconvo- lution microscopy is one of the best-described processing meth- ods in the microscopical sciences and is based on mathematical models that aim to reverse the distortions that take place in opti- cal instruments. Tere are numerous variants of deconvolution algorithms, and they all come with their own advantages and disadvantages, essentially differing in reconstruction quality and speed. Since there is not one perfect method for all image conditions, ZEN imaging soſtware implements know-how from more than 20 published deconvolution variants to deliver best user experience and valid results at all times. A major ben- efit of the WF deconvolution approach is that no light from the
www.microscopy-today.com • 2020 November
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