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Light Microscopy Techniques


25 × multiphoton Olympus SCALEVIEW objective, allowed the researchers to observe contiguous brain tissue up to 8 mm deep, several times deeper than ever before. Lens focus through even the deepest tissue was assured with specialized correction collars on the objectives [ 7 ].


The announcement of SCALE was followed in 2013 by CLARITY, which came out of Karl Deisseroth’s laboratory at Stanford University [ 8 ]. CLARITY, another promising clearing technique, involves creating a stable tissue-hydrogel hybrid out of thick biological tissue without changing the tissue’s structure [ 9 ]. CLARITY has the potential to help researchers extract vital neurobiological data from intact tissue deep within specimens ( Figure 5 ).


Clearing techniques such as SCALE and CLARITY


are signifi cant because they allow researchers to visualize neurological connections through the dense, light-scattering tissue of the brain. Because they do not require specimen slicing, these clearing reagants have improved the ability to map neural pathways continuously. Although these deep-imaging techniques have great potential, they are currently limited to fi xed-tissue applications.


Super-Resolution Microscopy


Researchers are always seeking to enhance resolution at any depth within a specimen. Super-resolution light microscopy includes a wide spectrum of imaging soſt ware, hardware, and methodologies that can improve the lateral ( x , y ) resolution of a microscope at various depths. Such resolution can even extend beyond the theoretical diff raction limit of a light optical system [ 10 ]. Many super-resolution techniques employ time- resolved localization of photoswitchable fluorophores and sequential activation to build ultra-high-resolution images from acquired data.


While super resolution can be achieved by selectively


deactivating fl uorophores, as in stimulated emission depletion (STED) microscopy [ 11 ], other techniques provide benefits with respect to axial resolution (depth). Structured illumination microscopy (SIM) is an example. First demonstrated by a group of Janelia Farm researchers in 2008 [ 12 ], SIM scanners have been shown to achieve z -axis resolution to about 300 nm [ 13 ]. Super-resolution imaging has enormous potential for helping researchers image phenomena they could not see before. T ese methodologies present challenges as well. Most super-resolution techniques require expensive hardware such as specialized photoswitching probes and specifi c laser systems designed for localization [ 14 ]. They also involve rigorous sample preparation, making the methodologies difficult to implement in some laboratories. In addition, super resolution depends on the use of complex mathematical models, making its computational, storage, and upkeep requirements challenging. Several companies now off er aff ordable soſt ware-based options that provide some of the benefits of hardware-based super resolution.


Conclusion


From capturing processes at the surface of a membrane to resolving phenomena at unprecedented depths, light


2016 May • www.microscopy-today.com 43


microscopy has evolved significantly over the past decade. Only after identifying the parameters of importance to the phenomena under study and understanding the pros and cons of each prospective microscopy technique can the best imaging methodology and equipment be matched to a given application.


References [1] D Murphy and MW Davidson , Fundamentals of Light Microscopy and Electronic Imaging , 2nd ed. , Wiley-Blackwell , Hoboken , 2013 .


[2] NL T ompson et al ., Biophys J 33 ( 1981 ) 435 – 54 . [3] E Betzig et al ., Science 313 ( 2006 ) 1642 – 45 . [4] J Pawley , ed., Handbook of Biological Confocal Microscopy , 3rd ed. , Springer , New York , 2006 .


[5] W Denk et al ., Science 248 ( 1990 ) 73 – 76 . [6] H Hama et al ., Nat Neurosci 14 ( 2011 ) 1481 – 88 . [7] B Brinkman , Microscopy Today 21 ( 2013 ) 10 – 13 . [8] K Chung and K Deisseroth , Nat Methods 10 ( 2013 ) 508 – 13 .


[9] R Tomer et al ., Nat Protoc 9 ( 2014 ) 1682 – 97 . [10] JA Conchello and JW Lichtman , Nat Methods 2 ( 2005 ) 920 – 31 .


[11] SW Hell and J Wichmann , Opt Lett 19 ( 1994 ) 780 – 82 . [12] L Shao et al ., Nat Methods 8 ( 2011 ) 1044 – 46 . [13] MGL Gustafsson , P Natl Acad Sci 102 ( 2005 ) 13081 – 86 . [14] MJ Rust et al ., Nat Methods 3 ( 2006 ) 793 – 96 .


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