Abstract
The visualization of infection processes in tissues and organs by immunolabeling is a key method in modern infection biology. The ability to observe and study the distribution, tropism, and abundance of pathogens inside of organ tissues provides pivotal data on disease development and progression. Using conventional microscopy methods, immunolabeling is mostly restricted to thin sections obtained from paraffin-embedded or frozen samples. However, the limited 2D image plane of these thin sections may lead to the loss of crucial information on the complex structure of an infected organ and the cellular context of the infection. Modern multicolor, immunostaining-compatible tissue clearing techniques now provide a relatively fast and inexpensive way to study high-volume 3D image stacks of virus-infected organ tissue. By exposing the tissue to organic solvents, it becomes optically transparent. This matches the sample’s refractive indices and eventually leads to a significant reduction of light scattering. Thus, in combination with long free working distance objectives, large tissue sections up to 1 mm in size can be imaged by conventional confocal laser scanning microscopy (CLSM) at high resolution. Here, we describe a protocol to apply deep-tissue imaging after tissue clearing to visualize rabies virus distribution in infected brains in order to study topics like virus pathogenesis, spread, tropism, and neuroinvasion.
Original language | English |
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Article number | e59402 |
Journal | Journal of Visualized Experiments |
Volume | 2019 |
Issue number | 146 |
DOIs | |
Publication status | Published - 30 Apr 2019 |
Externally published | Yes |
Bibliographical note
Funding Information:The authors thank Thomas C. Mettenleiter and Verena te Kamp for critically reading the manuscript. This work was supported by the Federal Excellence Initiative of Mecklenburg Western Pomerania and the European Social Fund (ESF) Grant KoInfekt (ESF/14-BM-A55-0002/16) and an intramural collaborative research grant on Lyssaviruses at the Friedrich-Loeffler-Institute (Ri-0372).
Publisher Copyright:
© 2019 Journal of Visualized Experiments.