Imaging immune cells in context

Image shows a piece of white adipose tissue cleared and imaged in 3D using our EMOVI protocol (Hofmann et al., Frontiers in Immunology, 2021). Vessels in magenta, MHC-II expressing cells in cyan. Copyright: Keppler lab

During her work in London, Selina first started imaging (confocal, 2-photon and TIRFM). Recently, the Keppler lab implemented 3D imaging of organs or large tissue pieces in our workflow. We use confocal, widefield, as well as light sheet microscopy to image cleared organs or large tissue pieces. We are always interested in exploring new applications for our method – get in touch!

Our EMOVI approach:

Optical clearing and volumetric imaging of large tissue pieces or whole organs holds great promise for the in-depth study of cells in their natural surroundings. A better understanding of cell-cell or cell-niche interactions is crucial to understand the complexity of inflammatory or pathophysiological processes. A multitude of optical clearing methods followed by 3D imaging have been developed for this purpose, thereby mainly focusing on structural components and nerval architecture. We recently developed and used EMOVI (efficient tissue clearing and multi-organ volumetric imaging) to quantify changes in sparse cell populations in the lung during systemic inflammation. Furthermore, we found that tissue clearing permits volumetric multiplex imaging and quantitative histo-cytometry as measure of disease activity in nephritic kidneys. Check-out our recent publication for more details!

Frontiers | Efficient Tissue Clearing and Multi-Organ Volumetric Imaging Enable Quantitative Visualization of Sparse Immune Cell Populations During Inflammation | Immunology (frontiersin.org)

Imaris to FlowJo file conversion tool:

Histo-cytometry maps the positioning and phenotypic identity of cell stained with multiplexed antibodies and hence operates analogous to flow cytometric analysis with the addition of the spatial organization of immune cells in tissues. To translate fluorescence data into volumetric, representative surfaces or point-like spots, we use Imaris. For each object, a variety of parameters is calculated. For surfaces, these parameters include for example the position (x, y, z), volume, sphericity, median fluorescence intensities for all channels, volume and the distance to the closest object of defined surfaces/spots. All statistics relevant for analysis can then be exported as a collection of csv files and subsequently edited and concatenated into a single summary file with our open-access standalone Python application:

https://gitlab.com/kepplerlab/imaris_statistics_converter

Please cite your sources!

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