Our approach allows us to perform the quantitative analysis of a Aspergillus fumigatus Conidia distribution in whole-mount optically cleared mouse lung. Aspergillus fumigatus Conidia can penetrate the respiratory tract and cause life-threatening diseases in immunocompromised patients. The respiratory tract of mammalians is a system of airways of different generations.
Each generation is characterized by the different structures of airway walls and immune cell populations. In the bronchial branches, aspergillus fumigatus Conidia be eliminated by mucociliary clearance. However, in available space, the mucociliary clearance doesn't work and Conidia must be cleared by the immune cells, such as alveolar macrophages and neutrophils.
As is the spatial temporal aspects of Conidia distribution in the airways are important. However, not properly investigated question in understanding of the disease mechanisms. Here we present an experimental setup for the quantitative analysis of Aspergillus fumigatus Conidia distribution in the airways of infected mice.
I apply 50 microliters of labeled Aspergillus fumigatus Conidia to mouse. Six hours later, harvest the lung. Fix overnight in 2%formaldehyde and stain with labeled Streptavidin conjugate.
Then subject the specimen to the optical clearing. Place the specimen in the glass bottle filled with 50%methanol water solution and place it onto the sample mixer at room temperature for one hour. Replace 50%methanol with 100%methanol and put it under the sample mixer for two hours.
Prepare a mixture consisting of one part benzyl alcohol, and two parts of benzyl-benzoate. Transfer the specimen to it 24 well plate and cover it with the benzyl-benzoate mixture for at least 30 minutes. The sample is ready for imaging.
Lay the sample on the sample holder. And place the holder in the microscope. After turning the microscope system on and opening the software, Turn the transmission light on and select an X objective.
Visually locate your sample in the transmission light and proceed to the acquisition tab. Select confocal laser microscopy Lambda mode. Switch the appropriate lasers on.
Set the spectral range of the detector and select the dichroic mirror. Adjust the detector gain and narrow the pinhole to one area unit. Set pixel resolution to 512 by 512.
Switch on the zed-stack mode and start live imaging. Find the focal plane where both dice are visible. Expand the zed-stack panel.
Using the focusing wheel, find and select the lowest and the highest planes of the sample. Position the focal plane next to the bottom of the sample. Turn the zed-stack mode off and dial scan mode on.
If needed, adjust X, Y position and the number of tiles. Turn zed-stack and dial scan modes on. Set zed-sectioning step to five microns.
Set the scanning speed. Start the experiment. Imaging typically takes several hours dependent on the land size and the scanning speed.
The obtained image is then spectrally unmixed. For spectral unmixing, use linear unmixing option. Select the regions corresponding to the land areas labeled with streptavidin and Conidia.
Start unmixing. After unmixing save the unmixed file. To stich tiles from the obtained image, open the image.
Select methods, geometric stitching. Select the image in the input window. In stitching options, select new output and fuse tiles option.
Use reference mode with selected channel corresponding to the airway fluorescence. Open the image in a surplus 3D view. If needed, change colors used for their presentation.
Here we show airways channel in gray shades and Conidia channel in purple. To create an airway mask, use add new surface tool. Select airway channel and choose the smoothing parameter of 10 microns.
Various filters can be used to exclude out of airway signal. First, visually inspect the surface and set the intensity threshold. Additional filters, like surface area can also be applied to exclusively select airways, but not pleura or blood vessels.
Next, manually delete the selected surface fragment that do not belong to the airways. Now you can observe the created surface and investigate the missing parts to correct them later on. Create a mask for the airway surface using options edit and mask all.
Select the airway channel and set work cells outside the surface to 0.001. Here we showed the resulting mask in orange. Save Conidia channel and the airway mask in two separate folders, as T of F series.
Open the file with the airway mask image. Make the image binary. Follow process binary, make binary.
To resemble the mask thickness, apply several dilate 3D functions. Follow plugins, process, dilate 3D. You can also use macros functionality to do this.
After several cycles of dilate process, use the fill holes function. Follow process, binary, fill holes. To fill the residual holes in the mask manually, open the region of interest manager.
follow analyze, tools, ROI manager. Using polygon selection tool select an area that should be filled at one particular projection. Do this several times for few projections in zed-stack so that you can interpolate shapes of the regions of interest.
To interpolate the select shapes, select all of them and press more, interpolate ROIs. Then fill all the ROIs with white. Use the fill holes procedure once again.
To reassemble the mask thickness back after filling the holes, apply the erode 3D function. Follow plugins, process, erode 3D. You can also use micros for this.
The number of iterations in erode 3D and LA 3D should be equal. Launch the Conidia count application. Press the add files button and select the folders with DIFF files with the prepared airway mask and the Conidia fluorescence images.
Set a custom threshold between zero and one. Press OK and see the results. Using this approach, we perform the quantitative analysis of the Conidia inside and outside the bronchial tree of mice six hours after Conidia application.
The data suggests that upon our original application, the majority of Conidia penetrate the alveola space and allocated there at the beginning of the inflammatory immune response. Three-dimensional imaging of the mouse lungs with confocal laser scanning microscopy allows identification of Aspergillus fumigatus Conidia in the airways. Processing of three-dimensional images using this protocol permits to perform quantitative analysis of Conidia distribution inside and outside the bronchial branches.
Our approach can also be utilized to estimate the kinetics of the Conidia elimination from the aways of mice and to compare anatomical Conidia distribution in the immunocompetent and immunocompromised mice. Additionally, using this approach, one can analyze the distribution of the microparticles or nano particles agglomerates in the airways.
