We describe the method for quantitative analysis of the distribution of Aspergillus fumigatus conidia (3 µm in size) in the airways of mice. The method also can be used for the analysis of microparticles and nanoparticle agglomerate distribution in the airways in various pathological condition models.
Aspergillus fumigatus conidia are airborne pathogens that can penetrate human airways. Immunocompetent people without allergies exhibit resistance and immunological tolerance, while in immunocompromised patients, conidia can colonize airways and cause severe invasive respiratory disorders. Various cells in different airway compartments are involved in the immune response that prevents fungal invasion; however, the spatio-temporal aspects of pathogen elimination are still not completely understood. Three-dimensional (3D) imaging of optically cleared whole-mount organs, particularly the lungs of experimental mice, permits detection of fluorescently labeled pathogens in the airways at different time points after infection. In the present study, we describe an experimental setup to perform a quantitative analysis of A. fumigatus conidia distribution in the airways. Using fluorescent confocal laser scanning microscopy (CLSM), we traced the location of fluorescently labeled conidia in the bronchial branches and the alveolar compartment 6 hours after oropharyngeal application to mice. The approach described here was previously used for detection of the precise pathogen location and identification of the pathogen-interacting cells at different phases of the immune response. The experimental setup can be used to estimate the kinetics of the pathogen elimination in different pathological conditions.
On a daily basis, people inhale airborne pathogens, including spores of opportunistic fungi Aspergillus fumigatus (A. fumigatus conidia) that can penetrate the respiratory tract1. The respiratory tract of mammals is a system of airways of different generations that are characterized by the different structures of the airway walls2,3,4. Tracheobronchial walls consist of several cell types among which are ciliated cells that provide the mucociliary clearance5. In the alveoli, there are no ciliated cells and the penetrating alveolar space pathogens cannot be eliminated by the mucociliary clearance6. Moreover, each airway generation is a niche for multiple immune cell populations and subsets of these populations are unique for certain airway compartments. Thus, alveolar macrophages reside in the alveolar compartments, while both the trachea and conducting airways are lined with the intraepithelial dendritic cells7,8.
The approximate size of A. fumigatus conidia is 2-3.5 µm9. Since the diameter of small airways in humans and even in mice exceeds 3.5 µm, it was suggested that conidia can penetrate the alveolar space2,10,11. In fact, histological examination showed the fungal growth in the alveoli of the patients suffering from aspergillosis12. Conidia were also detected in the alveoli of infected mice using live imaging of the thick lung slices13. Simultaneously, conidia were detected in the luminal side of the bronchial epithelium of mice14.
Three-dimensional (3D) imaging of the optically cleared whole-mount mouse lungs permits morphometric analysis of the airways15. Particularly, the quantitative analysis of the visceral pleural nerve distribution was performed using optically cleared mouse lung specimens15. Recently, Amich et al.16 investigated the fungal growth after intranasal application of conidia to the immunocompromised mice using a light-sheet fluorescence microscopy of optically cleared mouse lung specimens. The precise location of the resting conidia in the airways at different time points after the infection is important for identifying the cell populations that can provide sufficient antifungal defense in certain phases of inflammation. However, due to the relatively small size, the spatio-temporal aspects of A. fumigatus conidia distribution in the airways are poorly characterized.
Here, we present an experimental setup for the quantitative analysis of A. fumigatus conidia distribution in the airways of infected mice. Using fluorescent confocal laser scanning microscopy (CLSM) of optically cleared lungs of mice that received an oropharyngeal application of the fluorescently labeled A. fumigatus conidia, we obtain 3D images and perform the image processing. Using 3D imaging of the whole-mount lung lobe, we have previously shown the distribution of A. fumigatus conidia in the conducting airway of mice 72 hours after conidia application8.
All methods concerning laboratory animals described here have been approved by the Institutional Animal Care and Use Committee (IACUC) at the Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences (protocol number 226/2017).
1. A. fumigatus conidia application
2. Specimen preparation
3. Mouse lung lobe optical clearing
4. Mouse lung lobe imaging with CLSM
5. Spectral unmixing and stitching
6. Image processing: surface rendering
7. Image processing: mask correction
8. Conidia quantitative analysis
Following the protocol above, the 3D image showing the airways and A. fumigatus conidia in the lung lobe of a mouse was obtained (Figure 1A). Streptavidin (that was used for airway visualization) labeled bronchi and bronchioles15. Additionally, the large vessels, which are easily distinguishable from the airways by their morphology, and pleura are visualized in the airway channel (Figure 1A-C). The creation of the airway surface and mask permitted removal of the vessel and the pleura projections in the airway channel; however, the integrity of the airway surface is destroyed due to the weak signal of streptavidin in several bronchial branches (Figure 1B-C). The further processing of the airway mask permits the repair of the missing fragments (Figure 1D).
The distribution of A. fumigatus conidia in the lungs of mice was estimated using the left or right superior lung lobes at different time points after conidia application. For the right superior lung lobe, the image consists of approximately 30 tiles and around 250 Z-stacks. After stitching, the image that was acquired with the resolution 512 × 512 had an image size of 2360 × 2815 pixels and the size of one pixel is 2.77 µm × 2.77 µm, which is comparable with the size of A. fumigatus conidia that is 2-3.5 µm9.
The enlarged image of the distal airway region demonstrates that detection of the precise location of conidia (inside or outside the bronchial branches) is quite difficult due to the complexity of the image and the small size of conidia in relation to the size of the airways (Figure 2A). Precise examination revealed that conidia were located both inside and outside the bronchial branches (Figure 2B-C).
The threshold settings of the conidia channel greatly influence the resulting number of conidia (Figure 2B-C). To make the unbiased quantitative analysis we developed an app in the programming and numeric computing platform that allows estimating the number of conidia inside and outside the airway mask, avoiding the manual threshold setting. The app acts based on the following algorithm. First, the conidia channel is segmented into a binary 3D stack of images using an optimal threshold value. As was described above, the selected imaging resolution permits the identification of one conidium as one pixel. The usage of streptavidin for airway labeling permits visualization of bronchi but not alveoli15. Therefore, conidia residing in bronchi are defined as conidia pixels inside the airways mask, while conidia residing in alveoli are defined as conidia pixels outside the airway mask. Considering this, in the next step of the algorithm, a binary AND operation is performed for the airway mask image and the conidia image to extract pixels of conidia that reside in bronchi. Similarly, the remaining conidia pixels are extracted to obtain the number of conidia in alveoli. The resulting percentage of conidia in bronchi and alveoli relative to the overall amount of conidia in the lung is presented in the bar chart and the output table of the app user interface.
Using this approach, the quantitative analysis of the conidia distribution in the airways of mice was performed for the time point of 6 hours after conidia application (Figure 2D). The data suggest that upon oropharyngeal application, the majority of conidia penetrate the alveolar space and locate there at the beginning of the inflammatory immune response.
Figure 1. The principle of airway image processing. (A) 3D image of the right superior lung lobe of a mouse, 24 hours after conidia application showing biotin-rich structures (streptavidin, white) and A. fumigatus conidia (magenta). Steptavidin-positive large vessels are indicated with fine arrows. (B,C) The surface (green) and the mask (orange) for the airways. The missing airway fragments in the surface and the mask are indicated with arrows; the excessive structures with arrowheads. Scale bar is 1000 µm. (D) The airway mask after the corrections. Please click here to view a larger version of this figure.
Figure 2. Conidia image processing and quantitative analysis. (A) Enlarged image of the distal airways (Streptavidin, grey shades) and A. fumigatus conidia (magenta) that are presented in Figure 1A. Scale bar is 300 µm B, C. Enlarged image arbitrary boxed on (A) is represented as a Z-slice with a high threshold (B) and a low threshold value (C). Conidia inside the airway are indicated with arrows, and outside with arrowheads. Scale bar is 150 µm. (D) Quantitative analysis of conidia in the bronchial branches and the alveolar space. The data are shown as the median and interquartile range for 4 mice, 6 h after receiving A. fumigatus conidia. Please click here to view a larger version of this figure.
Whole-organ 3D imaging permits obtaining of the data without dissection of the specimen, which is of great importance for investigating the spatial aspects of the anatomical distribution of the pathogen in the organism. There are several techniques and modifications of tissue optical clearing that help to overcome the laser light scattering and allow whole-organ imaging15,16,18,19. One of the custom tissue clearing approaches consists of methanol-based tissue dehydration and delipidation followed by optical clearing with BABB. The approach was developed more than 100 years ago and has several modifications. In our work, we use the simplest modification that was described by Scott et al.15 Such an approach is optimal for usage with fluorescently labeled pathogens. Moreover, the fluorochromes with high photostability are preferable for prolonged imaging. Unfortunately, visualization of transgenic TdTomato A. fumigatus conidia is not possible using this method, due to the high sensitivity of TdTomato to BABB (data not shown). Thus, the approach that we describe here permits successful detection of resting or fixed pathogens, but cannot be used for imaging of growing A. fumigatus conidia or hyphae. Additionally, the immunohistochemical staining of the specimen with the high-affinity binding substances is preferable. Thus, we also faced trouble trying to apply the fluorescently labeled antibodies to visualize vessels and lymphatics in whole-mount lung lobe specimens. However, Scott et al.15 visualized nerve fibers using two-step staining with antibodies against PGP 9.5. This indicates that some antibodies can be used for staining with the following optical clearing using BABB. We also lost the fluorescent signal from the 0.1 µm fluorescent latex particles after BABB clearing, while the usage of clearing-enhanced 3D (Ce3D) clearing solution18 did not affect the fluorescent signal of the particles.
In the present approach, we use streptavidin to label airways. Streptavidin binds endogenous biotin that is considered to be expressed in Clara cells (and the alveolar type II epithelial cells to a lesser extent)20. As Clara cells (also known as Club cells) in the absence of inflammation reside in the bronchi and bronchioles, but not in the alveolar compartment, streptavidin staining visualizes only bronchial branches. Therefore, in the present approach, all the conidia outside the streptavidin-labeled airways were determined as being located in the alveolar space. For the more precise detection of the conidia location, some other airway markers, such as SOX9, should be used3. In case, when antibody usage is necessary Ce3D or three-dimensional imaging of solvent-cleared organs (3DISCO)19 techniques are more appropriate than BAAB-based optical clearing. However, BABB optical clearing is the most simple and time-consuming approach, and therefore is the most advantageous for the in advance fluorescently labeled conidia detection in the marked with streptavidin airways.
3D imaging of the mouse lungs can also be performed using optical projection tomography integrated microscopy3. However, due to the limitation in the resolution of tomography, CLSM is more suitable for the simultaneous imaging of the airways and 3 µm conidia. In our case, a single conidium is seen as one pixel. The processing of such images allowed quantification of conidia inside and outside the bronchial branches. The method also can be applied to compare anatomical conidia distribution in the immunocompetent and immunocompromised mice. The approach can also be utilized to estimate the kinetics of the conidia elimination from the airways of mice. Moreover, the combination of the approach of the whole airway morphometry that was developed by Scott et al.15 with the algorithm for unbiased conidia quantitative analysis can be helpful in the precise location of A. fumigatus conidia and other particles of comparable size in different generations of the bronchial tree.
The authors have nothing to disclose.
The authors thank Prof. Sven Krappmann (University Hospital Erlangen and FUA Erlangen-Nürnberg, Germany) for providing the Aspergillus fumigatus conidia strain AfS150. The authors thank MIPT Press Office. V.B. acknowledges the Ministry of Science and Higher Education of the Russian Federation (#075-00337-20-03, project FSMG-2020-0003). The work regarding A. fumigatus conidia imaging and quantification was supported by RSF № 19-75-00082. The work regarding airways imaging was supported by RFBR № 20-04-60311.
Alexa Fluor 594 NHS Ester | ThermoFisher | A20004 | |
Aspergillus fumigatus conidia | ATCC | 46645 | The strain AfS150, a ATCC 46645 derivative |
Benzyl alcohol | Panreac | 141081.1611 | 98.0-100 % |
Benzyl benzoate | Acros | AC10586-0010 | 99+% |
C57Bl/6 mice | Pushchino Animal Breeding Centre (Russia) | Male. 12 – 30 week old. | |
Catheter | Venisystems | G715-A01 | 18G |
Cell imaging coverglass-bottom chamber | Eppendorf | 30742028 | 4 or 8 well chamber with coverglass bottom |
Centrifuge | Eppendorf | 5804R | Any centrifuge provided 1000 g can be used |
Confocal laser scanning microscope | ZEISS | ZEISS LSM780 | |
Dimethyl sulfoxide | Sigma-Aldrich | 276855 | ≥99.9% |
FIJI image processing package | FIJI | Free software | |
Forcep | B. Braun Aesculap | BD557R | Toothed |
Forcep | B. Braun Aesculap | BD321R | Fine-tipped |
Forcep | Bochem | 1727 | Smooth |
Glass bottle | DURAN | 242101304 | With groung-in lid |
Graphic Editor Photoshop | Adobe Inc | Adobe Photoshop CS | |
GraphPad Software | GraphPad | Prism 8 | |
Imaris Microscopy Imaging Software | Oxford Instruments | Free trial is avalable https://imaris.oxinst.com/microscopy-imaging-software-free-trial | |
Isoflurane | Karizoo | ||
NaHCO3 | Panreac | 141638 | |
Objective | ZEISS | 420640-9800-000 | Plan-Apochromat, 10 × (NA = 0.3) |
Paraformaldehyde | Sigma-Aldrich | 158127 | |
PBS | Paneco | P060Π | |
Pipette | ProLine | 722020 | 5 to 50 μL |
Powdered milk | Roth | T145.2 | |
Sample mixer | Dynal | MXIC1 | |
Scissors | B. Braun | BC257R | Blunt |
Shaker | Apexlab | GS-20 | 50-300 rpm |
Skalpel | Bochem | 12646 | |
Silk thread | B. Braun | 3 USP | |
Streptavidin, Alexa Fluor 488 conjugate | ThermoFisher | S11223 | |
Test tube | SPL Lifesciences | 50050 | 50 mL |
Tris (hydroxymethyl aminomethane) | Helicon | H-1702-0.5 | Mr 121.14; CAS Number: 77-86-1 |
Triton X-100 | Amresco | Am-O694-0.1 | |
ZEN microscope software | ZEISS | ZEN2012 SP5 | https://www.zeiss.com/microscopy/int/products/microscope-software/zen.html |