Name: Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues
Uploaded: 2024-03-01T14:35:00
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Description: Read this Scientific Article Protocol about Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues at JoVE.com

The work was supported by Swiss National Science Foundation 310030_156818, 310030_182315, and NCCR_ 180541 AntiResist (to DB).

Whole&#45;Organ Pathogen Tracking via Fluorescent Reporter Quantification

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The authors have nothing to disclose.

The local tissue context of bacterial pathogens is crucial for determining local host attacks, bacterial adaptations, the local outcome of the host pathogen interactions and antimicrobial chemotherapy, and the individual contributions to overall disease outcome. Imaging micrometer sized bacteria in centimeters size organs has been challenging. Serial two-photon (STP) tomography provides sufficient spatial resolution to detect individual bacterial cells in entire organs, automatized sectioning and imaging, and sufficient throughput (~1 organ per day)11. While host antigens can be stained in vivo, pathogen cells should express suitable fluorescent proteins to ensure comprehensive detection of intracellular pathogen cells. The resulting data sets (0.5-1.5 TeraByte per organ) pose substantial challenges for IT infrastructures for data analysis and storage.
There are several critical steps in this method. First, a pathogen strain with detectable and homogeneous expression of fluorescent protein GFP or YFP is required. Ideally, a chromosomal expression cassette25 is used to minimize fluorescence heterogeneity due to plasmid copy-number variation. Sufficient fluorescence intensity is required but excessive levels of fluorescent protein should be avoided to avoid fitness impairments of the pathogen23. Appropriate expression levels can be obtained by selection of an appropriate promoter and fine-tuning of the ribosomal binding site25 or the entire 5&#39; untranslated region (UTR)26. Second, the perfusion fixation should involve an initial wash with buffer to remove as many erythrocytes as possible from the blood circulation. This is particularly critical for spleen and liver (although complete erythrocyte removal from these organs is difficult). Remaining erythrocytes absorb light in the visible part of the spectrum compromising imaging quality27. Third, storage of the fixed tissues in the cryoprotectant is critical to reduce tissue autofluorescence, which is particularly high in inflamed tissues and can overshadow the comparatively weak fluorescence of the pathogen cells11. Fourth, the effective cross-linking of the tissue to the surrounding agarose block is critical for smooth vibratome-cutting without the tissue jumping out of the agarose block. Fifth, fluorescent signals and their identification as pathogen cells must be independently verified using orthogonal approaches such as staining with antibodies to pathogen components (such as lipopolysaccharide for Gram-negative bacteria) and confocal microscopy of the sections retrieved from the tomograph11. Some infected tissues contain auto-fluorescent particles with similar shape and overlapping fluorescence spectra that can be easily misinterpreted as pathogen cells. Sixth, the quantity of pathogen cells within microcolonies should be compared to orthogonal approaches such as confocal microscopy to assess the accuracy. The overall bacterial loads based on these calculations should be verified by comparison to orthogonal approaches such as flow cytometry and plating.
Important modifications of the widely used STP protocol include placing a narrow bandpass filter 510/20 nm in front of photomultiplier 211, to reduce interference of green-yellow autofluorescence that is particularly strong in infected and inflamed liver, spleen, and Peyer&#39;s patches. The strong autofluorescence and increased light scattering of such organs compared to brain (which dominates other applications of STP) also generates a need for more effective correction for uneven illumination. As another modification, this protocol employs the CIDRE approach22 for this purpose (Figure 3) and AI-based segmentation of bacteria. Finally, tissue preprocessing was altered by including an incubation step in cryoprotectant at -20 &#176;C which reduces tissue autofluorescence and thus facilitates detection of small pathogen cells with relatively weak fluorescence11.
Troubleshooting might be necessary if no pathogen signals can be detected, or segmentation yields insufficient sensitivity (too many pathogen cells are missed) or insufficient precision (too many background particles are segmented as pathogen cells). If background tissue autofluorescence is detectable but there are too few pathogen signals, the pathogens might contain insufficient amounts of fluorescent proteins. This can be tested using confocal microscopy of tissue sections from the same infected tissue or flow cytometry of tissue homogenates19,28. Underlying reasons could be insufficient expression levels or instability of the expression cassette. Mitigation strategies could include alternative promoters to drive expression, codon-adaptation of the genes encoding the fluorescent protein for the pathogen species, employment of episomal constructs with higher copy number, or stabilization of expression cassettes by chromosomal integration or balanced-lethal complementation29. The choice of fluorescent protein is also important, but detection is possible with GFP.mut2, mWasabi, YPet, and TIMERbac. If segmentation is inaccurate, this might be caused by too weak pathogen fluorescence which could be addressed as described above, or too high tissue autofluorescence background. Extensive perfusion of wash solution or prolonged incubation in storage buffer immediately before embedding in the agarose block and tomography might resolve these issues. Finally, sufficient training of the neural network is required for precise classification, but excessive training can lead to overfitting that impairs performance for new samples.
Currently, no other method can image entire organs at sufficient spatial resolution in 3D for detecting individual bacteria. Future improvements in tissue clearing and light-sheet microscopy might achieve similar resolution. This might enable imaging at higher speed and with more fluorescent channels.
An important limitation of STP is the in-plane pixel resolution of ~0.5 &#181;m and vertical resolution of 5 to 10 &#181;m, which is insufficient for resolving closely located bacteria, e.g., within a densely packed microcolony. However, it is possible to retrieve tissue sections after tomography for secondary high-resolution confocal microscopy of selected tissue parts. Another limitation of STP is the availability of only three fluorescence channels, which restricts the number of fluorophores that can be imaged simultaneously. Again, secondary analysis of retrieved tissue sections with multiplexing methods can reveal the location and intensity of many more markers for selected tissue parts. This information could be integrated into the overall 3D structure of the surrounding tissue as determined with STP.
In conclusion, this protocol enables detailed investigations of host-pathogen interactions at the local and whole-organ level. The protocol should be easily adaptable to other pathogens (provided they can be obtained as fluorescent strains), other organs, and different host species.

Infections occur in tissues with complex anatomy and compartmentalized physiology. The diverse microenvironments that co-exist in the infected tissue can determine the fates of local pathogen subsets and their contributions to overall disease outcome1,2,3. However, comprehensive 3D mapping of microbial pathogens in cm-sized tissues remains challenging4. Imaging of the brain and other organs is a highly active research field with constantly improving experimental strategies5, but many methods still lack the sub-&#181;m resolution that would be required to identify &#181;m-sized bacterial pathogens confidently. In contrast, serial two-photon (STP) tomography6 enables automated multi-color, deformation-free imaging of entire tissues with sub-&#181;m in-plane resolution, yielding full volumetric datasets. This method combines repeated physical sectioning of the tissue using a vibratome with intermittent two-photon imaging of the emerging block faces with infrared light. STP tomography has been widely used for mapping thin axons in the brain to establish connectivity maps7,8,9,10.
STP tomography also enables 3D mapping of individual microbial pathogen cells (Salmonella, Toxoplasma) in the entire infected tissues11,12 using a tomograph. Second-harmonic generation reveals collagen sheaths around arteries and in fibrous bands such as the trabeculae of spleen, thus providing anatomical context. In vivo injected fluorescent antibodies can be used to stain host cells to reveal interactions between individual pathogen cells and infiltrating immune cells such as neutrophils. Here, the pipeline involving processing of the tissue, imaging, stitching of imaging tiles with illumination correction, stacking of images in three dimensions, and segmentation using machine-learning tools, is described. This pipeline yields 3D positions of individual pathogens cells and microcolonies within their host context. Counting the number of individual cells within microcolonies remains difficult because of resolution limits, but such numbers can be estimated based on the integrated brightness of the microcolony. The pipeline can be readily adapted to other infection models if recombinant GFP- or YFP-expressing pathogens are available.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Chemicals</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Agarose Low Melt</td>
			<td>Roth</td>
			<td>Art. 6351.5 25g</td>
			<td></td>
		</tr>
		<tr>
			<td>Boric acid</td>
			<td>Sigma-Aldrich</td>
			<td>6768-500G</td>
			<td></td>
		</tr>
		<tr>
			<td>Instant adhesive Loctite 435</td>
			<td>Henkel</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Paraformaldehyde</td>
			<td>Sigma-Aldrich</td>
			<td>P6148</td>
			<td></td>
		</tr>
		<tr>
			<td>Poly(ethylene glycol)</td>
			<td>Sigma-Aldrich</td>
			<td>P5413-1kg</td>
			<td></td>
		</tr>
		<tr>
			<td>Polyvinylpyrrolidone</td>
			<td>Sigma-Aldrich</td>
			<td>PVP-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium borohydride</td>
			<td>Sigma-Aldrich</td>
			<td>71321-25g</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium hydroxide</td>
			<td>Merck</td>
			<td>106453</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium periodate</td>
			<td>Sigma-Aldrich</td>
			<td>311448-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium phosphate dibasic</td>
			<td>Sigma-Aldrich</td>
			<td>71640-250G</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium phosphate monobasic dihydrate</td>
			<td>Sigma-Aldrich</td>
			<td>71500-1KG</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium tetraborate</td>
			<td>Sigma-Aldrich</td>
			<td>221732-100g</td>
			<td></td>
		</tr>
		<tr>
			<td>Sucrose</td>
			<td>AppliChem</td>
			<td>A4734,1000</td>
			<td></td>
		</tr>
		<tr>
			<td>Tris-buffered saline (TBS)</td>
			<td>Merck</td>
			<td>T5912-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma-Aldrich</td>
			<td>9002-93-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum filtration 500</td>
			<td>TPP</td>
			<td>TPP99250</td>
			<td></td>
		</tr>
		<tr>
			<td>Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Blade</td>
			<td>Campden Instruments Limited&#160;</td>
			<td>01-01-4692</td>
			<td></td>
		</tr>
		<tr>
			<td>MAITAI Laser</td>
			<td>Spectra-Physics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Peel away plastic mold</td>
			<td>Sigma-Aldrich</td>
			<td>E6032-1CS</td>
			<td></td>
		</tr>
		<tr>
			<td>TissueCyte 1000 tomograph</td>
			<td>TissueVision</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Antibody/dyes</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>DAPI</td>
			<td>Merck</td>
			<td>D9542-5MG</td>
			<td></td>
		</tr>
		<tr>
			<td>Primary antibodies</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>anti-LPS Salmonella, rabbit</td>
			<td>Sifin</td>
			<td>REF TS 1624</td>
			<td></td>
		</tr>
		<tr>
			<td>anti-CD169-PE, clone 3D6.112</td>
			<td>Biolegend&#160;</td>
			<td>142403</td>
			<td></td>
		</tr>
		<tr>
			<td>anti-Ly-6G-PE, clone 1A8</td>
			<td>Biolegend&#160;</td>
			<td>127608</td>
			<td></td>
		</tr>
		<tr>
			<td>Secondary antibodies</td>
			<td>Invitrogen</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>chicken anti-rabbit Alexa 647</td>
			<td>Invitrogen</td>
			<td>A-21443</td>
			<td></td>
		</tr>
		<tr>
			<td>Software</td>
			<td>Company</td>
			<td>Version</td>
			<td></td>
		</tr>
		<tr>
			<td>Fiji</td>
			<td>Image J</td>
			<td>1.54g or later</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>2017b/2018b or later</td>
			<td></td>
		</tr>
		<tr>
			<td>Orchestrator (tomograph)</td>
			<td>TissueVision</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Visualization software Imaris</td>
			<td>Oxford Instruments</td>
			<td>9.9.0 or later</td>
			<td></td>
		</tr>
	</tbody>
</table>

high&#45;resolution three&#45;dimensional whole&#45;organ tomography of microbial infections

Most infections take place within three-dimensional host tissues with intricate anatomy and locally varying host physiology. The positioning of pathogen cells within this diverse environment significantly affects their stress levels, responses, fate, and contribution to the overall progression of the disease and treatment failure. However, due to the technical difficulties in locating &#181;m-sized pathogen cells within cm-sized host organs, this area of research has been relatively unexplored. Here, we present a method for addressing this challenge. We employ serial two-photon tomography and AI-enhanced image analysis to locate individual Salmonella cells throughout the entire spleen, liver lobes, and whole lymph nodes of infected mice. Using fluorescent reporters and in vivo antibody administration, the replication rate of single Salmonella cells, their local interaction with specific immune cells, and bacterial responses to antibiotics can be determined. These methodologies open avenues for a comprehensive examination of infections, their prevention, and treatment within the three-dimensional tissue context.

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

High&#45;Resolution Three&#45;Dimensional Whole&#45;Organ Tomography of Microbial Infections

The described procedure enables detection of individual Salmonella cells in entire mouse organs such as spleen, liver, mesenteric lymph nodes, and Peyer&#39;s patches11 (Figure 5 and Figure 6). It also detects Toxoplasma gondii parasites in mouse brain12. Some infected tissues including liver, Peyer&#39;s patches, and spleen emit substantial autofluorescence in the green-yellow range. Autofluorescence is further enhanced by the fixation with paraformaldehyde, which is necessary to preserve the tissue structure. Detection of green fluorescence from GFP, mWasabi, and the green component of TIMERbac against this autofluorescence background is improved by putting a narrow bandpass filter 510/20 nm (transmitting most GFP emissions but blocking a large part of the autofluorescence spectrum) in front of photomultiplier 2 (which collects green emissions) and by reducing tissue autofluorescence by storing fixed tissues for 3 or more days in cryoprotectant11. However, bacteria should still express at least a few thousand copies per cell of GFP or other fluorescent proteins. On the other hand, excessive fluorescent protein levels should be avoided to minimize fitness costs that could lead to attenuated virulence23.
Correct segmentation of fluorescent bacteria in the tissues can be confirmed by immunohistochemistry of retrieved tissue sections after imaging. Specifically, objects stained with an antibody to bacterial surface components such as lipopolysaccharide can be aligned with the fluorescence image obtained by STP tomography (Figure 5). It is important to note that some stained Salmonella cells lack fluorescent proteins and thus detectable fluorescence in both confocal microscopy and STP tomography. These cells are Salmonella that have been killed by the host immune system as demonstrated by flow cytometric sorting and growth cultures from single sorted cells as well as the close correlation between the number of colony-forming units on agar plates and the number of fluorescent Salmonella cells as determined by flow cytometry24. In addition, plasmid loss can result in non-fluorescent viable cells and this needs to be tested by plating on media with and without appropriate antibiotics corresponding to the selection marker on the plasmid. For pSC101-derived plasmids, plasmid loss in vivo is rare19. For most chromosomally integrated expression cassettes such as sifB::gfp used in11, loss of expression is undetectable in vivo. If the segmentation is inconsistent with immunohistochemistry data, the segmentation pipeline needs to be modified.
The resolution of STP tomography is insufficient to resolve individual bacterial cells within densely packed microcolonies. However, the total fluorescence intensity of the microcolony enables estimation of the number of Salmonella cells. This requires a fluorescent strain with highly homogenous fluorescence levels such as Salmonella sifB::gfp11. Combining the estimated number of Salmonella cells for all microcolonies and single cells yields total bacterial tissue loads that are consistent with alternative methods such as plating or flow cytometry of tissue homogenates11. Plating and flow cytometry cannot be done directly from the same tissues because they need to be perfusion-fixed for STP tomography. Instead, they have to be done with additional animals that are not fixed. If the median bacterial loads as determined by the various approaches differ by more than 3-fold, the viability of fluorescent bacteria might be compromised (in case of lower colony-forming units) or some bacteria might have lost the fluorescent reporter construct (in case of higher colony-forming units). Control experiment will be required to identify the source of such discrepancies.
STP tomography provides the localization of bacterial cells within the 3D structure of the infected tissues. The second harmonic signals of collagen provide anatomical landmarks such as arteries and trabeculae. In addition, host cells can be stained in vivo by injecting an antibody to surface markers prior to perfusion (Figure 5 and Figure 6). This staining provides additional landmarks for tissue compartments and specific microenvironments including inflammation foci (intracellular markers and some compartments with diffusional barriers such as the splenic white pulp or the brain are not easily accessible suitable for this in vivo staining). This approach revealed the splenic white pulp as a tissue compartment that enables long-term survival of Salmonella during antimicrobial chemotherapy11.
Finally, Salmonella replicating at moderate or slow rates can be identified and localized within the 3D structure of tissues using strains that express timerbac, a single cell reporter for replication rate11,15.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66469/66469fig01.jpg" /> 
Figure 1: Procedure for whole-organ imaging using serial two-photon (STP) tomography. (A-B) The organ is harvested after transcardial perfusion and stored overnight in 4% paraformaldehyde (PFA) at 4 &#176;C. (C-E) The organ is embedded in oxidized agarose and cross-linked, then the tissue is scanned and sliced using STP tomography. The slices are collected for subsequent immunohistochemistry. (F-J) Computational analysis pipelines for quantifying bacterial numbers, confirmation of fluorescent bacteria, and 3D reconstruction of bacterial positions. <a href="https://www.jove.com/files/ftp_upload/66469/66469fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66469/66469fig02.jpg" /> 
Figure 2: Serial two-photon (STP) tomography setup. (A) Tomograph which incorporates (B) 2-photon imaging with (C) automated serial tissue sectioning. (D) Collected tissue slices for follow-up investigations. <a href="https://www.jove.com/files/ftp_upload/66469/66469fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66469/66469fig03.jpg" /> 
Figure 3: Tile stitching and illumination correction. Tiles are stitched and uneven illumination is corrected for using corrected intensity distributions using regularized energy minimization (CIDRE). <a href="https://www.jove.com/files/ftp_upload/66469/66469fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66469/66469fig04.jpg" /> 
Figure 4: Segmentation of Salmonella expressing the green fluorescent protein (GFP) using a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN). (A) Representative images of GFP objects segmented by SVM (left) and corresponding images (right) with regions segmented by SVM (Scale bar: 10 &#181;m). (B) Clustered red-green-blue (RGB) values distribution for the segmented regions. (C)&#160;Representative images of non-GFP objects falsely identified by the SVM as bacteria (left). CNN correctly dismisses them as background (right, scale bar: 10 &#181;m). <a href="https://www.jove.com/files/ftp_upload/66469/66469fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66469/66469fig05.jpg" /> 
Figure 5: Detection of Salmonella expressing the green fluorescent protein (GFP) by tomography and confirmation by immunohistochemistry. Images of the same section acquired by tomography (left) or confocal microscopy after staining with an antibody to Salmonella lipopolysaccharide (right). Neutrophils (red) were stained by in vivo injection of a PE-labeled anti-Ly-6G antibody prior to perfusion. <a href="https://www.jove.com/files/ftp_upload/66469/66469fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66469/66469fig06.jpg" /> 
Figure 6: 3D reconstruction and localization of Salmonella in infected mouse spleen. (A) Three-dimensional (3D) reconstruction of a 5 mm thick spleen slice and stained in vivo before perfusion with anti-CD169 antibody (red). The blue signal represents collagen detected by second harmonics. (B) One optical plane of the 3D stack shown in (A). The positions of Salmonella cells or microcolonies are indicated by stars. (C) 3D reconstruction of Salmonella positions&#160;(stars) in infected liver. The arteries are visible based on their collagen sheaths (blue). Scale bar: 1 mm. <a href="https://www.jove.com/files/ftp_upload/66469/66469fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Read this Scientific Article Protocol about Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues at JoVE.com

Here, we describe a procedure that enables organ-wide detection of pathogenic bacteria during infection and quantification of fluorescent reporter activities.

Most infections take place within three-dimensional host tissues with intricate anatomy and locally varying host physiology. The positioning of pathogen ...