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 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 µm-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.
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-µm resolution that would be required to identify µm-sized bacterial pathogens confidently. In contrast, serial two-photon (STP) tomography6 enables automated multi-color, deformation-free imaging of entire tissues with sub-µm 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.
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' 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'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 °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 µm and vertical resolution of 5 to 10 µm, 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.
The authors have nothing to disclose.
The work was supported by Swiss National Science Foundation 310030_156818, 310030_182315, and NCCR_ 180541 AntiResist (to DB).
Chemicals | |||
Agarose Low Melt | Roth | Art. 6351.5 25g | |
Boric acid | Sigma-Aldrich | 6768-500G | |
Instant adhesive Loctite 435 | Henkel | ||
Paraformaldehyde | Sigma-Aldrich | P6148 | |
Poly(ethylene glycol) | Sigma-Aldrich | P5413-1kg | |
Polyvinylpyrrolidone | Sigma-Aldrich | PVP-100G | |
Sodium borohydride | Sigma-Aldrich | 71321-25g | |
Sodium hydroxide | Merck | 106453 | |
Sodium periodate | Sigma-Aldrich | 311448-100G | |
Sodium phosphate dibasic | Sigma-Aldrich | 71640-250G | |
Sodium phosphate monobasic dihydrate | Sigma-Aldrich | 71500-1KG | |
Sodium tetraborate | Sigma-Aldrich | 221732-100g | |
Sucrose | AppliChem | A4734,1000 | |
Tris-buffered saline (TBS) | Merck | T5912-1L | |
Triton X-100 | Sigma-Aldrich | 9002-93-1 | |
Vacuum filtration 500 | TPP | TPP99250 | |
Equipment | |||
Blade | Campden Instruments Limited | 01-01-4692 | |
MAITAI Laser | Spectra-Physics | ||
Peel away plastic mold | Sigma-Aldrich | E6032-1CS | |
TissueCyte 1000 tomograph | TissueVision | ||
Antibody/dyes | |||
DAPI | Merck | D9542-5MG | |
Primary antibodies | |||
anti-LPS Salmonella, rabbit | Sifin | REF TS 1624 | |
anti-CD169-PE, clone 3D6.112 | Biolegend | 142403 | |
anti-Ly-6G-PE, clone 1A8 | Biolegend | 127608 | |
Secondary antibodies | Invitrogen | ||
chicken anti-rabbit Alexa 647 | Invitrogen | A-21443 | |
Software | Company | Version | |
Fiji | Image J | 1.54g or later | |
MATLAB | MathWorks | 2017b/2018b or later | |
Orchestrator (tomograph) | TissueVision | ||
Visualization software Imaris | Oxford Instruments | 9.9.0 or later |