3.1 Sample preparation pipeline and the power of tissue autofluorescence
To achieve the multi-scale observation of spatially-distributed biological signals of interest, we developed a sample preparation pipeline that includes two imaging modalities: high-volume optical projection tomography and high-resolution confocal microscopy after reverse OPT (RevOPT, Fig. 1). The pipeline is divided into four phases, sample preparation (Fig. 1 a-k), imaging and image processing (Fig. 1 l-m), reverse OPT (Fig. 1 n-r) and secondary imaging (Fig. 1 s), spanning a duration of approximately three weeks. First, tissue preservation, autofluorescence quenching and tissue permeabilisation are performed to prepare the samples for staining. This is followed by fluorescent antibody staining of select markers. A clearing step precedes the acquisition of optical projections over a 360° sample rotation. Next, a filtered back-projection algorithm is used to reconstruct the projections into a 3D image as described previously29. Using RevOPT we revert the sample to a state compatible with freezing in Optimal Cutting Temperature (OCT) compound, allowing for cryostat sectioning and counterstaining (Fig. 1, p-r). Finally, in amongst several methods requiring thin sectioning including electron microscopy or single-molecule FISH, we selected confocal microscopy to image regions of interest identified by OPT with improved resolution.
Tissue autofluorescence is an inherent signal produced by extracellular matrix components and certain pigmented cell types. In OPT, autofluorescence quenching is required to reduce noise and retain targeted fluorescent signals29 (Fig. 1, steps a and c). However, low levels of autofluorescence enable the discrimination of the outer and inner layers of the gut when samples are illuminated at 415nm spectrally filtered between 400-440nm, whilst emission is collected within the range of 455-520nm29. A longitudinal portrayal of the gut (Fig. 2a) provides an overview of the structures present in the tissue. In reconstructions made up of 1200 projections, well-resolved villi can be observed in a 3D visualisation software (Fig. 2b). When taking a cross-sectional view, the mucosal layers can be distinguished from the villi in the OPT scan (Fig. 2c; mu = muscularis, sm = submucosa, m = mucosa and L = lumen) whilst a greater resolution is achieved by confocal microscopy on the same sample having undergone RevOPT (Fig. 2d). During RevOPT, counterstaining is possible and demonstrated here by the staining of DNA with DAPI (Fig. 2d). The improved preservation of cross-sectional structure in OPT is evident when comparing the virtual section (Fig. 2c) and its histological counterpart (Fig. 2d), with loss of tissue and distortion being apparent in the confocal image.
We implemented a virtual unfolding technique30 (for more detail, see supplementary SI Fig. 1) to observe the gut tissue from within the lumen, with sections spanning from this point to the serosa (Fig. 2e, section closest to lumen). Villous density could be calculated by segmenting the unfolded image and finding local maxima (Fig. 2f). This can be performed for the whole tissue region or applied to smaller regions of interest to probe different areas of the tissue. In this healthy tissue, overall villous density is mostly homogeneous (Fig. 2f). This data can be transformed into a quantitative visualisation of different sectors (Fig. 2g). Virtual unfolding also yields a straightened image (Fig. 2h) of the tissue cross section seen in Fig. 2c.
Virtual unfolding of 3D-reconstructed data can lead to detailed visualizations of structures that are difficult to visualise in a 3D image such as Fig. 2a or in a virtual cross-section as in Fig. 2c. We found a suspected lymphoid follicle in the autofluorescence channel of a different sample (top view Fig. 2i, side view Fig. 2j), whose structural context is made clear by virtual unfolding (Fig. 2k and straightened Fig. 2l). The follicle is made up of three lobes, with a concentration of fluorescent vessels in the centre. In the areas surrounding the follicle, gaps in the villi suggests the potential presence of lymphatic vasculature. Typically, large vascular networks are difficult to observe by visualization of cross-sections. In Fig. 2m, an example of such a network is shown, highlighting the added value that processing the autofluorescence channel can bring to gut structure characterization.
3.2 Cell-type specific signal distribution throughout the gut volume
OPT can also be used for visualisation of cell types according to stainings of selective markers. To demonstrate this, we chose to stain the gut immune compartment, due to its structured organisation under healthy conditions and its common deregulation in intestinal diseases (e.g. IBD31) and other systemic disorders (e.g. metabolic diseases32,33, autoimmunity34 and neurodegeneration35,36). For this we stained CD45-positive cells using fluorescently labelled antibodies. In healthy adult mice, immune cells are found interspersed at regular intervals or compartmentalised in gut-associated lymphoid structures (GALTs) known as isolated lymphoid follicles (ILFs, Fig. 3a triangle). Overlaying the autofluorescence channel reveals other adjacent structures such as blood vessels and luminal dietary fibers (Fig. 3a, cross and square respectively). In order to view the three-dimensional characteristics of such an OPT image, a movie is provided in the supplementary information (SI Movie 1).
It is known that age- and microbiota-dependent education of the immune system is responsible for the formation of lymphoid structures such as Peyer’s patches and ILFs37,38. We confirm that with OPT, we are able to identify differences in the immune cell compartments in the contexts of young (14 days) SPF mice and old (30+ weeks) germ-free mice (Fig. 3b and 3c respectively), compared to the old SPF sample shown in Fig. 3a. At a young age under normal rearing conditions, no dense regions of immune cells are observed (Fig. 3b). Intestines of older, germ-free mice also exhibit reduced CD45-positive cell clusters in the mucosal layers. A two-channel cross-sectional view of these samples (Fig. 3d-f) frames the immune signal within the structured layers of the gut. An isolated lymphoid follicle is located within the sub-mucosal layers and is surrounded by smaller, less dense CD45-positive clusters in figure 3d. Conversely, there is no specific fluorescence in both old germ-free and young SPF animals, thus indicating a lack of well-defined GALT structures in these mouse models (Fig. 3b, 3c, 3e, and 3f).
OPT reconstructions can thus be used for broader, organ-scale characterisation of the mouse intestine. Furthermore, current 3D image processing tools allow for accurate quantification of different parameters. We segmented the ILFs in the 625nm channel alone (Fig. 3g) and found their size ranges from approximately 1 to 5 million μm3 (Fig. 3h). Their spatial distribution along the small intestine is uniform, averaging at 500μm in between ILFs (Fig. 3i).
3.3 gutOPT pipeline for multi-modal imaging and high resolution characterisation of the gut
Because sample preparation for optical projection tomography is compatible with downstream processing for additional imaging modalities, we wondered whether we could incorporate a single pipeline for imaging at different scales. We performed reverse-OPT (Fig. 1 n and o) on the samples shown in Fig. 3 and imaged them using confocal microscopy (Fig. 4).
We wondered whether we could use OPT to pre-select regions of interest, and retrace them and image them using high-resolution techniques. To do this, we selected isolated lymphoid follicles in the OPT reconstruction and calculated their distance from the edge of the sample (Fig. 4a i and ii). Once samples had undergone RevOPT and were mounted in optimal cutting temperature (OCT) compound, the depth of each cryosection was used to track the localization of the ROIs. We find that the fluorescence signal was maintained from the OPT staining, and sections do not require further immuno-staining for confocal imaging.
Specifically, we find that preselected ILF regions observed by OPT are high-density cell clusters rich in CD45-positive cells (Fig. 4b and c). In both ROIs containing ILFs, the calculated distances were accurate, and we find the immune cell-dense region situated within the submucosa as expected from the OPT reconstructions and their known localisation39,40. Areas lacking GALTs in 3D (Fig. 4a iii) only contain sparse positive cells in the lamina propria at higher resolution (Fig. 4d). By measuring the immune cell density in the whole-sectioned GALT regions we find that the signal density threshold for visibility in OPT is approximately 400 fluorescent cells per mm2 of DAPI signal (Fig. 4e). We imaged the adult germ-free mice that display no GALTs by OPT (Fig. 4f). Here, we find no CD45+ cells along the length of the villi nor in the submucosa, confirming that no ILFs are present, and suggesting that the lack of a microbiome indeed alters the immune compartment in the gut (Fig. 4f, triangle). The number of immune cells is also significantly reduced compared to that observed in the gut of SPF mice (Fig. 4g). Thus, OPT can be used to identify specific structures and markers of interest using tissue-wide staining, and given a sufficiently dense fluorescent signal ROIs can be traced by confocal microscopy using RevOPT and cryosectioning.