Many areas of biomedical research have been held back by limited possibilities to extract simultaneous global and regional information of a studied biological process or event. Commonly, high-resolution studies of specific cell niches or regions of interest (ROIs) within a greater body of tissue does not allow for the resultant data to be put into a larger context. Due to technical limitations, information on spatial origin of the studied region, neighboring cell types, and the global state of the organ studied is often lost. Conversely, technologies allowing whole organ imaging do not generally provide the resolution required for detailed cell-level analyses. A protocol allowing for high-resolution analysis to be directed by a prior global assessment of the disease state of an entire organ would thus constitute a significant asset for many areas of research. A good example comes from different murine models for development of type-1 diabetes (T1D). Our present understanding of the natural history of this disease1 rests to a large extent on analysis of rodent models, in particular the nonobese diabetic (NOD) mouse.2, 3 This model is characterized by the autoreactive T-cell mediated destruction of pancreatic -cells, leading to diabetes.4, 5, 6 While it is well established that insulitis progresses over an extended time period, detailed understanding of its kinetics, as well as the cellular and molecular mechanisms underlying observed regional variations, is largely lacking due to limitations in existing technology. In particular, high-magnification imaging alone [e.g., confocal laser scanning microscopy (CLSM) on isolated islets of Langerhans7], while providing excellent details about individual islets, does not provide an overview of disease progression at the level of the whole pancreas. Further, it does not generate information regarding the spatial origin of the analyzed islet, and importantly, does not permit studies of neighboring/interacting cell types and molecules.
We have previously adapted the concept of optical projection tomography (OPT)8, 9 to allow for imaging of specifically labeled structures within intact mouse organs, including the pancreas.10 To develop a means by which both the details of the autoimmune attack at the islet level, and also information of the global state of the pancreatic gland, could be studied in the same specimen, we postulated that four criteria would have to be met. The methodology should allow for; 1. global (whole organ) assessments of molecularly labeled pancreatic constituents (e.g., insulin producing islets); 2. selection of ROIs based on the expression of specific markers and the spatial position, shape, and size of individual objects; 3. high resolution analyses of user-defined ROIs identified based on the global assessment; and 4. high resolution 2-D and 3-D analyses of two or more molecular markers within the ROI. To develop a protocol that would meet these criteria, we turned to the NOD mouse model for T1D.
Animals and Organ Preparation
Intact female NOD pancreata from our local breeding colony (NOD/Bom) were isolated at week 16 and stained for insulin as described.10 All animals used in this study were used with approval of the Ethical Committee on Animal Experiments for Northern Sweden.
Optical Projection Tomography
OPT scanning was performed using the Bioptonics 3001 OPT scanner (Bioptonics, Edinburgh, United Kingdom) modified with an exciter and emitter E610lpv2 filter (Chroma, Rockingham, Vermont), which significantly improved the signal-to-noise ratio and obviated the use of the background subtraction protocol previously applied for assessing -cell mass.10 Scans were performed with an in-house modified mount that could better bear the weight of the adult pancreatic specimen. Tomographic reconstructions were generated using the NRecon V1.5.0 (SkyScan, Kontich, Belgium) software, orthogonal planes were assessed using DataViewer V1.3.2 (SkyScan), and volume renderings were created using Bioptonics Viewer V1.61 (Bioptonics). Islet -cell volumes were quantified using a measurement protocol created in the quantification software module for Volocity v4.3.2 (Improvision, Coventry, United Kingdom). Image stacks of tomographic sections were manually edited to remove any reconstruction artifacts (e.g., the occasional apparent merging of large islets located very close to each other). Thereafter, a “find objects by intensity” task was applied to the measurement protocol. This protocol selects voxels according to specified intensity threshold values. This value was manually edited to exclude pixels with intensity values lower than those normally contributing the labeled objects (islets). Next, a fine filter ( kernel) was applied to avoid selection of voxels not contributing to individual islets. Potential artifacts such as dust particles were identified in interactive 3-D models and deselected from the measurements window. Measured objects were finally exported to the Excel 2007 (Microsoft Corporation, Redmond, Washington) software for statistical analysis.
Isolation of Biopsies
Scanned pancreatic specimens were immersed in Murrays clear benzyl alcohol (AL0161, Scharlau, Barcelona, Spain): [Benzyl benzoate (154839, MP Biomedicals, Illkirch, France) in a 1:2 ratio] in a glass petri dish and labeled objects (islets) were visualized in a Nikon SMZ800 stereo microscope equipped for fluorescence using an EX , DM 595/ EM filter set. Guided by interactive volume reconstructions, ROIs selected on islet morphology, size, 3-D distribution, or all parameters combined, could be easily located. ROIs were carefully isolated as biopsies from the agarose embedded specimen using glass capillaries ( inner diameter) whose ends had been ground sharp using an Arkansas stone. The isolated biopsies were washed in methanol, rehydrated into Tris buffered Triton x-100 supplemented saline, and immunostained with a marker for infiltrating T lymphocytes (CD3). Antibodies used were rabbit anti-CD3 (C7930, 1:200, Sigma, St. Louis, Missouri) and Alexa 488 goat antirabbit (1:500, Invitrogen, Carlsbad, California). Thereafter, the biopsies were dehydrated in methanol, cleared in Murray’s clear, and transferred to -deep microwells for confocal scanning. ROI biopsies were analyzed on a Nikon C1 confocal microscope fitted with argon and a helium/neon laser. Samples were scanned through a Nikon Plan Fluor objective. Companion images were scanned at and step size. Volume, isosurface, and wire-frame renderings were generated using the Imaris v3.3.2 software (Bitplane, Zurich, Switzerland).
Obtaining parameters for region-of-interest selection
The OPT scan data [Fig. 1a ] were reconstructed providing tomographic sections throughout the volume of each pancreas [Figs. 1a through 1g, 1i, 1j and other data not shown]. 3-D volume reconstructions were subsequently generated based on the signal from the insulin-specific antibodies and from tissue autofluorescence [Fig. 1b]. These are fully interactive and allow for free tilting, zoom, and virtual clipping of the specimen (Video 1 ). Based on the tomographic data, specific islet -volumes were quantified. This generated a full histogram of islet -cell volumes and information of corresponding centroid coordinates [Fig. 1h and other data not shown]. In the example depicted in Fig. 1b, the pancreas ( duodenal-NOD) contained 93 insulin labeled islets having islet -cell volumes ranging from (which equals the maximum spatial resolution for the current sample at utilized zoom factor) to [Fig. 1h]. The total islet -cell volume for the current specimen was . As compared to stage matched NOD H2b congenic pancreata, this volume corresponds to approximately a 50% reduction in insulin cell mass. The control specimen depicted in Figs. 1i and 1j contained 1063 islets with a total -cell volume of .10.1117/1.3000430.1
Isolation of regions of interest
To address the spatial character of infiltrating cell types, individual islets were selected based on their position within the whole pancreas (e.g., peripheral location or near the main duct), their shape (e.g., spherical or elongated), or their size. In the current example, two neighboring islets with different shapes and volumes were selected [smaller elongated (assigned yellow pseudocolor in Fig. 1b) and dumbbell-shaped larger (assigned red pseudocolor in Fig. 1b)]. 3-D quantification of the two islets provided information of their individual -cell volume. In the current example, the “red” islet -cell volume was and the spherical “yellow” islet . Volumetric data could be cross-referenced back to the 3-D model and served as a converse selection criterion [red and yellow in Figs. 1b and 1e through 1h]. Although these data provide both global and regional information, the current resolution of the OPT technology does not permit cell-resolution analyses of individual islets when addressing specimens on a current scale. Selected islets were therefore isolated as microbiopsies, in the current example containing both the “red” and “yellow” islets. This was made possible by using the interactive volume reconstructions as reference guides to accurately guide the biopsy capillary into the fixed pancreas specimen [Video 1 and Fig. 2a ]. After the biopsy was removed, the pancreas was subjected to another round of OPT scanning to confirm that the selected ROI had been properly isolated [Figs. 1c and 1d].
Confocal analyses of regions of interest
To address the amount and spatial distribution of infiltrating cell types in relationship to the selected islets, the biopsies were stained with antibodies against a marker for infiltrating T-lymphocytes (CD3), and CLSM scanned. This allowed for the generation of high-resolution orthogonal sections planes (data not shown) throughout the ROI (down to depth when using the objective), as well as for the generation of volume-, isosurface-, and wire-frame renderings of the selected islet -cell volumes and surrounding infiltrate [Figs. 2b through 2k], altogether facilitating a detailed 3-D assessment of the infiltration process (islets depicted correspond to the “red” and “yellow” islets in Fig. 1). In the current example, the two islets displayed insulitis varying from partly [Figs. 2b through 2f] to fully T-cell enclosed islets [Figs. 2g through 2k]. Comparing OPT with CLSM data of 24 islets indicated a 1-D variation of , which translates into a volumetric variation of per islet between the two techniques.
In this study we provide a protocol that allows for site-directed analysis based on a global overview of the composition of the pancreas (i.e., volume and spatial distribution of molecularly labeled objects within the full volume of the gland). By applying this approach to the NOD model of type-1 diabetes, we demonstrate that all required criteria for such analyses have been fulfilled, and we provide detailed 3-D images of the autoimmune attack on individual islets of Langerhans. Although not falling within the scope of this brief work, it should be possible to expand these analyses to cover the infiltration dynamics of other subpopulations of the autoimmune attack during this process. Further, we anticipate that the developed methodology could easily be adapted to studies of combined global and region-specific character also in other organ systems, e.g., when high-resolution analyses of rare cell niches or events in a larger body of tissue are required.
This work was supported by grants from the Swedish Research Council (Holmberg and Ahlgren), the Juvenile Diabetes Foundation (Sharpe, Holmberg, and Ahlgren), The Kempe Foundation, (Ahlgren), and Biotech Grants from the Umeå University Medical Faculty (Holmberg and Ahlgren).