Abstract
Aims/hypothesis
It is generally accepted that structural and functional quantitative imaging of individual islets would be beneficial to elucidate the pathogenesis of type 1 diabetes. We here introduce functional optical coherence imaging (FOCI) for fast, label-free monitoring of beta cell destruction and associated alterations of islet vascularisation.
Methods
NOD mouse and human islets transplanted into the anterior chamber of the eye (ACE) were imaged with FOCI, in which the optical contrast of FOCI is based on intrinsic variations of the index of refraction resulting in a faster tomographic acquisition. In addition, the phase sensitivity allows simultaneous label-free acquisition of vascularisation.
Results
We demonstrate that FOCI allows longitudinal quantification of progressive autoimmune insulitis, including the three-dimensional quantification of beta cell volume, inflammation and vascularisation. The substantially increased backscattering of islets is dominated by the insulin–zinc nanocrystals in the beta cell granules. This translates into a high specificity for the functional beta cell volume of islets. Applying FOCI to a spontaneous mouse model of type 1 diabetes, we quantify the modifications of the pancreatic microvasculature accompanying the progression of diabetes and reveal a strong correlation between increasing insulitis and density of the vascular network of the islet.
Conclusions/interpretation
FOCI provides a novel imaging technique for investigating functional and structural diabetes-induced alterations of the islets. The label-free detection of beta cell volume and infiltration together with vascularisation offers a unique extension to study ACE-transplanted human islets. These results are contributing to a deeper understanding of human islet transplant rejection and label-free in vivo monitoring of drug efficacy.
Similar content being viewed by others
Introduction
Diabetes mellitus develops as a functional impairment in insulin production, sometimes in association with insulin resistance. In both major types of diabetes mellitus, the progressive dysfunction of the beta cell causes the disease development. While type 1 diabetes is the result of an autoimmune attack on beta cells, type 2 diabetes is considered to be driven by metabolic factors associated with sedentary lifestyle and obesity, albeit with accumulating evidence of low-grade inflammation. Modifications of the pancreatic microvasculature are likely to accompany the progression of both types of diabetes. Alterations in vascular variables, such as transient vasoconstriction, vasodilation, increased blood flow and vascular leakage, are necessary preludes to inflammation as they orchestrate the influx of diverse cell types and affect local homeostasis [1–6]. To fully appreciate how these events contribute to the pathogenesis, improved methods allowing longitudinal, high-resolution monitoring of vascularisation and affected tissues close to the cellular level during its natural progression are warranted and constitute the key objectives of this study.
To date, longitudinal non-invasive, intra-vital imaging of the pancreas has been mainly restricted to non-optical imaging modalities such as MRI and computed tomography (CT). While the medical potential of these modalities is indisputable, they are limited by a restricted set of reagents targeting specific tissues and by a relatively low spatial resolution [4, 7, 8]. Optical microscopy on the other hand is mainly limited to preclinical studies because of a reduced penetration depth [9–12]. Optical projection tomography (OPT) has been used to image the adult mouse pancreas and retrieve the three-dimensional (3D) and undistorted structure of the tissue [13]. While OPT can quantify inflammation at different stages of disease [14, 15], it is limited by being an ex vivo technique that requires sample fixation and immunolabelling.
In accordance with these objectives, we opted for a non-invasive imaging method, extended-focus optical coherence microscopy (xfOCM) [16], which allows label-free longitudinal visualisation and quantification of pancreatic ducts, blood vessels and individual islets of Langerhans ex vivo and in vivo. The technique requires only a two-dimensional scan to obtain a 3D image. This intrinsic multiplex advantage results in a fast parallel acquisition of entire depth profiles without depth scanning, with an in vivo penetration depth of ∼300 μm in the pancreas. However, optical pancreas imaging requires laparotomy, restricting this technique to transversal studies [17–19]. Recently, the anterior chamber of the eye (ACE) has been used to study transplanted pancreatic islets [20–22]. Engrafted on the iris, islets could now be repeatedly imaged using the mouse eye as a natural body window. Using this approach, we elucidate important structural and functional alterations induced during the progression of autoimmune diabetes.
Methods
Animals
Mice of genotype C57BL/6 (B6).Rag2−/− were backcrossed with NOD mice for the generation of NOD.Rag2−/− mice, as previously described [23]. NOD.Foxp3-green fluorescent protein (GFP) mice were generated by speed-congenic backcrossing of the B6.Foxp3-GFP (Jax stock number 006772) with NOD mice, as previously described [21]. BALB/c mice were purchased from Taconic (Ejby, Denmark). All animals were bred and maintained in a specific pathogen-free environment at the animal facilities at Lund University. Guinea pigs were purchased from Harlan Laboratories (Indianapolis, IN, USA). Mice with ZnT8 (also known as Slc30a8) knockout (KO) and littermates were generated as previously described [24]. The Ethics Committee at Lund University and the Swiss cantonal veterinary authorities approved all experimental animal procedures. Experiments were performed after mice were genotyped, without randomisation or blinding. No data were specifically included or excluded.
Pancreatic islet isolation, culture and ACE transplantation
Mouse pancreatic islets were isolated according to the protocol previously described [25]. Between 10 and 50 cultured islets were transplanted per eye, as previously described [21].
For experiments, including the study of healthy control islet grafts, 6–8-week old BALB/c or B6.Foxp3-GFP recipient mice were ACE transplanted with islets from BALB/c or B6.Rag2 −/− donors, respectively. For inflamed islet grafts, 10-week-old NOD.Foxp3-GFP recipient mice were ACE transplanted with islets from NOD.Rag2 −/− donors. Human islets from non-diabetic cadaver donors were provided by the Nordic Islet Transplantation Program (www.nordicislets.org), coordinated by O. Korsgren, Uppsala University, and were transplanted into 6–8-week-old female NOD.Rag2 −/− mice (see the electronic supplementary materials [ESM] Methods for details). The study was approved by the Ethics Committee at Lund University.
Functional optical coherence imaging
Mouse head and eyeball were restrained as previously described [20] and anaesthesia was maintained at 1% isoflurane. Functional optical coherence imaging (FOCI) integrates the xfOCM instrument equipped with a Zeiss Neofluar objective × 10; NA 0.3, Carl Zeiss (Jena, Germany) with an improved acquisition (customised optical coherence microscopy [OCM] spectrometer) and optimised scanning modality for longitudinal functional imaging. A fast processing unit allows real-time monitoring (structure and blood flow), with a fully equipped platform for small-animal imaging. Details of the xfOCM instrument, data acquisition, scanning protocols and analysis are given in the ESM Methods.
Immunohistochemistry
Graft-bearing eyes or pancreas were isolated from mice after perfusion with 4% paraformaldehyde/PBS, cryosectioned parallel to the iris (section thickness 8–10 μm) and subjected to immunohistochemical staining as previously described [21] (see ESM Methods).
Ex vivo imaging with dark-field optical coherence microscopy
Cryosections were mounted with DABCO mounting medium and imaged using the dual system combining dark-field OCM (dfOCM) [26] with confocal fluorescence (ESM Fig. 1) (see ESM Methods).
Statistics
Groups were compared by the Mann–Whitney test or unpaired Student’s t test, as indicated in the respective figure legend, and data are expressed as means ± SEM.
Results
Beta cell specificity in OCM
The optical contrast in xfOCM is based on the scattering characteristics of the investigated tissue. As we have previously shown, xfOCM imaging can be used to identify pancreatic islets in surgically exposed pancreases of live animals; the strong light scattering observed correlates strongly with insulin-producing beta cells [17]. As OCM is sensitive to changes in refractive index and because pancreatic beta cells have a high zinc content [27], we hypothesised that the origin of the strong OCM signal is dominantly caused by the zinc-insulin crystals in the pancreatic beta cells. To test this hypothesis, thick pancreas sections of ZnT8–KO mice, which lack zinc-insulin crystals in the secretory granules of beta cells [24, 27], were subjected to immunohistochemistry (IHC) using anti-insulin antibodies and analysed simultaneously by OCM and confocal fluorescence microscopy. In order to suppress the specular reflection from the cover slip, we used a dark-field OCM (dfOCM) configuration combined with a confocal fluorescence channel (ESM Fig. 1). Figure 1 shows transverse virtual sections at the selected depth position of this dfOCM image stack and the corresponding fluorescence images. While control pancreases showed a good correlation between dfOCM and insulin staining (Fig. 1a), the dfOCM signal in the ZnT8-KO mouse pancreases was significantly diminished (Fig. 1b, d), indicating that zinc within beta cells contributes to the OCM signal. In addition, we imaged the pancreases of guinea pigs, as they have a divergent insulin unable to form the hexameric crystals with zinc [28]. Therefore, beta cells in guinea pigs contain both insulin and zinc, but do not store insulin as zinc-insulin crystals. Guinea pig beta cells did not show a strong scattering in dfOCM images (Fig. 1c, d). While it is likely that additional, unidentified nanocrystals contribute to the islet scattering, these findings underline that this label-free approach provides a high specific contrast for the functional beta cell volume.
Longitudinal quantification of beta cell volume and islet vascularisation using FOCI
Having established that the xfOCM signal specifically reflects the beta cell volume, we next applied this technique in longitudinal studies. While longitudinal imaging of the pancreas in situ is feasible, the necessary laparotomy does not allow the relocation of the same islet at successive imaging sessions. To overcome this, we combined xfOCM with longitudinal imaging of islets transplanted into the ACE of recipient mice to establish FOCI (ESM Figs 2 and 3). Isolated syngeneic islets were transplanted into the ACE of healthy mice and repeatedly imaged by FOCI. Using this approach, we demonstrated how the islets were growing during the course of the study (Fig. 2a–e). These data extend previous reports based on two-photon microscopy showing that, after an initial phase of vascularisation, syngeneic islets transplanted into the ACE are sustained and function over several months [20, 25].
Using FOCI, we next visualised and quantified the microvasculature in the transplanted islets over a 2 month period. As illustrated in Fig. 2a–d, f, we found that, in parallel with islet growth, re-vascularisation of the grafted islet occurred rapidly over the first 2 weeks to form a capillary network.
An important advantage of the label-free FOCI technology is the potential to obtain high-resolution images of tissue structures, such as the human islets of Langerhans, that are not easily subjected to labelling with specific cues. We exploited these features for assessing islets isolated from human organ donors and transplanted into the ACE of immunodeficient NOD.Rag2 −/− mice. All 14 individual NOD.Rag2 −/− recipient mice receiving human islet grafts accepted multiple islet grafts. Depending on the quality of the donor material obtained, the successful engraftment of individual islets varied between three and five and up to 15 islets out of approximately 20 injected islets. Successfully engrafting islets displayed evidence of beginning re-vascularisation within 2–3 days and become fully re-vascularised 2–3 weeks later. In these islets, no signs of rejection could be detected for up to 25 weeks. As illustrated in Fig. 3 and as suggested in a previous report [29], transplanted islets were found to be functional and secreting insulin after ACE transplantation. The anatomical organisation of the transplanted islets was intact, with beta cells scattered between alpha and delta cells and distributed over the entire islet volume (Fig 3a). This well-established difference from the anatomical organisation of rodent islets is also illustrated in the non-homogeneous tissue structure revealed in the OCM image of the human islet (Fig. 3b).
FOCI detects inflammation in pancreatic islets under autoimmune attack
We next applied FOCI to follow the progressive autoimmune destruction of beta cells in the NOD mouse model for type 1 diabetes. To determine if the cells infiltrating the ACE-transplanted islets could be visualised in a label-free manner using FOCI, we next analysed 14-week-old non-diabetic NOD mice transplanted with syngeneic islets 4 weeks prior to analysis.
As reported previously [21] and shown in Fig. 4, NOD reporter mice can be used to visualise specific inflammatory cellular populations infiltrating the pancreatic islet. To interpret the altered OCM signal in vivo during the longitudinal study, we used NOD.Foxp3-GFP reporter mice to visualise the recruitment of forkhead box P3 (FOXP3)+ regulatory T cells to the site of inflammation along with other inflammatory leucocytes. The strong GFP signal indicating inflammation in the NOD islets, not observed in B6.Foxp3-GFP recipient mice (Fig. 4a), matched completely the reduced FOCI signal in the NOD islet (Fig. 4b). This supports the notion that this approach could be used to discriminate the beta cell volume from the infiltrated volume (Fig. 4b, ESM Fig. 4). The B6 control mice did not show such alterations in the FOCI signal. To confirm this, the mice were submitted to conventional IHC of the grafted islet. As illustrated in Fig. 4c and ESM Fig. 5, the insulin staining matched the brighter part of the segmented xfOCM image, while the significantly lower FOCI signal corresponded to the area stained with a pan-leucocyte marker. These findings confirm that FOCI can detect inflammation in addition to the beta cell volume. While the high OCM signal is specific to insulin, the lower OCM signal cannot be solely attributed to infiltration. Indeed, other non-beta endocrine cells and vessels also give weaker scattering. To estimate the extent to which the endocrine cell and vessel volume contribute to the non-beta cell volume in inflamed islets, we tried to quantify the non-beta cell volume in non-inflamed healthy wild-type islets. After initial fluctuations of the non-beta cell volume shortly after transplantation, it remained stable, making up approximately 3% of the total islet volume (ESM Fig. 6). While the mean value of the non-beta cell volume in the non-inflamed area is consistent with the literature [30], the significant deviation shows the limitation of FOCI technology for such a small volume.
To confirm that the insulitis identified in the ACE-transplanted NOD islets could be similarly detected by OCM in the pancreas, we next analysed images of surgically exposed pancreas from aged non-diabetic NOD mice. As illustrated in Fig. 5a–c, we observed alterations in the refractory pattern in some of the in situ imaged NOD islets, but not in control B6 mice (Fig. 5d). This OCM signal was lower than the signal detected in beta cells and different from the signal detected from the exocrine tissue. These images potentially reflected insulitis spanning from unaffected to peri-islet infiltrates and full-blown intra-islet infiltration. By imaging thick pancreatic sections of NOD mice stained with a pan-leucocyte marker (Fig. 6), we further confirmed that, as in the ACE model, insulitis can be captured in situ by OCM in the pancreas, though with the limitation of getting only single snapshots of the disease.
Insulitis correlates with attenuated vascularisation density
Using the longitudinal FOCI platform, we next monitored the vascular network of the islets during progressive inflammation (Fig. 7). Ten-week-old recipient mice with an ongoing autoimmune process were used, anticipating that the insulitis in the ACE-transplanted islets of these mice would progress rapidly. As expected, Foxp3-GFP+ cells were found to be recruited, along with other inflammatory cells, to the islet graft and accumulate over time (Fig. 7a), resulting in progressive beta cell destruction. This is clearly distinguishable in the orthogonal FOCI view (Fig. 7b), which is additionally illustrated in a 3D representation (Fig. 7c–e, ESM Video 1-3) and quantified (Fig. 7f).
As expected, we could visualise a gradual increase in the total islet vascular network shortly after transplantation (ESM Fig. 7). Counter intuitively, we noted a decrease in the microvasculature density in infiltrated regions of inflamed islets, with only a few big vessels remaining (Fig. 7a, g, h, Fig. 4d and ESM Videos 1–3). This decrease in the microvasculature density was not observed in islets displaying only limited infiltration (ESM Fig. 7 and ESM Video 4), indicating that the observed attenuation of microvasculature correlates with the severity of inflammation. Additionally, an attenuation of the microvascular network was observed in the inflamed islets imaged in situ in the surgically exposed pancreases (Fig. 5a–c), but not in control B6 mice in situ (Fig. 5d) or B6 islet grafts in the ACE (Fig. 2, Fig. 4d).
To confirm the regression of microvasculature observed by FOCI, we used IHC to analyse sections of pancreatic islets from aged non-diabetic NOD.Foxp3-GFP mice or ACE-transplanted islets from the same mice (4 weeks post transplantation) (see ESM Methods for details). This proves that the vessel density, as revealed by anti-CD31, was decreased in inflamed areas compared with non-affected areas of the islets (Fig. 8a–c, i). In contrast, high endothelial venules (HEVs) [31] expressing mucosal addressin cell adhesion molecule 1 (MAdCAM-1) predominated within inflamed islet areas (Fig. 8e–g, j). This supports previous observations of lymphocyte-HEV recognition in the development of insulitis in NOD mice [8, 15, 32, 33]. Interestingly, a similar distribution of CD31+ vs HEVs was also seen in inflamed ACE-transplanted NOD islets (Fig. 8d, h, i, j).
Discussion
Our label-free 3D images are based on next-generation OCM, denoted functional optical coherence imaging for its substantial extension into functional imaging. Compared with related imaging techniques such as OCT [34] or optical frequency domain imaging (OFDI) [35], FOCI has a micrometric resolution over an extended depth range. In contrast to conventional confocal microscopy, FOCI offers several advantages for islet imaging. First, it allows for simultaneous, label-free imaging of the islet structure and the vasculature. As the FOCI contrast is caused by the intrinsic light scattering of tissue it does not require genetically modified mice or an extrinsic biomarker to detect the beta cell volume and functional vascularisation. In addition, it is not affected by autofluorescence. Second, the coherent amplification of the backscattered light results in a higher signal-to-noise ratio. This increased contrast enables quantitative segmentation of the beta cell volume, vascularisation and infiltration. Third, the multiplex advantage of FOCI translates into a parallel acquisition of depth profiles, circumventing the need for voxel per voxel scanning in depth. The fast acquisition increases the time resolution allowing for visualising the vasculature of whole islets in 30 s. By applying the phase sensitivity of FOCI, blood flow can be quantified, a critical variable to monitor during the progression of diabetes. Finally, FOCI uses a broadband near infra-red (NIR) light source (<5 mW on the cornea) to acquire a full depth, resulting in a substantially lower power exposure and minimises the risk of photo-damage of fragile islet structures and microvasculature.
The lack of direct, non-invasive technologies to visualise inflammation in the pancreas has been a key obstacle to the early detection of type 1 diabetes and the rapid assessment of the effectiveness of therapeutic intervention. To highlight the usefulness of FOCI applied to the ACE model, we analysed and compared islets from a mouse model of type 1 diabetes. The location and anatomy of the pancreas make the study of this disease at the organ level difficult, requiring compromises on resolution for longitudinal studies in the timescale of disease progression. While direct examination of fixed pancreases has yielded valuable insight into the processes leading to diabetes, such classic attempts only provide a snapshot of the disease. Thus, non-invasive imaging strategies to monitor changes within the islets associated with diabetes are being sought to fill this gap. By applying the FOCI technique, we revealed several surprising features of the disease.
The longitudinal recording of the autoimmune-induced islet alterations in the ACE provided detailed monitoring and quantification of the decreasing beta cell volume and of the impact on the islet vasculature during the inflammatory process. An observed decrease in the vascular density in areas affected by insulitis coincided with a promotion of MAdCAM-1-expressing HEV-like structures. Altered vasculature has previously been associated with type 1 diabetes [2–4, 36]. More specifically, Villalta et al [36] have reported an increase rather than a decrease in vasculature when comparing pancreases from NOD mice prior to insulitis onset and NOD mice with advanced insulitis. This apparently conflicting statement may, in part, be due to differences in the overall assessment. We compared the vascularisation of infiltrated and non-infiltrated areas on the same individual islets, whereas Villalta et al [36] observed the increase in vasculature comparing the whole islets isolated from 4-week-old NOD mice with 18-week-old NOD mice. We therefore argue that the increase in vasculature under those conditions may be part of a developmentally controlled increase in islet vascularisation. HEVs have been linked to the promotion of guidance of lymphocyte trafficking and recruitment into inflammatory sites [31]. The observed overall reduction in the microvasculature is counterintuitive and remains to be understood in the larger framework of its impact on the pathogenesis of autoimmune diabetes.
The label-free detection of beta cell volume and vascularisation offered by the FOCI technique provides the unique possibility of studying ACE-transplanted human islets. Interestingly, the acceptance rate of xenografting human islets to NOD.Rag2−/− mice was similar to that of grafting syngeneic NOD islets. This may be surprising, as recombination activating gene 2 (RAG2)-deficient mice have been shown to retain functional natural killer (NK) cells, which have been suggested to contribute to xenograft rejection [37]. However, the role of NK cells in the rejection of xenotransplanted solid organs remains debated [38] and we cannot fully explain the mechanisms.
Abbreviations
- 3D:
-
Three-dimensional
- ACE:
-
Anterior chamber of the eye
- B6:
-
C57BL/6
- CT:
-
Computed tomography
- dfOCM:
-
Dark-field OCM
- FOCI:
-
Functional optical coherence imaging
- FOXP3:
-
Forkhead box P3
- GFP:
-
Green fluorescent protein
- HEV:
-
High endothelial venule
- IHC:
-
Immunohistochemistry
- KO:
-
Knockout
- MAdCAM-1:
-
Mucosal addressin cell adhesion molecule 1
- OCM:
-
Optical coherence microscopy
- OPT:
-
Optical projection tomography
- xfOCM:
-
Extended-focus OCM
References
Pober JS, Cotran RS (1990) The role of endothelial cells in inflammation. Transplantation 50:537–544
Papaccio G, Pisanti FA, Montefiano RD, Graziano A, Latronico MV (2002) Th1 and Th2 cytokines exert regulatory effects upon islet microvascular areas in the NOD mouse. J Cell Biochem 86:651–664
Medarova Z, Greiner DL, Ifediba M et al (2011) Imaging the pancreatic vasculature in diabetes models. Diabetes Metab Res Rev 27:767–772
Medarova Z, Castillo G, Dai G, Bolotin E, Bogdanov A, Moore A (2007) Noninvasive magnetic resonance imaging of microvascular changes in type 1 diabetes. Diabetes 56:2677–2682
Dai C, Brissova M, Reinert RB et al (2013) Pancreatic islet vasculature adapts to insulin resistance through dilation and not angiogenesis. Diabetes 62:4144–4153
Carlsson PO, Sandler S, Jansson L (1998) Pancreatic islet blood perfusion in the nonobese diabetic mouse: diabetes-prone female mice exhibit a higher blood flow compared with male mice in the prediabetic phase. Endocrinology 139:3534–3541
Gaglia JL, Guimaraes AR, Harisinghani M et al (2011) Noninvasive imaging of pancreatic islet inflammation in type 1A diabetes patients. J Clin Invest 121:442–445
Denis MC, Mahmood U, Benoist C, Mathis D, Weissleder R (2004) Imaging inflammation of the pancreatic islets in type 1 diabetes. Proc Natl Acad Sci U S A 101:12634–12639
Nyman LR, Wells KS, Head WS et al (2008) Real-time, multidimensional in vivo imaging used to investigate blood flow in mouse pancreatic islets. J Clin Invest 118:3790–3797
Lindsay RS, Corbin K, Mahne A et al (2015) Antigen recognition in the islets changes with progression of autoimmune islet infiltration. J Immunol 194:522–530
Coppieters K, Martinic MM, Kiosses WB, Amirian N, von Herrath M (2010) A novel technique for the in vivo imaging of autoimmune diabetes development in the pancreas by two-photon microscopy. Plos One 5
Coppieters K, Amirian N, von Herrath M (2012) Intravital imaging of CTLs killing islet cells in diabetic mice. J Clin Invest 122:119–131
Alanentalo T, Asayesh A, Morrison H et al (2007) Tomographic molecular imaging and 3D quantification within adult mouse organs. Nat Methods 4:31–33
Alanentalo T, Loren CE, Larefalk A, Sharpe J, Holmberg D, Ahlgren U (2008) High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas. J Biomed Opt 13:054070
Alanentalo T, Hornblad A, Mayans S et al (2010) Quantification and three-dimensional imaging of the insulitis-induced destruction of beta-cells in murine type 1 diabetes. Diabetes 59:1756–1764
Leitgeb RA, Villiger M, Bachmann AH, Steinmann L, Lasser T (2006) Extended focus depth for Fourier domain optical coherence microscopy. Opt Lett 31:2450–2452
Villiger M, Goulley J, Friedrich M et al (2009) In vivo imaging of murine endocrine islets of Langerhans with extended-focus optical coherence microscopy. Diabetologia 52:1599–1607
Berclaz C, Pache C, Bouwens A et al (2015) Combined Optical Coherence and Fluorescence Microscopy to assess dynamics and specificity of pancreatic beta-cell tracers. Sci Rep 5:10385
Berclaz C, Goulley J, Villiger M et al (2012) Diabetes imaging-quantitative assessment of islets of Langerhans distribution in murine pancreas using extended-focus optical coherence microscopy. Biomed Opt Express 3:1365–1380
Speier S, Nyqvist D, Cabrera O et al (2008) Noninvasive in vivo imaging of pancreatic islet cell biology. Nat Med 14:574–578
Schmidt-Christensen A, Hansen L, Ilegems E et al (2013) Imaging dynamics of CD11c(+) cells and Foxp3(+) cells in progressive autoimmune insulitis in the NOD mouse model of type 1 diabetes. Diabetologia 56:2669–2678
Abdulreda MH, Faleo G, Molano RD et al (2011) High-resolution, noninvasive longitudinal live imaging of immune responses. Proc Natl Acad Sci U S A 108:12863–12868
Soderstrom I, Bergman ML, Colucci F, Lejon K, Bergqvist I, Holmberg D (1996) Establishment and characterization of RAG-2 deficient non-obese diabetic mice. Scand J Immunol 43:525–530
Lemaire K, Ravier MA, Schraenen A et al (2009) Insulin crystallization depends on zinc transporter ZnT8 expression, but is not required for normal glucose homeostasis in mice. Proc Natl Acad Sci U S A 106:14872–14877
Nyqvist D, Kohler M, Wahlstedt H, Berggren PO (2005) Donor islet endothelial cells participate in formation of functional vessels within pancreatic islet grafts. Diabetes 54:2287–2293
Villiger M, Pache C, Lasser T (2010) Dark-field optical coherence microscopy. Opt Lett 35:3489–3491
Lemaire K, Chimienti F, Schuit F (2012) Zinc transporters and their role in the pancreatic beta-cell. J Diabetes Investig 3:202–211
Conlon JM (2001) Evolution of the insulin molecule: insights into structure-activity and phylogenetic relationships. Peptides 22:1183–1193
Chmelova H, Cohrs CM, Chouinard JA et al (2015) Distinct roles of beta cell mass and function during type 1 diabetes onset and remission. Diabetes 64:2148–2160
Kim A, Miller K, Jo J, Kilimnik G, Wojcik P, Hara M (2009) Islet architecture: a comparative study. Islets 1:129–136
Mebius RE, Streeter PR, Michie S, Butcher EC, Weissman IL (1996) A developmental switch in lymphocyte homing receptor and endothelial vascular addressin expression regulates lymphocyte homing and permits CD4+ CD3- cells to colonize lymph nodes. Proc Natl Acad Sci U S A 93:11019–11024
Hanninen A, Taylor C, Streeter PR et al (1993) Vascular addressins are induced on islet vessels during insulitis in nonobese diabetic mice and are involved in lymphoid cell binding to islet endothelium. J Clin Invest 92:2509–2515
Hanninen A, Jaakkola I, Jalkanen S (1998) Mucosal addressin is required for the development of diabetes in nonobese diabetic mice. J Immunol 160:6018–6025
Huang D, Swanson EA, Lin CP et al (1991) Optical coherence tomography. Science 254:1178–1181
Yun S, Tearney G, de Boer J, Iftimia N, Bouma B (2003) High-speed optical frequency-domain imaging. Opt Express 11:2953–2963
Villalta SA, Lang J, Kubeck S et al (2013) Inhibition of VEGFR-2 reverses type 1 diabetes in NOD mice by abrogating insulitis and restoring islet function. Diabetes 62:2870–2878
Chen D, Weber M, Lechler R, Dorling A (2006) NK-cell-dependent acute xenograft rejection in the mouse heart-to-rat model. Xenotransplantation 13:408–414
Griesemer A, Yamada K, Sykes M (2014) Xenotransplantation: immunological hurdles and progress toward tolerance. Immunol Rev 258:241–258
Acknowledgements
We thank M. Sison (School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Switzerland) for his help with the dfOCM setup.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Funding
This work was partly financially supported by the Swiss National Science Foundation Grant (20320L-150191, 206021-139141), the Commission for Technology and Innovation (CTI, Bern, grant 17537.2 PFLS-LS), the Novo Nordisk Foundation, Diabetesförbundet, Barndiabetesförbundet and by the Swedish Research Council. D. Szlag acknowledges support from the Scientific Exchange Programme between Switzerland and the New Member States of the European Union and the project Enhancing Educational Potential of Nicolaus Copernicus University in the Disciplines of Mathematical and Natural Sciences grant.
Duality of interest
The authors declare that there is no duality of interest associated with this manuscript.
Contribution statement
CB, AS-C, DS, JE, AG-B, TL and DH conceived and designed the experiments. AS-C, LH, CB, JE, AB and DS performed, acquired and analysed the FOCI data. AS-C performed and analysed the immunohistochemistry experiments. DS implemented the vascularisation extraction. CB performed the analysis and quantification of the FOCI images. CB, JE, JG, MV, AG-B and FS performed, acquired and analysed the Znt8 and guinea pig experiments. CB, AS-C, DS, TL and DH drafted the manuscript. JE, LH, AB, MV, JG, FS and AG-B critically revised the manuscript. All authors approved the final version to be published and agreed to be accountable for all aspects of the work. DH is guarantor of this work.
Additional information
Corinne Berclaz, Anja Schmidt-Christensen and Daniel Szlag contributed equally to this study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM Methods
(PDF 358 kb)
ESM Fig. 1
(PDF 84 kb)
ESM Fig. 2
(PDF 165 kb)
ESM Fig. 3
(PDF 233 kb)
ESM Fig. 4
(PDF 172 kb)
ESM Fig. 5
(PDF 135 kb)
ESM Fig. 6
(PDF 35 kb)
ESM Fig. 7
(PDF 223 kb)
ESM Video 1
3D rendering of the infiltration process over time. 3D rendering of a FOCI-imaged islet engrafted in the eye of a NOD.Foxp3-GFP recipient mouse at 1 week, 3 weeks and 4 weeks post transplantation (corresponding to Fig. 7) showing the progressive loss of beta cell volume and decrease of vasculature density in the center of the islet (inflamed area) over time. Beta cell volume is shown in green and vasculature in red. (MOV 3750 kb)
ESM Video 2
3D rendering of an infiltrated islet graft. 3D image rendering (FOCI) of islet graft in the eye of a NOD.Foxp3-GFP recipient mouse at 4 weeks post transplantation (corresponding to Fig. 7 and ESM Video 1). This movie shows raw data of the islet structure (green) and vascularisation (red). Left upper movie shows the islet engrafted on the iris. On the right upper part, the structure was “thresholded” to reveal the beta cell volume together with vascularisation. Lower left part shows the corresponding vascularisation of the islet only. A slicing through the islet (lower right) reveals the vascularisation combined with the islet structure at different depths, scale bar: 100 μm. (MOV 3754 kb)
ESM Video 3
Quantification of the beta cell volume and infiltrated volume. 3D rendering of a FOCI segmented islet engrafted in the eye of a NOD.Foxp3-GFP recipient mouse at 4 weeks post transplantation (corresponding to Fig. 7). The movie shows the islet segmented for beta cell volume (green) and infiltrated volume (blue). A slicing through the islet reveals the structure of the islet for different depths, scale bar: 100 μm. (MOV 3778 kb)
ESM Video 4
3D rendering of a non-infiltrated islet graft. 3D rendering of a FOCI imaged non-infiltrated islet graft in the eye of a NOD.Foxp3-GFP recipient mouse at 5 weeks post transplantation. This movie shows raw data of the islet structure (green) and vascularisation (red). Left upper movie shows the islet engrafted on the iris. On the right part, the structure was “thresholded” to reveal the beta cell volume together with vascularisation. Lower left part shows the corresponding vascularisation of the islet only. A slicing through the islet (lower right) reveals the vascularisation combined with the islet structure at different depths, scale bar: 100 μm. (MOV 3785 kb)
Rights and permissions
About this article
Cite this article
Berclaz, C., Schmidt-Christensen, A., Szlag, D. et al. Longitudinal three-dimensional visualisation of autoimmune diabetes by functional optical coherence imaging. Diabetologia 59, 550–559 (2016). https://doi.org/10.1007/s00125-015-3819-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00125-015-3819-x