Infusing pancreatic islets into the portal vein currently represents the preferred approach for islet transplantation, despite considerable loss of islet mass almost immediately after implantation. ...Therefore, approaches that obviate direct intravascular placement are urgently needed. A promising candidate for extrahepatic placement is the omentum. We aimed to develop an extracellular matrix skeleton from the native pancreas that could provide a microenvironment for islet survival in an omental flap. To that end, we compared different decellularization approaches, including perfusion through the pancreatic duct, gastric artery, portal vein, and a novel method through the splenic vein. Decellularized skeletons were compared for size, residual DNA content, protein composition, histology, electron microscopy, and MR imaging after repopulation with isolated islets. Compared to the other approaches, pancreatic perfusion via the splenic vein provided smaller extracellular matrix skeletons, which facilitated transplantation into the omentum, without compromising other requirements, such as the complete depletion of cellular components and the preservation of pancreatic extracellular proteins. Repeated MR imaging of iron-oxide-labeled pancreatic islets showed that islets maintained their position in vivo for 49 days. Advanced environmental scanning electron microscopy demonstrated that islets remained integrated with the pancreatic skeleton. This novel approach represents a proof-of-concept for long-term transplantation experiments.
We have recently described a magnetic resonance (MR) method for detection of rat pancreatic islets transplanted into the liver after labeling with superparamagnetic iron oxide nanoparticles. The aim ...of this work was to study whether this technique could be applicable over a longer period after transplantation and whether it could help to detect islet rejection.
Islets from Lewis and Wistar rats were cultured in the presence of iron oxide nanoparticles. Two thousand of Lewis (n=6) or Wistar (n=8) iron-labeled islets were transplanted into the portal vein of Lewis diabetic animals. Serial MR imaging of the liver were performed at 1, 2, 3, 4, 5, and 6 weeks.
Although all allogeneic islets were rejected by 12 days, syngeneic animals remained normoglycemic throughout the study. At week 1, the labeled islets were visualized on MR scans as distinct hypointense spots homogeneously distributed in the liver. While their number declined only insignificantly in the syngeneic group, in the allogeneic group the number of spots gradually decreased until approximately 35% of their initial count. Although syngeneic islets showed a normal histology, the allogeneic islets were completely rejected. Iron particles, localized in macrophages, were detected only in the syngeneic islets and were absent in the rejected islet structures. In vitro incubation tests did not reveal any differences in insulin secretion between labeled and nonlabeled islets.
MR imaging of iron-labeled pancreatic islets can be used for verification of the technical success of the transplantation procedure itself and for the detection of the decreasing relative islet mass due to rejection.
Diabetic peripheral neuropathy (DPN) is a common complication of diabetes with potential severe consequences. Its pathogenesis involves hyperglycemia-linked mechanisms, which may include changes in ...the expression of neurotrophic growth factors. We analyzed the expression of 29 factors potentially related to nerve degeneration and regeneration in skin biopsies from 13 type 1 diabetic pancreas and kidney recipients with severe DPN including severe depletion of intraepidermal nerve fibers (IENF) in lower limb skin biopsies (group Tx1 1st examination). The investigation was repeated after a median 28-month period of normoglycemia achieved by pancreas transplantation (group Tx1 2nd examination). The same tests were performed in 13 stable normoglycemic pancreas and kidney recipients 6–12 years posttransplantation (group Tx2), in 12 matched healthy controls (group HC), and in 12 type 1 diabetic subjects without severe DPN (group DM). Compared to DM and HC groups, we found a significantly higher (p < 0.05–0.001) expression of NGF (nerve growth factor), NGFR (NGF receptor), NTRK1 (neurotrophic receptor tyrosine kinase 1), GDNF (glial cell-derived neurotrophic factor), GFRA1 (GDNF family receptor alpha 1), and GFAP (glial fibrillary acidic protein) in both transplant groups (Tx1 and Tx2). Enhanced expression of these factors was not normalized following the median 28-month period of normoglycemia (Tx1 2nd examination) and negatively correlated with IENF density and with electrophysiological indices of DPN (vibration perception threshold, electromyography, and autonomic tests). In contrast to our expectation, the expression of most of 29 selected factors related to neural regeneration was comparable in subjects with severe peripheral nerve fiber depletion and healthy controls and the expression of six factors was significantly upregulated. These findings may be important for better understanding the pathophysiology of nerve regeneration and for the development of intervention strategies.
Variability of pancreatic donors may significantly impact the success of islet isolation. The aim of this study was to evaluate donor factors associated with isolation failure and to investigate ...whether immunohistology could contribute to organ selection. Donor characteristics were evaluated for both successful ( n = 61 ) and failed ( n = 98 ) islet isolations. Samples of donor pancreatic tissue ( n = 78 ) were taken for immunohistochemical examination. Islet isolations with 250000 islet equivalents were considered successful. We confirmed that BMI of less than 25 kg/m2 ( P < 0.001 ), cold ischemia time more than 8 hours ( P < 0.01 ), hospitalization longer than 96 hours ( P < 0.05 ), higher catecholamine doses ( P < 0.05 ), and edematous pancreases ( P < 0.01 ) all unfavorably affected isolation outcome. Subsequent immunohistochemical examination of donor pancreases confirmed significant differences in insulin-positive areas ( P < 0.001 ). ROC analyses then established that the insulin-positive area in the pancreas could be used to predict the likely success of islet isolation ( P < 0.001 ). At the optimal cutoff point (>1.02%), sensitivity and specificity were 89% and 76%, respectively. To conclude, while the insulin-positive area, determined preislet isolation, as a single variable, is sufficient to predict isolation outcome and helps to improve the success of this procedure, its combination with the established donor scoring system might further improve organ selection.
We previously developed a deep learning-based web service (IsletNet) for an automated counting of isolated pancreatic islets. The neural network training is limited by the absent consensus on the ...ground truth annotations. Here, we present a platform (IsletSwipe) for an exchange of graphical opinions among experts to facilitate the consensus formation. The platform consists of a web interface and a mobile application. In a small pilot study, we demonstrate the functionalities and the use case scenarios of the platform. Nine experts from three centers validated the drawing tools, tested precision and consistency of the expert contour drawing, and evaluated user experience. Eight experts from two centers proceeded to evaluate additional images to demonstrate the following two use case scenarios. The Validation scenario involves an automated selection of images and islets for the expert scrutiny. It is scalable (more experts, images, and islets may readily be added) and can be applied to independent validation of islet contours from various sources. The Inquiry scenario serves the ground truth generating expert in seeking assistance from peers to achieve consensus on challenging cases during the preparation for IsletNet training. This scenario is limited to a small number of manually selected images and islets. The experts gained an opportunity to influence IsletNet training and to compare other experts' opinions with their own. The ground truth-generating expert obtained feedback for future IsletNet training. IsletSwipe is a suitable tool for the consensus finding. Experts from additional centers are welcome to participate.
In vitro labeling of pancreatic islets by iron nanoparticles enables their detection as hypoitnense spots on serial magnetic resonance (MR) images. We report the first results of a pilot trial aiming ...to test the feasibility and safety of this technique in humans.
Islets were labeled in culture with 5 μL/mL ferucarbotran for 6 to 48 hr and transplanted into the portal vein (12 infusions) in 8 C-peptide negative recipients. The liver area was examined the next day and 1, 4, and 24 weeks posttransplant using a 3T MR scanner.
In all recipients, significant C-peptide levels and near-normal HbA1c values were achieved with 50% to 80% insulin dose reduction. No side effects related to the labeling procedure were documented. Typically, a significant islet spot number decrease (on average 60%) was detected at week 1 with subsequent only slight decrease for up to 24 weeks. In two subjects with labeling period of less than 6 and 10 hr, only few islet spots were detected corresponding to poor islet visualization in phantoms labeled for the same period of time.
Pancreatic islets (PI) visualization was safe and successful in all recipients but was less efficient if labeling period was less than 16 hr. Significant decrease of islet spots occurred at week 1, suggesting early islet destruction or impaired engraftment. Afterward, the islet spot numbers remained stable for up to 24 weeks. Data show that MR detection of ferucarbotran-labeled islets enables their long-term noninvasive visualization and correlates with sustained C-peptide production.
Three magnetic resonance (MR)/fluorescence imaging probes were tested for visualization, cellular distribution, and survival of labeled pancreatic islets in vitro and following transplantation. As T1 ...contrast agents (CAs), gadolinium(III) complexes linked to β‐cyclodextrin (Gd‐F‐βCD) or bound to titanium dioxide (TiO2@RhdGd) were tested. As a T2 CA, perovskite manganite nanoparticles (LSMO@siF@si) were examined. Fluorescein or rhodamine was incorporated as a fluorescent marker in all probes. Islets labeled with gadolinium(III) CAs were visible as hyperintense spots on MR in vitro, but detection in vivo was inconclusive. Islets labeled with LSMO@siF@si CA were clearly visible as hypointense spots or areas on MR scans in vitro as well as in vivo. All CAs were detected inside the islet cells by fluorescence. Although the vitality and function of the labeled islets was not impaired by any of the tested CAs, results indicate that LSMO@siF@si CA is a superior marker for islet labeling, as it provides better contrast enhancement within a shorter scan time.
Seeing the light: We evaluated three novel magnetic resonance/fluorescence imaging probes for visualization, cellular distribution, and survival of labeled pancreatic islets in vitro and in vivo. One of these contrast agents, based on perovskite‐like manganite nanoparticles, was shown to provide better contrast enhancement within a shorter scan time than existing agents, indicating potential utility for future imaging of transplanted pancreatic islets.
Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we ...describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R
2
= 1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R
2
= 0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.
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