Surgery is usually reserved for people with chronic pancreatitis who have pain that does not respond to other treatments. A total pancreatectomy, which involves the complete removal of the pancreas, can relieve the pain of chronic pancreatitis.

An islet cell autotransplant has the potential to prevent diabetes, or to at least make diabetes milder and more easily managed. The goal of a total pancreatectomy with islet cell autotransplant is to improve a person's quality of life by decreasing the level of pain thus reducing the need for narcotic medication and minimizing the amount of insulin the patient will need to take to help with blood sugar levels.

A determination of how well the transplanted cells will function will take several months, and in some people up to a year. Some patients might not need to take insulin shots or test their blood sugar daily, although most patients will.

Once the total pancreatectomy stage is complete, the pancreas is immediately preserved and taken to the cell laboratory where the isolation of the islet cells begins.

An infusion catheter will be inserted into a vessel leading to the patient's liver to be used for the islet cell transplant. The Interventional Radiologist will verify the placement of the infusion catheter. The islet cell infusion through the catheter into the patient's liver will take about 45 minutes.

It is important that during this time, the islet cells be given an opportunity to rest in order to fully graft and begin producing insulin on their own. A single scar will result from the incision. The patient will also have small scars from drain and transplant sites.

Because the islets come from the patient's own pancreas, there is no chance of tissue rejection. However, it may take some time for the islet cells to produce enough insulin to maintain normal blood glucose levels.

The anesthesiologist will manage the patient's pain after surgery using epidural medication along with IV medication. During the outpatient visits, we will work with the patient and the Behavioral Medicine team to slowly adjust and lower the levels of pain medication.

A two-week follow-up appointment with the surgeon, diabetes management team, and behavioral medicine team will be made before the patient leaves the hospital. Appointments are monthly as needed and then at six months and one year.

Although this varies among patients depending on pain tolerance, coping mechanisms and support systems, the patient will be encouraged to return to normal activities —things such as showering, driving, walking up stairs, light lifting, returning to work, etc.

We advise that the patient avoid heavy lifting or straining for six to eight weeks after surgery. It is very important that the patient adhere to the recommended diet in order to continue to manage blood sugar and pancreatic insufficiency. Health Medical Services Digestive Health Patients GI Surgery Chronic Pancreatitis Surgery Islet Cell Transplant Surgery.

Digestive Disease Center. About The DDC G. Digestive Diseases. Small Intestine. Digestive Organs. Chronic Pancreatitis Surgery. Islet Cell Transplant Surgery. Laparoscopic Surgery. Rectal Surgery. Medical Tests. Abdominal Scans.

Barium Radiology. Function Studies. Interventional Radiology. Symptoms and Conditions. The chamber holder and ×40, 1. All calculations were made using built-in functions of the Igor Pro 8 software Wavemetrics.

The R-GECO1 data is presented as the fluorescence intensity, F, normalized to the initial fluorescence, F0. The heatmaps in Fig. SC- and primary islets were transduced with adenovirus expressing the FRET-based cAMP reporter Epac-S H ref.

The chamber was mounted on the thermostated stage of a TIRF imaging setup based on an Eclipse Ti Nikon microscope with a ×60, 1. The filters were mounted in a filter wheel Sutter Instruments , which, together with the camera was controlled by MetaFluor software.

The experiments for Supplementary Fig. For metabolite tracing assays, SC-islets or primary islets were used for each technical replicate of 13 C 6 -glucose labeling or 13 C 5 -glutamine labeling. CLM Samples were analyzed on a Thermo Q Exactive Focus Quadrupole Orbitrap mass spectrometer coupled with a Thermo Dionex UltiMate HPLC system Thermo Fisher Scientific.

Xcalibur v. Confirmation of metabolite peak specificity was achieved using commercially available standards Merck, Cambridge Isotope Laboratories and Santa Cruz Biotechnology.

LC-MS data quality was monitored throughout the run with running standard mixes, inhouse quality controls and blanks for detecting carry over. Peak integration and metabolite isotopologue identification was accomplished using TraceFinder SP2 software v.

Specificity of labeled peaks and isotopologues were confirmed using cell line controls, blank control samples and nonlabeled islet samples pre- and postincubation.

Natural abundance was assayed using nonlabeled cell samples, and confirmed with correction calculations using IsoCor software on a subset of data DNA normalization was also in strong agreement with levels of essential amino acids that could not be metabolized from glucose in each sample data not shown.

A third and final measurement was taken after another 1-min incubation Measurement C. NOD-SCID-Gamma NSG, Jackson Laboratories, catalog no.

Mice were anesthetized with isoflurane and the kidney exposed. A small opening to the kidney capsule was made and the capsule separated with a glass rod. The tubing was inserted in the opening and the SC-islets implanted using a Hamilton syringe.

The kidney capsule was then closed by cautery before wound closure. Nonfasted blood samples were collected from the saphenous vein monthly. Some mice received a single high-dose streptozotocin STZ, catalog no.

To test the functionality of the SC-islet grafts, the mice were subjected to an intraperitoneal glucose tolerance test. The mice were weighed, and blood glucose was measured before the test. To prepare single-cell suspensions from grafted SC-islet cells, grafts were first carefully retrieved from dissected kidneys.

Kidney capsule was gently removed and graft tissue was scraped with a scalpel, avoiding the kidney parenchyme. Recovered graft tissue was then minced with a scalpel in small pieces and dissociated into single cells as described above. Single cells from in vitro and graft-recovered SC-islets were encapsulated using the 10x Genomics Chromium platform.

We included two reference scRNAseq datasets in our analysis; 1 sample of pancreatic endocrine progenitor cells 48 and 12 samples of primary adult human pancreatic islets We performed raw data fastq processing with 10x Genomics Cell Ranger v.

The reads were mapped to a hybrid of human and mouse reference genomes GRCh The filtered counts were analyzed with Seurat The counts were normalized, scaled and analyzed for principal component analysis PCA with default methods.

The variable genes top 1, were identified separately for each sample and combined during the analysis for a total of 6, variable genes. The integrated harmonized principal components PC were used to build the uniform manifold approximation and projection UMAP , find the neighboring cells using shared nearest neighbor and identify cell clusters using default Seurat methods.

To reduce background RNA contamination from disrupted cells we used SoupX 79 with clusters identified with Seurat, and known cell type specific marker genes GCG , TTR , INS , IAPP , SST , GHRL , PPY , COL3A1 , CPA1 , CLPS , REG1A , CELA3A , CTRB1 , CTRB2 , PRSS2 , CPA2 , KRT19 , VTCN1 to estimate the level of contamination.

The Seurat analysis was then repeated with the adjusted counts with the following modifications. Cells with less than 1, UMI counts or expressed genes were excluded. During clustering the resolution was adjusted to 0. The clusters were reordered by similarity and identified to cell types by the differentially expressed genes corresponding to known marker genes.

We focused the analysis on the identified endocrine cells by selecting clusters that expressed endocrine markers CHGA, INS, GCG or SST.

We then rebuilt the UMAP and clustering on those cells. To improve gene expression representation we used a denoising and imputation method with Rmagic Differentially expressed genes among clusters and sample types were identified with the FindMarkers function in Seurat, using MAST 81 , with a log fold-change threshold of 0.

We inferred the differentiation trajectories of the beta populations using RNA velocity. We performed pseudotime analysis on the beta cell populations cells using Monocle2 ref.

The data was reanalyzed using clusterCells to identify the different timepoint populations. The 2, genes with lowest q-value identified with differentialGeneTes t function were used for dimensionality reduction using DDRTree in the reduceDimension function.

The list of genes can be found in Supplementary Table 4. Aggregated signature gene scores were calculated using AddModuleScore function in Seurat. The following mature beta cell marker genes were used to calculate the mature beta signature: INS , G6PC2 , HOPX , UCN3 , IAPP , CHGB , MAFA and SIX3.

To compare the SC-derived cell populations described by Veres et al. Although we would ideally have liked to use the same preprocessing steps as in our analysis, the dataset of Veres et al.

was produced with a different scRNAseq method inDrops , which presents variable barcode location and higher error rate 87 , precluding the use of Cell Ranger for mapping and read counting.

Instead we used the read counts UMI and metadata provided in the GEO submission GSE as a starting point for the comparison. Since the analysis by Veres et al. used a different genome annotation, we had to exclude genes that were not included in both datasets retaining 19, shared genes.

We combined the datasets with those of Seurat 77 and normalized the expression with default settings. The variable genes top 1, per sample were identified separately for each sample, the data was scaled, and the top 50 PCs were identified with default settings.

The resulting datasets were harmonized with Harmony 78 using sample ID as grouping variable, theta set to 2, using 50 clusters and maximum iterations per cluster set to 40 and maximum iterations for harmony set to The harmonized PCA values were used as input for UMAP, and the UMAP values were used to identify neighboring cells with default settings.

Clustering was carried out at resolution set to 2. Clusters that were highly correlated with previously identified cell types were identified using clustifyr Some clusters were annotated manually as we had excluded the nonendocrine cells for our endocrine cell dataset.

Morphological data represents population-wide observations from independent differentiation experiments. Insulin secretion, respirometry and metabolomics data represents samples of independent SC-islet differentiation experiments or islet donors.

In vivo data is derived from independent animals. Transcriptomics data represents data on the level of single cells, which are pooled from two to three independent differentiation experiments per timepoint. Statistical methods used are represented in each figure legend. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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Download references. We thank C. Wollheim for invaluable feedback on the manuscript. Grym, A. Laitinen, S. Eurola and V. Parekh are thanked for expert technical support, and J. Juutila, S. Andersson and J. Morikka for the processing and acquisition of metabolite tracing data. We thank FIMM Single-Cell Analytics unit supported by HiLIFE and Biocenter Finland for scRNA sequencing services.

We are grateful to the Nordic Network for Islet Transplantation supported by the strategic grant consortium Excellence of Diabetes Research in Sweden, EXODIAB , and the IsletCore, University of Alberta, Canada, for providing human islets and P. MacDonald for providing IsletCore summary statistics.

This study was supported by the Academy of Finland grant and MetaStem Center of Excellence grant to T. and the Helsinki University Hospital Research Funds to T. and an EMBO Long-Term Fellowship ALT to D. Additional funding was provided by The Swedish Research Council S.

the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement INNODIA and INNODIA HARVEST. and Harry B. Helmsley Charitable Trust to T.

Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland. Bioinformatics and Genomics Program, Centre for Genomic Regulation CRG , The Barcelona Institute of Science and Technology BIST , Barcelona, Spain.

Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas CIBERDEM , Barcelona, Spain. Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden. Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.

Metabolomics Unit, Institute for Molecular Medicine Finland, Helsinki, Finland. Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.

Molecular and Integrative Bioscience Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

You can also search for this author in PubMed Google Scholar. conceived and conceptualized the study, developed differentiation protocols, differentiated SC-islets, analyzed the single-cell transcriptomic data and wrote the manuscript. conceptualized and performed the metabolomic analyses, differentiated SC-islets and wrote the manuscript.

developed differentiation protocols, performed and analyzed SC-islet differentiation-, insulin secretion- and IHC experiments and wrote the manuscript.

developed differentiation protocols, performed and analyzed differentiation and animal experiments and participated in the writing of the manuscript.

and M. performed and analyzed cell physiology experiments. performed and analyzed differentiation and animal experiments, H. and J. participated in the differentiation experiment analysis. assisted in the single-cell transcriptomic experiments and J. in the data analysis. helped in the metabolomic analysis pipeline.

and E. performed electron microscopical analyses. and P. helped in the analysis of metabolic data and participated in the manuscript writing. acquired funding and participated in the differentiation and animal experiments. and A. supervised the cell physiology experiments, acquired funding, and participated in manuscript writing.

conceived and supervised the study, provided resources, acquired funding and wrote the manuscript. Correspondence to Timo Otonkoski.

Nature Biotechnology thanks Maike Sander and the other, anonymous, reviewer s for their contribution to the peer review of this work. Cell identity quantification of single cells passing quality control in the integrated full and endocrine filtered dataset.

Differentially expressed genes between endpoint in vitro maturation, endpoint in vivo maturation and adult beta cells. Differentially expressed genes between in vitro SC-beta cells with Mature High signature versus Mature Low signature. Open Access This article is licensed under a Creative Commons Attribution 4.

Reprints and permissions. Functional, metabolic and transcriptional maturation of human pancreatic islets derived from stem cells. Nat Biotechnol 40 , — Download citation. Received : 18 March Accepted : 11 January Published : 03 March Issue Date : July Anyone you share the following link with will be able to read this content:.

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Skip to main content Thank you for visiting nature. nature nature biotechnology articles article. Download PDF. Subjects Experimental models of disease Regenerative medicine Stem-cell differentiation Stem-cell research Type 1 diabetes.

Abstract Transplantation of pancreatic islet cells derived from human pluripotent stem cells is a promising treatment for diabetes. Main The generation of functional pancreatic beta cells from human pluripotent stem cells hPSCs is a main goal of stem cell research, aiming to provide a renewable and consistent source of cells for the treatment of diabetes.

Results SC-islets present organotypic cytoarchitecture and function We devised an optimized differentiation protocol by combining previous advances in the generation of SC-islets 8 , 9 , 13 , 14 Fig. Full size image. Discussion Here, we describe an optimized protocol to generate human SC-islets that display glucose-sensitive insulin release and endocrine cell composition similar to that of primary islets.

Methods In vitro culture and differentiation of hPSCs Human embryonic stem cell line H1 WA01, WiCell was used for most of this study. In vitro culture of primary adult islets Primary islets were provided by the Nordic Network for Islet Transplantation Uppsala University and University of Alberta IsletCore Canada.

In vitro tests of insulin secretion Static tests of insulin secretion were carried out in 1.