Penn Human Pancreas Procurement and Analysis Program Abstract Utilizing our existing infrastructure and scientific collaborations, we have assembled 6 cores with expertise ranging from pancreas procurement and islet isolation to data integration for a comprehensive and integrated Human Pancreas Procurement and Analysis Program based at the University of Pennsylvania. Core A will procure a spectrum of human pancreata and detailed donor medical history; perform high resolution HLA typing by next generation sequencing; isolate islets; and distribute islets and tissues to the other Cores for further analysis or processing. Core B will perform physiological phenotyping on the isolated islets. Core C will quantify and characterize memory T cell subsets by flow cytometry and single cell qPCR analysis; characterize suppressive activity of Tregs and the ability of related effector cells to be suppressed; B cell phenotyping; and generate chromatin accessibility maps of enhancers in pathogenic cell types. Core D will perform multiple advanced modalities for the molecular profiling of isolated islets including RNAseq and microRNAseq of sorted islet cell populations; mass cytometry for single cell quantification of more than 20 cell surface and intracellular markers; and single cell RNAseq. Core E will process tissues using multiple modalities that will allow for analysis using advanced technologies such as multiplexed immunoflourescent staining, combinatorial barcoded FISH (combFISH), whole slide imaging, and quantitative image analysis of protein markers and immune cell infiltrates. This Core will adapt 2-dimensional mass cytometry to pancreatic sections utilizing multiplexed ion beam imaging (MIBI) technology. This Core will also archive tissues as well as DNA and blood, and facilitate sample distribution to HPPAP approved researchers. Finally, Core F will assemble, annotate and maintain an open access database for the Program and its member-researchers, and collaborate with the HIRN in the sharing of data from both programs. The entire Program will be executed by an Administrative Core consisting of the PIs, with assistance from an Executive Committee consisting of the core leaders. The Administrative Core will interface with an external committee to review applications for HPPAP biosample use, and will collaborate with the HIRN. The Program will also interact with the HPPAP member/PANC DB user community to provide a richly annotated source of physiologic, genomic and immunologic data on the tissue-based landscape governing T1D.

Public Health Relevance

Penn Human Pancreas Procurement and Analysis Program Narrative The past decades have seen a dramatic improvement in our ability to phenotype and molecularly profile human tissues relevant to the etiology of Type 1 diabetes with unprecedented resolution, at the genomic, epigenomic, protein, and functional levels. Here we will employ state-of-the-art technologies to determine all aspects of pancreas biology as it pertains to type 1 diabetes, juvenile organ donors, and other cases of beta- cell dysfunction. We will profile both the endocrine and immune systems with multiple modalities, and make the vast data accumulated available through the highly accessible PANC-DB to be developed here. This comprehensive profiling of the natural history of Type 1 diabetes will pave the way for future discoveries of new treatment modalities for diabetes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
High Impact Research and Research Infrastructure Cooperative Agreement Programs—Multi-Yr Funding (UC4)
Project #
1UC4DK112217-01
Application #
9236460
Study Section
Special Emphasis Panel (ZDK1-GRB-S (O4)S)
Program Officer
Arreaza-Rubin, Guillermo
Project Start
2016-09-20
Project End
2020-09-19
Budget Start
2016-09-20
Budget End
2020-09-19
Support Year
1
Fiscal Year
2016
Total Cost
$12,249,589
Indirect Cost
$4,187,426
Name
University of Pennsylvania
Department
Surgery
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Manduchi, Elisabetta; Chesi, Alessandra; Hall, Molly A et al. (2018) Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS. Pac Symp Biocomput 23:548-558
Olson, Randal S; Cava, William La; Mustahsan, Zairah et al. (2018) Data-driven advice for applying machine learning to bioinformatics problems. Pac Symp Biocomput 23:192-203
Wang, Yue J; Kaestner, Klaus H (2018) Single-Cell RNA-Seq of the Pancreatic Islets--a Promise Not yet Fulfilled? Cell Metab :
Urbanowicz, Ryan J; Olson, Randal S; Schmitt, Peter et al. (2018) Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform 85:168-188
Manduchi, Elisabetta; Williams, Scott M; Chesi, Alessandra et al. (2018) Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS. Hum Genet 137:413-425
Urbanowicz, Ryan J; Meeker, Melissa; La Cava, William et al. (2018) Relief-based feature selection: Introduction and review. J Biomed Inform 85:189-203
Serr, Isabelle; Scherm, Martin G; Zahm, Adam M et al. (2018) A miRNA181a/NFAT5 axis links impaired T cell tolerance induction with autoimmune type 1 diabetes. Sci Transl Med 10:
Moore, Jason H; Shestov, Maksim; Schmitt, Peter et al. (2018) A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods. Pac Symp Biocomput 23:259-267
Piette, Elizabeth R; Moore, Jason H (2018) Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV). BioData Min 11:6
Rosenfeld, Aaron M; Meng, Wenzhao; Luning Prak, Eline T et al. (2018) ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data. Front Immunol 9:2107

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