HPAP T1D: Integrated Human Pancreas Analysis Program (Univ. Pennsylvania)

Contact PI: Ali Naji, MD, PhD, University of Pennsylvania (UC4 DK112217)

Klaus Kaestner, PhD, Investigator, University of Pennsylvania
Michael Feldman, MD, PhD, co-Investigator, University of Pennsylvania
Jason Moore, PhD, co-Investigator, University of Pennsylvania
Michael Betts, PhD, co-Investigator, University of Pennsylvania
Doris Stoffers, MD, PhD, co-Investigator, University of Pennsylvania

 

Summary of Project Abstract & Publications

Start Date: September 20, 2016

 

Abstract

The UC4DK112217 team headed by Dr. Ali Naji at the University of Pennsylvania combines expertise ranging from pancreas procurement and islet isolation to data integration and analysis, supporting the Human Pancreas Analysis Program (HPAP) through six cores. Core A (headed by Dr. Naji) will procure a spectrum of human pancreata and detailed donor medical history in close collaboration with Dr. Atkinson’s team from the University of Florida; 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 (headed by Dr. Stoffers) will perform physiological phenotyping on the isolated islets, which will also be coordinated closely with Dr. Powers’ group at Vanderbilt University. Core C (headed by Dr. Betts) will quantify and characterize memory T cell subsets by flow cytometry and single cell analysis; characterize suppressive activity of Tregs and the ability of related effector cells to be suppressed; perform B cell phenotyping; and generate chromatin accessibility maps of enhancers in pathogenic immune cell types. Core D (Dr. Kaestner) 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 (Dr. Feldman) will process tissues and analyze them using advanced technologies such as multiplexed immunoflourescent staining, whole slide imaging, quantitative image analysis of protein markers and immune cell infiltrates, and collaborate with Dr. Powers’ team to integrate their advance imaging modalities. This Core will also develop 2-dimensional mass cytometry to pancreatic sections using laser ablation technology. Finally, Core F (headed by Dr. Moore) 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 UC4 programs.

Meet the Grant Team 

Investigators

Ali Naji, MD, PhD University of Pennsylvania

Klaus Kaestner, PhD University of Pennsylvania

 

Co-Investigators

Doris Stoffers, MD, PhD University of Pennsylvania

Jason Moore, PhD University of Pennsylvania

Michael Betts, PhD University of Pennsylvania

Michael Feldman, MD, PhD University of Pennsylvania

 

Publications

• Genetic Variation in Type 1 Diabetes Reconfigures the 3D Chromatin Organization of T Cells and Alters Gene Expression.
• Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies
• Integration of genetic and clinical information to improve imputation of data missing from electronic health records
• ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites
• Automated discovery of test statistics using genetic programming
• Multiplexed In Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes
• ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data
• Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
• Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS
• Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)
• Investigating the parameter space of evolutionary algorithms
• Data-driven advice for applying machine learning to bioinformatics problems
• A miRNA181a/NFAT5 axis links impaired T cell tolerance induction with autoimmune type 1 diabetes
• A Heuristic Method for Simulating Open-data of Arbitrary Complexity that can be used to Compare and Evaluate Machine Learning Methods
• Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS
• PMLB: a Large Benchmark Suite for Machine Learning Evaluation and Comparison
• Evolutionarily derived networks to inform disease pathways