Pancreatlas: Applying an Adaptable Framework to Map the Human Pancreas in Health and Disease

Summary Human tissue phenotyping generates complex spatial information from numerous imaging modalities, yet images typically become static figures for publication, and original data and metadata are rarely available. While comprehensive image maps exist for some organs, most resources have limited support for multiplexed imaging or have non-intuitive user interfaces. Therefore, we built a Pancreatlas resource that integrates several technologies into a unique interface, allowing users to access richly annotated web pages, drill down to individual images, and deeply explore data online. The current version of Pancreatlas contains over 800 unique images acquired by whole-slide scanning, confocal microscopy, and imaging mass cytometry, and is available at https://www.pancreatlas.org. To create this human pancreas-specific biological imaging resource, we developed a React-based web application and Python-based application programming interface, collectively called Flexible Framework for Integrating and Navigating Data (FFIND), which can be adapted beyond Pancreatlas to meet countless imaging or other structured data-management needs.

[1]  Insulitis in Autoantibody-Positive Pancreatic Donor With History of Gestational Diabetes Mellitus , 2017, Diabetes Care.

[2]  Scott M. Palmer,et al.  LungMAP: The Molecular Atlas of Lung Development Program , 2017, American journal of physiology. Lung cellular and molecular physiology.

[3]  Allan R. Jones,et al.  An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.

[4]  Jeremy L. Muhlich,et al.  Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser , 2020 .

[5]  Morten Nielsen,et al.  The Immune Epitope Database and Analysis Resource Program 2003–2018: reflections and outlook , 2019, Immunogenetics.

[6]  Onur Yukselen,et al.  DEBrowser: interactive differential expression analysis and visualization tool for count data , 2018, bioRxiv.

[7]  A MusenMark,et al.  Enabling enrichment analysis with the Human Disease Ontology , 2011 .

[8]  Ardan Patwardhan,et al.  Trends in the Electron Microscopy Data Bank (EMDB) , 2017, Acta crystallographica. Section D, Structural biology.

[9]  Mark A. Musen,et al.  Enabling enrichment analysis with the Human Disease Ontology , 2011, J. Biomed. Informatics.

[10]  Jeremy L. Muhlich,et al.  Online narrative guides for illuminating tissue atlas data and digital pathology images , 2020, bioRxiv.

[11]  Ardan Patwardhan,et al.  EMPIAR: a public archive for raw electron microscopy image data , 2016, Nature Methods.

[12]  J. Schug,et al.  Multiplexed In Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes. , 2019, Cell metabolism.

[13]  José L. V. Mejino,et al.  Pushing the envelope: challenges in a frame-based representation of human anatomy , 2004, Data Knowl. Eng..

[14]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[15]  J. Niland,et al.  The Integrated Islet Distribution Program answers the call for improved human islet phenotyping and reporting of human islet characteristics in research articles , 2019, Diabetologia.

[16]  Melinda R. Dwinell,et al.  Three Ontologies to Define Phenotype Measurement Data , 2012, Front. Gene..

[17]  Xosé M. Fernández,et al.  The 27th annual Nucleic Acids Research database issue and molecular biology database collection , 2019, Nucleic Acids Res..

[18]  K. Kaestner,et al.  NIH Initiative to Improve Understanding of the Pancreas, Islet, and Autoimmunity in Type 1 Diabetes: The Human Pancreas Analysis Program (HPAP) , 2019, Diabetes.

[19]  Nuno A. Fonseca,et al.  The RNASeq-er API—a gateway to systematically updated analysis of public RNA-seq data , 2017, Bioinform..

[20]  Allan R. Jones,et al.  Transcriptional Landscape of the Prenatal Human Brain , 2014, Nature.

[21]  Yukako Tohsato,et al.  SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena , 2016, Bioinform..

[22]  Salil S. Bhate,et al.  Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging , 2017, Cell.

[23]  Jessica A. Turner,et al.  The Ontology for Biomedical Investigations , 2016, PloS one.

[24]  K. Henrick,et al.  New electron microscopy database and deposition system. , 2002, Trends in biochemical sciences.

[25]  M. Drumm,et al.  Cystic fibrosis-related diabetes is caused by islet loss and inflammation. , 2018, JCI insight.

[26]  Shila Ghazanfar,et al.  The human body at cellular resolution: the NIH Human Biomolecular Atlas Program , 2019, Nature.

[27]  M. Atkinson,et al.  Islet amyloidosis in a child with type 1 diabetes , 2019, Islets.

[28]  Ardan Patwardhan,et al.  A call for public archives for biological image data , 2018, Nature Methods.

[29]  Jamie A Davies,et al.  GUDMAP: the genitourinary developmental molecular anatomy project. , 2008, Journal of the American Society of Nephrology : JASN.

[30]  C. Rueden,et al.  Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.

[31]  R. Baldock,et al.  The GUDMAP database – an online resource for genitourinary research , 2011, Development.

[32]  J. Niland,et al.  The Integrated Islet Distribution Program Answers the Call for Improved Human Islet Phenotyping and Reporting of Human Islet Characteristics in Research Articles , 2019, Diabetes.

[33]  Diane C. Saunders,et al.  Decreased pancreatic acinar cell number in type 1 diabetes , 2020, Diabetologia.

[34]  Neil A Ranson,et al.  Securing the future of research computing in the biosciences , 2019, PLoS Comput. Biol..

[35]  M. Atkinson,et al.  Islet Microvasculature Alterations With Loss of Beta-cells in Patients With Type 1 Diabetes , 2018, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[36]  Bálint Antal,et al.  Image Data Resource: a bioimage data integration and publication platform , 2017, Nature Methods.

[37]  Anna Zhukova,et al.  Modeling sample variables with an Experimental Factor Ontology , 2010, Bioinform..

[38]  Chris Allan,et al.  Publishing and sharing multi-dimensional image data with OMERO , 2015, Mammalian Genome.