Functional Genomics of the Endocrine Pancreas

The Pancreas Clone Set and PancChip, New Resources for Diabetes Research

  1. L. Marie Scearce1,
  2. John E. Brestelli1,
  3. Shannon K. McWeeney2,
  4. Catherine S. Lee1,
  5. Joan Mazzarelli2,
  6. Deborah F. Pinney2,
  7. Angel Pizarro2,
  8. Christian J. Stoeckert, Jr.2,
  9. Sandra W. Clifton3,
  10. M. Alan Permutt4,
  11. Juliana Brown5,
  12. Douglas A. Melton5 and
  13. Klaus H. Kaestner1
  1. 1Department of Genetics, University of Pennsylvania, Philadephia, Pennsylvania
  2. 2Center for Bioinformatics, University of Pennsylvania, Philadephia, Pennsylvania
  3. 3Genome Sequencing Center, Washington University, St. Louis, Missouri
  4. 4Department of Internal Medicine, Washington University, St. Louis, Missouri
  5. 5Department of Molecular and Cellular Biology, Harvard University, Boston, Massachusetts


    Over the past 5 years, microarrays have greatly facilitated large-scale analysis of gene expression levels. Although these arrays were not specifically geared to represent tissues and pathways known to be affected by diabetes, they have been used in both type 1 and type 2 diabetes research. To prepare a tool that is particularly useful in the study of type 1 diabetes, we have assembled a nonredundant set of 3,400 clones representing genes expressed in the mouse pancreas or pathways known to be affected by diabetes. We have demonstrated the usefulness of this clone set by preparing a cDNA glass microarray, the PancChip, and using it to analyze pancreatic gene expression from embryonic day 14.5 through adulthood in mice. The clone set and corresponding array are useful resources for diabetes research.


    • Address correspondence and reprints requests to Klaus H. Kaestner, Department of Genetics, University of Pennsylvania, 415 Curie Blvd., Philadelphia, PA 19104. E-mail: kaestner{at}

      Received for publication 2 January 2002 and accepted in revised form 6 May 2002. Posted on the World Wide Web at on 7 June 2002.

      DoTS, Database of Transcribed Sequences; EST, expressed sequence tag; GO, gene ontology; GUS, Genomics Unified Schema; NIDDK, National Institute of Diabetes and Digestive and Kidney Diseases.

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