Early window of diabetes determinism in NOD mice, dependent on the complement receptor CRIg, identified by noninvasive imaging

Nat Immunol. 2012 Feb 26;13(4):361-8. doi: 10.1038/ni.2233.

Abstract

All juvenile mice of the nonobese diabetic (NOD) strain develop insulitis, but there is considerable variation in their progression to diabetes. Here we used a strategy based on magnetic resonance imaging (MRI) of magnetic nanoparticles to noninvasively visualize local effects of pancreatic-islet inflammation to predict the onset of diabetes in NOD mice. MRI signals acquired during a narrow early time window allowed us to sort mice into groups that would progress to clinical disease or not and to estimate the time to diabetes development. We exploited this approach to identify previously unknown molecular and cellular elements correlated with disease protection, including the complement receptor of the immunoglobulin superfamily (CRIg), which marked a subset of macrophages associated with diabetes resistance. Administration of a fusion of CRIg and the Fc portion of immunoglobulin resulted in lower MRI signals and diabetes incidence. In addition to identifying regulators of disease progression, we show here that diabetes is set at an early age in NOD mice.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Separation
  • Diabetes Mellitus, Type 1 / diagnosis*
  • Diabetes Mellitus, Type 1 / genetics
  • Diabetes Mellitus, Type 1 / immunology*
  • Disease Progression
  • Early Diagnosis*
  • Female
  • Ferric Compounds
  • Flow Cytometry
  • Gene Expression Profiling
  • Islets of Langerhans / pathology
  • Macrophages / immunology*
  • Macrophages / metabolism*
  • Magnetic Resonance Imaging
  • Male
  • Metal Nanoparticles
  • Mice
  • Mice, Inbred NOD
  • Receptors, Complement / immunology
  • Receptors, Complement / metabolism*

Substances

  • Ferric Compounds
  • Receptors, Complement
  • VSIG4 protein, mouse
  • ferric oxide

Associated data

  • GEO/GSE35096