Glitter in the darkness? Non‐fibrillar β‐amyloid plaque components significantly impact the β‐amyloid PET signal

Medientyp: E-Artikel
Titel: Glitter in the darkness? Non‐fibrillar β‐amyloid plaque components significantly impact the β‐amyloid PET signal
Beteiligte: Biechele, Gloria; Monasor, Laura Sebastian; Wind, Karin; Blume, Tanja; Parhizkar, Samira; Arzberger, Thomas; Sacher, Christian; Beyer, Leonie; Eckenweber, Florian; Gildehaus, Franz Josef; von Ungern‐Sternberg, Barbara; Willem, Michael; Bartenstein, Peter; Cumming, Paul; Rominger, Axel; Herms, Jochen; Lichtenthaler, Stefan F; Haass, Christian; Tahirovic, Sabina; Brendel, Matthias
Erschienen: Wiley, 2021
Erschienen in: Alzheimer's & Dementia
Sprache: Englisch
DOI: 10.1002/alz.051983
ISSN: 1552-5260; 1552-5279
Schlagwörter: Psychiatry and Mental health ; Cellular and Molecular Neuroscience ; Geriatrics and Gerontology ; Neurology (clinical) ; Developmental Neuroscience ; Health Policy ; Epidemiology
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Beschreibung: <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>β‐amyloid PET (Aβ‐PET) is an important tool for quantification of amyloidosis in the brain of suspected Alzheimer’s disease (AD) patients and transgenic AD mouse models. Despite the excellent correlation of Aβ‐PET with gold standard immunohistochemical assessments, the relative contributions of fibrillar and non‐fibrillar Aβ components to the <jats:italic>in vivo</jats:italic> Aβ‐PET signal remain unclear. Thus, we obtained two murine cerebral amyloidosis models that present with distinct Aβ plaque compositions and performed regression analysis between immunohistochemistry and Aβ PET to determine the biochemical contributions to Aβ‐PET signal <jats:italic>in vivo</jats:italic>.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>We investigated groups of <jats:italic>App<jats:sup>NL‐G‐F</jats:sup> </jats:italic> and APPPS1 mice three, six and 12 months of age by longitudinal [<jats:sup>18</jats:sup>F]‐florbetaben Aβ‐PET and with immunohistochemical analysis of the fibrillar and total Aβ burdens. We then applied group level inter‐modality regression models using age and genotype matched sets of fibrillar/ non‐fibrillar Aβ data (predictors) and Aβ‐PET results (outcome) for both transgenic models. An independent group of double‐hit APPPS1 mice with dysfunctional microglia due to knock‐out of triggering receptor expression on myeloid cells 2 (Trem2<jats:sup>‐/‐</jats:sup>) served for validation and evaluation of translational impact.</jats:p></jats:sec><jats:sec><jats:title>Result</jats:title><jats:p>Neither fibrillar nor non‐fibrillar Aβ content alone sufficed to explain the Aβ‐PET findings in either transgenic AD model (Figure 1). A regression model compiling fibrillar and non‐fibrillar Aβ together with the estimate of individual heterogeneity and age at scanning could explain a 93% of variance of the Aβ‐PET signal (p<0.001; Figure 2). Fibrillar Aβ burden had a 16‐fold higher contribution to the Aβ‐PET signal when compared to non‐fibrillar Aβ. However, given the relatively greater abundance of non‐fibrillar Aβ, we estimate that non‐fibrillar Aβ produced 79±25% of the net <jats:italic>in vivo</jats:italic> Aβ‐PET signal in <jats:italic>App<jats:sup>NL‐G‐F</jats:sup> </jats:italic> mice, and 25±12% in the APPPS1 mice. Corresponding results in groups of APPPS1/Trem2<jats:sup>‐/‐</jats:sup> and APPPS1/Trem2<jats:sup>+/+</jats:sup> mice validated the calculated regression factors and revealed that the altered fibrillarity due to Trem2 knockout impacts the Aβ‐PET signal (Figure 3).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Taken together, the <jats:italic>in vivo</jats:italic> Aβ‐PET signal derives from the composite of fibrillar and non‐fibrillar Aβ plaque components. While fibrillar Aβ has inherently higher PET tracer binding, the greater abundance of non‐fibrillar Aβ plaque in AD model mice contributes importantly to the PET signal.</jats:p></jats:sec>

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