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@ARTICLE{Biechele:164000,
author = {Biechele, Gloria and Sebastian Monasor, Laura and Wind,
Karin and Blume, Tanja and Parhizkar, Samira and Arzberger,
Thomas and Sacher, Christian and Beyer, Leonie and
Eckenweber, Florian and Gildehaus, Franz-Josef and von
Ungern-Sternberg, Barbara and Willem, Michael and
Bartenstein, Peter and Cumming, Paul and Rominger, Axel and
Herms, Jochen and Lichtenthaler, Stefan F and Haass,
Christian and Tahirovic, Sabina and Brendel, Matthias},
title = {{G}litter in the {D}arkness? {N}onfibrillar β-{A}myloid
{P}laque {C}omponents {S}ignificantly {I}mpact the
β-{A}myloid {PET} {S}ignal in {M}ouse {M}odels of
{A}lzheimer {D}isease.},
journal = {Journal of nuclear medicine},
volume = {63},
number = {1},
issn = {0022-3123},
address = {New York, NY},
publisher = {Soc.},
reportid = {DZNE-2022-00669},
pages = {117 - 124},
year = {2022},
abstract = {β-amyloid (Aβ) PET is an important tool for
quantification of amyloidosis in the brain of suspected
Alzheimer 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 nonfibrillar Aβ components
to the in vivo Aβ PET signal remain unclear. Thus, we
obtained 2 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
in vivo. Methods: We investigated groups of AppNL-G-F and
APPPS1 mice at 3, 6, and 12 mo of age by longitudinal
18F-florbetaben Aβ PET and with immunohistochemical
analysis of the fibrillar and total Aβ burdens. We then
applied group-level intermodality regression models using
age- and genotype-matched sets of fibrillar and nonfibrillar
Aβ data (predictors) and Aβ PET results (outcome) for both
Aβ mouse models. An independent group of double-hit APPPS1
mice with dysfunctional microglia due to knockout of
triggering receptor expression on myeloid cells 2 (Trem2-/-)
served for validation and evaluation of translational
impact. Results: Neither fibrillar nor nonfibrillar Aβ
content alone sufficed to explain the Aβ PET findings in
either AD model. However, a regression model compiling
fibrillar and nonfibrillar 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).
Fibrillar Aβ burden had a 16-fold higher contribution to
the Aβ PET signal than nonfibrillar Aβ. However, given the
relatively greater abundance of nonfibrillar Aβ, we
estimate that nonfibrillar Aβ produced $79\%$ ± $25\%$ of
the net in vivo Aβ PET signal in AppNL-G-F mice and $25\%$
± $12\%$ in APPPS1 mice. Corresponding results in separate
groups of APPPS1/Trem2-/- and APPPS1/Trem2+/+ mice validated
the calculated regression factors and revealed that the
altered fibrillarity due to Trem2 knockout impacts the Aβ
PET signal. Conclusion: Taken together, the in vivo Aβ PET
signal derives from the composite of fibrillar and
nonfibrillar Aβ plaque components. Although fibrillar Aβ
has inherently higher PET tracer binding, the greater
abundance of nonfibrillar Aβ plaque in AD-model mice
contributes importantly to the PET signal.},
keywords = {Plaque, Amyloid / PET signal (Other) / amyloid (Other) /
fibrillar (Other) / mouse (Other) / nonfibrillar (Other)},
cin = {AG Tahirovic / AG Herms / Neuropathology / Brainbank / AG
Lichtenthaler / AG Haass},
ddc = {610},
cid = {I:(DE-2719)1140003 / I:(DE-2719)1110001 /
I:(DE-2719)1140013 / I:(DE-2719)1110006 /
I:(DE-2719)1110007},
pnm = {352 - Disease Mechanisms (POF4-352)},
pid = {G:(DE-HGF)POF4-352},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:34016733},
pmc = {pmc:PMC8717179},
doi = {10.2967/jnumed.120.261858},
url = {https://pub.dzne.de/record/164000},
}