Hippocampal glucose uptake as a surrogate of metabolic change of microglia in Alzheimer’s disease View Full Text


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Article Info

DATE

2021-08-31

AUTHORS

Hongyoon Choi, Yoori Choi, Eun Ji Lee, Hyun Kim, Youngsun Lee, Seokjun Kwon, Do Won Hwang, Dong Soo Lee

ABSTRACT

BackgroundDynamically altered microglia play an important role in the progression of Alzheimer’s disease (AD). Here, we found a close association of the metabolic reconfiguration of microglia with increased hippocampal glucose uptake on [18F]fluorodeoxyglucose (FDG) PET.MethodsWe used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer’s Disease Neuroimaging Initiative were analyzed. FDG uptake in the hippocampus was compared according to the diagnosis, AD, mild cognitive impairment, and controls. The correlation analysis between hippocampal FDG uptake and soluble TREM2 in cerebrospinal fluid was performed.ResultsIn the animal study, 8- and 12-month-old 5xFAD mice showed higher FDG uptake in the hippocampus than wild-type mice. Cellular FDG uptake tests showed that FDG activity in hippocampal microglia was increased in the AD model, while FDG activity in non-microglial cells of the hippocampus was not different between the AD model and wild-type. scRNA-seq data showed that changes in glucose metabolism signatures including glucose transporters, glycolysis and oxidative phosphorylation, mainly occurred in microglia. A subset of microglia with higher glucose transporters with defective glycolysis and oxidative phosphorylation was increased according to disease progression. In the human imaging study, we found a positive association between soluble TREM2 and hippocampal FDG uptake. FDG uptake in the hippocampus at the baseline scan predicted mild cognitive impairment conversion to AD.ConclusionsWe identified the reconfiguration of microglial glucose metabolism in the hippocampus of AD, which could be evaluated by FDG PET as a feasible surrogate imaging biomarker for microglia-mediated inflammation. More... »

PAGES

190

References to SciGraph publications

  • 2018-05-21. Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease in MOLECULAR NEURODEGENERATION
  • 2016-01-12. Increased cerebrospinal fluid soluble TREM2 concentration in Alzheimer’s disease in MOLECULAR NEURODEGENERATION
  • 2010-12-31. Amyloid-β and tau — a toxic pas de deux in Alzheimer's disease in NATURE REVIEWS NEUROSCIENCE
  • 2014-09-30. Cortical hypermetabolism in MCI subjects: a compensatory mechanism? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-04-13. Spatial reconstruction of single-cell gene expression data in NATURE BIOTECHNOLOGY
  • 2017-01-30. [18F]FDG PET signal is driven by astroglial glutamate transport in NATURE NEUROSCIENCE
  • 2018-09-28. TREM2 — a key player in microglial biology and Alzheimer disease in NATURE REVIEWS NEUROLOGY
  • 2019-07-02. Author Correction: Soluble TREM2 ameliorates pathological phenotypes by modulating microglial functions in an Alzheimer’s disease model in NATURE COMMUNICATIONS
  • 2016-01-18. Microglial brain region−dependent diversity and selective regional sensitivities to aging in NATURE NEUROSCIENCE
  • 2019-01-02. Double-slit photoelectron interference in strong-field ionization of the neon dimer in NATURE COMMUNICATIONS
  • 2017-09-01. Microglia emerge as central players in brain disease in NATURE MEDICINE
  • 2016-04-04. Near-optimal probabilistic RNA-seq quantification in NATURE BIOTECHNOLOGY
  • 2016-03-07. Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis in NATURE IMMUNOLOGY
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    http://scigraph.springernature.com/pub.10.1186/s12974-021-02244-6

    DOI

    http://dx.doi.org/10.1186/s12974-021-02244-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1140783132

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34465358


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    31 schema:description BackgroundDynamically altered microglia play an important role in the progression of Alzheimer’s disease (AD). Here, we found a close association of the metabolic reconfiguration of microglia with increased hippocampal glucose uptake on [18F]fluorodeoxyglucose (FDG) PET.MethodsWe used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer’s Disease Neuroimaging Initiative were analyzed. FDG uptake in the hippocampus was compared according to the diagnosis, AD, mild cognitive impairment, and controls. The correlation analysis between hippocampal FDG uptake and soluble TREM2 in cerebrospinal fluid was performed.ResultsIn the animal study, 8- and 12-month-old 5xFAD mice showed higher FDG uptake in the hippocampus than wild-type mice. Cellular FDG uptake tests showed that FDG activity in hippocampal microglia was increased in the AD model, while FDG activity in non-microglial cells of the hippocampus was not different between the AD model and wild-type. scRNA-seq data showed that changes in glucose metabolism signatures including glucose transporters, glycolysis and oxidative phosphorylation, mainly occurred in microglia. A subset of microglia with higher glucose transporters with defective glycolysis and oxidative phosphorylation was increased according to disease progression. In the human imaging study, we found a positive association between soluble TREM2 and hippocampal FDG uptake. FDG uptake in the hippocampus at the baseline scan predicted mild cognitive impairment conversion to AD.ConclusionsWe identified the reconfiguration of microglial glucose metabolism in the hippocampus of AD, which could be evaluated by FDG PET as a feasible surrogate imaging biomarker for microglia-mediated inflammation.
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