Microglia-PET imaging as a surrogate marker for post-stroke neuroinflammation in preclinical mouse models and clinical cases: quantitative PET data analysis using biokinetic modeling and machine learning including information from multiparametric MRI scans

Background Ischemic stroke is the second leading cause of death and the third main cause of long-term disability worldwide, which explains the need for novel therapies to improve neurological recovery. Microglia, brain resident immune cells, are a suitable target for such a therapy. These cells express 18 kDa translocator protein (TSPO) when activated, which enables neuroinflammation monitoring using positron emission tomography (PET) with TSPO tracers, such as [18F]GE-180. However, the signal in PET images originates not only from specific binding of the tracer to the receptor of interest; it is contaminated by non-specific binding and free tracer in both tissue and blood. Gold-standard quantification of [18F]GE-180 specific binding is currently performed using pharmacokinetic modeling, which requires a longer scanning time and continuous arterial blood sampling. This is not only burdensome for the hospital staff, but also associated with additional risks and discomfort for the patient. Aim The aim of this work was to establish a simplified [18F]GE-180 PET scanning protocol for a mouse ischemic stroke model and translate it into human PET by integrating additional potentially relevant information using machine learning (ML) and taking a well-established pharmacokinetic modeling method as the ground truth. Materials and Methods Mouse study: Six mice after photothrombotic stroke (PT) and six sham mice were included in the study and scanned using a dedicated small-animal PET/MR scanner. For a half of the mice, we acquired four serial 0–90 min post injection (p.i.) scans per mouse (analysis cohort) and calculated quantitative TSPO binding estimates (distribution volume ratio, DVR) as well as semi-quantitative estimates (standardized uptake volume ratio, SUVR) for five late 10 min time frames. We compared how well the obtained SUVRs approximated DVR by means of linear fitting and Pearson correlation coefficient. The other half of the mice received 60-90 min p.i. [18F]GE-180 PET and was used as a validation cohort. Human study: 18 subjects after acute ischemic stroke received 0-90 min p.i. [18F]GE-180 PET along with a number of MRI sequences. Five manual venous blood samples were drawn during the PET scan and their activity concentration was measured. Based on dynamic PET data, a quantitative TSPO binding estimate was calculated voxelwise. We trained an ML algorithm using these estimates as the ground truth and three late 10 min PET frames, the ASL image, voxel co-ordinates, the lesion mask, and the five plasma activity concentrations as input features. Using Shapley Additive Explanations, we determined that the three late PET frames and the plasma activity concentrations had the highest impact on the model’s performance. We then tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots. Results The mouse study showed that the 60–70, 70–80, and 80–90 min p.i. frames produce the closest approximation for 90 min scan-based DVR in both sham and PT mice. The human study demonstrated on an individual voxel basis an additional value of the late plasma activity concentration in approximating the quantitative 90 min scan-based TSPO estimate. The 70-80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest semi-quantitative estimate in the ischemic lesion. Conclusion Reliable simplified TSPO quantification in patients after acute ischemic stroke is achievable by using a short late PET frame divided by a late plasma activity concentration and can thus replace full quantification based on a 90 min dynamic scan. The ML-based procedure of estimating feature importance used in this work can be applied for other conditions and other tracers in the future.Hintergrund Der ischämische Schlaganfall ist weltweit die zweithäufigste Todesursache und die dritthäufigste Ursache für Langzeitbehinderungen, was den Bedarf an neuartigen Therapien zur Verbesserung der neurologischen Erholung erklärt. Mikroglia, Immunzellen im Gehirn, sind ein geeignetes Ziel für eine solche Therapie. Diese Zellen exprimieren das 18 kDa-Translokatorprotein (TSPO), wenn sie aktiviert sind, was die Messung von Neuroinflammation mittels Positronen-Emissions-Tomographie (PET) mit TSPO-Tracern wie [18F]GE-180 ermöglicht. Das Signal in den PET-Bildern stammt jedoch nicht nur von der spezifischen Bindung des Tracers an den betreffenden Rezeptor, sondern wird auch durch unspezifische Bindungen und freien Tracer im Gewebe und im Blut kontaminiert. Die Goldstandard-Quantifizierung der spezifischen Bindung von [18F]GE-180 wird derzeit mit Hilfe pharmakokinetischer Modelle durchgeführt, was eine längere Messzeit und eine kontinuierliche arterielle Blutentnahme erfordert. Dies ist nicht nur für das Krankenhauspersonal belastend, sondern auch mit zusätzlichen Risiken und Unannehmlichkeiten für die Patienten verbunden. Zielsetzung Ziel dieser Arbeit war es, ein vereinfachtes [18F]GE-180-PET-Scanprotokoll für ein ischämisches Schlaganfallmodell bei Mäusen zu erstellen und es auf die PET-Untersuchung bei Menschen zu übertragen, indem zusätzliche potenziell relevante Informationen mit Hilfe von maschinellem Lernen (ML) integriert werden und eine wohl etablierte pharmakokinetische Modellierungsmethode als Grundwahrheit verwendet wird. Material und Methoden Mausstudie: Sechs Mäuse nach photothrombotischem Schlaganfall (PT) und sechs Mäuse nach identischer Versuchsdurchführung, jedoch ohne Schlaganfall (sham), wurden in die Studie aufgenommen und mit einem dedizierten Kleintier-PET/MR-Scanner untersucht. Für die Hälfte der Mäuse wurden vier serielle Messungen 0-90 Minuten nach der Injektion (p.i.) pro Maus (Analysekohorte) durchgeführt und die TSPO_Bindung quantitativ geschätzt (Distribution Volume Ratio, DVR). Zusätzlich wurden semi-quantitative Schätzungen (Standardized Uptake Volume Ratio, SUVR) für fünf späte 10 min Zeitfenster berechnet. Wir verglichen die Eignung der SUVRs als Näherung für die DVR mittels linearer Anpassung und Pearson-Korrelationskoeffizient. Die andere Hälfte der Mäuse erhielt 60-90 min p.i. [18F]GE-180-PET und wurde als Validierungskohorte verwendet. Humanstudie: 18 Probanden erhielten nach einem akuten ischämischen Schlaganfall 0-90 min p.i. [18F]GE-180-PET zusammen mit einer Reihe von MRT-Sequenzen. Fünf manuelle venöse Blutproben wurden während des PET-Scans entnommen und ihre Aktivitätskonzentration gemessen. Auf der Grundlage der dynamischen PET-Daten wurde eine quantitative Schätzung der TSPO-Bindung voxelweise berechnet. Wir trainierten einen ML-Algorithmus, der diese Schätzungen als Grundwahrheit und drei späte 10 min PET-Bilder, das ASL-Bild, Voxelkoordinaten, die Läsionsmaske und die fünf Plasmaaktivitätskonzentrationen als Eingangsmerkmale verwendete. Unter Verwendung von Shapley Additive Explanations stellten wir fest, dass die drei späten PET-Bilder und die Plasmaaktivitätskonzentrationen den größten Einfluss auf die Qualität des Modells hatten. Anschließend testeten wir eine vereinfachte Quantifizierungsmethode, die darin bestand, ein spätes PET-Bild durch eine Plasmaaktivitätskonzentration zu dividieren. Alle Kombinationen von Bildern/Proben wurden anhand von Konkordanz-Korrelationskoeffizienten und Bland-Altman-Diagrammen verglichen. Ergebnisse Die Mausstudie zeigte, dass die 60-70, 70-80 und 80-90 min p.i. Zeitfenster die beste Näherung an die 90 min Scan basierte DVR sowohl bei den Sham- als auch bei den PT-Mäusen produzieren. Die Humanstudie zeigte auf der Basis individueller Voxel einen zusätzlichen Wert der späten Plasmaaktivitätskonzentration für die Näherung an die quantitative 90-min Scan-basierten TSPO-Schätzung. Die Division der Werte im 70-80 min p.i. Zeitfenster mit dem Messwert der 30 min p.i. Plasmaprobe ergab die genaueste semi-quantitative Schätzung in der ischämischen Läsion. Schlussfolgerung Eine zuverlässige vereinfachte TSPO-Quantifizierung bei Patienten nach einem akuten ischämischen Schlaganfall ist durch die Verwendung eines kurzen späten PET-Zeitfensters geteilt durch eine späte Plasmaaktivitätskonzentration möglich und kann somit eine vollständige Quantifizierung auf der Grundlage eines 90 min dynamischen Scans ersetzen. Das in dieser Arbeit verwendete ML-basierte Verfahren zur Schätzung der Relevanz verschiedener Merkmale kann in Zukunft auch für andere Erkrankungen und Tracer angewendet werden

Reduced Acquisition Time [18F]GE-180 PET Scanning Protocol Replaces Gold-Standard Dynamic Acquisition in a Mouse Ischemic Stroke Model

Aim Understanding neuroinflammation after acute ischemic stroke is a crucial step on the way to an individualized post-stroke treatment. Microglia activation, an essential part of neuroinflammation, can be assessed using [ 18 F]GE-180 18 kDa translocator protein positron emission tomography (TSPO-PET). However, the commonly used 60–90 min post-injection (p.i.) time window was not yet proven to be suitable for post-stroke neuroinflammation assessment. In this study, we compare semi-quantitative estimates derived from late time frames to quantitative estimates calculated using a full 0–90 min dynamic scan in a mouse photothrombotic stroke (PT) model. Materials and Methods Six mice after PT and six sham mice were included in the study. For a half of the mice, we acquired four serial 0–90 min scans per mouse (analysis cohort) and calculated standardized uptake value ratios (SUVRs; cerebellar reference) for the PT volume of interest (VOI) in five late 10 min time frames as well as distribution volume ratios (DVRs) for the same VOI. We compared late static 10 min SUVRs and the 60–90 min time frame of the analysis cohort to the corresponding DVRs by linear fitting. The other half of the animals received a static 60–90 min scan and was used as a validation cohort. We extrapolated DVRs by using the static 60–90 min p.i. time window, which were compared to the DVRs of the analysis cohort. Results We found high linear correlations between SUVRs and DVRs in the analysis cohort for all studied 10 min time frames, while the fits of the 60–70, 70–80, and 80–90 min p.i. time frames were the ones closest to the line of identity. For the 60–90 min time window, we observed an excellent linear correlation between SUVR and DVR regardless of the phenotype (PT vs . sham). The extrapolated DVRs of the validation cohort were not significantly different from the DVRs of the analysis group. Conclusion Simplified quantification by a reference tissue ratio of the late 60–90 min p.i. [ 18 F]GE-180 PET image can replace full quantification of a dynamic scan for assessment of microglial activation in the mouse PT model

Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (beta-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-mu PET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes >= 12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas beta-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different beta-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models

Depletion and activation of microglia impact metabolic connectivity of the mouse brain

AimWe aimed to investigate the impact of microglial activity and microglial FDG uptake on metabolic connectivity, since microglial activation states determine FDG-PET alterations. Metabolic connectivity refers to a concept of interacting metabolic brain regions and receives growing interest in approaching complex cerebral metabolic networks in neurodegenerative diseases. However, underlying sources of metabolic connectivity remain to be elucidated.Materials and methodsWe analyzed metabolic networks measured by interregional correlation coefficients (ICCs) of FDG-PET scans in WT mice and in mice with mutations in progranulin (Grn) or triggering receptor expressed on myeloid cells 2 (Trem2) knockouts ((-/-)) as well as in double mutant Grn(-/-)/Trem2(-/-) mice. We selected those rodent models as they represent opposite microglial signatures with disease associated microglia in Grn(-/-) mice and microglia locked in a homeostatic state in Trem2(-/-) mice;however, both resulting in lower glucose uptake of the brain. The direct influence of microglia on metabolic networks was further determined by microglia depletion using a CSF1R inhibitor in WT mice at two different ages. Within maps of global mean scaled regional FDG uptake, 24 pre-established volumes of interest were applied and assigned to either cortical or subcortical networks. ICCs of all region pairs were calculated and z-transformed prior to group comparisons. FDG uptake of neurons, microglia, and astrocytes was determined in Grn(-/-) and WT mice via assessment of single cell tracer uptake (scRadiotracing).ResultsMicroglia depletion by CSF1R inhibition resulted in a strong decrease of metabolic connectivity defined by decrease of mean cortical ICCs in WT mice at both ages studied (6-7 m;p = 0.0148, 9-10 m;p = 0.0191), when compared to vehicle-treated age-matched WT mice. Grn(-/-), Trem2(-/-) and Grn(-/-)/Trem2(-/-) mice all displayed reduced FDG-PET signals when compared to WT mice. However, when analyzing metabolic networks, a distinct increase of ICCs was observed in Grn(-/-) mice when compared to WT mice in cortical (p < 0.0001) and hippocampal (p < 0.0001) networks. In contrast, Trem2(-/-) mice did not show significant alterations in metabolic connectivity when compared to WT. Furthermore, the increased metabolic connectivity in Grn(-/-) mice was completely suppressed in Grn(-/-)/Trem2(-/-) mice. Grn(-/-) mice exhibited a severe loss of neuronal FDG uptake (- 61%, p < 0.0001) which shifted allocation of cellular brain FDG uptake to microglia (42% in Grn(-/-) vs. 22% in WT).ConclusionsPresence, absence, and activation of microglia have a strong impact on metabolic connectivity of the mouse brain. Enhanced metabolic connectivity is associated with increased microglial FDG allocation

[18F]F-DED PET imaging of reactive astrogliosis in neurodegenerative diseases: preclinical proof of concept and first-in-human data

ObjectivesReactive gliosis is a common pathological hallmark of CNS pathology resulting from neurodegeneration and neuroinflammation. In this study we investigate the capability of a novel monoamine oxidase B (MAO-B) PET ligand to monitor reactive astrogliosis in a transgenic mouse model of Alzheimer`s disease (AD). Furthermore, we performed a pilot study in patients with a range of neurodegenerative and neuroinflammatory conditions.MethodsA cross-sectional cohort of 24 transgenic (PS2APP) and 25 wild-type mice (age range: 4.3-21.0 months) underwent 60 min dynamic [F-18]fluorodeprenyl-D2 ([F-18]F-DED), static 18 kDa translocator protein (TSPO, [F-18]GE-180) and beta-amyloid ([F-18]florbetaben) PET imaging. Quantification was performed via image derived input function (IDIF, cardiac input), simplified non-invasive reference tissue modelling (SRTM2, DVR) and late-phase standardized uptake value ratios (SUVr). Immunohistochemical (IHC) analyses of glial fibrillary acidic protein (GFAP) and MAO-B were performed to validate PET imaging by gold standard assessments. Patients belonging to the Alzheimer's disease continuum (AD, n = 2), Parkinson's disease (PD, n = 2), multiple system atrophy (MSA, n = 2), autoimmune encephalitis (n = 1), oligodendroglioma (n = 1) and one healthy control underwent 60 min dynamic [F-18]F-DED PET and the data were analyzed using equivalent quantification strategies.ResultsWe selected the cerebellum as a pseudo-reference region based on the immunohistochemical comparison of age-matched PS2APP and WT mice. Subsequent PET imaging revealed that PS2APP mice showed elevated hippocampal and thalamic [F-18]F-DED DVR when compared to age-matched WT mice at 5 months (thalamus: + 4.3%;p = 0.048), 13 months (hippocampus: + 7.6%, p = 0.022) and 19 months (hippocampus: + 12.3%, p < 0.0001;thalamus: + 15.2%, p < 0.0001). Specific [F-18]F-DED DVR increases of PS2APP mice occurred earlier when compared to signal alterations in TSPO and beta-amyloid PET and [F-18]F-DED DVR correlated with quantitative immunohistochemistry (hippocampus: R = 0.720, p < 0.001;thalamus: R = 0.727, p = 0.002). Preliminary experience in patients showed [F-18]F-DED V-T and SUVr patterns, matching the expected topology of reactive astrogliosis in neurodegenerative (MSA) and neuroinflammatory conditions, whereas the patient with oligodendroglioma and the healthy control indicated [F-18]F-DED binding following the known physiological MAO-B expression in brain.Conclusions[F-18]F-DED PET imaging is a promising approach to assess reactive astrogliosis in AD mouse models and patients with neurological diseases

Deciphering sources of PET signals in the tumor microenvironment of glioblastoma at cellular resolution

Various cellular sources hamper interpretation of positron emission tomography (PET) biomarkers in the tumor microenvironment (TME). We developed an approach of immunomagnetic cell sorting after in vivo radiotracer injection (scRadiotracing) with three-dimensional (3D) histology to dissect the cellular allocation of PET signals in the TME. In mice with implanted glioblastoma, translocator protein (TSPO) radiotracer uptake per tumor cell was higher compared to tumor-associated microglia/macrophages (TAMs), validated by protein levels. Translation of in vitro scRadiotracing to patients with glioma immediately after tumor resection confirmed higher single-cell TSPO tracer uptake of tumor cells compared to immune cells. Across species, cellular radiotracer uptake explained the heterogeneity of individual TSPO-PET signals. In consideration of cellular tracer uptake and cell type abundance, tumor cells were the main contributor to TSPO enrichment in glioblastoma;however, proteomics identified potential PET targets highly specific for TAMs. Combining cellular tracer uptake measures with 3D histology facilitates precise allocation of PET signals and serves to validate emerging novel TAM-specific radioligands

Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia.

BACKGROUND: Genetic mutations underlying familial Alzheimer\u27s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain. METHODS: We engineered a novel App knock-in mouse model (App RESULTS: Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App DISCUSSION: Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks

Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation position and therefore degrades the spatial resolution. A way to address this issue is depth-of-interaction (DOI) estimation. In this work we propose two machine learning-based algorithms, a dense and a convolutional neural network (NN), as well as a multiple linear regression (MLR)-based method to estimate DOI in depolished PET detector arrays with single-sided readout. The algorithms were tested on an 8× 8 array of 1.53× 1.53× 15 mm3 crystals and a 4× 4 array of 3.1× 3.1× 15 mm3 crystals, both made of Ce:LYSO scintillators and coupled to a 4× 4 array of 3× 3 mm3 silicon photomultipliers (SiPMs). Using the conventional linear DOI estimation method resulted in an average DOI resolution of 3.76 mm and 3.51 mm FWHM for the 8× 8 and the 4× 4 arrays, respectively. Application of MLR outperformed the conventional method with average DOI resolutions of 3.25 mm and 3.33 mm FWHM, respectively. Using the machine learning approaches further improved the DOI resolution, to an average DOI resolution of 2.99 mm and 3.14 mm FWHM, respectively, and additionally improved the uniformity of the DOI resolution in both arrays. Lastly, preliminary results obtained by using only a section of the crystal array for training showed that the NN-based methods could be used to reduce the number of calibration steps required for each detector array

Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (β-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-µPET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes ≥12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas β-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different β-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models