Bone marrow adipose tissue (BMAT) comprises >10% of total adipose mass, yet unlike white or brown adipose tissues (WAT or BAT) its metabolic functions remain unclear. Herein, we address this critical gap in knowledge. Our transcriptomic analyses revealed that BMAT is distinct from WAT and BAT, with altered glucose metabolism and decreased insulin responsiveness. We therefore tested these functions in mice and humans using positron emission tomography-computed tomography (PET/CT) with¹⁸F-fluorodeoxyglucose. This revealed that BMAT resists insulin- and cold-stimulated glucose uptake, while further in vivo studies showed that, compared to WAT, BMAT resists insulin-stimulated Akt phosphorylation. Thus, BMAT is functionally distinct from WAT and BAT. However, in humans basal glucose uptake in BMAT is greater than in axial bones or subcutaneous WAT and can be greater than that in skeletal muscle, underscoring the potential of BMAT to influence systemic glucose homeostasis. These PET/CT studies characterise BMAT function in vivo, establish new methods for BMAT analysis, and identify BMAT as a distinct, major adipose tissue subtype.

Background—: Abdominal aortic aneurysms are a major cause of death. Prediction of aneurysm expansion and rupture is challenging and currently relies on the simple measure of aneurysm diameter. Using MRI, we aimed to assess whether areas of cellular inflammation correlated with the rate of abdominal aortic aneurysm expansion. Methods and Results—: Stable patients (n=29; 27 male; age, 70±5 years) with asymptomatic abdominal aortic aneurysms (4.0 to 6.6 cm) were recruited from a surveillance program and imaged using a 3-T MRI scanner before and 24 to 36 hours after administration of ultrasmall superparamagnetic particles of iron oxide (USPIO). The change in T2* value on T2*-weighted imaging was used to detect accumulation of USPIO within the abdominal aortic aneurysm. Histological examination of aneurysm tissue confirmed colocalization and uptake of USPIO in areas with macrophage infiltration. Patients with distinct mural uptake of USPIO had a 3-fold higher growth rate (n=11, 0.66 cm/y; P =0.020) than those with no (n=6, 0.22 cm/y) or nonspecific USPIO uptake (n=8, 0.24 cm/y) despite having similar aneurysm diameters (5.4±0.6, 5.1±0.5, and 5.0±0.5 cm, respectively; P >0.05). In 1 patient with an inflammatory aneurysm, there was a strong and widespread uptake of USPIO extending beyond the aortic wall. Conclusions—: Uptake of USPIO in abdominal aortic aneurysms identifies cellular inflammation and appears to distinguish those patients with more rapidly progressive abdominal aortic aneurysm expansion. This technique holds major promise as a new method of risk-stratifying patients with abdominal aortic aneurysms that extends beyond the simple anatomic measure of aneurysm diameter. Clinical Trial Registration—: URL: http://www.clinicaltrials.gov . Unique identifier: NCT00794092.

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.

Background—: Cell therapy is an emerging and exciting novel treatment option for cardiovascular disease that relies on the delivery of functional cells to their target site. Monitoring and tracking cells to ensure tissue delivery and engraftment is a critical step in establishing clinical and therapeutic efficacy. The study aims were (1) to develop a Good Manufacturing Practice–compliant method of labeling competent peripheral blood mononuclear cells with superparamagnetic particles of iron oxide (SPIO), and (2) to evaluate its potential for magnetic resonance cell tracking in humans. Methods and Results—: Peripheral blood mononuclear cells 1–5×10 ⁹ were labeled with SPIO. SPIO-labeled cells had similar in vitro viability, migratory capacity, and pattern of cytokine release to unlabeled cells. After intramuscular administration, up to 10 ⁸ SPIO-labeled cells were readily identifiable in vivo for at least 7 days using magnetic resonance imaging scanning. Using a phased-dosing study, we demonstrated that systemic delivery of up to 10 ⁹ SPIO-labeled cells in humans is safe, and cells accumulating in the reticuloendothelial system were detectable on clinical magnetic resonance imaging. In a healthy volunteer model, a focus of cutaneous inflammation was induced in the thigh by intradermal injection of tuberculin. Intravenously delivered SPIO-labeled cells tracked to the inflamed skin and were detectable on magnetic resonance imaging. Prussian blue staining of skin biopsies confirmed iron-laden cells in the inflamed skin. Conclusions—: Human peripheral blood mononuclear cells can be labeled with SPIO without affecting their viability or function. SPIO labeling for magnetic resonance cell tracking is a safe and feasible technique that has major potential for a range of cardiovascular applications including monitoring of cell therapies and tracking of inflammatory cells. Clinical Trial Registration—: URL: http://www.clinicaltrials.gov ; Unique identifier: NCT00972946, NCT01169935.