Imaging metabolic syndrome

Abstract

Metabolic syndrome is a fast growing public health burden for almost all the developed countries and many developing nations. Despite intense efforts from both biomedical and clinical scientists, many fundamental questions regarding its aetiology and development remain unclear, partly due to the lack of suitable imaging technologies to visualize lipid composition and distribution, insulin secretion, beta-cell mass and functions in vivo. Such technologies would not only impact on our understanding of the complexity of metabolic disorders such as obesity and diabetes, but also aid in their diagnosis, drug development and assessment of treatment efficacy. In this article we discuss and propose several strategies for visualization of physiological and pathological changes that affect pancreas and adipose tissue as a result of the development of metabolic diseases.

Figure 1. Imaging modalities, their spatial resolution and applications

Bioimaging encompasses a range of imaging modalities that provide spatial and functional analyses from molecules in the angstrom level to human brain and heart in the centimetre level. NMR, nuclear magnetic resonance; SERS, surface-enhanced Raman scattering; CT, computed tomography; MRI, magnetic resonance imaging; PET, positron emission tomography.

Figure 2. Localized 1D and 2D MRS of skeletal muscle

1. 1D spectrum including the olefinic protons −(CH=CH−) obtained by 1D point-resolved spectroscopy (PRESS) from soleus muscle. The CH₃ and (CH₂)_n from the IMCL and EMCL pools are seen, along with creatine and trimethyl amine (TMA) protons.

2. Localized 2D correlation spectrum (L-COSY) recorded from soleus muscle of a normal weight healthy subject. The spectrum shows the diagonal and cross-peaks from various resonances of both IMCL and EMCL pools. The cross-peaks due to olefinic (−CH=CH−) and allylic methylene protons (CH₂CH=CH) and diallylic methylene protons (−CH=CH−CH₂−CH=CH−) permits estimation of degree of unsaturation.

Figure 3. Cellular and molecular regulation of insulin secretion

The cellular events leading to insulin secretion start with a rise in glucose level in the blood, which quickly leads to glucose uptake into pancreatic β-cells. Glucose in the cells then undergoes glycolysis and Krebs cycle to produce ATP, resulting in an increased ATP/ADP ratio, and consequent closure of K_ATP-channels. Membrane depolarization from K_ATP-channel closure opens L-type calcium channels, allowing calcium influx into the cells, which then triggers insulin granule exocytosis and the release of insulin into blood. GluT-2, glucose transporter 2; GK, glucokinase; TCA, tricarboxylic acid cycle; Syt7, synaptotagmin-7; K-ATP, ATP-sensitive potassium channel; L-Ca, L-type calcium channel.

Figure 4. An optical sensor for visualizing insulin granule exocytosis

1. The sensor is based on a chimeric fusion protein that consists of a secretory granule resident protein, phogrin and two fluorescent proteins: a highly pH-sensitive pHluorin inside the secretory granules and a red mCherry.

2. Schematic showing the design strategy of the optical sensor. pHluorin is inside the acidic lumen and remains non-fluorescent at resting state. Upon exocytosis, pHluorin faces the extracellular fluid at neutral pH, and becomes highly fluorescent, while mCherry remains in the cytosol during the process, and serves as a label for granule tracking and a standard for ratiometric quantification (adapted from Lu et al, 2009).

3. A time-lapse showing detected exocytosis events in insulin-secreting cells. Arrows indicate exocytosed insulin granules. SP, signal peptide; TMR, transmembrane region. The images are unpublished observations by Gustavsson.

Figure 5. Basic concept and applications of surface-enhanced Raman spectroscopy

1. Basic concept of SERS. Raman scattering occurs at very low intensities in solution phase when molecules (oval-shaped grains) are distant from the metallic nanoparticles (yellow spheres). When the molecule is close to the nanosurface, upon laser excitation, the intensity is enhanced by the interaction of the molecule with the surface electrons.

2. Scanning EM image of silver and gold bimetallic SERS substrate by deep UV lithography on silicon wafers designed for glucose sensing.

3. Raman spectrum of glucose. Glucose sensing using surface functionalized bimetallic SERS substrate shown in 5B in SERS mode. The peaks indicate different vibrational levels in the molecules. Note that the narrowness of the peaks allows structurally similar multiple analytes to be detected simultaneously.

4. Glucose quantification by SERS-based glucose sensing. Areas-under-curves of the vibrational bands of glucose Raman spectrum at 519, 1067, 1131 and 1365 cm⁻¹ are plotted against glucose concentrations.

Figure 6. Schematic work flow of DOFLA in α- and β-cell screening and its application for in vivo imaging

1. DOFLA preparation and high throughput screening in α- and β-cells to identify a probe specific for each of the cell types.

2. Structure of an α-cell selective probe Glucagon Yellow.

3. Examples of some potential modifications to convert Glucagon Yellow into PET or SPECT probes. The ^99mTc ligand could also be replaced by a Gd chelate to form an MRI contrast agent.

Figure 7. Hyperpolarized ¹³C and an example of its applications in metabolic research

1. The NMR signal is proportional to the difference between two populations of nuclei in distinct energy levels. Hyperpolarization of the ¹³C nuclei produces a large difference as compared to the thermal equilibrium state thus increasing the NMR signal. N⁺/N⁻: number of nuclei in high/low energy state.

2. The thermal equilibrium ¹³C spectrum of urea at 9.4 T averaged for 65 h is depicted on the left while the analysis of the same sample, hyperpolarized by the DNP-MR method and acquired in 1 s is presented on the right (adapted from Ardenkjaer-Larsen et al, 2003).

3. Time course of spectra acquired every second over a 60 s period after injection of hyperpolarized [1-¹³C]pyruvate from the heart of a male Wistar rat (adapted from Tyler et al, 2008). As the pyruvate signal decays, one can observe the generation of lactate, alanine and bicarbonate. Chemical shift imaging of these metabolites from a perfused rat heart depicts the biodistribution. The proton image is given for anatomical reference. The images are unpublished observations by Lee.