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Metals play an essential role in biological systems and are required as structural or catalytic co-factors in many proteins. Disruption of the homeostatic control and/or spatial distributions of metals can lead to disease. Imaging technologies have been developed to visualize elemental distributions across a biological sample. Measurement of elemental distributions by imaging mass spectrometry and imaging X-ray fluorescence are increasingly employed with technologies that can assess histological features and molecular compositions. Data from several modalities can be interrogated as multimodal images to correlate morphological, elemental, and molecular properties. Elemental and molecular distributions have also been axially resolved to achieve three-dimensional volumes, dramatically increasing the biological information. In this review, we provide an overview of recent developments in the field of metal imaging with an emphasis on multimodal studies in two and three dimensions. We specifically highlight studies that present technological advancements and biological applications of how metal homeostasis affects human health.
Copyright © 2020 Elsevier Ltd. All rights reserved.
Selective inversion recovery (SIR) is a quantitative magnetization transfer (qMT) method that provides estimates of parameters related to myelin content in white matter, namely the macromolecular pool-size-ratio (PSR) and the spin-lattice relaxation rate of the free pool (R), without the need for independent estimates of ∆B, B, and T. Although the feasibility of performing SIR in the human brain has been demonstrated, the scan times reported previously were too long for whole-brain applications. In this work, we combined optimized, short-TR acquisitions, SENSE/partial-Fourier accelerations, and efficient 3D readouts (turbo spin-echo, SIR-TSE; echo-planar imaging, SIR-EPI; and turbo field echo, SIR-TFE) to obtain whole-brain data in 18, 10, and 7 min for SIR-TSE, SIR-EPI, SIR-TFE, respectively. Based on numerical simulations, all schemes provided accurate parameter estimates in large, homogenous regions; however, the shorter SIR-TFE scans underestimated focal changes in smaller lesions due to blurring. Experimental studies in healthy subjects (n = 8) yielded parameters that were consistent with literature values and repeatable across scans (coefficient of variation: PSR = 2.2-6.4%, R = 0.6-1.4%) for all readouts. Overall, SIR-TFE parameters exhibited the lowest variability, while SIR-EPI parameters were adversely affected by susceptibility-related image distortions. In patients with relapsing remitting multiple sclerosis (n = 2), focal changes in SIR parameters were observed in lesions using all three readouts; however, contrast was reduced in smaller lesions for SIR-TFE, which was consistent with the numerical simulations. Together, these findings demonstrate that efficient, accurate, and repeatable whole-brain SIR can be performed using 3D TFE, EPI, or TSE readouts; however, the appropriate readout should be tailored to the application.
Copyright © 2020 Elsevier Inc. All rights reserved.
The presynaptic dopamine transporter mediates rapid reuptake of synaptic dopamine. Although cell surface DAT trafficking recently emerged as an important component of DAT regulation, it has not been systematically investigated. Here, we apply our single quantum dot (Qdot) tracking approach to monitor DAT plasma membrane dynamics in several heterologous expression cell hosts with nanometer localization accuracy. We demonstrate that Qdot-tagged DAT proteins exhibited highly heterogeneous membrane diffusivity dependent on the local membrane topography. We also show that Qdot-tagged DATs were localized away from the flat membrane regions and were dynamically retained in the membrane protrusions and cell edges for the duration of imaging. Single quantum dot tracking of wildtype DAT and its conformation-defective coding variants (R60A and W63A) revealed a significantly accelerated rate of dysfunctional DAT membrane diffusion. We believe our results warrant an in-depth investigation as to whether compromised membrane dynamics is a common feature of brain disorder-derived DAT mutants.
Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.
PURPOSE - This study aims to systematically evaluate the accuracy and precision of pool size ratio (PSR) measurements from quantitative magnetization transfer (qMT) acquisitions using simplified models in the context of assessing injury-associated spatiotemporal changes in spinal cords of non-human primates. This study also aims to characterize changes in the spinal tissue pathology in individual subjects, both regionally and longitudinally, in order to demonstrate the relationship between regional tissue compositional changes and sensorimotor behavioral recovery after cervical spinal cord injury (SCI).
METHODS - MRI scans were recorded on anesthetized monkeys at 9.4 T, before and serially after a unilateral section of the dorsal column tract. Images were acquired following saturating RF pulses at different offset frequencies. Models incorporating two pools of protons but with differing numbers of variable parameters were used to fit the data to derive qMT parameters. The results using different amounts of measured data and assuming different numbers of variable model parameters were compared. Behavioral impairments and recovery were assessed by a food grasping-retrieving task. Histological sections were obtained post mortem for validation of the injury.
RESULTS - QMT fitting provided maps of pool size ratio (PSR), the relative amounts of immobilized protons exchanging magnetization compared to the "free" water. All the selected modeling approaches detected a lesion/cyst at the site of injury as significant reductions in PSR values. The regional contrasts in the PSR maps obtained using the different fittings varied, but the 2-parameter fitting results showed strong positive correlations with results from 5-parameter modeling. 2-parameter fitting results with modest (>3) RF offsets showed comparable sensitivity for detecting demyelination in white matter and loss of macromolecules in gray matter around lesion sites compared to 5-parameter fitting with fully-sampled data acquisitions. Histology confirmed that decreases of PSR corresponded to regional demyelination around lesion sites, especially when demyelination occurred along the dorsal column on the injury side. Longitudinally, PSR values of injured dorsal column tract and gray matter horns exhibited remarkable recovery that associated with behavioral improvement.
CONCLUSION - Simplified qMT modeling approaches provide efficient and sensitive means to detect and characterize injury-associated demyelination in white matter tracts and loss of macromolecules in gray matter and to monitor its recovery over time.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
Mammals have two types of photoreceptors, rods and cones. While rods are exceptionally sensitive and mediate vision at very low illumination levels, cones operate in daylight and are responsible for the bulk of visual perception in most diurnal animals, including humans. Yet the mechanisms of phototransduction in cones is understudied, largely due to unavailability of pure cone outer segment (COS) preparations. Here we present a novel mathematical model of cone phototransduction that explicitly takes into account complex cone geometry and its multiple physical scales, faithfully reproduces features of the cone response, and is orders of magnitude more efficient than the standard 3D diffusion model. This is accomplished through the mathematical techniques of homogenization and concentrated capacity. The homogenized model is then computationally implemented by finite element method. This homogenized model permits one to analyze the effects of COS geometry on visual transduction and lends itself to performing large numbers of numerical trials, as required for parameter analysis and the stochasticity of rod and cone signal transduction. Agreement between the nonhomogenized, (i.e., standard 3D), and homogenized diffusion models is reported along with their simulation times and memory costs. Virtual expression of rod biochemistry on cone morphology is also presented for understanding some of the characteristic differences between rods and cones. These simulations evidence that 3D cone morphology and ion channel localization contribute to biphasic flash response, i.e undershoot. The 3D nonhomogenized and homogenized models are contrasted with more traditional and coarser well-stirred and 1D longitudinal diffusion models. The latter are single-scale and do not explicitly account for the multi-scale geometry of the COS, unlike the 3D homogenized model. We show that simpler models exaggerate the magnitude of the current suppression, yield accelerated time to peak, and do not predict the local concentration of cGMP at the ionic channels.
Imaging apoptosis could provide an early and specific means to monitor tumor responses to treatment. To date, despite numerous attempts to develop molecular imaging approaches, there is still no widely-accepted and reliable method for in vivo imaging of apoptosis. We hypothesized that the distinct cellular morphologic changes associated with treatment-induced apoptosis, such as cell shrinkage, cytoplasm condensation, and DNA fragmentation, can be detected by temporal diffusion spectroscopy imaging (TDSI). Cetuximab-induced apoptosis was assessed in vitro and in vivo with cetuximab-sensitive (DiFi) and insensitive (HCT-116) human colorectal cancer cell lines by TDSI. TDSI findings were complemented by flow cytometry and immunohistochemistry. Cell cycle analysis and flow cytometry detected apoptotic cell shrinkage in cetuximab-treated DiFi cells, and significant apoptosis was confirmed by histology. TDSI-derived parameters quantified key morphological changes including cell size decreases during apoptosis in responsive tumors that occurred earlier than gross tumor volume regression. TDSI provides a unique measurement of apoptosis by identifying cellular characteristics, particularly cell shrinkage. The method will assist in understanding the underlying biology of solid tumors and predict tumor response to therapies. TDSI is free of any exogenous agent or radiation, and hence is very suitable to be incorporated into clinical applications.
Although insulin resistance consistently occurs with type 1 diabetes, its predominant driver is uncertain. We therefore determined the relative contributions of hyperglycemia and iatrogenic hyperinsulinemia to insulin resistance using hyperinsulinemic-euglycemic clamps in three participant groups ( = 10/group) with differing insulinemia and glycemia: healthy control subjects (euinsulinemia and euglycemia), glucokinase-maturity-onset diabetes of the young (GCK-MODY; euinsulinemia and hyperglycemia), and type 1 diabetes (hyperinsulinemia and hyperglycemia matching GCK-MODY). We assessed the contribution of hyperglycemia by comparing insulin sensitivity in control and GCK-MODY and the contribution of hyperinsulinemia by comparing GCK-MODY and type 1 diabetes. Hemoglobin A was normal in control subjects and similarly elevated for type 1 diabetes and GCK-MODY. Basal insulin levels in control subjects and GCK-MODY were nearly equal but were 2.5-fold higher in type 1 diabetes. Low-dose insulin infusion suppressed endogenous glucose production similarly in all groups and suppressed nonesterified fatty acids similarly between control subjects and GCK-MODY, but to a lesser extent for type 1 diabetes. High-dose insulin infusion stimulated glucose disposal similarly in control subjects and GCK-MODY but was 29% and 22% less effective in type 1 diabetes, respectively. Multivariable linear regression showed that insulinemia-but not glycemia-was significantly associated with muscle insulin sensitivity. These data suggest that iatrogenic hyperinsulinemia predominates in driving insulin resistance in type 1 diabetes.
© 2019 by the American Diabetes Association.
For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.