Predictors of Prostate-specific Membrane Antigen (PSMA/FOLH1) Expression in a Genomic Database.

OBJECTIVE: To assess predictors of prostate-specific membrane antigen (PSMA) expression in a genomic database; positron emission tomography with PSMA-targeted radiopharmaceuticals is increasingly being utilized. METHODS: The de-identified Decipher Biosciences database, which includes expression for more than 46,000 coding and noncoding genes per patient, was queried for expression of FOLH1 (PSMA). Prostate cancer patients who underwent radical prostatectomy and received the Decipher Test were included in the analysis. PSMA expression was compared to the Gleason Grade Group, Decipher risk category (a validated 22 biomarker genomic score), basal versus luminal molecular subtype, and androgen receptor activity. Multivariable regression analyses were performed. RESULTS: The Decipher de-identified Decipher Biosciences database contained 16,807 men who underwent prostatectomy with the average age being 65-year old and most being Gleason Grade Group 2 (35%) or 3 (27%). Higher Grade Group was associated with higher PSMA expression except in Grade Group 5 [Grade group: 1 (0.66), 2 (0.84), 3 (0.99), 4 (1.07), 5 (0.99), P <.001]. Luminal subtype was found to have much higher PSMA expression when compared to basal (1.01 vs 0.68, P <.001). The androgen receptor activity signature demonstrated a dramatic difference between basal (0.19) and luminal (0.62) subtypes (P <.001). In the multivariable model, luminal patients, high androgen receptor activity scores, and high Grade Groups were significantly associated with higher FOLH1 percentile rank (P <.001). CONCLUSION: High PSMA expression (FOLH1) was associated with high androgen receptor activity and luminal subtype. Genomic tests could aid in predicting, interpreting, and/or directing PSMA theranostics.

An accurate and cost-effective alternative method for measuring cell migration with the circular wound closure assay.

Cell migration is important in many physiological and pathological processes. Mechanisms of two-dimensional cell migration have been investigated most commonly by evaluating rates of cell migration into linearly scratched zones on the surfaces of culture plates. Here, we present a detailed description of a simple adaptation for the well-known and popular wound closure assay, using a circular wound instead of a straight line. This method demonstrates improved precision, reproducibility, and sampling objectivity for measurements of wound sizes as compared with classic scratch assays, enabling more accurate calculations of migration rate. The added benefits of the method are simplicity and low cost as compared with commercially available assays for generating circular wounds.

Comparison of B0 versus B0 and B1 field inhomogeneity correction for glycosaminoglycan chemical exchange saturation transfer imaging.

PURPOSE: The study compares glycosaminoglycan chemical exchange saturation transfer (gagCEST) imaging of intervertebral discs corrected for solely B0 inhomogeneities or both B0 and B1 inhomogeneities. METHODS: Lumbar intervertebral discs of 20 volunteers were examined with T2-weighted and gagCEST imaging. Field inhomogeneity correction was performed with B0 correction only and with correction of both B0 and B1. GagCEST effects measured by the asymmetric magnetization transfer ratio (MTRasym) and signal-to-noise ratio (SNR) were compared between both methods. RESULTS: Significant higher MTRasym and SNR values were obtained in the nucleus pulposus using B0 and B1 correction compared with B0-corrected gagCEST. The GagCEST effect was significantly different in the nucleus pulposus compared with the annulus fibrosus for both methods. CONCLUSION: The B0 and B1 field inhomogeneity correction method leads to an improved quality of gagCEST imaging in IVDs compared with only B0 correction.

A correlative and quantitative imaging approach enabling characterization of primary cell-cell communication: Case of human CD4+ T cell-macrophage immunological synapses.

Cell-to-cell communication engages signaling and spatiotemporal reorganization events driven by highly context-dependent and dynamic intercellular interactions, which are difficult to capture within heterogeneous primary cell cultures. Here, we present a straightforward correlative imaging approach utilizing commonly available instrumentation to sample large numbers of cell-cell interaction events, allowing qualitative and quantitative characterization of rare functioning cell-conjugates based on calcium signals. We applied this approach to examine a previously uncharacterized immunological synapse, investigating autologous human blood CD4+ T cells and monocyte-derived macrophages (MDMs) forming functional conjugates in vitro. Populations of signaling conjugates were visualized, tracked and analyzed by combining live imaging, calcium recording and multivariate statistical analysis. Correlative immunofluorescence was added to quantify endogenous molecular recruitments at the cell-cell junction. By analyzing a large number of rare conjugates, we were able to define calcium signatures associated with different states of CD4+ T cell-MDM interactions. Quantitative image analysis of immunostained conjugates detected the propensity of endogenous T cell surface markers and intracellular organelles to polarize towards cell-cell junctions with high and sustained calcium signaling profiles, hence defining immunological synapses. Overall, we developed a broadly applicable approach enabling detailed single cell- and population-based investigations of rare cell-cell communication events with primary cells.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

Spectral unmixing techniques for optoacoustic imaging of tissue pathophysiology.

A key feature of optoacoustic imaging is the ability to illuminate tissue at multiple wavelengths and therefore record images with a spectral dimension. While optoacoustic images at single wavelengths reveal morphological features, in analogy to ultrasound imaging or X-ray imaging, spectral imaging concedes sensing of intrinsic chromophores and externally administered agents that can reveal physiological, cellular and subcellular functions. Nevertheless, identification of spectral moieties within images obtained at multiple wavelengths requires spectral unmixing techniques, which present a unique mathematical problem given the three-dimensional nature of the optoacoustic images. Herein we discuss progress with spectral unmixing techniques developed for multispectral optoacoustic tomography. We explain how different techniques are required for accurate sensing of intrinsic tissue chromophores such as oxygenated and deoxygenated haemoglobin versus extrinsically administered photo-absorbing agents and nanoparticles. Finally, we review recent developments that allow accurate quantification of blood oxygen saturation (sO2) by transforming and solving the sO2 estimation problem from the spatial to the spectral domain.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

The Impact of Somatostatin Receptor-Directed PET/CT on the Management of Patients with Neuroendocrine Tumor: A Systematic Review and Meta-Analysis.

Somatostatin receptor (SSTR) imaging is widely used for guiding the management of neuroendocrine tumor (NET) patients. 68Ga-DOTATATE approval by the U.S. Food and Drug Administration has triggered widespread clinical interest in SSTR PET/CT throughout the United States. Here, we performed a systematic review and meta-analysis to evaluate the impact of SSTR PET/CT on the management of patients with NETs. Methods: A comprehensive literature search was performed using The National Center for Biotechnology Information PubMed online database, applying the following key words: "management" AND "PET" AND "neuroendocrine". Fourteen of 190 studies were deemed suitable based on the following inclusion criteria: original research, cohort study, number of patients 10 or more, and reported change in management after SSTR PET/CT. Change in management across studies was determined by a random-effects model. Results: A total of 1,561 patients were included. Overall, change in management occurred in 44% (range, 16%-71%) of NET patients after SSTR PET/CT. In 4 of 14 studies, SSTR PET/CT was performed after an 111In-Octreotide scan. In this subgroup, additional information by SSTR PET/CT led to a change in management in 39% (range, 16%-71%) of patients. Seven of 14 studies differentiated between inter- and intramodality changes, with most changes being intermodality (77%; intramodality, 23%). Conclusion: The management was changed in more than one third of patients undergoing SSTR PET/CT even when performed after an 111In-Octreotide scan. Intermodality changes were 3 times more likely than intramodality changes, underlining the clinical impact of SSTR PET/CT.

Suite PET/CT neuroimaging for the diagnosis of Parkinson's disease: statistical parametric mapping analysis.

OBJECTIVES: The aim of this study was to investigate the topographical distribution of dopamine transporter (DAT), dopamine D2 receptor, and glucose metabolism in Parkinson's disease (PD) using PET/computed tomography (CT) scanning and statistical parametric mapping (SPM) analysis. PARTICIPANTS AND METHODS: Seventy-four patients (58 PD patients and 16 normal controls) underwent DAT, D2 receptor, and glucose brain PET/CT scans using C-methyl-N-2-ß-carbomethoxy-3-ß-(4-fluorophenyl) tropane (C-ß-CFT), C-raclopride (C-RAC), and fluorine-18-fluorodeoxyglucose (F-FDG) radiotracers for the respective scans. All three PET/CT procedures were performed in each participant. The uptake patterns were analyzed using SPM software. RESULTS: Striatal DAT binding was lower in PD patients than in controls, whereas D2 receptor binding did not differ between PD patients and controls. D2 receptor binding was increased in the putamen in only the 12 drug-naive patients. Glucose uptake was also slightly lower in the cingulate gyrus of PD patients than in the controls. CONCLUSION: Suite PET/CT scans using the ligands C-ß-CFT, C-RAC, and F-FDG PET/CT are valuable for diagnosing PD. SPM-based analysis of static PET/CT scan data is potentially of great clinical use.

The role of 68Ga-DOTA-NOC PET/CT in evaluating neuroendocrine tumors: real-world experience from two large neuroendocrine tumor centers.

OBJECTIVE: Our aim was to assess the role of Ga-DOTA-NOC PET/CT as a tool for the management of neuroendocrine tumors (NETs), evaluating the clinical impact on patients from two large NET centers in different geopolitical settings. PATIENTS AND METHODS: This is a retrospective study of patients with NETs who underwent Ga-DOTA-NOC PET/CT at Royal Liverpool University Hospital (UK) and at Mount Lebanon Hospital (Lebanon). Indications for imaging and findings of the PET/CT along with demographic and clinical outcome data were recorded and evaluated. RESULTS: Four hundred and forty-five patients fulfilled the inclusion criteria, with a median age at the time of diagnosis of 56 (range: 3-90) years; 248 (55.7%) patients were male.Ga-DOTA-NOC PET/CT was indicated for staging in 193 (43.4%) patients, for diagnosis in 124 (27.9%) patients, for follow-up in 97 (21.7%) patients, and for identification of a primary NET site in 31 (7%) patients.One hundred and four (27.9%) patients underwent Ga-DOTA-NOC PET/CT for the primary diagnosis of NET, of whom 66 (52.7%) patients presented with a clinical suspicion of NET, 10 (8.3%) patients presented with a biochemical suspicion of NET only, and 48 (38.8%) patients presented with a suspicious NET lesion discovered on another imaging modality. The most common clinical presentation was typical carcinoid syndrome [4 (33%) patients].Results on the basis of histology were used as the gold standard for the diagnosis in 57% of patients and the remaining on the basis of follow-up as per established clinical consensus. Sensitivity, specificity, negative-predictive value, and positive-predictive value of PET/CT were 87.1, 97.7, 79.6, and 98.7%, respectively, for the entire sample. Accuracy was measured using the receiver operating characteristic curve analysis with an area under the curve of 0.924 (95% confidence interval: 0.874-0.974). CONCLUSION: Ga-DOTA-NOC PET/CT is a highly sensitive and specific study for the diagnosis and follow-up of patients with neuroendocrine tumors. These results support the use of Ga-DOTA-NOC PET/CT contributing significantly toward the clinical management of NET patients.

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform.

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an "off-the-shelf," open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

Multiple objects detection in biological images using a marked point process framework.

The marked point process framework has been successfully developed in the field of image analysis to detect a configuration of predefined objects. The goal of this paper is to show how it can be particularly applied to biological imagery. We present a simple model that shows how some of the challenges specific to biological data are well addressed by the methodology. We further describe an extension to this first model to address other challenges due, for example, to the shape variability in biological material. We finally show results that illustrate the MPP framework using the "simcep" algorithm for simulating populations of cells.

Impact of Reimbursement Cuts on the Sustainability and Accessibility of Dopamine Transporter Imaging.

PURPOSE: Dopamine transporter single-photon emission computed tomography imaging utilizing iodine-123 ioflupane is accurate for differentiation of Parkinson disease from essential tremor. This study evaluates how reimbursement for I-123 ioflupane imaging changed between 2011 (year of FDA approval) and 2014 (year after loss of pass-through status for hospital-based outpatient imaging from CMS). METHODS: I-123 ioflupane reimbursement data for our institution's hospital-based imaging were compared between two periods: (1) July 2011 to October 2012, and (2) 2014. For each time period separately and in combination, averages and ranges of reimbursement for private insurance and CMS were analyzed and compared. A model to ensure recouping of radiopharmaceutical costs was developed. RESULTS: Review yielded 247 studies from July 2011 to October 2012 and 94 studies from 2014. Average reimbursement per study fell from $2,469 (US dollars) in 2011 to 2012 to $1,657 in 2014. CMS reduced average reimbursement by $1,148 in 2014 because of loss of radiopharmaceutical pass-through status. Average reimbursements from CMS versus private payors markedly differed in 2011 to 2012 at $2,266 versus $2,861, respectively, and in 2014 at $1,118 versus $3,470, respectively. Between 2011 to 2012 and 2014, the CMS percentage increased from 54% to 78%. Assuming that I-123 ioflupane cost $2,000, our model based on 2014 data predicts a practice with greater than 60% CMS patients would no longer recover radiopharmaceutical costs. CONCLUSIONS: Reimbursement levels, payor mix, scanner location, and radiopharmaceutical costs are all critical, variable factors for modeling the financial viability of I-123 ioflupane imaging and, by extrapolation, future radiopharmaceuticals.

Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study.

UNLABELLED: The MRI study within the German National Cohort, a large-scale, population-based, longitudinal study in Germany, comprises comprehensive characterization and phenotyping of a total of 30 000 participants using 3-Tesla whole-body MR imaging. A multi-centric study design was established together with dedicated core facilities for e. g. managing incidental findings or providing quality assurance. As such, the study represents a unique opportunity to substantially impact imaging-based risk stratification leading to personalized and precision medicine. Supported by the developments in the field of computational science, the newly developing scientific field of radiomics has large potential for the future. In the present article we provide an overview on population-based imaging and Radiomics and conceptualize the rationale and design of the MRI study within the German National Cohort. KEY POINTS: • Population-based imaging and Radiomics constitute two emerging fields with great oppertunities and challenges for Radiology.• As part of the MRI-study of the NAKO approximately 30 000 subjects will undergo 3 Tesla whole-body MRI.• MR Imaging data is publicly accessable and will provide important insights into the natural history of disease processes and personalized risk profiles of the general population. Citation Format: • Schlett CL, Hendel T, Weckbach S et al. Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study. Fortschr Röntgenstr 2016; 188: 652 - 661.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

Focal impairment in myocardial fatty acid imaging in the left anterior descending artery area, a strong predictor for cardiac death in hemodialysis patients without obstructive coronary artery disease.

PURPOSE: We investigated whether impaired patterns of myocardial fatty acid imaging were associated with cardiac death in dialysis patients without coronary lesions. METHODS: We prospectively enrolled 155 hemodialysis patients without obstructive coronary artery disease, who had been examined by single-photon emission computed tomography (SPECT) using the iodinated fatty acid analogue BMIPP. Uptake of BMIPP on SPECT was graded in 17 segments on a five-point scale (0, normal; 4, absent) and assessed as BMIPP summed scores. Of the enrolled 155 participants, we analyzed 95 who had BMIPP summed scores ≥ 6 (52 men and 43 women, 65 ± 11 years). BMIPP scores ≥ 2 in ≥ 2 consecutive segments in SPECT were defined as focal, and the others as non-focal pattern. RESULTS: Of 95 participants analyzed, 42 (44.2 %) showed focal and 53 (55.8 %) non-focal type. During follow-up for 5.1 ± 2.0 years, 42 died of cardiac events. The occurrence of cardiac death was higher in the focal than in the non-focal group (30/42 [71.4 %] versus 12/53 [22.6 %], p = 0.001). In stepwise Cox hazard analysis, focal pattern was associated with cardiac death (hazard ratio 2.266), independent of impairment of BMIPP SPECT (BMIPP summed scores ≥ 12). The predictive potential of BMIPP SPECT for cardiac death was higher (p < 0.001) in the left anterior descending artery area compared with other coronary territories. CONCLUSIONS: Focal impairment in myocardial fatty acid imaging in the left anterior descending area may strongly predict cardiac death in this population.

The use of fluorescence microscopy and image analysis for rapid detection of non-producing revertant cells of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002.

BACKGROUND: Ethanol production via genetically engineered cyanobacteria is a promising solution for the production of biofuels. Through the introduction of a pyruvate decarboxylase and alcohol dehydrogenase direct ethanol production becomes possible within the cells. However, during cultivation genetic instability can lead to mutations and thus loss of ethanol production. Cells then revert back to the wild type phenotype. A method for a rapid and simple detection of these non-producing revertant cells in an ethanol producing cell population is an important quality control measure in order to predict genetic stability and the longevity of a producing culture. Several comparable cultivation experiments revealed a difference in the pigmentation for non-producing and producing cells: the accessory pigment phycocyanin (PC) is reduced in case of the ethanol producer, resulting in a yellowish appearance of the culture. Microarray and western blot studies of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002 confirmed this PC reduction on the level of RNA and protein. METHODS: Based on these findings we developed a method for fluorescence microscopy in order to distinguish producing and non-producing cells with respect to their pigmentation phenotype. By applying a specific filter set the emitted fluorescence of a producer cell with a reduced PC content appeared orange. The emitted fluorescence of a non-producing cell with a wt pigmentation phenotype was detected in red, and dead cells in green. In an automated process multiple images of each sample were taken and analyzed with a plugin for the image analysis software ImageJ to identify dead (green), non-producing (red) and producing (orange) cells. RESULTS: The results of the presented validation experiments revealed a good identification with 98 % red cells in the wt sample and 90 % orange cells in the producer sample. The detected wt pigmentation phenotype (red cells) in the producer sample were either not fully induced yet (in 48 h induced cultures) or already reverted to a non-producing cells (in long-term photobioreactor cultivations), emphasizing the sensitivity and resolution of the method. CONCLUSIONS: The fluorescence microscopy method displays a useful technique for a rapid detection of non-producing single cells in an ethanol producing cell population.

Stochastic approach to the molecular counting problem in superresolution microscopy.

Superresolution imaging methods--now widely used to characterize biological structures below the diffraction limit--are poised to reveal in quantitative detail the stoichiometry of protein complexes in living cells. In practice, the photophysical properties of the fluorophores used as tags in superresolution methods have posed a severe theoretical challenge toward achieving this goal. Here we develop a stochastic approach to enumerate fluorophores in a diffraction-limited area measured by superresolution microscopy. The method is a generalization of aggregated Markov methods developed in the ion channel literature for studying gating dynamics. We show that the method accurately and precisely enumerates fluorophores in simulated data while simultaneously determining the kinetic rates that govern the stochastic photophysics of the fluorophores to improve the prediction's accuracy. This stochastic method overcomes several critical limitations of temporal thresholding methods.

Extracting meaning from biological imaging data.

Biological imaging continues to improve, capturing continually longer-term, richer, and more complex data, penetrating deeper into live tissue. How do we gain insight into the dynamic processes of disease and development from terabytes of multidimensional image data? Here I describe a collaborative approach to extracting meaning from biological imaging data. The collaboration consists of teams of biologists and engineers working together. Custom computational tools are built to best exploit application-specific knowledge in order to visualize and analyze large and complex data sets. The image data are summarized, extracting and modeling the features that capture the objects and relationships in the data. The summarization is validated, the results visualized, and errors corrected as needed. Finally, the customized analysis and visualization tools together with the image data and the summarization results are shared. This Perspective provides a brief guide to the mathematical ideas that rigorously quantify the notion of extracting meaning from biological image, and to the practical approaches that have been used to apply these ideas to a wide range of applications in cell and tissue optical imaging.