Measuring Absolute Blood Perfusion in Mice Using Dynamic Contrast-Enhanced Ultrasound

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Abbas Shirinifard, Suresh Thiagarajan, Melissa D. Johnson, Christopher Calabrese, András Sablauer*

Department of Information Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA; Department of Pathology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA; Department of Small Animal Imaging, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA.

DOI: 10.1016/j.ultrasmedbio.2017.02.004

We investigated the feasibility of estimating absolute tissue blood perfusion using dynamic contrast-enhanced ultrasound (CEUS) imaging in mice. We developed a novel method of microbubble administration and a model-free approach to estimate absolute kidney perfusion, and explored the kidney as a reference organ to estimate absolute perfusion of a neuroblastoma tumor. We performed CEUS on the kidneys of CD1 nude mice using the VisualSonics VEVO 2100 imaging system. We estimated individual kidney blood perfusion using the burst–replenishment (BR) technique. We repeated the kidney imaging on the mice after a week. We performed CEUS imaging of a neuroblastoma mouse xenograft tumor along with its right kidney using two sets of microbubble administration parameters to estimate absolute tumor blood perfusion. We performed statistical tests at a significance level of 0.05. Our estimated absolute kidney perfusion (425 ± 123 mL/min/100 g) was within the range of previously reported values. There was no statistical difference between the estimated absolute kidney blood perfusions from the 2 wk of imaging (paired t-test, p = 0.09). We estimated the absolute blood perfusion in the neuroblastoma tumor to be 16.49 and 16.9 mL/min/100 g for the two sets of microbubble administration parameters (Wilcoxon rank-sum test, p = 0.6). We have established the kidney as a reliable reference organ in which to estimate absolute perfusion of other tissues. Using a neuroblastoma tumor, we have determined the feasibility of estimating absolute blood perfusion in tissues using contrast-enhanced ultrasound imaging.

Box-Counting Method of 2D Neuronal Image: Method Modification and Quantitative Analysis Demonstrated on Images from the Monkey and Human Brain

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Nemanja Rajković,1, Bojana Krstonošić,2, Nebojša Milošević,1

1 Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, 11000 Belgrade, Serbia; 2 Department of Anatomy, School of Medicine, University of Novi Sad, Hajduk Veljkova 21, 21000 Novi Sad, Serbia.

DOI: 10.1155/2017/8967902

This study calls attention to the difference between traditional box-counting method and its modification. The appropriate scaling factor, influence on image size and resolution, and image rotation, as well as different image presentation, are showed on the sample of asymmetrical neurons from the monkey dentate nucleus. The standard BC method and its modification were evaluated on the sample of 2D neuronal images from the human neostriatum. In addition, three box dimensions (which estimate the space-filling property, the shape, complexity, and the irregularity of dendritic tree) were used to evaluate differences in the morphology of type III aspiny neurons between two parts of the neostriatum.

Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells

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Joseph J. Kim,1,2, Neal K. Bennett,1, Mitchel S. Devita,3, Sanjay Chahar,4, Satish Viswanath,5, Eunjee A. Lee,6, Giyoung Jung,6,7, Paul P. Shao,8, Erin P. Childers,9, Shichong Liu,10, Anthony Kulesa,1,11, Benjamin A. Garcia,10, Matthew L. Becker,9, Nathaniel S. Hwang,6, Anant Madabhushi,5, Michael P. Verzi,4, Prabhas V. Moghe,1,12

1 Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, USA; 2 Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA; 3 Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, New Jersey, USA; 4 Department of Genetics, Rutgers University, Piscataway, New Jersey, USA; 5 Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; 6 School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea; 7 Division of Heath Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA; 8 Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA; 9 Department of Polymer Science, University of Akron, Akron, Ohio, USA; 10 Epigenetics Program, Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; 11 Department of Biological Engineering,
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; 12 Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, USA.

DOI: 10.1038/srep39406

While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone post-translational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.

Image-guided genomics of phenotypically heterogeneous populations reveals vascular signalling during symbiotic collective cancer invasion

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J. Konen,1, E. Summerbell,1, B. Dwivedi,2, K. Galior,3, Y. Hou,4, L. Rusnak,1, A. Chen,1, J. Saltz,5, W. Zhou,2,6, L.H. Boise,2,6, P. Vertino,2,7, L. Cooper,4, K. Salaita,3, J. Kowalski,2,8, A.I. Marcus,2,6

1 Graduate Program in Cancer Biology, Emory University, 1365C Clifton Road, Atlanta, Georgia 30322, USA; 2 Winship Cancer Institute, Emory University, 1365C Clifton Road, Atlanta, Georgia 30322, USA; 3 Department of Chemistry, Emory University, 506 Atwood Drive, Atlanta, Georgia 30322, USA; 4 Department of Biomedical Informatics, Emory University, 36 Eagle Row, Atlanta, Georgia 30322, USA; 5 Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York 11794, USA; 6 Department of Hematology and Medical Oncology, Emory University, 1365C Clifton Road, Atlanta, Georgia 30322, USA; 7 Department of Radiation Oncology, Emory University, 1365C Clifton Road, Atlanta, Georgia 30322, USA; 8 Department of Biostatistics and Bioinformatics, Emory University, 1365C Clifton Road, Atlanta, Georgia 30322, USA.

DOI: 10.1038/ncomms15078

Phenotypic heterogeneity is widely observed in cancer cell populations. Here, to probe this heterogeneity, we developed an image-guided genomics technique termed spatiotemporal genomic and cellular analysis (SaGA) that allows for precise selection and amplification of living and rare cells. SaGA was used on collectively invading 3D cancer cell packs to create purified leader and follower cell lines. The leader cell cultures are phenotypically stable and highly invasive in contrast to follower cultures, which show phenotypic plasticity over time and minimally invade in a sheet-like pattern. Genomic and molecular interrogation reveals an atypical VEGF-based vasculogenesis signalling that facilitates recruitment of follower cells but not for leader cell motility itself, which instead utilizes focal adhesion kinase-fibronectin signalling. While leader cells provide an escape mechanism for followers, follower cells in turn provide leaders with increased growth and survival. These data support a symbiotic model of collective invasion where phenotypically distinct cell types cooperate to promote their escape.

Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes

Nick E. Phillips,1, Cerys Manning,2, Nancy Papalopulu,2 , Magnus Rattray,2

1 The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; 2 Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

DOI: 10.1371/journal.pcbi.1005479

Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.

Toward greener electrochemical synthesis of composition-tunable luminescent CdX-based (X = Te, Se, S) quantum dots for bioimaging cancer cells

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Denilson V. Freitasa, Sérgio G.B. Passosa, Jéssica M.M. Diasa, Alexandra Mansurb, Sandhra M. Carvalhob, Herman Mansurb, Marcelo Navarroa

a Department of Fundamental Chemistry, Federal University of Pernambuco, Cidade Universitária, 50670-901, Recife, PE, Brazil; b Department of Metallurgical and Materials Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.

DOI: 10.1016/j.snb.2017.04.185

A novel versatile and clean colloidal processing route was developed for synthesizing CdX (X = Te, Se or S) quantum dots (QDs) based on the simultaneous production of the respective precursors via cadmium sacrificial anode electrochemical methodology. The CdX QDs stabilized by 3-mercaptopropionic acid (MPA) were synthesized in one-pot electrochemical process using aqueous medium at room temperature with full reaction time of approximately 16 min. The CdX quantum dots conjugates were extensively characterized by UV-vis, PL, FTIR, TEM-EDS, XRD, DLS, XPS, and Zeta potential, evidencing that very stable colloidal quantum dots were produced with uniform narrow-size distributions and average nanoparticle diameter of approximately 3.0 nm and quantum yield (QY) of approximately 10%. The cell viability response of HeLa cells toward the CdX-MPA conjugates clearly demonstrated that they are non-toxic based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) mitochondrial metabolic activity assay for 24 h of incubation with HeLa cervical cancer cells. Moreover, they showed effective fluorescent activity with time-dependent intensities for evaluating continuous endocytosis of cancer cells, thus, offering innumerous possibilities to be used as fluorescent nanoprobes for bioimaging and biosensing of cancer cells.

 

Computational prediction of drug-drug interactions based on drugs functional similarities

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Reza Ferdousia, b, Reza Safdaria , Yadollah Omidib

a Department of Health Information Management, School of Allied-Health Sciences, Tehran University of Medical Sciences, Tehran, Iran; b Research Centre for Pharmaceutical Nanotechnology, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.

DOI: 10.1016/j.jbi.2017.04.021

Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and side effects. Prediction and identification of DDIs are extremely vital for patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2 189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2 394 766 potential drug pairs interactions were studied. The model was able to predict over 250 000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for detection of possible DDIs based on functional similarities of drugs.