Shifting the optimal stiffness for cell migration

Figure 2

Benjamin L. Bangasser,1, Ghaidan A. Shamsan,1, Clarence E. Chan,1, Kwaku N. Opoku,1, Erkan Tüzel,1, Benjamin W. Schlichtmann,1, Jesse A. Kasim,1, Benjamin J. Fuller,1, Brannon R. McCullough,1, Steven S. Rosenfeld,2, David J. Odde,1

1 Department of Biomedical Engineering, University of Minnesota, 312 Church Street SE, Minneapolis, Minnesota 55455, USA; 2 Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.

DOI: 10.1038/ncomms15313

Cell migration, which is central to many biological processes including wound healing and cancer progression, is sensitive to environmental stiffness, and many cell types exhibit a stiffness optimum, at which migration is maximal. Here we present a cell migration simulator that predicts a stiffness optimum that can be shifted by altering the number of active molecular motors and clutches. This prediction is verified experimentally by comparing cell traction and F-actin retrograde flow for two cell types with differing amounts of active motors and clutches: embryonic chick forebrain neurons (ECFNs; optimum 1 kPa) and U251 glioma cells (optimum 100 kPa). In addition, the model predicts, and experiments confirm, that the stiffness optimum of U251 glioma cell migration, morphology and F-actin retrograde flow rate can be shifted to lower stiffness by simultaneous drug inhibition of myosin II motors and integrin-mediated adhesions.

 

Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease

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Maxwell B. Wang,1, Julia P. Owen,1, Pratik Mukherjee,1,2, Ashish Raj,3

1 Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America; 2 Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, California, United States of America; 3 Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.

DOI: 10.1371/journal.pcbi.1005550

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

High-Throughput Quantification of Nanoparticle Degradation Using Computational Microscopy and Its Application to Drug Delivery Nanocapsules

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Aniruddha Ray†‡, Shuoran Li§, Tatiana Segura‡§∥⊥, Aydogan Ozcan*†‡⊥# 
Electrical Engineering Department; Bioengineering Department; §Department of Chemical and Biomolecular Engineering; Department of Medicine, Dermatology; #Department of Surgery, David Geffen School of Medicine; California NanoSystems  Institute (CNSI), University of California, Los Angeles, California 90095, United States
Design and synthesis of degradable nanoparticles are very important in drug delivery and biosensing fields. Although accurate assessment of nanoparticle degradation rate would improve the characterization and optimization of drug delivery vehicles, current methods rely on estimating the size of the particles at discrete points over time using, for example, electron microscopy or dynamic light scattering (DLS), among other techniques, all of which have drawbacks and practical limitations. There is a significant need for a high-throughput and cost-effective technology to accurately monitor nanoparticle degradation as a function of time and using small amounts of sample. To address this need, here we present two different computational imaging-based methods for monitoring and quantification of nanoparticle degradation. The first method is suitable for discrete testing, where a computational holographic microscope is designed to track the size changes of protease-sensitive protein-core nanoparticles following degradation, by periodically sampling a subset of particles mixed with proteases. In the second method, a sandwich structure was utilized to observe, in real-time, the change in the properties of liquid nanolenses that were self-assembled around degrading nanoparticles, permitting continuous monitoring and quantification of the degradation process. These cost-effective holographic imaging based techniques enable high-throughput monitoring of the degradation of any type of nanoparticle, using an extremely small amount of sample volume that is at least 3 orders of magnitude smaller than what is required by, for example, DLS-based techniques.

Quantitative assessment of Cerenkov luminescence for radioguided brain tumor resection surgery

Figure 7.

Justin S Klein, Gregory S Mitchell, Simon R Cherry

Department of Biomedical Engineering, University of California, Davis, CA, United States of America

DOI: 10.1088/1361-6560/aa6641

Cerenkov luminescence imaging (CLI) is a developing imaging modality that detects radiolabeled molecules via visible light emitted during the radioactive decay process. We used a Monte Carlo based computer simulation to quantitatively investigate CLI compared to direct detection of the ionizing radiation itself as an intraoperative imaging tool for assessment of brain tumor margins. Our brain tumor model consisted of a 1 mm spherical tumor remnant embedded up to 5 mm in depth below the surface of normal brain tissue. Tumor to background contrast ranging from 2:1 to 10:1 were considered. We quantified all decay signals (e±, gamma photon, Cerenkov photons) reaching the brain volume surface. CLI proved to be the most sensitive method for detecting the tumor volume in both imaging and non-imaging strategies as assessed by contrast-to-noise ratio and by receiver operating characteristic output of a channelized Hotelling observer.

First demonstration of multi-color 3-D in vivo imaging using ultra-compact Compton camera

Figure 5

 

Aya Kishimoto,1, Jun Kataoka,1, Takanori Taya,1, Leo Tagawa,1, Saku Mochizuki,1, Shinji Ohsuka,2, Yuto Nagao,3, Keisuke Kurita,3, Mitsutaka Yamaguchi,3, Naoki Kawachi,3, Keiko Matsunaga,4, Hayato Ikeda,4, Eku Shimosegawa,4, Jun Hatazawa,4

1 Waseda University, Graduate School of Advanced Science and Engineering, Tokyo, Japan; 2 Hamamatsu Photonics K. K., Central Research Laboratory, Sizuoka, Japan; 3 National Institutes for Quantum and Radiological Science and Technology, Gunma, Japan; 4 Osaka University Graduate School of Medicine, Medical Imaging Center for Translational Research, Osaka, Japan.

DOI: 10.1038/s41598-017-02377-w

In the field of nuclear medicine, single photon emission tomography and positron emission tomography are the two most common techniques in molecular imaging, but the available radioactive tracers have been limited either by energy range or difficulties in production and delivery. Thus, the use of a Compton camera, which features gamma-ray imaging of arbitrary energies from a few hundred keV to more than MeV, is eagerly awaited along with potential new tracers which have never been used in current modalities. In this paper, we developed an ultra-compact Compton camera that weighs only 580 g. The camera consists of fine-pixelized Ce-doped Gd3Al2Ga3O12 scintillators coupled with multi-pixel photon counter arrays. We first investigated the 3-D imaging capability of our camera system for a diffuse source of a planar geometry, and then conducted small animal imaging as pre-clinical evaluation. For the first time, we successfully carried out the 3-D color imaging of a live mouse in just 2 h. By using tri-color gamma-ray fusion images, we confirmed that 131I, 85Sr, and 65Zn can be new tracers that concentrate in each target organ.

 

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.