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Supplementary Figure 4 from Role for Stromal Heterogeneity in Prostate Tumorigenesis

Supplementary Figure 1 from Role for Stromal Heterogeneity in Prostate Tumorigenesis

Supplementary Figure 3 from Role for Stromal Heterogeneity in Prostate Tumorigenesis

Supplementary Figure 2 from Role for Stromal Heterogeneity in Prostate Tumorigenesis

In most biological processes, diffusion plays a critical role in transferring various bio-molecules to transfer desirable locations in an effective and energy-efficient manner. How fast molecules are transferred is measured by diffusion coefficients. Since each bio-molecules, in particular, signaling molecules have their unique diffusion coefficients and quantifying the diffusion coefficients help us to understand various time scales of both physiological and pathological processes in biological systems. Moreover, since diffusion profiles of a diffusant vary in different micro-environments of cell membranes, accurate diffusion coefficient also can provide a good picture of membrane landscapes as well as interactions of different membrane constituents. Currently, only a few experimental methods are available to assess the diffusion coefficient of a biomolecule of interest in live cells including Fluorescence Recovery After Photobleaching (FRAP). FRAP was developed to study diffusion processes of biomolecules in the cell membranes in the 1970s. Albeit its long history, the main principle of FRAP analysis has remained unchanged since its inception: fitting FRAP data to a theoretical diffusion model for the best fitting diffusion coefficient or using the relation between the half time of recovery and ROI size. In this study, we developed a flexible yet versatile confocal FRAP data analysis framework based on linear regression analysis which allows FRAP users to determine the diffusion from either single or multiple FRAP data points without data fitting. We also validated this approach for a series of fluorescently labeled soluble and membrane-bound proteins and lipids.

This study identified the subgroups (latent classes) of Korean college students according to the influence of perfectionism on career stress and indecision, and explored the effects of sub-factors of perfectionism on career stress and indecision for each subgroup. Also, the study examined how individual self-esteem and stress coping styles affect the subgroup classification. Data from 476 South Korean college students were analyzed via mixture regression and logistic regression. Four latent classes were identified. In class 1, career indecision increased as self-oriented perfectionism increased, and career stress increased as socially prescribed perfectionism increased. In class 2, career stress increased as self-oriented perfectionism increased, whereas both career stress and career indecision decreased as others-oriented perfectionism increased. In class 3, both career stress and career indecision increased as others-oriented perfectionism increased. In class 4, career stress and career indecision decreased as others-oriented perfectionism increased, while career stress increased as socially prescribed perfectionism increased. In differentiating the classes, self-esteem and coping styles were analyzed as predictor variables. The results indicated that self-esteem helped to distinguish class 1 from class 2, and class 1 from class 3. Avoidance-oriented coping style could distinguish class 1 from class 3. Career counselors would benefit by noting that the influence of each sub-trait of perfectionism on career-related issues may vary by latent class, and that self-esteem and coping styles may moderate the effects of perfectionism on career-related issues.

Biofuel cells, which convert chemical energy into electrical energy at mild temperature and over moderate pH ranges, have been considered some of the most promising candidates for powering biomedical devices. However, most biofuel cells provide low power output and short-term operational stability due to their poor electron transfer. To address these issues, we use a unique amphiphilic assembly method to generate hybrid biofuel cells with high power output and good operational stability. This approach can induce favorable interfacial interactions between electrocatalysts and significantly improve the electron transfer kinetics of electrodes. In this study, glucose oxidase (in aqueous media) is repeatedly assembled with hydrophobic metal nanoparticles (in nonpolar media) on a conductive textile. The formed biofuel cell exhibits remarkably high power output (7.3 mW cm−2) and good operational durability. We believe that our assembly approach can provide a basis for preparing a variety of high-performance bioelectrochemical devices, including biofuel cells.

This study explored the effects of ethnic/racial matching on psychotherapy outcomes via the number of counseling sessions attended by clients, using clinical data collected from a university’s counseling center. A total sample of 644 clients (193 men, 448 women) was selected and the ethnic/racial distribution of the sample comprised 499 White/European American (77.5%), 81 African American (12.6%), 33 Asian American (5.1%), and 31 Hispanic clients (4.8%). As a result of a multilevel mediation analysis, it was found that the clients working with counselors of matching ethnicity/race attended more number of counseling sessions than those with counselors of different ethnicity/race. Moreover, the ethnic/racial matching between clients and counselors led to a higher improvement of global assessment of function (GAF). The analysis also revealed that the client–counselor ethic/racial matching showed significant, direct effects on the improvement of GAF. The effects of ethnic/racial matching on the number of counseling sessions and GAF improvement significantly differed at the counselor-level. Clinical implications to multicultural counseling and training are provided.

Since its introduction in the 1970s, Fluorescence Correlation Spectroscopy (FCS) has become a standard biophysical and physical chemistry tool to investigate not only a diffusion but also a broad range of biochemical processes including binding kinetics and anomalous diffusion. Since the derivation of FCS equations for many biochemical processes shares many common steps with the diffusion FCS equation, it is important to understand the mathematical theory behind the ​diffusion FCS equation. However, because the derivation of FCS equations requires advanced Fourier Transform and inverse Fourier Transform theory, which most biologists and biochemists are not familiar with, it is often treated as a black box in practice. In this study, we provide a simple and straightforward step-by-step derivation of FCS equations for free ​diffusion based on calculus-level mathematics, so that FCS equations and its applications can be accessible to a broad audience. Additionally, we compare our derivation with the conventional Fourier Transform and inverse Fourier Transform theory based approach.

Diffusion of proteins and lipids in lipid membranes plays a pivotal role in almost all aspects of cellular biology, including motility, exo-/endocytosis and signal transduction. For this reason, gaining a detailed understanding of membrane structure and function has long been a major area of cell biology research. To better elucidate this structure-function relationship, various tools have been developed for diffusion measurements, including Fluorescence Recovery After Photobleaching (FRAP). Because of the complexity of cellular microenvironments, biological diffusion is often correlated over time and described by a time-dependent diffusion coefficient, D(t), although the underlying mechanisms are not fully understood. Since D(t) provides important information regarding cellular structures, such as the existence of subresolution barriers to diffusion, many efforts have been made to quantify D(t) by FRAP assuming a single power law, D(t) = Γt α - 1 where Γ and α are transport coefficient and anomalous exponent. However, straightforward approaches to quantify a general form of D(t) are lacking. In this study, we develop a novel mathematical and computational framework to compute the mean square displacement of diffusing molecules and diffusion coefficient D(t) from each individual time point of confocal FRAP data without the single power law assumption. Additionally, we developed an auxiliary equation for D(t) which can readily distinguish normal diffusion or single power law anomalous diffusion from other types of anomalous diffusion directly from FRAP data. Importantly, by applying this approach to FRAP data from a variety of membrane markers, we demonstrate the single power law anomalous diffusion assumption is not sufficient to describe various types of D(t) of membrane proteins. Lastly, we discuss how our new approaches can be applied to other fluorescence microscopy tools such as Fluorescence Correlation Spectroscopy (FCS) and Single Particle Tracking (SPT).

Despite previous research efforts on identifying the risk and protective factors of problematic Internet use (PIU), the specific mechanism among these factors are largely unknown. Thus, the present study examined the effect of adolescents' social relationships on their PIU, as well as the effect of self-esteem as a mediator and the effect of self-control as a moderator. Survey data from 750 South Korean middle and high school students in Seoul and Gyeonggi areas were analyzed using structural equation modeling. The results are as follows. First, PIU is significantly associated with relationship with peers and with mother. Second, self-esteem did not mediate the effect of communication with mother on PIU, but was found to partially mediate the effect of peer relationship on PIU. Third, adolescents' self-control significantly moderated the indirect effect of peer relationship on PIU via self-esteem. Through detailed analyses, this study identified self-esteem as a mediator and self-control as a moderator in the relationship between social relationships and PIU. Implications of these results for understanding the relationship among social relationships, self-esteem, self-control, and PIU are discussed.

This paper presents a high-efficiency digital class-D audio amplifier using a composite interpolation filter for portable audio devices. The proposed audio amplifier is composed of an interpolation filter, a delta-sigma modulator, and a class-D output stage. To reduce power consumption, the designed interpolation filter has an optimized composite structure that uses a direct-form symmetric and Lagrange FIR filters. Compared to the filters with homogeneous structures, the hardware cost and complexity are reduced by about half by the optimization. The coefficients of the digital deltasigma modulator are also optimized for low power consumption. The class-D output stage has gate driver circuits to reduce shoot-through current. The implemented class-D audio amplifier exhibited a high efficiency of 87.8 % with an output power of 57 mW at a load impedance of 16 6 and a power supply voltage of 1.8 V. An outstanding signal-to-noise ratio of 90 dB and a total harmonic distortion plus noise of 0.03 % are achieved for a single-tone input signal with a frequency of 1 kHz.

Quantitative measurements of diffusion can provide important information about how proteins and lipids interact with their environment within the cell and the effective size of the diffusing species. Confocal fluorescence recovery after photobleaching (FRAP) is one of the most widely accessible approaches to measure protein and lipid diffusion in living cells. However, straightforward approaches to quantify confocal FRAP measurements in terms of absolute diffusion coefficients are currently lacking. Here, we report a simplified equation that can be used to extract diffusion coefficients from confocal FRAP data using the half time of recovery and effective bleach radius for a circular bleach region, and validate this equation for a series of fluorescently labeled soluble and membrane-bound proteins and lipids. We show that using this approach, diffusion coefficients ranging over three orders of magnitude can be obtained from confocal FRAP measurements performed under standard imaging conditions, highlighting its broad applicability.

The original version of this article was published with the following error. This has been corrected with this erratum.

A model of ligand-induced intracellular calcium (Ca2+) responses incorporating phospholipase C (PLC) and protein kinase C (PKC) is developed for the purpose of understanding the mechanisms underlying the observed temporal patterns of intracellular calcium (Ca(i)2+) under sustained agonist stimulation. Some studies have suggested that inhibition of ligand receptors and PLC by PKC could generate sinusoidal Ca2+ oscillations, while PKC-independent Ca2+-induced Ca2+ release (CICR) via IP(3)-gated Ca2+ channels on the endoplasmic reticulum (ER) is believed to be responsible for baseline spiking. However, some evidence also indicates that baseline spiking can be observed under high-PKC activity, or under low-PKC activity with low agonist stimulus, as well. Insight into the basis of these observations regarding the role of PKC in Ca(i)2+ response patterns can be gained by developing and analyzing a mathematical model of Ca(i)2+ responses. We do this herein and find that (1) interaction of CICR and the sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA) pump is enough to generate both types of Ca(i)2+ oscillations, (2) there exist four possible Ca(i)2+ response patterns under sustained agonist stimulus: a sub-threshold response (SR), baseline spiking, sinusoidal oscillations (SO) and transient with plateau, and (3) the IP(3) concentration, which is controlled by the strength of the interaction between PKC and PLC, can be used to predict the Ca(i)2+ response patterns. From this analysis we conclude that the different patterns of Ca(i)2+ oscillations can be understood as a generic consequence of the interactions between CICR via the IP(3)-gated Ca(2+) channels in response to changes in the level of IP(3), and re-uptake into the ER/SR via the SERCA pump. PKC, in conjunction with PLC, can act as a switch between different Ca(i)2+ response patterns by modulating the cytosolic IP(3) level, which determines the Ca(i)2+ patterns.

Fluorescence recovery after photobleaching (FRAP) is a powerful, versatile, and widely accessible tool to monitor molecular dynamics in living cells that can be performed using modern confocal microscopes. Although the basic principles of FRAP are simple, quantitative FRAP analysis requires careful experimental design, data collection, and analysis. In this unit, we discuss the theoretical basis for confocal FRAP, followed by step-by-step protocols for FRAP data acquisition using a laser-scanning confocal microscope for (1) measuring the diffusion of a membrane protein, (2) measuring the diffusion of a soluble protein, and (3) analysis of intracellular trafficking. Finally, data analysis procedures are discussed, and an equation for determining the diffusion coefficient of a molecular species undergoing pure diffusion is presented.

Summary The glycosphingolipid GM1 binds cholera toxin (CT) on host cells and carries it retrograde from the plasma membrane (PM) through endosomes, the trans -Golgi (TGN), and the endoplasmic reticulum (ER) to induce toxicity. To elucidate how a membrane lipid can specify trafficking in these pathways, we synthesized GM1 isoforms with alternate ceramide domains and imaged their trafficking in live cells. Only GM1 with unsaturated acyl chains sorted efficiently from PM to TGN and ER. Toxin binding, which effectively crosslinks GM1 lipids, was dispensable, but membrane cholesterol and the lipid raft-associated proteins actin and flotillin were required. The results implicate a protein-dependent mechanism of lipid sorting by ceramide structure and provide a molecular explanation for the diversity and specificity of retrograde trafficking by CT in host cells.

The B subunit of cholera toxin (CTXB) is generally accepted as a marker of lipid rafts. Compared to other raft markers or lipid-anchored proteins, CTXB exhibits relatively slow diffusion. A variety of mechanisms could potentially account for this slow diffusion of CTXB, including crosslinking of small raft domains, confinement by the actin cytoskeleton, association with caveolae, incorporation into actively maintained domains, or molecular crowding effects in response to elevated membrane protein density. We evaluated the role of each of these mechanisms in controlling the lateral diffusion of CTXB in the current study by employing fluorescence recovery after photobleaching (FRAP) of fluorescently labeled CTXB following actin depolymerization, ATP depletion, cholesterol depletion, labeling across a range of CTXB concentrations, or in caveolin-1 knockout MEFs. Of these conditions, only cholesterol depletion significantly altered the diffusional mobility of CTXB. Furthermore, we tested whether the slow diffusion of CTXB is an intrinsic property of its receptor by examining the effects of CTXB on the diffusion of a fluorescent GM1 analog. The results of this experiment showed that CTXB slows the diffusion of its receptor. However, binding of CTXB to cells did not affect the diffusion of another raft marker (YFP-GL-GPI), a non-raft marker (YFP-GT46), or a fluorescent lipid analog (DiIC16). Taken together, these data suggest that CTXB diffusion is not limited by actin corrals, caveolae, molecular crowding effects, or the intrinsically slow diffusion of GM1. In addition, they suggest that crosslinking of small rafts induced by CTXB binding does not substantially alter the dynamics of membrane domains enriched in other types of raft or non-raft proteins or lipids.

Fluorescence recovery after photobleaching (FRAP) using confocal laser scanning microscopes (confocal FRAP) has become a valuable technique for studying the diffusion of biomolecules in cells. However, two-dimensional confocal FRAP sometimes yields results that vary with experimental setups, such as different bleaching protocols and bleaching spot sizes. In addition, when confocal FRAP is used to measure diffusion coefficients (D) for fast diffusing molecules, it often yields D-values that are one or two orders-of-magnitude smaller than that predicted theoretically or measured by alternative methods such as fluorescence correlation spectroscopy. Recently, it was demonstrated that this underestimation of D can be corrected by taking diffusion during photobleaching into consideration. However, there is currently no consensus on confocal FRAP theory, and no efforts have been made to unify theories on conventional and confocal FRAP. To this end, we generalized conventional FRAP theory to incorporate diffusion during photobleaching so that analysis by conventional FRAP theory for a circular region of interest is easily applicable to confocal FRAP. Finally, we demonstrate the accuracy of these new (to our knowledge) formulae by measuring D for soluble enhanced green fluorescent protein in aqueous glycerol solution and in the cytoplasm and nucleus of COS7 cells.

There is an old adage that says “To see is to believe,” and it still seems to be true in many fields of biology. For an experimental validation of hypotheses, modern biology takes advantage of various fluorescence-based techniques (fluorescence microscopy, digital image analysis) for visualization and quantification. Fluorescence Recovery after Photobleaching (FRAP), a widely used fluorescence-based technique to visualize and quantify the diffusion of fluorescently tagged proteins, is one good example. The first observation of fluorescence was made by Sir John Frederick William Herschel (1845) from a quinine solution, and the concept of fluorescence was first named after fluorite by Sir George G. Stokes (1857). On a microscopic scale, fluorescence was first observed by August K?ler (1904), who discovered that a biological tissue could autofluoresce under ultraviolet light irradiation. Later, it became popular in the biological field after M. Haitinger (1933) succeeded in staining histological specimens with fluorescent dyes for the first time, which is called the technique of secondary fluorescence, distinguishing it from autofluorescent tissue previously observed by M. Haitinger. Haitinger and others extended the application of the technique of secondary fluorescence to stain not only specific tissues but also bacteria, or other pathogens that are not autofluorescent [1]. Although the technique of secondary fluorescence demonstrated that nonfluorescent cells can be made fluorescent, it was a nonspecific staining technique. The breakthrough in a specific immunofluorescence staining technique was provided by Albert Coons in 1941 by attaching a fluorescent dye to an antibody [2]. Later, Coons and N.H. Kaplan developed the fluorescein isothiocyanate (FITC) immunofluorescence technique [3].

Fluorescence Recovery After Photobleaching (FRAP) has been a versatile tool to study transport and reaction kinetics in live cells. Since the fluorescence data generated by fluorescence microscopy are in a relative scale, a wide variety of scalings and normalizations are used in quantitative FRAP analysis. Scaling and normalization are often required to account for inherent properties of diffusing biomolecules of interest or photochemical properties of the fluorescent tag such as mobile fraction or photofading during image acquisition. In some cases, scaling and normalization are also used for computational simplicity. However, to our best knowledge, the validity of those various forms of scaling and normalization has not been studied in a rigorous manner. In this study, we investigate the validity of various scalings and normalizations that have appeared in the literature to calculate mobile fractions and correct for photofading and assess their consistency with FRAP equations. As a test case, we consider linear or affine scaling of normal or anomalous diffusion FRAP equations in combination with scaling for immobile fractions. We also consider exponential scaling of either FRAP equations or FRAP data to correct for photofading. Using a combination of theoretical and experimental approaches, we show that compatible scaling schemes should be applied in the correct sequential order; otherwise, erroneous results may be obtained. We propose a hierarchical workflow to carry out FRAP data analysis and discuss the broader implications of our findings for FRAP data analysis using a variety of kinetic models.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

In an important research article in Biophysical Journal, Feder et al. (1) introduced the idea that proteins in cell membranes undergo a form of restricted, time-dependent lateral mobility due to anomalous diffusion. In doing so, they provided a plausible explanation for why the presence of the so-called immobile fraction is often required to fully describe fluorescence recovery after photobleaching (FRAP) measurements when a free-diffusion model is used to fit the data. These findings helped to shape current models of cell-membrane organization, and models incorporating anomalous diffusion have subsequently been applied more generally to other cellular compartments as well. Feder et al. started with the assumption that the mean-squared displacement (MSD; 〈x2〉) of anomalously diffusing molecules is related to a certain power of time by 〈x2〉=Γtα, where Γ and α are referred to as a transport coefficient and an anomalous exponent, respectively. By analogy to the relationship between the MSD and diffusion coefficient D for purely diffusing molecules in R2, 〈x2〉=4Dt, the authors introduced a time-dependent diffusion coefficient as 〈x2〉=Γtα=(Γtα−1)t=4DFeder(t)t, (1) where DFeder(t)=14Γtα−1 (2) for Γ>0 and 0

Although Ca(i)(2+) waves in networks of astrocytes in vivo are well documented, propagation in vivo is much more complex than in culture, and there is no consensus concerning the dominant roles of intercellular and extracellular messengers [inositol 1,4,5-trisphosphate (IP(3)) and adenosine-5'-triphosphate (ATP)] that mediate Ca(i)(2+) waves. Moreover, to date only simplified models that take very little account of the geometrical struture of the networks have been studied. Our aim in this paper is to develop a mathematical model based on realistic cellular morphology and network connectivity, and a computational framework for simulating the model, in order to address these issues. In the model, Ca(i) (2+) wave propagation through a network of astrocytes is driven by IP(3) diffusion between cells and ATP transport in the extracellular space. Numerical simulations of the model show that different kinetic and geometric assumptions give rise to differences in Ca(i)(2+) wave propagation patterns, as characterized by the velocity, propagation distance, time delay in propagation from one cell to another, and the evolution of Ca(2+) response patterns. The temporal Ca(i)(2+) response patterns in cells are different from one cell to another, and the Ca(i)(2+) response patterns evolve from one type to another as a Ca(i)(2+) wave propagates. In addition, the spatial patterns of Ca(i)(2+) wave propagation depend on whether IP(3), ATP, or both are mediating messengers. Finally, two different geometries that reflect the in vivo and in vitro configuration of astrocytic networks also yield distinct intracellular and extracellular kinetic patterns. The simulation results as well as the linear stability analysis of the model lead to the conclusion that Ca(i)(2+) waves in astrocyte networks are probably mediated by both intercellular IP(3) transport and nonregenerative (only the glutamate-stimulated cell releases ATP) or partially regenerative extracellular ATP signaling.

One of the most dominant methods cells use for a large class of cellular processes is reaction (or binding) diffusion kinetics, which are controlled by kinetic constants such as diffusion coefficients and on/off binding rate constants. Fluorescence recovery after photobleaching (FRAP) can be used to determine these kinetic constants in living cells. While an analytic expression for FRAP formulae for pure diffusion has been available for some time, an analytic FRAP formula for the binding diffusion model has not been reported yet. Here, we present an analytic FRAP formula for the binding diffusion model in an explicit form allowing for diffusion of the bound complex for either a uniform circle laser profile or a Gaussian laser profile.