Metabolite Imager: customized spatial analysis of metabolite distributions in mass spectrometry imaging

Mass spectrometry (MS) is currently the most utilized analytical instrument for evaluating the metabolite composition of a biological sample at both the qualitative and quantitative level. The exponential growth of raw data generated through increasingly versatile mass spectrometers requires sophisticated algorithms to process and visualize the raw data to address biological questions. The structural and quantitative diversity of a single species’ metabolome (e.g. all metabolite species) under different experimental conditions itself forms a very large and complex dataset to analyze. We have developed a free, Java-based metabolomics application “Metabolite Imager” (www.metaboliteimager.com) that enables customized analysis and visualization of the metabolite distributions in tissues acquired through MS-based imaging approaches. Metabolite Imager algorithms perform customized targeted searching of metabolites through user-defined and publicly-available databases enabling the analysis of spatial distributions of large metabolite numbers in tissue sections. Metabolite Imager’s automated, two-dimensional image generator has several customizable features for producing high-resolution images. Additional Metabolite Imager algorithms support identifying targeted and unknown detected metabolites in selected tissue regions using spatially-based enrichment analysis that could impact metabolic engineering strategies. Co-localization algorithms of metabolites and selected ions by m/z enable analysis of precursor-product relationships in situ that will be important for expanding the biological context of metabolic pathways.     

PECAM-1 isoform-specific activation of MAPK/ERKs and small GTPases: implications in inflammation and angiogenesis

Platelet-endothelial cell adhesion molecule-1 (PECAM-1/CD31) is expressed on the surface of endothelial cells (EC) and leukocytes. PECAM-1 plays an important role in endothelial-leukocyte and endothelial-endothelial cell-cell interactions. The anti-PECAM-1 antibody-mediated blockade of these interactions inhibits transendothelial migration (TEM) of leukocytes and angiogenesis. PECAM-1 may accommodate these processes through the regulation of cell adhesive and migratory mechanisms. How PECAM-1 regulates these dynamic processes remain unknown. Here we show that PECAM-1 transduces outside-in signals, which activate MAPK/ERKs and small GTPases. This occurs through PECAM-1-mediated formation of intracellular-signaling complexes, Shc/Grb2/SOS1 and/or Crkl/C3G, which is initiated by PECAM-1 engagement on the surface of leukocytes and/or EC. Src, SHP2, and alternative PECAM-1 pre-mRNA splicing play a regulatory role in these signaling events. Our findings reveal that PECAM-1 engagement on the cell surface can transduce "outside-in" signals and activate MAPK/ERKs and small GTPases, impacting both cadherin-mediated cell-cell and integrin-mediated cell-matrix interactions. Thus, we propose PECAM-1 is an important mediator of vascular barrier and regulator of leukocyte and EC adhesion and migration.     

Nonhomogeneous spatial distributions of populations

Summary Spatial inhomogeneities such as nonconstant population densities usually will be attributed to random effects or to an inhomogeneous substrate. Such an explanation may be incorrect since from certain chemical reactions it is known that the interaction of species together with diffusion may generate nonhomogeneous spatial structures. However, the effect of boundary conditions has been so far neglected. In this paper nonlinear and linear interaction-diffusion models are investigated under various side-conditions by analytic methods and by computer simulations. A remarkable fact, as compared with earlier results in the field, is the example of an interaction-diffusion process which in the whole space has only the constant as a stable limit distribution, whereas the introduction of a side condition, e.g. a population reservoir or a barrier, leads to standing spatial population waves.     

Towards an integrated computational tool for spatial analysis in macroecology and biogeography

Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of sam 's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/ ).     

Measurement of non-randomness in spatial distributions

The measurement of departure from randomness in spatial distributions has widespread application in ecological work. Several “indices of non-randomness” are compared with regard to their dependence on sample number, sample size and density. Criteria for the best choice of index for specific situations are discussed. A new coefficient C_x is proposed for use with positively contagious distributions and tests of significance are given. When C_x and another index (S²/m−1) are used for positive and negative contagion respectively, values ranging from −1 through 0 (random) to +1 are obtained, regardless of sample number, sample size or density.     

A computational approach to discovering the functions of bacterial phytochromes by analysis of homolog distributions

Background  

Phytochromes are photoreceptors, discovered in plants, that control a wide variety of developmental processes. They have also been found in bacteria and fungi, but for many species their biological role remains obscure. This work concentrates on the phytochrome system of Agrobacterium tumefaciens, a non-photosynthetic soil bacterium with two phytochromes. To identify proteins that might share common functions with phytochromes, a co-distribution analysis was performed on the basis of protein sequences from 138 bacteria.     

Contributions of satellite remote sensing to analysis of spatial variability in zooplankton distributions

Zooplankton populations in Lake Tahoe and other large lakes often exhibit considerable variability due to changing predator assemblages and interactions between littoral and pelagial regions. Wind-induced advection of Emerald Bay populations of Bosmina longirostris into Lake Tahoe was hypothesized to be the mechanism for Bosmina's reappearance in the main body of that lake in late 1978 following an absence of more than three years (Threlkeld 1981).Landsat satellite imagery is examined here to determine if a significant spatial-temporal interaction in water quality in Emerald Bay and Lake Tahoe consistent with this hypothesis occurred during the period when Bosmina reappeared. Densitometer measurements of Landsat MSS band 4 and 5 images were compared by ANOVA; significant station-season interactions were detected, and enlarged Landsat film images revealed plumes, streaks and other surface features which may have been associated with the hypothesized mixing events in Lake Tahoe. A major limitation of this method is the need for pre-flyover water truth data for interpretation of detectable surface features.     

Cluster size distributions: signatures of self-organization in spatial ecologies

Three different lattice-based models for antagonistic ecological interactions, both nonlinear and stochastic, exhibit similar power-law scalings in the geometry of clusters. Specifically, cluster size distributions and perimeter-area curves follow power-law scalings. In the coexistence regime, these patterns are robust: their exponents, and therefore the associated Korcak exponent characterizing patchiness, depend only weakly on the parameters of the systems. These distributions, in particular the values of their exponents, are close to those reported in the literature for systems associated with self-organized criticality (SOC) such as forest-fire models; however, the typical assumptions of SOC need not apply. Our results demonstrate that power-law scalings in cluster size distributions are not restricted to systems for antagonistic interactions in which a clear separation of time-scales holds. The patterns are characteristic of processes of growth and inhibition in space, such as those in predator-prey and disturbance-recovery dynamics. Inversions of these patterns, that is, scalings with a positive slope as described for plankton distributions, would therefore require spatial forcing by environmental variability.     

Strategy and software for the statistical spatial analysis of 3D intracellular distributions

The localization of proteins in specific domains or compartments in the 3D cellular space is essential for many fundamental processes in eukaryotic cells. Deciphering spatial organization principles within cells is a challenging task, in particular because of the large morphological variations between individual cells. We present here an approach for normalizing variations in cell morphology and for statistically analyzing spatial distributions of intracellular compartments from collections of 3D images. The method relies on the processing and analysis of 3D geometrical models that are generated from image stacks and that are used to build representations at progressively increasing levels of integration, ultimately revealing statistical significant traits of spatial distributions. To make this methodology widely available to end‐users, we implemented our algorithmic pipeline into a user‐friendly, multi‐platform, and freely available software. To validate our approach, we generated 3D statistical maps of endomembrane compartments at subcellular resolution within an average epidermal root cell from collections of image stacks. This revealed unsuspected polar distribution patterns of organelles that were not detectable in individual images. By reversing the classical ‘measure‐then‐average’ paradigm, one major benefit of the proposed strategy is the production and display of statistical 3D representations of spatial organizations, thus fully preserving the spatial dimension of image data and at the same time allowing their integration over individual observations. The approach and software are generic and should be of general interest for experimental and modeling studies of spatial organizations at multiple scales (subcellular, cellular, tissular) in biological systems.     

Dimerization of VEGF receptors and implications for signal transduction: a computational study

Vascular endothelial growth factor (VEGF) is a potent cytokine involved in the induction of neovascularization. Secreted as a cysteine-linked dimer, it has two binding sites at opposite poles through which it may bind VEGF receptors (VEGFRs), receptor tyrosine kinases found on the surface of endothelial and other cells. The binding of a VEGF molecule to two VEGFR molecules induces transphosphorylation of the intracellular domains of the receptors, leading to signal transduction. The dominant mechanism of receptor dimerization is not clear: the receptors may be present in an inactive pre-dimerized form, VEGF binding first to one of the receptors, the second receptor then ideally located for dimerization; or VEGF may bind receptor monomers on the cell surface, which then diffuse and bind to available unligated receptor monomers to complete the activation. Both processes take place and one or other may dominate on different cell types. We demonstrate the impact of dimerization mechanism on the binding of VEGF to the cell surface and on the formation of active signaling receptor complexes. We describe two methods to determine which process dominates, based on binding and phosphorylation assays. The presence of two VEGF receptor populations, VEGFR1 and VEGFR2, can result in receptor heterodimer formation. Our simulations predict that heterodimers will comprise 10-50% of the active, signaling VEGF receptor complexes, and that heterodimers will form at the expense of homodimers of VEGFR1 when VEGFR2 populations are larger. These results have significant implications for VEGF signal transduction and interpretation of experimental studies. These results may be applicable to other ligand-receptor pairs, in particular PDGF.     

Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales

Abstract. Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alternative methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive models and classification tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better accuracy than GLM with quadratic and polynomial terms. This is probably because distributions at finer scales represent a linear sub‐sample of entire realized niches of species. In contrast to GLM, the performance of GAM was constant across scales being more data‐oriented. The predictive accuracy of GAM was always at least equal to other techniques, suggesting that this modelling approach is more robust to variations of scale because it can deal with any response shape.     

The spatial distributions of randomly coiling polynucleotides

Statistical mechanical averages of vectors and tensors characterizing the allowed configurations of randomly coiling polynucleotides have been calculated for chains of 2⁰–2¹⁰ repeating units. Specifically, the persistence vector p = 〈 r 〉 has been obtained as a function of chain length. Configurational averages of the Cartesian tensors formed from the displacement vector ρ = r – p have been computed up to and including the tensor of seventh rank. From these tensors the three-dimensional spatial distributions of end-to-end vectors have been constructed to provide comprehensive pictures of the directional tendencies of the randomly coiling polynucleotide. The elements of the third and fourth moment tensors, however, give sufficient information to represent accurately the qualitative features of the spatial distributions. For long chains, more than 2⁶ (64) repeating units, the spatial distributions assume spherically symmetric shapes that can be adequately characterized by one-dimensional radial distribution functions. These radial distribution functions agree well with the radial distributions calculated from Monte Carlo samples containing more than 5000 chains. The constraints of fixed bond lengths, fixed bond angles, and hindered internal rotations severely distort the spatial distributions of short polynucleotide chains to mushroom-shaped volumes. These skewed distributions provide information useful to the analysis of small, single-stranded loops, bulges, and circles. The formation of these structures requires the termini of the polynucleotides to lie within specifically delineated volumes with respect to coordinate systems affixed to the first bonds of the chains. The extent to which these loop closure volumes overlap the three-dimensional spatial distributions provides estimates of loop formation that are much more reliable than earlier studies based upon the radial distribution function.     

Vector-based RNAi,a novel tool for isoform-specific knock-down of VEGF and anti-angiogenesis gene therapy of cancer

Vascular endothelial growth factor (VEGF) carries out multifaceted functions in tumor development, and it exists as at least five isoforms with distinct biologic activities and clinical implications. Several strategies have been developed to block VEGF for cancer therapy; however, the approach to target-specific VEGF isoform(s) has not been explored to date. In the present study, we show that DNA vector-based RNA interference (RNAi), in which RNAi sequences targeting murine VEGF isoforms are inserted downstream of an RNA polymerase III promoter, has potential applications in isoform-specific "knock-down" of VEGF. Large molecular weight VEGF isoforms were specifically reduced in vitro in the presence of isoform-specific RNAi constructs. Additionally, H1 promoter may be superior to U6 promoter when used for vector-based RNAi of VEGF isoforms. This strategy provides a novel tool to study the function of various VEGF isoforms and may contribute to VEGF isoform-specific treatment in cancer.     

Mechanisms of coronary angiogenesis in response to stretch: role of VEGF and TGF-beta

To test the hypotheses that cyclic stretch of 1) cardiac myocytes produces factors that trigger angiogenic events in coronary microvascular endothelial cells (CMEC) and 2) CMEC enhances the expression of growth factors, cardiac myocytes and CMEC were subjected to cyclic stretch in a Flexercell Strain Unit. Vascular endothelial growth factor (VEGF) but not basic fibroblast growth factor mRNA and protein levels increased approximately twofold in myocytes after 1 h of stretch. CMEC DNA synthesis increased approximately twofold when conditioned medium from stretched myocytes or VEGF protein was added, and addition of VEGF neutralizing antibody blocked the increase. CMEC migration and tube formation increased with the addition of conditioned media but were markedly attenuated by VEGF neutralizing antibody. Myocyte transforming growth factor-beta [corrected] (TGF-beta) increased 2.5-fold after 1 h of stretch, and the addition of TGF-beta neutralizing antibodies inhibited the stretch-induced upregulation of VEGF. Stretch of CMEC increased VEGF mRNA in these cells (determined by Northern blot and RT-PCR) and increased the levels of VEGF protein (determined by ELISA analysis) in the conditioned media. Therefore, cyclic stretch of cardiac myocytes and CMEC appears to be an important primary stimulus for coronary angiogenesis through both paracrine and autocrine VEGF pathways. These data indicate that 1) CMEC DNA synthesis, migration, and tube formation are increased in response to VEGF secreted from stretched cardiac myocytes; 2) VEGF in CMEC subjected to stretch is upregulated and secreted; and 3) TGF-beta signaling may regulate VEGF expression in cardiac myocytes.     

VEGF promotes tumorigenesis and angiogenesis of human glioblastoma stem cells

There is increasing evidence for the presence of cancer stem cells (CSCs) in malignant brain tumors, and these CSCs may play a pivotal role in tumor initiation, growth, and recurrence. Vascular endothelial growth factor (VEGF) promotes the proliferation of vascular endothelial cells (VECs) and the neurogenesis of neural stem cells. Using CSCs derived from human glioblastomas and a retrovirus expressing VEGF, we examined the effects of VEGF on the properties of CSCs in vitro and in vivo. Although VEGF did not affect the property of CSCs in vitro, the injection of mouse brains with VEGF-expressing CSCs led to the massive expansion of vascular-rich GBM, tumor-associated hemorrhage, and high morbidity, suggesting that VEGF promoted tumorigenesis via angiogenesis. These results revealed that VEGF induced the proliferation of VEC in the vascular-rich tumor environment, the so-called stem cell niche.