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The co-occurrence of species and the co-diversity of sites in neutral models of biodiversity.
PUBLISHED: 01-01-2013
Patterns of co-occurrence of species are widely used to assess the fit of ecological neutral models to empirical patterns. The mathematically equivalent patterns of co-diversity of sites, in contrast, have been considered only indirectly and analyses normally are focused on the spatial distribution of species richness, rather than on the patterns of species sharing. Here we use two analytical tools (range-diversity plots and rank plots) to assess the predictions of simple neutral models in relation to patterns of co-occurrence and co-diversity. Whereas a fully stochastic null model predicts zero average among species and among sites, neutral models generate systems with low levels of covariance among species and high levels of positive covariance among sites. These patterns vary with different combinations of dispersal and speciation rates, but are always linked to the shape, symmetry, and spread of the range-size and species-richness frequency distributions. Non-homogeneous patterns of diversity and distribution arise in neutral models because of the spatial arrangement of sites and their concomitant similarity, which is reflected also in the spread of the range-size frequency distribution. The nearly null covariance among species, in contrast, implies low variance in species richness of sites and very slim frequency distributions. In real world assemblages of Mexican volant and non-volant mammals, patterns of range-size and species-richness frequency distribution are similar to those generated by neutral models. However, when the comparison includes the covariance both for species (co-occurrence) and for sites (co-diversity), empirical patterns differ significantly from the predictions of neutral models. Because of the mathematical links between the covariance in the distribution of species and the variance of species-richness values and between the covariance in species sharing among sites and the variance of range-size values, a full understanding of patterns of diversity calls for the simultaneous analysis of co-occurrence and co-diversity.
Authors: Angela J. Brandt, Gaston A. del Pino, Jean H. Burns.
Published: 03-13-2014
Coexistence theory has often treated environmental heterogeneity as being independent of the community composition; however biotic feedbacks such as plant-soil feedbacks (PSF) have large effects on plant performance, and create environmental heterogeneity that depends on the community composition. Understanding the importance of PSF for plant community assembly necessitates understanding of the role of heterogeneity in PSF, in addition to mean PSF effects. Here, we describe a protocol for manipulating plant-induced soil heterogeneity. Two example experiments are presented: (1) a field experiment with a 6-patch grid of soils to measure plant population responses and (2) a greenhouse experiment with 2-patch soils to measure individual plant responses. Soils can be collected from the zone of root influence (soils from the rhizosphere and directly adjacent to the rhizosphere) of plants in the field from conspecific and heterospecific plant species. Replicate collections are used to avoid pseudoreplicating soil samples. These soils are then placed into separate patches for heterogeneous treatments or mixed for a homogenized treatment. Care should be taken to ensure that heterogeneous and homogenized treatments experience the same degree of soil disturbance. Plants can then be placed in these soil treatments to determine the effect of plant-induced soil heterogeneity on plant performance. We demonstrate that plant-induced heterogeneity results in different outcomes than predicted by traditional coexistence models, perhaps because of the dynamic nature of these feedbacks. Theory that incorporates environmental heterogeneity influenced by the assembling community and additional empirical work is needed to determine when heterogeneity intrinsic to the assembling community will result in different assembly outcomes compared with heterogeneity extrinsic to the community composition.
24 Related JoVE Articles!
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Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture
Authors: Abraham Kim, German Kilimnik, Charles Guo, Joshua Sung, Junghyo Jo, Vipul Periwal, Piotr Witkowski, Philip Dilorio, Manami Hara.
Institutions: University of Chicago, National Institutes of Health, University of Chicago, University of Massachusetts.
The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.
Cellular Biology, Issue 49, beta-cells, islets, large-scale analysis, pancreas
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Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
Authors: R. Craig Stillwell, Ian Dworkin, Alexander W. Shingleton, W. Anthony Frankino.
Institutions: University of Houston, Michigan State University.
The scaling of body parts is a central feature of animal morphology1-7. Within species, morphological traits need to be correctly proportioned to the body for the organism to function; larger individuals typically have larger body parts and smaller individuals generally have smaller body parts, such that overall body shape is maintained across a range of adult body sizes. The requirement for correct proportions means that individuals within species usually exhibit low variation in relative trait size. In contrast, relative trait size can vary dramatically among species and is a primary mechanism by which morphological diversity is produced. Over a century of comparative work has established these intra- and interspecific patterns3,4. Perhaps the most widely used approach to describe this variation is to calculate the scaling relationship between the size of two morphological traits using the allometric equation y=bxα, where x and y are the size of the two traits, such as organ and body size8,9. This equation describes the within-group (e.g., species, population) scaling relationship between two traits as both vary in size. Log-transformation of this equation produces a simple linear equation, log(y) = log(b) + αlog(x) and log-log plots of the size of different traits among individuals of the same species typically reveal linear scaling with an intercept of log(b) and a slope of α, called the 'allometric coefficient'9,10. Morphological variation among groups is described by differences in scaling relationship intercepts or slopes for a given trait pair. Consequently, variation in the parameters of the allometric equation (b and α) elegantly describes the shape variation captured in the relationship between organ and body size within and among biological groups (see 11,12). Not all traits scale linearly with each other or with body size (e.g., 13,14) Hence, morphological scaling relationships are most informative when the data are taken from the full range of trait sizes. Here we describe how simple experimental manipulation of diet can be used to produce the full range of body size in insects. This permits an estimation of the full scaling relationship for any given pair of traits, allowing a complete description of how shape covaries with size and a robust comparison of scaling relationship parameters among biological groups. Although we focus on Drosophila, our methodology should be applicable to nearly any fully metamorphic insect.
Developmental Biology, Issue 56, Drosophila, allometry, morphology, body size, scaling, insect
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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
Authors: Ashwin Prakash, Jason Bechtel, Alexei Fedorov.
Institutions: University of Toledo Health Science Campus.
Non-coding genomic regions in complex eukaryotes, including intergenic areas, introns, and untranslated segments of exons, are profoundly non-random in their nucleotide composition and consist of a complex mosaic of sequence patterns. These patterns include so-called Mid-Range Inhomogeneity (MRI) regions -- sequences 30-10000 nucleotides in length that are enriched by a particular base or combination of bases (e.g. (G+T)-rich, purine-rich, etc.). MRI regions are associated with unusual (non-B-form) DNA structures that are often involved in regulation of gene expression, recombination, and other genetic processes (Fedorova & Fedorov 2010). The existence of a strong fixation bias within MRI regions against mutations that tend to reduce their sequence inhomogeneity additionally supports the functionality and importance of these genomic sequences (Prakash et al. 2009). Here we demonstrate a freely available Internet resource -- the Genomic MRI program package -- designed for computational analysis of genomic sequences in order to find and characterize various MRI patterns within them (Bechtel et al. 2008). This package also allows generation of randomized sequences with various properties and level of correspondence to the natural input DNA sequences. The main goal of this resource is to facilitate examination of vast regions of non-coding DNA that are still scarcely investigated and await thorough exploration and recognition.
Genetics, Issue 51, bioinformatics, computational biology, genomics, non-randomness, signals, gene regulation, DNA conformation
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Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction
Authors: Jenny Magnes, Kathleen Susman, Rebecca Eells.
Institutions: Vassar College, Vassar College.
Soil and aquatic microscopic organisms live and behave in a complex three-dimensional environment. Most studies of microscopic organism behavior, in contrast, have been conducted using microscope-based approaches, which limit the movement and behavior to a narrow, nearly two-dimensional focal field.1 We present a novel analytical approach that provides real-time analysis of freely swimming C. elegans in a cuvette without dependence on microscope-based equipment. This approach consists of tracking the temporal periodicity of diffraction patterns generated by directing laser light through the cuvette. We measure oscillation frequencies for freely swimming nematodes. Analysis of the far-field diffraction patterns reveals clues about the waveforms of the nematodes. Diffraction is the process of light bending around an object. In this case light is diffracted by the organisms. The light waves interfere and can form a diffraction pattern. A far-field, or Fraunhofer, diffraction pattern is formed if the screen-to-object distance is much larger than the diffracting object. In this case, the diffraction pattern can be calculated (modeled) using a Fourier transform.2 C. elegans are free-living soil-dwelling nematodes that navigate in three dimensions. They move both on a solid matrix like soil or agar in a sinusoidal locomotory pattern called crawling and in liquid in a different pattern called swimming.3 The roles played by sensory information provided by mechanosensory, chemosensory, and thermosensory cells that govern plastic changes in locomotory patterns and switches in patterns are only beginning to be elucidated.4 We describe an optical approach to measuring nematode locomotion in three dimensions that does not require a microscope and will enable us to begin to explore the complexities of nematode locomotion under different conditions.
Bioengineering, Issue 68, Caenorhabditis elegans, C. elegans, diffraction, video analysis, freely swimming, autocorrelation, laser, locomotion
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Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology
Authors: Andreas Florian Haas, Ben Knowles, Yan Wei Lim, Tracey McDole Somera, Linda Wegley Kelly, Mark Hatay, Forest Rohwer.
Institutions: San Diego State University, University of California San Diego.
Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.
Environmental Sciences, Issue 93, dissolved organic carbon, particulate organic matter, nutrients, DAPI, SYBR, microbial metagenomics, viral metagenomics, marine environment
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Improved In-gel Reductive β-Elimination for Comprehensive O-linked and Sulfo-glycomics by Mass Spectrometry
Authors: David B. Nix, Tadahiro Kumagai, Toshihiko Katoh, Michael Tiemeyer, Kazuhiro Aoki.
Institutions: University of Georgia, University of Georgia, Ishikawa Prefectural University.
Separation of proteins by SDS-PAGE followed by in-gel proteolytic digestion of resolved protein bands has produced high-resolution proteomic analysis of biological samples. Similar approaches, that would allow in-depth analysis of the glycans carried by glycoproteins resolved by SDS-PAGE, require special considerations in order to maximize recovery and sensitivity when using mass spectrometry (MS) as the detection method. A major hurdle to be overcome in achieving high-quality data is the removal of gel-derived contaminants that interfere with MS analysis. The sample workflow presented here is robust, efficient, and eliminates the need for in-line HPLC clean-up prior to MS. Gel pieces containing target proteins are washed in acetonitrile, water, and ethyl acetate to remove contaminants, including polymeric acrylamide fragments. O-linked glycans are released from target proteins by in-gel reductive β-elimination and recovered through robust, simple clean-up procedures. An advantage of this workflow is that it improves sensitivity for detecting and characterizing sulfated glycans. These procedures produce an efficient separation of sulfated permethylated glycans from non-sulfated (sialylated and neutral) permethylated glycans by a rapid phase-partition prior to MS analysis, and thereby enhance glycomic and sulfoglycomic analyses of glycoproteins resolved by SDS-PAGE.
Chemistry, Issue 93, glycoprotein, glycosylation, in-gel reductive β-elimination, O-linked glycan, sulfated glycan, mass spectrometry, protein ID, SDS-PAGE, glycomics, sulfoglycomics
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Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria
Authors: Jeremy C. Henderson, John P. O'Brien, Jennifer S. Brodbelt, M. Stephen Trent.
Institutions: The University of Texas at Austin, The University of Texas at Austin, The University of Texas at Austin.
Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS can be subdivided into three domains: the distal O-polysaccharide, a core oligosaccharide, and the lipid A domain consisting of a lipid A molecular species and 3-deoxy-D-manno-oct-2-ulosonic acid residues (Kdo). The lipid A domain is the only component essential for bacterial cell survival. Following its synthesis, lipid A is chemically modified in response to environmental stresses such as pH or temperature, to promote resistance to antibiotic compounds, and to evade recognition by mediators of the host innate immune response. The following protocol details the small- and large-scale isolation of lipid A from gram-negative bacteria. Isolated material is then chemically characterized by thin layer chromatography (TLC) or mass-spectrometry (MS). In addition to matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, we also describe tandem MS protocols for analyzing lipid A molecular species using electrospray ionization (ESI) coupled to collision induced dissociation (CID) and newly employed ultraviolet photodissociation (UVPD) methods. Our MS protocols allow for unequivocal determination of chemical structure, paramount to characterization of lipid A molecules that contain unique or novel chemical modifications. We also describe the radioisotopic labeling, and subsequent isolation, of lipid A from bacterial cells for analysis by TLC. Relative to MS-based protocols, TLC provides a more economical and rapid characterization method, but cannot be used to unambiguously assign lipid A chemical structures without the use of standards of known chemical structure. Over the last two decades isolation and characterization of lipid A has led to numerous exciting discoveries that have improved our understanding of the physiology of gram-negative bacteria, mechanisms of antibiotic resistance, the human innate immune response, and have provided many new targets in the development of antibacterial compounds.
Chemistry, Issue 79, Membrane Lipids, Toll-Like Receptors, Endotoxins, Glycolipids, Lipopolysaccharides, Lipid A, Microbiology, Lipids, lipid A, Bligh-Dyer, thin layer chromatography (TLC), lipopolysaccharide, mass spectrometry, Collision Induced Dissociation (CID), Photodissociation (PD)
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Flying Insect Detection and Classification with Inexpensive Sensors
Authors: Yanping Chen, Adena Why, Gustavo Batista, Agenor Mafra-Neto, Eamonn Keogh.
Institutions: University of California, Riverside, University of California, Riverside, University of São Paulo - USP, ISCA Technologies.
An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.
Bioengineering, Issue 92, flying insect detection, automatic insect classification, pseudo-acoustic optical sensors, Bayesian classification framework, flight sound, circadian rhythm
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Discovery of New Intracellular Pathogens by Amoebal Coculture and Amoebal Enrichment Approaches
Authors: Nicolas Jacquier, Sébastien Aeby, Julia Lienard, Gilbert Greub.
Institutions: University Hospital Center and University of Lausanne.
Intracellular pathogens such as legionella, mycobacteria and Chlamydia-like organisms are difficult to isolate because they often grow poorly or not at all on selective media that are usually used to cultivate bacteria. For this reason, many of these pathogens were discovered only recently or following important outbreaks. These pathogens are often associated with amoebae, which serve as host-cell and allow the survival and growth of the bacteria. We intend here to provide a demonstration of two techniques that allow isolation and characterization of intracellular pathogens present in clinical or environmental samples: the amoebal coculture and the amoebal enrichment. Amoebal coculture allows recovery of intracellular bacteria by inoculating the investigated sample onto an amoebal lawn that can be infected and lysed by the intracellular bacteria present in the sample. Amoebal enrichment allows recovery of amoebae present in a clinical or environmental sample. This can lead to discovery of new amoebal species but also of new intracellular bacteria growing specifically in these amoebae. Together, these two techniques help to discover new intracellular bacteria able to grow in amoebae. Because of their ability to infect amoebae and resist phagocytosis, these intracellular bacteria might also escape phagocytosis by macrophages and thus, be pathogenic for higher eukaryotes.
Immunology, Issue 80, Environmental Microbiology, Soil Microbiology, Water Microbiology, Amoebae, microorganisms, coculture, obligate intracellular bacteria
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Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster
Authors: Pietro Laneve, Angela Giangrande.
Institutions: Institut de Génétique et de Biologie Moléculaire et Cellulaire, Istituto Italiano di Tecnologia.
The last decades have witnessed the explosion of scientific interest around gene expression control mechanisms at the RNA level. This branch of molecular biology has been greatly fueled by the discovery of noncoding RNAs as major players in post-transcriptional regulation. Such a revolutionary perspective has been accompanied and triggered by the development of powerful technologies for profiling short RNAs expression, both at the high-throughput level (genome-wide identification) or as single-candidate analysis (steady state accumulation of specific species). Although several state-of-art strategies are currently available for dosing or visualizing such fleeing molecules, Northern Blot assay remains the eligible approach in molecular biology for immediate and accurate evaluation of RNA expression. It represents a first step toward the application of more sophisticated, costly technologies and, in many cases, remains a preferential method to easily gain insights into RNA biology. Here we overview an efficient protocol (Enhanced Northern Blot) for detecting weakly expressed microRNAs (or other small regulatory RNA species) from Drosophila melanogaster whole embryos, manually dissected larval/adult tissues or in vitro cultured cells. A very limited amount of RNA is required and the use of material from flow cytometry-isolated cells can be also envisaged.
Molecular Biology, Issue 90, Northern blotting, Noncoding RNAs, microRNAs, rasiRNA, Gene expression, Gcm/Glide, Drosophila melanogaster
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Quantitative Analysis and Characterization of Atherosclerotic Lesions in the Murine Aortic Sinus
Authors: Daniel E. Venegas-Pino, Nicole Banko, Mohammed I. Khan, Yuanyuan Shi, Geoff H. Werstuck.
Institutions: McMaster University, McMaster University.
Atherosclerosis is a disease of the large arteries and a major underlying cause of myocardial infarction and stroke. Several different mouse models have been developed to facilitate the study of the molecular and cellular pathophysiology of this disease. In this manuscript we describe specific techniques for the quantification and characterization of atherosclerotic lesions in the murine aortic sinus and ascending aorta. The advantage of this procedure is that it provides an accurate measurement of the cross-sectional area and total volume of the lesion, which can be used to compare atherosclerotic progression across different treatment groups. This is possible through the use of the valve leaflets as an anatomical landmark, together with careful adjustment of the sectioning angle. We also describe basic staining methods that can be used to begin to characterize atherosclerotic progression. These can be further modified to investigate antigens of specific interest to the researcher. The described techniques are generally applicable to a wide variety of existing and newly created dietary and genetically-induced models of atherogenesis.
Medicine, Issue 82, atherosclerosis, atherosclerotic lesion, Mouse Model, aortic sinus, tissue preparation and sectioning, Immunohistochemistry
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High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities
Authors: Colin W. Bell, Barbara E. Fricks, Jennifer D. Rocca, Jessica M. Steinweg, Shawna K. McMahon, Matthew D. Wallenstein.
Institutions: Colorado State University, Oak Ridge National Laboratory, University of Colorado.
Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be assimilated for energy and nutrients. Measuring soil microbial enzyme activity is crucial in understanding soil ecosystem functional dynamics. The general concept of the fluorescence enzyme assay is that synthetic C-, N-, or P-rich substrates bound with a fluorescent dye are added to soil samples. When intact, the labeled substrates do not fluoresce. Enzyme activity is measured as the increase in fluorescence as the fluorescent dyes are cleaved from their substrates, which allows them to fluoresce. Enzyme measurements can be expressed in units of molarity or activity. To perform this assay, soil slurries are prepared by combining soil with a pH buffer. The pH buffer (typically a 50 mM sodium acetate or 50 mM Tris buffer), is chosen for the buffer's particular acid dissociation constant (pKa) to best match the soil sample pH. The soil slurries are inoculated with a nonlimiting amount of fluorescently labeled (i.e. C-, N-, or P-rich) substrate. Using soil slurries in the assay serves to minimize limitations on enzyme and substrate diffusion. Therefore, this assay controls for differences in substrate limitation, diffusion rates, and soil pH conditions; thus detecting potential enzyme activity rates as a function of the difference in enzyme concentrations (per sample). Fluorescence enzyme assays are typically more sensitive than spectrophotometric (i.e. colorimetric) assays, but can suffer from interference caused by impurities and the instability of many fluorescent compounds when exposed to light; so caution is required when handling fluorescent substrates. Likewise, this method only assesses potential enzyme activities under laboratory conditions when substrates are not limiting. Caution should be used when interpreting the data representing cross-site comparisons with differing temperatures or soil types, as in situ soil type and temperature can influence enzyme kinetics.
Environmental Sciences, Issue 81, Ecological and Environmental Phenomena, Environment, Biochemistry, Environmental Microbiology, Soil Microbiology, Ecology, Eukaryota, Archaea, Bacteria, Soil extracellular enzyme activities (EEAs), fluorometric enzyme assays, substrate degradation, 4-methylumbelliferone (MUB), 7-amino-4-methylcoumarin (MUC), enzyme temperature kinetics, soil
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
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Using Coculture to Detect Chemically Mediated Interspecies Interactions
Authors: Elizabeth Anne Shank.
Institutions: University of North Carolina at Chapel Hill .
In nature, bacteria rarely exist in isolation; they are instead surrounded by a diverse array of other microorganisms that alter the local environment by secreting metabolites. These metabolites have the potential to modulate the physiology and differentiation of their microbial neighbors and are likely important factors in the establishment and maintenance of complex microbial communities. We have developed a fluorescence-based coculture screen to identify such chemically mediated microbial interactions. The screen involves combining a fluorescent transcriptional reporter strain with environmental microbes on solid media and allowing the colonies to grow in coculture. The fluorescent transcriptional reporter is designed so that the chosen bacterial strain fluoresces when it is expressing a particular phenotype of interest (i.e. biofilm formation, sporulation, virulence factor production, etc.) Screening is performed under growth conditions where this phenotype is not expressed (and therefore the reporter strain is typically nonfluorescent). When an environmental microbe secretes a metabolite that activates this phenotype, it diffuses through the agar and activates the fluorescent reporter construct. This allows the inducing-metabolite-producing microbe to be detected: they are the nonfluorescent colonies most proximal to the fluorescent colonies. Thus, this screen allows the identification of environmental microbes that produce diffusible metabolites that activate a particular physiological response in a reporter strain. This publication discusses how to: a) select appropriate coculture screening conditions, b) prepare the reporter and environmental microbes for screening, c) perform the coculture screen, d) isolate putative inducing organisms, and e) confirm their activity in a secondary screen. We developed this method to screen for soil organisms that activate biofilm matrix-production in Bacillus subtilis; however, we also discuss considerations for applying this approach to other genetically tractable bacteria.
Microbiology, Issue 80, High-Throughput Screening Assays, Genes, Reporter, Microbial Interactions, Soil Microbiology, Coculture, microbial interactions, screen, fluorescent transcriptional reporters, Bacillus subtilis
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Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae
Authors: Melissa N. Patterson, Patrick H. Maxwell.
Institutions: Rensselaer Polytechnic Institute.
Saccharomyces cerevisiae has been an excellent model system for examining mechanisms and consequences of genome instability. Information gained from this yeast model is relevant to many organisms, including humans, since DNA repair and DNA damage response factors are well conserved across diverse species. However, S. cerevisiae has not yet been used to fully address whether the rate of accumulating mutations changes with increasing replicative (mitotic) age due to technical constraints. For instance, measurements of yeast replicative lifespan through micromanipulation involve very small populations of cells, which prohibit detection of rare mutations. Genetic methods to enrich for mother cells in populations by inducing death of daughter cells have been developed, but population sizes are still limited by the frequency with which random mutations that compromise the selection systems occur. The current protocol takes advantage of magnetic sorting of surface-labeled yeast mother cells to obtain large enough populations of aging mother cells to quantify rare mutations through phenotypic selections. Mutation rates, measured through fluctuation tests, and mutation frequencies are first established for young cells and used to predict the frequency of mutations in mother cells of various replicative ages. Mutation frequencies are then determined for sorted mother cells, and the age of the mother cells is determined using flow cytometry by staining with a fluorescent reagent that detects bud scars formed on their cell surfaces during cell division. Comparison of predicted mutation frequencies based on the number of cell divisions to the frequencies experimentally observed for mother cells of a given replicative age can then identify whether there are age-related changes in the rate of accumulating mutations. Variations of this basic protocol provide the means to investigate the influence of alterations in specific gene functions or specific environmental conditions on mutation accumulation to address mechanisms underlying genome instability during replicative aging.
Microbiology, Issue 92, Aging, mutations, genome instability, Saccharomyces cerevisiae, fluctuation test, magnetic sorting, mother cell, replicative aging
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Cortical Source Analysis of High-Density EEG Recordings in Children
Authors: Joe Bathelt, Helen O'Reilly, Michelle de Haan.
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.  In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis. 
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials 
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Authors: Nikki M. Curthoys, Michael J. Mlodzianoski, Dahan Kim, Samuel T. Hess.
Institutions: University of Maine.
Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.
Basic Protocol, Issue 82, Microscopy, Super-resolution imaging, Multicolor, single molecule, FPALM, Localization microscopy, fluorescent proteins
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Authors: Johannes Felix Buyel, Rainer Fischer.
Institutions: RWTH Aachen University, Fraunhofer Gesellschaft.
Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.
Bioengineering, Issue 83, design of experiments (DoE), transient protein expression, plant-derived biopharmaceuticals, promoter, 5'UTR, fluorescent reporter protein, model building, incubation conditions, monoclonal antibody
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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
Authors: C. R. Gallistel, Fuat Balci, David Freestone, Aaron Kheifets, Adam King.
Institutions: Rutgers University, Koç University, New York University, Fairfield University.
We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.
Behavior, Issue 84, genetics, cognitive mechanisms, behavioral screening, learning, memory, timing
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Basics of Multivariate Analysis in Neuroimaging Data
Authors: Christian Georg Habeck.
Institutions: Columbia University.
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Authors: Viktor Martyanov, Robert H. Gross.
Institutions: Dartmouth College.
SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference1. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data1. In this article, we utilize a web version of SCOPE2 to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs3,4 and has been used in other studies5-8. The three algorithms that comprise SCOPE are BEAM9, which finds non-degenerate motifs (ACCGGT), PRISM10, which finds degenerate motifs (ASCGWT), and SPACER11, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail1,2,9-11.
Genetics, Issue 51, gene regulation, computational biology, algorithm, promoter sequence motif
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Layers of Symbiosis - Visualizing the Termite Hindgut Microbial Community
Authors: Jared Leadbetter.
Institutions: California Institute of Technology - Caltech.
Jared Leadbetter takes us for a nature walk through the diversity of life resident in the termite hindgut - a microenvironment containing 250 different species found nowhere else on Earth. Jared reveals that the symbiosis exhibited by this system is multi-layered and involves not only a relationship between the termite and its gut inhabitants, but also involves a complex web of symbiosis among the gut microbes themselves.
Microbiology, issue 4, microbial community, symbiosis, hindgut
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Electroporation of Mycobacteria
Authors: Renan Goude, Tanya Parish.
Institutions: Barts and the London School of Medicine and Dentistry, Barts and the London School of Medicine and Dentistry.
High efficiency transformation is a major limitation in the study of mycobacteria. The genus Mycobacterium can be difficult to transform; this is mainly caused by the thick and waxy cell wall, but is compounded by the fact that most molecular techniques have been developed for distantly-related species such as Escherichia coli and Bacillus subtilis. In spite of these obstacles, mycobacterial plasmids have been identified and DNA transformation of many mycobacterial species have now been described. The most successful method for introducing DNA into mycobacteria is electroporation. Many parameters contribute to successful transformation; these include the species/strain, the nature of the transforming DNA, the selectable marker used, the growth medium, and the conditions for the electroporation pulse. Optimized methods for the transformation of both slow- and fast-grower are detailed here. Transformation efficiencies for different mycobacterial species and with various selectable markers are reported.
Microbiology, Issue 15, Springer Protocols, Mycobacteria, Electroporation, Bacterial Transformation, Transformation Efficiency, Bacteria, Tuberculosis, M. Smegmatis, Springer Protocols
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.