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An ultra-fast metabolite prediction algorithm.
Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.
Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.
18 Related JoVE Articles!
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Authors: Xiaojing Liu, Zheng Ser, Ahmad A. Cluntun, Samantha J. Mentch, Jason W. Locasale.
Institutions: Cornell University, Cornell University.
Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.  Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.
Chemistry, Issue 87, high-resolution mass spectrometry, metabolomics, positive/negative switching, low mass calibration, Orbitrap
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Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
Authors: Cuong H. Le, Jun Han, Christoph H. Borchers.
Institutions: University of Victoria, University of Victoria.
Mass spectrometry imaging (MSI) determines the spatial localization and distribution patterns of compounds on the surface of a tissue section, mainly using MALDI (matrix assisted laser desorption/ionization)-based analytical techniques. New matrices for small-molecule MSI, which can improve the analysis of low-molecular weight (MW) compounds, are needed. These matrices should provide increased analyte signals while decreasing MALDI background signals. In addition, the use of ultrahigh-resolution instruments, such as Fourier transform ion cyclotron resonance (FTICR) mass spectrometers, has the ability to resolve analyte signals from matrix signals, and this can partially overcome many problems associated with the background originating from the MALDI matrix. The reduction in the intensities of the metastable matrix clusters by FTICR MS can also help to overcome some of the interferences associated with matrix peaks on other instruments. High-resolution instruments such as the FTICR mass spectrometers are advantageous as they can produce distribution patterns of many compounds simultaneously while still providing confidence in chemical identifications. Dithranol (DT; 1,8-dihydroxy-9,10-dihydroanthracen-9-one) has previously been reported as a MALDI matrix for tissue imaging. In this work, a protocol for the use of DT for MALDI imaging of endogenous lipids from the surfaces of mammalian tissue sections, by positive-ion MALDI-MS, on an ultrahigh-resolution hybrid quadrupole FTICR instrument has been provided.
Basic Protocol, Issue 81, eye, molecular imaging, chemistry technique, analytical, mass spectrometry, matrix assisted laser desorption/ionization (MALDI), tandem mass spectrometry, lipid, tissue imaging, bovine lens, dithranol, matrix, FTICR (Fourier Transform Ion Cyclotron Resonance)
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Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy
Authors: R. Craig Everroad, Seiji Yoshida, Yuuri Tsuboi, Yasuhiro Date, Jun Kikuchi, Shigeharu Moriya.
Institutions: RIKEN Advanced Science Institute, Yokohama City University, RIKEN Plant Science Center, Nagoya University.
Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level1. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography–mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra2,3. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment4,5,6. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals7,8,9,10,11,12. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community13,14, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer2. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.
Molecular Biology, Issue 62, environmental metabolomics, metabolic profiling, microbial ecology, plankton, NMR spectroscopy, PCA
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The ITS2 Database
Authors: Benjamin Merget, Christian Koetschan, Thomas Hackl, Frank Förster, Thomas Dandekar, Tobias Müller, Jörg Schultz, Matthias Wolf.
Institutions: University of Würzburg, University of Würzburg.
The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation2-8. The ITS2 Database9 presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank11 accurately reannotated10. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold12 (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling13. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold. The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST14 search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE15,16 and ProfDistS17 for multiple sequence-structure alignment calculation and Neighbor Joining18 tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure. In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.
Genetics, Issue 61, alignment, internal transcribed spacer 2, molecular systematics, secondary structure, ribosomal RNA, phylogenetic tree, homology modeling, phylogeny
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Terahertz Microfluidic Sensing Using a Parallel-plate Waveguide Sensor
Authors: Victoria Astley, Kimberly Reichel, Rajind Mendis, Daniel M. Mittleman.
Institutions: Rice University .
Refractive index (RI) sensing is a powerful noninvasive and label-free sensing technique for the identification, detection and monitoring of microfluidic samples with a wide range of possible sensor designs such as interferometers and resonators 1,2. Most of the existing RI sensing applications focus on biological materials in aqueous solutions in visible and IR frequencies, such as DNA hybridization and genome sequencing. At terahertz frequencies, applications include quality control, monitoring of industrial processes and sensing and detection applications involving nonpolar materials. Several potential designs for refractive index sensors in the terahertz regime exist, including photonic crystal waveguides 3, asymmetric split-ring resonators 4, and photonic band gap structures integrated into parallel-plate waveguides 5. Many of these designs are based on optical resonators such as rings or cavities. The resonant frequencies of these structures are dependent on the refractive index of the material in or around the resonator. By monitoring the shifts in resonant frequency the refractive index of a sample can be accurately measured and this in turn can be used to identify a material, monitor contamination or dilution, etc. The sensor design we use here is based on a simple parallel-plate waveguide 6,7. A rectangular groove machined into one face acts as a resonant cavity (Figures 1 and 2). When terahertz radiation is coupled into the waveguide and propagates in the lowest-order transverse-electric (TE1) mode, the result is a single strong resonant feature with a tunable resonant frequency that is dependent on the geometry of the groove 6,8. This groove can be filled with nonpolar liquid microfluidic samples which cause a shift in the observed resonant frequency that depends on the amount of liquid in the groove and its refractive index 9. Our technique has an advantage over other terahertz techniques in its simplicity, both in fabrication and implementation, since the procedure can be accomplished with standard laboratory equipment without the need for a clean room or any special fabrication or experimental techniques. It can also be easily expanded to multichannel operation by the incorporation of multiple grooves 10. In this video we will describe our complete experimental procedure, from the design of the sensor to the data analysis and determination of the sample refractive index.
Physics, Issue 66, Electrical Engineering, Computer Engineering, Terahertz radiation, sensing, microfluidic, refractive index sensor, waveguide, optical sensing
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Authors: Haipeng Xing, Willey Liao, Yifan Mo, Michael Q. Zhang.
Institutions: Stony Brook University, Cold Spring Harbor Laboratory, University of Texas at Dallas.
ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.
Genetics, Issue 70, Bioinformatics, Genomics, Molecular Biology, Cellular Biology, Immunology, Chromatin immunoprecipitation, ChIP-Seq, histone modifications, segmentation, Bayesian, Hidden Markov Models, epigenetics
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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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FIBS-enabled Noninvasive Metabolic Profiling
Authors: Alireza Behjousiar, Antony Constantinou, Karen M. Polizzi, Cleo Kontoravdi.
Institutions: Imperial College London, Imperial College London.
In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be validated for predictive purposes. Ideally such systems would be low volume, which precludes sampling and destructive analyses when time course data are to be obtained. What is needed is an in situ monitoring tool which can report the necessary information in real-time and noninvasively. An interesting option is the use of fluorescent, protein-based in vivo biological sensors as reporters of intracellular concentrations. One particular class of in vivo biosensors that has found applications in metabolite quantification is based on Förster Resonance Energy Transfer (FRET) between two fluorescent proteins connected by a ligand binding domain. FRET integrated biological sensors (FIBS) are constitutively produced within the cell line, they have fast response times and their spectral characteristics change based on the concentration of metabolite within the cell. In this paper, the method for constructing Chinese hamster ovary (CHO) cell lines that constitutively express a FIBS for glucose and glutamine and calibrating the FIBS in vivo in batch cell culture in order to enable future quantification of intracellular metabolite concentration is described. Data from fed-batch CHO cell cultures demonstrates that the FIBS was able in each case to detect the resulting change in the intracellular concentration. Using the fluorescent signal from the FIBS and the previously constructed calibration curve, the intracellular concentration was accurately determined as confirmed by an independent enzymatic assay.
Bioengineering, Issue 84, metabolite monitoring, in vivo biosensors, in situ monitoring, mammalian cell culture, bioprocess engineering, medium formulation
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
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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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A Protocol for Computer-Based Protein Structure and Function Prediction
Authors: Ambrish Roy, Dong Xu, Jonathan Poisson, Yang Zhang.
Institutions: University of Michigan , University of Kansas.
Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.
Biochemistry, Issue 57, On-line server, I-TASSER, protein structure prediction, function prediction
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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
Authors: Takayuki Tohge, Alisdair R. Fernie.
Institutions: Max-Planck-Institut.
Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.
Plant Biology, Issue 64, Genetics, Bioinformatics, Metabolomics, Plant metabolism, Transcriptome analysis, Functional annotation, Computational biology, Plant biology, Theoretical biology, Spectroscopy and structural analysis
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Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts
Authors: Norbert W. Lutz, Evelyne Béraud, Patrick J. Cozzone.
Institutions: Aix-Marseille Université, Aix-Marseille Université.
Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.
Neuroscience, Issue 91, metabolomics, brain tissue, rodents, neurochemistry, tissue extracts, NMR spectroscopy, quantitative metabolite analysis, cerebral metabolism, metabolic profile
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Authors: James Smadbeck, Meghan B. Peterson, George A. Khoury, Martin S. Taylor, Christodoulos A. Floudas.
Institutions: Princeton University.
The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (, a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
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Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
Authors: Charmion Cruickshank-Quinn, Kevin D. Quinn, Roger Powell, Yanhui Yang, Michael Armstrong, Spencer Mahaffey, Richard Reisdorph, Nichole Reisdorph.
Institutions: National Jewish Health, University of Colorado Denver.
Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital aspect of metabolomics is sample preparation whereby inconsistent techniques generate unreliable results. This technique encompasses protein precipitation, liquid-liquid extraction, and solid-phase extraction as a means of fractionating metabolites into four distinct classes. Improved enrichment of low abundance molecules with a resulting increase in sensitivity is obtained, and ultimately results in more confident identification of molecules. This technique has been applied to plasma, bronchoalveolar lavage fluid, and cerebrospinal fluid samples with volumes as low as 50 µl.  Samples can be used for multiple downstream applications; for example, the pellet resulting from protein precipitation can be stored for later analysis. The supernatant from that step undergoes liquid-liquid extraction using water and strong organic solvent to separate the hydrophilic and hydrophobic compounds. Once fractionated, the hydrophilic layer can be processed for later analysis or discarded if not needed. The hydrophobic fraction is further treated with a series of solvents during three solid-phase extraction steps to separate it into fatty acids, neutral lipids, and phospholipids. This allows the technician the flexibility to choose which class of compounds is preferred for analysis. It also aids in more reliable metabolite identification since some knowledge of chemical class exists.
Bioengineering, Issue 89, plasma, chemistry techniques, analytical, solid phase extraction, mass spectrometry, metabolomics, fluids and secretions, profiling, small molecules, lipids, liquid chromatography, liquid-liquid extraction, cerebrospinal fluid, bronchoalveolar lavage fluid
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MALDI-Mass Spectrometric Imaging for the Investigation of Metabolites in Medicago truncatula Root Nodules
Authors: Erin Gemperline, Lingjun Li.
Institutions: University of Wisconsin- Madison, University of Wisconsin- Madison.
Most techniques used to study small molecules, such as pharmaceutical drugs or endogenous metabolites, employ tissue extracts which require the homogenization of the tissue of interest that could potentially cause changes in the metabolic pathways being studied1. Mass spectrometric imaging (MSI) is a powerful analytical tool that can provide spatial information of analytes within intact slices of biological tissue samples1-5. This technique has been used extensively to study various types of compounds including proteins, peptides, lipids, and small molecules such as endogenous metabolites. With matrix-assisted laser desorption/ionization (MALDI)-MSI, spatial distributions of multiple metabolites can be simultaneously detected. Herein, a method developed specifically for conducting untargeted metabolomics MSI experiments on legume roots and root nodules is presented which could reveal insights into the biological processes taking place. The method presented here shows a typical MSI workflow, from sample preparation to image acquisition, and focuses on the matrix application step, demonstrating several matrix application techniques that are useful for detecting small molecules. Once the MS images are generated, the analysis and identification of metabolites of interest is discussed and demonstrated. The standard workflow presented here can be easily modified for different tissue types, molecular species, and instrumentation.
Basic Protocol, Issue 85, Mass Spectrometric Imaging, Imaging Mass Spectrometry, MALDI, TOF/TOF, Medicago truncatula, Metabolite, Small Molecule, Sublimation, Automatic Sprayer
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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
Authors: Nathaniel W. Snyder, Maya Khezam, Clementina A. Mesaros, Andrew Worth, Ian A. Blair.
Institutions: University of Pennsylvania .
Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.
Biochemistry, Issue 75, Chemistry, Molecular Biology, Cellular Biology, Physiology, Medicine, Pharmacology, Genetics, Genomics, Mass Spectrometry, MS, Metabolism, Metabolomics, untargeted, extraction, lipids, accurate mass, liquid chromatography, ultraperformance liquid chromatography, UPLC, high resolution mass spectrometry, HRMS, spectrometry
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RNA Secondary Structure Prediction Using High-throughput SHAPE
Authors: Sabrina Lusvarghi, Joanna Sztuba-Solinska, Katarzyna J. Purzycka, Jason W. Rausch, Stuart F.J. Le Grice.
Institutions: Frederick National Laboratory for Cancer Research.
Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.
Genetics, Issue 75, Molecular Biology, Biochemistry, Virology, Cancer Biology, Medicine, Genomics, Nucleic Acid Probes, RNA Probes, RNA, High-throughput SHAPE, Capillary electrophoresis, RNA structure, RNA probing, RNA folding, secondary structure, DNA, nucleic acids, electropherogram, synthesis, transcription, high throughput, sequencing
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