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.
22 Related JoVE Articles!
Laser Microdissection Applied to Gene Expression Profiling of Subset of Cells from the Drosophila Wing Disc
Institutions: University of Naples.
Heterogeneous nature of tissues has proven to be a limiting factor in the amount of information that can be generated from biological samples, compromising downstream analyses. Considering the complex and dynamic cellular associations existing within many tissues, in order to recapitulate the in vivo
interactions thorough molecular analysis one must be able to analyze specific cell populations within their native context. Laser-mediated microdissection can achieve this goal, allowing unambiguous identification and successful harvest of cells of interest under direct microscopic visualization while maintaining molecular integrity. We have applied this technology to analyse gene expression within defined areas of the developing Drosophila
wing disc, which represents an advantageous model system to study growth control, cell differentiation and organogenesis. Larval imaginal discs are precociously subdivided into anterior and posterior, dorsal and ventral compartments by lineage restriction boundaries. Making use of the inducible GAL4-UAS binary expression system, each of these compartments can be specifically labelled in transgenic flies expressing an UAS-GFP transgene under the control of the appropriate GAL4-driver construct. In the transgenic discs, gene expression profiling of discrete subsets of cells can precisely be determined after laser-mediated microdissection, using the fluorescent GFP signal to guide laser cut.
Among the variety of downstream applications, we focused on RNA transcript profiling after localised RNA interference (RNAi). With the advent of RNAi technology, GFP labelling can be coupled with localised knockdown of a given gene, allowing to determinate the transcriptional response of a discrete cell population to the specific gene silencing. To validate this approach, we dissected equivalent areas of the disc from the posterior (labelled by GFP expression), and the anterior (unlabelled) compartment upon regional silencing in the P compartment of an otherwise ubiquitously expressed gene. RNA was extracted from microdissected silenced and unsilenced areas and comparative gene expression profiling determined by quantitative real-time RT-PCR. We show that this method can effectively be applied for accurate transcriptomics of subsets of cells within the Drosophila
imaginal discs. Indeed, while massive disc preparation as source of RNA generally assumes cell homogeneity, it is well known that transcriptional expression can vary greatly within these structures in consequence of positional information. Using localized fluorescent GFP signal to guide laser cut, more accurate transcriptional analyses can be performed and profitably applied to disparate applications, including transcript profiling of distinct cell lineages within their native context.
Developmental Biology, Issue 38, Drosophila, Imaginal discs, Laser microdissection, Gene expression, Transcription profiling, Regulatory pathways , in vivo RNAi, GAL4-UAS, GFP labelling, Positional information
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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
Single-cell Profiling of Developing and Mature Retinal Neurons
Institutions: Iowa State University.
Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and gene expression programs for extremely rare cell subsets has been a challenge using standard whole-tissue approaches. Gene expression profiling of individual cells allows for unprecedented access to cell types that comprise only a small percentage of the total tissue1-7
. In addition, this technique can be used to examine the gene expression programs that are transiently expressed in small numbers of cells during dynamic developmental transitions8
This issue of cellular diversity arises repeatedly in the central nervous system (CNS) where neuronal connections can occur between quite diverse cells9
. The exact number of distinct cell types is not precisely known, but it has been estimated that there may be as many as 1000 different types in the cortex itself10
. The function(s) of complex neural circuits may rely on some of the rare neuronal types and the genes they express. By identifying new markers and helping to molecularly classify different neurons, the single-cell approach is particularly useful in the analysis of cell types in the nervous system. It may also help to elucidate mechanisms of neural development by identifying differentially expressed genes and gene pathways during early stages of neuronal progenitor development.
As a simple, easily accessed tissue with considerable neuronal diversity, the vertebrate retina is an excellent model system for studying the processes of cellular development, neuronal differentiation and neuronal diversification. However, as in other parts of the CNS, this cellular diversity can present a problem for determining the genetic pathways that drive retinal progenitors to adopt a specific cell fate, especially given that rod photoreceptors make up the majority of the total retinal cell population11
. Here we report a method for the identification of the transcripts expressed in single retinal cells (Figure 1
). The single-cell profiling technique allows for the assessment of the amount of heterogeneity present within different cellular populations of the retina2,4,5,12
. In addition, this method has revealed a host of new candidate genes that may play role(s) in the cell fate decision-making processes that occur in subsets of retinal progenitor cells8
. With some simple adjustments to the protocol, this technique can be utilized for many different tissues and cell types.
Neuroscience, Issue 62, Single-cells, transcriptomics, gene expression, cell-type markers, retina, neurons, genetics
Setting-up an In Vitro Model of Rat Blood-brain Barrier (BBB): A Focus on BBB Impermeability and Receptor-mediated Transport
Institutions: VECT-HORUS SAS, CNRS, NICN UMR 7259.
The blood brain barrier (BBB) specifically regulates molecular and cellular flux between the blood and the nervous tissue. Our aim was to develop and characterize a highly reproducible rat syngeneic in vitro
model of the BBB using co-cultures of primary rat brain endothelial cells (RBEC) and astrocytes to study receptors involved in transcytosis across the endothelial cell monolayer. Astrocytes were isolated by mechanical dissection following trypsin digestion and were frozen for later co-culture. RBEC were isolated from 5-week-old rat cortices. The brains were cleaned of meninges and white matter, and mechanically dissociated following enzymatic digestion. Thereafter, the tissue homogenate was centrifuged in bovine serum albumin to separate vessel fragments from nervous tissue. The vessel fragments underwent a second enzymatic digestion to free endothelial cells from their extracellular matrix. The remaining contaminating cells such as pericytes were further eliminated by plating the microvessel fragments in puromycin-containing medium. They were then passaged onto filters for co-culture with astrocytes grown on the bottom of the wells. RBEC expressed high levels of tight junction (TJ) proteins such as occludin, claudin-5 and ZO-1 with a typical localization at the cell borders. The transendothelial electrical resistance (TEER) of brain endothelial monolayers, indicating the tightness of TJs reached 300 ohm·cm2
on average. The endothelial permeability coefficients (Pe) for lucifer yellow (LY) was highly reproducible with an average of 0.26 ± 0.11 x 10-3
cm/min. Brain endothelial cells organized in monolayers expressed the efflux transporter P-glycoprotein (P-gp), showed a polarized transport of rhodamine 123, a ligand for P-gp, and showed specific transport of transferrin-Cy3 and DiILDL across the endothelial cell monolayer. In conclusion, we provide a protocol for setting up an in vitro
BBB model that is highly reproducible due to the quality assurance methods, and that is suitable for research on BBB transporters and receptors.
Medicine, Issue 88, rat brain endothelial cells (RBEC), mouse, spinal cord, tight junction (TJ), receptor-mediated transport (RMT), low density lipoprotein (LDL), LDLR, transferrin, TfR, P-glycoprotein (P-gp), transendothelial electrical resistance (TEER),
A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Institutions: Institut Pasteur , Université Sorbonne Paris Cité, Dana Farber Cancer Institute.
Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia
luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps
protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.
Immunology, Issue 77, Genetics, Microbiology, Biochemistry, Molecular Biology, Cellular Biology, Biomedical Engineering, Infection, Cancer Biology, Virology, Medicine, Host-Pathogen Interactions, Host-Pathogen Interactions, Protein-protein interaction, High-throughput screening, Luminescence, Yeast two-hybrid, HT-GPCA, Network, protein, yeast, cell, culture
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Institutions: Joint Unit Hospices de Lyon-bioMérieux, BioMérieux, Hospices Civils de Lyon, Lyon 1 University, BioMérieux, Hospices Civils de Lyon, Hospices Civils de Lyon.
The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1
. ‘How to use PSA' remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2
or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4
. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g.
PCA3 in prostate cancer5,6
and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10
. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1
Medicine, Issue 81, Cancer Biology, Genetics, Molecular Biology, Prostate, Retroviridae, Biomarkers, Pharmacological, Tumor Markers, Biological, Prostatectomy, Microarray Analysis, Gene Expression, Diagnosis, Human Endogenous Retroviruses, HERV, microarray, Transcriptome, prostate cancer, Affymetrix
Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
Institutions: San Francisco State University.
Recently, disordered photonic materials have been suggested as an alternative to periodic crystals for the formation of a complete photonic bandgap (PBG). In this article we will describe the methods for constructing and characterizing macroscopic disordered photonic structures using microwaves. The microwave regime offers the most convenient experimental sample size to build and test PBG media. Easily manipulated dielectric lattice components extend flexibility in building various 2D structures on top of pre-printed plastic templates. Once built, the structures could be quickly modified with point and line defects to make freeform waveguides and filters. Testing is done using a widely available Vector Network Analyzer and pairs of microwave horn antennas. Due to the scale invariance property of electromagnetic fields, the results we obtained in the microwave region can be directly applied to infrared and optical regions. Our approach is simple but delivers exciting new insight into the nature of light and disordered matter interaction.
Our representative results include the first experimental demonstration of the existence of a complete and isotropic PBG in a two-dimensional (2D) hyperuniform disordered dielectric structure. Additionally we demonstrate experimentally the ability of this novel photonic structure to guide electromagnetic waves (EM) through freeform waveguides of arbitrary shape.
Physics, Issue 91, optics and photonics, photonic crystals, photonic bandgap, hyperuniform, disordered media, waveguides
RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
Institutions: Children's Mercy Hospital and Clinics, School of Medicine, University of Missouri-Kansas City.
The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the cell is affected by external signals (e.g.
drug treatment), or how cells differ between a healthy state and a diseased state. With the advent and continuous refinement of next-generation DNA sequencing technology, RNA-sequencing (RNA-seq) has become an increasingly popular method of transcriptome analysis to catalog all species of transcripts, to determine the transcriptional structure of all expressed genes and to quantify the changing expression levels of the total set of transcripts in a given cell, tissue or organism1,2
. RNA-seq is gradually replacing DNA microarrays as a preferred method for transcriptome analysis because it has the advantages of profiling a complete transcriptome, providing a digital type datum (copy number of any transcript) and not relying on any known genomic sequence3
Here, we present a complete and detailed protocol to apply RNA-seq to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is based on our recent published study entitled "RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin,"4
in which we successfully performed the first complete transcriptome analysis of human pulmonary microvascular endothelial cells treated with thrombin using RNA-seq. It yielded unprecedented resources for further experimentation to gain insights into molecular mechanisms underlying thrombin-mediated endothelial dysfunction in the pathogenesis of inflammatory conditions, cancer, diabetes, and coronary heart disease, and provides potential new leads for therapeutic targets to those diseases.
The descriptive text of this protocol is divided into four parts. The first part describes the treatment of human pulmonary microvascular endothelial cells with thrombin and RNA isolation, quality analysis and quantification. The second part describes library construction and sequencing. The third part describes the data analysis. The fourth part describes an RT-PCR validation assay. Representative results of several key steps are displayed. Useful tips or precautions to boost success in key steps are provided in the Discussion section. Although this protocol uses human pulmonary microvascular endothelial cells treated with thrombin, it can be generalized to profile transcriptomes in both mammalian and non-mammalian cells and in tissues treated with different stimuli or inhibitors, or to compare transcriptomes in cells or tissues between a healthy state and a disease state.
Genetics, Issue 72, Molecular Biology, Immunology, Medicine, Genomics, Proteins, RNA-seq, Next Generation DNA Sequencing, Transcriptome, Transcription, Thrombin, Endothelial cells, high-throughput, DNA, genomic DNA, RT-PCR, PCR
Cell Death Associated with Abnormal Mitosis Observed by Confocal Imaging in Live Cancer Cells
Institutions: Sheba Medical Center, Tel-Aviv University, Tel-Aviv University, Tel-Aviv University, Ecole Superieure de Biotechnologie Strasbourg, Tel-Aviv University.
Phenanthrene derivatives acting as potent PARP1 inhibitors prevented the bi-focal clustering of supernumerary centrosomes in multi-centrosomal human cancer cells in mitosis. The phenanthridine PJ-34 was the most potent molecule. Declustering of extra-centrosomes causes mitotic failure and cell death in multi-centrosomal cells. Most solid human cancers have high occurrence of extra-centrosomes. The activity of PJ-34 was documented in real-time by confocal imaging of live human breast cancer MDA-MB-231 cells transfected with vectors encoding for fluorescent γ-tubulin, which is highly abundant in the centrosomes and for fluorescent histone H2b present in the chromosomes. Aberrant chromosomes arrangements and de-clustered γ-tubulin foci representing declustered centrosomes were detected in the transfected MDA-MB-231 cells after treatment with PJ-34. Un-clustered extra-centrosomes in the two spindle poles preceded their cell death. These results linked for the first time the recently detected exclusive cytotoxic activity of PJ-34 in human cancer cells with extra-centrosomes de-clustering in mitosis, and mitotic failure leading to cell death. According to previous findings observed by confocal imaging of fixed cells, PJ-34 exclusively eradicated cancer cells with multi-centrosomes without impairing normal cells undergoing mitosis with two centrosomes and bi-focal spindles. This cytotoxic activity of PJ-34 was not shared by other potent PARP1 inhibitors, and was observed in PARP1 deficient MEF harboring extracentrosomes, suggesting its independency of PARP1 inhibition. Live confocal imaging offered a useful tool for identifying new molecules eradicating cells during mitosis.
Cancer Biology, Issue 78, Medicine, Cellular Biology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Genetics, Neoplastic Processes, Pharmacologic Actions, Live confocal imaging, Extra-centrosomes clustering/de-clustering, Mitotic Catastrophe cell death, PJ-34, myocardial infarction, microscopy, imaging
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
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
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Institutions: University of Ulm.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo
. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls.
DTI data analysis is performed in a variate fashion, i.e.
voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e.
differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
Medicine, Issue 77, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Neurodegenerative Diseases, nuclear magnetic resonance, NMR, MR, MRI, diffusion tensor imaging, fiber tracking, group level comparison, neurodegenerative diseases, brain, imaging, clinical techniques
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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
Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
Institutions: United States Department of Agriculture-Agricultural Research Service, University of California, Davis, University of Chile, University of California, Davis, URS Corporation Australia Pty. Ltd..
Advanced micrometeorological methods have become increasingly important in soil, crop, and environmental sciences. For many scientists without formal training in atmospheric science, these techniques are relatively inaccessible. Surface renewal and other flux measurement methods require an understanding of boundary layer meteorology and extensive training in instrumentation and multiple data management programs. To improve accessibility of these techniques, we describe the underlying theory of surface renewal measurements, demonstrate how to set up a field station for surface renewal with eddy covariance calibration, and utilize our open-source turnkey data logger program to perform flux data acquisition and processing. The new turnkey program returns to the user a simple data table with the corrected fluxes and quality control parameters, and eliminates the need for researchers to shuttle between multiple processing programs to obtain the final flux data. An example of data generated from these measurements demonstrates how crop water use is measured with this technique. The output information is useful to growers for making irrigation decisions in a variety of agricultural ecosystems. These stations are currently deployed in numerous field experiments by researchers in our group and the California Department of Water Resources in the following crops: rice, wine and raisin grape vineyards, alfalfa, almond, walnut, peach, lemon, avocado, and corn.
Environmental Sciences, Issue 82, Conservation of Natural Resources, Engineering, Agriculture, plants, energy balance, irrigated agriculture, flux data, evapotranspiration, agrometeorology
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
Institutions: University of Toronto, University of Toronto, University of Regina.
Phenotypes are determined by a complex series of physical (e.g.
protein-protein) and functional (e.g.
gene-gene or genetic) interactions (GI)1
. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2
. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7
, but GI information remains sparse for prokaryotes8
, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10
Here, we present the key steps required to perform quantitative E. coli
Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9
, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format.
Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g.
the 'Keio' collection11
) and essential gene hypomorphic mutations (i.e.
alleles conferring reduced protein expression, stability, or activity9, 12, 13
) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14
. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9
. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2
. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e.
slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2
as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9
Genetics, Issue 69, Molecular Biology, Medicine, Biochemistry, Microbiology, Aggravating, alleviating, conjugation, double mutant, Escherichia coli, genetic interaction, Gram-negative bacteria, homologous recombination, network, synthetic lethality or sickness, suppression
High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry
Institutions: Medical College of Wisconsin, Stanford University School of Medicine, Medical College of Wisconsin, Hong Kong University, Johns Hopkins University School of Medicine, Medical College of Wisconsin.
There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the heart, and improving strategies to accurately model heart disease. The potential of exploiting human induced pluripotent stem cell (hiPSC) technology to generate cardiac muscle “in a dish” for these applications continues to generate high enthusiasm. In recent years, the ability to efficiently generate cardiomyogenic cells from human pluripotent stem cells (hPSCs) has greatly improved, offering us new opportunities to model very early stages of human cardiac development not otherwise accessible. In contrast to many previous methods, the cardiomyocyte differentiation protocol described here does not require cell aggregation or the addition of Activin A or BMP4 and robustly generates cultures of cells that are highly positive for cardiac troponin I and T (TNNI3, TNNT2), iroquois-class homeodomain protein IRX-4 (IRX4), myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC2v) and myosin regulatory light chain 2, atrial isoform (MLC2a) by day 10 across all human embryonic stem cell (hESC) and hiPSC lines tested to date. Cells can be passaged and maintained for more than 90 days in culture. The strategy is technically simple to implement and cost-effective. Characterization of cardiomyocytes derived from pluripotent cells often includes the analysis of reference markers, both at the mRNA and protein level. For protein analysis, flow cytometry is a powerful analytical tool for assessing quality of cells in culture and determining subpopulation homogeneity. However, technical variation in sample preparation can significantly affect quality of flow cytometry data. Thus, standardization of staining protocols should facilitate comparisons among various differentiation strategies. Accordingly, optimized staining protocols for the analysis of IRX4, MLC2v, MLC2a, TNNI3, and TNNT2 by flow cytometry are described.
Cellular Biology, Issue 91, human induced pluripotent stem cell, flow cytometry, directed differentiation, cardiomyocyte, IRX4, TNNI3, TNNT2, MCL2v, MLC2a
Low Molecular Weight Protein Enrichment on Mesoporous Silica Thin Films for Biomarker Discovery
Institutions: The Methodist Hospital Research Institute, National Center for Nanoscience and Technology.
The identification of circulating biomarkers holds great potential for non invasive approaches in early diagnosis and prognosis, as well as for the monitoring of therapeutic efficiency.1-3
The circulating low molecular weight proteome (LMWP) composed of small proteins shed from tissues and cells or peptide fragments derived from the proteolytic degradation of larger proteins, has been associated with the pathological condition in patients and likely reflects the state of disease.4,5
Despite these potential clinical applications, the use of Mass Spectrometry (MS) to profile the LMWP from biological fluids has proven to be very challenging due to the large dynamic range of protein and peptide concentrations in serum.6
Without sample pre-treatment, some of the more highly abundant proteins obscure the detection of low-abundance species in serum/plasma. Current proteomic-based approaches, such as two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE) and shotgun proteomics methods are labor-intensive, low throughput and offer limited suitability for clinical applications.7-9
Therefore, a more effective strategy is needed to isolate LMWP from blood and allow the high throughput screening of clinical samples.
Here, we present a fast, efficient and reliable multi-fractionation system based on mesoporous silica chips to specifically target and enrich LMWP.10,11
Mesoporous silica (MPS) thin films with tunable features at the nanoscale were fabricated using the triblock copolymer template pathway. Using different polymer templates and polymer concentrations in the precursor solution, various pore size distributions, pore structures, connectivity and surface properties were determined and applied for selective recovery of low mass proteins. The selective parsing of the enriched peptides into different subclasses according to their physicochemical properties will enhance the efficiency of recovery and detection of low abundance species. In combination with mass spectrometry and statistic analysis, we demonstrated the correlation between the nanophase characteristics of the mesoporous silica thin films and the specificity and efficacy of low mass proteome harvesting. The results presented herein reveal the potential of the nanotechnology-based technology to provide a powerful alternative to conventional methods for LMWP harvesting from complex biological fluids. Because of the ability to tune the material properties, the capability for low-cost production, the simplicity and rapidity of sample collection, and the greatly reduced sample requirements for analysis, this novel nanotechnology will substantially impact the field of proteomic biomarker research and clinical proteomic assessment.
Bioengineering, Issue 62, Nanoporous silica chip, Low molecular weight proteomics, Peptidomics, MALDI-TOF mass spectrometry, early diagnostics, proteomics
Cortical Source Analysis of High-Density EEG Recordings in Children
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
Flat Mount Preparation for Observation and Analysis of Zebrafish Embryo Specimens Stained by Whole Mount In situ Hybridization
Institutions: University of Notre Dame.
The zebrafish embryo is now commonly used for basic and biomedical research to investigate the genetic control of developmental processes and to model congenital abnormalities. During the first day of life, the zebrafish embryo progresses through many developmental stages including fertilization, cleavage, gastrulation, segmentation, and the organogenesis of structures such as the kidney, heart, and central nervous system. The anatomy of a young zebrafish embryo presents several challenges for the visualization and analysis of the tissues involved in many of these events because the embryo develops in association with a round yolk mass. Thus, for accurate analysis and imaging of experimental phenotypes in fixed embryonic specimens between the tailbud and 20 somite stage (10 and 19 hours post fertilization (hpf), respectively), such as those stained using whole mount in situ
hybridization (WISH), it is often desirable to remove the embryo from the yolk ball and to position it flat on a glass slide. However, performing a flat mount procedure can be tedious. Therefore, successful and efficient flat mount preparation is greatly facilitated through the visual demonstration of the dissection technique, and also helped by using reagents that assist in optimal tissue handling. Here, we provide our WISH protocol for one or two-color detection of gene expression in the zebrafish embryo, and demonstrate how the flat mounting procedure can be performed on this example of a stained fixed specimen. This flat mounting protocol is broadly applicable to the study of many embryonic structures that emerge during early zebrafish development, and can be implemented in conjunction with other staining methods performed on fixed embryo samples.
Developmental Biology, Issue 89, animals, vertebrates, fishes, zebrafish, growth and development, morphogenesis, embryonic and fetal development, organogenesis, natural science disciplines, embryo, whole mount in situ hybridization, flat mount, deyolking, imaging
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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
Gibberella zeae Ascospore Production and Collection for Microarray Experiments.
Institutions: USDA, University of Minnesota/ Agroinnova, University of Torino, University of Minnesota.
Fusarium graminearum Schwabe (teleomorph Gibberella zeae) is a plant pathogen causing scab disease on wheat and barley that reduces crop yield and grain quality. F. graminearum also causes stalk and ear rots of maize and is a producer of mycotoxins such as the trichothecenes that contaminate grain and are harmful to humans and livestock (Goswami and Kistler, 2004).
The fungus produces two types of spores. Ascospores, the propagules resulting from sexual reproduction, are the main source of primary infection. These spores are forcibly discharged from mature perithecia and dispersed by wind (Francl et al 1999). Secondary infections are mainly caused by macroconidia which are produced by asexual means on the plant surface. To study the developmental processes of ascospores in this fungus, a procedure for their collection in large quantity under sterile conditions was required. Our protocol was filmed in order to generate the highest level of information for understanding and reproducibility; crucial aspects when full genome gene expression profiles are generated and interpreted. In particular, the variability of ascospore germination and biological activity are dependent on the prior manipulation of the material. The use of video for documenting every step in ascospore production is proposed in order to increase standardization, complying with the increasingly stringent requirements for microarray analysis. The procedure requires only standard laboratory equipment. Steps are shown to prevent contamination and favor time synchronization of ascospores.
Plant Biology, Issue 1, sexual cross, spore separation, MIAME standards
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Institutions: University of California, Los Angeles.
Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy. Insight is given into how computational models can provide information on the population dynamics of mosquitoes and the spread of transposable elements through A. gambiae subspecies. The ethical considerations of releasing genetically modified mosquitoes into the wild are discussed.
Cellular Biology, Issue 5, mosquito, malaria, popuulation, replacement, modeling, infectious disease
Basics of Multivariate Analysis in Neuroimaging Data
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