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Pubmed Article
A hierarchical poisson log-normal model for network inference from RNA sequencing data.
PLoS ONE
PUBLISHED: 01-01-2013
Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data.
ABSTRACT
Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient's hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study. RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment. In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.
23 Related JoVE Articles!
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Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing
Authors: Jerome E. Lee, Rui Yi.
Institutions: University of Colorado, Boulder, University of Colorado, Denver.
MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3’ and 5’ oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3’ and 5′ ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.
Molecular Biology, Issue 93, RNA, ligation, miRNA, miR-Seq, linker, oligonucleotide, high-throughput sequencing
52095
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MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR (qPCR)
Authors: Aida Gordanpour, Robert K. Nam, Linda Sugar, Stephanie Bacopulos, Arun Seth.
Institutions: University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Canada, Sunnybrook Health Sciences Centre, Toronto, Canada, Sunnybrook Research Institute.
MicroRNAs (miRNAs) are single-stranded, 18–24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process including development1, apoptosis2, and cell cycle regulation3. MiRNAs are estimated to regulate the expression of 30% to 90% of human genes4 by binding to their target messenger RNAs (mRNAs)5. Widespread dysregulation of miRNAs has been reported in various diseases and cancer subtypes6. Due to their prevalence and unique structure, these small molecules are likely to be the next generation of biomarkers, therapeutic agents and/or targets. Methods used to investigate miRNA expression include SYBR green I dye- based as well as Taqman-probe based qPCR. If miRNAs are to be effectively used in the clinical setting, it is imperative that their detection in fresh and/or archived clinical samples be accurate, reproducible, and specific. qPCR has been widely used for validating expression of miRNAs in whole genome analyses such as microarray studies7. The samples used in this protocol were from patients who underwent radical prostatectomy for clinically localized prostate cancer; however other tissues and cell lines can be substituted in. Prostate specimens were snap-frozen in liquid nitrogen after resection. Clinical variables and follow-up information for each patient were collected for subsequent analysis8. Quantification of miRNA levels in prostate tumor samples. The main steps in qPCR analysis of tumors are: Total RNA extraction, cDNA synthesis, and detection of qPCR products using miRNA-specific primers. Total RNA, which includes mRNA, miRNA, and other small RNAs were extracted from specimens using TRIzol reagent. Qiagen's miScript System was used to synthesize cDNA and perform qPCR (Figure 1). Endogenous miRNAs are not polyadenylated, therefore during the reverse transcription process, a poly(A) polymerase polyadenylates the miRNA. The miRNA is used as a template to synthesize cDNA using oligo-dT and Reverse Transcriptase. A universal tag sequence on the 5' end of oligo-dT primers facilitates the amplification of cDNA in the PCR step. PCR product amplification is detected by the level of fluorescence emitted by SYBR Green, a dye which intercalates into double stranded DNA. Specific miRNA primers, along with a Universal Primer that binds to the universal tag sequence will amplify specific miRNA sequences. The miScript Primer Assays are available for over a thousand human-specific miRNAs, and hundreds of murine-specific miRNAs. Relative quantification method was used here to quantify the expression of miRNAs. To correct for variability amongst different samples, expression levels of a target miRNA is normalized to the expression levels of a reference gene. The choice of a gene on which to normalize the expression of targets is critical in relative quantification method of analysis. Examples of reference genes typically used in this capacity are the small RNAs RNU6B, RNU44, and RNU48 as they are considered to be stably expressed across most samples. In this protocol, RNU6B is used as the reference gene.
Cancer Biology, Issue 63, Medicine, cancer, primer assay, Prostate, microRNA, tumor, qPCR
3874
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Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching
Authors: Kelley D. Sullivan, Edward B. Brown.
Institutions: University of Rochester, University of Rochester.
Multi-fluorescence recovery after photobleaching is a microscopy technique used to measure the diffusion coefficient (or analogous transport parameters) of macromolecules, and can be applied to both in vitro and in vivo biological systems. Multi-fluorescence recovery after photobleaching is performed by photobleaching a region of interest within a fluorescent sample using an intense laser flash, then attenuating the beam and monitoring the fluorescence as still-fluorescent molecules from outside the region of interest diffuse in to replace the photobleached molecules. We will begin our demonstration by aligning the laser beam through the Pockels Cell (laser modulator) and along the optical path through the laser scan box and objective lens to the sample. For simplicity, we will use a sample of aqueous fluorescent dye. We will then determine the proper experimental parameters for our sample including, monitor and bleaching powers, bleach duration, bin widths (for photon counting), and fluorescence recovery time. Next, we will describe the procedure for taking recovery curves, a process that can be largely automated via LabVIEW (National Instruments, Austin, TX) for enhanced throughput. Finally, the diffusion coefficient is determined by fitting the recovery data to the appropriate mathematical model using a least-squares fitting algorithm, readily programmable using software such as MATLAB (The Mathworks, Natick, MA).
Cellular Biology, Issue 36, Diffusion, fluorescence recovery after photobleaching, MP-FRAP, FPR, multi-photon
1636
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Performing Custom MicroRNA Microarray Experiments
Authors: Xiaoxiao Zhang, Yan Zeng.
Institutions: University of Minnesota , University of Minnesota .
microRNAs (miRNAs) are a large family of ˜ 22 nucleotides (nt) long RNA molecules that are widely expressed in eukaryotes 1. Complex genomes encode at least hundreds of miRNAs, which primarily inhibit the expression of a vast number of target genes post-transcriptionally 2, 3. miRNAs control a broad range of biological processes 1. In addition, altered miRNA expression has been associated with human diseases such as cancers, and miRNAs may serve as biomarkers for diseases and prognosis 4, 5. It is important, therefore, to understand the expression and functions of miRNAs under many different conditions. Three major approaches have been employed to profile miRNA expression: real-time PCR, microarray, and deep sequencing. The technique of miRNA microarray has the advantage of being high-throughput, generally less expensive, and most of the experimental and analysis steps can be carried out in a molecular biology laboratory at most universities, medical schools and associated hospitals. Here, we describe a method for performing custom miRNA microarray experiments. A miRNA probe set will be printed on glass slides to produce miRNA microarrays. RNA is isolated using a method or reagent that preserves small RNA species, and then labeled with a fluorescence dye. As a control, reference DNA oligonucleotides corresponding to a subset of miRNAs are also labeled with a different fluorescence dye. The reference DNA will serve to demonstrate the quality of the slide and hybridization and will also be used for data normalization. The RNA and DNA are mixed and hybridized to a microarray slide containing probes for most of the miRNAs in the database. After washing, the slide is scanned to obtain images, and intensities of the individual spots quantified. These raw signals will be further processed and analyzed as the expression data of the corresponding miRNAs. Microarray slides can be stripped and regenerated to reduce the cost of microarrays and to enhance the consistency of microarray experiments. The same principles and procedures are applicable to other types of custom microarray experiments.
Molecular Biology, Issue 56, Genetics, microRNA, custom microarray, oligonucleotide probes, RNA labeling
3250
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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays
Authors: Helene Mens, Mary Kearney, Ann Wiegand, Jonathan Spindler, Frank Maldarelli, John W. Mellors, John M. Coffin.
Institutions: NCI-Frederick, University of Pittsburgh, Tuffts University.
Amplifying viral genes and quantifying HIV-1 RNA in HIV-1 infected individuals with viral loads below the limit of detection by standard assays (below 50-75 copies/ml) is necessary to gain insight to viral dynamics and virus host interactions in patients who naturally control the infection and those who are on combination antiretroviral therapy (cART). Here we describe how to amplify viral genomes by single genome sequencing (the SGS protocol) 13, 19 and how to accurately quantify HIV-1 RNA in patients with low viral loads (the single-copy assay (SCA) protocol) 12, 20. The single-copy assay is a real-time PCR assay with sensitivity depending on the volume of plasma being assayed. If a single virus genome is detected in 7 ml of plasma, then the RNA copy number is reported to be 0.3 copies/ml. The assay has an internal control testing for the efficiency of RNA extraction, and controls for possible amplification from DNA or contamination. Patient samples are measured in triplicate. The single-genome sequencing assay (SGS), now widely used and considered to be non-labor intensive 3, 7, 12, 14, 15,is a limiting dilution assay, in which endpoint diluted cDNA product is spread over a 96-well plate. According to a Poisson distribution, when less than 1/3 of the wells give product, there is an 80% chance of the PCR product being resultant of amplification from a single cDNA molecule. SGS has the advantage over cloning of not being subjected to resampling and not being biased by PCR-introduced recombination 19. However, the amplification success of SCA and SGS depend on primer design. Both assays were developed for HIV-1 subtype B, but can be adapted for other subtypes and other regions of the genome by changing primers, probes, and standards.
Immunology, Issue 55, single genome sequencing, SGS, real-time PCR, single-copy assay, SCA, HIV-1, ultra-sensitive, RNA extraction
2960
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An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Authors: Justen Manasa, Siva Danaviah, Sureshnee Pillay, Prevashinee Padayachee, Hloniphile Mthiyane, Charity Mkhize, Richard John Lessells, Christopher Seebregts, Tobias F. Rinke de Wit, Johannes Viljoen, David Katzenstein, Tulio De Oliveira.
Institutions: University of KwaZulu-Natal, Durban, South Africa, Jembi Health Systems, University of Amsterdam, Stanford Medical School.
HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in sub-Saharan Africa continue to expand, individuals on ART should be closely monitored for the emergence of drug resistance. Surveillance of transmitted drug resistance to track transmission of viral strains already resistant to ART is also critical. Unfortunately, drug resistance testing is still not readily accessible in resource limited settings, because genotyping is expensive and requires sophisticated laboratory and data management infrastructure. An open access genotypic drug resistance monitoring method to manage individuals and assess transmitted drug resistance is described. The method uses free open source software for the interpretation of drug resistance patterns and the generation of individual patient reports. The genotyping protocol has an amplification rate of greater than 95% for plasma samples with a viral load >1,000 HIV-1 RNA copies/ml. The sensitivity decreases significantly for viral loads <1,000 HIV-1 RNA copies/ml. The method described here was validated against a method of HIV-1 drug resistance testing approved by the United States Food and Drug Administration (FDA), the Viroseq genotyping method. Limitations of the method described here include the fact that it is not automated and that it also failed to amplify the circulating recombinant form CRF02_AG from a validation panel of samples, although it amplified subtypes A and B from the same panel.
Medicine, Issue 85, Biomedical Technology, HIV-1, HIV Infections, Viremia, Nucleic Acids, genetics, antiretroviral therapy, drug resistance, genotyping, affordable
51242
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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
Authors: Stéphanie Beaucourt, Antonio V. Bordería, Lark L. Coffey, Nina F. Gnädig, Marta Sanz-Ramos, Yasnee Beeharry, Marco Vignuzzi.
Institutions: Institut Pasteur .
RNA viruses use RNA dependent RNA polymerases to replicate their genomes. The intrinsically high error rate of these enzymes is a large contributor to the generation of extreme population diversity that facilitates virus adaptation and evolution. Increasing evidence shows that the intrinsic error rates, and the resulting mutation frequencies, of RNA viruses can be modulated by subtle amino acid changes to the viral polymerase. Although biochemical assays exist for some viral RNA polymerases that permit quantitative measure of incorporation fidelity, here we describe a simple method of measuring mutation frequencies of RNA viruses that has proven to be as accurate as biochemical approaches in identifying fidelity altering mutations. The approach uses conventional virological and sequencing techniques that can be performed in most biology laboratories. Based on our experience with a number of different viruses, we have identified the key steps that must be optimized to increase the likelihood of isolating fidelity variants and generating data of statistical significance. The isolation and characterization of fidelity altering mutations can provide new insights into polymerase structure and function1-3. Furthermore, these fidelity variants can be useful tools in characterizing mechanisms of virus adaptation and evolution4-7.
Immunology, Issue 52, Polymerase fidelity, RNA virus, mutation frequency, mutagen, RNA polymerase, viral evolution
2953
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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Authors: Philippe Pérot, Valérie Cheynet, Myriam Decaussin-Petrucci, Guy Oriol, Nathalie Mugnier, Claire Rodriguez-Lafrasse, Alain Ruffion, François Mallet.
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
50713
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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
Authors: C. R. Gallistel, Fuat Balci, David Freestone, Aaron Kheifets, Adam King.
Institutions: Rutgers University, Koç University, New York University, Fairfield University.
We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.
Behavior, Issue 84, genetics, cognitive mechanisms, behavioral screening, learning, memory, timing
51047
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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
4273
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Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA
Authors: Srikumar Sengupta, Jennifer M. Bolin, Victor Ruotti, Bao Kim Nguyen, James A. Thomson, Angela L. Elwell, Ron Stewart.
Institutions: Morgridge Institute for Research, University of Wisconsin, University of California.
Whole transcriptome sequencing by mRNA-Seq is now used extensively to perform global gene expression, mutation, allele-specific expression and other genome-wide analyses. mRNA-Seq even opens the gate for gene expression analysis of non-sequenced genomes. mRNA-Seq offers high sensitivity, a large dynamic range and allows measurement of transcript copy numbers in a sample. Illumina’s genome analyzer performs sequencing of a large number (> 107) of relatively short sequence reads (< 150 bp).The "paired end" approach, wherein a single long read is sequenced at both its ends, allows for tracking alternate splice junctions, insertions and deletions, and is useful for de novo transcriptome assembly. One of the major challenges faced by researchers is a limited amount of starting material. For example, in experiments where cells are harvested by laser micro-dissection, available starting total RNA may measure in nanograms. Preparation of mRNA-Seq libraries from such samples have been described1, 2 but involves significant PCR amplification that may introduce bias. Other RNA-Seq library construction procedures with minimal PCR amplification have been published3, 4 but require microgram amounts of starting total RNA. Here we describe a protocol for the Illumina Genome Analyzer II platform for mRNA-Seq sequencing for library preparation that avoids significant PCR amplification and requires only 10 nanograms of total RNA. While this protocol has been described previously and validated for single-end sequencing5, where it was shown to produce directional libraries without introducing significant amplification bias, here we validate it further for use as a paired end protocol. We selectively amplify polyadenylated messenger RNAs from starting total RNA using the T7 based Eberwine linear amplification method, coined "T7LA" (T7 linear amplification). The amplified poly-A mRNAs are fragmented, reverse transcribed and adapter ligated to produce the final sequencing library. For both single read and paired end runs, sequences are mapped to the human transcriptome6 and normalized so that data from multiple runs can be compared. We report the gene expression measurement in units of transcripts per million (TPM), which is a superior measure to RPKM when comparing samples7.
Molecular Biology, Issue 56, Genetics, mRNA-Seq, Illumina-Seq, gene expression profiling, high throughput sequencing
3340
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Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells
Authors: Geoffrey Berguet, Jan Hendrickx, Celine Sabatel, Miklos Laczik, Sharon Squazzo, Ignacio Mazon Pelaez, Rini Saxena, Helene Pendeville, Dominique Poncelet.
Institutions: Diagenode S.A., Diagenode Inc..
Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a technique of choice for studying protein-DNA interactions. ChIP-seq has been used for mapping protein-DNA interactions and allocating histones modifications. The procedure is tedious and time consuming, and one of the major limitations is the requirement for high amounts of starting material, usually millions of cells. Automation of chromatin immunoprecipitation assays is possible when the procedure is based on the use of magnetic beads. Successful automated protocols of chromatin immunoprecipitation and library preparation have been specifically designed on a commercially available robotic liquid handling system dedicated mainly to automate epigenetic assays. First, validation of automated ChIP-seq assays using antibodies directed against various histone modifications was shown, followed by optimization of the automated protocols to perform chromatin immunoprecipitation and library preparation starting with low cell numbers. The goal of these experiments is to provide a valuable tool for future epigenetic analysis of specific cell types, sub-populations, and biopsy samples.
Molecular Biology, Issue 94, Automation, chromatin immunoprecipitation, low DNA amounts, histone antibodies, sequencing, library preparation
52150
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RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
Authors: Dilyara Cheranova, Margaret Gibson, Suman Chaudhary, Li Qin Zhang, Daniel P. Heruth, Dmitry N. Grigoryev, Shui Qing Ye.
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
4393
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A Practical Guide to Phylogenetics for Nonexperts
Authors: Damien O'Halloran.
Institutions: The George Washington University.
Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this topic and so it presents inherent problems. Here we compile a practical introduction to phylogenetics for nonexperts. We outline in a step-by-step manner, a pipeline for generating reliable phylogenies from gene sequence datasets. We begin with a user-guide for similarity search tools via online interfaces as well as local executables. Next, we explore programs for generating multiple sequence alignments followed by protocols for using software to determine best-fit models of evolution. We then outline protocols for reconstructing phylogenetic relationships via maximum likelihood and Bayesian criteria and finally describe tools for visualizing phylogenetic trees. While this is not by any means an exhaustive description of phylogenetic approaches, it does provide the reader with practical starting information on key software applications commonly utilized by phylogeneticists. The vision for this article would be that it could serve as a practical training tool for researchers embarking on phylogenetic studies and also serve as an educational resource that could be incorporated into a classroom or teaching-lab.
Basic Protocol, Issue 84, phylogenetics, multiple sequence alignments, phylogenetic tree, BLAST executables, basic local alignment search tool, Bayesian models
50975
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Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
Authors: Bernd Rädle, Andrzej J. Rutkowski, Zsolt Ruzsics, Caroline C. Friedel, Ulrich H. Koszinowski, Lars Dölken.
Institutions: Max von Pettenkofer Institute, University of Cambridge, Ludwig-Maximilians-University Munich.
The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene expression. Along with a better understanding of the involved molecular mechanisms, precise measurements of the underlying kinetics have become increasingly important. Here, these powerful methodologies face major limitations due to intrinsic properties of the template samples they study, i.e. total cellular RNA. In many cases changes in total cellular RNA occur either too slowly or too quickly to represent the underlying molecular events and their kinetics with sufficient resolution. In addition, the contribution of alterations in RNA synthesis, processing, and decay are not readily differentiated. We recently developed high-resolution gene expression profiling to overcome these limitations. Our approach is based on metabolic labeling of newly transcribed RNA with 4-thiouridine (thus also referred to as 4sU-tagging) followed by rigorous purification of newly transcribed RNA using thiol-specific biotinylation and streptavidin-coated magnetic beads. It is applicable to a broad range of organisms including vertebrates, Drosophila, and yeast. We successfully applied 4sU-tagging to study real-time kinetics of transcription factor activities, provide precise measurements of RNA half-lives, and obtain novel insights into the kinetics of RNA processing. Finally, computational modeling can be employed to generate an integrated, comprehensive analysis of the underlying molecular mechanisms.
Genetics, Issue 78, Cellular Biology, Molecular Biology, Microbiology, Biochemistry, Eukaryota, Investigative Techniques, Biological Phenomena, Gene expression profiling, RNA synthesis, RNA processing, RNA decay, 4-thiouridine, 4sU-tagging, microarray analysis, RNA-seq, RNA, DNA, PCR, sequencing
50195
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
50319
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
50341
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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
51216
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Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
Authors: Anne Katchy, Cecilia Williams.
Institutions: University of Houston.
Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen receptors (ERs), ERα, and ERβ. ERα is frequently upregulated in breast cancer and drives the proliferation of breast cancer cells. The ERs function as transcription factors and regulate gene expression. Whereas ERα's regulation of protein-coding genes is well established, its regulation of noncoding microRNA (miRNA) is less explored. miRNAs play a major role in the post-transcriptional regulation of genes, inhibiting their translation or degrading their mRNA. miRNAs can function as oncogenes or tumor suppressors and are also promising biomarkers. Among the miRNA assays available, microarray and quantitative real-time polymerase chain reaction (qPCR) have been extensively used to detect and quantify miRNA levels. To identify miRNAs regulated by estrogen signaling in breast cancer, their expression in ERα-positive breast cancer cell lines were compared before and after estrogen-activation using both the µParaflo-microfluidic microarrays and Dual Labeled Probes-low density arrays. Results were validated using specific qPCR assays, applying both Cyanine dye-based and Dual Labeled Probes-based chemistry. Furthermore, a time-point assay was used to identify regulations over time. Advantages of the miRNA assay approach used in this study is that it enables a fast screening of mature miRNA regulations in numerous samples, even with limited sample amounts. The layout, including the specific conditions for cell culture and estrogen treatment, biological and technical replicates, and large-scale screening followed by in-depth confirmations using separate techniques, ensures a robust detection of miRNA regulations, and eliminates false positives and other artifacts. However, mutated or unknown miRNAs, or regulations at the primary and precursor transcript level, will not be detected. The method presented here represents a thorough investigation of estrogen-mediated miRNA regulation.
Medicine, Issue 84, breast cancer, microRNA, estrogen, estrogen receptor, microarray, qPCR
51285
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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
51673
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Analysis of RNA Processing Reactions Using Cell Free Systems: 3' End Cleavage of Pre-mRNA Substrates in vitro
Authors: Joseph Jablonski, Mark Clementz, Kevin Ryan, Susana T. Valente.
Institutions: The Scripps Research Institute, City College of New York.
The 3’ end of mammalian mRNAs is not formed by abrupt termination of transcription by RNA polymerase II (RNPII). Instead, RNPII synthesizes precursor mRNA beyond the end of mature RNAs, and an active process of endonuclease activity is required at a specific site. Cleavage of the precursor RNA normally occurs 10-30 nt downstream from the consensus polyA site (AAUAAA) after the CA dinucleotides. Proteins from the cleavage complex, a multifactorial protein complex of approximately 800 kDa, accomplish this specific nuclease activity. Specific RNA sequences upstream and downstream of the polyA site control the recruitment of the cleavage complex. Immediately after cleavage, pre-mRNAs are polyadenylated by the polyA polymerase (PAP) to produce mature stable RNA messages. Processing of the 3’ end of an RNA transcript may be studied using cellular nuclear extracts with specific radiolabeled RNA substrates. In sum, a long 32P-labeled uncleaved precursor RNA is incubated with nuclear extracts in vitro, and cleavage is assessed by gel electrophoresis and autoradiography. When proper cleavage occurs, a shorter 5’ cleaved product is detected and quantified. Here, we describe the cleavage assay in detail using, as an example, the 3’ end processing of HIV-1 mRNAs.
Infectious Diseases, Issue 87, Cleavage, Polyadenylation, mRNA processing, Nuclear extracts, 3' Processing Complex
51309
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Analysis of Gene Expression in Emerald Ash Borer (Agrilus planipennis) Using Quantitative Real Time-PCR
Authors: Binny Bhandary, Swapna Priya Rajarapu, Loren Rivera-Vega, Omprakash Mittapalli.
Institutions: The Ohio State University.
Emerald ash borer (EAB, Agrilus planipennis) is an exotic invasive pest, which has killed millions of ash trees (Fraxinus spp) in North America. EAB continues to spread rapidly and attacks ash trees of different ages, from saplings to mature trees. However, to date very little or no molecular knowledge exists for EAB. We are interested in deciphering the molecular-based physiological processes at the tissue level that aid EAB in successful colonization of ash trees. In this report we show the effective use of quantitative real-time PCR (qRT-PCR) to ascertain mRNA levels in different larval tissues (including midgut, fat bodies and cuticle) and different developmental stages (including 1st-, 2nd-, 3rd-, 4th-instars, prepupae and adults) of EAB. As an example, a peritrophin gene (herein named, AP-PERI1) is exemplified as the gene of interest and a ribosomal protein (AP-RP1) as the internal control. Peritrophins are important components of the peritrophic membrane/matrix (PM), which is the lining of the insect gut. The PM has diverse functions including digestion and mechanical protection to the midgut epithelium.
Cellular Biology, Issue 39, quantitative real time-PCR, peritrophin, emerald ash borer, gene expression
1974
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Basics of Multivariate Analysis in Neuroimaging Data
Authors: Christian Georg Habeck.
Institutions: Columbia University.
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience
1988
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