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.
23 Related JoVE Articles!
DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
Institutions: University of California, Davis.
Microarray expression profiling of the nervous system provides a powerful approach to identifying gene activities in different stages of development, different physiological or pathological states, response to therapy, and, in general, any condition that is being experimentally tested1
. Expression profiling of neural tissues requires isolation of high quality RNA, amplification of the isolated RNA and hybridization to DNA microarrays. In this article we describe protocols for reproducible microarray experiments from brain tumor tissue2
. We will start by performing a quality control analysis of isolated RNA samples with Agilent's 2100 Bioanalyzer "lab-on-a-chip" technology. High quality RNA samples are critical for the success of any microarray experiment, and the 2100 Bioanalyzer provides a quick, quantitative measurement of the sample quality. RNA samples are then amplified and labeled by performing reverse transcription to obtain cDNA, followed by in vitro transcription in the presence of labeled nucleotides to produce labeled cRNA. By using a dual-color labeling kit, we will label our experimental sample with Cy3 and a reference sample with Cy5. Both samples will then be combined and hybridized to Agilent's 4x44 K arrays. Dual-color arrays offer the advantage of a direct comparison between two RNA samples, thereby increasing the accuracy of the measurements, in particular for small changes in expression levels, because the two RNA samples are hybridized competitively to a single microarray. The arrays will be scanned at the two corresponding wavelengths, and the ratio of Cy3 to Cy5 signal for each feature will be used as a direct measurement of the relative abundance of the corresponding mRNA. This analysis identifies genes that are differentially expressed in response to the experimental conditions being tested.
Genetics, Issue 49, microarray, RNA, expression profiling, dual-color labeling
Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
Institutions: University of Sydney, Royal Prince Alfred Hospital, Department of Anatomical Pathology, Concord Repatriation General Hospital.
The current prognosis and classification of CRC relies on staging systems that integrate histopathologic and clinical findings. However, in the majority of CRC cases, cell dysfunction is the result of numerous mutations that modify protein expression and post-translational modification1
A number of cell surface antigens, including cluster of differentiation (CD) antigens, have been identified as potential prognostic or metastatic biomarkers in CRC. These antigens make ideal biomarkers as their expression often changes with tumour progression or interactions with other cell types, such as tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs).
The use of immunohistochemistry (IHC) for cancer sub-classification and prognostication is well established for some tumour types2,3
. However, no single ‘marker’ has shown prognostic significance greater than clinico-pathological staging or gained wide acceptance for use in routine pathology reporting of all CRC cases.
A more recent approach to prognostic stratification of disease phenotypes relies on surface protein profiles using multiple 'markers'. While expression profiling of tumours using proteomic techniques such as iTRAQ is a powerful tool for the discovery of biomarkers4, it is not optimal for routine use in diagnostic laboratories and cannot distinguish different cell types in a mixed population. In addition, large amounts of tumour tissue are required for the profiling of purified plasma membrane glycoproteins by these methods.
In this video we described a simple method for surface proteome profiling of viable cells from disaggregated CRC samples using a DotScan CRC antibody microarray. The 122-antibody microarray consists of a standard 82-antibody region recognizing a range of lineage-specific leukocyte markers, adhesion molecules, receptors and markers of inflammation and immune response5
, together with a satellite region for detection of 40 potentially prognostic markers for CRC. Cells are captured only on antibodies for which they express the corresponding antigen. The cell density per dot, determined by optical scanning, reflects the proportion of cells expressing that antigen, the level of expression of the antigen and affinity of the antibody6
For CRC tissue or normal intestinal mucosa, optical scans reflect the immunophenotype of mixed populations of cells. Fluorescence multiplexing can then be used to profile selected sub-populations of cells of interest captured on the array. For example, Alexa 647-anti-epithelial cell adhesion molecule (EpCAM; CD326), is a pan-epithelial differentiation antigen that was used to detect CRC cells and also epithelial cells of normal intestinal mucosa, while Phycoerythrin-anti-CD3, was used to detect infiltrating T-cells7
. The DotScan CRC microarray should be the prototype for a diagnostic alternative to the anatomically-based CRC staging system.
Immunology, Issue 55, colorectal cancer, leukocytes, antibody microarray, multiplexing, fluorescence, CD antigens
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
Glycan Profiling of Plant Cell Wall Polymers using Microarrays
Institutions: University of Melbourne, University of Melbourne, CSIRO Plant Industry, Black Mountain Laboratories, University of Copenhagen.
Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g.
wood, paper, textile and biofuel industries)1,2
. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3
. Furthermore, their composition is also modified during development1
, or in response to environmental cues4
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5
. However, relatively few of the biosynthetic genes have been functionally characterized 4,5
. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6
. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7
. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9
have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 & 2
that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12
and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.
Plant Biology, Issue 70, Molecular Biology, Cellular Biology, Genetics, Genomics, Proteomics, Proteins, Cell Walls, Polysaccharides, Monoclonal Antibodies, Microarrays, CoMPP, glycans, Arabidopsis, tissue collection
Using a Pan-Viral Microarray Assay (Virochip) to Screen Clinical Samples for Viral Pathogens
Institutions: University of California, San Francisco, University of California, San Francisco.
The diagnosis of viral causes of many infectious diseases is difficult due to the inherent sequence diversity of viruses as well as the ongoing emergence of novel viral pathogens, such as SARS coronavirus and 2009 pandemic H1N1 influenza virus, that are not detectable by traditional methods. To address these challenges, we have previously developed and validated a pan-viral microarray platform called the Virochip with the capacity to detect all known viruses as well as novel variants on the basis of conserved sequence homology1
. Using the Virochip, we have identified the full spectrum of viruses associated with respiratory infections, including cases of unexplained critical illness in hospitalized patients, with a sensitivity equivalent to or superior to conventional clinical testing2-5
. The Virochip has also been used to identify novel viruses, including the SARS coronavirus6,7
, a novel rhinovirus clade5
, XMRV (a retrovirus linked to prostate cancer)8
, avian bornavirus (the cause of a wasting disease in parrots)9
, and a novel cardiovirus in children with respiratory and diarrheal illness10
. The current version of the Virochip has been ported to an Agilent microarray platform and consists of ~36,000 probes derived from over ~1,500 viruses in GenBank as of December of 2009. Here we demonstrate the steps involved in processing a Virochip assay from start to finish (~24 hour turnaround time), including sample nucleic acid extraction, PCR amplification using random primers, fluorescent dye incorporation, and microarray hybridization, scanning, and analysis.
Immunology, Issue 50, virus, microarray, Virochip, viral detection, genomics, clinical diagnostics, viral discovery, metagenomics, novel pathogen discovery
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
A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies
Institutions: University of Bern.
Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a ‘donor’ block into a ‘recipient’ block and then used for a variety of biomarker applications. The construction of conventional TMAs is labor intensive, imprecise, and time-consuming. Here, a protocol using next-generation Tissue Microarrays (ngTMA) is outlined. ngTMA is based on TMA planning and design, digital pathology, and automated tissue microarraying. The protocol is illustrated using an example of 134 metastatic colorectal cancer patients. Histological, statistical and logistical aspects are considered, such as the tissue type, specific histological regions, and cell types for inclusion in the TMA, the number of tissue spots, sample size, statistical analysis, and number of TMA copies. Histological slides for each patient are scanned and uploaded onto a web-based digital platform. There, they are viewed and annotated (marked) using a 0.6-2.0 mm diameter tool, multiple times using various colors to distinguish tissue areas. Donor blocks and 12 ‘recipient’ blocks are loaded into the instrument. Digital slides are retrieved and matched to donor block images. Repeated arraying of annotated regions is automatically performed resulting in an ngTMA. In this example, six ngTMAs are planned containing six different tissue types/histological zones. Two copies of the ngTMAs are desired. Three to four slides for each patient are scanned; 3 scan runs are necessary and performed overnight. All slides are annotated; different colors are used to represent the different tissues/zones, namely tumor center, invasion front, tumor/stroma, lymph node metastases, liver metastases, and normal tissue. 17 annotations/case are made; time for annotation is 2-3 min/case. 12 ngTMAs are produced containing 4,556 spots. Arraying time is 15-20 hr. Due to its precision, flexibility and speed, ngTMA is a powerful tool to further improve the quality of TMAs used in clinical and translational research.
Medicine, Issue 91, tissue microarray, biomarkers, prognostic, predictive, digital pathology, slide scanning
Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples
Institutions: Institute for Hepatitis and Virus Research, Thomas Jefferson University , Drexel University College of Medicine, Van Andel Research Institute, Serome Biosciences Inc..
In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that allows for the glycosylation profiling of specific proteins. Glycosylation of proteins is the most prevalent post-translational modification found on proteins, and leads diversified modifications of the physical, chemical, and biological properties of proteins. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases. However, current methods to study protein glycosylation typically are too complicated or expensive for use in most normal laboratory or clinical settings and a more practical method to study protein glycosylation is needed. The new protocol described in this study makes use of a chemically blocked antibody microarray with glycan-binding protein (GBP) detection and significantly reduces the time, cost, and lab equipment requirements needed to study protein glycosylation. In this method, multiple immobilized glycoprotein-specific antibodies are printed directly onto the microarray slides and the N-glycans on the antibodies are blocked. The blocked, immobilized glycoprotein-specific antibodies are able to capture and isolate glycoproteins from a complex sample that is applied directly onto the microarray slides. Glycan detection then can be performed by the application of biotinylated lectins and other GBPs to the microarray slide, while binding levels can be determined using Dylight 549-Streptavidin. Through the use of an antibody panel and probing with multiple biotinylated lectins, this method allows for an effective glycosylation profile of the different proteins found in a given human or animal sample to be developed.
Glycosylation of protein, which is the most ubiquitous post-translational modification on proteins, modifies the physical, chemical, and biological properties of a protein, and plays a fundamental role in various biological processes1-6
. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases 7-12
. In fact, most current cancer biomarkers, such as the L3 fraction of α-1 fetoprotein (AFP) for hepatocellular carcinoma 13-15
, and CA199 for pancreatic cancer 16, 17
are all aberrant glycan moieties on glycoproteins. However, methods to study protein glycosylation have been complicated, and not suitable for routine laboratory and clinical settings. Chen et al.
has recently invented a chemically blocked antibody microarray with a glycan-binding protein (GBP) detection method for high-throughput and multiplexed profile glycosylation of native glycoproteins in a complex sample 18
. In this affinity based microarray method, multiple immobilized glycoprotein-specific antibodies capture and isolate glycoproteins from the complex mixture directly on the microarray slide, and the glycans on each individual captured protein are measured by GBPs. Because all normal antibodies contain N-glycans which could be recognized by most GBPs, the critical step of this method is to chemically block the glycans on the antibodies from binding to GBP. In the procedure, the cis
-diol groups of the glycans on the antibodies were first oxidized to aldehyde groups by using NaIO4
in sodium acetate buffer avoiding light. The aldehyde groups were then conjugated to the hydrazide group of a cross-linker, 4-(4-N-MaleimidoPhenyl)butyric acid Hydrazide HCl (MPBH), followed by the conjugation of a dipeptide, Cys-Gly, to the maleimide group of the MPBH. Thus, the cis-diol groups on glycans of antibodies were converted into bulky none hydroxyl groups, which hindered the lectins and other GBPs bindings to the capture antibodies. This blocking procedure makes the GBPs and lectins bind only to the glycans of captured proteins. After this chemically blocking, serum samples were incubated with the antibody microarray, followed by the glycans detection by using different biotinylated lectins and GBPs, and visualized with Cy3-streptavidin. The parallel use of an antibody panel and multiple lectin probing provides discrete glycosylation profiles of multiple proteins in a given sample 18-20
. This method has been used successfully in multiple different labs 1, 7, 13, 19-31
. However, stability of MPBH and Cys-Gly, complicated and extended procedure in this method affect the reproducibility, effectiveness and efficiency of the method. In this new protocol, we replaced both MPBH and Cys-Gly with one much more stable reagent glutamic acid hydrazide (Glu-hydrazide), which significantly improved the reproducibility of the method, simplified and shorten the whole procedure so that the it can be completed within one working day. In this new protocol, we describe the detailed procedure of the protocol which can be readily adopted by normal labs for routine protein glycosylation study and techniques which are necessary to obtain reproducible and repeatable results.
Molecular Biology, Issue 63, Glycoproteins, glycan-binding protein, specific protein glycosylation, multiplexed high-throughput glycan blocked antibody microarray
Performing Custom MicroRNA Microarray Experiments
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
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Institutions: University of Washington.
Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e
., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g
., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori
, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e
., perform group-level analysis) based on a common cortical coordinate space.
Neuroscience, Issue 68, Magnetoencephalography, MEG, Electroencephalography, EEG, audition, attention, inverse imaging
Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry
Institutions: University of Exeter.
A wide range of methods are currently available for determining the dissociation constant between a protein and interacting small molecules. However, most of these require access to specialist equipment, and often require a degree of expertise to effectively establish reliable experiments and analyze data. Differential scanning fluorimetry (DSF) is being increasingly used as a robust method for initial screening of proteins for interacting small molecules, either for identifying physiological partners or for hit discovery. This technique has the advantage that it requires only a PCR machine suitable for quantitative PCR, and so suitable instrumentation is available in most institutions; an excellent range of protocols are already available; and there are strong precedents in the literature for multiple uses of the method. Past work has proposed several means of calculating dissociation constants from DSF data, but these are mathematically demanding. Here, we demonstrate a method for estimating dissociation constants from a moderate amount of DSF experimental data. These data can typically be collected and analyzed within a single day. We demonstrate how different models can be used to fit data collected from simple binding events, and where cooperative binding or independent binding sites are present. Finally, we present an example of data analysis in a case where standard models do not apply. These methods are illustrated with data collected on commercially available control proteins, and two proteins from our research program. Overall, our method provides a straightforward way for researchers to rapidly gain further insight into protein-ligand interactions using DSF.
Biophysics, Issue 91, differential scanning fluorimetry, dissociation constant, protein-ligand interactions, StepOne, cooperativity, WcbI.
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
A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays
Institutions: McMaster University .
Microarrays have found use in the development of high-throughput assays for new materials and discovery of small-molecule drug leads. Herein we describe a guided material screening approach to identify sol-gel based materials that are suitable for producing three-dimensional protein microarrays. The approach first identifies materials that can be printed as microarrays, narrows down the number of materials by identifying those that are compatible with a given enzyme assay, and then hones in on optimal materials based on retention of maximum enzyme activity. This approach is applied to develop microarrays suitable for two different enzyme assays, one using acetylcholinesterase and the other using a set of four key kinases involved in cancer. In each case, it was possible to produce microarrays that could be used for quantitative small-molecule screening assays and production of dose-dependent inhibitor response curves. Importantly, the ability to screen many materials produced information on the types of materials that best suited both microarray production and retention of enzyme activity. The materials data provide insight into basic material requirements necessary for tailoring optimal, high-density sol-gel derived microarrays.
Chemistry, Issue 78, Biochemistry, Chemical Engineering, Molecular Biology, Genetics, Bioengineering, Biomedical Engineering, Chemical Biology, Biocompatible Materials, Siloxanes, Enzymes, Immobilized, chemical analysis techniques, chemistry (general), materials (general), spectroscopic analysis (chemistry), polymer matrix composites, testing of materials (composite materials), Sol-gel, microarray, high-throughput screening, acetylcholinesterase, kinase, drug discovery, assay
Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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
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
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
Institutions: The University of Texas Graduate School of Biomedical Sciences at Houston.
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
Genetics, Issue 93, EML Cells, Self-renewal, Differentiation, Hematopoietic precursor cell, RNA-Sequencing, Data analysis
Using Coculture to Detect Chemically Mediated Interspecies Interactions
Institutions: University of North Carolina at Chapel Hill .
In nature, bacteria rarely exist in isolation; they are instead surrounded by a diverse array of other microorganisms that alter the local environment by secreting metabolites. These metabolites have the potential to modulate the physiology and differentiation of their microbial neighbors and are likely important factors in the establishment and maintenance of complex microbial communities. We have developed a fluorescence-based coculture screen to identify such chemically mediated microbial interactions. The screen involves combining a fluorescent transcriptional reporter strain with environmental microbes on solid media and allowing the colonies to grow in coculture. The fluorescent transcriptional reporter is designed so that the chosen bacterial strain fluoresces when it is expressing a particular phenotype of interest (i.e.
biofilm formation, sporulation, virulence factor production, etc
.) Screening is performed under growth conditions where this phenotype is not
expressed (and therefore the reporter strain is typically nonfluorescent). When an environmental microbe secretes a metabolite that activates this phenotype, it diffuses through the agar and activates the fluorescent reporter construct. This allows the inducing-metabolite-producing microbe to be detected: they are the nonfluorescent colonies most proximal to the fluorescent colonies. Thus, this screen allows the identification of environmental microbes that produce diffusible metabolites that activate a particular physiological response in a reporter strain. This publication discusses how to: a) select appropriate coculture screening conditions, b) prepare the reporter and environmental microbes for screening, c) perform the coculture screen, d) isolate putative inducing organisms, and e) confirm their activity in a secondary screen. We developed this method to screen for soil organisms that activate biofilm matrix-production in Bacillus subtilis
; however, we also discuss considerations for applying this approach to other genetically tractable bacteria.
Microbiology, Issue 80, High-Throughput Screening Assays, Genes, Reporter, Microbial Interactions, Soil Microbiology, Coculture, microbial interactions, screen, fluorescent transcriptional reporters, Bacillus subtilis
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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
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
Strategies for Study of Neuroprotection from Cold-preconditioning
Institutions: The University of Chicago Medical Center.
Neurological injury is a frequent cause of morbidity and mortality from general anesthesia and related surgical procedures that could be alleviated by development of effective, easy to administer and safe preconditioning treatments. We seek to define the neural immune signaling responsible for cold-preconditioning as means to identify novel targets for therapeutics development to protect brain before injury onset. Low-level pro-inflammatory mediator signaling changes over time are essential for cold-preconditioning neuroprotection. This signaling is consistent with the basic tenets of physiological conditioning hormesis, which require that irritative stimuli reach a threshold magnitude with sufficient time for adaptation to the stimuli for protection to become evident.
Accordingly, delineation of the immune signaling involved in cold-preconditioning neuroprotection requires that biological systems and experimental manipulations plus technical capacities are highly reproducible and sensitive. Our approach is to use hippocampal slice cultures as an in vitro
model that closely reflects their in vivo
counterparts with multi-synaptic neural networks influenced by mature and quiescent macroglia / microglia. This glial state is particularly important for microglia since they are the principal source of cytokines, which are operative in the femtomolar range. Also, slice cultures can be maintained in vitro
for several weeks, which is sufficient time to evoke activating stimuli and assess adaptive responses. Finally, environmental conditions can be accurately controlled using slice cultures so that cytokine signaling of cold-preconditioning can be measured, mimicked, and modulated to dissect the critical node aspects. Cytokine signaling system analyses require the use of sensitive and reproducible multiplexed techniques. We use quantitative PCR for TNF-α to screen for microglial activation followed by quantitative real-time qPCR array screening to assess tissue-wide cytokine changes. The latter is a most sensitive and reproducible means to measure multiple cytokine system signaling changes simultaneously. Significant changes are confirmed with targeted qPCR and then protein detection. We probe for tissue-based cytokine protein changes using multiplexed microsphere flow cytometric assays using Luminex technology. Cell-specific cytokine production is determined with double-label immunohistochemistry. Taken together, this brain tissue preparation and style of use, coupled to the suggested investigative strategies, may be an optimal approach for identifying potential targets for the development of novel therapeutics that could mimic the advantages of cold-preconditioning.
Neuroscience, Issue 43, innate immunity, hormesis, microglia, hippocampus, slice culture, immunohistochemistry, neural-immune, gene expression, real-time PCR
A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
Institutions: Universite de Montreal, Universite de Montreal, Universite de Montreal.
There are several lines of evidence supporting the role of de novo
mutations as a mechanism for common disorders, such as autism and schizophrenia. First, the de novo
mutation rate in humans is relatively high, so new mutations are generated at a high frequency in the population. However, de novo
mutations have not been reported in most common diseases. Mutations in genes leading to severe diseases where there is a strong negative selection against the phenotype, such as lethality in embryonic stages or reduced reproductive fitness, will not be transmitted to multiple family members, and therefore will not be detected by linkage gene mapping or association studies. The observation of very high concordance in monozygotic twins and very low concordance in dizygotic twins also strongly supports the hypothesis that a significant fraction of cases may result from new mutations. Such is the case for diseases such as autism and schizophrenia. Second, despite reduced reproductive fitness1
and extremely variable environmental factors, the incidence of some diseases is maintained worldwide at a relatively high and constant rate. This is the case for autism and schizophrenia, with an incidence of approximately 1% worldwide. Mutational load can be thought of as a balance between selection for or against a deleterious mutation and its production by de novo
mutation. Lower rates of reproduction constitute a negative selection factor that should reduce the number of mutant alleles in the population, ultimately leading to decreased disease prevalence. These selective pressures tend to be of different intensity in different environments. Nonetheless, these severe mental disorders have been maintained at a constant relatively high prevalence in the worldwide population across a wide range of cultures and countries despite a strong negative selection against them2
. This is not what one would predict in diseases with reduced reproductive fitness, unless there was a high new mutation rate. Finally, the effects of paternal age: there is a significantly increased risk of the disease with increasing paternal age, which could result from the age related increase in paternal de novo
mutations. This is the case for autism and schizophrenia3
. The male-to-female ratio of mutation rate is estimated at about 4–6:1, presumably due to a higher number of germ-cell divisions with age in males. Therefore, one would predict that de novo
mutations would more frequently come from males, particularly older males4
. A high rate of new mutations may in part explain why genetic studies have so far failed to identify many genes predisposing to complexes diseases genes, such as autism and schizophrenia, and why diseases have been identified for a mere 3% of genes in the human genome. Identification for de novo
mutations as a cause of a disease requires a targeted molecular approach, which includes studying parents and affected subjects. The process for determining if the genetic basis of a disease may result in part from de novo
mutations and the molecular approach to establish this link will be illustrated, using autism and schizophrenia as examples.
Medicine, Issue 52, de novo mutation, complex diseases, schizophrenia, autism, rare variations, DNA sequencing
Pyrosequencing: A Simple Method for Accurate Genotyping
Institutions: Washington University in St. Louis.
Pharmacogenetic research benefits first-hand from the abundance of information provided by the completion of the Human Genome Project. With such a tremendous amount of data available comes an explosion of genotyping methods. Pyrosequencing(R) is one of the most thorough yet simple methods to date used to analyze polymorphisms. It also has the ability to identify tri-allelic, indels, short-repeat polymorphisms, along with determining allele percentages for methylation or pooled sample assessment. In addition, there is a standardized control sequence that provides internal quality control. This method has led to rapid and efficient single-nucleotide polymorphism evaluation including many clinically relevant polymorphisms. The technique and methodology of Pyrosequencing is explained.
Cellular Biology, Issue 11, Springer Protocols, Pyrosequencing, genotype, polymorphism, SNP, pharmacogenetics, pharmacogenomics, PCR
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