MOLECULAR & CELLULAR NEUROBIOLOGY 
Master Course Cognitive Neuroscience - Radboud University, Nijmegen
 

Chapter 5: Molecular biological research methodology
           Molecular biology and Recombinant DNA technology Detection of DNA, RNA and protein Generation of gene expression atlases of the CNS
           Techniques used in Molecular Biology    Detection of RNA Gene transfer - transgenic animals
           Genetic transmission    In situ hybridization Optogenetics
           Genetic mapping    PCR Cloning
           Genomic and cDNA libraries    Microarray and RNA-seq analysis Stem cells
   Bioinformatics - data analysis    CRISPR-cas genome editing
  ChIP-chip/seq  
 

Microarray analysis

 

DNA microarray technology (also known as DNA arrays, DNA chips, gene chip or biochips) started to appear during the second half of the 1990s and has historically evolved from the initial experimental reports published in the mid 1970s which indicated that labelled nucleic acids could be used to monitor the expression of nucleic acid molecules attached to a solid support. In a broad sense, the technology may be defined as a high-throughput,  large­scale and versatile technology used for parallel gene expression analysis for thousands of genes of known and unknown function (e.g. comparative mRNA expression profiling and genome-wide analysis of mRNA expression), or DNA homology analysis for detecting polymorphisms and mutations in both prokaryotic and eukaryotic genomic DNA. Either 25-nucleotide long fragments of known DNA sequences (oligonucleotide arrays for sequence variation studies) or cDNA fragments (cDNA arrays for expression profile studies) are immobilised on glass surfaces on a 1.3cm x 1.3cm microarray in a predetermined order (grid). Thousands of fragments can be stored on a single chip. The sample of interest (tumour, tissue, species) to be examined for gene expression profile should be available in a form that will allow RNA extraction. The RNA is labelled with fluorescent and hybridised with the fragments on the microarray. Hybridisation events are captured by scanning the surface of the microarray with a laser scanning device and measuring the fluorescence intensity at each position in the microarray. The fluorescence intensity of each spot on the array is proportional to the level of expression of the gene represented by that spot. DNA microarrays have been used to understand the cell cycle, haematopoietic differentiation, interferon gamma treatment and cancer classification. The ability to monitor the expression levels of thousands of genes simultaneously offers the opportunity to expand the analysis of cancer genetics beyond single–candidate gene approaches. Microarrays are capable of monitoring the expression levels of the entire human genome using nanograms of total RNA. The major advantage of gene arrays is thus that they can provide information on thousands of targets in a single experiment. The challenge is, however, the interpretation of the microarray data. The key is to develop methods for recognizing meaningful gene expression patterns and distinguishing those patterns from noise.

DNA Microarray experiments

The principle of DNA microarray technology is based on the fact that complementary sequences of DNA can be used to hybridise immobilised DNA molecules. This involves three major multi-stage steps; 1. Manufacturing of microarrays: This step involves the availability of a chip or a glass slide with its special surface chemistry, the robotics used for producing microarrays by spotting the DNA (targets) onto the chip or for their in situ synthesis. Some array manufacturers offer custom analysis services and may perform the probe labeling and hybridization reactions as a service.
2. Sample preparation and array hybridisation step: This step involves mRNA or DNA isolation followed by fluorescent labelling of cDNA probes, hybridisation of the sample to the immobilised target DNA and removing the unhybridized cDNA .
3. Image acquisition and data analysis: Finally, this step involves microarray scanning, and image analysis using sophisticated software programs that allows us to quantify and interpret the data.

However, there are four major steps in performing a typical microarray experiment in the laboratory:

1. Sample preparation and labelling. Isolatie total RNA containing messenger RNA (mRNA) that ideally represents a quantitative copy of genes expressed at the time of sample collection (experimental sample & reference sample). This step is crucial, simply because the overall success of any microarray experiment depends on the quality of the RNA.The sample mRNA extracted from the biological sample of interest and the reference are then separately converted into complementary DNA (cDNA) using a reverse-transcriptase enzyme. This step also requires a short primer to initiate cDNA synthesis. Next, each cDNA (Sample and Control) are labelled with a different tracking molecule, often fluorescent cyanine dyes (i.e. Cy3 and Cy5)
2. Hybridisation (the process of joining two complementary strands of DNA to form a double-stranded molecule). The labelled cDNAs (Sample and Control) are mixed together and then competitively hybridised against denatured PCR product or cDNA molecules spotted on a glass slide. Ideally, each molecule in the labelled cDNA will only bind to its appropriate complementary target sequence on the immobilised array.
3. Washing. First to remove any labelled cDNA that did not hybridise on the array, and secondly to increase stringency of the experiment to reduce cross hybridisation. The later is achieved by either increasing the temperature or lowering the ionic strength of the buffers.
4. Image acquisition and Data analysis: produce an image of the surface of the hybridised array. The slide is dried and placed into a laser scanner to determine how much labelled cDNA (probe) is bound to each target spot. Laser excitation of the incorporated targets yield an emission with characteristic spectra, which is measured using a confocal laser microscope.

Validation

Differences in expression of specific sequences are often validated by another method of analysis, such as RT-PCR or Northern blot analysis. These same methods can be used for relative or absolute quantitation of specific messages of interest identified by array analysis.

Click here for video 1 on microarrays and here for video 2 on microarrays.

Click DNA microarray for an animation.

Click DNA microarray for a movie.

 

Microarray - technique

Preparation and hybridisation

 

Microarray - construction

 

 
Microarray - results

                                       Subarray with up- and downregulated genes indicated - duplicate experiment

 

                                   Deviation indicates upregulation (red) or downregulation (green)

 

Example

Neuronal plasticity of the brain dopamine system is thought to underlie the behavioral changes elicited by chronic exposure to drugs of abuse such as cocaine. To identify the potential mechanisms responsible for these adaptive changes, three genetically distinct mouse models of altered dopaminergic function have been used.  These genetic animal models that recapitulate the pharmacological models of "behavioral sensitization" associated with exposure to psychostimulants (dopamine transporter knockout; DAT-KO); tricyclic antidepressants (norepinephrine transporter knockout, NET-KO) and reserpine (vesicular monoamine transporter knockout, VMAT2+/-), all demonstrate enhanced behavioral responses to direct or indirect dopamine receptor agonists.

By microarray expression profiling of 36,000 genes/ESTs (expressed sequence tags), a handful of commonly up- and down-regulated genes in the striatum of the three mutant lines was identified. Comparison of this profile with that of normal mice pharmacologically sensitized to cocaine, further restricted this collection to only two down-regulated genes, giving insight into a possible general mechanism underlying drug-induced neuronal and behavioral plasticity.

         

      

 

RNA sequencing (RNA-seq) analysis

RNA-seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Conceptually, the RNAseq approach is very simple. Sequence reads are generated from random locations along each RNA by either sequencing sheared double-stranded cDNA libraries (strandless RNA-seq), or by sequencing fragmented RNA populations (stranded RNA-seq). After sequencing en masse, the short reads are then mapped back against the appropriate reference genome or catalogues of all exon-junction sequences to provide a global survey of transcriptome activity (see figure at the bottom). Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes (the complete set of transcripts in a cell).

Advantages of RNA-seq for investigating the transcriptome

RNA-seq has several advantages over microarrays:

1. Gene-expression profiling by RNA-seq has been shown to be very robust and highly quantitative. The reproducibility of the approach has been shown to be extremely high (Pearson correlations of 0.99 have been reported for replicate RNA-seq runs) and raw tag counts (typically between 20 and 40 nucleotides) correlate well with quantitative real-time PCR results. For a microarray experiment, where image-derived intensities are used to determine relative abundance of transcripts, the dynamic range of expression is constrained to a maximum of four to five orders of magnitude. Although rare transcripts can be detected by prolonging image exposure, the image becomes saturated for the most highly expressed transcripts, and the relative expression of these transcripts is lost. In contrast, the dynamic range of RNAseq is potentially unlimited, as tag counts are used to directly determine transcript abundance.

2. RNA-seq is more sensitive than microarray platforms. When sequence depths of 10-100 million reads per biological sample are compared with expression arrays, many genes whose activities are below detection limit on the array are readily observed. Importantly, this sensitivity is tuneable by altering sequencing depth. No more than 3-4 Gb is needed to obtain near complete coverage of mammalian transcriptomes.

3. Expression profiling by RNA-seq is more precise than hybridization-based approaches, where RNAs sharing more than 75% sequence identity to the probe will cross-hybridize. The high levels of sequence identity used for mapping (96-100%) allow one to profile highly homologous transcripts that would otherwise be confounded by cross-hybridization in microarray-based experiments. Repetitive sequences have always been excluded from array probe designs for this very reason, and these elements can now be profiled using RNA-seq.

4. Unlike arrays that use a defined set of probes to interrogate RNA samples, RNA-seq requires no previous assumptions about which parts of the genome are transcriptionally active. This provides the opportunity for transcriptome discovery, and large amounts of novel expression have been reported. As much as 25% of all observed expression in RNA sequence experiments falls outside known exons for mammalian genomes.

Biological insights from RNA-seq

Until now, one of the major challenges in transcriptomics has been how to survey which of these known RNAs are present in a single biological state. In addition to monitoring gene activity, RNA-seq can study alternative splicing events, and the usage of promoters and 3'-untranslated regions (3'-UTRs). These events can be detected by counting tags that match the portions of sequence unique to each transcript. These so-called 'diagnostic' sequences may correspond to cassette exons or the junction sequences arising from specific exon combinations.

The use of RNA-seq has, for the first time, enabled researchers to rapidly place genome-wide surveys of both known and novel transcriptional complexity into a biological context. In addition to identifying the presence and relative abundance of known transcripts, RNA-seq has regularly identified novel transcriptional content and complexity. RNA-seq is not just a means for measuring the relative abundance of transcripts, it is a massive-scale survey of sequence content, enabling the simultaneous analysis of gene expression and screening for sequence variation; RNA-seq can identify novel single nucleotide polymorphisms (SNPs) in exons.

Challenges for RNA-seq technology

As with any new technology, there are currently various limitations to RNA-seq that will need to be addressed. In species where genome builds are not complete, or where there is limited EST and mRNA characterization, inferring the scale and scope of transcriptional complexity will be challenging. Mapping of the RNA sequence tags to the genome sequence provides much needed precision in distinguishing the expression of homologous genomic sequences, but it is still not possible to discern the origin of a sequence tag that maps to more than one location. This means that parts of the transcriptome are undecipherable (known as multi-mapping or ambiguous regions). Furthermore, current RNA-seq methods are not yet suitable for samples where only very small amounts of RNA are available.

Despite these caveats, RNA-seq is heralding a new period in transcriptomics and is bringing much-needed sensitivity and discrimination to global gene expression assays. The power of the new sequencing technologies means it is now feasible to sequence the complete transcriptome in short random fragments, thus providing the opportunity to measure the expression of all known transcripts as well as systematically screening for novel expression. Being able to accurately survey sequence variation and gene activity simultaneously should enable a single experiment to yield large amounts of diverse information, for example, screening for mutations, monitoring allele-specific expression and studying post-transcriptional events, such as RNA editing, simultaneously in a single pathological sample.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

The identification of differential exon splicing by RNAseq. In this hypothetical genome-browser view, RNAseq tags (shown in red) are aligned to the genome sequence, giving a quantitative view of tag densities across the locus. Genome-aligned reads identify individual exons and exon-exon junction usage can be monitored by matching tags to a reference set of junction sequences. Differential exon-exon junction usage can be used to identify canonical and alternative splicing events.

 
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