23 Transcriptome Analysis: Microarrays Charles Hindmarch Clinical Sciences, University of Bristol, UK 23.1 Basic ‘how-to-do’ and ‘why-do’ section Investigations into the genomic response to environmental fluctuation or pathology have previously been limited to the weight of supporting literature, researcher intuition and the availability of appropriate probes. Microarray allows a non-biased approach to identifying which genes change their expression in a particular tissue or cell type in response to a given physiological challenge. A microarray is a technology that allows simultaneous measurement of the expression of hundreds, thousands or tens of thousands of genes. In its simplest form, a microarray is a library of cDNA or oligonucleotide probes that have been immobilized onto a substrate such as nylon, glass or quartz. Each of these probes is a sequence that will hybridize to a specific and known messenger ribonucleic acid (mRNA) sequence according to Crick-Watson base pair rules – one probe, one gene. Because microarray interrogates the 1 transcriptome, the first step is to extract and purify RNA from each sample. Total RNA is composed of different populations, of which only 5 per cent is considered to be coding (mRNA), with the remaining fractions being ribosomal and non-coding small RNA species such as micro-RNAs. Given that the total RNA concentration of a single cell is in the low picogram range, obtaining sufficient mRNA to perform transcriptomic analysis can be a challenge. However, amplification protocols address these needs. Selective amplification of mRNA during reverse transcription is ensured through the use of specialized Essential Guide to Reading Biomedical Papers: Recognising and Interpreting Best Practice, First Edition. Edited by Phil Langton. Ó 2013 by John Wiley & Sons, Ltd. Published 2013 by John Wiley & Sons, Ltd. 1 Transcriptome – all mRNA transcripts from the sample.
Transcript
CH23 09/26/2012 12:12:4 Page 203
23Transcriptome Analysis:Microarrays
Charles HindmarchClinical Sciences, University of Bristol, UK
23.1 Basic ‘how-to-do’ and ‘why-do’ sectionInvestigations into the genomic response to environmental fluctuation or pathology
have previously been limited to the weight of supporting literature, researcher
intuition and the availability of appropriate probes. Microarray allows a non-biased
approach to identifying which genes change their expression in a particular tissue or
cell type in response to a given physiological challenge.
A microarray is a technology that allows simultaneous measurement of the
expression of hundreds, thousands or tens of thousands of genes. In its simplest
form, a microarray is a library of cDNA or oligonucleotide probes that have been
immobilized onto a substrate such as nylon, glass or quartz. Each of these probes
is a sequence that will hybridize to a specific and known messenger ribonucleic
acid (mRNA) sequence according to Crick-Watson base pair rules – one probe,
one gene.
Because microarray interrogates the 1transcriptome, the first step is to extract and
purify RNA from each sample. Total RNA is composed of different populations, of
which only �5 per cent is considered to be coding (mRNA), with the remaining
fractions being ribosomal and non-coding small RNA species such as micro-RNAs.
Given that the total RNA concentration of a single cell is in the low picogram range,
obtaining sufficient mRNA to perform transcriptomic analysis can be a challenge.
However, amplification protocols address these needs. Selective amplification of
mRNA during reverse transcription is ensured through the use of specialized
Essential Guide to Reading Biomedical Papers: Recognising and Interpreting Best Practice, First Edition.
Edited by Phil Langton.
� 2013 by John Wiley & Sons, Ltd. Published 2013 by John Wiley & Sons, Ltd.
1 Transcriptome – all mRNA transcripts from the sample.
CH23 09/26/2012 12:12:4 Page 204
primers bearing a T7 promoter region that binds to the polyadenylation site on the
transcript. During the second round of amplification and subsequent in vitro
transcription, two goals are achieved:
1. Fluorescent label can be incorporated into the synthesized material.
2. Sufficient amount of this material is available for hybridization to the microarray.
Finally, it is necessary for the product of these reactions to be fragmented to allow
an efficient and reproducible hybridization to the probes on the array. You may
wonder why fragmentation is necessary, but the necessity emerges from the facts
that the probes on the microarray are of a limited size and that the second round
synthesis will faithfully reproduce the cDNA, regardless of length.
The earliest chips were ‘spotted’ arrays, which consisted of a library of probes
that were printed onto a glass slide using a head of needles. For these experiments,
the control and the treated sample needed to be labelled differently (e.g. Cy3 or Cy5)
and the samples combined prior to hybridization. Using different dyes carries the
potential for bias (that the dye might affect hybridization efficiency), and this was
controlled using a ‘dye-swap’ control that repeated the experiment, but with each
sample incorporating the ‘other’ dye.
These early array experiments were plagued with technical difficulties, such
as spotting irregularities (often called doughnuts, on account of their ring
shaped misprint), and analytical challenges such as normalization strategies
(see below). However, these pioneer chips became the basis upon which all
current whole genome analysis experiments are now based. Modern microarrays
eliminate many of the experimental problems that the spotted ancestors encoun-
tered, mainly because of the high throughput manufacturing with which the
technology is produced. These developments bring the following advantages:
� Chip-to-chip variation is so small that control and treated samples can be
hybridized to different microarrays.
� Separate chips are used for separate samples, a single label (biotin) can be used
and the issues that surrounded dye-bias are eliminated.
� Probes are often synthesized directly from sequences that have been uploaded
to public databases, so probe error is minimized and changes in gene
annotation/function can be easily updated.
� Individual probes are often made of multiple overlapping smaller probes,
so that some statistical inference of the level of non-specific hybridization can
be drawn.
Regardless of the technology employed, microarray data relies on the relative
hybridization ratio between the control sample and a treated sample to each single
204 CH23 TRANSCRIPTOME ANALYSIS: MICROARRAYS
CH23 09/26/2012 12:12:4 Page 205
probe in the library on the chip. The result of a microarray experiment comparing
two (or more) conditions is a list of probe identifiers that relate to genes, and
expression values in which the end user has confidence are:
� expressed in the tissue/sample;
� differentially expressed by some prescribed fold-change cut-off criterion;
� significantly different between the treatments.
Having satisfied these criteria, this list of genes represents the minimum usage of
the available data. More advanced bioinformatic analysis of such lists can establish
gene-function, generate functional networks and drive hypothesis detection. It is
important to note that while microarray is a hypothesis machine, it does not stand
alone in experimental biology. The validation of some of the identified genes using
an independent technique (e.g. qPCR – see Primer 20) will satisfy that the ‘false
discovery rate’ (see below) is low and will give confidence in those other
significantly regulated genes revealed in the experiment.
23.2 Required controls23.2.1 Sample collection
Special care is required when collecting biological samples, extracting RNA and
sample preparation, because RNA is very liable to enzymatic degradation by both
endogenous and introduced ribonucleases (RNases). Fortunately, several commer-
cially available chemicals ensure that RNases can be controlled and, together with
experimental diligence, degradation can be kept at bay. All tools required for the
dissection or culture of biological material under study must be free of RNase
contamination, and any reagents required must be made using RNase-free solutions
and chemicals and must be handled in an aseptic manner at all times. It is also
appropriate to claim an area of the laboratory within which only RNAwork will be
performed (Figure 23.1a).
Once extracted, tissue or cells should be stored in an appropriate manner to
protect against endogenous RNase degradation. Commercially available reagents
that RNase-protect samples are available, but ultra-low temperatures help to ensure
that samples are kept ‘safe’.
Successful microarray experiments are dependent on the quality and quantity of
the biological samples that are used. Experiments based on tissue dissections, are
subject to huge sources of error that can result from including RNA from
neighbouring tissues that either contaminate or dilute the biological signal being
studied. It is therefore important for dissections to be maintained by a single
competent operator, and for those dissections to be consistent between samples and
based on appropriate anatomical atlas for that species.
23.2 REQUIRED CONTROLS 205
CH23 09/26/2012 12:12:4 Page 206
Figure 23.1 Overview of the features of microarray experiments. A: Meticulous attention toRNase control is required in microarray experiments. It is a good idea to establish a clean spacewithin which RNase contamination can be controlled. B: The number of publications in theliterature that use the term ‘microarray’ in the text has increased dramatically in the past 10 years.With over 47,000 publications, the quantity of data available is immense. C: Before and afternormalisation of microarray data. In this box andwhisker plot, each sample represents the spread of31,099 data points and the bold bar represents the median expression value. Normalisation ofmicroarrays ensures that variations in the data that are due to some technical aspect or someuncontrolled biological aspect are ‘ironed out’ between samples. D: Principle components analysis(PCA) establishes the degree of variability between samples; each point on this PCA representsa single sample and the expression of over 30,000 probes. Four groups of samples emerge from thisanalysis that correspond to three different treatments. E: The Venn diagram, useful for comparingdifferent lists of regulated genes so that those commonly regulated elements may be distinguishedfrom those that are unique to a particular condition. F. Clustering of gene expression data can act asa quality control check and identify gene expression patterns across multiple datasets; here the fivegroups are from the same brain region but the top two on the right cluster differently because theyare from a different strain of animal than the other three. Courtesy of Dr. Charles Hindmarch.
206 CH23 TRANSCRIPTOME ANALYSIS: MICROARRAYS
CH23 09/26/2012 12:12:4 Page 207
RNA quality should be assessed following extraction to ensure that the material is
of sufficient quantity and quality for use on the microarray. Technologies that rely
on spectrophotometric analysis of RNA can establish the concentration, based on
the absorbance at 260 nm (A260¼ 1¼ 40mg/ml), and quality, based on the ratio
between the absorbance at 260 nm and 280 nm (indicating contamination with
proteins). RNA integrity (if the sample is degraded) can be assessed by running the
sample on a denaturing gel or a microcapillary system that will show bands (or
spikes) for the 18s and 28s ribosomal RNA in the samples whose integrity are a good
proxy for mRNA quality.
Usually, the handling of RNA in an array experiment is boiled down to just a
simple statement such as ‘all dissections were performed in an RNase-free manner’
or ‘quality control was assured in each sample using spectrophotometric analysis’.
It is not always the case that samples were collected and stored in laboratory
conditions (collection of RNA in the field or biopsy following surgery, for example).
In these cases, the integrity of the RNA might warrant specific reference to quality
and consistency between samples.
23.2.2 Sample replication and sample pooling
The microarray experiment is not unlike any other; each measurement (each
genome expression microarray) is a snapshot of a continuous biological process
that varies according to treatment. To ensure that this snapshot is an appropriate
one for the biological process, it is necessary to introduce both biological and
technical replicates into a microarray experiment (see also Primer 2). These
replicates are required to account for 2variations in the biology and 3technical
aspects of the experiment that cannot adequately be controlled for within the
experiment.
While both technical and biological replication can be achieved by increasing
the number of arrays used in the experiment, pooling of sample from different
animals onto a single microarray is a good way to reduce population level
effects on the experiment. When using an outbred animal population, for
example, each array can be turned into a microcosm of the animal population.
When pooling, each array should represent samples that are independent from
one another. The methodology of the paper should explicitly state the number of
microarrays that have been used for each condition and the exact nature of the
biological tissue that has been hybridized onto each array. Such information is a
requirement on the public databases to which most journals require microarray
data to be submitted.
2 ‘Variations in the biology’ include circadian rhythm, oestrous cycling, outbred strain, etc.3 ‘Technical aspects of the experiment’ include experimenter variation, surgical precision and other such
errors.
23.2 REQUIRED CONTROLS 207
CH23 09/26/2012 12:12:4 Page 208
23.2.3 Normalization
Microarray data walks a fine line between the 4false positive result and the 5false
negative result. Because microarrays represent so many probes, it can often be
difficult to work out how to handle the data so as to avoid false positive results
without being so stringent that you introduce false negative results. Careful
normalization and statistical testing with appropriate multiple test correction
(see below) is critical for minimizing false results in array experiments.
The process of normalization ensures that any technical (e.g. background
signal between two arrays) or uncontrolled (e.g. different experimental batch)
aspect of the experiment is removed from the data, so that the differences that
remain result from the treatment under study. Figure 23.1C shows the expression
value of nine microarrays; four independent control replicates and five inde-
pendent treated replicates. The plot for each sample shows the range of
expression values and the bold horizontal line shows the median value of
each array. Clearly, the median value is more variable in the control state,
and a difference between the medians of the control and the treated arrays
appears to exist.
Two assumptions about microarray experiments need to be made in order to
understand the reasons for normalization:
� First, it is expected that all arrays within the same condition should be broadly
similar because they are replicates;
� Second, we expect the majority of the 31,099 probes on the array not to change,
even in response to a treatment.
With these assumptions made, it is generally accepted that variations in the
total expression range must come from non-controlled factors. Normalization
nudges all the microarrays onto the same playing field so that these experimental
artefacts do not overemphasize the expression of a gene (false positive result) or,
indeed, mask it (false negative result). There are several different normalization
strategies available, and their choice depends on various factors, including sample
size and data quality. These should be outlined in the methodology of the micro-
array paper.
23.2.4 Multiple testing correction
In any experiment in which differences between one or more treatments and a
control are sought, it is necessary to decide whether observed differences are
4 False positive: the data says the mRNA is up-regulated when it is not.5 False negative: the data says the mRNA is not regulated when it is.
208 CH23 TRANSCRIPTOME ANALYSIS: MICROARRAYS
CH23 09/26/2012 12:12:5 Page 209
likely to be due to real differences among the groups (see both Primers 2 and 3).
Statistical testing allows the following question to be answered; is the difference
between two sets of data a product of chance or is it significantly different? If the
probability that the result occurred by chance is less than five per cent (p< 0.05),
then we can be 95 per cent confident that the difference is real. This five per cent
can be considered a false discovery rate (FDR). A problem exists, however, when
more than one test is being performed, because the false discovery rate changes
with the number of tests, according the equation:
FDR ¼ p-value cut-of f � number of tests
The result is that when tens of thousands of tests are performed as with an array
experiment, the overall FDR will be near 100 per cent and there will be no
confidence in any result. Multiple test correction is a statistical technique that
accounts for the changing FDR by modifying the p-value threshold in proportion to
the number of tests being performed, with the result that the corrected false
discovery rate is always below 5 per cent. Most of the multiple test corrections
rank the observed p-values of each test performed and correct as a function of the
total number of tests. The main difference between the various correction protocols
available is their stringency; given the high number of tests performed in an array
experiment, even the seemingly smallest p-value can be rendered insignificant and
can produce a false negative result.
23.2.5 Microarray data
Each probe on a microarray is assigned a unique identifier to distinguish it from all
other probes and to provide a reference, so that they can be updated with changing
descriptions and annotation. The basic information usually desired includes, but is
not restricted to:
� Internal ID – unique identifier of probe assigned by the manufacturer
� Gene symbol
� Gene Description
� Genbank ID
� Unigene cluster ID
This is complemented by information gained from the experiment, such as:
� average raw expression value in a particular condition;
� p-values of probe;
23.2 REQUIRED CONTROLS 209
CH23 09/26/2012 12:12:5 Page 210
� fold-change regulation between particular conditions;
� statistics involved in selecting differentially regulated genes.
23.2.6 Visualizing microarray data
Reading gene lists in journal articles is very boring – fact! It is a real challenge for
authors to find interesting ways both to share the findings of their data and to engage
with the scientific community about their meaning; figures in journal articles should
be designed to convey those patterns evident in the data. Microarray data is usually
presented in a few simple formats that have almost become as recognizable and easy
to interpret to the lay scientist as a Western blot or a histogram.
The principal component analysis (PCA) allows the expression profile of all
probes from each sample to be used to plot the relationship between the samples
in a three-dimensional space. Each ‘component’ allows the degree of variation in
a particular direction to be plotted, with the first component being the largest
degree of separation, and each subsequent component representing smaller
separations. Figure 23.1d represents 20 microarrays with four different treatments
under investigation, and the PCA is performed on all 31,099 probes on each array.
It is satisfying to see that the gene expression profiles correlate to treatment.
The PCA is also an excellent method to identify outlier microarrays whose
primary quality control should be checked to ensure that the array experiment was
a success.
The heat map is a way to show the expression change of individual genes
between two conditions using blocks of colour, but it is still just a gene list,
albeit nicely coloured, so it has a limit on the number of genes that the reader can
digest. A heat map becomes more useful when combined with a second analysis,
such as clustering (Figure 23.1f). Such analysis will allow the author to show
you that some conditions are more similar to each other than are others. For
example, Figure 23.1f shows five conditions that have clustered according to the
strain of animal used, suggesting that gene expression is highly strain-dependent
(Hindmarch et al., 2007).
Using conventional graphs can also be useful, but again only for relatively small
numbers of genes, otherwise they lose their usefulness. The Venn diagram
(Figure 23.1e) allows different lists of significantly regulated genes to be compared,
so that both the number and proportion of genes that overlap between the two
experiments can be visualized. In Figure 23.1e, a list of genes that are regulated by
treatment A is compared to a list of genes that is affected by treatment B. The
intersection between these two represents a list of genes that are regulated by
both treatments. The sections that do not intersect, therefore, list genes that are
affected by either treatment A or B. These lists now start to become more useful in
downstream and more advance analysis that, for example, may seek to establish the
function of those genes affected only by treatment B.
210 CH23 TRANSCRIPTOME ANALYSIS: MICROARRAYS
CH23 09/26/2012 12:12:5 Page 211
23.2.7 Advanced analysis
The advances in microarray experiments have certainly increased the amount of
expression information available, but these data are being been generated at a faster
rate than can be understood. The number of experiments that include microarrays
has been increasing sharply year-on-year since 2000. At the time this primer was
written (April 2012), the term ‘microarray’ returned over 47,000 hits on the website
Pubmed (Figure 23.1B; www.ncbi.nlm.nih.gov/sites/entrez), with each publication
potentially reporting the expression of hundreds or thousands of probes in a
particular paradigm.
The non-biased approach to data collection is essentially undermined by an
inadequate and heavily biased approach to investigation and analysis; scientists are
still resorting to studying the genes on the microarray that they know! In order to
investigate further the physiological functions of the genes regulated in an experi-
ment, a Gene Ontology analysis can be performed. Each gene is annotated with
various functional ontologies called GO terms, and such terms are split into three
broad domains:
� Cellular component.
� Molecular function.
� Biological Process.
Within these domains, the vocabulary is well controlled and subject to constant
flux in the light of novel research. GO analysis relies on the probability that a
particular term will appear in any given gene list above that of pure chance (on the
entire data set – see Hindmarch et al., 2011).
Searching for patterns in vast fields of data that may convey meaning is a
substantial challenge and has led to the development of novel approaches that have
become associated with the term bioinformatics. The novel analytical approaches
that characterize bioinformatics are ongoing and require the collaboration of
biologists, mathematicians and computer scientists. Using these new methods,
the complexity of life is being modelled, tested and re-modelled. Network recon-
struction strategies are starting to make patterns from transcriptomic (and proteo-
mic) data and are attempting to identify those genes in a list that are most important
for the stability of the network and, thus, which are best to target in order to modify
and manipulate a network and therefore, perhaps, the disease state that the network
represents.
23.2.8 Experimental pipeline
The pipeline presented here is highly simplified and every stage requires careful
optimization prior to the ‘final experiment’ being performed.Microarray experiments
23.2 REQUIRED CONTROLS 211
CH23 09/26/2012 12:12:5 Page 212
can be expensive, so care and attention should be lavished on each of the following
steps:
1. Establish a biological question and design a robust experiment that will
incorporate both biological and technical replicates.
2. Dissect/obtain the biological material from the experimental groups in the
study and store in a manner that will reduce the risk of RNase degradation. Be
prepared to validate RNA stability.
3. Extract and purify the RNA from these samples in a single batch (if possible) to
