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Edited by
Richard A. Shimkets
Gene Expression
Profiling
Methods and Protocols
Volume 258
METHODS IN MOLECULAR BIOLOGYTM
METHODS IN MOLECULAR BIOLOGYTM
Edited by
Richard A. Shimkets
Gene Expression
Profiling
Methods and Protocols

Technical Considerations 1
1
From: Methods in Molecular Biology, Vol. 258: Gene Expression Profiling: Methods and Protocols
Edited by: R. A. Shimkets © Humana Press Inc., Totowa, NJ
1
Technical Considerations
in Quantitating Gene Expression
Richard A. Shimkets
1. Introduction
Scientists routinely lecture and write about gene expression and the abun-
dance of transcripts, but in reality, they extrapolate this information from a vari-
ety of measurements that different technologies may provide. Indeed, there are
many reasons that applying different technologies to transcript abundance may
give different results. This may result from an incomplete understanding of the
gene in question or from shortcomings in the applications of the technologies.
The first key factor to appreciate in measuring gene expression is the way that
genes are organized and how this influences the transcripts in a cell. Figure 1
depicts some of the scenarios that have been determined from sequence analyses
of the human genome. Most genes are composed of multiple exons transcribed
with intron sequences and then spliced together. Some genes exist entirely
between the exons of other genes, either in the forward or reverse orientation.
This poses a problem because it is possible to recover a fragment or clone that
could belong to multiple genes, be derived from an unspliced transcript, or be
the result of genomic DNA contaminating the RNA preparation. All of these
events can create confusing and confounding results. Additionally, the gene dup-
lication events that have occurred in organisms that are more complex have led
to the existence of closely related gene families that coincidentally may lie near
each other in the genome. In addition, although there are probably less than 50,000
human genes, the exons within those genes can be spliced together in a variety
of ways, with some genes documented to produce more than 100 different tran-
scripts (1).

2 Shimkets
Therefore, there may be several hundred thousand distinct transcripts, with
potentially many common sequences. Gene biology is even more interesting
and complex, however, in that genetic variations in the form of single nucleo-
tide polymorphisms (SNPs) frequently cause humans and diploid or polyploid
model systems to have two (or more) distinct versions of the same transcript.
This set of facts negates the possibility that a single, simple technology can
accurately measure the abundance of a specific transcript. Most technologies
probe for the presence of pieces of a transcript that can be confounded by closely
related genes, overlapping genes, incomplete splicing, alternative splicing, geno-
mic DNA contamination, and genetic polymorphisms. Thus, independent meth-
ods that verify the results in different ways to the exclusion of confounding vari-
ables are necessary, but frequently not employed, to gain a clear understanding
of the expression data. The specific means to work around these confounding
variables are mentioned here, but a blend of techniques will be necessary to
achieve success.
2. Methods and Considerations
There are nine basic considerations for choosing a technology for quantitating
gene expression: architecture, specificity, sensitivity, sample requirement, cover-
age, throughput, cost, reproducibility, and data management.
2.1. Architecture
We define the architecture of a gene-expression analysis system as either an
open system, in which it is possible to discover novel genes, or a closed system
in which only known gene or genes are queried. Depending on the application,
there are numerous advantages to open systems. For example, an open system may
detect a relevant biological event that affects splicing or genetic variation. In
addition, the most innovative biological discovery processes have involved the
Fig. 1. Typical gene exon structure.

discovery of novel genes. However, in an era where multiple genome sequences
have been identified, this may not be the case. The genomic sequence of an orga-
nism, however, has not proven sufficient for the determination of all of the tran-
scripts encoded by that genome, and thus there remain prospects for novelty
regardless of the biological system. In model systems that are relatively unchar-
acterized at the genomic or transcript level, entire technology platforms may
be excluded as possibilities. For example, if one is studying transcript levels in
a rabbit, one cannot comprehensively apply a hybridization technology because
there are not enough transcripts known for this to be of value. If one simply
wants to know the levels of a set of known genes in an organism, a hybridization
technology may be the most cost-effective, if the number of genes is sufficient
to warrant the cost of producing a gene array.
2.2. Specificity
The evolution of genomes through gene or chromosomal fragment duplica-
tions and the subsequent selection for their retention, has resulted in many gene
families, some of which share substantial conservation at the protein and nucleo-
tide level. The ability for a technology to discriminate between closely related
gene sequences must be evaluated in this context in order to determine whether
one is measuring the level of a single transcript, or the combined, added levels
of multiple transcripts detected by the same probing means. This is a double-
edged sword because technologies with high specificity, may fail to identify one
allele, or may do so to a different degree than another allele when confronted
with a genetic polymorphism. This can lead to the false positive of an expres-
sion differential, or the false negative of any expression at all. This is addressed
in many methods by surveying multiple samples of the same class, and prob-
ing multiple points on the same gene. Methods that do this effectively are pre-
ferred to those that do not.
2.3. Sensitivity
The ability to detect low-abundance transcripts is an integral part of gene dis-
covery programs. Low-abundance transcripts, in principle, have properties that
are of particular importance to the study of complex organisms. Rare transcripts
frequently encode for proteins of low physiologic concentrations that in many
cases make them potent by their very nature. Erythropoietin is a classic exam-
ple of such a rare transcript. Amgen scientists functionally cloned erythropoietin
long before it appeared in the public expressed sequence tag (EST) database.
Genes are frequently discovered in the order of transcript abundance, and a
simple analysis of EST databases correctly reveals high, medium, and low abun-
dance transcripts by a direct correlation of the number of occurrences in that

4 Shimkets
database (data not shown). Thus, using a technology that is more sensitive has
the potential to identify novel transcripts even in a well-studied system.
Sensitivity values are quoted in publications for available technologies at con-
centrations of 1 part in 50,000 to 1 part in 500,000. The interpretation of these
data, however, should be made cautiously both upon examination of the method
in which the sensitivity was determined, as well as the sensitivity needed for the
intended use. For example, if one intends to study appetite-signaling factors and
uses an entire rat brain for expression analysis, the dilution of the target cells
of anywhere from 1 part in 10,000 to 1 part in 100,000 allows for only the most
abundant transcripts in the rare cells to be measured, even with the most sensi-
tive technology available. Reliance on cell models to do the same type of analy-
sis, where possible, suffers the confounding variable that isolated cells or cell
lines may respond differently in culture at the level of gene expression. An ideal
scenario would be to carefully micro dissect or sort the cells of interest and study
them directly, provided enough samples can be obtained.
In addition to the ability of a technology to measure rare transcripts, the sen-
sitivity to discern small differentials between transcripts must be considered.
The differential sensitivity limit has been reported for a variety of techniques
ranging from 1.5-fold to 5-fold, so the user must determine how important
small modulations are to the overall project and choose the technology while
taking this property into account as well.
2.4. Sample Requirement
The requirement for studying transcript abundance levels is a cell or tissue
substrate, and the amount of such material needed for analysis can be prohibi-
tively high with many technologies in many model systems. To use the above
example, dozens of dissected rat hypothalami may be required to perform a glo-
bal gene expression study, depending on the quantitating technology chosen.
Samples procured by laser-capture microdissection can only be used in the mea-
suring of a small number of transcripts and only with some technologies, or
must be subjected to amplification technologies, which risk artificially altering
transcript ratios.
2.5. Coverage
For open architecture systems where the objective is to profile as many tran-
scripts as possible and identify new genes, the number of independent tran-
scripts being measured is an important metric. However, this is one of the most
difficult parameters to measure, because determining what fraction of unknown
transcripts is missing is not possible. Despite this difficulty, predictive models
can be made to suggest coverage, and the intuitive understanding of the tech-
nology is a good gage for the relevance and accuracy of the predictive model.

The problem of incomplete coverage is perhaps one of the most embarrass-
ing examples of why hundreds of scientific publications were produced in the
1970’s and 1980’s having relatively little value. Many of these papers reported
the identification of a single differentially expressed gene in some model sys-
tem and expounded upon the overwhelmingly important new biological path-
way uncovered. Modern analysis has demonstrated that even in the most sim-
ilar biological systems or states, finding 1% of transcripts with differences is
common, with this number increasing to 20% of transcripts or more for sys-
tems when major changes in growth or activation state are signaled. In fact, the
activation of a single transcription factor can induce the expression of hundreds
of genes. Any given abundantly altered transcript without an understanding of
what other transcripts are altered, is similar to independent observers describing
the small part of an elephant that they can see. The person looking at the trunk
describes the elephant as long and thin, the person observing an ear believes it
to be flat, soft and furry, and the observer examining a foot describes the ele-
phant as hard and wrinkly. Seeing the list of the majority of transcripts that are
altered in a system is like looking at the entire elephant, and only then can it be
accurately described. Separating the key regulatory genes on a gene list from
the irrelevant changes remains one of the biggest challenges in the use of tran-
script profiling.
2.6. Throughput
The throughput of the technology, as defined by the number of transcript
samples measured per unit time, is an important consideration for some projects.
When quick turnaround is desired, it is impractical to print microarrays, but
where large numbers of data points need to be generated, techniques where
individual reactions are required are impractical. Where large experiments on
new models generate significant expense, it may be practical to perform a higher
throughput, lower quality assay as a control prior to a large investment. For
example, prior to conducting a comprehensive gene profiling experiment in a
drug dose-response model, it might be practical to first use a low throughput
technique to determine the relevance of the samples prior to making the invest-
ment with the more comprehensive analysis.
2.7. Cost
Cost can be an important driver in the decision of which technologies to
employ. For some methods, substantial capital investment is required to obtain
the equipment needed to generate the data. Thus, one must determine whether
a microarray scanner or a capillary electrophoresis machine is obtainable, or if
X-ray film and a developer need to suffice. It should be noted that as large com-
panies change platforms, used equipment becomes available at prices dramati-

6 Shimkets
cally less than those for brand new models. In some cases, homemade equip-
ment can serve the purpose as well as commercial apparatuses at a fraction of
the price.
2.8. Reproducibility
It is desired to produce consistent data that can be trusted, but there is more
value to highly reproducible data than merely the ability to feel confident about
the conclusions one draws from them. The ability to forward-integrate the find-
ings of a project and to compare results achieved today with results achieved
next year and last year, without having to repeat the experiments, is key to
managing large projects successfully. Changing transcript-profiling technolo-
gies often results in datasets that are not directly comparable, so deciding upon
and persevering with a particular technology has great value to the analysis of
data in aggregate. An excellent example of this is with the serial analysis of
gene expression (SAGE) technique, where directly comparable data have been
generated by many investigators over the course of decades and are available
online (http://www.ncbi.nlm.nih.gov).
2.9. Data Management
Management and analysis of data is the natural continuation to the discussion
of reproducibility and integration. Some techniques, like differential display,
produce complex data sets that are neither reproducible enough for subsequent
comparisons, nor easily digitized. Microarray and GeneCalling data, however,
can be obtained with software packages that determine the statistical signifi-
cance of the findings and even can organize the findings by molecular function
or biochemical pathways. Such tools offer a substantial advance in the genera-
tion of accretive data. The field of bioinformatics is flourishing as the number
of data points generated by high throughput technologies has rapidly exceeded
the number of biologists to analyze the data.
Reference
1. Ushkaryov, Y. A. and Sudhof, T. C. (1993) Neurexin IIIα: extensive alternative
splicing generates membrane-bound and soluble forms. Proc. Natl. Acad. Sci. USA
90, 6410–6414.

Technology Summary 7
7
2
Gene Expression Quantitation Technology Summary
Richard A. Shimkets
Summary
Scientists routinely talk and write about gene expression and the abundance of
transcripts, but in reality they extrapolate this information from the various mea-
surements that a variety of different technologies provide. Indeed, there are many
reasons why applying different technologies to the problem of transcript abun-
dance may give different results, owing to an incomplete understanding of the
gene in question or from shortcomings in the applications of the technologies.
There are nine basic considerations for making a technology choice for quantitat-
ing gene expression that will impact the overall outcome: architecture, specific-
ity, sensitivity, sample requirement, coverage, throughput, cost, reproducibility,
and data management. These considerations will be discussed in the context of
available technologies.
Key Words: Architecture, bioinformatics, coverage, quantitative, reproducibility,
sensitivity, specificity, throughput
1. Introduction
Owing to the intense interest of many groups in determining transcript levels
in a variety of biological systems, there are a large number of methods that have
been described for gene-expression profiling. Although the actual catalog of
all techniques developed is quite extensive, there are many variations on simi-
lar themes, and thus we have reduced what we present here to those techniques
that represent a distinct technical concept. Within these groups, we discovered
that there are methods that are no longer applied in the scientific community,
not even in the inventor’s laboratory. Thus, we have chosen to focus the methods
chapters of this volume on techniques that are in common use in the community

8 Shimkets
at the time of this writing. This work also introduces two novel technologies,
SEM-PCR and the Invader Assay, that have not been described previously.
Although these methods have not yet been formally peer-reviewed by the sci-
entific community, we feel these approaches merit serious consideration.
In general, methods for determining transcript levels can be based on tran-
script visualization, transcript hybridization, or transcript sequencing (Table 1).
The principle of transcript visualization methods is to generate transcripts
with some visible label, such as radioactivity or fluorescent dyes, to separate
the different transcripts present, and then to quantify by virtue of the label the
relative amount of each transcript present. Real-time methods for measuring
label while a transcript is in the process of being linearly amplified offer an
advantage in some cases over methods where a single time-point is measured.
Many of these methods employ the polymerase chain reaction (PCR), which is
an effective way of increasing copies of rare transcripts and thus making the
techniques more sensitive than those without amplification steps. The risk to
any amplification step, however, is the introduction of amplification biases that
occur when different primer sets are used or when different sequences are ampli-
fied. For example, two different genes amplified with gene-specific primer sets
in adjacent reactions may be at the same abundance level, but because of a ther-
modynamic advantage of one primer set over the other, one of the genes might
give a more robust signal. This property is a challenge to control, except by mul-
tiple independent measurements of the same gene. In addition, two allelic vari-
ants of the same gene may amplify differently if the polymorphism affects the
secondary structure of the amplified fragment, and thus an incorrect result may
be achieved by the genetic variation in the system. As one can imagine, tran-
script visualization methods do not provide an absolute quantity of transcripts
per cell, but are most useful in comparing transcript abundance among multiple
states.
Transcript hybridization methods have a different set of advantages and disad-
vantages. Most hybridization methods utilize a solid substrate, such as a micro-
array, on which DNA sequences are immobilized and then labeled. Test DNA
or RNA is annealed to the solid support and the locations and intensities on the
solid support are measured. In another embodiment, transcripts present in two
samples at the same levels are removed in solution, and only those present at
differential levels are recovered. This suppression subtractive hybridization
method can identify novel genes, unlike hybridizing to a solid support where
information generated is limited to the gene sequences placed on the array.
Limitations to hybridization are those of specificity and sensitivity. In addi-
tion, the position of the probe sequence, typically 20–60 nucleotides in length,
is critical to the detection of a single or multiple splice variants. Hybridization
methods employing cDNA libraries instead of synthetic oligonucleotides give

Technology Summary 9
inconsistent results, such as variations in splicing and not allowing for the test-
ing of the levels of putative transcripts predicted from genomic DNA sequence.
Hybridization specificity can be addressed directly when the genome sequence
of the organism is known, because oligonucleotides can be designed specifically
to detect a single gene and to exclude the detection of related genes. In the ab-
sence of this information, the oligonucleotides cannot be designed to assure
specificity, but there are some guidelines that lead to success. Protein-coding
regions are more conserved at the nucleotide level than untranslated regions,
so avoiding translated regions in favor of regions less likely to be conserved is
useful. However, a substantial amount of alternative splicing occurs immedi-
ately distal to the 3' untranslated region and thus designing in proximity to regions
following the termination codon may be ideal in many cases. Regions contain-
ing repetitive elements, which may occur in the untranslated regions of tran-
scripts, should be avoided.
Several issues make the measurement of transcript levels by hybridization a
relative measurement and not an absolute measurement. Those experienced with
hybridization reactions recognize the different properties of sequences anneal-
ing to their complementary sequences, and thus empirical optimization of tem-
peratures and wash conditions have been integrated into these methods.
Principle disadvantages to hybridization methods, in addition to those of
any closed system, center around the analysis of what is actually being mea-
sured. Typically, small regions are probed and if an oligonucleotide is designed
to a region that is common to multiple transcripts or splice variants, the result-
ing intensity values may be misleading. If the oligonucleotide is designed to an
exon that is not used in one sample of a comparison, the results will indicate
lack of expression, which is incorrect. In addition, hybridization methods may
be less sensitive and may yield a negative result when a positive result is clearly
present through visualization.
The final class of technologies that measure transcript levels, transcript sequenc-
ing, and counting methods can provide absolute levels of a transcript in a cell.
These methods involve capturing the identical piece of all genes of interest,
typically the 3' end of the transcript, and sequencing a small piece. The number
of times each piece was sequenced can be a direct measurement of the abun-
dance of that transcript in that sample. In addition to absolute measurement,
other principle advantages of this method include the simplicity of data inte-
gration and analysis and a general lack of problems with similar or overlapping
transcripts. Principle disadvantages include time and cost, as well as the fact
that determining the identity of a novel gene by only the 10-nucleotide tag is
not trivial.
We would like to mention two additional considerations before providing
detailed descriptions of the most popular techniques. The first is contamination

10
Shimkets
Table 1
Common Gene Expression Profiling Methods
Kits Service Detect Detect
Technique Class Architecture Available Available Alt. Splicing SNPs
5'-nuclease assay/real-time RT-PCR Visualization Open Yes No No No
AFLP (amplified-fragment length Visualization Open No No No Yes
polymorphism fingerprinting)
Antisense display Visualization Open No No No No
DDRT-PCR Visualization Open Yes No No No
(differential display RT-PCR)
DEPD (digital expression Visualization Open No No Yes No
pattern display)
Differential hybridization Hybridization Open No No No No
(differential cDNA library screening)
DSC (differential subtraction chain) Hybridization Open No No No No
GeneCalling Visualization Open No Yes Yes Yes
In situ Hybridization Hybridization Closed Yes No No No
Invader Assay Visualization Closed Yes Yes No Yes
Microarray hybridization Hybridization Closed Yes Yes No No
Molecular indexing Visualization Open No No No No
(and computational methods)
MPSS (massively parallel Sequencing Open No No No No
signature sequencing)
Northern-Blotting Hybridization Closed Yes No No No
(Dot-/Slot-Blotting)
Nuclear run on assay/nuclease S1 analysis Visualization Closed Yes No No No
ODD (ordered differential display) Visualization Open No No No No
Quantitative RT-PCR Visualization Closed Yes Yes No No

Technology
Summary
11
RAGE (rapid analysis of gene expression) Visualization Open No No Yes No
RAP-PCR (RNA arbitrarily primed Visualization Open No No No No
PCR fingerprinting)
RDA (representational difference analysis) Visualization Open No No No No
RLCS (restriction landmark cDNA scanning) Visualization Open No No No No
RPA (ribonuclease protection assay) Visualization Open No No No No
RSDD (reciprocal subtraction Visualization Open No No No No
differential display)
SAGE (serial analysis of gene expression) Sequencing Open Yes No No No
SEM-PCR Visualization Closed No Yes No No
SSH (suppression subtractive hybridization) Hybridization Open Yes No Yes No
Suspension arrays with microbeads Hybridization Closed No No No No
TALEST (tandem arrayed ligation Sequencing Open No No No No
of expressed sequence tags)

12 Shimkets
of genomic or mitochondrial DNA or unspliced RNA contamination in mes-
senger RNA preparations. Even using oligo-dT selection and DNAse digestion,
DNA and unspliced RNA tends to persist in many RNA preparations. This is
evidenced by an analysis of the human expressed sequence tag (EST) database
for sequences obtained that are clearly intronic or intragenic. These sequences
tile the genome evenly and comprise from 0.5% to up to 5% of the ESTs in a
given sequencing project, across even the most experienced sequencing centers
(unpublished observation). Extremely sensitive technologies can detect the con-
taminating genomic DNA and give false-positive results. A common mistake
when using quantitative PCR methods involves the use of gene-specific primers
to design the primers within the same exon. This often yields a positive result
because a few copies of genomic DNA targets will be present. By designing
primer sets that span large introns, a positive result excludes both genomic DNA
contamination as well as unspliced transcripts. This is not always possible, of
course, in the cases of single-exon genes like olfactory G protein-coupled recep-
tors and in organisms like saccharomyces and fungi where multi-exon genes
are not common. In these cases, a control primer set that will only amplify geno-
mic DNA can aid dramatically in the interpretation of the results.
A final, and practical consideration is to envision the completion of the pro-
ject of interest, because using different quantitation methods will result in the
need for different follow-up work. For example, if a transcript counting method
that reveals 10 nucleotides of sequence is used, how will those data be fol-
lowed up? What prioritization criteria for the analysis will be used, and how will
the full-length sequences and full-length clones, for those genes be obtained?
This may sound like a trivial concern, but in actuality, the generation of large
sets of transcript-abundance data may create a quantity of follow-up work that
may be unwieldy or even unreasonable. Techniques that capture the protein-
coding regions of transcripts, such as GeneCalling, reveal enough information
for many novel genes that may help prioritize their follow-up, rather than 3'-
based methods where there is little ability to prioritize follow-up without a larger
effort. Beginning with the completion of the project in mind allows the researcher
to maximize the time line and probability for completion, as well as produce
the best quality research result in the study of gene expression.

StaRT-PCR 13
13
3
Standardized RT-PCR and the Standardized
Expression Measurement Center
James C. Willey, Erin L. Crawford, Charles R. Knight,
K. A. Warner, Cheryl A. Motten, Elizabeth A. Herness,
Robert J. Zahorchak, and Timothy G. Graves
Summary
Standardized reverse transcriptase polymerase chain reaction (StaRT-PCR) is
a modification of the competitive template (CT) RT method described by
Gilliland et al. StaRT-PCR allows rapid, reproducible, standardized, quantitative
measurement of data for many genes simultaneously. An internal standard CT is
prepared for each gene, cloned to generate enough for >109 assays and CTs for
up to 1000 genes are mixed together. Each target gene is normalized to a reference
gene to control for cDNA loaded in a standardized mixture of internal standards
(SMIS) into the reaction. Each target gene and reference gene is measured rela-
tive to its respective internal standard within the SMIS. Because each target gene
and reference gene is simultaneously measured relative to a known number of
internal standard molecules in the SMIS, it is possible to report each gene expres-
sion measurement as a numerical value in units of target gene cDNA molecules/
106 reference gene cDNA molecules. Calculation of data in this format allows for
entry into a common databank, direct interexperimental comparison, and combi-
nation of values into interactive gene expression indices.
Key Words: cDNA, expression, mRNA, quantitative, RT- PCR, StaRT-PCR
1. Introduction
With the recent completion of the human genome project, attention is now
focusing on functional genomics. In this context, a key task is to understand
normal and pathological function by empirically correlating gene expression
patterns with known and newly discovered phenotypes. As with other areas of
science, progress in this area will accelerate greatly when there is an accepted
standardized way to measure gene expression (1,2).

14 Willey et al.
Standardized reverse transcriptase-polymerase chain reaction (StaRT-PCR)
is a modification of the competitive template (CT) reverse transcriptase (RT)
method described by Gilliland et al. (3). StaRT-PCR allows rapid, reproduci-
ble, standardized, and quantitative measurement of data for many genes simul-
taneously (4–15). An internal standard CT is prepared for each target gene and
reference gene (e.g., β-actin and GAPDH), then cloned to generate enough for
>109 assays. Internal standards for up to 1000 genes are quantified and mixed
together in a standardized mixture of internal standards (SMIS). Each target gene
is normalized to a reference gene to control for cDNA loaded into the reaction.
Each target gene and reference gene is measured relative to its respective inter-
nal standard in the SMIS. Because each target gene and reference gene is simul-
taneously measured relative to a known number of internal standard molecules
that have been combined into the SMIS, it is possible to report each gene expres-
sion measurement as a numerical value in units of target gene cDNA molecules/
106 reference gene cDNA molecules. Calculation of data in this format allows
for entry into a common databank (5), direct interexperimental comparison (4–
15), and combination of values into interactive gene expression indices (8,9,11).
With StaRT-PCR, as is clear in the schematic presented in Fig. 1A, expres-
sion of each reference gene (e.g., β-actin) or target gene (e.g., Gene 1–6) in a
sample (for example sample A) is measured relative to its respective internal
standard in the SMIS. Because in each experiment the internal standard for
each gene is present at a fixed concentration relative to all other internal stan-
dards, it is possible to quantify the expression of each gene relative to all others
measured. Furthermore, it is possible to compare data from analysis of sample A
to those from analysis of all other samples, represented as B1-n. This result is a
continuously expanding virtual multiplex experiment. That is, data from an ever-
expanding number of genes and samples may be entered into the same database.
Because the number of molecules for each standard is known, it is possible to
calculate all data in the form of molecules/reference gene molecules.
In contrast, for other multigene methods, such as multiplex real-time RT-
PCR or microarrays, represented in Fig. 1B, expression of each gene is directly
compared from one sample to another and data are in the form of fold differ-
ences. Because of intergene variation in hybridization efficiency and/or PCR
amplification efficiency, and the absence of internal standards to control for these
sources of variation, it is not possible to directly compare expression of one gene
to another in a sample or to obtain values in terms of molecules/molecules of
reference gene.
In numerous studies, StaRT-PCR has provided both intralaboratory (4–15)
and interlaboratory reproducibility (6) sufficient reproducability to detect two-
fold differences in gene expression. StaRT-PCR identifies interactive gene
expression indices associated with lung cancer (8–10), pulmonary sarcoidosis

Exploring the Variety of Random
Documents with Different Content

A CHILD’S PRESENT TO HIS CHILD-
SAVIOUR
Go, pretty child, and bear this flower
Unto thy little Saviour;
And tell Him, by that bud now blown,
He is the Rose of Sharon known.
When thou hast said so, stick it there
Upon His bib, or stomacher;
And tell Him, for good handsel[A] too,
That thou hast brought a whistle new,
Made of a clean straight oaten reed,
To charm his cries at time of need.
Tell Him, for coral thou hast none,
But if thou hadst, He should have one;
But poor thou art, and known to be
Even as moneyless as He.
Lastly, if thou canst win a kiss
From those mellifluous lips of His,
Then never take a second on,
To spoil the first impression.
Robert Herrick
[A] handsel: a gift for good luck.

A CHRISTMAS CAROL
There’s a song in the air!
There’s a star in the sky!
There’s a mother’s deep prayer
And a baby’s low cry!
And the star rains its fire while the Beautiful sing,
For the manger of Bethlehem cradles a king.
There’s a tumult of joy
O’er the wonderful birth,
For the virgin’s sweet boy
Is the Lord of the earth,
Ay! the star rains its fire and the Beautiful sing,
For the manger of Bethlehem cradles a king.
In the light of that star
Lie the ages impearled;
And that song from afar
Has swept over the world.
Every hearth is aflame, and the Beautiful sing
In the homes of the nations that Jesus is King.
We rejoice in the light,
And we echo the song
That comes down through the night
From the heavenly throng.
Ay! we shout to the lovely evangel they bring,
And we greet in His cradle our Saviour and King.
Josiah Gilbert Holland

There are in Paradise
Souls neither great nor wise,
Yet souls who wear no less
The crown of faithfulness.
My master bade me watch the flock by night;
My duty was to stay. I do not know
What thing my comrades saw in that great light,
I did not heed the words that bade them go,
I know not were they maddened or afraid;
I only know I stayed.
The hillside seemed on fire; I felt the sweep
Of wings above my head; I ran to see
If any danger threatened these my sheep.
What though I found them folded quietly,
What though my brother wept and plucked my sleeve,
They were not mine to leave.
Thieves in the wood and wolves upon the hill,
My duty was to stay. Strange though it be,
I had no thought to hold my mates, no will
To bid them wait and keep the watch with me.
I had not heard that summons they obeyed;
I only know I stayed.
Perchance they will return upon the dawn
With word of Bethlehem and why they went.
I only know that watching here alone,
I know a strange content.
I have not failed that trust upon me laid;
I ask no more—I stayed.
Theodosia Garrison

Included by permission of the author and of The Century Company.

Good King Wenceslas looked out
On the Feast of Stephen,
When the snow lay round about,
Deep, and crisp, and even.
Brightly shone the moon that night
Though the frost was cruel,
When a poor man came in sight,
Gath’ring winter fuel.
“Hither, page, and stand by me,
If thou know’st it, telling.
Yonder peasant, who is he?
Where and what his dwelling?”
“Sire, he lives a good league hence,
Underneath the mountain;
Right against the forest fence,
By Saint Agnes’ fountain.”
“Bring me flesh, and bring me wine,
Bring me pine-logs hither;
Thou and I shall see him dine,
When we bear them thither.”
Page and monarch, forth they went,
Forth they went together;
Through the rude wind’s wild lament
And the bitter weather.
“Sire, the night is darker now,
And the wind blows stronger;
Fails my heart, I know not how,
I can go no longer.”
“Mark my footsteps, good my page;
T d th i th b ldl

Tread thou in them boldly:
Thou shalt find the winter rage
Freeze thy blood less coldly.”
In his master’s steps he trod,
Where the snow lay dinted;
Heat was in the very sod
Where the saint has printed.
Therefore, Christian men, be sure,
Wealth or rank possessing,
Ye who now will bless the poor,
Shall yourselves find blessing.
Translated from the Latin by J. M.
Neale

We Three Kings of Orient are,
Bearing gifts we traverse afar,
Field and fountain,
Moor and mountain,
Following yonder star.
Chorus
O Star of wonder, Star of night,
Star with Royal Beauty bright,
Westward leading.
Still proceeding,
Guide us to Thy perfect Light.
Gaspard: Born a king on Bethlehem plain,
Gold I bring to crown Him again;
King forever,
Ceasing never
Over us all to reign.
Chorus: O Star of wonder....
Melchior: Frankincense to offer have I,
Incense owns a deity nigh;
Prayer and praising
All men raising,
Worship Him God on high.
Balthazar: Myrrh is mine; its bitter perfume
Breathes a life of gathering gloom;
Sorrowing, sighing,
Bleeding, dying,
Sealed in a stone-cold tomb.

Glorious now behold Him arise,
King and God, and Sacrifice;
Heav’n sings Allelujah:
Allelujah,
The earth replies.
J. H. Hopkins, Jr.

GOD REST YE, MERRY GENTLEMEN
God rest ye, merry gentlemen; let nothing you dismay,
For Jesus Christ, our Saviour, was born on Christmas-day.
The dawn rose red o’er Bethlehem, the stars shone through the
gray,
When Jesus Christ, our Saviour, was born on Christmas-day.
God rest ye, little children; let nothing you affright,
For Jesus Christ, your Saviour, was born this happy night;
Along the hills of Galilee the white flocks sleeping lay,
When Christ, the child of Nazareth, was born on Christmas-day.
God rest ye, all good Christians; upon this blessed morn
The Lord of all good Christians was of a woman born:
Now all your sorrows He doth heal, your sins He takes away;
For Jesus Christ, our Saviour, was born on Christmas-day.
Dinah Maria Mulock

THE WASSAIL SONG
Here we come a-wassailing
Among the leaves so green,
Here we come a-wandering
So fair to be seen.
Love and joy come to you
And to your wassail too,
And God bless you, and send you
A happy New Year.
We are not daily beggars
That beg from door to door,
But we are neighbours’ children
That you have seen before.
Good Master and good Mistress,
As you sit by the fire,
Pray think of us poor children
Who are wandering in the mire.
Bring us out a table
And spread it with a cloth;
Bring us out a mouldy cheese
And some of your Christmas loaf.
God bless the master of this house,
Likewise the mistress too;
And all the little children
That round the table go.

Old Devonshire Carol
Included by permission of The H. W. Gray Company.

WASSAILER’S SONG
Wassail! Wassail! all over the town,
Our bread it is white, our ale it is brown;
Our bowl is made of a maplin tree;
We be good fellows all;—I drink to thee.
Here’s to our horse, and to his right ear,
God send master a happy new year;
A happy new year as ever he did see,—
With my wassail bowl I drink to thee.
Here’s to our mare, and to her right eye,
God send our mistress a good Christmas pie;
A good Christmas pie as e’er I did see,—
With my wassailing bowl I drink to thee.
Here’s to our cow, and to her long tail,
God send our master us never may fail
Of a cup of good beer: I pray you draw near,
And our jolly wassail it’s then you shall hear.
Be here any maids? I suppose here be some;
Sure they will not let young men stand on the cold stone!
Sing hey, O, maids! come trole back the pin,
And the fairest maid in the house let us all in.
Come, butler, come, bring us a bowl of the best;
I hope your souls in heaven will rest;
But if you do bring us a bowl of the small,
Then, down fall butler, and bowl and all.

CAROL IN PRAISE OF THE HOLLY
AND IVY
(Holly and Ivy Made a Great Party)
Holly and Ivy made a great party,
Who should have the mastery
In lands where they go.
Then spake Holly, “I am fierce and jolly,
I will have the mastery
In lands where we go.”
Then spake Ivy, “I am loud and proud,
And I will have the mastery
In lands where we go.”
Then spake Holly, and bent him down on his knee,
“I pray thee, gentle Ivy,
Essay me no villany
In the lands where we go.”
Fifteenth Century Carol

CEREMONIES FOR CHRISTMAS
Come, bring with a noise,
My merry, merry boys,
The Christmas log to the firing,
While my good dame, she
Bids ye all be free,
And drink to your heart’s desiring.
With the last year’s brand
Light the new block, and
For good success in his spending,
On your psalteries play,
That sweet luck may
Come while the log is a-tending.
Drink now the strong beer,
Cut the white loaf here,
The while the meat is a-shredding;
For the rare mince-pie
And the plums stand by
To fill the paste that’s a-kneading.
Robert Herrick

CHRISTMAS EVE—ANOTHER
CEREMONY
Come, guard this night the Christmas-pie,
That the thief, though ne’er so sly,
With his flesh-hooks, don’t come nigh
To catch it.
From him, who alone sits there,
Having his eyes still in his ear,
And a deal of nightly fear
To watch it.

ANOTHER TO THE MAIDS
Wash your hands, or else the fire
Will not tend to your desire;
Unwashed hands, ye maidens, know,
Dead the fire, though ye blow.
Robert Herrick

OUR JOYFUL FEAST
So, now is come our joyful feast,
Let every soul be jolly!
Each room with ivy leaves is drest,
And every post with holly.
Though some churls at our mirth repine,
Round your brows let garlands twine,
Drown sorrow in a cup of wine,
And let us all be merry!
Now all our neighbours’ chimneys smoke,
And Christmas logs are burning;
Their ovens with baked meats do choke,
And all their spits are turning.
Without the door let sorrow lie,
And if for cold it hap to die,
We’ll bury it in Christmas pie,
And evermore be merry!
George Wither

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