Note: Descriptions are shown in the official language in which they were submitted.
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QUANTITATIVE ASSAY FOR EXPRESSION OF
GENES IN MICROARRAY
Background of the Invention
The United States government has rights in this invention by virtue of
grant ROlAG09278 from the U.S. National Institute on Aging to Eugenia
Wang and from the Defense Advanced Research Projects Agency of the U.
S. Department of Defense to E: Wang.
The present invention is in the area of a method for detecting
quantitative as well as qualitative levels of expression of genes in a
microarray, for example, in combinatorial libraries.
Microarray techniques offer biologists a systematic way to survey
DNA and RNA variation. Until recently, the only tools available to scientists
were Northern blot analysis, RNase protection or RT-PCR to assay
differential expression. These techniques are limited to use with a few genes
at a time. In contrast, microarray techniques provide a means of generating a
global view of huge numbers of gene expressions simultaneously which has
attracted great interest, and they are becoming standard tools of both
molecular biology research and clinical diagnostics. As the first step of
expression profiling experiments, the analysis and quantification of the array
images exert an important impact on the accuracy of the subsequent data
mining and exploration.
Microarrays typically contain at separate sites nanomolar (less than
picogram) quantities of individual genes, cDNAs, or Expressed Sequence
Tags ("ESTs") (partial gene sequences) on a substrate such as a
nitrocellulose or silicon plate, or photolithographically prepared glass
substrate. Microarrays containing approximately a thousand ESTs are
commercially available from Affymatrix. Clontech sells arrays of gene-
specific cDNA fragments, with approximately half the number of
Affymatrix's ESTs, designed for specific research areas such as tumor
research or broad applications.
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Once fabricated, the arrays are hybridized to cDNA probes using
standard techniques with gene-specific primer mixes. The nucleic acid to be
analyzed - the target - is isolated, amplified and labeled, typically with a
fluorescent reporter group, radiolabel or phosphorous label probe. After the
hybridization reaction is completed, the array is inserted into the scanner,
where patterns of hybridization are detected. The hybridization data are
collected as light emitted from the reporter groups already incorporated into
the target, which is now bound to the probe array. Probes that perfectly
match the target generally produce stronger signals than those that have
mismatches. Since the sequence and position of each probe on the array are
known, by complementarity, the identity of the target nucleic acid applied to
the probe array can be determined.
There are a variety of labels that are used. cDNAs and ESTs can be
detected by autoradiography or phosphorimaging (32P). Fluorescent dyes
1 S are also used, and are commercially available from suppliers such as
Clontech.
The mapping and sequencing phase of the human genome project is
well ahead of schedule. So far, complete genomic sequences of 17 model
organisms, including the eukaryotes S. cerevisiae and C. elegans, have been
finished. The complete human genome sequence is expected to be available
this year. However, of the genes already sequenced, currently the function of
only approximately 20% of 53,000 human genes, or 25% of 6,200 yeast open
reading frames, are known. The next phase of the human genome project will
be dealing with understanding the functions of the remaining 80% of the genes.
The main approach to studying a new gene's function is by determining its
pattern of expression - when, where and how strongly it is expressed.
Techniques currently implemented to investigate gene expression levels
include Northern blots (Alwine, et al. Proc. Natl Acad. Sci. USA 74, 5350-5354
(1977)), RT-PCR (Martorana, et al. BioTechniques 27, 136-144 (1999), Wen,
et al. Proc. Natl. Acad. Sci. USA 95, 334-339 (1998)), differential display
(Liang and Pardee Science 257, 967-971 (1992)), sequencing of DNAs from
cDNA libraries (Okubo, K. et al. Nature Genet. 2, 173-179 (1992)), and serial
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analysis of gene expression- SAGE (Velculescu, V.E. et al. Cell 88, 243-251
(1997)). All of these approaches except SAGE can process simultaneously
only several dozens or hundreds of samples. Keeping in mind the huge volume
of information that should be received in the near future (expression patterns
of
tens of thousands of genes under different conditions, and in different
organisms), it seems that only high-throughput methods like cDNA or
oligonucleotide microarrays will be powerful enough to resolve this challenge.
cDNA microarrays have already been successfully implemented in a
number of cases, including: light- and dark-grown A. thaliana seedlings
(Desprez , et al. Plant J. 14, 643-652 (1998)), heat shock and phorbol ester-
regulated genes in human T cells (Shena, et al. Proc. Natl Acad. Sci. USA 93,
10614-10619 (1996)), young and old mice (Lee, Science 285, 1390-1393
(1999)), and temporal programs of gene expressions in human fibroblasts in
response to serum stimulation (Iyer, V.R. et al. Science 283, 83-87 (1999)).
Many aspects of microarray implementation were recently reviewed in a
special issue of Nature Genetics 21, suppl. (1999). So far, in most cases cDNA
arrays have been printed on glass slides. Glass as a support has many
advantages; it is a durable, non-porous material which has low peculiar
fluorescence. But on the other hand, the load of each dot in a microarray on
glass is lower than on nylon membranes. As a consequence, with microarrays
on glass it is necessary to use very high concentrations of fluorescently
labeled
probe, typically 50-200 ~g of total RNA or several ~g of mRNA per array, in
each hybridization (Duggan, et al. Nature Genetics 21, suppl., 10-14 (1999)).
Such quantities of RNA are usually not available, and this fact limits
possible
cDNA microarray implementations. Another problem concerning the cDNAs
compromising the microarray is that PCR products extracted from clone inserts
of cDNA libraries (Gerhold, et al. TIBS 24, 168-173 (1999)) are probably not
optimal hybridization probes, because clone inserts can be of very different
lengths (0.3 -3.0 kb) and GC content, and therefore have different melting
temperatures. This means that the efficacy of hybridization of different
probes
in microarrays under particular hybridization conditions can differ very
significantly, making expression profiles dependent on experimental
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conditions. Finally, if cDNA targets for hybridization on arrays on glass are
fluorescently labeled, while this approach allows direct comparison between
control and test samples in one experiment (Cheung, et al. Nature Genetics 21,
suppl., 15-19 (1999)), the sensitivity of fluorescent probes is lower than
that of
radioactive or enzyme-coupled probes. Fluorescent probes can also bleach
during analysis, making it impossible to rescan an array; and last but not
least,
the price of laser scanners and other equipment necessary to analyze
fluorescence still remains too high for broader implementation of cDNA
microarrays.
Microarrays provide a simple and comprehensive way to describe
huge numbers of genes simultaneously. While array preparation techniques
have matured in recent years, as reported by Cheung, et al., Nature Genet.
21, 15-19 (1999) and Brown and Botstein Nature Genet. 21, 33-37 (1999),
the techniques for quantifying and analyzing the array data are still in an
evolving stage. As noted above, arrays in general have found a wide range
of applications, such as investigating normal biological and disease
processes, and profiling differential gene expression. The analysis of array
data has therefore attracted considerable research interest. As the first step
of
expression profiling experiments, the analysis and quantification of the array
images exert an important impact on the accuracy of the subsequent data
mining and exploration. However, despite the high cost of commercial
packages (Bowtell, Nature Genet. 21, 25-32 (1999)), the procedures for
quantifying and analyzing remain time-consuming and tedious. Some
commercial packages use a rigid template grid to extract the gene
expressions, and require an accuracy of up to one pixel in overlaying the
template. A simple comparison between two arrays may take several hours
for a first-time user. In addition, most commercially available software
packages rely on the human operator to not only align the array, but also
evaluate the reliability of the results. Moreover, all of the currently
available
systems for detection of gene expression have disadvantages. Bleeding due
to an overabundance of a specific gene's presence or expression is a problem
with some commercially available filters, with bleeding from one spot to
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adjacent spots, obscuring the results for the adjacent spots. Fluorescent
labels fade, so that permanent records must be made by alternative
techniques. Sensitivity of detection is low.
Some of these problems have been minimized using more specific
primer mixes. A cDNA probe generated by random priming distributes the
isotropic label among nearly all RNA species. Many labeled species will
contribute only to non-specific background hybridization, or will cross-
hybridize to many different cDNA fragments. The proportion of label that is
in sequences complementary to genes represented on the array is minimal.
Label can be concentrated into poly A+ RNA using oligo(dT) primers.
However, most cells contain mRNA from many thousands of genes at any
given time, so the probe primarily consists of sequences not represented on
the array that can contribute to undesirable cross-hybridization. Selection of
probes to exclude common sequences helps. Another approach to address
these problems is to decrease the number of genes on the array. Still another
approach is to apply multiple samples to each array, so that results can be
averaged, and anomalous results readily identified.
However, none of these techniques have eliminated problems with
analysis of the microarrays. There remains a need for sensitive, accurate,
reproducible means for analysis of microarrays of genes and ESTs. There is
also a need for quantitative detection, not just qualitative detection.
It is therefore an object of the present invention to provide a method
and materials for accurately detecting gene expression in microarrays of
large numbers of genes, cDNAs or ESTs.
It is a further object of the present invention to provide a method and
materials for quantitating as well as detecting expression of genes in
microarrays.
It is another object of the present invention to provide a non-
radioactive or non-fluorescent assay for gene expression for use in
microarrays, Northern blots, Southern blots or other techniques using DNA
hybridization.
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Summary of the Invention
A method has been developed for detection of gene expression or
hybridization in microarrays, for example, in combinatorial libraries where
quantities are very small and spots located very closely, resulting in
uncomfortable situations where intense reaction can spill over into the
adjacent spots, and therefore obscure the accuracy of the reaction of the
neighboring sites. The assay uses a digoxigenin enzyme assay for detection.
The examples demonstrate the utility of the enzymatic detection
system. The transcriptionally regulated profile of E-box-related genes
specific
to a given cultured cell sample was determined by unique digoxigenin (DIG)-
labeled cDNAs produced from RNAs isolated from the culture of interest. This
specific enzymatic labeling probe allows the end result of detecting
hybridization reaction intensity by colorimetric evaluation of alkaline
phosphatase-coupled antibody to DIG. The enzymatic deposit on each locus of
the E-box microarray is readily analyzed by an upright microscope attached to
a CCD camera, without the problem of the long delay needed for exposure time
with radioactive probes; or the photobleaching and high background reaction
problem associated with the fluorescent probe approach. The enzymatic
approach provides a user-friendly designer approach to custom-adapt the gene
screening task to analyze a subgroup of gene expressions controlled by the
same molecular modality. The assay is very sensitive, enabling detection of as
little as 0.02 nanograms.
A method for enhancing the reliability of analysis of expression of
DNA in microarray formats has also been developed, using software analysis
that normalizes the spots. This process uses deformable template techniques
to quantify large-scale array data automatically, despite possible spatial
distortion of the arrays. Each node in the deformable template represents a
gene spot, and iterates according to the gradient descent rule, which
minimizes an energy function combining data mismatch energy and template
deformation energy.
The utility of the normalization method for analysis was
demonstrated in a study to identify families of genes in the mouse liver
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whose transcript levels are altered by aging. Commercially available cDNA
microarrays were used to analyze liver mRNA levels from young versus
aged C57BL/6 male mice. Hybridization of RT-cDNAs from young and
aged mouse livers to a mouse cDNA microarray of 588 genes (ClonTech
Atlas Mouse cDNA Expression Array) shows specific, coordinated age-
associated changes in the expression of certain families of genes whose
activities are critical to tissue function and repair and maintenance of the
normal physiological state. These gene families include tumor suppressors,
cell cycle regulators, and various stress response and signaling pathway
components. 32P-labeled ssRT-cDNA was used as the probes for the
microarray hybridization assays, with mRNA levels ranging from very high
to very low abundance. The process described herein was demonstrated to
yield superior results.
Brief Description of the Drawings
Figures 1 a, 1 b, 1 c, I d and 1 a show an apparatus for making
microarrays.
Figure 2a-a demonstrates all the steps of template definition,
iteration, unreliable region detection and spot labeling. It is easy to
distinguish the over-expressed spots, labeled as '*', and those distorted by
the
shadow of over-expressed spots, labeled as '?'. The labels of the spots are
confirmed by visual inspection on the original image, where arrows indicate
the positions of spots in the shadow of the over-expressed ones. Figure 2a is
a raw image of part of a ClonTech cDNA array. The array quantification
method was tested by analyzing ClonTech AtlasTM cDNA Filter Arrays,
which comprise 588 cDNA elements spotted in duplicate. Following the
user's manual provided by the manufacturer, an autoradiograph was obtained
after the procedures of radioactivity labeling, array hybridization and
rinsing.
The digital images are obtained by scanning the autoradiographs upright at
300 DPI, with 8-bit gray scale and gamma correction disabled. A 3*3 out-
range pixel smoothing filter was applied to reduce salt-and-pepper noise. The
initial position overlays a grid template on the original image, which is one
block of a ClonTech filter. Users are required to provide the position of
spots
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in the left-top and right-bottom corners via a graphical user interface. In
equations (5) and (6), parameters are set as ~,; _ ~p; =1 and r/ = 50. Figure
2b
is the initial alignment with a prototype template. Figure 2c is the fine
alignment by automatic iteration of a deformable template. Figure 2d shows
the unreliable regions (yellow) detected by mathematical morphology. The
structuring element is chosen as a disk with the same size as an ideal sample
spot. Figure 2e shows automatic labeling of the spots, corresponding to the
unreliable regions ('*' over-expressed spots; '?' - spots distorted by the
shadow; 'o' - normal spots).
Figures 3a-3d show the qualitative and quantitative evaluation of
repeatability and reliability. Two blocks (transcription factors and general
DNA-binding proteins) of ClonTech AtlasTM mouse cDNA arrays were
quantified using the same deformable template method. The imaging
conditions are the same as in Figure 2a. Two independent operators are
required to provide the initial position (left-top and right-bottom corners)
of
the prototype template (Figure 3a). Each operator repeats the same procedure
on one sample three times. For the sake of simplicity, the normalized
intensity of a spot of [0, 0.33] is defined as low-abundance, [0.33, 0.67] is
defined as medium-abundance, and [0.67, 1] is defined as high-abundance.
Relative error versus intensity demonstrates that the errors in quantifying
low-abundance spots are found to be higher than in quantifying medium- and
high-abundance spots. Maximal error in ratio versus intensity demonstrates
that the errors in ratios among low-abundance spots are found to be higher
than the rest of the spots (Figure 3b). An example of undesirable
normalization compares the gene expressions in the two samples being
compared to control genes (Figure 3c). Line L1 is the ideal case of two
identical samples: S,; = Sz; , i =1,..., N ; line L2 is the central line of
the real
samples; line L3 is the central line of the real controls. The normalization
procedure can be understood as rotating the coordinate space by angle
B(L2, L3) . Thus the reliability of normalization can be evaluated by the
angles 9(Ll, L3) and B(L2, L3) (Figure 3d). The larger the angles, the less
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reliable the normalization. In this case, B(Ll, L3) _ -29.30° ,
B(L2, L3) _ -14.58° . An example of desirable normalization is
seen in
Figure : e(Ll, L3) _ -0.96° , 8(L2, L3) = 2.13 °
Figure 4 shows cDNA microarray hybridization for evaluation of E-box
binding-related gene expression. The matrix position, with each gene's
abbreviation, is written underneath each locus of three repeats of dots with
identical amounts deposited; the X-coordinates denote the number 1, 2, 3, 4,
and 5 positions, and Y-coordinates denote the "a" through "o" positions. The
matrix location for each gene triplet is then identified as X,Y coordinates.
For
example, Sk denotes the position of N-Myc, and 3d denotes the position of
Mad. The same coordinates are also included in Table 4.
Figures Sa and b show the expression profiles of E-box binding-related
gene expressions in Hela cells. Figure Sa - total RNA was labeled with
digoxigenin in RT reaction with gene specific primers; Figure Sb - mRNA was
labeled with digoxigenin in RT reaction with oligo(dT) primers. Arrows within
the matrix show positions of I - Hela DNA (positive control); II - lambda
DNA (negative control); Ill-UBC; IV-RPL-13A; V-MBP-1; VI-HPRTl.
The distance between dots can be measured by the bar of lmm.
Figure 6 shows the hybridization of products of multiplex PCR with 5
pairs of primers with a cDNA microarray. Arrows within the matrix point to: I
-Mrdb; II - c-Myc p64; III - TFII-1; IV - ODC 1; V- cdc25A; VI - Hela
genomic DNA.
Figure 7 shows the relationship between concentrations of 5 genes
including Mrdb, c-Myc, TFII-1, ODC1, and cdc25a, and intensity of
hybridization signals. Logarithmic approximation is shown. Dot intensity is
represented by the arbitrary units on the Y-axis; concentration is measured as
ng/ml on the X-axis.
Figures 8a and 8b show the expression profiles of E-box-related genes
in Hela cells (Figure 8a), and normal human lymphocytes (Figure 8b). Arrows
within the matrix show positions of: I - Aldolase C; II - Mad4;111- MBP-1.
Figures 9a, b and c depict a pairwise comparison of E-box gene
expression in Hela cells and human lymphocytes. Two independent
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hybridizations are averaged for each type of cell. Figure 9a - Three-
dimensional, and Figure 9b - two-dimensional, representations of differences
in gene expression. Each panel corresponds to one column in Figure 8a, and
each bar represents an individual gene. Figure 9c - Distribution of genes with
common gain or loss of expression in dependence on relative ratio value. The
relative fold ratio between samples S 1 and S2 is computed as
RDM~Shs2~ -
172Qx~S~~s,~
which yields a value in the range of [-1,+1]. Positive values correspond to up-
regulation, and negative values correspond to down-regulation, of genes in
sample S2. The relative fold ratio has a similar meaning to that of
conventional
fold ratio, except that the value is normalized and symmetric, with clear
physical interpretation. Ro,,,,(S1,S2) _ X0.5 corresponds to a two-fold up- or
down-regulation in normalizing the two samples; a set of housekeeping genes
of relatively constant expression levels were selected as controls, and linear
normalization was applied.
Detailed Description of the Invention
Two methodologies have been developed to enhance resolution and
sensitivity of microarray analysis of gene detection. The first is a
chromophore detection assay to facilitate determination of the amount of
expression in microarrays, Northern blots, Southern blots, and other
techniques for determination of DNA hybridization; the second is a fast and
reliable approach to analyzing generic arrays using a deformable template to
extract the expression spots in the array, which is capable of identifying the
unreliable expressions automatically. This automated iteration reduces the
human error in quantification to a large extent, and optimizes the processing
time in comparing arrays ten-fold compared to the existing packages.
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Apparatus for Making Microarrays
Figures 1 a-1 a show an apparatus for forming microarrays of
biological materials. As shown Figures 1 a and 1 b, the base of the apparatus
is a vibration isolation table 1. Mounted on the table 1 by means of a first
horizontal linear guide 2, is a platform 14. The platform 14 is connected
through a carriage (not shown) to a drive mechanism (not shown) such as a
lead screw in the guide 2. The horizontal linear guide 2 carries a computer
controlled motor Sa connected to the drive mechanism, which effects
movement of the platform 14 back and forth along the first linear guide 2.
The motor Sa is linked to a computer 13 via an amplifier 15 and a motion
control board 28.
The platform 14 is designed to hold detachable sample reservoirs 11
in predetermined positions 18. As shown in Figure 1 b, a sample reservoir 11
is a 96-well microtiter plate. The platform 14 also holds a series of
1 S substrates 12 which are held in place by means of suction, created by
drawing a vacuum through the holes 21 on the platform underneath the
substrate.
As seen in Figures la and lb, at opposite sides of the table 1 are
vertical risers 6, having upper ends that carry a second horizontal linear
guide 3 mounted substantially transversely, above and straddling the
platform 14 and the first linear guide 2. The second horizontal linear guide 3
carries a computer controlled motor Sb connected to a drive mechanism (not
shown) such as a lead screw which is in threaded engagement with a carriage
(not shown) which can be moved along the guide 3 by operation of the motor
Sb, linked to the computer 13 via an amplifier 16 and the motion control
board 28.
A third guide 4 is attached to the second linear guide 3 by means of
the carriage such that the third linear guide 4 is substantially perpendicular
to
the first linear guide 2 and the second linear guide 3. By means of the
computer controlled motor Sb, the third linear guide 4 can be moved back
and forth by the carriage along the axis of the second linear guide 3. A drive
mechanism within the third linear guide 4, e.g. a lead screw that is meshed
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with the carriage, enables the third linear guide 4 to be moved vertically by
a
further computer controlled motor Sc and positioned in any desired vertical
location within the range of movement. Computer control is achieved by
connection of motor Sa to an amplifier 17 which is connected to the motion
control board 28. Use of linear guide 2, linear guide 3, linear guide 4 and
the
three carriages, provides for motion in three dimensions.
As shown in Figure 1 c, near the lower end of the third linear guide 4,
is attached a sampling manifold 9 which contains four sampling needles 8
spaced linearly along the manifold at intervals to allow for simultaneous
sample pick-up by all four sampling needles 8 from four sample locations in
sample reservoirs 11. The sampling manifold 98 can be moved between two
positions by activation of a pneumatic cylinder 10 connected between the
sampling manifold 9 and third linear guide 4. As shown in Figure lc in solid
lines, the sampling manifold 9 is in the "down" position, for sampling and
cleaning. When the third linear guide 4 is being re-positioned, the sampling
manifold is pivoted to the "up" position, as shown by the broken lines.
The base of the third linear guide 4 has piezoelectric inkjets 7
mounted thereon, the sampling needles 8 being connected to the piezoelectric
inkjets 7 by microline tubing conduits 34 (Figure lc). Each sampling needle
8 is connected through a conduit 34 to a micropump 35 and thence to a
microvalve 36. Each microvalve 36 is adjustable so that the fluid delivered
from the pump 35 can be directed selectively to the corresponding
piezoelectric inkjet 7 or to the waste.
As shown in Figure lb, there are two gravity overflow reservoirs 24,
25, positioned on opposite sides of the first linear guide 2 at the rear of
the
platform, on isolation table 1. The reservoirs 24, 25 contain cleaning
solutions that can, when desired, be pumped through the interior of the
apparatus. As shown in Figure 1 d, such overflow reservoirs 24, 25 are
provided with a fluid in-feed aperture 40 in the lower portion of the overflow
reservoir, into which is pumped the solution of interest from a fluid
reservoir
41, through a pump 44. In the upper portion of the overflow reservoir is a
fluid overflow aperture 42 out of which the liquid in the overflow reservoir
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returns to the fluid reservoir 41. The overflow reservoir is further provided
with openings 43 on the top to allow for insertion of the sampling needles 8.
Also mounted to the vibration isolation table 1 are two cleaning
boxes 26, 27 (Figure 1 b) positioned on opposite sides of the first linear
guide
2 outwardly of the gravity overflow reservoirs 24, 25. As shown in Figure
le, the top of each box is provided with openings 51, 52 to accommodate the
sampling needles 8 and the piezoelectric inkjets 7, respectively. Each box
26, 27 is provided with nozzles 50 on the interior sides thereof for the
delivery of wash fluid from a wash fluid reservoir (not shown) onto the
exterior surfaces of the sampling needles 8 and the piezoelectric inkjets 7.
The lower part of the boxes are provided with an exit port 53 through which
the waste wash fluid is sucked off into a vacuum trap.
In operation, a number of substrates 12 are placed on the platform 14
and selected samples are loaded into the sample reservoirs 11. The platform
14 mounted on the first linear guide 2 is moved into position such that the
sampling manifold 9 is in position for sampling. The sampling manifold 9 is
lowered by actuation of the pneumatic cylinder 10, and the third linear guide
4 is lowered, to place the sampling needles 8 into the sample reservoirs 11.
The quantities of sample to be placed on the microarray are taken up through
the sampling needles 8 by way of the micropumps 35 and delivered through
the conduits 34 to the piezoelectric inkjets 7, and the sampling manifold 9
retracted. The piezoelectric inkjets 7 are positioned over the substrate 12
and
the piezoelectric inkjets 7 deliver the samples onto the substrate 12. This
process is repeated such that multiple samples are delivered to the selected
substrates 12 at the predetermined locations to form a microarray.
To prevent cross-contamination of samples, the sampling needles 8,
the conduit 34, the micropump 35, the microvalves 36, and the piezoelectric
inkjets 7 are cleaned between take-up of different samples. This is done
using the two gravity overflow reservoirs, one 24 containing saline and the
other 25 containing water. Saline or water is taken up by sampling needle 8
and delivered through the conduit 34 to the piezoelectric inkjets 7 to flush
the
system. Following this the cleaning boxes 26, 27, are used to spray water
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and/or air on the exterior surfaces of the sampling needles 8 and the
piezoelectric inkjets 7.
The whole of the process may be automatized. Motion control,
digital actuation, sample processing, and micropumping can all be controlled
by means of a computer using specialized computer programs designed
therefor. Certain functions such as the recirculation of fluid through the
gravity overflow reservoirs can be run continuously during operation and do
not require computer control.
A variety of liquid reagents can be dispensed using the described
apparatus. For example, the liquids may contain DNA, RNA, modified
nucleic acids and nucleic acid analogues, peptides, antibodies, antigens,
enzymes, or cells. The apparatus can also dispense activator or inhibitor
fluids. An activator fluid is one which makes possible coupling to the
substrate, or causes a synthesis reaction with a previously deposited reagent.
An inhibitor fluid protects an area on the substrate to prevent the material
in
the area from reacting.
Piezoelectric inkjets 7 preferably are drop-on-demand printer heads
which are able to deliver small metered amounts of liquids quickly and
accurately. The amount of material delivered will depend on the specific
use, and may be, for example, 10 to 1000 picolitres (pl), preferably 20 to
100 pl, and most preferred 35 pl. Examples of sample reservoirs include
96-well and 384-well microtiter plates and EppendorfrM tubes. Examples of
sampling devices include sampling needles, which may be made of stainless
steel bore tubing and may include syringe tips.
The gravity overflow reservoirs and the cleaning boxes may be
located in any suitable position.
The micropumps 35 may be activated intermittently or continuously.
Intermittent activation may be achieved using an AC -~ DC relay under the
control of the motion control board.
Components of the apparatus may be provided separately for
assembly, together with instructions for assembly and use of the apparatus.
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Digoxi;;enin Enzymatic Detection Assay
As demonstrated in Example 2, an extremely sensitive and accurate
means for visualizing the extent of hybridization between nucleic acid
molecules has been developed. The assay utilizes digoxigenin (DIG) to label
target nucleic acid molecules, such as cDNA produced from gene-specific
primers, with subsequent incubation with anti-digoxigenin antibody conjugated
with an enzyme such as alkaline phosphatase (AP), and colorimetric or
chemiluminescent detection.
The method includes the steps of labeling the nucleic acid molecules,
typically cDNA, hybridizing the labelled molecules, rinsing to remove material
that did not hybridize, incubating the hybridized material with enzyme
conjugated anti-digoxigenin antibody, typically alkaline phosphatase, staining
for revelation of bound enzyme, and scanning for data acquisition. This
method is fast, requiring a maximum of two days, and can detect samples of
four micrograms or less.
The assay can be used to detect as little as 0.02 nanograms of nucleic
acid. This means the starting material; i.e., the DNA or RNA isolated from the
targeted tissues can be as little as one to two micrograms. The starting
materials can be labeled by the digoxigen method, then the labeled nucleic
acid
used for hybridization with the microarrays. Methods currently used by
Affymatrix and ClonTech both require using mRNA as the starting material;
which is difficult to obtain, requiring at least 100 to 1,000 micrograms of
total
RNA. This is in contrast to the one to two micrograms total RNA required for
the 0.02 nanograms which can be detected with the assay described herein.
The assay uses an enzyme that can cleave a chromogenic,
chemiluminescent or colorimetric substrate. In the preferred embodiment, the
enzyme is alkaline phosphatase and the substrate is Disodium 3-(4-
methoxyspiro { 1,2-dioxetane-3,2'-(5' -chloro)tricyclo [3.3 .1.1 ) decan} -4-
yl)
phenyl phosphate - CSPD. In the preferred embodiment, following antibody
incubation, the reacted substrate is stained with 5-Bromo-4-chloro-3-indolyl-
phosphate, toluidine salt (BCIP), and Nitro blue tetrazolium chloride (NBT) in
detection buffer.
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Extraction of gene expression from arranges
In an ideal array image, the gene expressions are represented as
equally spaced spots of the same size. However, due to random disturbance
in printing the spots, or the warping of the membranes caused by undesirable
temperatures, the spots can be displaced from their ideal positions. Simple
grid templates are insufficient to extract the gene expressions. To overcome
the variability of these spots, a deformable template, with shape-varying
ability, has been developed to keep track of the distortion of gene spots.
Active shape models (Cootes, et al., Computer Vision and Image
Understanding 61, 38-59 (1995); Ostu, N. IEEE Trans. on Syst. Man &
Cybern. 8, 62-66 (1978); and Adryan, et al., BioTech. 26, 1174-1179 (1999))
and deformable templates (Serra, J. Image Analysis and Mathematical
Morphology, Academic Press, London, 1982; Haralick, et al. IEEE Trans. on
Patt. Anal. Mach. Intell. 9, 532-550 (1987); Ostu, N. IEEE Trans. on Syst.
Man & Cybern. 8, 62-66 (1978)) were developed to detect and locate
distorted objects by incorporating prior information concerning the shape of
desired objects into the development of an active'snake' model.
Generally, deformable template matching techniques integrate model-
driven and data-driven analysis by an energy function and a set of
regularization parameters. Two factors are taken into account: data mismatch
and template deformation. Usually, criterion functions are defined to
quantify these two factors: one measures how much the input pattern differs
from the deformed template, and the other measures the degree to which the
template is deformed. In template matching or classification applications,
optimal matching is achieved by minimizing a weighted sum of these two
criteria. The weighting factors are called regularization parameters, which
provide a trade-off between template deformation and data mismatch.
Template representation
As described in the example, a raw image is scanned from a
ClonTech cDNA mouse array. Each sample spot in the three-dimensional
view of the input image of the array corresponds to a local minimum in the
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gray level space. The following model is then used in order to extract
pertinent information.
An array of gene samples is represented as a set of N circle spots of
the same size. Each spot is a circle represented by S(C(P, r), I ) , where
C(P, r) is a circle centered at Euclidean coordinate P _ (x, y) , and with
radius r . The intensity of the spot is an average value I of pixel
intensities
inside the circle. The radius of a microarray spot is determined by the
printing and imaging conditions, and can be measured and set as a constant
over the whole array. Therefore, the prototype template is based on prior
knowledge about a generic array, which can be set as a grid of evenly spaced
circles around the array. The objective of using a deformable template is to
find the optimal position of the centers of all spots regarding both data
mismatch and template deformation factors.
Data mismatch energy
The data mismatch energy measures the fitness of the deformed
template to the input pattern. Since the input image is in gray level, and the
gene expressions are represented by the integral intensities of local regions,
data mismatch energy can be defined in term of the integral intensities:
2
N
1 l'--
jJ(f cp ~ - ,fmin ~dY (1)
2 t=I I P-P I<r
Defining potential function, IO' ~ = j ff ~l')~dY , at each spot, the data
P-P I <r
mismatch energy is simplified to:
N
~'~ = 1 ~~Pt ' IO~-Ir"
2 ;_,
Here ~p; is the weight of the i-th node, and r is a predefined radius for the
sample spots. The potential function is an integration of gray level values
over a local region, which has smoothing abilities. Experimental results
exhibit its robustness to minor perturbation and noise.
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Template deformation energy
The template deformation energy measures the deviation of the
deformed template from the prototype template. The template deformation
to translation happens due to warping of membranes. In order to quantify the
degree of deformation, the deformation energy for each spot is defined as the
Euclidean distance of the deformed control node P; from its predefined
position P,.o
Ez = 1 ~ ~~ Ip~ - pro I -~'~r ' ~~x~ - xro )z + ~Yr - Yro )Z ~ ~ )
2 ,-~
Here, ~,; is the flexibility of the i-th node. The minimization of the
template
deformation energy helps to prevent the nodes from being attracted by
perturbations and noise in local backgrounds, and thus improves the
robustness of the proposed algorithm.
Relaxation
Combining the template deformation and data mismatch energies,
one can define an overall energy function as:
E = aE~ + EZ ; a is a regularization parameter.
(4)
The localization procedure of sample spots corresponds to the
minimization of the overall energy. The minima of this function can be
obtained by using global optimization methods, such as dynamic
programming, greedy searching algorithm, neural networks, etc., yet at the
cost of excessive computing. Since each spot of an array is printed at a
predefined position, it is assumed that the gene spots are presented in a
regular array, for which the initial position can be chosen by predetermined
sites according to the number of loci in the entire matrix. It is also assumed
that the prototype array template can be placed close enough to the input
array. Thus the array spots can be localized by fording a local minimum
around the initial grid. This can be realized by a deterministic gradient
descent technique:
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~; _-~~ =-w(aw;(I(P;>~ax.~+~'~(x~-x~o»
(s)
w; n~; ~~(a~~;(I(p;) y;)+a,;(v;-v;o»
(6)
i =1, 2, ..., N , and rl is the learning rate.
In an isotopic array, the flexibility and weight of each spot are set to
be the same. The positions of the target centers are regressed to minimize the
energy defined by (4), which can be interpreted as a set of nodes on a rubber
band; the positions of the nodes tend to gravitate to the bottom of a local
minimum of the energy field. In order to restrict the movement of the nodes
and avoid tuning an optimal value for the learning rate, the nodes are moved
one unit in each iteration, according to the sign of ~x and Dy . For example,
to extract gene expressions from one function group of an AtlasTM cDNA
array, a 3 * 3 out-range pixel smoothing filter (Ekstrom, M. P. Digital Image
Processing Techniques, Academic Press, London, 1984) is applied to reduce
salt-and-pepper noise. The membrane is warped during the procedure of
hybridization and washing, so that it is impossible to find an exact match
between the membrane under investigation and a predefined grid template.
However, after less than five iterations, all nodes in the deformable template
come to a stable position, which minimizes the overall energy function
defined by (4). Visual inspection confirms good correspondence between the
deformed grid template and the gene spots.
Detection of unreliable regions
In array hybridization, some strong signals are observed in
autoradiographs, colloquially called 'bleeding' spots. These over-expressed
signals cause problems in quantification of not only themselves, but also the
genes in their neighborhoods. Most commercial packages for analyzing
array data either require the users to check bleeding genes and their
neighbors visually, or simply ignore the existence of these spots. Failure to
detect overexpressed spots may lead to erroneous results in comparing two
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gene expression profiles. The objective of the approach described herein is to
identify the bleeding regions automatically, and alarm the users to potential
errors in the quantification result. Thus, the users are relieved from tedious
and subjective evaluation of the problematic regions in their membranes.
The following basic assumptions have been made about the spots in a
generic array:
The spots are mutually exclusive.
The change of intensity within each spot has a certain range.
Each spot is a connected component with limited size.
Therefore, the identification of over-expressed spots can be simplified as
filtering out the spots larger than a predefined size. Based on shape,
mathematical morphology provides an efficient approach to process digital
images, and has been widely used in solving image processing problems
which were difficult to solve by linear filters. Appropriately used,
mathematical morphological operations tend to simplify image data by
preserving their essential shape characteristics and eliminating
irrelevancies.
The basic mathematical morphological operations are erosion and dilation.
Based on the composition of erosion and dilation, opening and closing are
defined. Considering the case of a binary image, let A be the set of points
representing the binary 'on' pixels of the original binary image, and B be the
set of points representing binary 'on' pixels of the structuring element. The
basic morphological operations are defined below:
Dilation of a binary image A by binary structuring element B,
AO+B={b+a~forsomebEBandaEA}
Erosion of a binary image A by binary structuring element B,
AOB = { p ~ b + p E A for every b E B}
Opening of a binary image A by binary structuring element B,
A~B=(AOB)O+ B
Closing of a binary image A by binary structuring element B,
A ~ B = (A O+ B)OB
In order to distinguish the over-expressed spots from the normally
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expressed spots, a disk structuring element, C, whose size is the upper limit
of a spot on the array being studied, was selected.
C = {(x, y) ~ x2 + y2 < RZ}, where R is the maximal radius of an ideal spot.
The procedure of identifying the over-expressed spots can be
described as follows:
Step 1. Binarize the input image F = { f (x, y) ~ 0 <_ x < M, 0 _< y < N} by
global thresholding techniques, such as Ostu's thresholding method (Ostu,
N.A. IEEE Trans. on Syst. Man & Cybern. 8, 62-66 (1978)) based on optimal
discriminant analysis:
G={g(x,y)~ 0_<x<M, 0<_ y<N},
g(x' y) 0 'otherwise > T ' T is Ostu's optimal threshold
Step 2. Filter out the normal size spots by morphological opening with
structuring element C:
G'=G~C={g'(x~Y)}
Consequently, the unreliable regions are represented as a set:
UR = { (x, y) ~ g' (x, y) =1, (x, y) E F} (9)
Step 3. Identify control node P,. _ (x;, y; ) as an over-expressed spot if
(x;,y;)EUR.
In fact, quantification problems reside not only in over-expressed
spots, but also those spots in their neighborhoods. During the iterations of
locating the real positions of the spots, some control nodes are quickly
attracted by the over-expressed spots, since a low data mismatch energy field
is formed around these spots. In this system, a gene expression spot is
labeled
as unreliable, if the corresponding control node moves far from its initial
position.
Target measurement
In quantitative analysis with microarrays, it is assumed that the
amount of fluorescent light or radioactivity emitted from each spot is
representative of the amount of labeled nucleic acid probe associated with
that spot (AtlasTM cDNA Expression Arrays User Manual, Protocol
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#PT3140-1, Version #PR91208, pp7-9, 1998). The intensity of each spot
displayed in a 3D plot shows that the assumption of hemispheric shape for a
sample spot is not sufficient for quantification purposes. The following
integration function is used to calculate the volume of each spot:
I ~P ) - ~~~'~'xdydz =
P-P I <r I P-P I <r
In discrete images, the integration is replaced by summation, and uses the
area
of a circle to normalize the volume:
I(I')=
' ~ I P-P I <r
in which P; is the target node obtained from the deformable template
matching iterations. The normalized volume is a real value within a range
~0, Im~ ~, where Im~ is the upper limit of a pixel value. For 8-bit and 16-bit
gray-scale images, ImaK equals 255 and 65535 respectively.
Performance Test' Automatic Processingyvs. Manual Operation
The system was trained based on 24 sub-images from two ClonTech
filters. The training stage includes selection of the optimal regularization
parameter in the definition of energy. The system was tested on 204 sub-
images from 17 ClonTech filters, and 50 bio-chip microarray images printed
in house. Testing results show that the proposed model can extract the spots
satisfactorily, when they are mutually exclusive. However, some spots are
attracted by the over-expressed ones, and thus cause distortion to the
quantification procedure. Although the system is able to identify these
erroneous spots automatically, an ultimate solution requires improvement in
the design of optimal probes and experimental conditions. Table 1 lists the
comparison among three systems in analyzing AtlasTM Arrays, including
AtlasImageTM 1.0 released by ClonTech (http://www.clontech.com), EstBlot
(Adryan, et al., BioTech 26, 1174-1179 (1999)) developed by Johannes
Gutenberg University, and ArrayAnalyzer developed in our lab. Compared
with the existing systems, our system performs several times faster. The
system error is measured by repeating the quantification procedure on one
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array six times. Two factors are considered in evaluating the system:
Average error ~Cl~rr = Avg( ~' ) and Maximal error M~rr = Max( ~' ) , in which
,m ,u r=~ ft
,u; and o-; are the average value and standard deviation in measuring the i-th
spot among several repeats. Table 2 lists the comparison of two systems on
both ClonTech filters and the microarrays printed in house, and it is clear
that the system described herein outperforms the existing system.
In order to quantify the system error involved in the proposed array image
analysis method, two samples are compared, and the following aspects of the
comparison procedure analyzed: error in quantification, error in ratio. and
error in normalization. The relative error in quantification is defined as
~; l,u; , where ~; and ,u; are the standard deviation and average value of the
i-th spot among the six independent tests. To compare the gene expression of
two samples, we define a relative fold ratio instead of the conventional fold
ratio. For samples S, and SZ , the relative fold ratio is computed as
R,z,,- (S, , Sz ) _ (Sz S' ) , which is a value in the range [-l,+1] ;
positive
max(S, , SZ )
values indicate up-regulation and negative values indicate down-regulation
from S, to SZ . The relative fold ratio has a similar meaning to that of the
conventional one, except that the value is normalized and easy to understand.
For instance, R,t,; _ ~0.5 corresponds to up- or down-regulation at two
times. The relationship between the abundance of the genes and the average
of maximum error in ratio comparison. Regarding genes with normalized
abundance in [0, 0.33], (0.33, 0.67], (0.67, 1] as low, medium, and high
abundance, the maximum error in ratio for each category is 1.89%, 4.89%
and 14.54%, respectively. It is clear that the ratios among low abundance
genes are more sensitive to the initial positions of the prototype template in
the quantification. Therefore, the system is more reliable in dealing with
medium- and high- abundance spots. Table 3 lists the relationship between
the automatic evaluation (reliability labels) of the spots and the
quantitative
errors. Quantitative studies confirm that the automatically identified
'unreliable' loci suffer from more errors than the other two categories.
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Actually, the percentage of 'unreliable' spots can be used as an indicator of
the quality of the hybridization procedure.
ArrayAnalyzer automates most of the quantification and comparison
functions. It processes different function groups of an Atlas cDNA array
separately, and therefore enables the system to be readily generalized to the
processing of all types of arrays. Meanwhile, ArrayAnalyzer is robust to
variation among different users. The quantification of spots largely depends
on the nature of the input image, rather than the subjective judgement of the
users. While most commercial systems rely on human operators to evaluate
the reliability of the results, ArrayAnalyzer automatically identifies the
potential error caused by over-expressed spots and their shadow. One of the
unique features of this system is to discard unreliable quantification and
exclude misleading results in advance. It is worth noting that due to
sequence-dependent hybridization characteristics and variations inherent in
any hybridization reaction, Atlas data and any other array data should be
considered only semi-quantitative. It is always necessary to verify any
interesting results of array experiments with other assays to measure the
level
of RNA via Northern and/or semi-quantitative RT-PCR methods.
In summary, the process defined by the computer program presented
here addresses some of the problems of unreliability inherent in
indiscriminate microarray design. A further solution is to design a microarray
in a cluster pattern, in a "divide-and-conquer" fashion, to group the genes
according to their intrinsic level of abundance, and then array them in the
template. The "divide-and-conquer" approach also provides versatility,
allowing follow-up study of a selected cluster of genes in a focused effort.
Emerging reports show the power of the microarray analysis approach to
determine gene expression changes in a template of 18,000 to 20,000 genes.
The method described herein allows the detection of areas where most
changes occur, permitting an in-depth follow-up verification via other
molecular methodologies such as Northern blotting analysis and/or semi-
quantitative RT-PCR analysis. Furthermore, selected areas can also be used
for in-depth analysis, to compare computer automated analysis versus
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manual densitometric tracing. Ultimately, the computational work involved
in any microarray analysis should document multigene changes with
maximal reliability and repeatability, and minimal human error. This
problem can be dealt with by high-powered mathematical modeling and
computer programs, but more importantly it needs to be borne in mind when
the microarrays are designed. Therefore, "designer biochips" to cluster genes
according to their level of abundance in a biological system, and the use of
computerized automatic processing of data analysis, ease the task of
bioinformatics in the current race of high-throughput technology.
Example 1: Comparison of DNA from young and old animals using a
Deformable Template.
Total RNA was isolated from frozen liver by extracting homogenized
liver with guanidium/phenol solution, according to the technique described in
P. Chomcynski and N. Sacchi, Anal Biochem. 162, 156 (1987). The aqueous
phase was further extracted with (25:24:1 ) phenol: CHC13:IAA, and glycogen
was removed by centrifugation. RNA was recovered by ethanol precipitation,
quantified by spectroscopy, and qualified by gel electrophoresis. 1.5 ~g of
high
quality Dnase-treated total RNA was used in the [alpha-32P] dATP labeled
MMLV reverse transcriptase cDNA synthesis, using 588 optimized primers
(ClonTech). Unincorporated 32P-labeled nucleotides were removed from the
labeled cDNA probes by profiling with Chroma Spin spin columns (ClonTech),
using gravity elution. Fractions containing the cDNA/RNA probes were
converted to single stranded cDNA at 68°C with 1 M NaOH, and
neutralized
with 1 M NaH2P04[pH 7.0]. The probes were hybridized overnight at 68° C
to
prehybridized (68° C for 30 minutes) ClonTech mouse microarrays,
containing
588 PCR gene products, in the presence of C°t-1 DNA and sheared salmon
sperm, using ExpressHyb hybridization solution (ClonTech). The membranes
were washed X4 with prewarmed 2X SSC, 1% SDS, followed by two
additional washes with pre-warmed O.1X SSC, 0.5% SDS. The membranes
were then wrapped wet in plastic wrap for autoradiography and
phosphorimaging.
In analyzing a ClonTech Atlas mouse cDNA expression array, the
CA 02367413 2001-10-09
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autoradiograph film is scanned at 300DPI, 8 bit gray scale, by a typical
commercially available flatbed scanner, Saphir Linotype-Hell. The acquired
image is enhanced and analyzed by a software package, ArrayAnalyzer,
developed in-house. In order to deal with membranes with undesirable
warping, a deformable template is defined to quantify the gene expressions
automatically. Each node in the deformable template iterates according to the
gradient descent rule, which minimizes an energy function combining data
mismatch energy and template deformation energy. An ideal gene spot is
modeled as a circle with a predefined radius; thus the gene expressions can be
quantified by integrated intensities. ArrayAnalyzer is also capable of
identifying "bleeding" regions and thus alarm the user of potential errors in
the
quantification. In the later analysis, these regions are carefully checked,
and are
excluded from the discussion. To compare the gene expression of two samples,
a relative fold ratio was defined instead of the conventional fold ratio; the
relative fold ratio between samples show that, taking account of human errors
in the operation, the pairwise comparison is more reliable in medium and high
abundance genes. The experimental results described in the text are based on
the average of three repeats on each filter. In normalizing the two samples,
Lambda DNA was selected as a negative control, and HPRT, MOD, and
G3PDH were selected as positive controls. A linear normalization method is
applied.
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Table 1 Performance of three systems in quantifying two ClonTech
Atlas arrays
Software AtlasImageTM EstBlot ArrayAnalyzer
1.0
Overall aligrunent 20 mini M 15 min M 1 * M
~ ~ 9
sec~2~
Fine tune the alignment ~3 0 min M S min M 0.5 A
* 9
sec
Adjust background 15 min M 3 min A 0.5 A
sec
Check the reliability 15 min M N/A 20 sec A
of samples
Repeat for the second 1 hour M 0 A 1.5*9 M &
array A
sec
Compare the two arrays 15 min M & 4 min A I *6 A
A sec
Customizing report (tabular10 min M & 0.5 A 0.5 A
data) A min min
Customizing report (graphicalN/A N/A 5 min M &
A
data)
(1) The processing time of AtlasImageTM 1.0 is that estimated in the
manufacturer's instruction.
(2) The processing times of EstBlot and ArrayAnalyzer are tested on a
Pentium~ 400, 384MB RAM.
In this table, 'M' refers to manual operation, and 'A' refers to automatic
processing. Operator '*' in measuring the processing time of ArrayAnalyzer
means the repeat times of the same procedure. Since ArrayAnalyzer is
designed for generic array analysis, each group of genes and housekeeping
genes in ClonTech's cDNA array is treated as an individual array. Thus, the
quantification function is applied nine times for each array (6 times for
functional groups and 3 times for control genes), and the comparison
1 S function is applied six times.
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Table 2 Comparison of system errors in quantification
Displacement EstBlot ArrayAnalyzer ArrayAnalyzer
(3-10 pixels) (ClonTech (ClonTech (Microarrays printed in
filters) filters) house)
11.49 6.97 0.93
err (
l)
Merr (%) 55.86 33.10 5.01
Two independent operators are required to provide the initial position (left-
top
and right-bottom corners) of the prototype template. Each operator repeats the
same procedure on one sample for three times. During testing, it was observed
that human operators could displace the initial position of a grid template
from
three up to ten pixels in aligning. For the six repeats, two factors are
N
considered in evaluating the system: Average error ,uerr = Avg( ~~~' ) and
~-~ /'I
Maximal error M~r, = Max( 6' ) , in which ,u; and o~; are the average value
and
r=~
r
standard deviation in measuring the i-th spot.
Table 3 Comparison of errors for spots with different labels
Label Number (total err (%) ft(Max(Rn,~ ) - Nlin(R~,,~ ))
number is 98)
O (normal) 69 (70.4°/°) 6.56 0.1039
? (unreliable) 22 (22.45%) 9.46 0.2177
* (over-expressed) 7 (7.14%) 3.18 0.04
The experimental conditions are the same as in Table 3. This table lists the
relationship between the automatic evaluation (reliability labels) of the
spots
and the quantitative errors. Two factors are considered: average error
err = Avg( 6~ ) , and the average of maximum error in ratio comparison. It is
;n
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WO 00/60126 PCT/US00/09526
shown that the automatically identified 'unreliable' loci suffer from more
errors than the other two categories. Actually, the percentage of 'unreliable'
spots can be used as an indicator of the quality of the hybridization
procedure.
Example 2: Digoxigenin Enzymatic Detection for Microarray Analysis
of E-Box Binding Related Gene Expression.
Realizing the advantages and problems of cDNA microarrays for
expression profiling, a new approach was developed based on utilizing
digoxigenin (DIG) to label target cDNA produced from gene-specific primers,
with subsequent incubation with anti-digoxigenin antibody conjugated with
alkaline phosphatase (AP), and colorimetric or chemiluminescent detection. A
set of genes containing the E-box binding element (CACGTG), located in
promoter regions of many genes, was selected as the probes. Probably the best-
known representative of E-box-binding genes is c-Myc, whose transactivating
activity plays crucial roles in the regulation of cell cycle, proliferation
and
apoptosis (Eilers, M. Mol. Cells 9, 1-6 (1999); Dang, C.V. Mol. Cell Biol. 19,
1-11 (1999); Facchini and Penn FASEB Journal 12,633-651 (1998)). Genes
interacting with or regulating expression for c-Myc, as well as some target
genes whose expression is E-box-binding-dependent, are included in this
microarray. These custom-designed microarrays, combined with the enzymatic
approach to label hybridization probes, allow the development of an
inexpensive, user-friendly system for high-throughput gene screening assay of
specific subgroups of gene expressions.
Materials and Methods
Selection of probes for arrayin~
E-box-binding proteins, as well as c-Myc-regulating, -interacting and
target genes, were chosen from different data bases - GeneAtlas
(http://www.citi2.fr/GENATLAS), GeneCards
(http://bioinfo.weizmann.ac.il/cards), GenBank
(http://www.ncbi.nkm.nih.gov/Web/Genbank) and PubMed
(http://www.ncbi.nlm.nih.gov/PubMed). Unigene
(http://www.ncbi.nlm.nih.govltJniGene/index.html) cluster numbers and
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sequences were used to identify genes and verify their uniqueness. Nine
housekeeping genes, as well as HeLa cell DNA, were selected as positive
controls; as negative controls, lambda DNA and 2xSSC (2x standard salt
solution - 0.3 M NaCI, 30 mM Na citrate, pH 7.0) were chosen. For each gene,
a pair of primers was generated with the help of Primer3 software (Rosen and
Skaletsky (1998) Primer3. Code available at htty//www-~enome.wi.mit.edu/
genome software/other/primer3.html.). The program parameters were chosen
in such a way that the melting temperature of the amplicon should be close to
80°C but not more than 88°C or less than 75°C, the length
of the amplicon was
to be generally around 450 by (with a few outlyers between 300 and 700 bp),
with primer annealing temperature about 60°C, and average length of
primers
23 bp. Sequences of all amplicons have been carefully verified using
proprietary software (BLASTN, FASTA), to avoid homology with repetitive
elements and other related sequences, and also to distinguish between genes
from the same family. A full list of all selected genes is represented in
Table 4.
DNA. RNA and mRNA isolation
Total RNA and DNA were isolated from approximately 10 8 HeLa cell
cultures and human peripheral lymphocytes isolated from fresh blood aliquots
using Trizol reagent (Gibco BRL, Burlington, ON). DNA and RNA
concentrations and quality were determined by spectrophotometric and gel
electrophoresis analysis in 0.8 or 2% agarose gels, respectively. Poly(A)+RNA
was isolated from 150 p.g of total RNA using the Oligotex mRNA kit (Qiagen,
Mississauga, ON), according to the manufacturer's instructions.
Amplification and purification of probes
10 p,g of total RNA was reverse-transcribed in 40 ~1 reaction, using 200
U of MMLV (Gibco BRL, Burlington, ON) according to the manufacturer's
instructions. Two PCR reactions for each pair of primers were conducted in a
total volume of 100 ~1, in a GeneAmp PCR system 9700 (PE Applied
Biosystems, Norwalk, CT). Each 50 ~l reaction (10 mM Tris-HCI, pH 8.6, 50
mM .KCI, 0.1 % Triton X-100, 1.5 mM MgCl2, 0.5 mM of each dNTP, 20 pM
of each primer, 1.25 U of Taq DNA polymerase (Amersham Pharmacia
Biotech, Baie d'Urfe, QC) and 10 p,l of RT reaction or 100 ng of genomic
CA 02367413 2001-10-09
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DNA) was thermal-cycled as follows: first cycle at 94°C for 5 min, 35
cycles
at 94°C for 45 sec, at 60°C for 1 min and at 72°C for 30
sec, the last cycle at
72°C for 7 min. Probes that could not be amplified in RT-PCR were
extracted
from genomic DNA, with the condition that the primers were selected in the 3'
region of a gene. Size and yield of PCR products were determined by gel
electrophoresis in 2% agarose. Then PCR products were purified from solution
or agarose gel bands, following preparative agarose gel electrophoresis (if by-
products were determined), using GFX columns (Amersham Pharmacia
Biotech, Baie d'Urfe, QC). After purification, concentrations of all probes
were estimated by agarose gel electrophoresis, and adjusted to approximately
100 ng/~1.
Robotic arrayin~
Purified PCR products in 2x standard salt solution (SSC) were arrayed
in triplicates from 384-well plates, utilizing a GeneMachinesTM OmniGrid
microarrayer (Genomic Instrumentation Services, San Carlos, CA) equipped
with ChipMaker2 tips (Telechem International, San Jose, CA). The spacing
between dots was 400 Vim. Microarrays were printed on Hybond-N or
Hybond-N+ nylon membranes (Amersham Pharmacia Biotech, Baie d'Urfe,
QC), attached to standard glass slides with tape. Before and after each 10
slides with membranes, regular slides were inserted to inspect printing
quality.
After arraying, membranes were UV irradiated at 50 mJ (GS Gene linker, Bio-
Rad, Hercules, CA) to immobilize the DNA; then fragments of membranes
containing arrays (approximately 1 x 1.5 cm) were cut off, denaturated in
boiling water for 5 min, rinsed in 0.1 % SDS for 5 min, and used for
prehybridization. After the UV irradiation step, membranes can be stored
attached to glass slides.
Preparation of DIG-labeled cDNA for hybridization
An initial mix of gene-specific primers (GSP) was produced. For this
purpose, 1 nM of each primer that was used in RT-PCR reactions to prepare
probes was mixed in a total volume of 250 ~1. Digoxigenin (DIG)-labeled
targets were produced in RT reaction as follows: 1 ~l of GSP, 4 ~.g of total
RNA, and RNAse-free water in total volume of 14 ~1 were heated at
65°C for
31
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
15 min to denature the RNA, and then kept at room temperature for 5 min for
primer annealing. Alternatively, 2 ~.g of mRNA and 400 ng of oligo(dT)lz-~s
primers were used. The reaction mix, containing 8 ~1 of Sx first strand buffer
supplied by the enzyme's manufacturer, 2 ~,1 of 10 mM mix of dATP, dCTP
and dGTP (final concentration 500 ~.M each), 4 ~,1 of 0.1 M DDT, 0.7 ~1
RNAguard, 31 U/~,1 (Amersham Pharmacia Biotech, Baie d'Urfe, QC), 10 ~,1
of a 2 mM mix of 19:1 dTTP:DIG-11-dUTP (Roche, Laval, QC) and 2 ~1 (200
U/~1) of Moloney marine leukemia virus reverse transcriptase (MMLV RT)
(Gibco BRL, Burlington, ON), was added. Reaction was carried out at
37°C
for 1 h, followed by enzyme degradation at 94°C for 5 min in GeneAmp
9700.
Alternatively, Omniscript reverse transcriptase (Qiagen, Mississauga, ON) was
used according to the manufacturer's instructions. Labeling reactions were
purified on GFX columns; this step eliminates all labeled products shorter
than
100 bp, as well as unincorporated nucleotides, primers and protein.
After purification, efficacy of labeling was estimated as follows: 1 ~1
of 1:100, 1:1000, 1:10000 and 1:100000 dilutions were spotted on Hybond-N
membrane, together with dilutions of control DIG-labeled DNA at known
concentrations (10-0.01 pg/~l) as standardization for our assays (Roche,
Laval,
QC); after immobilization with UV, the membrane was incubated with alkaline
phosphatase (AP)-conjugated antibody to DIG (Anti-DIG-AP), rinsed, and
stained with chemiluminescent substrate, Disodium 3-(4-methoxyspiro{1,2-
dioxetane-3,2'-(5'-chloro)tricyclo[3.3.1.13°7]decan}-4-yl) phenyl
phosphate -
CSPD (Roche, Laval, QC), according to the manufacturer's instructions.
Hybridization and processing
For hybridization and pre-hybridization, DIG Easy Hyb buffer (Roche,
Laval, QC), or formamide buffer containing 50% deionized formamide, Sx
SSC, 2% blocking solution (Roche, Laval, QC), 0.1% N-lauroylsarcosine,
0.02% SDS, 100 ~g/ml denaturated salmon DNA, were used. Membranes
were pre-hybridized at 42°C for 2 h in a hybridization oven (Autoblot,
Bellco,
Vineland, NJ). Hybridization was performed at 42°C overnight in 1 ml
or less
of hybridization solution, in 5-ml Falcon tubes. The concentration of labeled
32
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
probes in the hybridization mix constituted 10 ng/ml. Before hybridization the
probes were denaturated at 65°C for 10 min in hybridization solution.
Afterwards, hybridization membranes were rinsed (unless mentioned
specially) twice with IxSSC, 0.1% SDS for 15 min at room temperature, and
then with prewarmed O.IxSSC, 0.1% SDS for 15 min at 68°C.
Alternatively,
membranes were rinsed in more stringent conditions, i.e. twice in 2xSSC, 0.1%
SDS at 68°C. for 30 min, and twice in O.IxSSC, 0.1% SDS at
68°C for 30 min.
After equilibration for 5 min in rinsing buffer (0.3% Tween 20 in malefic
buffer
(0.1 M malefic acid, 0.15 M NaCI, pH 7.5)), membranes were blocked for 1.5 h
in 1 % blocking solution under slight agitation, and then treated for 30 min
in 10
ml of alkaline phosphatase-conjugated sheep anti-digoxigenin antibody (Roche,
Laval, QC), diluted 1:1000 for colorimetric staining, or 1:10000 for
chemiluminescent detection. Following antibody incubation, membranes were
rinsed three times for 15 min in rinsing buffer, equilibrated for 2 min in
detection buffer (0.1 M Tris-HCI, 0.15 M NaCI, pH 9.5), and stained with 175
~g/ml 5-Bromo-4-chloro-3-indolyl-phosphate, toluidine salt (BCIP), and 330
p.g/ml Nitro blue tetrazolium chloride (NBT) in detection buffer.
Alternatively,
1:100 dilution of CSPD was applied, and chemiluminescence was detected
according to the manufacturer's recommendations (Roche, Laval, QC) using
BioMax MR Kodak film.
Scanning and evaluation of arrays
Arrays were scanned on an Olympus microscope equipped with a
Multiscan-4 System (Applied Scientific Instrumentation, Eugene, OR) and a
color CCD Sony 950 camera. Data acquisition and montage of different fields
of view into one file were accomplished with the help of the Northern Eclipse
Imaging System (EMPIX Imaging, Missisauga, ON): Quantitative
measurements of intensity of enzymatic reaction at each dot, background
subtraction, normalization to housekeeping genes, and comparison of paired
hybridizations were all performed with an in-house software program.
Results
Selection of probes and primers
After careful evaluation of different data bases, 61 genes were selected
33
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WO 00/60126 PCT/US00/09526
for arraying, including 9 housekeeping genes. This set of genes contains 38 E-
box binding genes, together with the Myc (c-, N-, L l and L2) family, 5 c-Myc
regulating factors (ZFP161, nm23-H2S, MBP-1, RBMS 1 and RBMS2), 5 c-
Myc interacting genes (YY1, TFII-1, PAM, MM-l and alpha-tubulin), and 4 c-
Myc target genes (prothymosin alpha, MRDB, ODC1, and cdc25A). Positive
controls include 9 housekeeping genes with different levels of expression
(UBC, beta-actin, GADPH, HPRT1, phospholipase 2, HLA-C, PRS9, aldolase
C, and RPL13A), and also HeLa genomic DNA. Lambda DNA and 2xSSC (2x
standard salt solution), which was used as solvent for all probes, were
selected
as negative controls.
Primers for all genes were selected with the help of Primer3 software,
provided that they corresponded to the same conditions for PCR reaction, and
produced products of similar melting temperature. Most products were
produced from HeLa or lymphocyte cDNA. In case PCR amplification failed
from cDNA, primers were selected in the 3' region of these genes, and
amplicons were produced from HeLa genomic DNA. The average annealing
temperature of primers was 60.10.9°C, which allowed all PCR reactions
to be
in the 96-well format. Sizes and melting temperatures of products, and
annealing temperatures of primers, are represented in Table 4. The average
size of PCR products for arraying, and their melting temperature, were 44158
by and 803°C, respectively. Selecting these parameters allowed
hybridization
and post-hybridization rinsing in stringent conditions, decreasing drastically
the
possibility of cross-hybridization and background level.
Scrupulous selection of primers may be used to distinguish in some
cases between very close members of gene families (for example, USF1 and 2,
ID2, 3 and 4, members of the Myc family, and so on), or between two different
transcripts of c-Myc. As is well known, there are several different
transcription
forms of c-Myc, transcribed from different promoters, with varying regulation
properties (Bodescot and Brison Gene 174, 115-120 (1996)). Selecting primers
in the 1S' exon and the 2"a-3~a exons allowed discrimination between full-size
and truncated forms of c-Myc.
34
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WO 00/60126 PCT/US00/09526
Conditions influencing hybridization
Several parameters which probably influence the results of
hybridization with cDNA microarrays printed on nylon membranes were
carefully tested. First of all, gene profiling results were examined using
either
mRNA or total HeLa RNA. Surprisingly, the whole pattern of expression was
very similar, with the exception of a few genes (UBC, RPL-13A, MBP-1) the
signals from mRNA were several times higher; the most prominent difference
was found in UBC, where it approached 5-fold. Alternatively, signals for
HPRT1 and phospholipase A2 were higher with total RNA. In conditions
where quantity of mRNA is a limiting factor, total RNA can be used instead,
without significant differences in results of expression profiling.
Comparison of two reverse transcription enzymes, Moloney murine
leukemia virus (MMLV) (Gibco BRL, Burlington, ON) and OmniScript
(Qiagen, Mississauga, ON), used for production of digoxigenin-labeled targets
for hybridization, did not reveal any difference in expression profile when
gene-specific primers were used; but signal intensity was stronger after
labeling
with MMLV, especially after a day of staining (Table 5). When oligo(dT)
primers were used with mRNA, some significant differences in expression
levels of several genes were detected. Labeling with OmniScript produced 2-3
times more intense signals for RP-S9, RP-L13A, enolasel, N-Myc and MAD4.
To decide which buffer is better for hybridization with microarrays,
EasyHybTM (Roche, Laval, QC) and formamide-based buffers were compared.
The expression profile of HeLa mRNA was found to be independent of buffer
composition, but signals were higher after hybridization in formamide buffer
(Table 5), and addition of 2% blocking reagent further reduced background in
comparison with EasyHybTM, thereby facilitating subsequent scanning and
image evaluation.
No substantial differences were found in expression profile of HeLa
mRNA when rinsing conditions of different stringency were used (see
Materials and Methods). More stringent rinsing evenly lowered all signals, and
produced signals with sharper borders, rendering them easier to scan and
evaluate. Standard rinsing conditions are probably already stringent enough in
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
hybridizations with cDNA microarrays and gene-specific primers; therefore
standard rinsing is preferred, because it is not so time-consuming.
Comparison of positively charged (Hybond-N+) with neutral (Hybond-
N) nylon membranes revealed no differences in sensitivity. Aside from this
consideration, the neutral (Hybond-N) nylon membrane is preferable due to its
stronger texture for printing support. This strength was not found in the
positively charged Hybond-N+ membrane, which was found to retain visible
printing footprints, causing complications in image analysis and increased
background.
As may be seen from Table 5, increasing the staining time from
overnight to 1 day usually increased the overall strength of signals by only
10%. Longer staining time increased the background level of the reaction,
which compromised the possible advantage of higher sensitivity. Variations in
hybridization conditions can increase overall signal intensity by 30-40%.
However, the positive effects are not additive, and the maximum difference in
total intensity of microarrays approaches only 50%. The following conditions
for hybridization of DIG-labeled targets with the cDNA microarray are
optimal: printing probes on neutral nylon membrane, reverse transcription
reaction with total RNA, gene-specific primers and MMLV reverse
transcriptase, hybridization in formamide buffer, and standard rinsing
conditions. These conditions were implemented in the experiments described
in the following paragraphs.
Specificity, sensitivity and reproducibility of hybridization
To evaluate the specificity of cDNA microarray hybridization, 5 genes
(MRDB, ODC, TFII-1, cdc25A and c-Myc), covering the entire range of length
(368-711 bp) of arrayed products, were labeled in multiplex PCR reaction and
hybridized with cDNA arrays (Figure 6). As expected, only 5 samples on the
array were positive, as well as the HeLa genomic DNA as control, since it will
hybridize with the locus where HeLa genomic DNA was spotted at the highest
concentration at position 51, and negative show little or no detection at
positions
1 a and 1 b where spotted HeLa genomic DNA is of low quantity. In all, these
experiments demonstrate no signs of cross-hybridization (Figure 4).
36
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
To estimate the sensitivity and derive a calibration curve for cDNA
microarray hybridization, different concentrations of this 5-gene PCR mix (10,
4, l, 0.4, 0.1 and 0.04 ng/ml) were hybridized with arrays. The results of
this
experiment are presented in Figure 5. Linear dependence in semi-logarithmic
coordinates, with an obvious plateau in the region of 4-10 ng/ml, was observed
for all genes, with the same slope of 452. The lower limit of detection varies
slightly for different probes in the array, and corresponds to 40-100 pg/ml
per
individual gene. These results are close to the detection limit of the
digoxigenin system (10-30 pg/ml), according to the manufacturer (Roche,
Laval, QC). This level of sensitivity allows detection of mRNAs of
intermediate abundance, each representing more than 0.04% of total cell
mRNA. Taking into account this detection level, it is estimated that for
hybridization with a microarray containing about 70 genes of intermediate
abundance, 7 ng of labeled probe produced from gene-specific primers should
suffice. For the next hybridizations, a concentration of labeled probes of 10
ng/ml was selected. The yield of standard reverse transcription labeling
reaction with gene specific primers is about 20-40 ng; therefore, one labeling
reaction yields enough product for 2-4 independent hybridization reactions. In
contrast to unstable radioactive probes, DIG-labeled probes can be stored and
reused several times. Reusing hybridization mixes 2-3 times, after storing at -
20° C for several months, gave results quite concordant with the
original ones.
The arrays were scanned at a resolution of 3600 dpi, and results were
compared with results of microscope scanning. In general, variability between
replicated dots was higher in the case of the scanner, and linearity may be
influenced by the scanner's software. The scanner can be used for initial
evaluation of hybridization results, especially when chemilumenescence
detection is implemented.
Expression prof lint of Hela cells in comparison with human
lymphocytes
Expression profiles of E-box genes were determined in replicating
HeLa cells (Figure 8a) and normal human lymphocytes (Figure 8b). In
lymphocytes, the most prominent alteration consisted of more than 2-fold up-
37
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
regulation of E-box-related genes TCF4, MAD4 and Aldolase C.
Alternatively, down-regulation of c-Myc-regulating genes MBP 1 and Nm23-
H2S, and small down-regulation of c-Myc and up-regulation of N-Myc, were
registered in lymphocytes in comparison with HeLa cells. Expression of some
c-Myc interacting and target genes was down- (MM-l, ODC1) or up-regulated
(PAM, MrDb) in lymphocytes. Also, small up- (MITF, ID2) and down-
(TFEB) regulation was detected in expression of several E-box-binding genes
in lymphocytes, in comparison with HeLa cells. These results are shown in
Figures 9a, 9b and 9c.
Summary
cDNA and oligonucleotide microarrays are becoming an increasingly
powerful technique for investigating gene expression patterns. In spite of the
fast progress in this field, some limitations of the technique persist. One of
the
major obstacles is the requirement for a large amount of mRNA. Another
problem with existing microarray systems is data mining; while information on
expression of tens of thousands genes is absolutely vital to estimate the
functions of new genes, in some instances a researcher is interested in the
expression profile of only a subset of genes, in many physiological
conditions.
The significant differences in expression of 3-6 genes out of 61 are already
much more manageable than can be detected from ordinary microarrays with
massive numbers of genes, in the hundreds or thousands. For example, in
SAGA analysis of 45,000 genes, it was found that about only 1 % are
differentially expressed in normal and cancerous human cells. A similar
estimation resulted from analysis of expression profiles in young and old
mice;
expressions of only 1.8% of about 6,000 genes are changed more than 2-fold.
Printing microarrays on nylon filters, and using digoxigenin to label the
cDNA with gene-specific primers, permits use of as little as one to 4 ~g of
total
RNA per hybridization. This is the same sensitivity that can be attained with
radioactivity in the Clontech protocol, and it is much more sensitive than
ordinary microarrays, which need several ~.g of mRNA, and therefore require
100 to 1,000 micrograms of total RNA to begin with. In addition, DIG-labeled
38
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
probes of high labeling sensitivity can be stored for a long time, and reused
several times, in contrast to fluorescently or radioactively labeled ones.
Careful selection of genes for inclusion in a microarray, and using
digoxigenin for labeling, also helps avoid another disadvantage of radioactive
labeling: genes in the E-box microarray are all in the same category of
abundance (intermediate or low abundant). Excluding highly abundant genes
eliminates the problem of merging of strong signals. Merged signals in some
circumstances substantially complicate the process of scanning, and create
unreliable results during the data acquisition step.
Other advantages of using the enzymatic labeling approach,
superceding both the radioactive and fluorescent probe approaches, are the
time-saving and repeatability aspects. In general this process from start to
finish, including the steps needed for labeling cDNA, hybridization, rinsing,
incubation with the alkaline phosphatase conjugated anti-digoxigenin antibody,
staining for revelation of bound alkaline phosphatase, and scanning for data
acquisition, requires a maximum of two days. This is quite a time saving,
compared with the up to eight days' exposure required for radioactive 3zP or
33P
labeled probes. The advantage of the enzyme-labeled probes over fluorescent-
labeled probes is the cost savings in the data evaluation step, where the
method
requires an inexpensive routine upright microscope, whereas the fluorescent-
labeled probes require the use of an expensive laser detection system or a
confocal microscope set-up. This, plus the notorious fact that fluorescence
can
be easily bleached after the scanning process, makes our enzymatic approach
far superior, due to the ability to scan an array repeatedly with an
inexpensive
microscope, without losing the original signal intensity.
These results can be compared to the commercially available gene
expression array systems as follows:
39
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
Table 6: Comparative Characteristics of Gene Expression Array
Systems.
Affymetrix* Clontech Fluorescent DIG-Array
Printed Spot, NA 10 1-15 0.5-2
ng of cDNA
Total RNA per 5(0.5)** 2-5 50-200 2-4
hybridization
Sensitivity 1:100,000 1:20,000 1:100,000 1:100,000
( 1:2,000,000)
Difference detection twofold (10%) twofold twofold 40-50%
* routine use, current limit in parenthesis
** before amplification in an in vitro transcription reaction (typically 30-
100
fold).
40
CA 02367413 2001-10-09
WO 00/60126 PCT/US00/09526
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TABLE 5
Influence of different hybridization conditions on intensity of microarray
signals.
Total intensity
of microarray*
Hybridization conditions Overnight 1 day incubation
incubation withwith NBTBCIP
NBTBCIP
Two types of buffer
EasyHyb vs. Formamide 894 vs. 1270** 933 vs. 1360
Two types of reverse
transcriptase enzyme
OmniScript vs. MMLV 894 vs. 916 933 vs. 1379
Two types of rinsing
Standard vs. Stringent 1293 vs. 935 1399 vs. 1015
* - total intensity of all microarray dots after subtraction of background;
the same quantity of
DIG-labeled Hela cDNA was used in all hybridizations.
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