Improving Gene Expression Studies with the Mx4000™ Multiplex QPCR
System
Dr. Martinez, what research projects are you presently involved
with?
My research program focuses on connective tissue adaptation using a
variety of tissue types including bone, skeletal muscle, ligament, tendon,
skin, and heart. We study biological processes that impact collagen gene
expression, extracellular matrix [ECM] assembly, collagen maturation, and
turnover. NASA and funding from the American Heart Association supports
this research.
Our laboratory has established a long track record with NASA studying
the effects on collagen plasticity in cortical bone and skeletal muscle
during spaceflight. We used an animal model to investigate levels of
collagen degradation after long-term missions. NASA also supports our
studies on knee-ligament wound healing in the absence of load bearing
during recovery. These studies utilize the hindlimb unweighting model, and
ultimately, we hope this work provides orthopedic and flight surgeons with
knowledge to accelerate healing time of dense fibrous connective tissues
in astronauts who have been subjected to long-duration missions in
microgravity.
Why did you first investigate QPCR applications for your
research?
We have been interested in quantifying the expression of genes that
code for the proteins associated with the ECM. We are particularly
interested in mRNA expression of the collagen superfamily, proteoglycans,
growth factors, cytokines, and certain catalytic enzymes. Using Northern
blot analyses and ribonuclease protection assays did not provide the
sensitivity needed to detect differences in gene transcripts.
What techniques were you using before? What were the strengths and
weaknesses of these techniques?
We modified a method by Tsai and Wiltbank, which is used to absolutely
quantify the copies of mRNA per total RNA, using a standard curve
quantitative competitive RT-PCR approach. This is a fairly laborious
process. We first had to engineer cDNA constructions of our target and
competitor DNA fragments, insert them into a plasmid, perform in vitro
transcription to synthesize RNA, and use the
It's critical to have good-to-excellent technical
support because I do believe that a machine is as good as the service and
support that the company manufacturing it provides.
RNA as standards in our reverse-transcription reactions. Next, we
performed PCR reactions on the cDNA and generated a standard curve, and
then we placed our endpoint PCR on a 2% agarose gel along with our unknown
target bands. We characterized the light intensity from each of the PCR
bands using a gel documentation system, then generated a log curve using
our standards [R2 = 0.996] and compared our unknowns to the standard
curve.
In theory, this is a very sensitive way to quantitate mRNA using
competitive RT-PCR. However, this method is not only labor
intensive, it’s also expensive after you add up the costs of the labor,
consumables, time to make the constructs, purchasing the kits [in vitro
transcription + radiolabeled nucleotides], and so forth. So we were
looking for an economical, time-efficient, and sensitive way to quantitate
mRNA from hypocellular, dense fibrous connectives. This is when we decided
to move away from competitive RT-PCR methodology and begin to use QPCR.
How did you learn about QPCR technologies that could be useful in
your situation?
About two and a half years ago, I enrolled in the QPCR course offered
by Ambion and Duncan Talbot at the MD Anderson School of Medicine. I also
read the literature regarding TaqMan technology offered by Roche using the
LightCycler and Bio-Rad’s iCycler system.
What were some of the problems you encountered when using those
traditional approaches in your research projects?
I had experienced problems and challenges in several areas of my
research prior to implementing QPCR applications. For example, quantifying
endpoint PCR bands using a gel documentation system has its drawbacks. The
accuracy of the results depends on issues not associated with the PCR
reaction, such as challenges associated with RNA standards. RNA stays
viable for only a finite period of time, and degradation can occur at
–160°C if RNAses are present. Working with expensive agarose and
carcinogenic ethidium bromide to construct gels also presents problems.
The assay, including time to make the constructs, can take up to six
months to develop and optimize. Then it takes one to two days to run the
RT-PCR, analyze the gel, and compute the numbers using a spreadsheet
database.
Once you learned about some of the advantages of incorporating QPCR
technologies into your research protocols, how did you decide which
systems to evaluate?
While I was a graduate student, I studied fluorescence technology and
found that the highest signal obtained was with a xenon lamp source
matched with specific photomultiplier tubes (such as Turner Fluorimeter or
Waters 470 Fluorescence Detector for HPLC applications). So I knew I
wanted a photomultiplier tube system to amplify the fluorescence and ruled
out QPCR detection systems using CCD camera or laser technology.
Replacement of these laser light systems is a huge drain on a research
budget since it is so costly. In addition, I knew that I wanted
96-well format to maximize throughput, so I also did not consider QPCR
systems that provide alternate formats for sample processing.
Have you noticed a preference by the major scientific journals for
quantitative data as compared to the more traditional research methods,
and, if so, was this trend a factor in your decision to look at QPCR
systems?
More and more scientific review panels are requiring quantitative PCR
data in grant applications as opposed to traditional Northern blot- or
RPA-generated data. Since this is the case, I felt that I could
improve my chances of obtaining good preliminary data for use in future
grant proposals by having a system that could produce quantitative PCR
data.
What were the most important performance attributes to you in choosing
a QPCR system? Did you also seek the opinions of other researchers in
reviewing purchase options?
The main criteria I used to decide between available systems was high
throughput, high sensitivity—the ability to detect five to ten copies—and
software that was reliable and easy to use. In addition the excitation
light source was a huge factor as was overall cost for the QPCR system.
I was sold on Stratagene’s system during a demonstration by the QPCR
Applications Scientist
Stratagene's Applications Scientists who can
demonstrate the system and discuss QPCR applications—walk the walk and
talk the talk—by showing the Mx4000 system's capabilities, are a huge
selling factor.
and by speaking with my colleague at University of Texas Medical Center
laboratory, who is a beta site evaluator for Stratagene’s Mx4000 QPCR
system and also owns two competitive instruments. I saw for myself
how easy the Mx4000 is to use, and learned how to order sDNA amplicon
standards and TaqMan probes and primers. Stratagene’s Applications
Scientists who can demonstrate the system and discuss QPCR
applications—walk the walk and talk the talk—by showing the Mx4000
system’s capabilities, are a huge selling factor. I was able to get
comfortable with the knowledge that Stratagene’s Applications Scientists
would provide support for my laboratory in getting started with QPCR
applications.
Was having the ability to actually see the Mx4000 system in a lab and
speak with the Principal Investigator an important factor in your
decision-making process?
Yes, most definitely! Since UT was a beta evaluation site for the
system, I was sold on the product after seeing the results with my own
eyes. My discussions with the Applications Scientist made it clear
that Stratagene’s team understood my needs as well as the previous
technologies I’ve used. And I realized that the Mx4000 system would
save me time, money, and provide peace of mind in the long run.
What features of the Mx4000 system made you want to learn more
about the system?
The multiplex capability of the Mx4000 system was the most important
factor; that is, the amount of information we could obtain from one QPCR
run and perform the analyses in triplicate for statistical purposes. Also,
obtaining the data directly using the Mx4000 software protocols without
having to perform analysis algorithms using third-party software.
The real-time aspect of QPCR was also novel. Researching other web
sites and comparing specifications of the Mx4000 with competitors also
helped me make a decision.
* Patents Pending
|