Executive Summary
The BYU Department of Nutrition enlists students as research
assistants from a variety of majors. Research assistants are generally looking
for lab experience necessary for graduate programs. This project was created as
a solution to a question in Dr. Jason Kenealey’s lab. Dr. Kenealey’s lab
focuses on understanding the biochemical mechanisms and effects of naturally
occurring molecules on different cancers. Dr. Kenealey’s research assistants
use many different machines to assist their research that eventually export an
Excel file with results after the experiment is completed.
After finishing an experiment,
students will take the readout exported by the machine, format a new workbook,
copy and paste data from the readout to the new workbook, write in formulas to
analyze the data, and finally create graphics that clearly convey the data. The
process is cumbersome and repetitive, all while leaving plenty of room for
errors that skew data.
I recently started running
experiments using the Polymerase Chain-Reaction (PCR) ThermoCycler. The objective of the experiment is to
determine the quantity of transcriptions or copies of genes that are being expressed.
Cells treated with different pharmacological agents are lysed open to examine
mRNA content, which is an indication of genes being expressed. After a few
steps the mRNA is converted to more stable cDNA and quantitative PCR (qPCR) is
run against each of the different treated-cellular samples. qPCR uses different
primers to target specific genes of interest and amplify the number of gene
transcripts to readable levels. In order to control for differing levels of
overall cDNA content between different treatments, scientists use an internal
standard gene as a reference. The gene expression of this internal standard is
consistent regardless of treatment options. The change in expression of the
target genes is important to understand because expression of different genes
indicates a change in biological function. For example, cancer cells often decrease
the amount of important tumor-suppressing proteins (like p53) and the targets
of those tumor-suppressing proteins (like TP53INP, PUMA, NOXA, etc.). The
ability of pharmacological agents to upregulate (or in some cases downregulate)
the target genes of interest can be an important mechanism of their efficacy in
treating disease.
My system is designed to first
prompt the user to choose the file path of the readouts to be analyzed (with
the option to select an internal standard from a previous experiment on a
separate workbook). Then, the procedure accepts input from the user regarding
the control samples, the internal target gene, and the target gene of interest.
After, it analyzes the different readouts from the PCR machine to determine the
values of ΔCt(control), ΔCt(treated), fold change, and standard deviations for
each sample, important values in determining the number of transcripts in each
gene. Ultimately, this data is converted into an easy to read bar chart. The
system is designed to expand or contract to fit different amounts of sample,
target, and replicate numbers, and should accommodate any other students
running qPCR from the same machine. The output of my system is a workbook with
a copy of the original readout from the ThermoCycler along with a new summary
sheet that contains a formatted table summarizing the above stated values and chart.
Attachments
http://files.gove.net/shares/files/16w/jffmchm/Jeff_Mecham_Final_Project_Write_Up.pdfhttp://files.gove.net/shares/files/16w/jffmchm/Code_for_Final_Project_IS_520.xlsm
http://files.gove.net/shares/files/16w/jffmchm/Experiement_Trial_with_Target_Genes.xls
http://files.gove.net/shares/files/16w/jffmchm/Experiment_Trail_with_Internal_Ref_Gene_POLR2A.xls
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