20.109(S19):Complete data analysis (Day9)

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20.109(S19): Laboratory Fundamentals of Biological Engineering

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Spring 2019 schedule        FYI        Assignments        Homework        Class data        Communication
       1. Assessing ligand binding        2. Measuring gene expression        3. Engineering biomaterials              


Introduction

Today is the last laboratory day for Mod 2! You will complete the wet lab experiments by analyzing your qPCR data / comparing the results to the RNA-seq data and assessing your cell viability results. In addition, you should use the in-class time to think about how you will use the RNA-seq data in concert with the experimental results obtained at the bench.

Part 1: Examine qPCR results

Before you can apply the statistical tools you learned in Mod1 to your data, you must first normalize the expression levels of your gene of interest and p21. To account for any unintended biases in RNA purification and / or cDNA preparation, it is important to normalize the expression of the transcript of interest to expression of a housekeeping or constitutive gene. Ideally, the gene to which the data of interest are normalized is not responsive to the treatment tested. In our experiment, we used GAPDH because it is not expected to be responsive to etoposide treatment. How might you confirm this assumption?

  1. Review your data posted on the Class data page.
    • Remember you prepared DLD-1, DLD-1 +etoposide, BRCA2-/- , and BRCA2-/- +etoposide samples.
    • All samples were probed using the primers you designed to your gene of interest, along with the p21 and GAPDH primers.
    • Each reaction was completed in triplicate. Note: these are technical replicates.
    • The data are represented as the 'threshold cycle' CT or amplification cycle at which SYBR Green fluorescent signal was detected (review M2D5 Introduction).
  2. Normalize the p21 expression to GAPDH expression (ΔCT).
    • Subtract the GAPDH CT value from the p21 CT values using the appropriate treatment conditions, according to the screenshot below.
      Sp17 20.109 M2D9 qPCR normallization.png
  3. Exponentially transform each normalized value to the ΔCT expression.
    • ΔCT expression = 2-ΔCT.
  4. Average the replicates for each treatment, then calculate the 95% CI and t-test p-value.
    • With this information, graph your data with error bars and include information concerning any statistical significance.
  5. Are these results consistent with those from the RNA-seq data for p21 expression?
    • Load the data:
      • load("~/Desktop/RNA-seq data analysis/preprocessed_data.RData")
      • library("DESeq2")
    • Plot the reads for p21 (also called CDKN1A):
      • plotCounts(dds,"CDKN1A", intgroup="group")
    • If you would like to save the actual count values, save it to a variable:
      • data <- plotCounts(dds,"CDKN1A", intgroup="group", returnData=TRUE)

In case you need the data files to load: preprocessed_data.RData and afterAnalysis.RData

Part 2: Complete cell viability data analysis

The CellTiter-Glo Luminescent Cell Viability Assay is a method for quantifying the number of viable cells based on measuring the amount of ATP present. ATP is a proxy for the presence of metabolically active (alive) cells. In this assay, the cells are lysed and ATP is released from the active cells. In a reaction catalyzed by a propriety luciferase enzyme, luciferin, ATP, and oxygen result in oxyluciferin, AMP, PPi, carbon dioxide, and light. The light product is then measured using a luminometer.

Sp19 cell titer glo assay.png

Earlier in the week you used etoposide to treat DLD-1 and BRCA2-/- cells in an effort to probe the surprising results observed in previous semesters. By comparing the results of your treatment conditions to those used previously, you will further explore the effect of etoposide on viability.

  1. Retrieve your plate from the 37 °C incubator.
  2. Briefly look at the DLD1 and BRCA2-/- cells under the microscope.
    • Make a note of their confluency and morphology.
  3. Aspirate the spent media from each well.
    • Be careful not to cross-contaminate between the wells.
  4. Add 500 μL of PBS into each well to wash the cells and gently rock the plate from side-to-side.
  5. Aspirate the PBS from each well.
    • Be careful not to cross-contaminate between the wells.
  6. Add 250 μL of fresh media into each well.
  7. Obtain an aliquot of the CellTiter Glo reagent from the front laboratory bench.
  8. Thoroughly mix the CellTiter Glo reagent, then add 250 μL into each well of your plate.
    • After adding the reagent, pipet up and down ten times to mix.
  9. Move your plate to the plate shaker at the front laboratory bench and shake for 2 min.
  10. Remove your plate from the plate shaker and incubate on your benchtop for 10 min.
  11. Transfer 100 μL from each well into a white 96-well plate.
    • Be very careful that you add your samples to the appropriate wells according the plate map below.
    • Instructors will add media only control to the 96-well plate to obtain a value for background luminescence.
  12. When the plate is fully loaded, the teaching faculty will measure the luminescence using a Molecular Devices SpectraMax M3 Microplate Reader.
    • Be patient as more than one plate will need to be used to measure all of the team samples.
  13. You will receive an Excel spreadsheet with your data.
    • Transfer your luminescence values into the template (provided here) then post this spreadsheet to the Class data page.
  14. Lastly, analyze your data using the following information:
    • The data are represented as raw luminescent vales (arbitrary units).
    • Subtract the average of the three ‘no cells control’ from all of the luminescent vales. This is the background luminescence.
    • Normalize your luminescence values by dividing all the well values by the ‘no DNA damage controls’ value for the appropriate cell line. This gives you viability as the “% of control.”

Reagents

  • CellTiter Glo cell viability assay kit (Promega)

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