BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Lab 3: Hormones & Pizza! (Part 2)
Adapted by Greg Crowther from a previous version written by Doug Wacker
Pre-lab assignment
By the start of your lab section (Thursday, January 28th at 8:45am or Thursday, January 28th at 1:15pm)
you should (1) complete the pre-lab assignment posted to Canvas and (2) print or download this lab
handout, look it over, and bring it to lab.
Today’s objectives
 Test hypotheses about blood glucose levels, using the data collected in lab last week.
 Measure fasting and fed salivary cortisol levels, using saliva samples collected in lab last week.
Materials
* Salimetrics Salivary Cortisol Enzyme Immunoassay Kit
- 96-well plate with wells coated with cortisol-binding antibodies
- Cortisol standards (3, 1, 0.333, 0.111, 0.037, 0.012, and 0 µg/dL)
- Cortisol controls (“High” in red-cap vial, “Low” in blue-cap vial)
- Cortisol-enzyme conjugate (purple-cap vial containing yellow solution; contains cortisol fused
to HRP)
- Assay diluent (red solution)
- Wash buffer concentrate (10X; clear solution)
- TMB (tetramethylbenzidine) substrate solution (brown bottle, clear solution)
- Stop solution (clear; highly acidic!)
- Students’ saliva samples, previously centrifuged to remove particulate matter
* Containers
- Reagent reservoirs (for multichannel pipettor)
- 30mL centrifuge tube or 50mL beaker, 1/section
- 50mL graduated cylinder (for assay diluent), 1/section
- 100mL graduated cylinder (for diluting wash buffer), 1/section
- 1000mL graduated cylinder (for diluting wash buffer), 1/section
- 1000mL beaker (for diluting wash buffer), 1/section
* Pipettors
- P20 pipettor + tips, 1/section
- P200 pipettor + tips, 1/section
- Multichannel pipettor (200µL) + tips, 1/section
- Tip collection beaker, 1/section
* Other Equipment
- Laptops, 1/pair, with Microsoft Excel, Internet access, and printer access
- Plate rotator, 1/section
- BioTek Eon microplate reader, 1/section
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Conceptual overview
This week we will continue to generate and analyze datasets relevant to the endocrine system: blood
glucose levels and salivary cortisol levels.
As you know, blood glucose concentration is regulated by hormones like insulin (which lowers blood
[glucose] by stimulating glucose uptake and glycogen synthesis) and glucagon (which raises blood
[glucose] by stimulating gluconeogenesis, glycogen breakdown, and glucose release by the liver).
Cortisol also affects fuel metabolism; it promotes glucose generation and release by the liver (as
glucagon does), inhibits glucose use by most tissues, and promotes the breakdown of protein and fat.
You might wonder why we are measuring cortisol in saliva rather than in blood. Hormones like cortisol
travel to their target cells via the blood, so the concentration in the blood determines the effect on the
target cells. However, since salivary [cortisol] (essentially representing “spill-over” from the blood)
correlates well with blood [cortisol] (see Figure 1), and since saliva is easier to collect than blood, we can
get a reasonable idea of blood cortisol trends from saliva measurements.
Figure 1: Linear regression analysis
of serum versus salivary cortisol. (A)
Displays the correlation between
serum and salivary cortisol at rest;
(B) displays the correlation between
serum and salivary cortisol during
mental stress. Units are
micrograms per deciliter (mcg/dl) or
nanograms per deciliter (ng/dl).
Figure taken from J.H. Eisenach et
al., Anaesthesiology 121: 878-93,
2014.
Enzyme-linked immunosorbent assays (ELISAs): an alternative to radioimmunoassays (RIAs)
Last week you analyzed some hypothetical data from a radioimmunoassay (RIA) of insulin levels. This
week you will generate real data from an enzyme-linked immunosobent assay (ELISA), which works
according to a similar principle. Instead of measuring the radioactivity of ligands bound to an antibody,
ELISAs measure the catalytic activity of an enzyme (here, HRP). Just as higher amounts of regular
(nonradioactive) insulin lower the amount of regular insulin trapped by the antibody in a RIA, higher
amounts of the regular analyte (analyte: a substance whose concentration is analyzed) lower the
amount of enzyme-linked analyte trapped by the antibody in an ELISA. Figure 2 diagrams this protocol in
greater detail.
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Figure 2: How Salimetrics kits measure analytes such as cortisol. Step 1: An analyte can be any
substance to be analyzed; in our case, it is cortisol. The HRP-linked analyte in this assay is equivalent to
the radiolabeled insulin in the RIA. Step 2: Washing ensures that only analytes bound to antibodies
remain in the sample. This step is equivalent to the charcoal treatment in the RIA. Step 3: TMB
(tetramethylbenzidine) is a substrate of the enzyme horseradish peroxidase, which turns it into a bluegreen product. The more HRP is present, the more blue-green product is produced. Step 4: HRP’s
enzyme activity is stopped with sulfuric acid, which also turns the blue-green product yellow. The
“yellowness” of the sample – its absorbance at 450 nm – is then measured with a plate reader. The
degree of absorbance is equivalent to the degree of radioactivity (“bound counts”) in the RIA. Figure
taken from salimetricseurope.blogspot.com.
Detailed instructions: measuring salivary cortisol
The directions below are taken (with modifications) from the manual for Salimetrics® product 1-3002
(Salivary Cortisol Enzyme Immunoassay Kit). We will discuss which groups/tables will do which steps.
Step -1 (done beforehand): Bring all supplies and samples (listed in the Materials section above) to room
temperature.
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Step 0: Set up the plate map. Which samples will go into which wells of the 96-well plate? We will figure
this out as a class, remembering to include the seven cortisol standards as well as the “High” and “Low”
cortisol controls. We will create a record of the plate map (a photographed drawing or a
Word/Excel/PowerPoint file) that can be shared electronically.
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2
3
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5
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A
B
C
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Step 1: Prepare 1X wash buffer by diluting 10X Wash Buffer Concentrate 10-fold with room-temperature
deionized water (100 mL of 10X Wash Buffer Concentrate + 900 mL of deionized H2O). The 1X wash
buffer should wind up in a reservoir or weigh boat compatible with a multichannel pipette.
Step 2: Add 24 mL of assay diluent to a reservoir, weigh boat, or disposable tube.
Step 3: Using the plate map devised above, pipet 25 µL of standards, controls, and saliva samples into
the appropriate wells. Use 25 µL of assay diluent for the no-cortisol control well.
Step 4: Dilute the enzyme conjugate 1:1600 by adding 15 µL of the conjugate to the 24 mL of assay
diluent. Mix the diluted conjugate solution, then add 200 µL to each well using a multichannel pipette.
Step 5: Put the 96-well plate on a plate rotator for 5 minutes at 150-200 rpm, then incubate at room
temperature for 30 minutes.
Step 6: Wash the 96-well plate 4 times with 1X wash buffer (prepared in Step 0 above). Pipet 200 µL of
wash buffer into each well using a multichannel pipette; when all wells have been filled, discard the
liquid into a sink. Blot the plate with a paper towel after each wash.
Step 7: Add 200 µL of TMB substrate solution to each well with a multichannel pipette.
Step 8: Put the 96-well plate on a plate rotator for 5 minutes at 150-200 rpm, then incubate the plate in
the dark (cover it with paper towels) for an additional 10 minutes at room temperature.
Step 9: Add 50 µL of stop solution to each well using a multichannel pipette. This should cause blueishgreen wells to turn yellow. Perform this step and all subsequent steps with great care, using gloves,
because the stop solution is highly acidic! (The low pH is what stops the chemical reaction.)
Step 10: Put the 96-well plate on a plate rotator for 3 minutes at 150-200 rpm. This should complete the
color conversion from blue-green to yellow.
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Step 11: Wipe off the bottom of the 96-well plate, if necessary. Use a plate reader such as the BioTek
Eon to read each well’s absorbance at 450 nm, a wavelength corresponding to the yellow color. Open
the Gen5 software and create a new experiment using an existing protocol (your instructor will help
you). Let the reader do its thing, then export the data to an Excel file.
Next week you will generate a standard curve from the cortisol standards (like the standard curve you
previously generated for the radioimmunoassay!), and you will use this curve to determine each
person’s fasting and fed cortisol levels.
Detailed instructions: blood glucose analysis
Regarding blood glucose, your task this week is to answer three questions concerning the data collected.
A few possibilities are listed below, but feel free to venture beyond these according to your interests!
 Do blood glucose levels rise after pizza consumption?
 Does blood glucose level remain perfectly steady during an extended fast (e.g., 5 to 18 hours)?
 Does pizza consumption change blood glucose levels more than bagel/pastry/fruit
consumption?
 Does blood glucose change in direct proportion to the number of pizza pieces eaten?
 Does sex (male or female) affect fasting glucose levels and/or the response to feeding?
 Does one type of pizza boost blood glucose levels more than another type?
By the end of your lab period, each team should turn in a report (typed or hand-written) that includes
the following for each of your three questions:
 A. The research question
 B. Your initial hypothesis (does not need to have been supported by the data)
 C. Statistical test of hypothesis (e.g., t-test, ANOVA, linear regression, etc. – see Appendix)
 D. Your answer to your research question, justified with specific data/statistics
 E. Well-labeled graph of results (e.g., bar graph with means and standard deviations of the 2
groups, scatter plot with equation of linear fit, etc.)
 F. Any caveats, concerns, or other comments on this analysis
Appendix: basic statistical tests
In analyzing your blood glucose data, you will test your hypotheses with some basic statistical tests.
Three of the most common analyses are the t-test, ANOVA, and linear regression.
T-test
A t-test answers the question, “Are the means of these two groups of values more different than we
would expect to occur by chance?” For example, the class’s blood glucose values before eating pizza will
never be EXACTLY the same as its values after eating pizza, but the t-test tells the probability of seeing
the observed difference (or something more extreme), if there were truly no difference in glucose
values before and after eating pizza. This probability is the p value. For historical reasons, and by
popular convention, a p value of less than 0.05 is generally considered “statistically significant.” That is,
if an observed difference would occur less than 5% of the time if there were truly no difference between
groups, the difference is considered “real.”
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
T-tests come in several varieties: they can be paired or unpaired, and can be 1-tailed or 2-tailed.
 In a paired t-test, each specific value in one group is matched or paired with a specific value in
the other group. This is because both values came from the same person, or have some other
common factor. In studying the effect of pizza on blood glucose levels, one could do a paired ttest in which each subject’s pre-pizza value would be paired with his/her post-pizza value. The
alternative, an unpaired t-test, would be used if we made one group fast and measured their
fasting blood glucose levels, then fed pizza to a different group and measured that other group’s
fed blood glucose levels.
 In a 1-tailed t-test, the two groups are tested for a difference in a certain direction (e.g., the fed
glucose values will be HIGHER than the fasting glucose values). In a 2-tailed t-test, no direction
is specified (e.g., the fed glucose values will be DIFFERENT than the fasting glucose values). The
“tails” refer to the tails of a bell curve; in a 1-tailed test, you are focusing on one end (tail) of the
curve, i.e., a difference in one specific direction. It is important to specify the number of tails in
your t-test because the p value depends on the number of tails.
There are multiple ways to do a t-test in Excel. The simplest way is to use the following formula:
= T.TEST(array1,array2,tails,type)
where tails is the number of tails (1 or 2) and type is 1 (paired), 2 (unpaired, equal variances in the 2
samples), or 3 (unpaired, unequal variances in the 2 samples). If doing an unpaired t-test, we generally
prefer to use the unequal variances version, so as to make the fewest assumptions necessary.
The number you get out from this formula is simply a p value between 0 and 1.
More information about the Excel T.TEST function can be found online, e.g., at
https://support.office.com/en-us/article/T-TEST-function-d4e08ec3-c545-485f-962e276f7cbed055?ui=en-US&rs=en-US&ad=US
An alternative way of doing a t-test in Excel is to use the Analysis TookPak add-in. Activating this add-in
might be different in different versions of Excel, but on a typical PC, one proceeds as follows: File =>
Options => Add-Ins => Manage: Excel Add-Ins (click “Go…”) => Check the Analysis ToolPak => Click “OK.”
Once this is done, the DATA tab of Excel should include a Data Analysis option, from which you can
select various types of t-tests. In this option, you will need to specify a hypothesized mean difference
(generally 0) and an alpha level (generally 0.05, corresponding to the usual significance cutoff of p <
0.05).
ANOVA
In its simplest form, an ANOVA (short for analysis of variance) is essentially a t-test for comparing more
than two groups. An ANOVA compares the variation within each group to the variation between the
groups. The smaller the former is relative to the latter, the lower the p value. For example, imagine that
Groups A, B, and C have mean scores of 70, 71, and 72, respectively. These groups may or may not be
significantly different, depending on how much variation there is in each group. If each group’s
individual values were all within a tenth of a point of the group’s mean (e.g., Group A includes values of
69.9, 69.9, 70, 70, 70, 70, 70.1, and 70.1), the groups would be significantly different according to an
ANOVA. If the groups overlapped much more, they would not be statistically significantly different.
ANOVAs can be performed in Excel using the Analysis TookPak (which can be installed according to the
instructions above).
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BBIO 351: Principles of Anatomy & Physiology I
Winter 2016
Linear regression
A linear regression determines how much of the variability in a variable (say, Y) can be explained by
another variable (say, X). The closeness of the linear relationship between X and Y can be quantified
with Pearson’s correlation coefficient, r, or with the square of this value, R2, which is the fraction of the
variation in Y that can be explained by X. Values for r can range from -1 (a perfect negative correlation)
to 1 (a perfect positive correlation); values for R2 can range from 0 (no correlation) to 1 (a perfect
correlation). One can also ask whether the slope of a regression is significantly different than 0
according to the p value associated with the estimated slope.
Like t-tests, linear regressions can be performed in a couple of ways in Excel. You can make a scatterplot
of the data, and under Trendline options select a Linear trendline and “Display Equation on chart” and
“Display R-squared value on chart.” Or you use the Analysis ToolPak: select Data Analysis from the
DATA tab, and select Regression.
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