Chapter 1. Mirror Experiment Activity 9.5

Mirror Experiment Activity 9.5

The experiment described below explored the same concepts as the one described in Figure 9.5 in the textbook. Read the description of the experiment and answer the questions below the description to practice interpreting data and understanding experimental design.

Mirror Experiment activities practice skills described in the brief Experiment and Data Analysis Primers, which can be found by clicking on the “Resources” button on the upper right of your LaunchPad homepage. Certain questions in this activity draw on concepts described in the Experimental Design and Data and Data Presentation primers. Click on the “Key Terms” buttons to see definitions of terms used in the question, and click on the “Primer Section” button to pull up a relevant section from the primer.

Experiment

Background

As you have learned in Fig. 9.5, growth-promoting factors produced by platelets in the blood can cause fibroblasts to grow and divide. In Chapter 10, you will learn that fibroblasts are cells that contribute to skin structure; they generate a scaffold to which cells can attach and grow. Are the effects of platelet-produced factors specific only to fibroblasts? Can other types of cells, such as muscle cells, proliferate in response to signals produced by platelets?

Hypothesis

Russell Ross and his colleagues were interested in atherosclerosis, a disease that you may know better as hardening of the arteries. Although atherosclerosis is typically associated with cholesterol and diet, it also involves the proliferation of smooth muscle cells (SMCs) in the artery walls themselves. Ross and colleagues speculated that, as a result of artery damage (i.e., a plaque beginning to form), platelets in the blood may accumulate at a specific location in an artery and secrete growth-promoting factors. Researchers hypothesized that these factors might cause SMCs to proliferate, ultimately contributing to atherosclerosis. Ross and colleagues predicted that if SMCs were treated with serum or with plasma containing platelets (or their associated growth factors), SMCs would rapidly grow and divide.

Experiment

Ross and colleagues adopted a similar protocol to that discussed in Fig. 9.5. Researchers isolated SMCs from the arteries of monkeys and grew these cells in petri dishes. SMCs were then exposed to a variety of treatments generated from monkey blood: blood serum (derived from clotted blood in which platelets had presumably released growth factors), blood plasma (derived from unclotted blood that did not contain platelets or their associated growth factors), and blood plasma supplemented with monkey platelets. Researchers then determined which treatments (if any) resulted in the proliferation of SMCs by counting the number of cells in petri dishes after a 9-11 day culture period (Figure 1).

Figure 1

Photo credit: SPL/Science Source

Results

Much like the experiments of Kohler and Lipton discussed in Fig. 9.5, Ross and colleagues determined that SMCs treated with blood serum grew and rapidly divided, whereas cells treated only with blood plasma did not proliferate (or did so only slightly). When supplemented with platelets, blood plasma could cause SMCs to proliferate. This observation provided evidence that growth factors produced by platelets could cause other cells – aside from fibroblasts – to grow and divide.

Source

Ross, R., et al., 1974. A platelet-dependent serum factor that stimulates the proliferation of arterial smooth muscle cells in vitro. Proc Natl Acad Sci U S A. 71, 1207-10.

Question 1 of 8

Question

As you may recall from Fig. 9.5, blood plasma is derived from unclotted blood that (presumably) does not contain any platelet-produced factors. How could Ross and colleagues have collected blood plasma from freshly drawn monkey blood that met this criteria?

Researchers obtained blood plasma only by removing platelets from monkey blood.

Researchers obtained blood plasma only by heating extracted blood to the normal body temperature of monkeys, which inactivated any factors produced by platelets.

Researchers obtained blood plasma by treating monkey blood with anti-clotting chemicals and removing platelets from monkey blood.

Researchers obtained blood plasma only by treating monkey blood with anti-clotting chemicals or drugs.

Researchers did not do any of the above; they had to collect blood plasma from untreated, freshly drawn blood.

Correct.

Incorrect.

Question 6 of 8

Question

Ross and colleagues counted the number of cells in SMC cultures exposed to different treatments, and generated the graph in Figure 3 (this is a rendering of the actual graph depicted in Fig. 3 of Ross and colleagues’ paper). Reflected in the graph is the fact SMCs were then treated (depicted by the arrow in the graph) with either 5% blood serum, 5% blood plasma, 5% blood plasma supplemented with platelets, or 0% plasma/serum. Which of the following statements is true regarding the graph below?

SMCs exposed to 5% blood serum were the only cells that proliferated, and these cultures typically contained 10⁵ i.e., 100,000) cells.

SMCs exposed to either 5% blood serum or 5% blood plasma supplemented with platelets demonstrated (roughly) the same amount of proliferation during the culture period.

Following the culture period, all SMCs exposed to 0% serum/plasma had died.

SMCs exposed to 5% blood plasma demonstrate the least amount of proliferation of all four treatments.

SMCs exposed to 5% blood plasma did not proliferate at all during the culture period.

Figure 3

Data and Data Presentation

Graphing Data

Figure 1

Now we can be confident that our numbers are reliable. The next challenge is to present the data. Typically we do this with a graph. Different kinds of data lend themselves to different kinds of graphs. Our mammal species data is discrete—we have clear categories: A, B, C, D, E, and F. For discrete data, either a pie chart or a bar graph would be appropriate. A pie chart divides a circle into “cake slices,” each representing the proportion of the total contributed by a particular category. In our trapping study, we have a total of 61 animals, so the slice representing species A will make an angle at the center of the pie of 17/61 x 360 = 100°. A bar graph represents the frequency of each species as a column whose height is proportional to frequency.

Fig. 1

What about continuous data? Imagine that the data we collected is the body lengths of the mammals we trapped. In this case, we might choose a histogram, which looks similar to a bar chart; only here we have to impose our own categories on a continuum of data. Because they were discrete categories—different species—the columns in the bar graph may have gaps between them. In the histogram, by contrast, there are no gaps between the columns because the end of one range (1–20cm) is continuous with the beginning of the next (20–40cm).

Fig. 2

Often we are plotting two variables against each other. If, for example, we record the time of day that each mammal is trapped, we can plot the total number of mammals trapped over the course of the 24-hour period.

	Midnight–2am	2am–4am	4am–6am	6am–8am	8am–10am	10am–12am	12am–2pm	2pm–4pm	4pm–6pm	6pm–8pm	8pm–10pm	10pm–midnight
Number trapped	8	3	2	0	0	0	0	0	1	22	17	8
Cumulative number	8	11	13	13	13	13	13	13	14	36	53	61

Table

Often one variable is independent—time, for example, will elapse regardless of the mammal count. We plot this on the x-axis, the horizontal axis of the graph. The dependent variable—the values that vary as a function of the independent variable (in this case, time of day)—is plotted on the y-axis, the vertical axis of the graph. If there is reason to believe that consecutive measurements are related to each other, points can be connected to each other by a line. Plotting our data on a graph using the values of the independent and dependent variables as coordinates gives us a line graph. This is a good way to identify trends and patterns in data. Here we can see that the mammals in our forest plot tend to be inactive (and therefore unlikely to be trapped) during daylight hours.

Fig. 3

In science, data are typically presented as a scatterplot, in which points are specified by their (x,y) coordinates. Points are not joined to each other by lines unless there are specified connections among them. Here, plotted in a way similar to the line graph (with the independent variable on the x-axis) is a scatterplot showing the time taken to drive from home to campus for a large number of students. The independent variable is the distance traveled; the dependent variable is travel time because the distances are fixed but travel times vary. Overall, there is a positive correlation between travel time and distance (the further you live from campus, the longer, on average, it will take you to get there), but there is plenty of variation as well. Look at the eight points representing the eight students who live five miles from campus. The variation we see in travel time (from 6 minutes to 30 minutes) is a reflection of differences in driving speed, traffic conditions, and route.

Fig. 4

What if there are more than two variables? Three-dimensional plots can be informative (but can also cause the reader headaches). A popular modern solution to this problem is a so-called temperature plot, in which the third dimension is represented in two dimensions through color: red (hot) for a strong effect in the third dimension and blue (cool) for a weak effect.

Graphs are the mainstay of scientific presentation, but you will see many other ways of presenting data in your textbook. For example, studies showing how different genes interact with each other in the course of development are often illustrated using network diagrams that give the reader a direct sense of the “connectedness” of a particular gene (or node). Evolutionary trees reveal the branching pattern of evolution with species that are closely related having a more recent common ancestor than those that are more distantly related.

Methods of presenting data in science are not limited, even in textbooks, by standard approaches. The popular press has developed many graphics-intense ways of presenting data. Think of an electoral map after an election. You can view information on a number of levels: whether the state is red or blue, the name of the election winner, the size of his or her majority, and so on. Scientists are learning that they too can package information in ways that are simultaneously informative and attractive.

Correct.

Incorrect.