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You are testing a new hydrogel that you hope to use to make artificial corneas. In your first experiment you will test a range of different levels of cross-linking on corneal endothelial and corneal epithelial cell growth from five different donors on the hydrogels. For each of the following, identify whether they are experimental units, independent variables, treatments dependent variables, or factors.
treatments, yes these are the inputs that you are testing.
the different donors
independent variable. The different donors could also be considered as factors. The decision would depend on how you plan to do your analysis
corneal epithelial cells and corneal endothelial cells
Factors. You could call these independent variables but when it comes to the rest of your planning you should consider how you want to interpret the data: do you want to compare epithelial cell growth versus endothelial cell growth or do you want to discover what the best substrate for epithelial cell growth and what is the best substrate for endothelial cell growth.
cell growth rates
dependent variable, this is what you are measuring
experimental unit. Each hydrogel will give you one measurement.
For each of these approaches to compare cellular proliferation rates, identify if they are direct or indirect. If they are indirect, what are the alternative interpretations of the data?
Measuring the relative level metabolic activity in the different wells of your plate by determining the reduction of Resazurin or MTT
Indirect. The rationale that if you have more cells you will have more metabolic activity is why this approach is quite commonly used, coupled with the potential for high throughput measurements. However, metabolic activity is what you are measuring and it could be that whatever manipulation you are performing affects metabolism rather than proliferation. These aren’t bad assays to do, just be a little cautious with your interpretation.
Plate a known number of cells in a dish, count the cells in the dish a defined amount of time later eg after 48h.
Indirect. I wasn’t trying to trick you here, cell numbers will increase through proliferation. However, the increase will be the net difference between proliferation and cell death. You could argue that at low densities there will be little cell death however, because you aren’t measuring proliferation this is still an indirect measurement. It’s not necessarily a bad option, it really depends upon how focused your experiment needs to be on proliferation rates. For example if you wanted to say that a material supported cell growth then a change in cell numbers would be appropriate. However, if your question was a molecular biology-level analysis of cell division you might need a tighter measure. Key point – choose your assay to suit your question! Note that if added a second experiment that showed cell death rates were unchanged then this indirect measure would likely be appropriate.
Determining the relative number of cells in G0, G1, S and M phase of the cell cycle using flow cytometry of propidium iodide stained cells.
Here you are directly measuring the proportion of the population in different states of cell division. The assumption would be that if you had a greater proportion of the population in S or M phase then proliferation rate would be higher. However, those aspects of the cell cycle could also proceed less rapidly. To measure proliferation rate you would need some sort of time-course measure as well as the cell-cycle analyses. For example you might want to introduce a drug which causes cell cycle arrest at a specific phase of the cell cycle and then determine the amount of time for the whole population to reach that point.
Staining your cell population with Ki67. This protein is present in cells that are actively dividing.
This would give you a snap-shot of how many cells are actively dividing at the time that you process the sample. If more cells are actively dividing you would infer that proliferation rates were higher. However, cells might be proceeding through the cell cycle slower. The rate at which proliferation is occuring may therefore not be directly related to this positive population. This isn’t a bad assay, the point I am making is that there is an alternative explanation and when you report the results of this sort of experiment you would have to be aware that you could get the same results in way that doesn’t mean a change in proliferation rate.
Read this one last!
The four assays described here are all good experiments and are widely used. The caveats to their interpretation are traded off against the speed and ease of the experiments. There are ways to tweak the design a little bit to reduce or remove alternative interpretations and, fundamentally, using a combination of approaches would increase your confidence that you are making an appropriate assumption. During your design stage, think about each of measurements you plan to make and how else you could get the same result. It may not be possible, feasible or justifiable to do the direct of the assay. You have to be the one to decide what matters with respect to your question. What assay is good enough? The answer will depend on whether this is the first experiment on a novel project or an experiment to determine if something will enter clinical practice.