This case can be thought of as a set of replicate experiments. In each experiment one culture from a single source was treated and a second culture from the same source was untreated. The experiment was replicated 10 times, using 10 different sources. Since each replicate experiment consists of a pair of data points linked by the common origin of the respective cultures, you have 10 pairs of data points (two sets of paired data).
A paired t test is appropriate for the same reasons it was appropriate for problem #2. The paired method controls for experimental error that might be contributed by the 10 different sources.
Why not conduct all of the replicate experiments on cultures from a single source, eliminating all experimental error that is contributed by individual variability? Then we run the risk that the result won't hold for other cell lines.
You suspect your stem cell cultures decline due to a failure to produce sufficient superoxide dismutase to rid the cells of oxygen free radicals. You have an assay for the enzyme, but the results are subject to considerable random error. In addition, to conduct the assay you must destroy the culture. You prepared thirty cultures from the same source and sampled half of them, destroying them. You then sampled the other half 10 days later. Your null hypothesis is that superoxide dismutase activity will remain the same after 10 days. What statistical test will you run to identify a significant difference?