Biological and technical replicates for statistical analysis in cellular biology These are questions regarding basic statistics/reporting in biology. I have already read a couple of articles on this subject, but couldn't find a clear answer applying to my research. 
I have the following scenario:
I have 3 independent cell cultures from which I have analyzed 10 cells each with a confocal microscope and obtained a value X (protein interaction) for each measurement.
I also have 2 independent cell cultures from which I have analyzed 20 cells each obtaining value X again. These samples are my negative control.
Now I would like to compare these groups with a simple student's t-test and there are two thinkable scenarios:
(1) Average each independent sample's measurements as input for my statistical analysis: M1 (n=10), M2 (n=10), M3 (n=10) vs C1 (n=20), C2 (n=20)
(n=3 for samples vs n=2 for controls).
In this scenario I would group all biological/experimental replicates together and compare them against all controls.
or
(2) Compare M1 (n=10) vs C1+C2 (n=40), M2 (n=10) vs C1+C2 (n=40), M3 (n=10) vs C1+C2 (n=40). 
In general, this appears to me like comparing independent Day 1, Day 2 and Day 3 of an experiment to a control.
Q1: Would it be better to compare to C1 only instead of C1+C2?
In this scenario I would compare each biological replicate with the controls. This seems more appropriate to me, as an overall/grouped result could be heavily affected by errors.
Q2: But which solution is appropriate? 
Q3: Are these measurements for each independent cell culture technical replicates even though they are not performed on the same cell?
EDIT: As an alternative, I could report just the first experiment as add "Experiment was repeated with similar results" as seen in various publications. However, this appears to be the worst solution to me.
Thanks in advance!
 A: The mixed model is suitable for your situation. Put all of 70 Xs into one model. Treat M (experiment) and C (control) as fixed effect. Add the random intercept of cell culture as random effect. Based on the properties of X (protein interaction), you need to select a distribution. If X follows normal distribution conditional on treatment and cell culture, you can fit linear mixed model. It can be written as:
$$Y_{ij}=\beta_0 + \beta_1 X_{ij} + \lambda_i + \epsilon_{ij}$$ 
where $i$ index culture, $j$ index measurement, $Y_{ij}$ is X (protein interaction), $X_{ij} = 0$ for control and $=1$ for experiment. $\lambda_i$ is random intercept, $\epsilon_{ij}$ is error term, and both of them follow normal distributions with mean 0 and unknown variance, and they are independent. 
If  $\beta_1$ is significantly differ from 0, it means M and C have different means of X.
A: Effectively, you have five observations to compare the experimental treatment with the control treatment: 3 treatment and 2 control.
You can do either of two things.
First, you can compute the average of the measurements for each culture. Then do a t-test or a nonparametric test to compare the means of the two treatments.
Your control culture averages might be more reliable since they are based on more measurements. If you want to evaluate this you will need a hierarchical model.
You have two treatments. Nested within treatments is a random factor, culture number (numbered 1 to 5). Nested within each level of culture number you have another random factor, call it measure, numbered from 1 to 70. You can use an analysis of variance with these three factors and with this nesting. The denominator for the test to compare the two treatments still comes from variation among cultures. 
I recommend the first method, at least to start.
You should evaluate whether the measurements are skewed.
