This is indeed sounds tricky. Perhaps you can quantify the number of cells before RNA extraction and try to normalize using those numbers?

Thanks for the response Denis! So in this case I would take a cell count and then normalize to cell number. My only concern would be that I culture the cells for 7 days, collecting conditioned media each day. Would I be able to visually count the cells on each of the days and use this as a valid cell count? Or would it still be valid for me to take a final cell count at the end of the 7 day treatment period and use this as my cell count to normalize with.

Normalizing progesterone data from radioimmunoassay (RIA) or enzyme-linked immunosorbent assay (ELISA) is essential to account for variations in sample processing, instrumentation, and biological factors. Since you don't have total protein measurements, here are a few alternative normalization strategies you could consider:

1. Normalize to cell number: If you have cell counts for your treated and control groups, you can normalize the progesterone concentrations to the number of cells per well or per milliliter of conditioned medium. This approach assumes that the number of cells is proportional to the amount of hormone produced.

2. Normalize to DNA content: Measure the DNA content of your cells using a fluorescence-based assay (e.g., Hoechst staining) or spectrophotometry. Then, normalize the progesterone concentration to the DNA content, which provides an estimate of the number of cells present.

3. Normalize to a housekeeping gene: Measure the expression level of a stable housekeeping gene (e.g., actin, GAPDH) in your RT-PCR experiments. Use the relative expression levels of the housekeeping gene to normalize the progesterone concentrations. This method assumes that the housekeeping gene is expressed at a consistent level across all samples and treatments.

4. Normalize to a reference compound: If you have access to a known quantity of a reference compound (e.g., cortisol, estradiol), add it to your samples before performing the RIA or ELISA. Then, use the ratio of progesterone to the reference compound as a normalization factor. This method allows you to compare the absolute quantities of progesterone among samples.

5. Normalize to a percentage change: Calculate the percentage difference in progesterone concentration between the treated and control groups for each sample. Divide the progesterone concentration of each sample by the mean progesterone concentration of the corresponding control group. This method accounts for global changes in progesterone production between treatments and helps eliminate technical variability.

6. Combine multiple normalization methods: Consider combining two or more of the above methods to generate a comprehensive picture of your data. For instance, you could normalize to both cell number and a housekeeping gene to account for variations in cell density and gene expression.

All the best

Saif Wahid Thanks so much for your response! I have a couple questions, I have access to qPCR and generated cDNA, so I could therefore normalize to housekeeping gene. If I were to do this would I just run a qPCR for GAPDH and then use the ct values to normalize to? Also, if I were to combine methods such as housekeeping gene and percentage change would I divide the progesterone value by ct value for example and then by percentage change as well? Thanks again!

Yes, you can normalize your progesterone data to a housekeeping gene using qPCR. Here's how you can do it:

Regarding combining methods like housekeeping gene normalization and percentage change:

• If you want to combine housekeeping gene normalization with percentage change normalization, you would first normalize your progesterone data to the housekeeping gene as described above.
• Then, if you want to account for percentage changes, you can further divide the normalized progesterone values by the percentage change factor. This would mean that you're adjusting your progesterone data for both gene expression changes (using the housekeeping gene normalization) and percentage changes between treated and control groups.
• Essentially, you'd have two normalization steps: first to the housekeeping gene and then to the percentage change factor.

All the best