5:30 PM instructure-uploads.s3.amazonaws.com o0o Verizon LTE Orm1/2 are regulate

Question

5:30 PM instructure-uploads.s3.amazonaws.com o0o Verizon LTE Orm1/2 are regulated in response to disruption of sphingolipid synthesis. K Breslow et al Nature 463. 1048-1053 (2010) dol:10.103nature08787 nature a-Flag 3xFlag-Orm1 3xFlag-Orm2 Supplementary Figure 7. Wild type and phospho-mutant forms of Orm1 and Orm2 show similar expression levels. Lysates from strains expressing 3xFlag-tagged wildtype (WT) or phospho-mutant alleles of ORM 1 and ORM2 were prepared after growth for 12-16 hrin or 150 ng/ml myriocin. Westem blots of these lysates were probed against the Flag epitope and against Pgk1 as a loading control. Flag-Om

Explanation / Answer

1. Find the Linear Range

For quantitate analysis of an image you must ensure your image was captured in a manner sensitive enough to detect change, in what we call the “linear range”. If you are not working within the linear range – e.g. if your detector or film can no longer absorb photons, it is saturated and you have hit your limit of detection – you are losing data. You don’t want this

to prevent saturation on film, you must empirically determine your linear range. To do this you need to serially dilute a known amount of your protein lysate, preform your Western, and plot the quantitated density of these Western blot bands against the amount you know you loaded. You should then find a linear line indicating where data is captured quantitatively. This is where you want to work. To fix any saturation problems, you can try loading less total protein, less primary antibody dilution, try a new antibody, or reduce the film exposure length.

2.Subtract Background

most Western blots and image captures are infiltrated with random imperfections. For example, the left side of the blot may be a little darker (higher background) or your less abundant band might have more background or an annoying dark scratch. These differences can cause inconsistencies in your results. Many software packages can calculate background around your band of interest, using some variation of the “rolling ball” method (again, take time to understand your software). The background should be subtracted from both your bands of interest and the bands you are normalizing to. Perfection here is challenging; just do your best and let statistics estimate the real answer when you are all done

3.Normalize

These control proteins are often product from a “housekeeping gene” such as actin, beta-tubulin or a chaperone protein like Hsp70. However, as many of us have discovered, these proteins can unexpectedly change in our experimental conditions. And, due to their high abundance, they can also be challenging to acquire in the linear range. Sometimes choosing a random background band that doesn’t change is the best choice.

Steps to Normalize Your Protein Band of Interest:

Step 1: Determine the background-subtracted densities of your protein of interest (PI) and the normalizing control (NC).

Step 2: Identify the NC that has the highest density value.

Step 3: Divide all the NC values by the highest NC density value to get a relative NC value. If you do this correctly the highest density value will be 1, and the others a fraction of it (e.g., 0.97).

Step 4: Divide all of your PI values by the relative NC values in their respective lanes.

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