Now just repeat your experiment a few times. See the left-most bar in the graph below). To calculate the relative abundance, the BSID for each band should be divided by the BSID of its loading control band.įinally, to make the data more readable, these values can be normalised to the first band (or whichever is an appropriate experimental control) by dividing the relative abundance by the first value (which will become 1. When considering the BSID, note that it doesn’t matter how big the ROI is, because any extra background pixels will add zero to the integrated Intensity. Schematically the ROI now looks like this: This works out to be 1220 (the integrated intensity) minus the area (50 pixels) multiplied by the mean background (10), which gives an answer of 720. We have separately measured the mean background intensity to be 10.īy calculating the BSID, we’re removing the background component from each pixel (it’s important to remember that even the “signal” pixels will have a noise component). The ROI has an area of 50 pixels and an integrated density of 1220 (18 pixels with an intensity of 50 and 32 pixels of intensity 10). Imagine the following ROI encompassing a band (the light grey bit): To help to understand this point (because it’s an important one and is the reason why our ROIs need not be the same size), below is an example. We do this by calculating the total background for the band (the number of pixels multiplied by the mean background intensity) and subtracting this from the total integrated intensity.ĪSIDE: Background-Subtracted Integrated Density (BSID) The first step is to calculate the background-subtracted integrated density (BSID) for each band. Scrunching NumbersĪt this point you can copy your numbers out to your favourite spreadsheet application (like LibreOffice and OpenOffice) to do some basic maths. Measure the ROI and repeat for your bands and loading controls. It’s important to select all of the signal from the band but none of the signal from adjacent bands! See the examples below: Hit ‘m’ to measure the background.ĭraw a region encompassing your first band. This should be away from the bands and should avoid any smears or bright spots (unless these are representative of the background). Using the rectangle tool on the Fiji toolbar, draw a region of interest (ROI) to use for background. Make sure that you’re set to take measurements of Area, Mean Grey Intensity and Integrated Intensity ( ). We will take one measurement for each band plus a background. As long as your Min is greater than pure black (with a value of 1) and your Max is less than pure white (256, 406 in 8, 12 and 16-bit respectively), you’re set. Make sure that this is set to display the full range of your image.Īn alternative (which is maybe quicker and simpler) is simply to measure the whole image with Min and Max selected in. NOTE: The lookup table will represent the currently selected range in. On the left is the Greyscale LUT on the right is HiLo: Each square has an intensity value from 1 (black) to 256 (white). To help with understanding this, below is a lookup table for an 8-bit greyscale image. A quick way to do this is to use the HiLo lookup table which labels pure black values (IE zero) in blue and pure white (IE 256, 406 in 8, 12 and 16-bit respectively) in red. To correct this use a combination of which will change the pixel values and which will change the way the values are represented.Ĭheck two is to make sure that you don’t have an oversaturated or underexposed image. Some cameras will invert both the Intensity data and the Look Up Table so bands are low values and background are high values (but look bright and dark respectively). You can easily check this by hovering your cursor over the image in a bright area and a dark area.įiji will give you a readout of the intensity value in the Status bar. Firstly, it’s important to make sure that your background is actually dark. There are two checks that you should do before starting to analyse your blots. It will probably look something like this (the band at the top is very feint). I’m not going to go into the details of acquisition (this is PostAcquisition after all) so let’s assume that you have an image of your blot that has both your protein of interest and loading control in the same blot and that the image is greyscale (my example image is 16-bit so values range from 1 to 65536). In this post we’ll look at the best way to acquire and analyse the humble Western blot.Ī quick thanks to Rosalie Richards (of the Sée Lab) for supplying the lovely western blot used as an example in this post. Instead of using fluorescent labelled antibodies ( although this can be done), most WBs use ChemiLuminescence to detect the amount of protein present. Proteins are separated based on their size then labelled and identified using antibodies. The Western blot is a staple of many Research Labs.