使用ImageJ分析Western.docx

1、使用ImageJ分析Western使用ImageJ分析WesternBlot(2013-04-16 21:40:31)转载标签：imagejwesterndnaThe following information is an updated version of a method for using ImageJ to analyze western blots from a now-deprecated older page. Dont use the alternate methods discussed on the old page, as they are subject to way

2、 too much user bias.A pdf copy of this page is available.ImageJ (http:/rsb.info.nih.gov/ij/index.html) can be used to compare the density (aka intensity) of bands on an agar gel or western blot. This tutorial assumes that you have carried your gel or blot through the visualization step, so that you

3、have a digital image of your gel in .tif, .jpg, .png or other image formats (.tif would be the preferred format to retain the maximum amount of information in the original image). If you are scanning x-ray film on a flatbed scanner, make sure you use a scanner with the ability to scan transparencies

4、 (i.e. film). See the references at the end of this tutorial for a discussion of the various ways that you can screw this step up.The method outlined here uses the Gel Analysis method outlined in the ImageJ documentation: Gel Analysis. You may prefer to use it instead of the methods I outline below.

5、 There should be very little difference between the results obtained from the various methods. This version of the tutorial was created using ImageJ 1.42q on a Windows 7 64-bit install.1. Open the image file using FileOpen in ImageJ.2. The gel analysis routine requires the image to be a gray-scale i

6、mage. The simplest method to convert to grayscale is to go to ImageType8-bit. Your image should look like Figure 1.3. Choose the Rectangular Selections tool from the ImageJ toolbar. Draw a rectangle around the first lane. ImageJ assumes that your lanes run vertically (so individual bands are horizon

7、tal), so your rectangle should be tall and narrow to enclose a single lane. If you draw a rectangle that is short and wide, ImageJ will switch to assuming the lanes run horizontally (individual bands are vertical), leading to much confusion.4. After drawing the rectangle over your first lane, press

8、the 1 key or go to AnalyzeGelsSelect First Lane to set the rectangle in place. The 1st lane will now be highlighted and have a 1 in the middle of it.5. Use your mouse to click and hold in the middle of the rectangle on the 1st lane and drag it over to the next lane. You can also use the arrow keys t

9、o move the rectangle, though this is slower. Center the rectangle over the lane left-to-right, but dont worry about lining it up perfectly on the same vertical axis. Image-J will automatically align the rectangle on the same vertical axis as the 1st rectangle in the next step.6. Press 2 or go to Ana

10、lyzeGelsSelect Next Lane to set the rectangle in place over the 2nd lane. A 2 will appear in the lane when the rectangle is placed.7. Repeat Steps 5 + 6 for each subsequent lane on the gel, pressing 2 each time to set the rectangle in place (Figure 3).8. After you have set the rectangle in place on

11、the last lane (by pressing 2), press 3, or go to AnalyzeGelsPlot Lanes to draw a profile plot of each lane.9. The profile plot represents the relative density of the contents of the rectangle over each lane. The rectangles are arranged top to bottom on the profile plot. In the example western blot i

12、mage, the peaks in the profile plot (Figure 4) correspond to the dark bands in the original image (Figure 3). Because there were four lanes selected, there are four sections in the profile plot. Higher peaks represent darker bands. Wider peaks represent bands that cover a wider size range on the ori

13、ginal gel.10. Images of real gels or western blots will always have some background signal, so the peaks dont reach down to the baseline of the profile plot. Figure 5 shows a peak from a real blot where there was some background noise, so the peak appears to float above the baseline of the profile p

14、lot. It will be necessary to close off the peak so that we can measure its size.11. Choose the Straight Line selection tool from the ImageJ toolbar (Figure 6). For each peak you want to analyze in the profile plot, draw a line across the base of the peak to enclose the peak (Figure 5). This step req

15、uires some subjective judgment on your part to decide where the peak ends and the background noise begins.12. Note that if you have many lanes highlighted, the later lanes will be hidden at the bottom of the profile plot window. To see these lanes, press and hold the space bar, and use the mouse to

16、click and drag the profile plot upwards.13. When each peak has been closed off at the base with the Straight Line selection tool, select the Wand tool from the ImageJ toolbar (Figure 8).14. Using the spacebar and mouse, drag the profile plot back down until you are back at the first lane. With the W

17、and tool, click inside the peak (Figure 9). Repeat this for each peak as you go down the profile plot. For each peak that you highlight, measurements should pop up in the Results window that appears.15. When all of the peaks have been highlighted, go to AnalyzeGelsLabel Peaks. This labels each peak

18、with its size, expressed as a percentage of the total size of all of the highlighted peaks.16. The values from the Results window (Figure 10) can be moved to a spreadsheet program by selecting EditCopy All in the Results window. Paste the values into a spreadsheet.Note: If you accidentally click in

19、the wrong place with the Wand, the program still records that clicked area as a peak, and it will factor into the total area used to calculate the percentage values. Obviously this will skew your results if you click in areas that arent peaks. If you do happen to click in the wrong place, simple go

20、to AnalyzeGelLabel Peaks to plot the current results, which displays the incorrect values, but more importantly resets the counter for the Results window. Go back to the profile plot and begin clicking inside the peaks again, starting with the 1st peak of interest. The Results window should clear an

21、d begin showing your new values. When youre sure youve click in all of the correct peaks without accidentally clicking in any wrong areas, you can go back to AnalyzeGelsLabel Peaks and get the correct results.Data analysisWith your data pasted into a spreadsheet, you can now calculate the relative d

22、ensity of the peaks. As a reminder, the values calculated by ImageJ are essentially arbitrary numbers, they only have meaning within the context of the set of peaks that you selected on the single gel image youve been working on. They do not have units of g of protein or any other real-world units t

23、hat you can think of. The normal procedure is to express the density of the selected bands relative to some standard band that you also selected during this process.1. Place your data in a spreadsheet. One of the peaks should be your standard. In this example well use the 1st peak as the standard.2.

24、 In a new column next to the Percent column, divide the Percent value for each sample by the Percent value for the standard (the 1st peak in this case, 26.666).3. The resulting column of values is a measure of the relative density of each peak, compared to the standard, which will obviously have a r

25、elative density of 1.4. In this example, the 2nd lane has a higher Relative Density (1.86), which corresponds well with the size and darkness of that band in the original image (Figure 1). Recall that these data are for the upper row of bands on the original western blot image.5. If you want to comp

26、are the density of samples on multiple gels or blots, you will need to use the same standard sample on every gel to provide a common reference when you calculate Relative Density values. See the sections below for more detailed discussion of these requirements.6. In order to test for significant dif

27、ferences between treatments in an experiment, all of your gels or blots will need to be scanned and quantified using this method, and the values will be expressed in terms of Relative Density, or you can treat Relative Density as a fold-change value (i.e. a Relative Density difference of 2 between a

28、 control and treatment would indicate a 2-fold change in expression). If you will be using analysis of variance techniques to test your data, you may need to ensure that your Relative Density values are normally distributed and that there is homogeneity of variance among the different treatments.7.

29、It should be noted here that some researchers make the extra effort to include a set of serial dilutions of a known standard on each blot. Using the serial dilution curve and the quantification techniques outlined above, it should be possible to express your sample bands in terms of picograms or nan

30、ograms of protein.A more involved example using loading-controls.Well use Figure 12 as a representative western blot. On this blot, we will pretend that we loaded four replicate samples of protein (four pipette loads out of the same vial of homogenate), so we expect the densities in each lane to be

31、equivalent. The upper row of bars will represent our protein of interest. The lower set of bars will represent our loading-control protein, which is meant to ensure that an equal amount of total protein was loaded in each lane. This loading-control protein is a protein that is presumably expressed a

32、t a constant level regardless of the treatment applied to the original organisms, such as actin (though many people will question the assertion that actin will be expressed equivalently across treatments).Looking at Figure 12, we had hoped to load equivalent amounts of total protein in each lane, but after running the western blot, the size and intensity of the lower bars in each lane varies quite a lot. The two left lanes appear equivalent, but the 3rd lane has half the density (gray value) compared to lanes 1+2, while lane 4 has h