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Gel Imaging Capture and Image Analysis

Introduction – Protein Gel Imaging


What are the effects of imaging parameters on your 1D and 2D analysis?

Being aware of image data and how to capture it will not only save you time when it comes to analyse your gel and western blot images (read: less manual work).

It will save further questions around “is my analysis quantitative?” or “why aren’t all my spots detected?”

(hint: analysing 8-bit JPEG images won’t give you accurate results). We show you the impact that bad image acquisition can have on your analysis.

Do you know what effects 8-bit, JPEG images can have on your results? If so, move onto Section 2: Antibody Coverage Analysis

Why does this matter?

Electrophoretic gel and blot images carry data and that data can be compromised without the appropriate handling. The processes of recovering missing data from images is in many cases impossible.

That’s not all, the image displayed on a computer does not appear to change even when data has been accidentally discarded or changed.

We need to understand the limitations of the image displayed and concentrate on keeping the real numbers to gain quantitative results when using any image analysis software.

Bonus: Get a free checklist which includes 12 top tips for creating better images the first time – plus places you’ve been going wrong, so you can apply them correctly.

Introduction – Protein Gel Imaging


What are the effects of imaging parameters on your 1D and 2D analysis?

Being aware of image data and how to capture it will not only save you time when it comes to analyse your gel and western blot images (read: less manual work).

It will save further questions around “is my analysis quantitative?” or “why aren’t all my spots detected?”

(hint: analysing 8-bit JPEG images won’t give you accurate results). We show you the impact that bad image acquisition can have on your analysis.

Do you know what effects 8-bit, JPEG images can have on your results? If so, move onto Section 2: Antibody Coverage Analysis

Why does this matter?

Electrophoretic gel and blot images carry data and that data can be compromised without the appropriate handling. The processes of recovering missing data from images is in many cases impossible.

That’s not all, the image displayed on a computer does not appear to change even when data has been accidentally discarded or changed.

We need to understand the limitations of the image displayed and concentrate on keeping the real numbers to gain quantitative results when using any image analysis software.

Let’s begin.

1. Understanding The Digital Image

The digital image is made up of an array of numbers.

When we view these numbers on a display they are converted into pixels (coloured squares).

This pixel display allows us to gain an understanding of the contents of that image faster.

Those pixels that we see are only a representation of the numbers, we therefore cannot use them for quantitative measurement in pixel form.

Only scientific image analysis software can explore the numbers and keep the data safe.

Pixel values of an image

Figure 1. Small area of an image as pixel values and represented by pixels

Two images can look exactly the same but contain very different numbers (pixel values).

Additionally two images that look different can contain the same numbers. In summary – seeing isn’t believing (1).

In order to obtain meaningful and quantifiable images you need to capture as much of that data as possible and keep it safe ready for analysis.

2. Bit Depth

What is this image data? How can I capture it and how can I keep it safe?

As we know, each pixel has a numerical value.

The bit depth (8-bit, 16-bit) represents the number of these values or intensity levels that each pixel can take.

Images captured at a higher bit depth will have more data (or be it storage) available.

The number of available intensity levels increases exponentially as the bit depth increases.

Bit Depth Intensity Levels
8 256
10 1024
12 4096
16 65536

Figure 2. Table and chart showing the intensity levels of 8,10,12, 16 bit images. As you can see a 16-bit image will hold more than 25x more data than an 8-bit image.

You won’t be able to see this difference by simply looking at your images.

With image analysis software however, this distinction is clear:

Figure 3. Spot detection on an 8-bit image. (a) image view and (b) 3D view (c) A profile through one of these undetected spots shows it to have a maximum pixel intensity of 33, which is only 9 grey levels above background. Two low level spots are clearly undetected.

Figure 4. Spot detection on the same 2D gel image, but captured at 16-bit (a) image view and (b) 3D view. The two spots which were previously below the limits of detection for the 8-bit image are now clearly well detected. (c) A profile through one of these spots shows it to have a maximum pixel intensity of 8390, which is 2062 grey levels above background for this image.

Recommendation
As a rule, the more levels of grey represented in an image, the better the ability to differentiate low abundance spots from the background, and the greater the quantitative accuracy. This is further illustrated in Figures 1 and 2, comparing spot detection in an identical area on the same 2D gel, captured at 8-bit and 16-bit

3. Image Saturation in Gel Image Analysis

Image saturation can occur if we try to store a value that is above or below the capacity supported.

Instead the value closest value to it will be stored.

Similarly if we try to store a value that is not supported, the value closest to it will be stored instead.

Figure 5. Example of values trying to be stored in an image. (values do not represent any image file and are shown for informative purposes only)

This means that those values that have been rounded up (blue value 4) or the one that now sits at the maximum capacity (blue value 6) can’t be viewed as the data they should be (3.8 & 7).

That data they once held is lost. This is called image saturation.

If saturation occurs in gel image capture, spots will appear to have their peaks cut off and lead to unreliable quantitation.

Figure 6. Two saturated spots visualised in image analysis software with a wireframe representing the potential missing data.

Quantitative analysis based on saturated images will not be reliable.

Recommendation

When capturing your images you should optimise the image capture and use as much of the available greyscale levels as possible without saturation.

4. The Dynamic Range Effect of Protein Quantification

Dynamic range (often visualized in a histogram) refers to the range of greyscale levels actually being used by the image.

Figure 7. An example of a low dynamic range using linear and log plots of pixel histograms. Think about it this way, a limited dynamic range can not only impact on the quality of the image analysis, it may also compromise quantitative results when comparing data between images.

For example with a 16-bit image there are 0 – 65,535 values available and your data should lie somewhere inside that range.
It is good practice to optimise scanning so that the majority of the values are represented.
Image saturation can be indicated if you see any grey level values at the max and minimum levels or if the pixel count looks clipped.

Figure 8. Example of image clipping

Recommendation

When capturing your images we recommended only scanning the area of the gel you are interesting in. Perform any cropping at the time of scanning to remove blank parts of the scanner plate, labels etc. The extra areas provide no useful information, can ‘steal’ dynamic range, distort image statistics and increase storage requirements.

5. Adjusting the Dynamic Range

You can adjust the dynamic range in CCD camera systems by altering the exposure time, or in a laser based system by fine tuning the voltage of the PMT detector.

It should be as high as possible across all your images, without saturating.

You should consult your scanner documentation, or contact your scanner supplier for information on how to achieve this.

However you should not change settings between different images in the same study!

6. Image Resolution

Figure 9. Display resolution of an image

Image (or spatial) resolution relates to the number of pixels displayed per unit length of a digital image, and is often measured in dpi (dots per inch) or in microns (the size of the area each pixel represents).

 

Images with a higher spatial resolution have a greater number of pixels and have more image detail than those of lower spatial resolution, and hence, more data, are available for the analysis, with a result that quantitative measurements will be more reliable.

Try to scan at the best resolution for your images. In most situations, 300 dpi or 100 microns will provide an image that is large enough for accurate analysis and small enough for efficient processing. However, if your gels are small (e.g. mini gels), then you may need to increase the resolution to achieve this. As a rule of thumb, the active area of the gel (i.e., the area of spot material) should fall in the range 1000-1800 pixels in both horizontal and vertical directions. This range provides a good trade-off in information content and analysis performance.

That’s not all, if you attempt to increase the size of an image. It causes software to interpolate new pixels in between the pixels. In other words it takes a guess at what the new pixels should be. Increasing the size of an image doesn’t actually increase the resolution and could potentially create misleading artefacts.

Be warned, once you’ve captured your images refrain from making any changes that may change data. Let’s find out what you can do to your images that is an acceptable practice.

7. Contrast Stretching

Figure 10. The over processed example shows that the same intensity levels are present in the unprocessed image.

This can be another artefact from the capture software or an image editing package.

This indicates that some form of “Contrast Stretching” or “Histogram Equalisation” has been applied to the scanned image.

For example: if an image containing 100 intensity levels is stretched to fill an image format capable of recording 400 intensity levels…

…the image still only contains 100 unique intensity levels (25% of those available).

Why can contrast stretching be a problem?

The image may look to be a higher resolution but the precision has not been improved.

Contrast stretched images have pixel intensities that step up and do not improve spot detection or quantification.

Recommendation
This is normally optional on the capture software and you should not apply any contrast stretching or equalisation to the image.

8. File Types & Greyscale

File types

Imagine this, you have optimised your image capture for ready for quantitative analysis and you save the file as a JPEG.

You will have just lost data instantly and it cannot be retrieved.

(Yes, that means that even if you convert a JPEG image back to a TIFF the lost data will not be recovered).

If you are capturing an image for quantitative analysis, any data lost will inevitably impact your results as some file formats do not preserve that valuable data.

Recommnedation

For analysing your gels we recommend storing them in a lossless format such as TIFF. If possible use GEL or IMG/INF file formats, these often contain additional grayscale calibration information. Please consult your scanner documentation, or contact your scanner supplier for information on how to achieve this.

To present your images in publications you will need to choose a different file format. See image capture for publications.

Greyscale vs Colour

Colour images are made up of 3 different channels, red, green and blue.

A 24-bit colour image has 8-bits per channel.

When an image is converted from colour to grayscale, it’s effectively changed to an 8-bit grayscale image (go back to bit depth if you don’t know why this is bad).

Importing a colour image into our software is possible but you should be aware that they are 8-bit colour depth.

Recommendation
Always scan directly to grayscale as the imaging device will then do the conversion from colour in the most accurate and sensitive manner. It will also allow for a higher bit depth.

9. Gel Imaging Systems and Scanners

Image acquisition can be achieved using a variety of devices. These can be broadly categorised into three major types:

  • Laser Scanners
  • CCD Camera Systems
  • Document Scanners

Laser scanners

Laser devices are the most sophisticated and versatile image acquisition instruments, and are commonly used to detect some of the more recently developed fluorescent dyes such as Cy dyes, Sypro, ProQ and Deep Purple.

Multiple lasers and emission filters can be used to accommodate the wide variety of fluorescent dyes currently available.

Some instruments also benefit from confocal optics, which exclude signals from scattered light, thus enabling gels to be scanned whilst between low fluorescence glass plates.

This feature is particularly useful for DIGE applications.

Laser-based image capture devices can also be used with the common visible protein stains such as silver and Coomassie Blue, and also for phosphor-imaging of radioactive labelling.

CCD Camera Systems

CCD camera image acquisition systems can be used with either visible dyes or fluorescent stains.

They can be either fixed or scanning. Scanning cameras are used to compensate for the relatively low dimensions of high quality camera chips (typically less than 2000 x 2000 pixels), and function by generating a series of overlapping images, which are assembled to form the final image.

Some instruments use different modes of illumination; coming from the top (for fluorescent dyes such as Sypro), bottom (for visible dyes) or edge of the gel assembly. The latter facilitates DIGE applications, as it enables gels to be scanned whilst between low fluorescence glass plates.

Document Scanners

Standard commercial document scanners are often used as densitometers.

Flatbed scanners offer both transmittance and reflectance, and are used for imaging visible dyes like silver and Coomassie, and to scan autoradiographs or blots. In general, the scanners used for gel applications differ from commercial office scanners in that their optical path is modified to cope with the gel assembly and they are sealed units to protect against wet samples.

Laser vs CCD Camera vs Document Scanners

Laser Scanners CCD Camera Systems Document
Scanning Fixed
Image Resolution 10-250 50-200 >120 20-250
Dynamic Range 5 3-4 3-4 4-5
Scan speed slow slow medium fast
Wavelength high high high low
Silver, Coomassie
autoradiography
yes yes yes yes
Storage phodphor yes no no no
Single colour fluoresence
(Cy dyes, Sypro,
Deep Purple, ProQ)
yes yes yes no
Multicolour fluorescence
(DIGE)
yes yes limited no
Chemiluminescence yes yes yes no
Cost very high high medium low

We hope this guide will expand your imaging knowledge, improve the quality of your data, images and ultimately your research. All material in this document has been written by collating information from various sources. We have assembled a bibliography of further reading and where possible sources have been cited. It is intended as a guide and not a protocol or standard operating procedure. You should check parameters specific to your own sample, instruments and image capture software. Best practice is to run pilot experiments to optimise sample handling, gel running, image capture and image analysis.

  1. Seeing the Scientific Image (parts 1,2,3), John Russ, Proceedings Royal Microscopy Society 39(2); 39(3); 39(4) (2004).

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