Image alignment demystified: Part 4 – Key considerations to keep in mind
Welcome to the final chapter of our four-part series, “Image Alignment Demystified”. In our previous instalment, we shared a set of recommendations designed to enhance your alignment outcomes when utilising Melanie. Today, we’ll delve deeper into potential complications that may arise during the alignment process and illuminate key factors that require your consideration to secure optimal alignment outcomes.
Navigating the density of the spot pattern
The density of the spot pattern in your gel images can significantly impact alignment outcomes.
Sparse spot density
When the number of spots in your samples is limited, achieving accurate alignments can be challenging. When you’re dealing with a minimal presence of protein spots, introducing a protein standard into each sample can be an effective strategy. The proteins in such a standard, strategically distributed across the pI and MW ranges under investigation, act as control points that increase reproducibility by reducing alignment variation. However, it’s important to ensure that these standard proteins do not naturally coexist or overlap with proteins in your samples to avoid interference during quantification. As a beneficial side effect, these standards can also facilitate normalization.
Dense spot pattern
Conversely, a very dense spot pattern poses its own challenges, as multiple potential matches may exist for a spot in one image.
Use 2D-DIGE to minimize ambiguities
For successful gel alignment, inherent similarities in content should outweigh both biological and technical variations. This highlights the importance of performing a 2D-DIGE experiment that includes an internal standard. The latter provides a consistent reference for alignment by removing all biological variability.
Increase spot resolution
Related to this, any tactics for improving spot resolution will benefit the alignment. This can be achieved in several ways, notably with larger gel sizes, better image acquisition instruments and optimized image acquisition parameters such as resolution, image depth and dynamic range.
Troubleshooting spot count during alignment
Occasionally, the number of spots considered in the alignment may be off the mark. Here are two scenarios and how to address them:
Over-detection
While we’ve insisted on the importance of sufficient spot resolution, acquiring large gels with excessively high resolutions can lead to the detection of a high density of minuscule spots with the default detection parameters. This can potentially overwhelm the software because it sees too many options for alignment. Therefore, if an unusually high number of spots are detected, double-check your image acquisition resolution. If it is too high, the resolution of your images can be decreased in the Quality control step.
If the resolution is appropriate, and in line with the recommendations for your gel size, you may need to adjust the advanced alignment options in the Workflow options. Keep in mind that changing these options should be a last resort. Feel free to reach out to us if you believe the default settings are ill-suited for your data set. We’re here to help find the best possible solution for your images.
Under-detection
If you’re experiencing the opposite problem and not enough spots are being detected, tweaking the advanced alignment options may also be required. Remember, our team is always a call away to assist.
The art and science of cropping
Refining 2D gel images through cropping prior to analysis is beneficial for several reasons. First, it facilitates the elimination of non-relevant areas, honing in on specific regions laden with proteins of interest. This strategy also excels in excluding potential artifacts, noise, and poorly focused proteins that often reside near the gel edges and can potentially skew the analysis. Further, by reducing the image file size, cropping streamlines the storage process, accelerates the analysis procedure, and diminishes computational burdens, notably when handling sizable datasets.
Cropping images to approximately the same area during the quality control step can also enhance the performance of automatic alignment. However, it is important to avoid cropping too tightly. To ensure 100% matching in your images, the software detects spots only within the common area shared by all images. Consequently, if the cropping area is smaller in certain images, spots near the borders may go undetected. Striking a balance between sufficient cropping and maintaining detectability is crucial for precise alignment.
To use or not to use MW markers for alignment
A frequently asked question is whether it’s beneficial to use molecular weight (MW) markers for alignment. While it’s possible to do so by including the MW marker when cropping, their utility as alignment references is debatable due to several inherent limitations:
MW markers are typically situated on one side of the gel and therefore primarily correct vertical deformations in that area only. Additionally, because they are on the side of the gel, the bands can be distorted and fuzzy, complicating their use for accurate alignment. They also do not correct for deformations in the pI (horizontal) direction, and their distances from the immobilized pH gradient (IPG) strip can vary between gels, potentially complicating pI alignment. Lastly, they are unable to correct local running differences.
For these reasons, we usually advise excluding MW markers from the primary area of interest, and the alignment. However, in scenarios where the number of spots is extremely low, the inclusion of the MW markers may become a necessary compromise.
Revisiting the alignment post spot detection
Once you have moved beyond the alignment step and your images have undergone spot detection, you can apply a range of modifications to your data. This includes the application of customized spot filters, spot editing and the annotation of proteins of interest. Therefore, if you are considering going back to the alignment stage to make adjustments, consider the following:
Know that the spot outlines that you will see post-spot detection represent the final spot detection results, not the initial detection results used for alignment. This is only true until you edit the alignment again. That’s when Melanie will generate a new detection pattern that is more suitable for alignment.
While simply viewing matches and alignment results is just fine, initiating any changes to the alignment will produce a fresh consensus spot pattern. Such a move will trigger a complete recalculation of all spots, nullifying previous edits and annotations, given that new quantification metrics are established, spots need to undergo re-filtration, statistical outcomes shift, and so on. Hence, caution is vital when editing or annotating spots and then revisiting the alignment step.
Remember, your project will be locked once spots have been detected. Consequently, you cannot edit it without a prompt from the software. Be mindful and considerate of the potential consequences before confirming any changes.
Once you’ve started editing or annotating spots, or adjusting their properties (such as spot exclusions or categorizations in Melanie Coverage), refrain from making further alignment edits or changes in the Alignment setup.
Conclusion
Image alignment is a delicate but critical aspect of gel electrophoresis analysis. Getting it right is non-negotiable, as any misalignment can skew the entire analytical narrative. While challenges and uncertainties can arise, being well-versed in both potential pitfalls and best practices is the key to ensuring accurate, reliable, and impactful results.
The aim of this blog series was to shed light on the nuances of image alignment, highlighting its core principles, quality assessment tools, practical guidelines and potential challenges. By consistently applying the strategies and insights shared, you can streamline your alignment efforts, leading to more reliable and informative results in your gel electrophoresis projects.
The journey to mastering image alignment is one of continual learning and refinement. As we close this series, we encourage you to experiment, explore, and evolve your understanding and practice of this pivotal process. By doing so, you will ensure your analysis stands on a solid foundation, ready to yield reliable insights into the fascinating world of proteins.