Introduction In the context of selecting or developing antibody reagents for HCP ELISA assays, multiple coverage analyses become essential to compare various experimental factors, including replicates, cell lines, process parameters, antigens, antibodies, and more. A critical question arises: Is there merit in analyzing image pairs from such comparisons individually, as is often the practice, or should these images be collectively analyzed in a single project? Overwhelmingly, the evidence leans toward the latter. In this discussion, we delve into why this […]

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. […]

Welcome back to our “Image Alignment Demystified” article series. In part 1, we took a deep dive into why alignment is pivotal in 2D gel electrophoresis analysis and explored its fundamental principles. Part 2 focused on the criteria and tools to help you assess alignment quality. In this chapter, we continue our journey by offering a practical roadmap with 10 recommendations and guidelines to ensure you achieve the best alignment results, thereby maximizing the reliability and precision of spot correspondences. […]

Welcome back to our four-part series, “Image Alignment Demystified.” In our previous article, we acquainted ourselves with the foundational concepts of image alignment in the world of Melanie. Here, we’re on a deeper exploration, navigating through the intricacies of alignment quality assessment and pinpointing potential alignment issues. While current alignment algorithms generally produce very convincing results, automatic alignment results should always be critically reviewed. That’s because relatively minor mismatches can have very impactful results. Things are not different for Melanie. […]

Welcome to the first part of our blog series on aligning 2D gel and blot electrophoresis images using Melanie software. Our goal is to demystify the process of image alignment and offer guidance on achieving accurate results. Throughout this series, we’ll focus on key takeaways instead of providing detailed descriptions of software steps and features, which can be found in the Expression Analysis and Coverage Analysis user guides. In this opening article, we’ll look at the core concepts and principles […]

Melanie allows you to export a table with all spot data, also called project data. You can use the exported file for further analysis of your experiment with third party software. Learn how and what data can be exported, in what format and what export options exist.

For each spot on an image, Melanie computes the following quantification measures, based on the calibrated pixel gray values: area, background, intensity, volume, volume ratio and relative volume.

Relevant expression changes in 2D gel electrophoresis experiments are often obscured by systematic experimental variation such as differences in sample preparation, sample loading, staining/labeling or image acquisition. These sources of variation will affect different 2D gels or images to different extents, complicating the comparison of protein abundance across 2D images. The process of compensating for these variations – unrelated to the biological expression changes – is called normalization.