How to automate data analysis for routine sample QC with PEAQ-DSC

Careful analysis of DSC thermograms is critical for accurate reporting of key thermal properties like melting temperature (T_m) and enthalpy (ΔH). However, manual processing can be time-consuming and prone to user bias, especially when analyzing complex thermograms with overlapping transition regions. To help save researchers time and reduce human variability, the automated data analysis features of the PEAQ DSC software can be easily implemented when performing routine quality control experiments on a biomolecule of interest.

Consider an example where thermal scans are collected on four mAb samples from the same batch that have been handled under varying storage conditions. Upon opening the experiment file, two transition regions are identified by the PEAQ DSC software but no fitting model has been applied (Figure 2). From this point, a series of subjective decisions must be made by the user to analyze the thermogram. 

Starting in the Buffer tab at the top of the analysis pane, the user must decide whether to apply a buffer subtraction to the data and subsequently select the buffer scan to be used (Figure 2, red box). An adequate buffer scan should have an elevated (more positive) differential power reading that mirrors the sample scan slope along the pre- and post-transition regions, as observed in the left panel of Figure 2. 

Next, the user must define the analysis region, or truncated range, by sliding the brackets within the middle panel (Figure 2, red arrows). In this example, a buffer subtraction has been applied and the truncated range has been defined as 35-100 °C.

In the Baseline tab in the analysis pane, the user must define the pre-transition temperature region using the left bracket and the post-transition temperature region using the right bracket (Figure 3, red arrows). The baseline type must also be selected (Figure 3, red box), which can impact the total area, ΔH, and standard deviation on these parameters. In this example, a spline (cubic polynomial) baseline was selected with a pre-transition temperature range of 35-57 °C and a post-transition temperature range of 95-100 °C.

Careful analysis of the thermogram reveals an inflection point suggesting a third broad transition peak overlapping with the two that were initially detected. Within the Fit tab of the analysis pane, additional transitions can be included in the fit of the data by right-clicking on the suspected T_m and selecting “Add Transition Guess” (Figure 4). After adding the third transition process to the deconvolution algorithm and selecting the appropriate fitting model, the fit of the experimental data can be iterated in order to minimize the reduced chi-square value (Figure 5).

Rather than manually repeating this process for the remaining three thermograms, the user can export the analysis settings from the first sample. By clicking “export” in the lower right corner (Figure 5, red box), the analysis settings are saved to the computer for future experiments on the same biomolecule. 

The remaining three samples can be quickly evaluated by importing the saved analysis settings file. This can be performed by selecting the next scan, returning to the Buffer tab at the top of the screen, and clicking “import” in the bottom right corner (Figure 6, red box).

The software will proceed to apply the user decisions from the previous analysis – buffer subtraction, truncated range, baseline type, transition region, fitting model, and number of transitions with respective T_m values as initial guesses – to the currently selected thermogram (Figure 7). This allows the user to quickly compare various batches or conditions for the same biomolecule. 

If a certain biomolecule is routinely evaluated across multiple experiments, the saved analysis file can be applied when setting up a new DSC sequence. Under the Scan Settings within the Experiment window, click on “Import” (Figure 8, red box). This feature will allow for automatic analysis of a thermogram upon opening the experiment file in the analysis window, using the nearest preceding buffer sample for buffer subtraction.

Automated data processing and analysis with PEAQ DSC enhances productivity and reduces user subjectivity, allowing researchers to devote more time to the interpretation and application of their results.

Interested in learning more about PEAQ DSC? Check out the links below to some other great resources.

• BLOG | How to choose between Differential Scanning Fluorimetery (DSF) vs Differential Scanning Calorimetry (DSC) in biopharmaceutical research
• BLOG | How DSC can complement your protein thermal stability studies
• WEBINAR | How to run mRNA-LNP measurements on MicroCal PEAQ-DSC