How does UniDec work
Mass spectrometry measures ion intensity at various mass-to-charge (m/z) ratios. Often, we want to separate the mass and the charge components of the m/z ratio, which is the goal of UniDec. UniDec starts by taking the one-dimensional m/z axis and creating a two-dimensional matrix of m/z values on one axis and charge values on the other axis. The intensities in this matrix represent the amount of the intensity from the m/z spectrum that is assigned to each charge state. In other words, each m/z data point has its own column of charge values in the charge vs. m/z matrix. This matrix is shown in the charge vs m/z plot after deconvolution. Because this plot represents the most direct outputs of the deconvolution, it is the first place to look for understanding the deconvolution.
UniDec uses a Bayesian algorithm that performs multiple iterations through three key steps to arrive at the most probable configuration of this charge vs. m/z matrix. In the first step of the iteration, a filter is applied based on expectations about the spectrum, such as a smooth charge state distribution. Here, charge states are smoothed with the assumption that the probability of a charge state is related to the presence of neighboring charge states that are m/(z-1) and m/(z+1) from the initial, such is the case in an ESI charge state distribution. Additional filters can be applied to smooth neighboring data points and/or neighboring masses with a known difference, such as different numbers of monomer units.
Second, UniDec takes the resulting filtered matrix of charge vs. m/z and generates a simulated spectrum from these values. To do this, it first sums along the charge axis to generate a one-dimensional mass spectrum from the two-dimensional matrix. Then, it can (optionally) convolve the simulated one-dimensional spectrum with a predicted peak shape that is specified by either manually entering the shape and FWHM or automatically determining the peak shape. You can also choose to use a peak shape of 0, in which case it will not add any peak shape convolution.
Finally, the intensities in the projected mass spectrum are compared to the actual data intensities. If it finds that a projected point has a higher intensity than the data, UniDec will lower the intensity of all the charge states for that m/z value in the charge vs. m/z matrix. If UniDec finds that a projected mass peak has a lower intensity than the actual m/z value, then the probability of those charge states (one column in the matrix) will be increased. Conversely, if the projection is too low, it will raise the intensity of that column. After adjusting intensities to match the data, it starts the cycle over by filtering the adjusted intensities.
UniDec goes through multiple iterations of these three steps until the intensities of the projected mass spectrum converge with the intensities of the actual mass spectrum. The result is a deconvolved mass spectrum based on the input data and the parameters set for the deconvolution. If these parameters are used incorrectly, the deconvolved mass spectrum may be distorted and not reflect the true masses of the samples analyzed. However, a solid understanding of the different parameters allows users to adjust the probabilities based on their system and gives flexibility to deconvolve challenging data.
For more information, take a look at these papers: