High-Throughput Deconvolution of Native Protein Mass Spectrometry Imaging Data Sets for Mass Domain Analysis

Abstract

Protein mass spectrometry imaging (MSI) with electrospray-based ambient ionization techniques, such as nanospray desorption electrospray ionization (nano-DESI), generates data sets in which each pixel corresponds to a mass spectrum populated by peaks corresponding to multiply charged protein ions. Importantly, the signal associated with each protein is split among multiple charge states. These peaks can be transformed into the mass domain by spectral deconvolution. When proteins are imaged under native/non-denaturing conditions to retain non-covalent interactions, deconvolution is particularly valuable in helping interpret the data. To improve the acquisition speed, signal-to-noise ratio, and sensitivity, native MSI is usually performed using mass resolving powers that do not provide isotopic resolution, and conventional algorithms for deconvolution of lower-resolution data are not suitable for these large data sets. UniDec was originally developed to enable rapid deconvolution of complex protein mass spectra. Here, we developed an updated feature set harnessing the high-throughput module, MetaUniDec, to deconvolve each pixel of native MSI data sets and transform m/z-domain image files to the mass domain. New tools enable the reading, processing, and output of open format .imzML files for downstream analysis. Transformation of data into the mass domain also provides greater accessibility, with mass information readily interpretable by users of established protein biology tools such as sodium dodecyl sulfate polyacrylamide gel electrophoresis.

Gábor Transform-Based Signal Isolation, Rapid Deconvolution, and Quantitation of Intact Protein Ions with Mass Spectrometry

“…1,11,21 In another example, current technology for nanoelectrospray desorption ionization (nano-DESI) MS imaging of biological tissues does not couple easily to LC, thus signals for many intact proteins are often detected simultaneously and may overlap severely in the resulting mass spectra. 8,22,23 For improved separation of clinical samples, immunocapture methods, such as Melon gel filtration, are often used as a preliminary separation method before analysis by LC/MS. 11,24 For biological analytes as large as (or larger than) intact mAbs, the mass spectrum can often be congested when the analyte constitutes a mixture of proteoforms or drug conjugation states that are not separated prior to or during the LC step.…”

“…While LC/MS methods can be extremely helpful in characterizing proteins from many biological samples, some complex samples still present major challenges, as some analytes may still coelute and exhibit suppression effects in electrospray ionization (ESI), and instrument response factors for different analytes can be extremely difficult to determine without pure reference analytes. , For example, blood serum samples containing monoclonal antibody (mAb) drugs present a background of endogenous antibodies that may not separate well by LC and are strongly overlapped with the antibody–drug signal in intact mass spectra. ,, In another example, current technology for nanoelectrospray desorption ionization (nano-DESI) MS imaging of biological tissues does not couple easily to LC, thus signals for many intact proteins are often detected simultaneously and may overlap severely in the resulting mass spectra. ,, For improved separation of clinical samples, immunocapture methods, such as Melon gel filtration, are often used as a preliminary separation method before analysis by LC/MS. , For biological analytes as large as (or larger than) intact mAbs, the mass spectrum can often be congested when the analyte constitutes a mixture of proteoforms or drug conjugation states that are not separated prior to or during the LC step. In addition to this spectral congestion, the signal from each protein of interest is often spread over many different charge states as a result of the stochastic charging process in ESI.…”

“…With the new automated signal selection and baseline-correction tools, the deconvolved mass spectra (“zero-charge” spectra) are easier and faster to generate and are much less sensitive to input parameters and baseline effects. We also analyze the quality of deconvolution through mass accuracy assessments, verification of charge state assignment, and comparison to other widely used deconvolution methods (an implementation of MaxEnt in the Agilent MassHunter BioConfirm v. 10.0 and the open-source, publicly available program UniDec from the Marty Group). ,,− The theory behind these deconvolution methods is described and compared elsewhere . New tools are introduced to aid in the identification of and “instant” mass determination for related peaks in charge distribution, validation of charge state assignments, and identification and removal of deconvolution artifacts.…”