iFAMS Imager: Analyte Deconvolution and Baseline Correction for Mass Spectrometry Imaging of Proteins

Histological and immunofluorescence imaging techniques are widely used for studying protein localization in biological tissues, from the identification of disease to the development of drugs with greater target specificity and lower toxicity. However, the complexity of such samples often requires the use of chemical labels and targeted analysis, thereby limiting proteome coverage. In contrast, mass spectrometry imaging (MSI) enables label-free, highly multiplexed analysis of tissue proteomes with cellular spatial resolution. The complexity of protein mixtures analyzed in MSI often leads to mass spectra at each pixel in the image with extensive signal overlap and a pronounced, curved baseline, which complicates data analysis and interpretation. Here, we introduce a parallelized Gábor-transform-based batch deconvolution workflow (“iFAMS Imager”) that resolves strongly overlapping protein signals in MSI and offers flexible options for baseline correction. Together, these advantages facilitate the removal of interferent signals strongly overlapped in m/z with those of the target analyte, resulting in high-fidelity images. Use of this open-source, publicly available software is demonstrated for imaging of common proteins in rat brain tissue.