Direct Tissue Profiling of Protein Complexes: Toward Native Mass Spectrometry Imaging

Native mass spectrometry seeks to probe noncovalent protein interactions in terms of protein quaternary structure, protein–protein and protein–ligand complexes. The ultimate goal is to link the understanding of protein interactions to the protein environment by visualizing the spatial distribution of noncovalent protein interactions within tissue. Previously, we have shown that noncovalently bound protein complexes can be directly probed via liquid extraction surface analysis from dried blood spot samples, where hemoglobin is highly abundant. Here, we show that the intact hemoglobin complex can be sampled directly from thin tissue sections of mouse liver and correlated to a visible vascular feature, paving the way for native mass spectrometry imaging

LESA FAIMS Mass Spectrometry for the Spatial Profiling of Proteins from Tissue

We have shown previously that coupling of high field asymmetric waveform ion mobility spectrometry (FAIMS), also known as differential ion mobility, with liquid extraction surface analysis (LESA) mass spectrometry of tissue results in significant improvements in the resulting protein mass spectra. Here, we demonstrate LESA FAIMS mass spectrometry imaging of proteins in sections of mouse brain and liver tissue. The results are compared with LESA mass spectrometry images obtained in the absence of FAIMS. The results show that the number of different protein species detected can be significantly increased by incorporating FAIMS into the workflow. A total of 34 proteins were detected by LESA FAIMS mass spectrometry imaging of mouse brain, of which 26 were unique to FAIMS, compared with 15 proteins (7 unique) detected by LESA mass spectrometry imaging. A number of proteins were identified including α-globin, 6.8 kDa mitochondrial proteolipid, macrophage migration inhibitory factor, ubiquitin, β-thymosin 4, and calmodulin. A total of 40 species were detected by LESA FAIMS mass spectrometry imaging of mouse liver, of which 29 were unique to FAIMS, compared with 24 proteins (13 unique) detected by LESA mass spectrometry imaging. The spatial distributions of proteins identified in both LESA mass spectrometry imaging and LESA FAIMS mass spectrometry imaging were in good agreement indicating that FAIMS is a suitable tool for inclusion in mass spectrometry imaging workflows

Raster-Mode Continuous-Flow Liquid Microjunction Mass Spectrometry Imaging of Proteins in Thin Tissue Sections

Mass spectrometry imaging by use of continuous-flow liquid microjunction sampling at discrete locations (array mode) has previously been demonstrated. In this Letter, we demonstrate continuous-flow liquid microjunction mass spectrometry imaging of proteins from thin tissue sections in raster mode and discuss advantages (a 10-fold reduction in analysis time) and challenges (suitable solvent systems, data interpretation) of the approach. Visualization of data is nontrivial, requiring correlation of solvent-flow, mass spectral data acquisition rate, data quality, and liquid microjunction sampling area. The latter is particularly important for determining optimum pixel size. The minimum achievable pixel size is related to the scan time of the instrument used. Here we show a minimum achievable pixel size of 50 μm (<i>x</i>-dimension) when using an Orbitrap Elite; however a pixel size of 600 μm is recommended in order to minimize the effects of oversampling on image accuracy

Hyperspectral Visualization of Mass Spectrometry Imaging Data

The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively

Hyperspectral Visualization of Mass Spectrometry Imaging Data

The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively