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The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly multimodal approach to analytical science, presents a significant challenge in the handling of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was also developed with extensibility in mind to stimulate development, comparisons, and evaluation of data analysis algorithms.

Previously we have shown that liquid extraction surface analysis (LESA) mass spectrometry is suitable for the analysis of intact proteins from a range of biological substrates. Here we show that LESA mass spectrometry may be coupled with high field asymmetric waveform ion mobility spectrometry (FAIMS) for top-down protein analysis directly from thin tissue sections (mouse liver, mouse brain) and from bacterial colonies (Escherichia coli) growing on agar. Incorporation of FAIMS results in significant improvements in signal-to-noise and reduced analysis time. Abundant protein signals are observed in single scan mass spectra. In addition, FAIMS enables gas-phase separation of molecular classes, for example, lipids and proteins, enabling improved analysis of both sets of species from a single LESA extraction.

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