Denoising of Mass Spectrometry Images via Inverse Maximum Signal Factors Analysis.

Improving signal-to-noise and, thereby, image contrast is one of the key challenges needed to expand the useful applications of mass spectrometry imaging (MSI). Both instrumental and data analysis approaches are of importance. Univariate denoising techniques have been used to improve contrast in MSI images with varying levels of success. Additionally, various multivariate analysis (MVA) methods have proven to be effective for improving image contrast. However, the distribution of important but low intensity ions can be obscured in the MVA analysis, leading to a loss of chemically specific information. In this work we propose inverse maximum signal factors (MSF) denoising as an alternative approach to both denoising and multivariate analysis for MSI imaging. This approach differs from the standard MVA techniques in that the output is denoised images for each original mass peak rather than the frequently difficult to interpret scores and loadings. Five tests have been developed to optimize and validate the resulting denoised images. The algorithm has been tested on a range of simulated data with different levels of noise, correlated noise, varying numbers of underlying components, and nonlinear effects. In the simulations, an excellent correlation between the true images and the denoised images was observed for peaks with an original signal-to-noise ratio as low as 0.1, as long as there was sufficient intensity in the sum of the selected peaks. The power of the approach was then demonstrated on two time-of-flight secondary ion mass spectrometry (ToF-SIMS) images that contained largely uncorrelated noise and a laser post-ionization matrix-assisted laser desorption/ionization mass spectrometry (MALDI-2-MS) image that contained strongly correlated noise. The improvements in signal-to-noise increased with decreasing intensity of the original peaks. A signal-to-noise improvement of as much as two orders of magnitude was achieved for very low intensity peaks. MSF denoising is a powerful addition to the suite of image processing techniques available for studying mass spectrometry images.

3D Molecular ToF-SIMS Imaging of Artificial Lipid Membranes Using a Discriminant Analysis-Based Algorithm.

Artificial lipid membranes play a growing role in technical applications such as biosensors in pharmacological research and as model systems in the investigation of biological lipid films. In the standard procedure for displaying the distribution of membrane components, fluorescence microscopy, the fluorophores used can influence the distribution of the components and usually not all substances can be displayed at the same time. The discriminant analysis-based algorithm used in combination with scanning time-of-flight secondary ion mass spectrometry (ToF-SIMS) enables marker-free, quantitative, simultaneous recording of all membrane components. These data are used for reconstruction of distribution patterns. In the model system used for this survey, a tear fluid lipid layer, the distribution patterns of all lipids correlate well in calculated ToF-SIMS images and epi-fluorescence microscopic images. All epi-fluorescence microscopically viewable structures are visible when using both positive and negative secondary ions and can be reproduced with high lateral resolution in the submicrometer range despite the very low signal intensity and a very low signal-to-noise ratio. In addition, three-dimensional images can be obtained with a subnanometer depth resolution. Furthermore, structures and the distribution of substances that cannot be made visible by epi-fluorescence microscopy can be displayed. This enables new insights that cannot be gained by epi-fluorescence microscopy alone.

Calculation of Membrane Lipid Ratios Using Single-Pixel Time-of-Flight Secondary Ion Mass Spectrometry Analysis.

Much evidence suggests that membrane domains, termed lipid rafts, which are enriched in sphingomyeline and cholesterol play important roles in the regulation of physiological and pathophysiological processes. A label-free quantitative imaging method for lipids is lacking at present. We report an algorithm which enables us to identify and calculate the percentages of the ingredients of lipid mixtures from single-pixel time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra in model systems. The algorithm is based on a linear mixing model. Discriminant analysis is used to reduce the dimension of the data space. Calculations were separately performed for positive and negative ion mass spectra. Phosphatidylcholine and sphingomyeline which have identical headgroups and cannot be easily distinguished from another by positive ion mass spectra were included in the analysis. The algorithm outlined may more generally be used to calculate the percentages of ingredients of mixtures from spectra acquired by quite different methods such as X-ray photoelectron spectroscopy.