Dead-time correction for time-of-flight secondary-ion mass spectral images: a critical issue in multivariate image analysis

Assessing dead time effects when attempting isotope ratio quantification by time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a quasi-non-destructive technique capable of analyzing the outer monolayers of a solid sample and detecting all elements of the periodic table and their isotopes. Its ability to analyze the outer monolayers resides in sputtering the sample surface with a low-dose primary ion gun, which, in turn, imposes the use of a detector capable of counting a single ion at a time. Consequently, the detector saturates when more than one ion arrives at the same time hindering the use of TOF-SIMS for quantification purposes such as isotope ratio estimation. Even though a simple Poisson-based correction is usually implemented in TOF-SIMS acquisition software to compensate the detector saturation effects, this correction is only valid up to a certain extent and can be unnoticed by the inexperienced user. This tutorial describes a methodology based on different practices reported in the literature for dealing with the detector saturation effects and assessing the validity limits of Poisson-based correction when attempting to use TOF-SIMS data for quantification purposes. As a practical example, a dried lithium hydroxide solution was analyzed by TOF-SIMS with the aim of estimating the 6Li/7Li isotope ratio. The approach presented here can be used by new TOF-SIMS users on their own data for understanding the effects of detector saturation, determine the validity limits of Poisson-based correction, and take into account important considerations when treating the data for quantification purposes.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

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.

Development of a Knudsen-type matrix coater for sample preparation for mass spectrometry imaging

The use of time-of-flight secondary ion mass spectrometry (SIMS) is of increasing interest for biological and medical applications due to its ability to provide chemical information on a submicrometer scale. However, the detection of larger biomolecules such as phospholipids and peptides is often inhibited by high fragmentation rates and low ionization efficiencies. One way to increase the secondary ion molecular yield is to chemically modify the surface using the matrix-enhanced SIMS approach, where an organic matrix is placed upon the surface. In this study, a Knudsen cell type matrix coater was developed in order to produce well-defined thicknesses of a matrix on a sample in order to study the effect of these matrix layers on the secondary ions. Using this technique, an order of magnitude enhancement of the useful ion yield for lipids was observed and clear enhancement of image contrast for lipids in brain tissue was demonstrated. The study shows that the layer thickness has a great influence on the emission of secondary ions, and therefore, its precise control is important for optimal yield enhancement.

Electrical conductivity and oxygen diffusion behaviour of the (La0.8Sr0.2)0.95CrxFe1−xO3−δ (x = 0.3, 0.5 and 0.7) A-site deficient perovskites

Oxygen transport is bulk diffusion limited and line scan measurements are shown to underestimate surface exchange coefficients.

ToF-SIMS imaging of capsaicinoids in Scotch Bonnet peppers (Capsicum chinense)

Peppers (Capsicum spp.) are well known for their ability to cause an intense burning sensation when eaten. This organoleptic response is triggered by capsaicin and its analogs, collectively called capsaicinoids. In addition to the global popularity of peppers as a spice, there is a growing interest in the use of capsaicinoids to treat a variety of human ailments, including arthritis, chronic pain, digestive problems, and cancer. The cellular localization of capsaicinoid biosynthesis and accumulation has previously been studied by fluorescence microscopy and electron microscopy, both of which require immunostaining. In this work, ToF-SIMS has been used to image the distribution of capsaicinoids in the interlocular septum and placenta of Capsicum chinense (Scotch Bonnet peppers). A unique cryo-ToF-SIMS instrument has been used to prepare and analyze the samples with minimal sample preparation. Samples were frozen in liquid propane, cryosectioned in vacuum, and analyzed without exposure to ambient pressure. ToF-SIMS imaging was performed at -110 °C using a Bi3 (+) primary ion beam. Molecular ions for capsaicin and four other capsaicinoids were identified in both the positive and negative ToF-SIMS spectra. The capsaicinoids were observed concentrated in pockets between the outer walls of the palisade cells and the cuticle of the septum, as well as in the intercellular spaces in both the placenta and interlocular septum. This is the first report of label-free direct imaging of capsaicinoids at the cellular level in Capsicum spp. These images were obtained without the need for labeling or elaborate sample preparation. The study demonstrates the usefulness of ToF-SIMS imaging for studying the distribution of important metabolites in plant tissues.

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.