Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets

The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable overview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the datasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate analysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. Three multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments and one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate algorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is shown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality significantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant amount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the analyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial scores into one three-dimensional view on the complete datacube.     

Infrared chemical imaging: Spatial resolution evaluation and super-resolution concept

Chemical imaging systems help to solve many challenges in various scientific fields. Able to deliver rapid spatial and chemical information, modern infrared spectrometers using Focal Plane Array detectors (FPA) are of great interest. Considering conventional infrared spectrometers with a single element detector, we can consider that the diffraction-limited spatial resolution is more or less equal to the wavelength of the light (i.e. 2.5-25 μm). Unfortunately, the spatial resolution of FPA spectroscopic setup is even lower due to the detector pixel size. This becomes a real constraint when micron-sized samples are analysed. New chemometrics methods are thus of great interest to overcome such resolution drawback, while keeping our far-field infrared imaging spectrometers. The aim of the present work is to evaluate the super-resolution concept in order to increase the spatial resolution of infrared imaging spectrometers using FPA detectors. The main idea of super-resolution is the fusion of several low-resolution images of the same sample to obtain a higher-resolution image. Applying the super-resolution concept on a relatively low number of FPA acquisitions, it was possible to observe a 30% decrease in spatial resolution.     

Constraining shape smoothness in multivariate curve resolution–alternating least squares

The resolution of complex multicomponent hyperspectral images with multivariate curve resolution–alternating least squares is mainly performed by using a limited number of constraints on the pure constituent distribution maps, such as non‐negativity or local constraints. This work proposes a constraint that works with the spatial information of the whole image and has been given the name shape smoothness constraint. Contrary to local constraints, shape smoothness constraint imposes a global character on the distribution map pattern. It uses two‐dimensional P‐splines to enforce smoothness of the global spatial features of the distribution maps generated within the alternating least squares procedure. This allows revealing main pattern(s) in the spatial data leaving high‐frequency signals, corresponding to fine‐scale structures in the image. This approach has been successfully applied on several simulated examples where imposing shape smoothness resulted in the recovery of the correct pattern for the image objects, which in turn provided more accurate distribution maps and spectral profiles. It was also shown that when information about the smoothness of the pattern(s) of the image constituents holds, the constraint can be a flexible and robust alternative for the resolution of real hyperspectral imaging data. Copyright © 2015 John Wiley & Sons, Ltd.     

Magnetic Resonance Imaging of Electrochemical Cells Containing Bulk Metal

The development of improved energy‐storage devices, as well as corrosion prevention and metal‐electrofinishing technologies, requires knowledge of local composition and transport behaviour in electrolytes near bulk metals, in situ and in real time. It remains a challenge to acquire such data and new analytical methods are required. Recent work shows that magnetic resonance imaging (MRI) is able to map concentration gradients and visualise electrochemical processes in electrochemical cells containing bulk metals. This recent work, along with the challenges, and solutions, associated with MRI of these electrochemical cells are reviewed.     

Friction-induced pattern formation and Turing systems

We investigate the possibility of Turing-type pattern formation during friction. Turing or reaction-diffusion systems describe variations of spatial concentrations of chemical components with time due to local chemical reactions coupled with diffusion. Turing systems can lead to a variety of complex spatial patterns evolving with time. During friction, the patterns can form at the sliding interface due to the mass transfer (diffusion), heat transfer, various tribochemical reactions, and wear. We present simulation data showing the possibility of such pattern formation. On the other hand, existing experimental data suggest that in situ tribofilms can form at the frictional interface due to a variety of friction-induced chemical reactions (oxidation, the selective transfer of Cu ions, etc.). These tribofilms as well as other frictional "secondary structures" can form various patterns (islands or honeycomb domains). This mechanism of pattern formation can be attributed to the Turing systems.     

Multimodality Imaging Agents with PET as the Fundamental Pillar

Positron emission tomography (PET) provides quantitative information in vivo with ultra‐high sensitivity but is limited by its relatively low spatial resolution. Therefore, PET has been combined with other imaging modalities, and commercial systems such as PET/computed tomography (CT) and PET/magnetic resonance (MR) have become available. Inspired by the emerging field of nanomedicine, many PET‐based multimodality nanoparticle imaging agents have been developed in recent years. This Minireview highlights recent progress in the design of PET‐based multimodality imaging nanoprobes with an aim to overview the major advances and key challenges in this field and substantially improve our knowledge of this fertile research area.     

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).     

Data Processing for 3D Mass Spectrometry Imaging

Data processing for three dimensional mass spectrometry (3D-MS) imaging was investigated, starting with a consideration of the challenges in its practical implementation using a series of sections of a tissue volume. The technical issues related to data reduction, 2D imaging data alignment, 3D visualization, and statistical data analysis were identified. Software solutions for these tasks were developed using functions in MATLAB. Peak detection and peak alignment were applied to reduce the data size, while retaining the mass accuracy. The main morphologic features of tissue sections were extracted using a classification method for data alignment. Data insertion was performed to construct a 3D data set with spectral information that can be used for generating 3D views and for data analysis. The imaging data previously obtained for a mouse brain using desorption electrospray ionization mass spectrometry (DESI-MS) imaging have been used to test and demonstrate the new methodology.     

Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Effect of 1,1,2,2-tetrachloroethane on the ionizing ability of complexes of HCl with DMF

The acidity function (H _o) of solutions of HCI (0–7.1 mol L^?1) in an equimolar mixture of DMF-1,1,2,2-tetrachloroethane (TCE) has been determined by the indicator method at 25 °C. Data on the relative ionizing activity of complexes of HCI with DMF with various compositions and structures have been obtained. In the ternary system HCI-DMF-TCE, the ionizing ability of the complexes of DMF with HCI of the compositions 2 : 1 and 1 : 1 with a quasiionic structure decreases compared to the HCI-DMF system, and that of the complex of the composition DMF · 2HC1 with a structure of an ion pair Me₂NHCOH⁺ · (CIHCI)^? with a strong centrosymmetrical H-bond in the anion increases.     

Utilization of spectral vector properties in multivariate chemometrics analysis of hyperspectral infrared imaging data for cellular studies

A suite of numerical techniques was utilized in a concerted fashion for the efficacious multivariate chemometrics analysis of hyperspectral infrared imaging data of exfoliated oral mucosa cells. Based on the vector representation of infrared spectrum a1xnu), spectral vector properties (SVP) are demonstrated to possess underpinning spectral information that was exploited in crucial chemometrics analyses; which include outlier spectra identification, selection for a subset of imaged mid-infrared spectra that contain good oral mucosa cell signals, and, for the first time, obtain major biochemical constituent spectra via the band-target entropy minimization (BTEM) curve resolution algorithm. The relative concentration spatial distribution of the major biochemical constituents observed, namely membrane lipids and various cellular protein structures (alpha-helix, beta-sheet, turns and bends), were subsequently acquired through multi-linear regression and were displayed as chemical contour maps. Amongst the set of numerical algorithms employed, two novel unsupervised clustering algorithms were developed and tested. One is useful for outlier spectra detection, and the other aids the selection of pertinent spatially distributed spectra that possess oral mucosa cell mid-infrared spectra with good signal-to-noise ratio. It is anticipated that this developed numerical suite will serve as an effective multivariate chemometrics protocol for cellular studies and biomedical diagnostics via infrared imaging.     

Direct imaging of single cells and tissue at sub‐cellular spatial resolution using transmission geometry MALDI MS

The need of cellular and sub‐cellular spatial resolution in laser desorption ionization (LDI)/matrix‐assisted LDI (MALDI) imaging mass spectrometry (IMS) necessitates micron and sub‐micron laser spot sizes at biologically relevant sensitivities, introducing significant challenges for MS technology. To this end, we have developed a transmission geometry vacuum ion source that allows the laser beam to irradiate the back side of the sample. This arrangement obviates the mechanical/ion optic complications in the source by completely separating the optical lens and ion optic structures. We have experimentally demonstrated the viability of transmission geometry MALDI MS for imaging biological tissues and cells with sub‐cellular spatial resolution. Furthermore, we demonstrate that in conjunction with new sample preparation protocols, the sensitivity of this instrument is sufficient to obtain molecular images at sub‐micron spatial resolution. Copyright © 2012 John Wiley & Sons, Ltd.     

Some considerations of coherency in topoenergetic terms

The spatial coherency of composite structures is discussed in topoenergetic terms by considering mixing experiments on water and aqueous solutions with HRMC. Two series of experiments are presented: on normal and shaken successively diluted aqueous solutions of Na₃PO₄· 12H₂O with methanol as structure developer, and on the solubility behaviour in water of a narrow size fraction of KCl crystals irradiated at 546 nm for different time multiples of 5 seconds. The amplitude of relative standard deviation of the integral mixing energy as a function of mixing time shows the contribution of different elementary processes by revealing the specific spectrum of the composite structure in water and aqueous solutions.The series of experiments denoted as A were hosted by the Interdisciplinary Research Center (Fundeni Hospital, Bucharest) and with the kind help of biologist Liliana Pislaru, physicist Emil Toroiman and technician Tatiana Culea, who contributed with careful weighings and irradiation of KCl samples.     

Challenges in Applying Photoemission Electron Microscopy to Biological Systems†

Photoemission electron microscopy (PEEM) is a unique surface‐sensitive instrument capable of providing real‐time images with high spatial resolution. While similar to the more common electron microscopies, scanning electron microscopy and transmission electron microscopy, the imaging technology relies on the photogeneration of electrons emitted from a sample through light excitation. This imaging technique has found prominence in surface and materials sciences, being well suited for imaging flat surfaces, and changes that occur to that surface as various parameters are changed (e.g. temperature, exposure to reactive gases). Biologically focused PEEM received significant attention in the 1970s, but was not aggressively advanced since that pioneering work. PEEM is capable of providing important insights into biological systems that extend beyond simple imaging. In this article, we identify and establish important issues that affect the acquisition and analysis of biological samples with PEEM. We will briefly review the biological impact and importance of PEEM with respect to our work. The article also concludes with a discussion of some of the current challenges that must be addressed to enable PEEM to achieve its maximum potential with biological samples.     

Fast,high resolution mass spectrometry imaging using a medipix pixelated detector

In mass spectrometry imaging, spatial resolution is pushed to its limits with the use of ion microscope mass spectrometric imaging systems. An ion microscope magnifies and then projects the original spatial distribution of ions from a sample surface onto a position-sensitive detector, while retaining time-of-flight mass separation capabilities. Here, a new type of position-sensitive detector based on a chevron microchannel plate stack in combination with a 512 × 512 complementary metal-oxide-semiconductor based pixel detector is coupled to an ion microscope. Spatial resolving power better than 6 μm is demonstrated by secondary ion mass spectrometry and 8–10μm spatial resolving power is achieved with laser desorption ionization. A detailed evaluation of key performance criteria such as spatial resolution, acquisition speed, and data handling is presented.     

Time of acquisition and high spatial resolution mass spectrometry imaging

The field of mass spectrometry imaging (MSI) is constantly evolving to analyze a diverse array of biological systems. A common goal is the need to resolve cellular and subcellular heterogeneity with high spatial resolution. As the field continues to progress towards high spatial resolution, other parameters must be considered when developing a practical method. Here, we discuss the impacts of high spatial resolution on the time of acquisition and the associated implications they have on an MSI analysis (e.g., area of the region of interest). This work presents a brief tutorial serving to evaluate high spatial resolution MSI relative to time of acquisition and data file size.     

Multivariate curve resolution-particle swarm optimization: A high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography–diode array detection (HPLC–DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC–MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.     

Molecular polarizability of organic compounds and their complexes: LIX. Molar volumes, intrinsic atomic solvation radii, and spatial structures of certain conformationally flexible compounds in solutions

The molar volumes and structures in individual liquids and solutions of a series of conformationally flexible compounds, such as alkanes and diaryl-substituted systems with sp ³-hybridized bridging atoms, were analyzed in terms of intrinsic solvation radii of atoms constituting the molecule. Intrinsic solvation atomic radii were determined for various molecules to show that they are larger than the van der Waals radii of the same atoms. An approach to parametrization of the intrinsic solvation radii of atoms constituting a molecule, using appropriate model compounds, was proposed. From the resulting values of intrinsic atomic solvation radii, the possible conformations of a series of diphenylmethanes, diphenylsilanes, diphenyl sulfides, diphenyl sulfoxides, and diphenyl sulfones in solutions were assessed.     

On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis

Hyperspectral imaging (HSI) is a method for exploring spatial and spectral information associated with the distribution of the different compounds in a chemical or biological sample. Amongst the multivariate image analysis tools utilized to decompose the raw data into a bilinear model, multivariate curve resolution alternating least squares (MCR‐ALS) can be applied to obtain the distribution maps and pure spectra of the components of the sample image. However, a requirement is to have the data in a two‐way matrix. Thus, a preliminary step consists of unfolding the raw HSI data into a single‐pixel direction. Consequently, through this data manipulation, the information regarding pixel neighboring is lost, and spatial information cannot directly be constrained on the component profiles in the current MCR‐ALS algorithm. In this short communication, we propose an adaptation of the MCR‐ALS framework, enabling the potential implementation of any variation of spatial constraint. This can be achieved by adding, at each least‐squares step, refolding/unfolding of the distribution maps for the components. The implementation of segmentation, shape smoothness, and image modeling as spatial constraints is proposed as a proof of concept. Copyright © 2015 John Wiley & Sons, Ltd.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.