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.     

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF‐SIMS data. Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF‐SIMS peak lists using up‐binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time‐of‐flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low‐density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF‐SIMS data. Artificial neural networks, in the form of self‐organising maps, are introduced and provide a non‐linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions. This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.     

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X‐ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth‐profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to ‘bin’ spectra on much more coarse a grid than they would like, perhaps even to unity mass bins even though much higher resolution is available, or select only part of an image for PCA analysis, even though PCA of the full data would be preferred. We apply the new ‘random vectors’ method of singular value decomposition proposed by Halko and co‐authors to time‐of‐flight (ToF)SIMS data for the first time. This increases the speed of calculation by a factor of several hundred, making PCA of these datasets practical on desktop PCs for the first time. For large images or 3D depth profiles we have implemented a version of this algorithm which minimises memory needs, so that even datasets too large to store in memory can be processed into PCA results on an ordinary PC with a few gigabytes of memory in a few hours. We present results from ToFSIMS imaging of a citrate crystal and a basalt rock sample, the largest of which is 134GB in file size corresponding to 67 111 mass values at each of 512 × 512 pixels. This was processed into 100 PCA components in six hours on a conventional Windows desktop PC. © 2015 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Correlative mass spectrometry imaging,applying time‐of‐flight secondary ion mass spectrometry and atmospheric pressure matrix‐assisted laser desorption/ionization to a single tissue section

Rationale

Mass spectrometry imaging (MSI) is a powerful tool for mapping the surface of a sample. Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) and atmospheric pressure matrix‐assisted laser desorption/ionization (AP‐MALDI) offer complementary capabilities. Here, we present a workflow to apply both techniques to a single tissue section and combine the resulting data for the example of human colon cancer tissue.

Methods

Following cryo‐sectioning, images were acquired using the high spatial resolution (1 μm pixel size) provided by TOF‐SIMS. The same section was then coated with a para‐nitroaniline matrix and images were acquired using AP‐MALDI coupled to an Orbitrap mass spectrometer, offering high mass resolution, high mass accuracy and tandem mass spectrometry (MS/MS) capabilities. Datasets provided by both mass spectrometers were converted into the open and vendor‐independent imzML file format and processed with the open‐source software MSiReader.

Results

The TOF‐SIMS and AP‐MALDI mass spectra show strong signals of fatty acids, cholesterol, phosphatidylcholine and sphingomyelin. We showed a high correlation between the fatty acid ions detected with TOF‐SIMS in negative ion mode and the phosphatidylcholine ions detected with AP‐MALDI in positive ion mode using a similar setting for visualization. Histological staining on the same section allowed the identification of the anatomical structures and their correlation with the ion images.

Conclusions

This multimodal approach using two MSI platforms shows an excellent complementarity for the localization and identification of lipids. The spatial resolution of both systems is at or close to cellular dimensions, and thus spatial correlation can only be obtained if the same tissue section is analyzed sequentially. Data processing based on imzML allows a real correlation of the imaging datasets provided by these two technologies and opens the way for a more complete molecular view of the anatomical structures of biological tissues.

     

Detecting changes in arabidopsis cell wall composition using time‐of‐flight secondary ion mass spectrometry

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was previously used to characterize lignocellulosic materials, including woody biomass. ToF‐SIMS can acquire both rapid spectral and spatial information about a sample's surface composition. In the present study, ToF‐SIMS was used to characterize the cell walls of stem tissue from the plant model organism, Arabidopsis thaliana. Using principal component analyses, ToF‐SIMS spectra from A. thaliana wild‐type (Col‐0), cellulose mutant (irx3), and lignin mutant (fah1) stem tissues were distinguished using ToF‐SIMS peaks annotated for wood‐derived lignocellulose, where spectra from the irx3 and fah1 were characterized by comparatively low polysaccharide and syringyl lignin content, respectively. Spatial analyses using ToF‐SIMS imaging furthermore differentiated interfascicular fiber and xylem vessels based on differences in the lignin content of corresponding cell walls. These new data support the applicability of ToF‐SIMS peak annotations based on woody biomass for herbaceous plants, including model plant systems like arabidopsis. Copyright © 2015 John Wiley & Sons, Ltd.     

Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth‐profiles in XPS, AES and SIMS. For large images or three‐dimensional (3D) imaging depth‐profiles, PCA has been difficult to apply until now simply because of the size of the matrices of data involved. In a recent paper, we described two algorithms, random vector 1 (RV1) and random vector 2 (RV2), that improve the speed of PCA and allow datasets of unlimited size, respectively. In this paper, we now apply the RV2 algorithm to perform PCA on full 3D time‐of‐flight SIMS data for the first time without subsampling. The dataset we process in this way is a 128 × 128 pixel depth‐profile of 120 layers, each voxel having a 70 439 value mass spectrum associated with it. This forms over a terabyte of data when uncompressed and took 27 h to process using the RV2 algorithm using a conventional windows desktop personal computer (PC). While full PCA (e.g. using RV2) is to be preferred for final reports or publications, a much more rapid method is needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a graphical processor unit (GPU) card containing 2880 individual processor cores. This increases the speed of calculation by a factor of around 4.1 compared with what is possible using a fast commercially available desktop PC having central processing units alone, and full PCA is performed in less than 7 s. The size of the dataset that can be processed in this way is limited by the size of the memory on the GPU card. This is typically sufficient for two‐dimensional images but not 3D depth‐profiles without sampling. We have therefore examined efficient sampling schemes that allow a good approximate solution to the PCA problem for large 3D datasets. We find that low‐discrepancy series such as Sobol series sampling gives more rapid convergence than random sampling, and we recommend such methods for routine use. Using the GPU and low‐discrepancy series together, we anticipate that any time‐of‐flight SIMS dataset, of whatever size, can be efficiently and accurately processed into PCA components in a maximum of around 10 s using a commercial PC with a widely available GPU card, although the longer RV2 approach is still to be preferred for the presentation of final results, such as in published papers. Copyright © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd     

Cluster TOF‐SIMS imaging of human skin remains: analysis of a South‐Andean mummy sample

A skin sample from a South‐Andean mummy dating back from the XI^th century was analyzed using time‐of‐flight secondary ion mass spectrometry imaging using cluster primary ion beams (cluster‐TOF‐SIMS). For the first time on a mummy, skin dermis and epidermis could be chemically differentiated using mass spectrometry imaging. Differences in amino‐acid composition between keratin and collagen, the two major proteins of skin tissue, could indeed be exploited. A surprising lipid composition of hypodermis was also revealed and seems to result from fatty acids damage by bacteria. Using cluster‐TOF‐SIMS imaging skills, traces of bio‐mineralization could be identified at the micrometer scale, especially formation of calcium phosphate at the skin surface. Mineral deposits at the surface were characterized using both scanning electron microscopy (SEM) in combination with energy‐dispersive X‐ray spectroscopy and mass spectrometry imaging. The stratigraphy of such a sample was revealed for the first time using this technique. More precise molecular maps were also recorded at higher spatial resolution, below 1 µm. This was achieved using a non‐bunched mode of the primary ion source, while keeping intact the mass resolution thanks to a delayed extraction of the secondary ions. Details from biological structure as can be seen on SEM images are observable on chemical maps at this sub‐micrometer scale. Thus, this work illustrates the interesting possibilities of chemical imaging by cluster‐TOF‐SIMS concerning ancient biological tissues. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterization of size‐segregated aerosols using ToF‐SIMS imaging and depth profiling

Size‐segregated particles were collected with a ten‐stage micro‐orifice uniform deposit impactor from a busy walkway in a downtown area of Hong Kong. The surface chemical compositions of aerosol samples from each stage were analyzed using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) operated in the static mode. The ToF‐SIMS spectra of particles from stage 2 (5.6–10 µm), stage 6 (0.56–1 µm), and stage 10 (0.056–0.1 µm) were compared, and the positive ion spectra from stage 2 to stage 10 were analyzed with principal component analysis (PCA). Both spectral analysis and PCA results show that the coarse‐mode particles were associated with inorganic ions, while the fine particles were associated with organic ions. PCA results further show that the particle surface compositions were size dependent. Particles from the same mode exhibited more similar surface features. Particles from stage 2 (5.6–10 µm), stage 6 (0.56–1 µm), and stage 10 (0.056–0.1 µm) were further selected as representatives of the three modes, and the chemical compositions of these modes of particles were examined using ToF‐SIMS imaging and depth profiling. The results reveal a non‐uniform chemical distribution from the outer to the inner layer of the particles. The coarse‐mode particles were shown to contain inorganic salts beneath the organics surface. The accumulation‐mode particles contained sulfate, nitrate, ammonium salts, and silicate in the regions below a thick surface layer of organic species. The nucleation‐mode particles consisted mainly of soot particles with a surface coated with sulfate, hydrocarbons, and, possibly, fullerenic carbon. The study demonstrated the capability of ToF‐SIMS depth profiling and imaging in characterizing both the surface and the region beneath the surface of aerosol particles. It also revealed the complex heterogeneity of chemical composition in size and depth distributions of atmospheric particles. Copyright © 2014 John Wiley & Sons, Ltd.     

Multivariate statistical analysis of non‐mass‐selected ToF‐SIMS data

Cluster LMIGs are now regarded as the standard primary ion guns on time‐of‐flight secondary ion mass spectrometers (ToF‐SIMS). The ToF‐SIMS analyst typically selects a bombarding species (cluster size and charge) to be used for material analysis. Using standard data collection protocols where the analyst uses only a single primary bombarding species, only a fraction of the ion‐beam current generated by the LMIG is used. In this work, we demonstrate for the first time that it is possible to perform ToF‐SIMS analysis when all of the primary ion intensity (clusters) are used; we refer to this new data analysis mode as non‐mass‐selected (NMS) analysis. Since each of the bombarding species has a different mass‐to‐charge ratio, they strike the sample at different times, and as a result, each of the bombarding species generates a spectrum. The resulting NMS ToF‐SIMS spectrum contains contributions from each of the bombarding species that are shifted in time. NMS spectra are incredibly complicated and would be difficult, if not impossible, to analyze using univariate methodology. We will demonstrate that automated multivariate statistical analysis (MVSA) tools are capable of rapidly converting the complicated NMS data sets into a handful of chemical components (represented by both spectra and images) that are easier to interpret since each component spectrum represents a unique and simpler chemistry. Copyright © 2008 John Wiley & Sons, Ltd.     

Simple statistically based alternatives to MAF for ToF‐SIMS spectral image analysis

The maximum autocorrelation factors technique (MAF) is becoming increasingly popular for the multivariate analysis of spectral images acquired with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments. In this article, we review the conditions under which the underlying chemical information can be separated from the large‐scale, non‐uniform noise characteristic of ToF‐SIMS data. Central to this pursuit is the ability to assess the covariance structure of the noise. Given a set of replicate images, the noise covariance matrix can be estimated in a straightforward way using standard statistical tools. Acquiring replicate images, however, is not always possible, and MAF solves a subtly different problem, namely, how to approximate the noise covariance matrix from a single image when replicates are not available. This distinction is important; the MAF approximation is not an unbiased statistical estimate of the noise covariance matrix, and it differs in a highly significant way from a true estimate for ToF‐SIMS data. Here, we draw attention to the fact that replicate measurements are made during the normal course of acquiring a ToF‐SIMS spectral image, rendering the MAF procedure unnecessary. Furthermore, in the common case that detector dead‐time effects permit no more than one ion of any specific species to be detected on a single primary ion shot, the noise covariance matrix can be estimated in a particularly simple way, which will be reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Spectral characterisation of eight glycerolipids and their detection in natural samples using time‐of‐flight secondary ion mass spectrometry

In recent years, time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) with cluster ion sources has opened new perspectives for the analysis of lipid biomarkers in geobiology and organic geochemistry. However, published ToF‐SIMS reference spectra of relevant compounds are still sparse, and the influence of the chemical environment (matrix) on the ionisation of molecules and their fragmentation is still not well explored. This study presents ToF‐SIMS spectra of eight glycerolipids as common target compounds in biomarker studies, namely ester‐ and ether‐bound phosphatidylethanolamine, ester‐ and ether‐bound phosphatidylcholine, ester‐bound phosphatidylglycerol, ester‐ and ether‐bound diglycerides and archaeol, obtained with a Bi cluster ion source. For all of these compounds, the spectra obtained in positive and negative analytical modes showed characteristic fragments that could clearly be assigned to e.g. molecular ions, functional groups and alkyl chains. By comparison with the reference spectra, it was possible to track some of these lipids in a pre‐characterised organic extract and in cryosections of microbial mats. The results highlight the potential of ToF‐SIMS for the laterally resolved analysis of organic biomarkers in environmental materials. The identification of the target compounds, however, may be hampered by matrix effects (e.g. adduct formation) and often require careful consideration of all spectral features and taking advantage of the molecular imaging capability of ToF‐SIMS. Copyright © 2009 John Wiley & Sons, Ltd.     

High‐Resolution Surface Chemical Analysis of a Trifunctional Pattern Made by Sequential Colloidal Shadowing

We present a new method for creating surface chemical patterns where three chemistries can be periodically arranged at alternate positions on a single substrate without the use of top‐down approaches. High‐resolution chemical imaging by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), with nanometer spatial resolution, is used to prove the success of the patterning and subsequent chemical modification steps. We use a combination of colloidal self‐assembly, plasma etching, self‐assembled monolayers (SAMs) and physical vapour deposition (PVD). The method utilizes a double colloid assembly process in which a first layer of close‐packed colloids is created, followed by plasma etching, coating with gold and deposition of a first SAM layer. A second particle layer is deposited on top of the first layer masking the interstitial spaces containing the first SAM. A second gold layer is deposited followed by a second SAM. After particle removal the surface consists of the pattern containing two different SAMs and a SiO₂ layer that can be readily functionalized with silanes. The possibility in the replacement of the two different thiols is investigated by X‐ray photoelectron spectroscopy (XPS) and it was found that no replacement is taking place. ToF‐SIMS imaging is used to show the periodicity of the chemical patterns by tracking unique fragment ions from the different surface regions. The patterning method is adaptable to create smaller or larger chemical patterns by appropriate choice of particle sizes. The patterns are useful for immobilizing biomolecules for cell studies or as multiplexed biosensors.     

Static TOF‐SIMS. A VAMAS interlaboratory study. Part II — accuracy of the mass scale and G‐SIMS compatibility

An interlaboratory study involving 32 time‐of‐flight static SIMS instruments from 13 countries has been conducted. In Part I of the analysis of data, we showed that 84% of instruments have excellent repeatabilities of better than 1.9% and that a relative instrument spectral response (RISR) can be used to evaluate variations between different generic types of instrument. Use of the RISR improves comparability between instruments by a factor of 33. Here, in Part II, we study the accuracy of the mass scale calibration in TOF‐SIMS and evaluate instrument compatibility with G‐SIMS. We show that the accuracy of calibration of the mass scale is much poorer than generally expected (?60 ppm for peaks <200 u and ?150 ppm for a large molecular peak at 647 u). This is a major issue for analysts. Elsewhere, we have developed a detailed study of the factors affecting the mass calibration and have developed a generic protocol that improves accuracy by a factor of 5. Here, this framework of understanding is used to interpret the results presented. Furthermore, we show that eight out of the ten participants submitting data for G‐SIMS could use operating conditions that generated G‐SIMS spectra of the PC reference material. This demonstrates that G‐SIMS may be conducted with a wide variety of instrument designs. © Crown Copyright 2007. Reproduced by permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd.     

High mass and spatial resolution mass spectrometry imaging of Nicolas Poussin painting cross section by cluster TOF‐SIMS

Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) imaging using cluster primary ion beams is used for the identification of the pigments in the painting of Rebecca and Eliezer at the Well by Nicolas Poussin. The combination of the high mass resolution of the technique with a sub‐micrometer spatial resolution offered by a delayed extraction of the secondary ions, together with the possibility to simultaneously identifying both minerals and organics, has proved to be the method of choice for the study of the stratigraphy of a paint cross section. The chemical compositions of small grains are shown with the help of a thorough processing of the data, with images of specific ions, mass spectra extracted from small regions of interest, and profiles drawn along the different painting layers. Copyright © 2016 John Wiley & Sons, Ltd.     

Image fusion combining SEM and ToF‐SIMS images

Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a high‐spatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter ‘A’ in the center (referred to below as the ‘A‐grid’), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter ‘C’ (referred to below as the ‘Cameca grid’). These were obtained using scanning electron microscopy (SEM) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF‐SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A‐Grid and the Cameca grid were determined to be 21 µm and 8 µm, respectively. When these values were compared to the original ToF‐SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Macromolecular Surface Design: Photopatterning of Functional Stable Nitrile Oxides

The efficient trapping of photogenerated thioaldehydes with functional shelf‐stable nitrile oxides in a 1,3‐dipolar cycloaddition is a novel and versatile photochemical strategy for polymer end‐group functionalization and surface modification under mild and equimolar conditions. The modular ligation in solution was followed in detail by electrospray ionization mass spectrometry (ESI‐MS). X‐ray photoelectron spectroscopy (XPS) was employed to analyze the functionalized surfaces, whereas time‐of‐flight secondary‐ion mass spectrometry (ToF‐SIMS) confirmed the spatial control of the surface functionalization using a micropatterned shadow mask. Polymer brushes were grown from the surface in a spatially confined regime by surface‐initiated atom transfer radical polymerization (SI‐ATRP) as confirmed by TOF‐SIMS, XPS as well as ellipsometry.     

Mitigating dead‐time effects during multivariate analysis of ToF‐SIMS spectral images

ToF‐SIMS spectra are formed by bombarding a surface with a pulse of primary ions and detecting the resultant ionized surface species using a time‐of‐flight mass spectrometer. Typically, the detector is a time‐to‐digital converter. Once an ion is detected using such detectors, the detector becomes insensitive to the arrival of additional ions for a period termed as the (detector) dead‐time. Under commonly used ToF‐SIMS data acquisition conditions, the time interval over which ions arising from a single chemical species reach the detector is on the order of the detector dead‐time. Thus, only the first ion reaching the detector at any given mass is counted. The event registered by the data acquisition system, then, is the arrival of one or more ions at the detector. This behavior causes ToF‐SIMS data to violate, in the general case, the assumption of linear additivity that underlies many multivariate statistical analysis techniques. In this article, we show that high‐mass‐resolution ToF‐SIMS spectral‐image data follow a generalized linear model, and we propose a data transformation and scaling procedure that enables such data sets to be successfully analyzed using standard methods of multivariate image analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

Secondary‐Ion Mass Spectrometry of Genetically Encoded Targets

Secondary ion mass spectrometry (SIMS) is generally used in imaging the isotopic composition of various materials. It is becoming increasingly popular in biology, especially for investigations of cellular metabolism. However, individual proteins are difficult to identify in SIMS, which limits the ability of this technology to study individual compartments or protein complexes. We present a method for specific protein isotopic and fluorescence labeling (SPILL), based on a novel click reaction with isotopic probes. Using this method, we added ¹⁹F‐enriched labels to different proteins, and visualized them by NanoSIMS and fluorescence microscopy. The ¹⁹F signal allowed the precise visualization of the protein of interest, with minimal background, and enabled correlative studies of protein distribution and cellular metabolism or composition. SPILL can be applied to biological systems suitable for click chemistry, which include most cell‐culture systems, as well as small model organisms.     

Identifying and minimizing buffer interferences in ToF‐SIMS analyses of lignocellulose

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) has demonstrated applicability to the analysis of lignocellulosic samples including pulp, paper, plants, and wood. One such application is to use ToF‐SIMS as a tool for detecting the activity of enzymes applied to degrade or modify plant biomass. The use of buffers for pH control of these enzymatic reactions can pose problems due to the nature of the ToF‐SIMS measurement. Specifically, inorganic species (e.g. salts) from buffer components could introduce several concerns for quantitative or semi‐quantitative ToF‐SIMS analysis. First, salts can produce additional peaks in the mass spectra, which may overlap with lignocellulose peaks of interest (mass interference). Second, salts can alter the chemical environment, or ‘matrix’, altering the ionization probability of lignocellulose‐related secondary ions during the sputtering mechanism of the ToF‐SIMS measurement (matrix effects). Third, salts may physically coat the lignocellulose surface, decreasing the signal from the lignocellulose, causing poor signal‐to‐noise in the analysis. The current work presents a simple approach for identifying interferences due to buffers, using both principal component analysis (PCA) and previously established lignocellulose‐relevant peak ratios. Furthermore, a simple acetic acid rinsing protocol is compared to distilled water rinsing and is evaluated and for its effectiveness in removing buffer‐related salts. The data shows that briefly rinsing lignocellulose samples in dilute acetic acid can be effective in restoring the validity of lignocellulose composition interpretations using ToF‐SIMS. Copyright © 2014 John Wiley & Sons, Ltd.