Mass spectral imaging of glycophospholipids, cholesterol, and glycophorin a in model cell membranes

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.

Cell and Tissue Imaging by TOF-SIMS and MALDI-TOF: An Overview for Biological and Pharmaceutical Analysis

The potential of mass spectrometry imaging (MSI) has been demonstrated in cell and tissue research since 1970. MSI can reveal the spatial distribution of a wide range of atomic and molecular ions detected from biological sample surfaces, it is a powerful and valuable technique used to monitor and detect diverse chemical and biological compounds, such as drugs, lipids, proteins, and DNA. MSI techniques, notably matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) and time of flight secondary ion mass spectrometry (TOF-SIMS), witnessed a dramatic upsurge in studying and investigating biological samples especially, cells and tissue sections. This advancement is attributed to the submicron lateral resolution, the high sensitivity, the good precision, and the accurate chemical specificity, which make these techniques suitable for decoding and understanding complex mechanisms of certain diseases, as well as monitoring the spatial distribution of specific elements, and compounds. While the application of both techniques for the analysis of cells and tissues is thoroughly discussed, a briefing of MALDI-TOF and TOF-SIMS basis and the adequate sampling before analysis are briefly covered. The importance of MALDI-TOF and TOF-SIMS as diagnostic tools and robust analytical techniques in the medicinal, pharmaceutical, and toxicology fields is highlighted through representative published studies.

Understanding mass spectrometry images: complexity to clarity with machine learning

The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the complexity and size of MSI data sets. Advances in this area have been particularly appealing for MSI of biological samples, which typically produce highly complicated data with often subtle relationships between features. There are many different machine learning approaches that have been applied to MSI data over the past two decades. In this review, we focus on a subset of non-linear machine learning techniques that have mostly only been applied in the past 5 years. Specifically, we review the use of the self-organizing map (SOM), SOM with relational perspective mapping (SOM-RPM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). While not their only functionality, we have grouped these techniques based on their ability to produce what we refer to as similarity maps. Similarity maps are color representations of hyperspectral data, in which spectral similarity between pixels-that is, their distance in high-dimensional space-is represented by relative color similarity. In discussing these techniques, we describe, briefly, their associated algorithms and functionalities, and also outline applications in MSI research with a strong focus on biological sample types. The aim of this review is therefore to introduce this relatively recent paradigm for visualizing and exploring hyperspectral MSI, while also providing a comparison between each technique discussed.

Label-Free Time-of-Flight Secondary Ion Mass Spectrometry Imaging of Sulfur-Producing Enzymes inside Microglia Cells following Exposure to Silver Nanowires

There are no methods sensitive enough to detect enzymes within cells, without the use of analyte labeling. Here we show that it is possible to detect protein ion signals of three different H₂S-synthesizing enzymes inside microglia after pretreatment with silver nanowires (AgNW) using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Protein fragment ions, including the fragment of amino acid (C₄H₈N⁺ = 70 amu), fragments of the sulfur-producing cystathionine-containing enzymes, and the Ag⁺ ion signal could be detected without the use of any labels; the cells were mapped using the C₄H₈N⁺ amino acid fragment. Scanning electron microscopy imaging and energy-dispersive X-ray chemical analysis showed that the AgNWs were inside the same cells imaged by TOF-SIMS and transformed chemically into crystalline Ag₂S within cells in which the sulfur-producing proteins were detected. The presence of these sulfur-producing cystathionine-containing enzymes within the cells was confirmed by Western blots and confocal microscopy images of fluorescently labeled antibodies against the sulfur-producing enzymes. Label-free TOF-SIMS is very promising for the label-free identification of H₂S-contributing enzymes and their cellular localization in biological systems. The technique could in the future be used to identify which of these enzymes are most contributory.

The influence of polyatomic primary ion chemistry on matrix effects in secondary ion mass spectrometry analysis

RATIONALE

The application of mass spectrometry imaging techniques to determine two- (2D) and three- (3D) dimensional chemical distribution ideally provides uniform, high sensitivity to multiple components and reliable quantification. These criteria are typically not met due to variations in sensitivity due to the chemistry of the analyte and surrounding surface chemistry. Here we explore the influence of projectile beam chemistry and sample chemistry in time-of-flight secondary ion mass spectrometry (TOF-SIMS). To the authors' knowledge this is the first time the combined effects of projectile chemistry and sample environment on the quantitative determination of mixed samples have been systematically studied.

METHODS

Secondary ion yields of lipid and amino acid mixtures were measured under 20 keV C₆₀ , Ar_n , and (H₂ O)_n cluster ion bombardment (n = 2000 or 4000) using TOF-SIMS. Ion suppression/enhancement effects were studied in dry sample films and in trehalose and water ice matrices.

RESULTS

The extent of the matrix effects and the secondary ion yield were found to depend on the chemistry of the primary ion beam and (for C₆₀ , Ar_n ) on the nature of the sample matrix. Under (H₂ O)_n bombardment the sample matrix had negligible effect on the analysis.

CONCLUSIONS

Compared with C₆₀ and Ar_n , water-containing cluster projectiles enhanced the sensitivity of TOF-SIMS determination of the chosen analytes and reduced the effect of signal suppression/enhancement in multicomponent samples and in different sample matrices. One possible explanation for this is that the (H₂ O)₄₀₀₀ projectile initiates on impact a nanoscale matrix environment that is very similar to that in frozen-hydrated samples in terms of the resulting ionisation effects. The competition between analytes for protons and the effect of the sample matrix are reduced with water-containing cluster projectiles. These chemically reactive projectile beams have improved characteristics for quantitative chemical imaging by TOF-SIMS compared with their non-reactive counterparts.

ToF-SIMS of tissues: "lessons learned" from mice and women

The ability to image cells and tissues with chemical and molecular specificity could greatly expand our understanding of biological processes. The subcellular resolution mass spectral imaging capability of time of flight secondary ion mass spectrometry (ToF-SIMS) has the potential to acquire chemically detailed images. However, the complexities of biological systems combined with the sensitivity of ToF-SIMS require careful planning of experimental methods. Tissue sample preparation methods of formalin fixation followed by paraffin embedding (FFPE) and OCT embedding are compared. Results show that the FFPE can potentially be used as a tissue sample preparation protocol for ToF-SIMS analysis if a cluster ion pre-sputter is used prior to analysis and if nonlipid related tissue features are the features of interest. In contrast, embedding tissue in OCT minimizes contamination and maintains lipid signals. Various data acquisition methodologies and analysis options are discussed and compared using mouse breast and diaphragm muscle tissue. Methodologies for acquiring ToF-SIMS 2D images are highlighted along with applications of multivariate analysis to better identify specific features in a tissue sections when compared to H&E images of serial sections. Identification of tissue features is necessary for researchers to visualize a molecular map that correlates with specific biological features or functions. Finally, lessons learned from sample preparation, data acquisition, and data analysis methods developed using mouse models are applied to a preliminary analysis of human breast tumor tissue sections.

Revealing cytokine-induced changes in the extracellular matrix with secondary ion mass spectrometry

Cell-secreted matrices (CSMs), where extracellular matrix (ECM) deposited by monolayer cell cultures is decellularized, have been increasingly used to produce surfaces that may be reseeded with cells. Such surfaces are useful to help us understand cell-ECM interactions in a microenvironment closer to the in vivo situation than synthetic substrates with adsorbed proteins. We describe the production of CSMs from mouse primary osteoblasts (mPObs) exposed to cytokine challenge during matrix secretion, mimicking in vivo inflammatory environments. Time-of-flight secondary ion mass spectrometry data revealed that CSMs with cytokine challenge at day 7 or 12 of culture can be chemically distinguished from one another and from untreated CSM using multivariate analysis. Comparison of the differences with reference spectra from adsorbed protein mixtures points towards cytokine challenge resulting in a decrease in collagen content. This is supported by immunocytochemical and histological staining, demonstrating a 44% loss of collagen mass and a 32% loss in collagen I coverage. CSM surfaces demonstrate greater cell adhesion than adsorbed ECM proteins. When mPObs were reseeded onto cytokine-challenged CSMs they exhibited reduced adhesion and elongated morphology compared to untreated CSMs. Such changes may direct subsequent cell fate and function, and provide insights into pathological responses at sites of inflammation.

Imaging lipids with secondary ion mass spectrometry

This review discusses the application of time-of-flight secondary ion mass spectrometry (TOF-SIMS) and magnetic sector SIMS with high lateral resolution performed on a Cameca NanoSIMS 50(L) to imaging lipids. The similarities between the two SIMS approaches and the differences that impart them with complementary strengths are described, and various strategies for sample preparation and to optimize the quality of the SIMS data are presented. Recent reports that demonstrate the new insight into lipid biochemistry that can be acquired with SIMS are also highlighted. This article is part of a Special Issue entitled Tools to study lipid functions.

Graphene nanoflakes as an efficient ionizing matrix for MALDI-MS based lipidomics of cancer cells and cancer stem cells

This study demonstrates that graphene nanoflakes can be efficiently used as a successful, interference free matrix for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS).

Image and Spectral Processing for ToF-SIMS Analysis of Biological Materials

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) instruments can rapidly produce large complex data sets. Within each spectrum, there can be hundreds of peaks. A typical 256×256 pixel image contains 65,536 spectra. If this is extended to a 3D image, the number of spectra in a given data set can reach the millions. The challenge becomes how to process these large data sets while taking into account the changes and differences between all the peaks in the spectra. This is particularly challenging for biological materials that all contain the same types of proteins and lipids, just in varying concentrations and spatial distributions. This data analysis challenge is further complicated by the limitations in the ion yield of higher mass, more chemically specific species, and potentially by the processing power of typical computers. Herein we briefly discuss analysis methodologies including univariate analysis, multivariate analysis (MVA) methods, and some of the limitations of ToF-SIMS analysis of biological materials.

Quantifying the molar percentages of cholesterol in supported lipid membranes by time-of-flight secondary ion mass spectrometry and multivariate analysis

The uneven cholesterol distribution among organelles and within the plasma membrane is postulated to be critical for proper cellular function. To study how interactions between cholesterol and specific lipid species contribute to the uneven cholesterol distribution between and within cellular membranes, model lipid membranes are frequently employed. Although the cholesterol distributions within membranes can be directly imaged without labels by using time-of-flight secondary ion mass spectrometry (TOF-SIMS), quantifying the cholesterol abundance at specific membrane locations in a label-free manner remains a challenge. Here, partial least-squares regression (PLSR) of TOF-SIMS data is used to quantitatively measure the local molar percentage (mol %) of cholesterol within supported lipid membranes. With the use of TOF-SIMS data from lipid membranes of known composition, a PLSR model was constructed that correlated the spectral variation to the mol % cholesterol in the membrane. The PLSR model was then used to measure the mol % cholesterol in test membranes and to measure cholesterol exchange between vesicles and supported lipid membranes. The accuracy of these measurements was assessed by comparison to the mol % cholesterol measured with conventional assays. By using this TOF-SIMS/PLSR approach to quantify the mol % cholesterol with location specificity, a better understanding of how the regional lipid composition influences cholesterol abundance and exchange in membranes may be obtained.

Secondary ion mass spectrometry imaging of molecular distributions in cultured neurons and their processes: comparative analysis of sample preparation

Neurons often exhibit a complex chemical distribution and topography; therefore, sample preparation protocols that preserve structures ranging from relatively large cell somata to small neurites and growth cones are important factors in secondary ion mass spectrometry (SIMS) imaging studies. Here, SIMS was used to investigate the subcellular localization of lipids and lipophilic species in neurons from Aplysia californica. Using individual neurons cultured on silicon wafers, we compared and optimized several SIMS sampling approaches. After an initial step to remove the high salt culturing media, formaldehyde, paraformaldehyde, and glycerol, and various combinations thereof, were tested for their ability to achieve cell stabilization during and after the removal of extracellular media. These treatments improved the preservation of cellular morphology as visualized with SIMS imaging. For analytes >250 Da, coating the cell surface with a 3.2 nm-thick gold layer increased the ion intensity; multiple analytes previously not observed or observed at low abundance were detected, including intact cholesterol and vitamin E molecular ions. However, once a sample was coated, many of the lower molecular mass (<200 Da) analyte signals were suppressed. The optimum approach depended on the analyte being studied; the approaches evaluated included rinsing with water and cell stabilization with glycerol and 4 % paraformaldehyde. The sample preparation methods described here enhance SIMS imaging of processes of individual cultured neurons over a broad mass range with enhanced image contrast.

ToF-SIMS depth profiling of cells: z-correction, 3D imaging, and sputter rate of individual NIH/3T3 fibroblasts

Proper display of three-dimensional time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data of complex, nonflat samples requires a correction of the data in the z-direction. Inaccuracies in displaying three-dimensional ToF-SIMS data arise from projecting data from a nonflat surface onto a 2D image plane, as well as possible variations in the sputter rate of the sample being probed. The current study builds on previous studies by creating software written in Matlab, the ZCorrectorGUI (available at http://mvsa.nb.uw.edu/), to apply the z-correction to entire 3D data sets. Three-dimensional image data sets were acquired from NIH/3T3 fibroblasts by collecting ToF-SIMS images, using a dual beam approach (25 keV Bi(3)(+) for analysis cycles and 20 keV C(60)(2+) for sputter cycles). The entire data cube was then corrected by using the new ZCorrectorGUI software, producing accurate chemical information from single cells in 3D. For the first time, a three-dimensional corrected view of a lipid-rich subcellular region, possibly the nuclear membrane, is presented. Additionally, the key assumption of a constant sputter rate throughout the data acquisition was tested by using ToF-SIMS and atomic force microscopy (AFM) analysis of the same cells. For the dried NIH/3T3 fibroblasts examined in this study, the sputter rate was found to not change appreciably in x, y, or z, and the cellular material was sputtered at a rate of approximately 10 nm per 1.25 × 10(13) ions C(60)(2+)/cm(2).

TOF-SIMS imaging of adsorbed proteins on topographically complex surfaces with Bi(3) (+) primary ions

Although previous studies have demonstrated that TOF-SIMS is a powerful method for the characterization of adsorbed proteins due to its specificity and surface sensitivity, it was unclear from earlier work whether the differences between proteins observed on uniform flat surfaces were large enough to facilitate clear image contrast between similar proteins in small areas on topographically complex samples that are more typical of biological tissues. The goal of this study was to determine whether Bi(3) (+) could provide sufficiently high sensitivity to provide clear identification of the different proteins in an image. In this study, 10 μm polystyrene microspheres were adsorbed with one of three different proteins, human serum albumin (HSA), bovine serum albumin (BSA), and hemoglobin. Spheres coated with HSA were then mixed with spheres coated with either BSA (a very similar protein) or hemoglobin (a dramatically different protein), and deposited on silicon substrates. Fluorescent labeling was used to verify the SIMS results. With maximum autocorrelation factors (MAF) processing, images showed clear contrast between both the very different proteins (HSA and hemoglobin) and the very similar proteins (HSA and BSA). Similar results were obtained with and without the fluorescent labels. MAF images were calculated using both the full spectrum and only characteristic amino acid fragments. Although better image contrast was obtained using the full spectrum, differences between the spheres were still evident when only the amino acid fragments were included in the analysis, suggesting that we are truly observing differences between the proteins themselves. These results demonstrate that TOF-SIMS, with a Bi(3) (+) primary ion, is a powerful technique for characterizing interfacial proteins not only on large uniform surfaces, but also with high spatial resolution on the topographically complex samples typical in biological analysis.

Identifying individual cell types in heterogeneous cultures using secondary ion mass spectrometry imaging with C60 etching and multivariate analysis

Tissue engineering approaches fabricate and subsequently implant cell-seeded and unseeded scaffold biomaterials. Once in the body, these biomaterials are repopulated with somatic cells of various phenotypes whose identification upon explantation can be expensive and time-consuming. We show that imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) can be used to distinguish mammalian cell types in heterogeneous cultures. Primary rat esophageal epithelial cells (REEC) were cultured with NIH 3T3 mouse fibroblasts on tissue culture polystyrene and freeze-dried before TOF-SIMS imaging. Results show that a short etching sequence with C(60)(+) ions can be used to clean the sample surface and improve the TOF-SIMS image quality. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used to identify peaks whose contributions to the total variance in the multivariate model were due to either the two cell types or the substrate. Using PLS-DA, unknown regions of cellularity that were otherwise unidentifiable by SIMS could be classified. From the loadings in the PLS-DA model, peaks were selected that were indicative of the two cell types and TOF-SIMS images were created and overlaid that showed the ability of this method to distinguish features visually.

Molecular depth profiling of buried lipid bilayers using C(60)-secondary ion mass spectrometry

An organic delta layer system made of alternating Langmuir-Blodgett multilayers of barium arachidate (AA) and barium dimyristoyl phosphatidate (DMPA) was constructed to elucidate the factors that control depth resolution in molecular depth profile experiments. More specifically, one or several bilayers of DMPA (4.4 nm) were embedded in relatively thick (51 to 105 nm) multilayer stacks of AA, resulting in a well-defined delta layer model system closely resembling a biological membrane. 3-D imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) depth profile analysis was performed on this system using a focused buckminsterfullerene (C(60)) cluster ion beam. The delta layer depth response function measured in these experiments exhibits similar features as those determined in inorganic depth profiling, namely an asymmetric shape with quasi-exponential leading and trailing edges and a central Gaussian peak. The effects of sample temperature, primary ion kinetic energy, and incident angle on the depth resolution were investigated. While the information depth of the acquired SIMS spectra was found to be temperature independent, the depth resolution was found to be significantly improved at low temperature. Ion induced mixing is proposed to be largely responsible for the broadening, rather than topography, as determined by atomic force microscopy (AFM); therefore, depth resolution can be optimized using lower kinetic energy, glancing angle, and liquid nitrogen temperature.

Surface view of the lateral organization of lipids and proteins in lung surfactant model systems-a ToF-SIMS approach

The lateral organization of domain structures is an extremely significant aspect of biomembrane research. Chemical imaging by mass spectrometry with its recent advancement in sensitivity and lateral resolution has become a highly promising tool in biological research. In this review, we focus briefly on the instrumentation, working principle and important concepts related to time-of-flight secondary ion mass spectrometry followed by an overview of lipid/protein fragmentation patterns and chemical mapping. The key issues addressed are the applications of time-of-flight secondary ion mass spectrometry in biological membrane research. Additionally, we briefly review our recent investigations based on time-of-flight secondary ion mass spectrometry to unravel the lateral distribution of lipids and surfactant proteins in lung surfactant model systems as an example that highlights the importance of fluidity and ionic conditions on lipid phase behavior and lipid-protein interactions.

Surface characterization of extracellular matrix scaffolds

Extracellular matrix (ECM) scaffolds prepared from different tissue sources or using different methods have been demonstrated to have distinctive effects upon cell adhesion patterns and the ability to support and maintain differentiated phenotypes. It is unknown whether the molecular composition or the ultrastructure of the ECM plays a greater role in determining the phenotype of the cells with which it comes into contact. However, when implanted, the topology and ligand landscape of the material will determine the host molecules that bind and the type and behavior of cells that mediate the host response. Therefore, a comprehensive understanding of surface characteristics is essential in the design of scaffolds for specific clinical applications. The surface characteristics of ECM scaffolds derived from porcine urinary bladder, small intestine, and liver as well as the effects of two commonly used methods of chemical cross-linking upon UBM were investigated. Electron microscopy and time of flight secondary ion mass spectroscopy were used to examine the surface characteristics of the scaffolds. The results show that ECM scaffolds have unique morphologic and structural properties which are dependant on the organ or tissue from which the scaffold is harvested. Furthermore, the results show that the surface characteristics of an ECM scaffold are changed through chemical cross-linking.

Sphingolipidomics: a valuable tool for understanding the roles of sphingolipids in biology and disease

The sphingolipidome is the portion of the lipidome that encompasses all sphingoid bases and their derivatives. Whereas the most studied sphingoid base is sphingosine [(2S,3R,4E)-2-aminooctadecene-1,3-diol], mammals have dozens of structural variants, and hundreds of additional types have been found in other eukaryotic organisms and some bacteria and viruses. Multiplying these figures by the N-acyl-derivatives ("ceramides") and the more than 500 phospho- and glyco- headgroups places the number of discrete molecular species in the tens of thousands or higher. Structure-specific, quantitative information about a growing fraction of the sphingolipidome can now be obtained using various types of chromatography coupled with tandem mass spectrometry, and application of these methods is producing many surprises regarding sphingolipid structure, metabolism, and function. Such large data sets can be difficult to interpret, but the development of tools that display results from genomic and lipidomic studies in a pathway relational, nodal, context can make it easier for investigators to deal with this complexity.