Time-of-flight Secondary Ion Mass Spectrometry Imaging Research Articles

Forensic differentiation of blue pen inks using time-of-flight secondary ion mass spectrometry and multivariate statistical methods

This study investigates the use of time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate statistical analysis for the identification of blue pen strokes in document forgery cases. Strokes of six commercially available blue pens were analysed using ToF-SIMS, and the data were interpreted using principal component analysis (PCA) and multivariate curve resolution (MCR). PCA effectively reduced the dimensionality of the complex ToF-SIMS data, enabling clear differentiation of the blue pen inks based on the obtained PCA scores. The first three principal components accounted for most of the variance, with PCA score plots showing distinct clusters of samples corresponding to the spectra of each pen. PCA analysis of the large-area ToF-SIMS images further revealed some characteristic spatial patterns and variations across the pen strokes; however, this analysis was less useful for the identification process. On the other hand, MCR provided a more chemically intuitive analysis by extracting pure component spectra from the complex mixtures. Each MCR factor was individually associated with a specific pen, allowing for exact identification of the pen stroke made. MCR score images displayed clear correlations with individual pen strokes, and the associated loadings identified signals at particular m/z values that were characteristic of each pen. This study shows that an efficient method for non-destructive forensic analysis of blue pen strokes can be achieved by combining ToF-SIMS with PCA and MCR. This analytical method provides reliable identification, which is crucial for forgery detection without requiring any sample pretreatment, i.e. the samples (pen strokes) were used just as they were made on paper.

Microglial activation induces nitric oxide signalling and alters protein S-nitrosylation patterns in extracellular vesicles.

Neuroinflammation is an underlying feature of neurodegenerative conditions, often appearing early in the aetiology of a disease. Microglial activation, a prominent initiator of neuroinflammation, can be induced through lipopolysaccharide (LPS) treatment resulting in expression of the inducible form of nitric oxide synthase (iNOS), which produces nitric oxide (NO). NO post-translationally modifies cysteine thiols through S-nitrosylation, which can alter function of the target protein. Furthermore, packaging of these NO-modified proteins into extracellular vesicles (EVs) allows for the exertion of NO signalling in distant locations, resulting in further propagation of the neuroinflammatory phenotype. Despite this, the NO-modified proteome of activated microglial EVs has not been investigated. This study aimed to identify the protein post-translational modifications NO signalling induces in neuroinflammation. EVs isolated from LPS-treated microglia underwent mass spectral surface imaging using time of flight-secondary ion mass spectrometry (ToF-SIMS), in addition to iodolabelling and comparative proteomic analysis to identify post-translation S-nitrosylation modifications. ToF-SIMS imaging successfully identified cysteine thiol side chains modified through NO signalling in the LPS treated microglial-derived EV proteins. In addition, the iodolabelling proteomic analysis revealed that the EVs from LPS-treated microglia carried S-nitrosylated proteins indicative of neuroinflammation. These included known NO-modified proteins and those associated with LPS-induced microglial activation that may play an essential role in neuroinflammatory communication. Together, these results show activated microglia can exert broad NO signalling changes through the selective packaging of EVs during neuroinflammation.

From surface to core: Comprehensive ToF-SIMS insights into pharmaceutical tablet analysis

This study presents an innovative approach to pharmaceutical analysis using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to investigate tablets containing paracetamol in the solid state without the need for traditional pretreatment methods (dissolution, filtration, etc.). Employing both MS1 and MS2 ToF-SIMS spectra, signals characteristic of paracetamol and a fragmentation mechanism were assigned. Some findings challenge previous interpretations of certain ToF-SIMS signals attributed to paracetamol, highlighting the need for reevaluation. Moreover, as the sample was used without any pretreatment (a purchased tablet), which has a complex matrix, the resulting ToF-SIMS mass spectra can suffer from spectral interferences. The use of multivariate statistical analysis resulted in the assignment of unique signals for paracetamol. Additionally, ToF-SIMS imaging was utilized with high mass and lateral resolution, employing the delayed-extraction ToF analyzer mode, to image the two-dimensional distribution of species on the tablet’s surface with an emphasis on paracetamol distribution. The use of a gas cluster ion beam (GCIB) sputtering source in association with ToF-SIMS enabled the visualization of the three–dimensional distribution of species within the tablet. This thorough investigation advances knowledge regarding the characterization of paracetamol and offers the examination of pharmaceuticals without sample pretreatment with elemental and molecular-specific information.

Leveraging ToF-SIMS imaging to investigate tenofovir disoproxil fumarate degradation at excipient interfaces in oral compressed tablets

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging has been used to study the hydrolysis of tenofovir disoproxil fumarate (TDF) to tenofovir monosoproxil (TM) within an oral compressed tablet. The ToF-SIMS images displayed a heterogenous distribution of the matrix components. Evaluation of the TM distribution revealed that it was primarily co-localized with areas of higher excipient concentration pointing toward excipient driven degradation. To support these observations, a compatibility study of TDF with each tablet component was performed via liquid chromatography. The ToF-SIMS imaging and compatibility study indicated that the excipient, Avicel® PH-102, was the primary driver of TM formation in the tablet. The hydrolysis degradation mechanism within the tablet is further rationalized through discussion of chemical and physical properties of the matrix components. The sum of this work demonstrates a new analytical workflow for probing and understanding matrix driven degradation in oral compressed tablets utilizing ToF-SIMS imaging.

Mesoporous Bioactive Glass-Incorporated Injectable Strontium-Containing Calcium Phosphate Cement Enhanced Osteoconductivity in a Critical-Sized Metaphyseal Defect in Osteoporotic Rats.

In this study, the in vitro and in vivo bone formation behavior of mesoporous bioactive glass (MBG) particles incorporated in a pasty strontium-containing calcium phosphate bone cement (pS100G10) was studied in a metaphyseal fracture-defect model in ovariectomized rats and compared to a plain pasty strontium-containing calcium phosphate bone cement (pS100) and control (empty defect) group, respectively. In vitro testing showed good cytocompatibility on human preosteoblasts and ongoing dissolution of the MBG component. Neither the released strontium nor the BMG particles from the pS100G10 had a negative influence on cell viability. Forty-five female Sprague-Dawley rats were randomly assigned to three different treatment groups: (1) pS100 (n = 15), (2) pS100G10 (n = 15), and (3) empty defect (n = 15). Twelve weeks after bilateral ovariectomy and multi-deficient diet, a 4 mm wedge-shaped fracture-defect was created at the metaphyseal area of the left femur in all animals. The originated fracture-defect was substituted with pS100 or pS100G10 or left empty. After six weeks, histomorphometrical analysis revealed a statistically significant higher bone volume/tissue volume ratio in the pS100G10 group compared to the pS100 (p = 0.03) and empty defect groups (p = 0.0001), indicating enhanced osteoconductivity with the incorporation of MBG. Immunohistochemistry revealed a significant decrease in the RANKL/OPG ratio for pS100 (p = 0.004) and pS100G10 (p = 0.003) compared to the empty defect group. pS100G10 showed a statistically higher expression of BMP-2. In addition, a statistically significant higher gene expression of alkaline phosphatase, osteoprotegerin, collagen1a1, collagen10a1 with a simultaneous decrease in RANKL, and carbonic anhydrase was seen in the pS100 and pS100G10 groups compared to the empty defect group. Mass spectrometric imaging by time-of-flight secondary ion mass spectrometry (ToF-SIMS) showed the release of Sr2+ ions from both pS100 and pS100G10, with a gradient into the interface region. ToF-SIMS imaging also revealed that resorption of the MBG particles allowed for new bone formation in cement pores. In summary, the current work shows better bone formation of the injectable pasty strontium-containing calcium phosphate bone cement with incorporated mesoporous bioactive glass compared to the bioactive-free bone cement and empty defects and can be considered for clinical application for osteopenic fracture defects in the future.

Effect of data preprocessing and machine learning hyperparameters on mass spectrometry imaging models

The self-organizing map (SOM) is a nonlinear machine learning algorithm that is particularly well suited for visualizing and analyzing high-dimensional, hyperspectral time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. Previously, we compared the capabilities of the SOM with more traditional linear techniques using ToF-SIMS imaging data. Although SOMs perform well with minimal data preprocessing and negligible hyperparameter optimization, it is important to understand how different data preprocessing methods and hyperparameter settings influence the performance of SOMs. While these investigations have been reported outside of the ToF-SIMS field, no such study has been reported for hyperspectral MSI data. To address this, we used two labeled ToF-SIMS imaging datasets, one of which was a polymer microarray dataset, while the other was semisynthetic hyperspectral data. The latter was generated using a novel algorithm that we describe here. A grid-search was used to evaluate which data preprocessing methods and SOM hyperparameters had the largest impact on the performance of the SOM. This was assessed using multiple linear regression, whereby performance metrics were regressed onto each variable defining the preprocessing-hyperparameter space. We found that preprocessing was generally more important than hyperparameter selection. We also found statistically significant interactions between several parameters studied, suggesting a complex interplay between preprocessing and hyperparameter selection. Importantly, we identified interesting trends, both dataset specific and dataset agnostic, which we describe and discuss in detail.

Advancements in ToF-SIMS imaging for life sciences.

In the last 2 decades, Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) has gained significant prominence as a powerful imaging technique in the field of life sciences. This comprehensive review provides an in-depth overview of recent advancements in ToF-SIMS instrument technology and its applications in metabolomics, lipidomics, and single-cell analysis. We highlight the use of ToF-SIMS imaging for studying lipid distribution, composition, and interactions in cells and tissues, and discuss its application in metabolomics, including the analysis of metabolic pathways. Furthermore, we review recent progress in single-cell analysis using ToF-SIMS, focusing on sample preparation techniques, in situ investigation for subcellular distribution of drugs, and interactions between drug molecules and biological targets. The high spatial resolution and potential for multimodal analysis of ToF-SIMS make it a promising tool for unraveling the complex molecular landscape of biological systems. We also discuss future prospects and potential advancements of ToF-SIMS in the research of life sciences, with the expectation of a significant impact in the field.

Expanding the Efficacy of Fingermark Enhancement Using ToF-SIMS.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been shown to enhance fingermark recovery compared to standard processes used by police forces, but there is no data to show how generally applicable the improvement is. Additionally, ToF-SIMS can be run in either positive or negative ion mode (or both), and there is no data on which mode of operation is most effective at revealing fingerprints. This study aims to fill these gaps by using ToF-SIMS to image fingerprints deposited on two common exhibit-type surfaces (polyethylene and stainless steel) using 10 donors and ageing fingerprints in either ambient, rainwater, or underground for 1 and 5 months. In all, 120 fingerprints were imaged using ToF-SIMS, and each was run in positive and negative modes. A fingerprint expert compared the fingerprint ridge detail produced by the standard process to the ToF-SIMS images. In over 50% of the samples, ToF-SIMS was shown to improve fingerprint ridge detail visualised by the respective standard process for all surfaces tested. In over 90% of the samples, the ridge detail produced by ToF-SIMS was equivalent to standard development across all different ageing and exposure conditions. The data shows that there is a benefit to running the ToF-SIMS in both positive and negative modes, even if no ridge detail was seen in one mode.

Properties of the fluoroacrylate and methacryloxypropyl-trimethoxysilane applied to a layer of Cu2O on bronze as either single or multi-component coatings

Various coatings have been developed and explored to protect bronze surfaces against the uncontrolled formation of different corrosion products when exposed to outdoor environments.In this research, the surfaces of artificially-formed oxidized bronze patinas (OB), consisting of Cu2O, were covered with either a single-component (fluoroacrylate, FA or methacryloxypropyl-trimethoxysilane, MS) or multi-component (a mixture of FA and MS, FA-MS) fluoropolymer coating and investigated. Variations in the concentration of each component in the coating were studied. Electrochemical tests were performed to determine the corrosion protection efficiency, followed by detailed surface analyses of the OBs, both uncoated and covered with single and multi-component coatings. A variety of investigative methods were used, including focused ion beam scanning electron microscopy (FIB-SEM), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and time-of-flight secondary ion mass spectrometry (ToF-SIMS).The coating made from a combination of FA and MS resulted in a very high protection efficiency. Despite the increased hydrophilicity of the single MS component, however, it was shown to efficiently protect the oxidized bronze surface. The FA-MS systems showed high hydrophobicity, but no improvement was measured in the efficiency of the corrosion protection when it was compared to the coating that contained 10% MS. According to XPS and ToF-SIMS imaging, the FA component of the FA-MS coating was not present only on the uppermost surface of the coating but throughout the whole coating, which could affect its corrosion protection efficiency.

The localization of the alkaloids in Coptis chinensis rhizome by time-of-flight secondary ion mass spectrometry.

Understanding the spatial distribution of active compounds can effectively evaluate the quality of decoction pieces of traditional Chinese medicine (TCM). Traditional methods are economical and practical but lack chemical information on the original distribution. Time-of-flight secondary ion mass spectrometry (TOF-SIMS), with the advantage of non-destructive detection of samples, can directly analyze the distribution of chemical compounds on the surface of various samples. In this study, TOF-SIMS image analysis technology was used to detect TCM for the first time. Taking Coptis rhizome (CR) as an example, a commonly used TCM, the distribution of the compounds in the cross-section of CR was studied. Meanwhile, ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UPLCQQQ-MS/MS) was used to verify the results of TOF-SIMS. The distribution of nine active compounds: berberine, epiberberine, coptisine, palmatine, columbamine, jatrorrhizine, tetrahydricheilanthifolinium, and oxyberberine, was well imaged in the cross-section of CR by TOF-SIMS. The content of berberine and epiberberine was the highest; Palmatine distribution in the pith was more than that in other parts; Oxyberberine was mainly concentrated in the cork and xylem rays. Normalization analysis showed contents of these compounds increased along with the growth years. The result was consistent with UPLC-QQQ-MS/MS. The TOF-SIMS method can display the spatial distribution status of the active compounds of herbs, providing a basis for selecting the medicine site with non-destructive and fast detection.

High spatial resolution ToF-SIMS imaging and image analysis strategies to monitor and quantify early phase separation in amorphous solid dispersions

Amorphous solid dispersions (ASDs) are formulations with enhanced drug solubility and dissolution rate compared to their crystalline counterparts, however, they can be inherently thermodynamically unstable. This can lead to amorphous phase separation and drug re-crystallisation, phenomena that are typically faster and more dominant at the product’s surfaces. This study investigates the use of high-resolution time of flight-secondary ion mass spectrometry (ToF-SIMS) imaging as a surface analysis technique combined with image-analysis for the early detection, monitoring and quantification of surface amorphous phase separation in ASDs. Its capabilities are demonstrated for two pharmaceutically relevant ASD systems with distinct re-crystallisation behaviours, prepared using hot melt extrusion (HME) followed by pelletisation or grinding: (1) paracetamol-hydroxypropyl methylcellulose (PCM-HPMC) pellets with drug loadings of 10%–50% w/w and (2) indomethacin-polyvinylpyrrolidone (IND-PVP) ground material with drug loadings of 20%–85% w/w. PCM-HPMC pellets showed intense phase separation, reaching 100% PCM surface coverage within 1–5 months. In direct comparison, IND-PVP HME ground material was more stable with only a moderate formation of isolated IND-rich clusters. Image analysis allowed the reliable detection and quantification of local drug-rich clusters. An Avrami model was applied to determine and compare phase separation kinetics. The combination of chemical sensitivity and high spatial resolution afforded by SIMS was crucial to enable the study of early phase separation and re-crystallisation at the surface. Compared with traditional methods used to detect crystalline material, such as XRPD, we show that ToF-SIMS enabled detection of surface physical instability already at early stages of drug cluster formation in the first days of storage.

Time of Flight Secondary Ion Mass Spectrometry imaging for precise localization of zirconium-labelled trastuzumab in xenograft cancer tumour tissues

The human epidermal growth factor receptor 2 (HER2) specific radiotracer zirconium-Desferrioxamine(DFO)-trastuzumab was visualized ex-vivo by Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging in ovarian and breast cancer xenograft tumor sections. Heterogeneous spatial distribution of [90Zr+] ions reflected the heterogeneous localization of trastuzumab, observed in parallel by immunohistochemistry staining in HER2+ tumors. Our results show that ToF-SIMS imaging is a quick and sensitive technique to image zirconium labelled biologics at microscale in tissues.

MS imaging and absorption methods visualizing sun filter skin spatial distribution and penetration.

Sunscreens must now be effective in protecting skin from ultraviolet, as well as visible/infrared radiation. Here, TriAsorB, a new broad-spectrum sun filter, was formulated with three other sunscreens and their distribution on human skin was studied using a standard penetration protocol and two novel mass spectrometry imaging techniques: atmospheric pressure matrix assisted laser desorption ionization (AP-MALDI) coupled to high resolution mass spectrometry and time of flight - secondary ion mass spectrometry (ToF-SIMS). The standard penetration protocol showed that sun filters absorption was very low, with most of the dose recovered at the surface (none entered the receptor fluid). Absorption was not increased in damaged skin. The results were confirmed by AP-MALDI and ToF-SIMS imaging of the spatial distribution of molecular species in cross-section samples of human skin. Each sun filter was detected on or in the stratum corneum, with a good homogenous coverage over the valleys and peaks of the skin, and correlated well with the distribution of endogenous biomarkers. In conclusion, conventional and novel imaging analysis methods showed that the sun filters remained mainly on the skin surface after topical application. Mass spectrometry imaging is a promising complementary approach to traditional skin penetration studies to visualize penetration of compounds.

Glutaraldehyde and uranyl acetate dual fixation combined sputtering/unroofing enables intracellular fatty acids TOF-SIMS imaging with organelle-corresponding subcellular distribution.

Fatty acids (FAs) have diverse functions in cellular activities. The intracellular distribution of FAs is critical for their functions. Imaging of FAs by time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been achieved. However, TOF-SIMS images of FAs so far do not have subcellular distribution due to inadequate sample preparation methods. In this study, we developed a chemical fixation method using glutaraldehyde (GA) with uranyl acetate (UA), which preserved cellular structure and intracellular FA distribution well. Combining GA+UA fixation with sputtering-based methods and unroofing-based methods, respectively, we successfully imaged intracellular lipids with the subcellular distribution.

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder.

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.

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.

An electroceutical approach enhances myelination via upregulation of lipid biosynthesis in the dorsal root ganglion

As the myelin sheath is crucial for neuronal saltatory conduction, loss of myelin in the peripheral nervous system (PNS) leads to demyelinating neuropathies causing muscular atrophy, numbness, foot deformities and paralysis. Unfortunately, few interventions are available for such neuropathies, because previous pharmaceuticals have shown severe side effects and failed in clinical trials. Therefore, exploring new strategies to enhance PNS myelination is critical to provide solution for such intractable diseases. This study aimed to investigate the effectiveness of electrical stimulation (ES) to enhance myelination in the mouse dorsal root ganglion (DRG)—an ex vivo model of the PNS. Mouse embryonic DRGs were extracted at E13 and seeded onto Matrigel-coated surfaces. After sufficient growth and differentiation, screening was carried out by applying ES in the 1–100 Hz range at the beginning of the myelination process. DRG myelination was evaluated via immunostaining at the intermediate (19 days in vitro (DIV)) and mature (30 DIV) stages. Further biochemical analyses were carried out by utilizing ribonucleic acid sequencing, quantitative polymerase chain reaction and biochemical assays at both intermediate and mature myelination stages. Imaging of DRG myelin lipids was carried out via time-of-flight secondary ion mass spectrometry (ToF-SIMS). With screening ES conditions, optimal condition was identified at 20 Hz, which enhanced the percentage of myelinated neurons and average myelin length not only at intermediate (129% and 61%) but also at mature (72% and 17%) myelination stages. Further biochemical analyses elucidated that ES promoted lipid biosynthesis in the DRG. ToF-SIMS imaging showed higher abundance of the structural lipids, cholesterol and sphingomyelin, in the myelin membrane. Therefore, promotion of lipid biosynthesis and higher abundance of myelin lipids led to ES-mediated myelination enhancement. Given that myelin lipid deficiency is culpable for most demyelinating PNS neuropathies, the results might pave a new way to treat such diseases via electroceuticals.

TOF-SIMS Imaging of Biological Tissue Sections and Structural Determination Using Tandem MS.

Over the past couple of years, imaging mass spectrometry (IMS) has arisen as a powerful tool to answer research questions in the biomedical field. Imaging mass spectrometry allows for label-free chemical imaging by providing full molecular information. The IMS technique best positioned for cell and tissue analysis is time-of-flight secondary ion mass spectrometry (ToF-SIMS) because it has the best spatial resolution of all the molecular IMS techniques and can detect many biochemical species and especially lipids with high sensitivity. Because one must rely on the mass and isotopic pattern of an ion in combination with positive correlations with lower mass fragments to help identify its structure, one major problem during ToF-SIMS experiments is the ambiguity when assigning a molecule to a certain mass peak. The solution are instruments with tandem MS capabilities as was already the case for many MALDI-ToF instruments more than a decade ago. It has been a few years since instruments with this capability were introduced and a number of interesting publications have been produced highlighting the advantages in biological SIMS work. Here, we present a protocol describing how tandem MS can be used to elucidate the structure of unknown or ambiguous mass peaks in biological tissue samples observed during ToF-SIMS imaging based on our experiences.

Femtosecond Laser Desorption Postionization MS vs ToF-SIMS Imaging for Uncovering Biomarkers Buried in Geological Samples.

The study of lipid molecular fossils by traditional biomarker analysis requires bulk sample crushing, followed by solvent extraction, and then the analysis of the extract by gas chromatography-mass spectrometry (GC-MS). This traditional analysis mixes all organic compounds in the sample regardless of their origins, with a loss of information on the spatial distribution of organic molecules within the sample. These shortcomings can be overcome using the chemical mapping of intact samples. Spectroscopic techniques such as UV fluorescence or Raman spectroscopy, laser ablation inductively coupled plasma mass spectrometry, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are among those elemental and molecular mapping techniques. This study employed femtosecond (fs) laser ablation combined with single-photon ionization, a method called fs-laser desorption postionization mass spectrometry (fs-LDPI-MS). A pulsed ∼75 fs, 800 nm laser was used to ablate the geological sample, which was then photoionized after a few microseconds by a pulsed 7.9 eV vacuum ultraviolet laser. An organic carbon-rich geological sample was used for this study to map hydrocarbon biomarkers in sediments that were previously studied by GC-MS. The petrography of this sample was examined by optical and fluorescence microscopy. It is demonstrated here that fs-LDPI-MS combined with petrography for multimodal imaging can expose buried compounds within the sample via in situ layer removal. When used in conjunction with traditional organic geochemical analysis, this method has the potential to determine the spatial distribution of organic biomarkers in geological material. Finally, fs-LDPI-MS imaging data are compared with ToF-SIMS imaging that is commonly used for such studies.

Sparse autoencoder-based feature extraction from TOF-SIMS image data of human skin structures.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a useful and versatile tool for surface analysis, enabling detailed compositional information to be obtained for the surfaces of diverse samples. Furthermore, in the case of two- or three-dimensional imaging, the measurement sensitivity in the higher molecular weight range can be improved by using a cluster ion source, thus further enriching the TOF-SIMS information. Therefore, appropriate analytical methods are required to interpret this TOF-SIMS data. This study explored the capabilities of a sparse autoencoder, a feature extraction method based on artificial neural networks, to process TOF-SIMS image data. The sparse autoencoder was applied to TOF-SIMS images of human skin keratinocytes to extract the distribution of endogenous intercellular lipids and externally penetrated drugs. The results were compared with those obtained using principal component analysis (PCA) and multivariate curve resolution (MCR), which are conventionally used for extracting features from TOF-SIMS data. This confirmed that the sparse autoencoder matches, and often betters, the feature extraction performance of conventional methods, while also offering greater flexibility.