Quantitative and Qualitative Analyses of Mass Spectra of OEL Materials by Artificial Neural Network and Interface Evaluation: Results from a VAMAS Interlaboratory Study.

Quantitative analysis of binary mixtures of tris(2-phenylpyridinato)iridium(III) (Ir(ppy)3) and tris(8-hydroxyquinolinato)aluminum (Alq3) by using an artificial neural network (ANN) system to mass spectra was attempted based on the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study (TW2 A31) to evaluate matrix-effect correction and to investigate interface determination. Monolayers of binary mixtures having different Ir(ppy)3 ratios (0, 0.25, 0.50, 0.75, and 1.00), and the multilayers containing these mixtures and pure samples were measured using time-of-flight secondary ion mass spectrometry (ToF-SIMS) with different primary ion beams, OrbiSIMS (SIMS with both Orbitrap and ToF mass spectrometers), laser desorption ionization (LDI), desorption/ionization induced by neutral clusters (DINeC), and X-ray photoelectron spectroscopy (XPS). The mass spectra were analyzed using a simple ANN with one hidden layer. The Ir(ppy)3 ratios of the unknown samples and the interfaces of the multilayers were predicted using the simple ANN system, even though the mass spectra of binary mixtures exhibited matrix effects. The Ir(ppy)3 ratios at the interfaces indicated by the simple ANN were consistent with the XPS results and the ToF-SIMS depth profiles. The simple ANN system not only provided quantitative information on unknown samples, but also indicated important mass peaks related to each molecule in the samples without a priori information. The important mass peaks indicated by the simple ANN depended on the ionization process. The simple ANN results of the spectra sets obtained by a softer ionization method, such as LDI and DINeC, suggested large ions such as trimers. From the first step of the investigation to build an ANN model for evaluating mixture samples influenced by matrix effects, it was indicated that the simple ANN method is useful for obtaining candidate mass peaks for identification and for assuming mixture conditions that are helpful for further analysis.

Quantitative analysis of ToF-SIMS data of a two organic compound mixture using an autoencoder and simple artificial neural networks.

RATIONALE: Matrix effects cause a nonlinear relationship between ion intensities and concentrations in mass spectrometry, including time-of-flight secondary ion mass spectrometry (ToF-SIMS). Here, two artificial neural network (ANN)-based methods, autoencoder-based and simple ANN methods, were employed for the quantitative and qualitative analyses of a two organic compound mixture via ToF-SIMS. METHODS: The multilayer model sample contained a mixture of Irganox 1010 and Fmoc-pentafluoro-L-phenylalanine (Fmoc-PFLPA). The sample's positive and negative ion depth profiles were collected through ToF-SIMS. ToF-SIMS-derived cross-sectional image datasets were analyzed using three unsupervised methods, namely principal component analysis (PCA), multivariate curve resolution (MCR), and use of a sparse autoencoder (SAE). The supervised simple ANN method was optimized based on the spectra and validated by predicting the test dataset ratios of Irganox 1010. RESULTS: The results obtained using the SAE demonstrated linear calibration curves and appropriate material distribution images. The Irganox 1010 and Fmoc-PFLPA positive and negative ion datasets exhibited >0.97 correlation coefficients. The PCA and MCR results demonstrated lower linearity than that of SAE. Moreover, SAE weights indicated the ions important for each organic compound. The simple ANN method accurately predicted the ratios in the test dataset and indicated the important ions. CONCLUSIONS: Both the supervised and unsupervised methods based on ANN, which were employed in regulating nonlinear relationships, were effective in the quantitative and qualitative analyses of the ToF-SIMS data of the two organic compound mixtures. Regarding qualitative analysis, both ANN-based methods indicated specific ions from the molecules in the sample.

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.

Model of local hydrogen permeability in stainless steel with two coexisting structures.

The dynamics of hydrogen in metals with mixed grain structure is not well understood at a microscopic scale. One of the biggest issues facing the hydrogen economy is "hydrogen embrittlement" of metal induced by hydrogen entering and diffusing into the material. Hydrogen diffusion in metallic materials is difficult to grasp owing to the non-uniform compositions and structures of metal. Here a time-resolved "operando hydrogen microscope" was used to interpret local diffusion behaviour of hydrogen in the microstructure of a stainless steel with austenite and martensite structures. The martensite/austenite ratios differed in each local region of the sample. The path of hydrogen permeation was inferred from the time evolution of hydrogen permeation in several regions. We proposed a model of hydrogen diffusion in a dual-structure material and verified the validity of the model by simulations that took into account the transfer of hydrogen at the interfaces.

Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study.

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.

Examination of beauty ingredient distribution in the human skin by time-of-flight secondary ion mass spectrometry.

In this study, the authors evaluated the distribution of low-abundance beauty ingredients in human skin tissues. The distribution of collagen tripeptide, a beauty ingredient, in the human skin was evaluated by applying multivariate curve resolution (MCR) to the time-of-flight secondary ion mass spectrometry mapping data, including reference information. The intensity of the secondary ion peaks was increased by the accumulation of secondary ion intensity in the depth direction obtained by argon cluster sputtering. Consequently, the collagen tripeptide distribution in the skin was evaluated by separating it from collagen peptide, although the conventional analysis was difficult because of the dilution of the collagen tripeptide in the skin. Additionally, the distribution of the collagen tripeptide in the skin may be determined with an autoencoder. In this study, the distribution of small amounts of beauty ingredients (the collagen tripeptide) in the skin was observed by integrating the secondary ion intensity in the depth direction with Ar cluster sputtering and applying MCR or autoencoder.

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and enables high-spatial-resolution chemical mapping. In this study, a combination of TOF-SIMS and data analysis was employed to evaluate biological materials composed of numerous proteins, including unknown ones. To interpret complicated TOF-SIMS data of human hair, an autoencoder, a dimensionality reduction method based on artificial neural networks, was applied. Autoencoders can be used to perform nonlinear analysis; therefore, they are more suitable than principal component analysis (PCA) for analyzing TOF-SIMS data, which are influenced by the matrix effect. As a model sample data, the TOF-SIMS depth profile of human hair, acquired via argon gas cluster ion beam sputtering and Bi3 2+ primary ion beam, was employed. Useful information, including the characteristic distributions of amino acids and permeated surfactants on the outermost surface of the hair, was extracted from the results obtained from the autoencoder. Furthermore, the autoencoder extracted more detailed features than did PCA. Therefore, autoencoders can become a powerful tool for TOF-SIMS data analysis.

Evaluation of secondary ions related to plant tissue using least absolute shrinkage and selection operator.

With regard to life sciences, it is important to understand biological functions such as metabolic reactions at the cellular level. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) that can provide chemical mappings at 100 nm lateral resolutions is useful for obtaining three-dimensional maps of biological molecules in cells and tissues. TOF-SIMS spectra generally contain several hundred to several thousand secondary ion peaks that provide detailed chemical information. In order to manage such a large number of peaks, data analysis methods such as multivariate analysis techniques have been applied to TOF-SIMS data of complex samples. However, the interpretation of the data analysis results is sometimes still difficult, especially for biological samples. In this study, TOF-SIMS data of resin-embedded plant samples were analyzed using one of the sparse modeling methods, least absolute shrinkage and selection operator (LASSO), to directly select secondary ions related to biological structures such as cell walls and nuclei. The same sample was measured by optical microscopy and the same measurement area as TOF-SIMS was extracted in order to prepare a target image for LASSO. The same area of the TOF-SIMS and microscope data were fused to evaluate the influence of the image fusion on the TOF-SIMS spectrum information using principal component analysis. Specifically, the authors examined onion mycorrhizal root colonized with Gigaspora margarita (an arbuscular mycorrhizal fungus). The results showed that by employing this approach using LASSO, important secondary ions from biological samples were effectively selected and could be clearly distinguished from the embedding resin.

Investigating matrix effects of different combinations of lipids and peptides on TOF-SIMS data.

Matrix effects, which cause a change in ion intensity, occur in mass spectrometry methods including time-of-flight secondary ion mass spectrometry (TOF-SIMS). Matrix effects often cause large issues in quantitative analysis because secondary ions related to a particular molecule could be dramatically enhanced or suppressed regardless of the concentration. To investigate matrix effects in biological samples, the authors evaluated mixed lipid {POPC [1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine, molecular weight (MW) 759.6]}, peptide [leu-enkephalin, neo-leu-enkephalin (amino acid sequence: YAGFL, MW 569.3), and neo-angiotensin II (amino acid sequence: DRVYIHAF, MW 1019.5)] samples. Matrix effect features were investigated by analyzing the concentration dependence of secondary ions in lipid-peptide mixed samples to develop a method that enables quantitative analysis using TOF-SIMS. Matrix effects depended on the lipid-peptide combination. Interestingly, some secondary ions possessed an intensity that was highly dependent on concentration.

OrbiSIMS Imaging Identifies Molecular Constituents of the Perialgal Vacuole Membrane of Paramecium bursaria with Symbiotic Chlorella variabilis.

The protist (mostly single-celled organisms), Paramecium bursaria, forms an intracellular symbiotic relationship with the single-celled algae, Chlorella variabilis, where P. bursaria provides nutrients (i.e., Ca2+, Mg2+, and K+), carbon dioxide for photosynthesis and protection from viruses, while C. variabilis provides oxygen, carbon fixation, and nutrients. Key to this successful relationship is the perialgal vacuole (PV) membrane, which surrounds C. variabilis and protects it from digestion by P. bursaria. The membrane is fragile and difficult to analyze using conventional methods therefore very little is known about the molecular composition. We used the OrbiSIMS, a new high-resolution mass spectrometer with subcellular resolution imaging, to study the compartmentalization of endosymbionts and elucidate biomolecular interactions between the host and endosymbiont. Ions from the region of interest, close to C. variabilis, and specific to the target samples containing PVs were found based on the chemical mapping and masses of the ions. We show chemical localizations of oligosaccharides in close proximity of C. variabilis endosymbionts in P. bursaria. These oligosaccharides are detected in host-endosymbiont samples containing PV membrane-bound algae and absent in free-living algae and digestive vacuole (DV) membrane-bound algae in P. bursaria.

Factors determining the reaction temperature of the solvent-free enzymatic synthesis of trehalose esters.

Solvent-free synthesis encourages the design of processes and products that reduce the use and generation of hazardous chemicals. Given the importance of developing greener methodologies, we sought to determine the factors influencing the reaction temperature required for solvent-free, enzymatic synthesis of sugar esters such as trehalose (TRE) esters, using Novozyme 435 as the enzyme catalyst. The use of lauric acid (La) and ethyl laurate (LaEt) as acyl donors did not affect the activation temperature for the generation of trehalose diesters (TDEs), despite the differences in corresponding by-products (water and ethanol). However, when glucose (GLU) and La were employed as reaction substrates as a comparison, glucose monoester (GME) generation readily occurred at much lower temperatures than with the TRE esters, even without a water collection device. Moreover, when the glass transition temperature (Tg) of the sugar substrates increased, a higher reaction temperature was required. These results suggest that while the activation temperature of the reaction did not correlate with the boiling point of the by-product, it did correlate with the glass transition temperature (Tg) of the trehalose substrates. Thus, our work demonstrates the importance of the physical state of amorphous matrices in determining the optimal reaction temperature of a solvent-free sugar synthesis.

Soft depth-profiling of mixed peptide/lipid samples by means of cluster induced desorption/ionization mass spectrometry-High depth resolution and low matrix effect.

Mixed peptide/lipid samples were analyzed with respect to their chemical composition by means of desorption/ionization induced by neutral SO2 clusters (DINeC) in combination with mass spectrometry (MS). Depth profiles of the mixed films indicated a segregation layer of lipid on top of all samples. The thickness of this layer as obtained by DINeC-MS was in the order of one nanometer what can be seen as an upper limit for the depth resolution of DINeC-MS. The relative amounts of the substance of peptide and lipid derived for the bulk material of mixed samples with different compositions were found to be close to the nominal values indicating a low matrix effect. Throughout the depth profiles, only intact molecular ions [M+H]+ as well as dimers of peptides and lipids were detectable, indicating the soft nature of DINeC even when used for depth profiling of biomolecular samples.

Evaluation of matrix effects on TOF-SIMS data of leu-enkephalin and 1,2-dioleoyl-sn-glycero-3-phosphocholine mixed samples.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is one of the most powerful methods to analyze biomolecules in biological tissues and cells because it provides detailed chemical structure information and chemical images with a high spatial resolution. However, in terms of quantitative analysis, there are issues such as matrix effects that often cause secondary ion intensity changes regardless of the actual concentration in a sample. For instance, the intensity of secondary ions related to peptides is generally suppressed when lipids coexist. Since the evaluation of biomolecules is crucial to understand biological phenomena, it is required to analyze peptides or lipids without matrix effects. Therefore, the mechanism of matrix effects regarding peptides and lipids in TOF-SIMS was investigated in this study. Leu-enkephalin (YGGFL, molecular weight of 555.3 Da) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC, C44H84NO8P, molecular weight 785.6 Da) were employed to prepare model samples. Model samples contain different weight ratios of these two molecules. The intensity of secondary ions related to the peptide or the lipid was compared with control samples containing pure leu-enkephalin or DOPC. As a result, it is indicated that the intensity of DOPC related secondary ions is strongly enhanced by coexisting leu-enkephalin, while the intensity of leu-enkephalin related secondary ions is suppressed by coexisting DOPC especially in a low concentration range of the peptide.

Evaluation of blood adsorption onto dialysis membranes by time-of-flight secondary ion mass spectrometry and near-field infrared microscopy.

Blood adsorption onto the inside surface of hollow fiber dialysis membranes was investigated by means of time-of-flight secondary ion mass spectrometry (TOF-SIMS) and near-field infrared microscopy (NFIR) in order to evaluate the biocompatibility and permeability of dialysis membranes. TOF-SIMS is useful for the imaging of particular molecules with a high spatial resolution of approximately 100 nm. In contrast, infrared spectra provide quantitative information and NFIR enables analysis with a high spatial resolution of less than 1 µm, which is close to the resolution of TOF-SIMS. A comparison was made of one of the most widely used dialysis membranes made of polysulfone (PSf), that has an asymmetric and inhomogeneous pore structure, and a newly developed asymmetric cellulose triacetate (ATA) membrane that also has an asymmetric pore structure, even though the conventional cellulose triacetate membrane has a symmetric and homogeneous pore structure. As a result, it was demonstrated that blood adsorption on the inside surface of the ATA membrane is more reduced than that on the PSf membrane. Graphical abstract Analysis of blood adsorption on inside surface of hollow fiber membrane.

Peptide Fragmentation and Surface Structural Analysis by Means of ToF-SIMS Using Large Cluster Ion Sources.

Peptide or protein structural analysis is crucial for the evaluation of biochips and biodevices, therefore an analytical technique with the ability to detect and identify protein and peptide species directly from surfaces with high lateral resolution is required. In this report, the efficacy of ToF-SIMS to analyze and identify proteins directly from surfaces is evaluated. Although the physics governing the SIMS bombardment process precludes the ability for researchers to detect intact protein or larger peptides of greater than a few thousand mass unit directly, it is possible to obtain information on the partial structures of peptides or proteins using low energy per atom argon cluster ion beams. Large cluster ion beams, such as Ar clusters and C60 ion beams, produce spectra similar to those generated by tandem MS. The SIMS bombardment process also produces peptide fragment ions not detected by conventional MS/MS techniques. In order to clarify appropriate measurement conditions for peptide structural analysis, peptide fragmentation dependency on the energy of a primary ion beam and ToF-SIMS specific fragment ions are evaluated. It was found that the energy range approximately 6 ≤ E/n ≤ 10 eV/atom is most effective for peptide analysis based on peptide fragments and [M + H] ions. We also observed the cleaving of side chain moieties at extremely low-energy E/n ≤ 4 eV/atom.

Evaluation of biomolecular distributions in rat brain tissues by means of ToF-SIMS using a continuous beam of Ar clusters.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides detailed chemical structure information and high spatial resolution images. Therefore, ToF-SIMS is useful for studying biological phenomena such as ischemia. In this study, in order to evaluate cerebral microinfarction, the distribution of biomolecules generated by ischemia was measured with ToF-SIMS. ToF-SIMS data sets were analyzed by means of multivariate analysis for interpreting complex samples containing unknown information and to obtain biomolecular mapping indicated by fragment ions from the target biomolecules. Using conventional ToF-SIMS (primary ion source: Bi cluster ion), it is difficult to detect secondary ions beyond approximately 1000 u. Moreover, the intensity of secondary ions related to biomolecules is not always high enough for imaging because of low concentration even if the masses are lower than 1000 u. However, for the observation of biomolecular distributions in tissues, it is important to detect low amounts of biological molecules from a particular area of tissue. Rat brain tissue samples were measured with ToF-SIMS (J105, Ionoptika, Ltd., Chandlers Ford, UK), using a continuous beam of Ar clusters as a primary ion source. ToF-SIMS with Ar clusters efficiently detects secondary ions related to biomolecules and larger molecules. Molecules detected by ToF-SIMS were examined by analyzing ToF-SIMS data using multivariate analysis. Microspheres (45 µm diameter) were injected into the rat unilateral internal carotid artery (MS rat) to cause cerebral microinfarction. The rat brain was sliced and then measured with ToF-SIMS. The brain samples of a normal rat and the MS rat were examined to find specific secondary ions related to important biomolecules, and then the difference between them was investigated. Finally, specific secondary ions were found around vessels incorporating microspheres in the MS rat. The results suggest that important biomolecules related to cerebral microinfarction can be detected by ToF-SIMS.

Structural analysis of the outermost hair surface using TOF-SIMS with gas cluster ion beam sputtering.

A hair cuticle, which consists of flat overlapping scales that surround the hair fiber, protects inner tissues against external stimuli. The outermost surface of the cuticle is covered with a thin membrane containing proteins and lipids called the epicuticle. In a previous study, the authors conducted a depth profile analysis of a hair cuticle's amino acid composition to characterize its multilayer structure. Time-of-flight secondary ion mass spectrometry with a bismuth primary ion source was used in combination with the C60 sputtering technique for the analysis. It was confirmed that the lipids and cysteine-rich layer exist on the outermost cuticle surface, which is considered to be the epicuticle, though the detailed structure of the epicuticle has not been clarified. In this study, depth profile analysis of the cuticle surface was conducted using the argon gas cluster ion beam (Ar-GCIB) sputtering technique, in order to characterize the structure of the epicuticle. The shallow depth profile of the cuticle surface was investigated using an Ar-GCIB impact energy of 5 keV. Compared to the other amino acid peaks rich in the epicuticle, the decay of 18-methyleicosanic acid (18-MEA) thiolate peak was the fastest. This result suggests that the outermost surface of the hair is rich in 18-MEA. In conclusion, our results indicate that the outermost surfaces of cuticles have a multilayer (lipid and protein layers), which is consistent with the previously proposed structure.

ToF-SIMS analysis of amyloid beta aggregation on different lipid membranes.

Amyloid beta (Aß) peptides are considered to be strongly related to Alzheimer's disease. Aß peptides form a ß-sheet structure on hard lipid membranes and it would aggregate to form amyloid fibrils, which are toxic to cells. However, the aggregation mechanism of Aß is not fully understood. To evaluate the influence of the lipid membrane condition for Aß aggregation, the adsorption forms of Aß (1-40) on mixture membranes of lipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and cholesterol ß-d-glucoside (ß-CG) were investigated by time-of-flight secondary ion mass spectrometry. As a result, Aß adsorbed along the localized DMPC lipid on the mixture lipid membranes, whereas it was adsorbed homogeneously on the pure DMPC and ß-CG membranes. Moreover, amino acid fragments that mainly existed in the n-terminal of Aß (1-40) peptide were strongly detected on the localized DMPC region. These results suggested that the Aß was adsorbed along the localized DMPC lipid with a characteristic orientation. These findings suggest that the hardness of the membrane is very sensitive to coexisting materials and that surface hardness is important for aggregation of Aß.

Peptide fragmentation caused by Ar cluster ions depending on primary ion energy.

RATIONALE: Time-of-flight secondary ion mass spectrometry (TOF-SIMS) with an Ar cluster ion beam as a primary ion source provides useful information in terms of peptide analysis. It is, however, difficult to interpret the spectra. The ToF-SIMS peptide spectra obtained with Ar clusters having different energies have been investigated in order to classify the secondary ions into the peptide fragment ions and those related to contaminants or the substrate. METHODS: Three peptides having different molecular weights from 600 to 1300 u were measured with Ar cluster beams having different energies per atom from 4 to 40 eV/atom. RESULTS: In the spectra normalized to a geometric average of all the spectra, the amino acid fragment ions are distinguished from other secondary ions. In the mass range above 600 u, the peptide fragment ions increase with mass while those not related to the peptide decrease with mass. CONCLUSIONS: Energy-dependence fragmentation helps in understanding the peptide spectra. Specific peptide fragment ions of the larger peptides are likely to be detected under lower energy than energy higher than 10 eV/atom. Although it is difficult to interpret the TOF-SIMS spectra of a peptide obtained with an Ar cluster ion beam, the secondary ions can be classified by comparing those obtained with different energy Ar cluster ion beams.