Characterization of poly(L-lysine)-graft-poly(ethylene glycol) assembled monolayers on niobium pentoxide substrates using time-of-flight secondary ion mass spectrometry and multivariate analysis

Control of protein adsorption onto solid surfaces is a critical area of biomaterials and biosensors research. Application of high performance surface analysis techniques to these problems can improve the rational design and understanding of coatings that control protein adsorption. We have used static time-of-flight secondary ion mass spectrometry (TOF-SIMS) to investigate several poly(L-lysine)-graft-poly(ethylene glycol) (PLL-g-PEG) adlayers adsorbed electrostatically onto negatively charged niobium pentoxide (Nb(2)O(5)) substrates. By varying the PEG graft ratio (i.e., the number of lysine monomers per grafted PEG chain) and the molecular weights of the PLL and PEG polymers, the amount of protein adsorption can be tailored between 1 and 300 ng/cm(2). Detailed multivariate analysis using principal component analysis (PCA) of the positive and negative ion TOF-SIMS spectra showed changes in the outermost surface of the polymer films that were related to the density and molecular weight of the PEG chains on the surface. However, no significant differences were noted due to PLL molecular weight, despite observed differences in the serum adsorption characteristics for adlayers of PLL-g-PEG polymers with different PLL molecular weights. From the PCA results, multivariate peak intensity ratios were developed that correlated with the thickness of the adlayer and the enrichment of the PEG chains and the methoxy terminus of the PEG chains at the outermost surface of the adlayer. Furthermore, partial least squares regression was used to correlate the TOF-SIMS spectra with the amount of protein adsorption, resulting in a predictive model for determining the amount of protein adsorption on the basis of the TOF-SIMS spectra. The accuracy of the prediction of the amount of serum adsorption depended on the molecular weight of the PLL and PEG polymers and the PEG graft ratio. The combination of multivariate analysis and static TOF-SIMS provides detailed information on the surface chemistry and insight into the mechanism for protein resistance of the coatings.     

Multivariate statistical analysis of concatenated time-of-flight secondary ion mass spectrometry spectral images. Complete description of the sample with one analysis

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS data set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately.     

TOF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettability

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used in a high-throughput fashion to obtain mass spectra from the surfaces of 576 novel acrylate-based polymers, synthesized using a combinatorial approach and in a micropatterned format. To identify variations in surface chemistry within the library, principal component analysis (PCA) was used. PCA clearly identified surface chemical commonality and differences within the library. The TOF-SIMS spectra were also used to determine the relationship between water contact angle (WCA) and the surface chemistry of the polymer library using partial least-squares regression (PLS). A good correlation between the TOF-SIMS data from the novel polymers and water contact angle was obtained. Examination of the PLS regression vector allowed surface moieties that correlate with high and low WCA to be identified. This in turn provided an insight into molecular structures that significantly influence wettability. This study demonstrates that multivariate analysis can be successfully applied to TOF-SIMS data from a large library of samples and highlights the potential of these techniques for building complex surface property/chemistry models.     

Application of time-of-flight-secondary ion mass spectrometry for the detection of enzyme activity on solid wood substrates

Time-of-flight-secondary ion mass spectrometry (TOF-SIMS) is a surface analysis technique that is herein demonstrated to be a viable tool for the detection of enzyme activity on solid substrates. Proof-of-principle experiments are presented that utilize commercial cellulase and laccase enzymes, which are known to modify major polymeric components of wood (i.e., cellulose and lignin, respectively). Enzyme activity is assessed through principle component analysis (PCA) as well as through peak ratios intended to measure selective enzymatic wood degradation. Spectral reproducibility of the complex wood substrates is found to be within 5% relative standard deviation (RSD), allowing for relative quantification of changes in wood composition. Procedures are also presented to identify and avoid the influence of mass interferences from protein adsorption by the enzyme solutions. The activity of a cellulase cocktail is clearly evident through the TOF-SIMS spectra and is supported by high-pressure liquid chromatography (HPLC) measurements of sugar release and by complementary X-ray photoelectron spectroscopy (XPS) measurements of the wood surfaces. Laccase activity, which is mediated through small organic molecules, can be detected in the TOF-SIMS spectra through a decrease in G and S lignin peaks. This work has positive implications for the development of qualitative, high-throughput screening assays for enzyme activity on industrially relevant, lignocellulosic substrates.     

Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition

Multivariate analysis has become increasingly common in the analysis of multidimensional spectral data. We previously showed that the multivariate analysis technique principal component analysis (PCA) is an excellent method for interpreting the static time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of adsorbed protein films. PCA is an unsupervised pattern recognition technique that loses resolution between spectra of different proteins as more proteins are added to the data set due to large within-group variation. The supervised pattern recognition techniques discriminant principal component analysis (DPCA) and linear discriminant analysis (LDA), which aim to control within-group variation while maximizing between-group separation to enhance discrimination between groups, were compared with PCA using data sets of TOF-SIMS spectra of proteins adsorbed onto mica and PTFE substrates. DPCA and LDA quantitatively improved discrimination between groups and provided different information about the data than PCA. LDA was able to classify unknown samples with a misclassification rate lower than PCA or DPCA. Both unsupervised and supervised pattern recognition techniques are useful for the interpretation and classification of static TOF-SIMS spectra of adsorbed protein films.     

Discriminating and imaging different phosphatidylcholine species within phase-separated model membranes by principal component analysis of TOF-secondary ion mass spectrometry images

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) enables chemically imaging the distributions of various lipid species in model membranes. However, discriminating the TOF-SIMS data of structurally similar lipids is very difficult because the high intensity, low mass fragment ions needed to achieve submicrometer lateral resolution are common to multiple lipid species. Here, we demonstrate that principal component analysis (PCA) can discriminate the TOF-SIMS spectra of four unlabeled saturated phosphatidylcholine species, 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), and 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC) according to variations in the intensities of their low mass fragment ions (m/z ≤ 200). PCA of TOF-SIMS images of phase-separated DSPC/DLPC and DPPC/DLPC membranes enabled visualizing the distributions of each phosphatidylcholine species with higher contrast and specificity than that of individual TOF-SIMS ion images. Comparison of the principal component (PC) scores images to atomic force microscopy (AFM) images acquired at the same membrane location before TOF-SIMS analysis confirmed that the PC scores images reveal the phase-separated membrane domains. The lipid composition within these domains was identified by projection of their TOF-SIMS spectra onto PC models developed using pure lipid standards. This approach may enable the identification and chemical imaging of structurally similar lipid species within more complex membranes.     

Application of multivariate curve resolution for analysis of FT-IR microspectroscopic images of in situ plant tissue

The chemometric techniques of multivariate curve resolution (MCR) are aimed at extracting the spectra and concentrations of individual components present in mixtures using a minimum set of initial assumptions. We present results from the application of alternating least squares (ALS) based MCR to the analysis of hyperspectral images of in situ biological material. The spectra of individual pure components were mathematically extracted and then identified by searching the spectra against a commercial library. No prior information about the chemical composition of the material was used in the data analysis. The spectra recovered by ALS-MCR analysis of an FT-IR microspectroscopic image of an 8-micron-cornkernel section matched very well the spectra of the corn storage protein, zein, and starch. Through the application of MCR, we were able to show the presence of a second spectrally different protein, which could not be easily seen using univariate analysis. These results demonstrate the value of multivariate curve resolution techniques for the analysis of biological tissue. The value of principal components analysis (PCA) for hyperspectral image analysis is also discussed.     

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.     

Single Nucleobase Identification Using Biophysical Signatures from Nanoelectronic Quantum Tunneling

Nanoelectronic DNA sequencing can provide an important alternative to sequencing‐by‐synthesis by reducing sample preparation time, cost, and complexity as a high‐throughput next‐generation technique with accurate single‐molecule identification. However, sample noise and signature overlap continue to prevent high‐resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest‐unoccupied and highest‐occupied molecular orbitals (LUMO and HOMO). Here, nine biophysical parameters are developed to better characterize molecular orbitals of individual nucleobases, intended for single‐molecule DNA sequencing using quantum tunneling of charges. For this analysis, theoretical models for quantum tunneling are combined with transition voltage spectroscopy to obtain measurable parameters unique to the molecule within an electronic junction. Scanning tunneling spectroscopy is then used to measure these nine biophysical parameters for DNA nucleotides, and a modified machine learning algorithm identified nucleobases. The new parameters significantly improve base calling over merely using LUMO and HOMO frontier orbital energies. Furthermore, high accuracies for identifying DNA nucleobases were observed at different pH conditions. These results have significant implications for developing a robust and accurate high‐throughput nanoelectronic DNA sequencing technique.     

Quantitative chemical derivatization technique in time-of-flight secondary ion mass spectrometry for surface amine groups on plasma-polymerized ethylenediamine film

A chemical derivatization technique in time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been developed to quantify the surface density of amine groups of plasma-polymerized ethylenediamine thin film deposited on a glass surface by inductively coupled plasma chemical vapor deposition. Chemical tags of 4-nitrobenzaldehyde or pentafluorobenzaldehyde were hybridized with the surface amine groups and were detected in TOF-SIMS spectra as characteristic molecular secondary ions. The surface amine density was controlled in a reproducible manner as a function of deposition plasma power and was also quantified using UV-visible spectroscopy. A good linear correlation was observed between the results of TOF-SIMS and UV-visible measurements as a function of plasma power. This shows that the chemical derivatization technique in TOF-SIMS analysis would be useful in quantifying the surface density of specific functional groups that exist on the organic surface.     

Surface characterization of hydroxyapatite and related calcium phosphates by XPS and TOF-SIMS

The surfaces of six biologically interesting calcium phosphate (CaP) phases (hydroxyapatite, dibasic calcium phosphate dihydrate, dibasic calcium phosphate, monobasic calcium phosphate, beta-tribasic calcium phosphate, octacalcium phosphate) have been examined by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS). The intensity of an O(1s) shake-up satellite correlates with the phosphate oxygen content. Together with the Ca/P and O/Ca XPS peak ratios, this feature helps provide identification of the CaP phase(s) present in the surface of unknown samples and establish their mole fractions, as proven with a bone sample. Contributions from carbonate impurities can be quantified using its C(1s) peak at 279.9 eV and subtracted from the O(1s) line shape to aid identification. Principal component analysis (PCA) has been applied successfully to analyze TOF-SIMS spectra of these six CaP phases. Multivariate analysis can help differentiate these CaP phases using the first two PCs, which are dominated by the relative intensities of only a few key ions: PO3-, O-, Ca+, CaOH+, PO2-, and OH-.     

Enhanced TOF-SIMS imaging of a micropatterned protein by stable isotope protein labeling

Patterning of biomolecules on surfaces is an increasingly important technological goal. Because the fabrication of biomolecule arrays often involves stepwise, spatially resolved derivatization of surfaces, spectroscopic imaging of these arrays is important in their fabrication and optimization. Although imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a powerful method for spatially resolved surface analysis, TOF-SIMS images of micropatterned proteins on organic substrates can be difficult to acquire, because of the lack of high intensity, protein-specific molecular ions that are essential for imaging under static conditions. In contrast, low-mass ions are of suitable intensity for imaging, but can originate from different chemical species on the surface. A potential solution to this problem is to utilize stable isotope labeled proteins, an approach that has heretofore not been explored in TOF-SIMS imaging of micropatterned proteins and peptides. To investigate the feasibility of stable isotope enhanced TOF-SIMS imaging of proteins, we synthesized 15N-labeled streptavidin by labeling of the protein during expression from a recombinant gene. The spatial distribution of streptavidin bound to biotin micropatterns, fabricated on a polymer and on a self-assembled monolayer on gold, was imaged by TOF-SIMS. Imaging of high-intensity, low-m/z secondary ions (e.g., C15N-) unique to streptavidin enabled unambiguous spatial mapping of the micropatterned protein with a lateral resolution of a few micrometers. TOF-SIMS imaging of micropatterned 15N-labeled streptavidin also illustrated the exquisite sensitivity of TOF-SIMS to low fractional coverage of protein (5 A effective thickness) in the background regions of the protein micropattern.     

Sulfonation of poly(N-vinylcarbazole) studied by combined time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy

A series of sulfonated poly(N-vinylcarbazole) (PVK) samples have been systematically studied by time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS). Negative TOF-SIMS results provided unambiguous evidence that sulfonate groups are chemically attached to the carbazole moiety of PVK. The positive SIMS spectrum of PVK was, however, little affected by the sulfonation reaction. The degree of sulfonation was quantitatively determined by XPS. Therefore, the combination of TOF-SIMS and XPS is useful to follow the sulfonation reaction, both qualitatively and quantitatively. The SIMS intensities of some characteristic fragments are linearly related to the degree of sulfonation, suggesting that quantitative analysis is possible from TOF-SIMS data.     

TOFSIMS-P: a web-based platform for analysis of large-scale TOF-SIMS data

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87?138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.     

Mass spectrometric imaging of lipids in brain tissue

The spatial distributions of various specific lipids in freeze-dried mouse brain sections were monitored using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Mouse brain sections were prepared by cryosectioning, rinsing in 0.15 M NH3HCOO, and freeze-drying, after which the samples were analyzed directly by TOF-SIMS, using Au3+ ions as primary ions. Positive and negative TOF-SIMS spectra of the tissue surface contained peaks from quasimolecular ions of a variety of specific lipids, including cholesterol, sulfatides, phosphatidylinositols, and phosphatidylcholines. Images showing the spatial signal intensity distributions of specific ions were recorded across analysis areas ranging from 100 x 100 microm(2) to 9 x 9 mm(2). The results demonstrate a highly complementary localization of cholesterol and phosphatidylcholine over dimensions from millimeter to micrometer range. Characteristic spatial distributions of several other lipids, including sulfatides and phosphatidylinositols, were observed. Principal component analysis was used to localize regions of the sample surface that show common spectral features. Spectra from different such regions showed large variations in lipid ion signals, indicating large variations in the lipid composition in different regions.     

Synthesis and characterization of ferrisilicate zeolite of pentasil group

Synthesis of ‘ferrisilicate’ a crystalline zeolite containing iron and silicon in the zeolite lattice positions and possessing remarkable molecular sieving properties and catalytic activity has been reported. The ferrisilicate was characterized by XRD, i.r., t.g./d.t.a., SEM, XPS, EPR, chemical analysis and water and hydrocarbon sorption measurement. The infrared spectra of ferrisilicate zeolite, in the mid infrared region indicated that Fe³⁺ ions were present in the zeolite framework. The presence of a signal at g = 4.3 in the EPR spectra was assigned to Fe³⁺ ions isomorphously substituted in the tetrahedral positions. The fact that Fe³⁺ ions in ferrisilicate zeolite were in trivalent state and indeed situated at the lattice sites was confirmed by XPS study. The catalytic activity of the ferrisilicate zeolite for xylene isomerization was lower than for its aluminium analogue, probably due to low concentration as well as the strength of strong acid sites.     

Disentangling dynamic changes of multiple cellular components during the yeast cell cycle by in vivo multivariate Raman imaging

Cellular processes are intrinsically complex and dynamic, in which a myriad of cellular components including nucleic acids, proteins, membranes, and organelles are involved and undergo spatiotemporal changes. Label-free Raman imaging has proven powerful for studying such dynamic behaviors in vivo and at the molecular level. To construct Raman images, univariate data analysis has been commonly employed, but it cannot be free from uncertainties due to severely overlapped spectral information. Here, we demonstrate multivariate curve resolution analysis for time-lapse Raman imaging of a single dividing yeast cell. A four-dimensional (spectral variable, spatial positions in the two-dimensional image plane, and time sequence) Raman data "hypercube" is unfolded to a two-way array and then analyzed globally using multivariate curve resolution. The multivariate Raman imaging thus accomplished successfully disentangles dynamic changes of both concentrations and distributions of major cellular components (lipids, proteins, and polysaccharides) during the cell cycle of the yeast cell. The results show a drastic decrease in the amount of lipids by ~50% after cell division and uncover a protein-associated component that has not been detected with previous univariate approaches.     

Matrix-assisted laser desorption/ionization mass spectrometry of discrete mass poly(butylene glutarate) oligomers

The mass dependency of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) response has been studied using equimolar mixtures of synthetic discrete mass poly(butylene glutarate) (PBG) oligomers of known structure having degrees of polymerization of 8, 16, 32, and 64. Mass discrimination observed was attributed to choice of matrix and detector saturation caused by higher laser intensity and inclusion of matrix ions in the MALDI spectra. Optimization of sample preparation and instrumental parameters provided uniform response over the mass ranged spanned by these four oligomers. The oligomer mixture was shown to serve as a model of more complex polymer distributions in the mass range 780-6000 Da, and application of the discrete mass oligomers as internal and calibration standards was demonstrated. Inclusion of PBG discrete mass oligomers as an internal standard in a quasi-equimolar mixture with polydispersed poly(butylene adipate) (PBA) indicated that some diminution of response occurred during the analysis of this mixture of materials. Reasons for differences in the corrected molecular weight averages of the polydispersed PBA obtained from measurements using MALDI and GPC were studied using individual discrete mass oligomers as calibration standards for GPC. The data indicated that differences in hydrodynamic volumes of PBG oligomers and PEG standards at similar masses resulted in an overestimation by GPC of the molecular weight averages of the PBA distribution.     

Identifying single bases in a DNA oligomer with electron tunnelling

It has been proposed that single molecules of DNA could be sequenced by measuring the physical properties of the bases as they pass through a nanopore. Theoretical calculations suggest that electron tunnelling can identify bases in single-stranded DNA without enzymatic processing, and it was recently experimentally shown that tunnelling can sense individual nucleotides and nucleosides. Here, we report that tunnelling electrodes functionalized with recognition reagents can identify a single base flanked by other bases in short DNA oligomers. The residence time of a single base in a recognition junction is on the order of a second, but pulling the DNA through the junction with a force of tens of piconewtons would yield reading speeds of tens of bases per second.     

Distinguishing monosaccharide stereo- and structural isomers with TOF-SIMS and multivariate statistical analysis

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is utilized to examine the mass spectra and fragmentation patterns of seven isomeric monosaccharides. Multivariate statistical analysis techniques, including principal component analysis (PCA), allow discrimination of the extremely similar mass spectra of stereoisomers. Furthermore, PCA identifies those fragment peaks that vary significantly between spectra. Heavy isotope studies confirm that these peaks are indeed sugar fragments, allow identification of the fragments, and provide clues to the fragmentation pathways. Excellent reproducibility is shown by multiple experiments performed over time and on separate samples. This study demonstrates the combined selectivity and discrimination power of TOF-SIMS and PCA and suggests new applications of the technique including differentiation of subtle chemical changes in biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.