Characterization of Adsorbed Protein Films by Time-of-Flight Secondary Ion Mass Spectrometry with Principal Component Analysis

Seredych, Mykola; Castner, David G.

doi:10.1021/la001209t

Cited by 407 publications

(475 citation statements)

References 74 publications

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“…36,37 By applying PCA, a ToF-SIMS data set can be reduced to two cross-product matrices: scores and loadings. Scores plots can be used to visualize the quantitative relationship among samples.…”

Section: Principal Component Analysismentioning

confidence: 99%

XPS, TOF-SIMS, NEXAFS, and SPR Characterization of Nitrilotriacetic Acid-Terminated Self-Assembled Monolayers for Controllable Immobilization of Proteins

2008

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For immobilization of proteins onto surfaces in a specific and controlled manner it is important to start with a well-defined surface that contains specific binding sites surrounded by a nonfouling background. For immobilizing histidine-tagged (his-tagged) proteins, surfaces containing nitrilotriacetic acid (NTA) headgroups and oligo(ethylene glycol) (OEG) moieties are a widely used model system. The surface composition, structure and reactivity of mixed NTA/OEG selfassembled monolayers (SAMs) on Au substrates were characterized in detail using x-ray photoelectron spectroscopy (XPS), near-edge x-ray absorption fine structure spectroscopy (NEXAFS), time-of-flight secondary ion mass spectrometry (ToF-SIMS) and surface plasmon resonance (SPR) biosensoring. XPS results for sequential adsorption of NTA thiols followed by OEG thiols showed that OEG molecules were incorporated into an incompletely formed NTA monolayer until a complete mixed SAM was formed. The surface concentration of NTA headgroups was estimated to be 0.9-1.3 molecule/nm 2 in the mixed NTA/OEG monolayers, compared to 1.9 molecule/nm 2 in pure NTA monolayers. Angle-dependent XPS indicated NTA headgroups were slightly reoriented toward an upright position after OEG incorporation and polarization-dependent NEXAFS results indicated increased ordering of the alkane chains of the molecules. Nitrogen-containing and OEG-related secondary ion fragments from the ToF-SIMS experiments confirmed the presence of NTA headgroups and OEG moieties in the monolayers. A multivariate peak intensity ratio was developed for estimating the relative NTA concentration in the outermost (10 Ǻ) of the monolayers. SPR measurements of a his-tagged, humanized antilysozyme variable fragment (HuLys Fv) immobilized onto Ni(II)-treated mixed NTA/OEG and pure NTA monolayers demonstrated the reversible, site-specific immobilization of his-tagged HuLys Fv (108 -205 ng/cm 2 ) with dissociation rates (k off ) between 1.0x10 −4 and 2.1x10 −5 s −1 , both depending on the NTA surface concentration and orientation. The monolayers without Ni(II) treatment exhibited low nonspecific adsorption of his-tagged HuLys Fv ( < 2ng/cm 2 ).

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Section: Principal Component Analysismentioning

confidence: 99%

XPS, TOF-SIMS, NEXAFS, and SPR Characterization of Nitrilotriacetic Acid-Terminated Self-Assembled Monolayers for Controllable Immobilization of Proteins

2008

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“…Wagner and Castner have used PCA to successfully cluster ToF-SIMS mass spectra generated from samples of single proteins. 4 A particular challenge for mass spectrometry techniques is distinguishing and characterizing isomers.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Monosaccharide Stereo- and Structural Isomers with TOF-SIMS and Multivariate Statistical Analysis

et al. 2006

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ABSTRACT. 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 which 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.1 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.

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“…PCA has therefore been an important method of analysing spectra and images in surface analysis for at least the last 25 years. There exist excellent examples [2][3][4][5][6][7][8] and analytical reviews [9][10][11] of its use in the literature, applied to a range of problems.…”

Section: Introductionmentioning

confidence: 99%

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

Cumpson

Fletcher

Sano

et al. 2016

Surface & Interface Analysis

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

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Characterization of Adsorbed Protein Films by Time-of-Flight Secondary Ion Mass Spectrometry with Principal Component Analysis

Cited by 407 publications

References 74 publications

XPS, TOF-SIMS, NEXAFS, and SPR Characterization of Nitrilotriacetic Acid-Terminated Self-Assembled Monolayers for Controllable Immobilization of Proteins

XPS, TOF-SIMS, NEXAFS, and SPR Characterization of Nitrilotriacetic Acid-Terminated Self-Assembled Monolayers for Controllable Immobilization of Proteins

Distinguishing Monosaccharide Stereo- and Structural Isomers with TOF-SIMS and Multivariate Statistical Analysis

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

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