Quantitative analysis of binary adsorbed protein films by time of flight secondary ion mass spectrometry

Wagner, Matt; Shen, Mingchao; Horbett, Thomas A.; Castner, David G.

doi:10.1002/jbm.a.10263

Cited by 60 publications

(51 citation statements)

References 30 publications

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“…A model is created capturing the covariance of the X and Y matrices that attempts to maximize the ability to predict the dependent variables in Y [31] in as few LVs as possible. [17] PLS model will tell us which of the independent variables X are the most important ones [21] by relating those that are either positively correlated or negatively correlated with the dependent variable Y. [14] By plotting the predicted Y against the experimental Y, the degree of accuracy of the model can be determined in the form of the linear regression factor R 2 .…”

Section: Multivariate Analysismentioning

confidence: 99%

“…[16] ) By analyzing the regression vector value, it has been possible to elucidate the surface chemistry (the ToF-SIMS fragments) that is implicated in a particular surface property. [14] The composition and structure of binary [17,18] and ternary [18] adsorbed protein films have been investigated by a combination of ToF-SIMS and PLS to provide information complementary to that from 125 I-radiolabeling [17,18] and XPS. [18] The use of PLS has demonstrated the possibility to quantify the amount of albumin adsorbed on fluoropolymers.…”

Section: Introductionmentioning

confidence: 99%

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Surface composition of insulin and albumin adsorbed on polymer substrates as revealed by multivariate analysis of ToF‐SIMS data

Henry

Bertrand

2008

Surface & Interface Analysis

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The structure and the composition of adsorbed protein films containing human serum albumin (HSA) and/or human insulin were studied with time-of-flight secondary ion mass spectrometry (ToF-SIMS). The proteins were adsorbed either on native polycarbonate (PC) membranes or on PC membranes treated with poly(N-vinylpyrrolidone) (PVP/PC). These membranes have been developed for their use as encapsulation membranes in a bioartificial pancreas. Principal component analysis (PCA) of the ToF-SIMS results allowed the determination of the main factors affecting the surface structure and composition of the protein layer. The substrate surface properties were found to be the most influencing factor. The amount and type of the adsorbed protein also influence the adsorbed protein layer surface composition and structure. A partial least squares (PLS) regression model allowed confirmation of the correlation between the adsorbed amount of protein and the surface composition of the protein layer.

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Section: Multivariate Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface composition of insulin and albumin adsorbed on polymer substrates as revealed by multivariate analysis of ToF‐SIMS data

Henry

Bertrand

2008

Surface & Interface Analysis

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“…ToF-SIMS analysis of proteins has been shown to generate mainly fragments of the 20 component amino acids, and not large fragments or even small peptides. It has been shown that the relative intensity of these amino acid fragments encodes information about the protein's composition, [4][5][6][7][8] conformation [9][10][11][12][13] and orientation. [14][15][16][17] However, it would be challenging to determine the identity of individual proteins in a complex mixture such as is found in cells or tissues using ToF-SIMS.…”

Section: Introductionmentioning

confidence: 99%

“…1,[3][4][5]8,[18][19][20][21][23][24][25][26] For this work, ToF-SIMS analysts have used a variety of methods to process and understand their data. These include a combination of traditional manual analysis and the application of multivariate analysis (MVA) methods.…”

Section: Introductionmentioning

confidence: 99%

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

Graham

Castner

2013

Mass Spectrometry

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

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“…[23] The second publication described a similar multivariant model to investigate the relation between endothelial cell growth and surface properties of plasma-deposited films. [24,25] In order to be of practical utility, the previously reported parallel synthesis of polymer libraries needs to be integrated with efficient screening assays, as well as appropriate computational methods for data handling and modeling. While it would be premature to speak in terms of ''high throughput'' assays at this point, the development of efficient rapidscreening assays for biologically relevant material properties that can facilitate the extensive testing of dozens or hundreds of polymers represents an important research challenge.…”

Section: Introductionmentioning

confidence: 99%

Integration of Combinatorial Synthesis, Rapid Screening, and Computational Modeling in Biomaterials Development

Smith

Seyda

Weber

et al. 2004

Macromol. Rapid Commun.

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Summary: The advent of high‐throughput combinatorial synthesis techniques in drug discovery has stimulated efforts to apply these techniques to the discovery of biomaterials. To be of practical utility, combinatorial approaches to biomaterials design require (i) the availability of parallel synthesis techniques to generate libraries of polymers, (ii) efficient assays for the rapid characterization of biorelevant material properties, and (iii) computational methods to efficiently model different biological responses in the presence of polymers. Here we report the integration of these methodologies and illustrate the potential of this approach to accelerate the development of new biomaterials. The parallel synthesis of a library of 112 biodegradable polyarylates has been reported previously. This library was used to develop efficient screening techniques to determine biorelevant polymer properties (fibrinogen adsorption, gene expression in macrophages, growth of fetal rat lung fibroblasts (RLFs)). A Surrogate (semiempirical) Model was developed (i) to determine molecular‐scale polymer properties that correlate to various biological responses, and (ii) to predict fibrinogen adsorption and RLF growth on polymeric surfaces. For 38 out of 45 polymers, the model predicted the amount of fibrinogen adsorbed correctly within the error of the experimental measurements. The growth of rat lung fibroblasts was correctly predicted by the model for 41 out of 48 polymers. The correlation factor between the model's predicted values and the experimentally determined data was 0.54 ± 0.09 and 0.69 ± 0.12 for fibrinogen adsorption and RLF growth, respectively. The results presented here demonstrate the utility of combinatorial and computational approaches for the rational design of polymers for biomedical applications.Design of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”).magnified imageDesign of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”).

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Quantitative analysis of binary adsorbed protein films by time of flight secondary ion mass spectrometry

Cited by 60 publications

References 30 publications

Surface composition of insulin and albumin adsorbed on polymer substrates as revealed by multivariate analysis of ToF‐SIMS data

Surface composition of insulin and albumin adsorbed on polymer substrates as revealed by multivariate analysis of ToF‐SIMS data

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

Integration of Combinatorial Synthesis, Rapid Screening, and Computational Modeling in Biomaterials Development

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