3D Molecular ToF-SIMS Imaging of Artificial Lipid Membranes Using a Discriminant Analysis-Based Algorithm
Artificial lipid membranes play a growing role in technical applications such as biosensors in pharmacological research and as model systems in the investigation of biological lipid films. In the standard procedure for displaying the distribution of membrane components, fluorescence microscopy, the...
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Veröffentlicht in: | Langmuir 2018-07, Vol.34 (30), p.8750-8757 |
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Sprache: | eng |
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Zusammenfassung: | Artificial lipid membranes play a growing role in technical applications such as biosensors in pharmacological research and as model systems in the investigation of biological lipid films. In the standard procedure for displaying the distribution of membrane components, fluorescence microscopy, the fluorophores used can influence the distribution of the components and usually not all substances can be displayed at the same time. The discriminant analysis-based algorithm used in combination with scanning time-of-flight secondary ion mass spectrometry (ToF-SIMS) enables marker-free, quantitative, simultaneous recording of all membrane components. These data are used for reconstruction of distribution patterns. In the model system used for this survey, a tear fluid lipid layer, the distribution patterns of all lipids correlate well in calculated ToF-SIMS images and epi-fluorescence microscopic images. All epi-fluorescence microscopically viewable structures are visible when using both positive and negative secondary ions and can be reproduced with high lateral resolution in the submicrometer range despite the very low signal intensity and a very low signal-to-noise ratio. In addition, three-dimensional images can be obtained with a subnanometer depth resolution. Furthermore, structures and the distribution of substances that cannot be made visible by epi-fluorescence microscopy can be displayed. This enables new insights that cannot be gained by epi-fluorescence microscopy alone. |
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ISSN: | 0743-7463 1520-5827 |
DOI: | 10.1021/acs.langmuir.8b01253 |