Label-free sub-micrometer 3D imaging of ciprofloxacin in native-state biofilms with cryo-time-of-flight secondary ion mass spectrometry

High spatial resolution mass spectrometry imaging has been identified as a key technology needed to improve understanding of the chemical components that influence antibiotic resistance within biofilms, which are communities of micro-organisms that grow attached to a surface. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers the unique ability for label-free 3D imaging of organic molecules with sub-micrometer spatial resolution and high sensitivity. Several studies of biofilms have been done with the help of ToF-SIMS, but none of those studies have shown 3D imaging of antibiotics in native-state hydrated biofilms with cell-level resolution. Because ToF-SIMS measurements must be performed in a high-vacuum environment, cryogenic preparation and analysis are necessary to preserve the native biofilm structure and antibiotic spatial distribution during ToF-SIMS measurements. In this study, we have investigated the penetration of the antibiotic ciprofloxacin into Bacillus subtilis biofilms using sub-micrometer resolution 3D imaging cryo-ToF-SIMS. B. subtilis biofilms were exposed to physiologically relevant levels of ciprofloxacin. The treated biofilms were then plunge-frozen in liquid propane and analyzed with ToF-SIMS under cryogenic conditions. Multivariate analysis techniques, including multivariate curve resolution (MCR) and inverse maximum signal factor (iMSF) denoising, were used to aid analysis of the data and facilitate high spatial resolution 3D imaging of the biofilm, providing individually resolved cells and spatially resolved ciprofloxacin intensity at "real world" concentrations.

Ligand-free preparation of polymer/CuInS2 nanocrystal films and the influence of 1,3-benzenedithiol on their photovoltaic performance and charge recombination properties

Bulk heterojunction solar cells based on conjugated polymer donors and fullerene-derivative acceptors have received much attention in the last decade. Alternative acceptors like organic non-fullerene acceptors or inorganic nanocrystals have been investigated to a lesser extent; however, they also show great potential. In this study, one focus is set on the investigation of the in situ growth of copper indium sulfide nanocrystals in a conjugated polymer matrix. This preparation method allows the fabrication of a hybrid active layer without long-chain ligands, which could hinder charge separation and transport. In contrast, surfactants for the passivation of the nanocrystal surface are missing. To tackle this problem, we modified the absorber layer with 1,3-benzenedithiol and investigated the influence on charge transfer and solar cell performance. Using ToF-SIMS measurements, we could show that 1,3-benzenedithiol is successfully incorporated and homogeneously distributed in the absorber layer, which significantly increases the power conversion efficiency of the corresponding solar cells. This can be correlated to an improved charge transfer between the nanocrystals and the conjugated polymer as revealed by transient absorption spectroscopy as well as prolonged carrier lifetimes as disclosed by transient photovoltage measurements.

Imaging mass spectrometry: Instrumentation, applications, and combination with other visualization techniques

Imaging Mass Spectrometry (IMS) is strengthening its position as a valuable analytical tool. It has unique ability to identify structures and to unravel molecular changes that occur in the precisely defined part of the sample. These unique features open new possibilities in the field of various aspects of biological research. In this review we briefly discuss the main imaging mass spectrometry techniques, as well as the nature of biological samples and molecules, which might be analyzed by such methodology. Moreover, a novel approach, where different analytical techniques might be combined with the results of IMS study, is emphasized and discussed. With such a fast development of IMS and related methods, we can foresee the promising future of this technique.

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

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.

Multivariate analysis of ToF-SIMS data from multicomponent systems: the why, when, and how

The use of multivariate analysis (MVA) methods in the processing of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data has become increasingly more common. MVA presents a powerful set of tools to aid the user in processing data from complex, multicomponent surfaces such as biological materials and biosensors. When properly used, MVA can help the user identify the major sources of differences within a sample or between samples, determine where certain compounds exist on a sample, or verify the presence of compounds that have been engineered into the surface. Of all the MVA methods, principal component analysis (PCA) is the most commonly used and forms an excellent starting point for the application of many of the other methods employed to process ToF-SIMS data. Herein we discuss the application of PCA and other MVA methods to multicomponent ToF-SIMS data and provide guidelines on their application and use.

Automated correlation and classification of secondary ion mass spectrometry images using a k-means cluster method

We present a novel method for correlating and classifying ion-specific time-of-flight secondary ion mass spectrometry (ToF-SIMS) images within a multispectral dataset by grouping images with similar pixel intensity distributions. Binary centroid images are created by employing a k-means-based custom algorithm. Centroid images are compared to grayscale SIMS images using a newly developed correlation method that assigns the SIMS images to classes that have similar spatial (rather than spectral) patterns. Image features of both large and small spatial extent are identified without the need for image pre-processing, such as normalization or fixed-range mass-binning. A subsequent classification step tracks the class assignment of SIMS images over multiple iterations of increasing n classes per iteration, providing information about groups of images that have similar chemistry. Details are discussed while presenting data acquired with ToF-SIMS on a model sample of laser-printed inks. This approach can lead to the identification of distinct ion-specific chemistries for mass spectral imaging by ToF-SIMS, as well as matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI).