Evaluation of chemical disposition in skin by stimulated Raman scattering microscopy.

Tracking drug disposition in the skin in a non-destructive and at least semi-quantitative fashion is a relevant objective for the assessment of local (cutaneous) bioavailability. Confocal Raman spectroscopy has been shown potentially useful in this regard and, importantly, recent advances have enabled the presence of applied chemicals in the viable epidermis below the stratum corneum (SC) to be determined without ambiguity and having addressed the challenges of (a) background signals from endogenous species and noise and (b) signal attenuation due to absorption and scattering. This study aimed to confirm these observations using a different vibrational spectroscopy approach - specifically, stimulated Raman scattering (SRS) microscopy - and the more conventional in vitro skin penetration test (IVPT). SRS is a nonlinear optical imaging technique which enables more precise location of the skin surface and enhanced skin depth resolution relative to confocal Raman microscopy. The method can also probe larger areas of the sample under investigation and identify the localization of the permeating chemical in specific structural components of the skin. Here, SRS was shown capable of tracking the uptake and distribution of 4-cyanophenol (CP), the same model compound used in the recent confocal Raman investigation, at depths beyond the SC following skin treatment with different vehicles and for different times. The SRS results correlated well with those from the confocal Raman experiments, and both were consistent with independent IVPT measurements. Acquired images clearly delineated CP preference for the intercellular lipid layers of the SC relative to the corneocytes. The stage is now set to apply these and other correlative techniques to examine commercial drug products.

Visualisation of drug distribution in skin using correlative optical spectroscopy and mass spectrometry imaging.

A correlative methodology for label-free chemical imaging of soft tissue has been developed, combining non-linear optical spectroscopies and mass spectrometry to achieve sub-micron spatial resolution and critically improved drug detection sensitivity. The approach was applied to visualise the kinetics of drug reservoir formation within human skin following in vitro topical treatment with a commercial diclofenac gel. Non-destructive optical spectroscopic techniques, namely stimulated Raman scattering, second harmonic generation and two photon fluorescence microscopies, were used to provide chemical and structural contrast. The same tissue sections were subsequently analysed by secondary ion mass spectrometry, which offered higher sensitivity for diclofenac detection throughout the epidermis and dermis. A method was developed to combine the optical and mass spectrometric datasets using image registration techniques. The label-free, high-resolution visualisation of tissue structure coupled with sensitive chemical detection offers a powerful method for drug biodistribution studies in the skin that impact directly on topical pharmaceutical product development.

Quantitative and Qualitative Analyses of Mass Spectra of OEL Materials by Artificial Neural Network and Interface Evaluation: Results from a VAMAS Interlaboratory Study.

Quantitative analysis of binary mixtures of tris(2-phenylpyridinato)iridium(III) (Ir(ppy)3) and tris(8-hydroxyquinolinato)aluminum (Alq3) by using an artificial neural network (ANN) system to mass spectra was attempted based on the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study (TW2 A31) to evaluate matrix-effect correction and to investigate interface determination. Monolayers of binary mixtures having different Ir(ppy)3 ratios (0, 0.25, 0.50, 0.75, and 1.00), and the multilayers containing these mixtures and pure samples were measured using time-of-flight secondary ion mass spectrometry (ToF-SIMS) with different primary ion beams, OrbiSIMS (SIMS with both Orbitrap and ToF mass spectrometers), laser desorption ionization (LDI), desorption/ionization induced by neutral clusters (DINeC), and X-ray photoelectron spectroscopy (XPS). The mass spectra were analyzed using a simple ANN with one hidden layer. The Ir(ppy)3 ratios of the unknown samples and the interfaces of the multilayers were predicted using the simple ANN system, even though the mass spectra of binary mixtures exhibited matrix effects. The Ir(ppy)3 ratios at the interfaces indicated by the simple ANN were consistent with the XPS results and the ToF-SIMS depth profiles. The simple ANN system not only provided quantitative information on unknown samples, but also indicated important mass peaks related to each molecule in the samples without a priori information. The important mass peaks indicated by the simple ANN depended on the ionization process. The simple ANN results of the spectra sets obtained by a softer ionization method, such as LDI and DINeC, suggested large ions such as trimers. From the first step of the investigation to build an ANN model for evaluating mixture samples influenced by matrix effects, it was indicated that the simple ANN method is useful for obtaining candidate mass peaks for identification and for assuming mixture conditions that are helpful for further analysis.

Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study.

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.

Method for Molecular Layer Deposition Using Gas Cluster Ion Beam Sputtering with Example Application In Situ Matrix-Enhanced Secondary Ion Mass Spectrometry.

We introduce a technique for the directed transfer of molecules from an adjacent reservoir onto a sample surface inside the vacuum chamber of a ToF-SIMS instrument using gas cluster ion beam (GCIB) sputtering. An example application for in situ matrix-enhanced secondary ion mass spectrometry (ME SIMS) is provided. This protocol has attractive features since most modern SIMS instruments are equipped with a GCIB gun. No solvents are required that would delocalize analytes at the surface, and the transfer of matrix molecules can be interlaced with SIMS depth profiling and 3D imaging sputtering and analysis cycles, which is not possible with conventional ME SIMS strategies. The amount of molecular deposition can be finely tuned, which is important for such a surface sensitive technique as SIMS. To demonstrate the concept, we used 2,5-DHB as a matrix for the enhancement of three drug molecules embedded in a tissue homogenate. By automatic operation of sputter deposition and erosion (cleanup) cycles, depth profiling could be achieved with ME SIMS with good repeatability (<4% RSD). Furthermore, we explored several different matrix compounds, including α-CHCA and aqueous solutions of Brønsted acids (formic acid) and 3-nitrobenzonitrile, a volatile compound known to spontaneously produce ions. The latter two matrix compounds were applied at cryogenic measurement conditions, which extend the range of matrices applicable for ME SIMS. Enhancement ratios range from 2 to 13, depending on the analytes and matrix. The method works in principle, but enhancement ratios for the drug molecules are rather limited at this point. Further study and optimization is needed, and the technique introduced here provides a tool to perform systematic studies of matrix compounds and experimental conditions for their potential for signal enhancement in ME SIMS.

Cryogenic OrbiSIMS Localizes Semi-Volatile Molecules in Biological Tissues.

OrbiSIMS is a recently developed instrument for label-free imaging of chemicals with micron spatial resolution and high mass resolution. We report a cryogenic workflow for OrbiSIMS (Cryo-OrbiSIMS) that improves chemical detection of lipids and other biomolecules in tissues. Cryo-OrbiSIMS boosts ionization yield and decreases ion-beam induced fragmentation, greatly improving the detection of biomolecules such as triacylglycerides. It also increases chemical coverage to include molecules with intermediate or high vapor pressures, such as free fatty acids and semi-volatile organic compounds (SVOCs). We find that Cryo-OrbiSIMS reveals the hitherto unknown localization patterns of SVOCs with high spatial and chemical resolution in diverse plant, animal, and human tissues. We also show that Cryo-OrbiSIMS can be combined with genetic analysis to identify enzymes regulating SVOC metabolism. Cryo-OrbiSIMS is applicable to high resolution imaging of a wide variety of non-volatile and semi-volatile molecules across many areas of biomedicine.

Correlative Hyperspectral Imaging Using a Dimensionality-Reduction-Based Image Fusion Method.

Chemical imaging techniques are increasingly being used in combination to achieve a greater understanding of a sample. This is especially true in the case of mass spectrometry imaging (MSI), where the use of different ionization sources allows detection of different classes of molecules across a range of spatial resolutions. There has been significant recent effort in the development of data fusion algorithms that attempt to combine the benefits of multiple techniques, such that the output provides additional information that would have not been present or obvious from the individual techniques alone. However, the majority of the data fusion methods currently in use rely on image registration to generate the fused data and therefore can suffer from artifacts caused by interpolation. Here, we present a method for data fusion that does not incorporate interpolation-based artifacts into the final fused data, applied to data acquired from multiple chemical imaging modalities. The method is evaluated using simulated data and a model polymer blend sample, before being applied to biological samples of mouse brain and lung.

Universal Sample Preparation Unlocking Multimodal Molecular Tissue Imaging.

A new tissue sample embedding and processing method is presented that provides downstream compatibility with numerous different histological, molecular biology, and analytical techniques. The methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matrix composed of hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) and sectioning using a cryomicrotome. The hydrogel was expected not to interfere with standard tissue characterization methods, histologically or analytically. We assessed the compatibility of this protocol with various mass spectrometric imaging methods including matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS). We also demonstrated the suitability of the universal protocol for extraction based molecular biology techniques such as rt-PCR. The integration of multiple analytical modalities through this universal sample preparation protocol offers the ability to study tissues at a systems biology level and directly linking results to tissue morphology and cellular phenotype.

OrbiSIMS Imaging Identifies Molecular Constituents of the Perialgal Vacuole Membrane of Paramecium bursaria with Symbiotic Chlorella variabilis.

The protist (mostly single-celled organisms), Paramecium bursaria, forms an intracellular symbiotic relationship with the single-celled algae, Chlorella variabilis, where P. bursaria provides nutrients (i.e., Ca2+, Mg2+, and K+), carbon dioxide for photosynthesis and protection from viruses, while C. variabilis provides oxygen, carbon fixation, and nutrients. Key to this successful relationship is the perialgal vacuole (PV) membrane, which surrounds C. variabilis and protects it from digestion by P. bursaria. The membrane is fragile and difficult to analyze using conventional methods therefore very little is known about the molecular composition. We used the OrbiSIMS, a new high-resolution mass spectrometer with subcellular resolution imaging, to study the compartmentalization of endosymbionts and elucidate biomolecular interactions between the host and endosymbiont. Ions from the region of interest, close to C. variabilis, and specific to the target samples containing PVs were found based on the chemical mapping and masses of the ions. We show chemical localizations of oligosaccharides in close proximity of C. variabilis endosymbionts in P. bursaria. These oligosaccharides are detected in host-endosymbiont samples containing PV membrane-bound algae and absent in free-living algae and digestive vacuole (DV) membrane-bound algae in P. bursaria.

Semiempirical Rules To Determine Drug Sensitivity and Ionization Efficiency in Secondary Ion Mass Spectrometry Using a Model Tissue Sample.

There is an increasing need in the pharmaceutical industry to reduce drug failure at late stage and thus reduce the cost of developing a new medicine. Since most drug targets are intracellular, this requires a better understanding of the drug disposition within a cell. Secondary ion mass spectrometry has been identified as a potentially important technique to do this, as it is label-free and allows imaging in 3D with subcellular resolution and recent studies have shown promise for amiodarone. An important analytical parameter is sensitivity, and we measure this in a bovine liver homogenate reference sample for 20 drugs representing important class types relevant to the pharmaceutical industry. We also measure the sensitivity for pure drug and show, for the first time, that the secondary ion mass spectrometry (SIMS) positive ionization efficiency for small molecules is a simple power-law relationship to the log P value. This discovery will be important for advancing the understanding of the SIMS ionization process in small molecules that has, until now, been elusive. This simple relationship is found to hold true for drug doped in the bovine liver homogenate reference sample, except for fluticasone, nicardipine, and sorafenib which suffer from severe matrix suppression. This relationship provides a simple semiempirical method to determine drug sensitivity for positive secondary ions. Furthermore, we show, on chosen models, how the use of different solvents during sample preparation can affect the ionization of analytes.