Spatial distribution of active compounds in stratum corneum-partitioning between corneocytes and lipid matrix

The interaction of active substances with molecular structures in stratum corneum (SC) is crucial for the efficacy and safety of cosmetic formulations and topical drugs. However, the molecular architecture of SC is highly complex and methods to unambiguously localize exogenous molecules within SC are lacking. Consequently, little is known about the distribution of actives within SC, and proposed penetration mechanisms through SC are typically limited to simple diffusion via a tortuous (lipid only) or transverse (across corneocytes and lipid matrix) pathway. In this work, 3D mass spectrometry imaging is used to determine the spatial distributions of four active substances at subcellular resolution in SC, including partitioning between the corneocytes and the intercellular lipid matrix. The results indicate that caffeine, 2-methyl resorcinol and oxybenzone are homogeneously distributed in the corneocytes but largely absent in the lipid matrix, despite considerable differences in lipophilicity. In contrast, the distribution- of jasmonic acid derivative is more inhomogeneous and indicates considerable localization to both the lipid phase and the corneocytes.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

Elucidating the molecular landscape of the stratum corneum

Skin is recognized as an intricate assembly of molecular components, which facilitate cell signaling, metabolism, and protein synthesis mechanisms in order to offer protection, regulation, and sensation to the body. Our study takes significant steps to characterize in more detail the complex chemistry of the skin, in particular by generating a better understanding of the uppermost layer, the stratum corneum. Using a state-of-the-art 3D OrbiSIMS technique, we were able to observe the depth distribution, in situ, for a wide range of molecular species. This unprecedented molecular characterization of skin provides information that has the potential to benefit research into fundamental processes, such as those associated with skin aging and disease, and the development and delivery of effective topical formulations.

Characterization of the molecular structure of skin, especially the barrier layer, the stratum corneum, is a key research priority for generating understanding to improve diagnostics, aid pharmaceutical delivery, and prevent environmental damage. Our study uses the recently developed 3D OrbiSIMS technique to conduct in situ analysis of ex vivo human skin tissue and reveals the molecular chemistry of skin in unprecedented detail, as a result of the step change in high mass resolving power compared with previous studies. This characterization exposes the nonhomogeneity of the stratum corneum, both laterally and as a function of depth. Chemical variations relating to fundamental biological processes, such as the epidermal cholesterol sulfate cycle, are visualized using in situ analysis. We are able to resolve the debate around the chemical gradients present within the epidermis, for example, whether palmitic acid is of sebaceous origin or a true component of the stratum corneum. Through in situ depth analysis of cryogenically preserved samples, we are able to propose that it is actually a component of both surface sebum and the intrinsic lipid matrix. This approach also suggests similarity between the epidermis compounds found in human and porcine skin as a function of depth. Since porcine skin is a widely used model for permeation testing this result has clinical relevance. In addition to using this technique for endogenous species, we have used it to demonstrate the permeation of a commercially important antiaging peptide into the human stratum corneum. Due to its chemical similarity to native skin components and exceptionally low effective concentration, this information was previously unattainable.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Secondary Ion Mass Spectrometry Imaging Reveals Changes in the Lipid Structure of the Plasma Membranes of Hippocampal Neurons following Drugs Affecting Neuronal Activity

The cellular functions of lipids in the neuronal plasma membranes have been increasingly acknowledged, particularly their association to neuronal processes and synaptic plasticity. However, the knowledge of their regulatory mechanisms in neuronal cells remains sparse. To address this, we investigated the lipid organization of the plasma membranes of hippocampal neurons in relation to neuronal activity using secondary ion mass spectrometry imaging. The neurons were treated with drugs, particularly tetrodotoxin (TTX) and bicuculline (BIC), to induce chronic activation and silencing. Distinct lipid organization was found in the plasma membrane of the cell body and the neurites. Moreover, significant alterations of the levels of the membrane lipids, especially ceramides, phosphatidylserines, phosphatidic acids, and triacylglycerols, were observed under the TTX and BIC treatments. We suggest that many types of membrane lipids are affected by, and may be involved in, the regulation of neuronal function.

Chemical Imaging of Retinal Pigment Epithelium in Frozen Sections of Zebrafish Larvae Using ToF-SIMS

Variants of the SLC24A5 gene, which encodes a putative potassium-dependent sodium-calcium exchanger (NCKX5) that most likely resides in the melanosome or its precursor, affect pigmentation in both humans and zebrafish (Danio rerio). This finding suggests that genetic variations influencing human skin pigmentation alter melanosome biogenesis via ionic changes. Gaining an understanding of how changes in the ionic environment of organelles impact melanosome morphogenesis and pigmentation will require a spatially resolved way to characterize the chemical environment of melanosomes in pigmented tissue such as retinal pigment epithelium (RPE). The imaging mass spectrometry technique most suited for this type of cell and tissue analysis is time-of-flight secondary ion mass spectrometry (ToF-SIMS) because it is able to detect many biochemical species with high sensitivity and with submicron spatial resolution. Here, we describe chemical imaging of the RPE in frozen-hydrated sections of larval zebrafish using cryo-ToF-SIMS. To facilitate the data interpretation, positive and negative polarity ToF-SIMS image data were transformed into a single hyperspectral data set and analyzed using principal component analysis. The combination of a novel protocol and the use of multivariate data analysis allowed us to discover new marker ions that are attributable to leucodopachrome, a metabolite specific to the biosynthesis of eumelanin. The described methodology may be adapted for the investigation of other classes of molecules in frozen tissues from zebrafish and other organisms.

Multimodal Imaging Mass Spectrometry to Identify Markers of Pulmonary Arterial Hypertension in Human Lung Tissue Using MALDI-ToF, ToF-SIMS, and Hybrid SIMS

Pulmonary arterial hypertension (PAH) is a rare and deadly disease affecting roughly 15-60 people per million in Europe with a poorly understood pathology. There are currently no diagnostic tools for early detection nor does a curative treatment exist. The lipid composition of arteries in lung tissue samples from human PAH and control patients were investigated using matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging. Using random forests as an IMS data analysis technique, it was possible to identify the ion at m/z 885.6 as a marker of PAH in human lung tissue. The m/z 885.6 ion intensity was shown to be significantly higher around diseased arteries and was confirmed to be a diacylglycerophosphoinositol PI(C18:0/C20:4) via MS/MS using a novel hybrid SIMS instrument. The discovery of a potential biomarker opens up new research avenues which may finally lead to a better understanding of the PAH pathology and highlights the vital role IMS can play in modern biomedical research.

Controlling orientation, conformation, and biorecognition of proteins on silane monolayers, conjugate polymers, and thermo-responsive polymer brushes: investigations using TOF-SIMS and principal component analysis

Control over orientation and conformation of surface-immobilized proteins, determining their biological activity, plays a critical role in biointerface engineering. Specific protein state can be achieved with adjusted surface preparation and immobilization conditions through different types of protein-surface and protein-protein interactions, as outlined in this work. Time-of-flight secondary ion mass spectroscopy, combining surface sensitivity with excellent chemical specificity enhanced by multivariate data analysis, is the most suited surface analysis method to provide information about protein state. This work highlights recent applications of the multivariate principal component analysis of TOF-SIMS spectra to trace orientation and conformation changes of various proteins (antibody, bovine serum albumin, and streptavidin) immobilized by adsorption, specific binding, and covalent attachment on different surfaces, including self-assembled monolayers on silicon, solution-deposited polythiophenes, and thermo-responsive polymer brushes. Multivariate TOF-SIMS results correlate well with AFM data and binding assays for antibody-antigen and streptavidin-biotin recognition. Additionally, several novel extensions of the multivariate TOF-SIMS method are discussed.

Graphical abstract

Measurement of Absolute Concentration at the Subcellular Scale

The concentration of a pharmaceutical drug or bioactive metabolite within the target organelle influences the effects elicited by the drug or metabolite. Although the relative concentrations of many compounds of interest within subcellular compartments have been measured, measurements of absolute concentrations in the organelle remain elusive. In this Perspective, we discuss a significant advance in using nano secondary ion mass spectrometry (nanoSIMS) to measure the absolute concentration of a ¹³C-labeled metabolite within secretory vesicles, as reported by Thomen et al. in the April issue of ACS Nano.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Enhanced vitamin C skin permeation from supramolecular hydrogels, illustrated using in situ ToF-SIMS 3D chemical profiling

Vitamin C (ascorbic acid) is a naturally occurring, powerful anti-oxidant with the potential to deliver numerous benefits to the skin when applied topically. However, topical use of this compound is currently restricted by an instability in traditional formulations and the delivery and eventual fate of precursor compounds has been largely unexplored. Time of flight secondary ion mass spectrometry (ToF-SIMS) is an emerging technique in the field of skin research and offers detailed chemical analysis, with high mass and spatial resolution, as well as profiling capabilities that allow analysis as a function of sample depth. This work demonstrates the successful use of ToF-SIMS to obtain, in situ, accurate 3D permeation profiles of both ascorbic acid and a popular precursor, ascorbyl glucoside, from ex vivo porcine skin. The significant permeation enhancing effect of a supramolecular hydrogel formulation, produced from an amphiphilic gemini imidazolium-based surfactant, was also demonstrated for both compounds. Using ToF-SIMS, it was also possible to detect and track the breakdown of ascorbyl glucoside into ascorbic acid, elucidating the ability of the hydrogel formulation to preserve this important conversion until the targeted epidermal layer has been reached. This work demonstrates the potential of ToF-SIMS to provide 3D permeation profiles collected in situ from ex vivo tissue samples, offering detailed analysis on compound localisation and degradation. This type of analysis has significant advantages in the area of skin permeation, but can also be readily translated to other tissue types.

A fusion of principal component analysis and singular value decomposition based multivariate denoising algorithm for free induction decay transversal data

The free induction decay (FID) transversal data determines the measurement accuracy of time-dependent geomagnetic fields, whereas the conservation of clean components and removal of noise cannot be easily achieved for this kind of data. Even though numerous techniques have been proven to be effective in improving the signal-to-noise ratio by filtering out frequency bands, how to efficiently reduce noise is still a crucial issue due to several restrictions, e.g., prior information requirement, stationary data assumption. To end this, a new multivariate algorithm based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD), namely, principal component analysis and decomposition (PCAD), was presented. This novel algorithm aims to reduce noise as well as cancel the interference of FID transversal data. Specifically, the PCAD algorithm is able to obtain the dominant principal components of the FID and that of the noise floor by PCA, in which an optimal number of subspaces could be retained via a cumulative percent of variance criterion. Furthermore, the PCA was combined with an SVD filter whose singular values corresponding to the interferences were identified, and then the noise was suppressed by nulling the corresponding singular values, which was able to achieve an optimum trade-off between the preservation of pure FID data and the denoising efficiency. Our proposed PCAD algorithm was compared with the widely used filter methods via extensive experiments on synthetic and real FID transversal data under different noise levels. The results demonstrated that this method can preserve the FID transversal data better and shows a significant improvement in noise suppression.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

A Time-of-Flight Secondary Ion Mass Spectrometry/Multivariate Analysis (ToF-SIMS/MVA) Approach To Identify Phase Segregation in Blends of Incompatible but Extremely Similar Resins

This work presents a data analysis extension to a well-established methodology for the assessment of organic coatings using imaging time-of-flight secondary ion mass spectrometry (ToF-SIMS). Such an approach produced results that can be analyzed using a multivariate analysis (MVA) procedure that performs the simultaneous processing of spatially and chemically related datasets. The coatings consist of two commercial resins that yield extremely similar spectra, and there are no peaks of sufficient intensity that are uniquely diagnostic of either material to provide an unambiguous identification of each. In order to resolve the problem, in addition to microtome-based sample preparation steps of tapers for the analysis through sample thickness, standard samples in cured and uncured conditions are introduced and measured in the same fashion as the specimens under investigation. The resulting ToF-SIMS imaging datasets have been processed using non-negative matrix factorization (NMF), which enabled identification of phase separation in the cured coatings.

Observation of endoplasmic reticulum tubules via TOF-SIMS tandem mass spectrometry imaging of transfected cells

Advances in three-dimensional secondary ion mass spectrometry (SIMS) imaging have enabled visualizing the subcellular distributions of various lipid species within individual cells. However, the difficulty of locating organelles using SIMS limits efforts to study their lipid compositions. Here, the authors have assessed whether endoplasmic reticulum (ER)-Tracker Blue White DPX^®, which is a commercially available stain for visualizing the endoplasmic reticulum using fluorescence microscopy, produces distinctive ions that can be used to locate the endoplasmic reticulum using SIMS. Time-of-flight-SIMS tandem mass spectrometry (MS²) imaging was used to identify positively and negatively charged ions produced by the ER-Tracker stain. Then, these ions were used to localize the stain and thus the endoplasmic reticulum, within individual human embryonic kidney cells that contained higher numbers of endoplasmic reticulum-plasma membrane junctions on their surfaces. By performing MS² imaging of selected ions in parallel with the precursor ion (MS¹) imaging, the authors detected a chemical interference native to the cell at the same nominal mass as the pentafluorophenyl fragment from the ER-Tracker stain. Nonetheless, the fluorine secondary ions produced by the ER-Tracker stain provided a distinctive signal that enabled locating the endoplasmic reticulum using SIMS. This simple strategy for visualizing the endoplasmic reticulum in individual cells using SIMS could be combined with existing SIMS methodologies for imaging intracellular lipid distribution and to study the lipid composition within the endoplasmic reticulum.

Imaging mass spectrometry for metabolites: technical progress, multimodal imaging, and biological interactions

Imaging mass spectrometry (IMS) allows the study of the spatial distribution of small molecules in biological samples. IMS is able to identify and quantify chemicals in situ from whole tissue sections to single cells. Both vacuum mass spectrometry (MS) and ambient MS systems have advanced considerably over the last decade; however, some limitations are still hard to surmount. Sample pretreatment, matrix or solvent choices, and instrument improvement are the key factors that determine the successful application of IMS to different samples and analytes. IMS with innovative MS analyzers, powerful MS spectrum databases, and analysis tools can efficiently dereplicate, identify, and quantify natural products. Moreover, multimodal imaging systems and multiple MS-based systems provide additional structural, chemical, and morphological information and are applied as complementary tools to explore new fields. IMS has been applied to reveal interactions between living organisms at molecular level. Recently, IMS has helped solve many previously unidentifiable relations between bacteria, fungi, plants, animals, and insects. Other significant interactions on the chemical level can also be resolved using expanding IMS techniques. WIREs Syst Biol Med 2017, 9:e1387. doi: 10.1002/wsbm.1387 For further resources related to this article, please visit the WIREs website.