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.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

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.

Completing the picture through correlative characterization

Natural and manufactured materials rely on complex hierarchical microstructures to deliver a suite of interesting properties. To predict and tailor their performance requires a joined-up knowledge of their multiphase microstructure, interfaces, chemistry and crystallography from the nanoscale to the macroscale. This Perspective reflects on how recent developments in correlative characterization can bring together multiple image modalities and maps of the local chemistry, structure and functionality to form rich multimodal and multiscale correlated datasets. The automated collection and digitization of multidimensional data is an essential part of the picture for developing multiscale modelling and 'big data'-driven machine learning approaches. These are needed to both improve our understanding of existing materials and exploit high-throughput combinatorial synthesis, processing and testing methods to develop materials with bespoke properties.

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.