Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Investigation of pectin deficiency in modulating the bioflavonoid profile of orange processing waste: A sustainable valorization of industrial waste

Orange processing waste (OPW) generated by the processing of oranges, as well as other citrus fruits, is a major source of pectin in the market nowadays. The residues generated during the pectin extraction process may contain many phytochemicals, including flavonoids. We use state-of-the-art techniques such as liquid chromatography high-resolution mass spectrometry (LC-HRMS/MS) and feature-based molecular network (FBMN) to annotate the flavonoids in OPWs. In particular, four flavonoids, hesperidin, naringin, diosmin, and hesperetin were quantified in the samples by LC-TDQ-MS. In total, 32 flavonoids from different classes were annotated, of which 16 were polymethoxylated flavonoids, 13 were flavonoid glycosides and 3 were flavanone aglycones. The results showed that flavonoid glycosides remain in high concentrations in OPWs from pectin factories even after pectin extraction by harsh conditions. The results show an exciting opportunity to harness the untapped potential of pectin factory waste as a renewable source for the extraction of glycoside flavonoids.

Review of Recent Advances in Gas-Assisted Focused Ion Beam Time-of-Flight Secondary Ion Mass Spectrometry (FIB-TOF-SIMS)

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a powerful chemical characterization technique allowing for the distribution of all material components (including light and heavy elements and molecules) to be analyzed in 3D with nanoscale resolution. Furthermore, the sample's surface can be probed over a wide analytical area range (usually between 1 µm² and 10⁴ µm²) providing insights into local variations in sample composition, as well as giving a general overview of the sample's structure. Finally, as long as the sample's surface is flat and conductive, no additional sample preparation is needed prior to TOF-SIMS measurements. Despite many advantages, TOF-SIMS analysis can be challenging, especially in the case of weakly ionizing elements. Furthermore, mass interference, different component polarity of complex samples, and matrix effect are the main drawbacks of this technique. This implies a strong need for developing new methods, which could help improve TOF-SIMS signal quality and facilitate data interpretation. In this review, we primarily focus on gas-assisted TOF-SIMS, which has proven to have potential for overcoming most of the aforementioned difficulties. In particular, the recently proposed use of XeF₂ during sample bombardment with a Ga⁺ primary ion beam exhibits outstanding properties, which can lead to significant positive secondary ion yield enhancement, separation of mass interference, and inversion of secondary ion charge polarity from negative to positive. The implementation of the presented experimental protocols can be easily achieved by upgrading commonly used focused ion beam/scanning electron microscopes (FIB/SEM) with a high vacuum (HV)-compatible TOF-SIMS detector and a commercial gas injection system (GIS), making it an attractive solution for both academic centers and the industrial sectors.

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.

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.

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Combining surface-sensitive microscopies for analysis of biological tissues after neural device implantation

In order to address the complexity of chemical analysis of biological systems, time-of-flight secondary ion mass spectrometry (ToF-SIMS), x-ray photoelectron spectroscopy (XPS), and x-ray photoemission electron microscopy (XPEEM) were used for combined surface imaging of a biological tissue formed around a surface neural device after implantation on a nonhuman primate brain. Results show patterns on biological tissue based on extracellular matrix (ECM) and phospholipid membrane (PM) molecular fragments, which were contrasted through principal component analysis of ToF-SIMS negative spectrum. This chemical differentiation may indicate severe inflammation on tissue with an early case of necrosis. Quantification of the elemental composition and the chemical bonding states on both ECM-rich and PM-rich features was possible through XPS analysis from survey and high-resolution spectra, respectively. Variable amounts of carbon (68%-80.5%), nitrogen (10%-2.4%), and oxygen (20.8%-16.5%) were detected on the surface of the biological tissue. Chlorine, phosphorous sodium, and sulfur were also identified in lower extends. Besides that, analysis of the C 1s high-resolution spectra for the same two regions (ECM and PM ones) showed that a compromise between C-C (41.8 at. %) and C-N/C-O (35.6 at. %) amounts may indicate a strong presence of amino acids and proteoglycans on the ECM fragment-rich region, while the great amount of C-C (70.1 at. %) on the PM fragment-rich region is attributed to the large chains of fatty acids connected to phospholipid molecules. The micrometer-scale imaging of these chemical states on tissue was accomplished through XPEEM analysis. The C-C presence was found uniformly distributed across the entire analyzed area, while C-N/C-O and C=O were in two distinct regions. The combination of ToF-SIMS, XPS, and XPEEM is shown here as a powerful, noninvasive approach to map out elemental and chemical properties of biological tissues, i.e., identification of chemically distinct regions, followed by quantification of the surface chemical composition in each distinct region.

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.

Correlated Materials Characterization via Multimodal Chemical and Functional Imaging

Multimodal chemical imaging simultaneously offers high-resolution chemical and physical information with nanoscale and, in select cases, atomic resolution. By coupling modalities that collect physical and chemical information, we can address scientific problems in biological systems, battery and fuel cell research, catalysis, pharmaceuticals, photovoltaics, medicine, and many others. The combined systems enable the local correlation of material properties with chemical makeup, making fundamental questions of how chemistry and structure drive functionality approachable. In this Review, we present recent progress and offer a perspective for chemical imaging used to characterize a variety of samples by a number of platforms. Specifically, we present cases of infrared and Raman spectroscopies combined with scanning probe microscopy; optical microscopy and mass spectrometry; nonlinear optical microscopy; and, finally, ion, electron, and probe microscopies with mass spectrometry. We also discuss the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for the interpretation of multidimensional data acquired from multimodal studies.

Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data

Time of flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface-sensitive characterization tool allowing the imaging of chemical properties over a wide range of organic and inorganic material systems. This technique allows precise studies of chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF-SIMS results is challenging because of the very large data volume and high dimensionality. Furthermore, investigation of samples with pronounced topographical features is complicated by systematic and measureable shifts in the mass spectrum. In this work we developed an approach for the interpretation of the ToF-SIMS data, based on the advanced data analytics. Along with characterization of the chemical composition, our approach allows extraction of the sample surface morphology from a time of flight registration technique. This approach allows one to perform correlated investigations of surface morphology, biological function, and chemical composition of Arabidopsis roots.

Adsorption of Malathion onto Copper and Iron Surfaces Relevant to Water Infrastructure

This study investigated the adsorption of malathion to copper and iron surfaces including microspheres and pipe specimens similar to those in drinking water infrastructure. The solid phase concentration of malathion on the virgin and used copper pipe specimens was generally between 0.2 - 1 mg/g. The adsorption capacity for copper and iron microspheres were greater than those of the pipe specimens because of their higher surface area-to-volume ratios. Copper materials adsorbed more malathion than comparable iron materials. XPS analysis of copper and iron surfaces revealed peaks at 164 eV (S 2p) and 135 eV (P 2p), which suggests that malathion chemically bonded to the surfaces of the specimens. Metal oxides likely formed stable bonds with phosphorus through pi conjugation. These findings are the first to show that malathion can chemically adhere to copper and iron pipe materials. This insight is critical for understanding the decontamination strategies needed for water networks.

Removing Beam Current Artifacts in Helium Ion Microscopy: A Comparison of Image Processing Techniques

The development of the helium ion microscope (HIM) enables the imaging of both hard, inorganic materials and soft, organic or biological materials. Advantages include outstanding topographical contrast, superior resolution down to <0.5 nm at high magnification, high depth of field, and no need for conductive coatings. The instrument relies on helium atom adsorption and ionization at a cryogenically cooled tip that is atomically sharp. Under ideal conditions this arrangement provides a beam of ions that is stable for days to weeks, with beam currents in the order of picoamperes. Over time, however, this stability is lost as gaseous contamination builds up in the source region, leading to adsorbed atoms of species other than helium, which ultimately results in beam current fluctuations. This manifests itself as horizontal stripe artifacts in HIM images. We investigate post-processing methods to remove these artifacts from HIM images, such as median filtering, Gaussian blurring, fast Fourier transforms, and principal component analysis. We arrive at a simple method for completely removing beam current fluctuation effects from HIM images while maintaining the full integrity of the information within the image.

Quantifying element incorporation in multispecies biofilms using nanoscale secondary ion mass spectrometry image analysis

Elucidating nutrient exchange in microbial communities is an important step in understanding the relationships between microbial systems and global biogeochemical cycles, but these communities are complex and the interspecies interactions that occur within them are not well understood. Phototrophic consortia are useful and relevant experimental systems to investigate such interactions as they are not only prevalent in the environment, but some are cultivable in vitro and amenable to controlled scientific experimentation. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful, high spatial resolution tool capable of visualizing the metabolic activities of single cells within a biofilm, but quantitative analysis of the resulting data has typically been a manual process, resulting in a task that is both laborious and susceptible to human error. Here, the authors describe the creation and application of a semiautomated image-processing pipeline that can analyze NanoSIMS-generated data, applied to phototrophic biofilms as an example. The tool employs an image analysis process, which includes both elemental and morphological segmentation, producing a final segmented image that allows for discrimination between autotrophic and heterotrophic biomass, the detection of individual cyanobacterial filaments and heterotrophic cells, the quantification of isotopic incorporation of individual heterotrophic cells, and calculation of relevant population statistics. The authors demonstrate the functionality of the tool by using it to analyze the uptake of (15)N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. The authors found that the degree of (15)N incorporation by individual cells was highly variable when labeled with (15)NH4 (+), but much more even when biofilms were labeled with (15)NO3 (-). In the (15)NH4 (+)-amended biofilms, the heterotrophic distribution of (15)N incorporation was highly skewed, with a large population showing moderate (15)N incorporation and a small number of organisms displaying very high (15)N uptake. The results showed that analysis of NanoSIMS data can be performed in a way that allows for quantitation of the elemental uptake of individual cells, a technique necessary for advancing research into the metabolic networks that exist within biofilms with statistical analyses that are supported by automated, user-friendly processes.

Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures.