Investigating lipid-lipid and lipid-protein interactions in model membranes by ToF-SIMS

Revealing local molecular distribution, orientation, phase separation, and formation of domains in artificial lipid layers: Towards comprehensive characterization of biological membranes

Lipids, together with molecules such as DNA and proteins, are one of the most relevant systems responsible for the existence of life. Selected lipids are able to assembly into various organized structures, such as lipid membranes. The unique properties of lipid membranes determine their complex functions, not only to separate biological environments, but also to participate in regulatory functions, absorption of nutrients, cell-cell communication, endocytosis, cell signaling, and many others. Despite numerous scientific efforts, still little is known about the reason underlying the variability within lipid membranes, and its biochemical significance. In this review, we discuss the structural complexity of lipid membranes, as well as the importance to simplify studied systems in order to understand phenomena occurring in natural, complex membranes. Such systems require a model interface to be analyzed. Therefore, here we focused on analytical studies of artificial systems at various interfaces. The molecular structure of lipid membranes, specifically the nanometric thickens of molecular bilayer, limits in a major extent the choice of highly sensitive methods suitable to study such structures. Therefore, we focused on methods that combine high sensitivity, and/or chemical selectivity, and/or nanometric spatial resolution, such as atomic force microscopy, nanospectroscopy (tip-enhanced Raman spectroscopy, infrared nanospectroscopy), phase modulation infrared reflection-absorption spectroscopy, sum-frequency generation spectroscopy. We summarized experimental and theoretical approaches providing information about molecular structure and composition, lipid spatial distribution (phase separation), organization (domain shape, molecular orientation) of lipid membranes, and real-time visualization of the influence of various molecules (proteins, drugs) on their integrity. An integral part of this review discusses the latest achievements in the field of lipid layer-based biosensors.

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.

Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound

Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.

Identifying exposition to low oxygen environment in human macrophages using secondary ion mass spectrometry and multivariate analysis

RATIONALE

Macrophages are innate immune cells presenting a strong phenotypic plasticity and deeply involved in tissue homeostasis. Oxygen environmental tension is a physical parameter that could influence their polarizations. In this study we use time-of-flight secondary ion mass spectrometry (TOF-SIMS) to describe how various polarizations are modified by a low oxygen exposure.

METHODS

TOF-SIMS experiments were performed using an IONTOF ToF-SIMS 5-100 (ION-TOF GmbH, Munster, Germany). Analysis was performed using a pulsed 25 keV Bi³⁺ beam, sputtering was performed using a 250 eV Cs beam. Cells were fixed by paraformaldehyde before TOF-SIMS analysis.

RESULTS

Multivariate analysis of the TOF-SIMS spectra provided ion species associated with the exposure of macrophages to low oxygen concentration. We were able to obtain some species, specific of a particular polarization, advocating for the use of macrophages as reporter cells of oxygen tension in tissues.

CONCLUSIONS

Our study demonstrates that macrophage molecular signature to low oxygen environment is dependent on their polarization. TOF-SIMS shows the clear capability to produce species revealing this exposition. This result opens the way to the use of TOF-SIMS as a tool to explore hypoxia in human tissues.

Structural determinants of protein partitioning into ordered membrane domains and lipid rafts

Increasing evidence supports the existence of lateral nanoscopic lipid domains in plasma membranes, known as lipid rafts. These domains preferentially recruit membrane proteins and lipids to facilitate their interactions and thereby regulate transmembrane signaling and cellular homeostasis. The functionality of raft domains is intrinsically dependent on their selectivity for specific membrane components; however, while the physicochemical determinants of raft association for lipids are known, very few systematic studies have focused on the structural aspects that guide raft partitioning of proteins. In this review, we describe biophysical and thermodynamic aspects of raft-mimetic liquid ordered phases, focusing on those most relevant for protein partitioning. Further, we detail the variety of experimental models used to study protein-raft interactions. Finally, we review the existing literature on mechanisms for raft targeting, including lipid post-translational modifications, lipid binding, and transmembrane domain features. We conclude that while protein palmitoylation is a clear raft-targeting signal, few other general structural determinants for raft partitioning have been revealed, suggesting that many discoveries lie ahead in this burgeoning field.

An overview of technologies for immobilization of enzymes and surface analysis techniques for immobilized enzymes

The current demands of sustainable green methodologies have increased the use of enzymatic technology in industrial processes. Employment of enzyme as biocatalysts offers the benefits of mild reaction conditions, biodegradability and catalytic efficiency. The harsh conditions of industrial processes, however, increase propensity of enzyme destabilization, shortening their industrial lifespan. Consequently, the technology of enzyme immobilization provides an effective means to circumvent these concerns by enhancing enzyme catalytic properties and also simplify downstream processing and improve operational stability. There are several techniques used to immobilize the enzymes onto supports which range from reversible physical adsorption and ionic linkages, to the irreversible stable covalent bonds. Such techniques produce immobilized enzymes of varying stability due to changes in the surface microenvironment and degree of multipoint attachment. Hence, it is mandatory to obtain information about the structure of the enzyme protein following interaction with the support surface as well as interactions of the enzymes with other proteins. Characterization technologies at the nanoscale level to study enzymes immobilized on surfaces are crucial to obtain valuable qualitative and quantitative information, including morphological visualization of the immobilized enzymes. These technologies are pertinent to assess efficacy of an immobilization technique and development of future enzyme immobilization strategies.

Changes in lipid distribution in E. coli strains in response to norfloxacin

Bacterial resistance to antibiotics has become an increasing threat, requiring not only the development of new targets in drug discovery, but more importantly, a better understanding of cellular response. In the current study, three closely related Escherichia coli strains, a wild type (MG1655) and an isogenic pair derived from the wild type (DPB635 and DPB636) are studied following exposure to sub lethal concentrations of antibiotic (norfloxacin) over time. In particular, genotype similarities between the three strains were assessed based on the lipid regulation response (e.g. presence/absence and up/down regulation). Lipid identification was performed using direct surface probe analysis (matrix-assisted laser desorption/ionization, MALDI), coupled to high-resolution mass spectrometry (Fourier transform ion cyclotron resonance mass spectrometry, FT-ICR MS) followed by statistical analysis of variability and reproducibility across batches using internal standards. Inspection of the lipid profile showed that for the MG1655, DPB635 and DPB636 E. coli strains, a similar distribution of the altered lipids was observed after exposure to norfloxacin antibiotic (e.g. fatty acids and glycerol phospholipids are up and down regulated, respectively). Additionally, variations in the lipid distribution resemble the extent to which each strain can combat the antibiotic exposure. That is, the topA66 topoisomerase I mutation of DPB636 translates into diminished response related to antibiotic sensitivity when compared to MG1655 and the DPB635 strains.

Low temperature thermal dependent Filgrastim adsorption behavior detected with ToF-SIMS

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) detected changes in Filgrastim (granulocyte colony stimulating growth factor, G-CSF) adsorption behavior at a solid interface when exposed to temperatures as low as 35 °C, i.e., before thermal denaturation, was detected by circular dichroism (CD) or dynamic light scattering (DLS). Biopharmaceuticals rely on maintaining sufficient conformation to impart correct biological function in vivo. Stability of such molecules is critical during synthesis, storage, transport, and administration. CD analysis indicated loss of structure at temperatures greater than ~60 °C, while DLS detected aggregation at ~42 °C. Furthermore, we demonstrate the nature of G-CSF interaction with a surface was altered rapidly and at relatively low temperatures. Specifically, after 10 min thermal treatment, changes in adsorption behavior occurred at 35 °C indicated by principal component analysis of spectra as primarily due to increasing yields of methionine fragments. This was likely to be due to either altering the preferential protein orientation upon adsorption or greater denaturation exposing the hydrophobic core. This investigation demonstrates the sensitivity of ToF-SIMS in studying biopharmaceutical adsorption and conformational change and can assist with studies into promoting their stability.

Instrumentation and software for mass spectrometry imaging--making the most of what you've got

Whilst it might be desirable to be able to purchase an up to date mass spectrometry platform and dedicate it to mass spectrometry imaging, this is not the situation initially for many laboratories. There are a variety of methods by which existing mass spectrometers can be upgraded/adapted to perform mass spectrometry imaging using MALDI, DESI or LAESI as the means of generating ions. The focus of this article is on relatively low cost adaptations of existing instrumentation with suggestions made for performance enhancements where appropriate. A brief description of attempts to perform SIMS imaging on quadrupole time of flight mass spectrometers is also given. The required software is described with particular emphasis on freeware packages which can be used to display/enhance data. Requirements for data pre-processing prior or statistical analysis are discussed along with the use of MATLAB® for the analysis itself.

Surface view of the lateral organization of lipids and proteins in lung surfactant model systems-a ToF-SIMS approach

The lateral organization of domain structures is an extremely significant aspect of biomembrane research. Chemical imaging by mass spectrometry with its recent advancement in sensitivity and lateral resolution has become a highly promising tool in biological research. In this review, we focus briefly on the instrumentation, working principle and important concepts related to time-of-flight secondary ion mass spectrometry followed by an overview of lipid/protein fragmentation patterns and chemical mapping. The key issues addressed are the applications of time-of-flight secondary ion mass spectrometry in biological membrane research. Additionally, we briefly review our recent investigations based on time-of-flight secondary ion mass spectrometry to unravel the lateral distribution of lipids and surfactant proteins in lung surfactant model systems as an example that highlights the importance of fluidity and ionic conditions on lipid phase behavior and lipid-protein interactions.