Improving secondary ion mass spectrometry image quality with image fusion

Probe-based mass spectrometry approaches for single-cell and single-organelle measurements

Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.

Nanoscale bond-selective imaging by computational fusion of atomic force microscopy and coherent anti-Stokes Raman scattering microscopy

Vibrational microscopy based on coherent Raman scattering is a powerful tool for high-speed chemical imaging, but its lateral resolution is bound to the optical diffraction limit. On the other hand, atomic force microscopy (AFM) provides nano-scale spatial resolution, yet with lower chemical specificity. In this study, we leverage a computational approach called pan-sharpening to merge AFM topography images and coherent anti-Stokes Raman scattering (CARS) images. The hybrid system combines the advantages of both modalities, providing informative chemical mapping with ∼20 nm spatial resolution. CARS and AFM images were sequentially acquired on a single multimodal platform, which facilitates image co-localization. Our image fusion approach allowed for discerning merged neighboring features previously invisible due to the diffraction limit and identifying subtle unobservable structures with the input from AFM images. Compared to tip-enhanced CARS measurement, sequential acquisition of CARS and AFM images enables higher laser power to be used and avoids any tip damage caused by the incident laser beams, resulting in a significantly improved CARS image quality. Together, our work suggests a new direction for achieving super-resolution coherent Raman scattering imaging of materials through a computational approach.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

An algorithm-assisted automated identification and enumeration system for sensitive hydrogen sulfide sensing under dark field microscopy

A sensitive H2S sensing strategy has been developed based on the automated identification and enumeration algorithm.

Robot-Assisted SpiderMass for In Vivo Real-Time Topography Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has shown to bring invaluable information for biological and clinical applications. However, conventional MSI is generally performed ex vivo from tissue sections. Here, we developed a novel MS-based method for in vivo mass spectrometry imaging. By coupling the SpiderMass technology, that provides in vivo minimally invasive analysis-to a robotic arm of high accuracy, we demonstrate that images can be acquired from any surface by moving the laser probe above the surface. By equipping the robotic arm with a sensor, we are also able to both get the topography image of the sample surface and the molecular distribution, and then and plot back the molecular data, directly to the 3D topographical image without the need for image fusion. This is shown for the first time with the 3D topographic MS-based whole-body imaging of a mouse. Enabling fast in vivo MSI bridged to topography paves the way for surgical applications to excision margins.

Enhancing hyperspectral EELS analysis of complex plasmonic nanostructures with pan-sharpening

Nanoscale hyperspectral techniques-such as electron energy loss spectroscopy (EELS)-are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities.

Evaluation of secondary ions related to plant tissue using least absolute shrinkage and selection operator

With regard to life sciences, it is important to understand biological functions such as metabolic reactions at the cellular level. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) that can provide chemical mappings at 100 nm lateral resolutions is useful for obtaining three-dimensional maps of biological molecules in cells and tissues. TOF-SIMS spectra generally contain several hundred to several thousand secondary ion peaks that provide detailed chemical information. In order to manage such a large number of peaks, data analysis methods such as multivariate analysis techniques have been applied to TOF-SIMS data of complex samples. However, the interpretation of the data analysis results is sometimes still difficult, especially for biological samples. In this study, TOF-SIMS data of resin-embedded plant samples were analyzed using one of the sparse modeling methods, least absolute shrinkage and selection operator (LASSO), to directly select secondary ions related to biological structures such as cell walls and nuclei. The same sample was measured by optical microscopy and the same measurement area as TOF-SIMS was extracted in order to prepare a target image for LASSO. The same area of the TOF-SIMS and microscope data were fused to evaluate the influence of the image fusion on the TOF-SIMS spectrum information using principal component analysis. Specifically, the authors examined onion mycorrhizal root colonized with Gigaspora margarita (an arbuscular mycorrhizal fungus). The results showed that by employing this approach using LASSO, important secondary ions from biological samples were effectively selected and could be clearly distinguished from the embedding resin.

Developing a Drug Screening Platform: MALDI-Mass Spectrometry Imaging of Paper-Based Cultures

Many potential chemotherapeutics fail to reach patients. One of the key reasons is that compounds are tested during the drug discovery stage in two-dimensional (2D) cell cultures, which are often unable to accurately model in vivo outcomes. Three-dimensional (3D) in vitro tumor models are more predictive of chemotherapeutic effectiveness than 2D cultures, and thus, their implementation during the drug screening stage has the potential to more accurately evaluate compounds earlier, saving both time and money. Paper-based cultures (PBCs) are an emerging 3D culture platform in which cells suspended in Matrigel are seeded into paper scaffolds and cultured to generate a tissue-like environment. In this study, we demonstrate the potential of matrix-assisted laser desorption/ionization-mass spectrometry imaging with PBCs (MALDI-MSI-PBC) as a drug screening platform. This method discriminated regions of the PBCs with and without cells and/or drugs, indicating that coupling PBCs with MALDI-MSI has the potential to develop rapid, large-scale, and parallel mass spectrometric drug screens.

Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology

Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".

Multimodal Chemical Analysis of the Brain by High Mass Resolution Mass Spectrometry and Infrared Spectroscopic Imaging

The brain functions through chemical interactions between many different cell types, including neurons and glia. Acquiring comprehensive information on complex, heterogeneous systems requires multiple analytical tools, each of which have unique chemical specificity and spatial resolution. Multimodal imaging generates complementary chemical information via spatially localized molecular maps, ideally from the same sample, but requires method enhancements that span from data acquisition to interpretation. We devised a protocol for performing matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance-mass spectrometry imaging (MSI), followed by infrared (IR) spectroscopic imaging on the same specimen. Multimodal measurements from the same tissue provide precise spatial alignment between modalities, enabling more advanced image processing such as image fusion and sharpening. Performing MSI first produces higher quality data from each technique compared to performing IR imaging before MSI. The difference is likely due to fixing the tissue section during MALDI matrix removal, thereby preventing analyte degradation occurring during IR imaging from an unfixed specimen. Leveraging the unique capabilities of each modality, we utilized pan sharpening of MS (mass spectrometry) ion images with selected bands from IR spectroscopy and midlevel data fusion. In comparison to sharpening with histological images, pan sharpening can employ a plethora of IR bands, producing sharpened MS images while retaining the fidelity of the initial ion images. Using Laplacian pyramid sharpening, we determine the localization of several lipids present within the hippocampus with high mass accuracy at 5 μm pixel widths. Further, through midlevel data fusion of the imaging data sets combined with k-means clustering, the combined data set discriminates between additional anatomical structures unrecognized by the individual imaging approaches. Significant differences between molecular ion abundances are detected between relevant structures within the hippocampus, such as the CA1 and CA3 regions. Our methodology provides high quality multiplex and multimodal chemical imaging of the same tissue sample, enabling more advanced data processing and analysis routines.

Correlative Microscopy Combining Secondary Ion Mass Spectrometry and Electron Microscopy: Comparison of Intensity-Hue-Saturation and Laplacian Pyramid Methods for Image Fusion

Correlative microscopy combining various imaging modalities offers powerful insights into obtaining a comprehensive understanding of physical, chemical, and biological phenomena. In this article, we investigate two approaches for image fusion in the context of combining the inherently lower-resolution chemical images obtained using secondary ion mass spectrometry (SIMS) with the high-resolution ultrastructural images obtained using electron microscopy (EM). We evaluate the image fusion methods with three different case studies selected to broadly represent the typical samples in life science research: (i) histology (unlabeled tissue), (ii) nanotoxicology, and (iii) metabolism (isotopically labeled tissue). We show that the intensity-hue-saturation fusion method often applied for EM-sharpening can result in serious image artifacts, especially in cases where different contrast mechanisms interplay. Here, we introduce and demonstrate Laplacian pyramid fusion as a powerful and more robust alternative method for image fusion. Both physical and technical aspects of correlative image overlay and image fusion specific to SIMS-based correlative microscopy are discussed in detail alongside the advantages, limitations, and the potential artifacts. Quantitative metrics to evaluate the results of image fusion are also discussed.

Multimodal image fusion with SIMS: Preprocessing with image registration

In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important. To ensure that two input images are of the same region, an implementation of the insight segmentation and registration toolkit (ITK) was developed to act as a preprocessing step before performing image fusion. This implementation of ITK allows for several degrees of movement between two input images to be accounted for, including translation, rotation, and scale transforms. First, the implementation was confirmed to accurately register two multimodal images by supplying a known transform. Once validated, two model systems, a copper mesh grid and a group of RAW 264.7 cells, were used to demonstrate the use of the ITK implementation to register a SIMS image with a microscopy image for the purpose of performing image fusion.

The Need for Speed in Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) has emerged as a powerful analytical tool enabling the direct molecular mapping of many types of tissue. Specifically, matrix-assisted laser desorption/ ionization (MALDI) represents one of the most broadly applicable IMS technologies. In recent years, advances in solid state laser technology, mass spectrometry instrumentation, computer technology, and experimental methodology have produced IMS systems capable of unprecedented data acquisition speeds (>50 pixels/second). In applications of this technology, throughput is an important consideration when designing an IMS experiment. As IMS becomes more widely adopted, continual improvements in experimental setups will be important to address biologically and clinically relevant time scales.

Co-registered Topographical, Band Excitation Nanomechanical, and Mass Spectral Imaging Using a Combined Atomic Force Microscopy/Mass Spectrometry Platform

The advancement of a hybrid atomic force microscopy/mass spectrometry imaging platform demonstrating the co-registered topographical, band excitation nanomechanical, and mass spectral imaging of a surface using a single instrument is reported. The mass spectrometry-based chemical imaging component of the system utilized nanothermal analysis probes for pyrolytic surface sampling followed by atmospheric pressure chemical ionization of the gas-phase species produced with subsequent mass analysis. The basic instrumental setup and operation are discussed, and the multimodal imaging capability and utility are demonstrated using a phase-separated polystyrene/poly(2-vinylpyridine) polymer blend thin film. The topography and band excitation images showed that the valley and plateau regions of the thin film surface were comprised primarily of one of the two polymers in the blend with the mass spectral chemical image used to definitively identify the polymers at the different locations. Data point pixel size for the topography (390 nm × 390 nm), band excitation (781 nm × 781 nm), and mass spectrometry (690 nm × 500 nm) images was comparable and submicrometer in all three cases, but the data voxel size for each of the three images was dramatically different. The topography image was uniquely a surface measurement, whereas the band excitation image included information from an estimated 20 nm deep into the sample and the mass spectral image from 110 to 140 nm in depth. Because of this dramatic sampling depth variance, some differences in the band excitation and mass spectrometry chemical images were observed and were interpreted to indicate the presence of a buried interface in the sample. The spatial resolution of the chemical image was estimated to be between 1.5 and 2.6 μm, based on the ability to distinguish surface features in that image that were also observed in the other images.