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

Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

Recent developments in ionization techniques for single-cell mass spectrometry

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

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.

Magnetic Sector Secondary Ion Mass Spectrometry on FIB-SEM Instruments for Nanoscale Chemical Imaging

The structural, morphological, and chemical characterization of samples is of utmost importance for a large number of scientific fields. Furthermore, this characterization very often needs to be performed in three dimensions and at length scales down to the nanometer. Therefore, there is a stringent necessity to develop appropriate instrumentational solutions to fulfill these needs. Here we report on the deployment of magnetic sector secondary ion mass spectrometry (SIMS) on a type of instrument widely used for such nanoscale investigations, namely, focused ion beam (FIB)–scanning electron microscopy (SEM) instruments. First, we present the layout of the FIB-SEM-SIMS instrument and address its performance by using specific test samples. The achieved performance can be summarized as follows: an overall secondary ion beam transmission above 40%, a mass resolving power (M/ΔM) of more than 400, a detectable mass range from 1 to 400 amu, a lateral resolution in two-dimensional (2D) chemical imaging mode of 15 nm, and a depth resolution of ∼4 nm at 3.0 keV of beam landing energy. Second, we show results (depth profiling, 2D imaging, three-dimensional imaging) obtained in a wide range of areas, such as battery research, photovoltaics, multilayered samples, and life science applications. We hereby highlight the system’s versatile capability of conducting high-performance correlative studies in the fields of materials science and life sciences.

Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.

Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis

Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.

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.

Highest resolution chemical imaging based on secondary ion mass spectrometry performed on the helium ion microscope

This paper is a review on the combination between Helium Ion Microscopy (HIM) and Secondary Ion Mass Spectrometry (SIMS), which is a recently developed technique that is of particular relevance in the context of the quest for high-resolution high-sensitivity nano-analytical solutions. We start by giving an overview on the HIM-SIMS concept and the underlying fundamental principles of both HIM and SIMS. We then present and discuss instrumental aspects of the HIM and SIMS techniques, highlighting the advantage of the integrated HIM-SIMS instrument. We give an overview on the performance characteristics of the HIM-SIMS technique, which is capable of producing elemental SIMS maps with lateral resolution below 20 nm, approaching the physical resolution limits, while maintaining a sub-nanometric resolution in the secondary electron microscopy mode. In addition, we showcase different strategies and methods allowing to take profit of both capabilities of the HIM-SIMS instrument (high-resolution imaging using secondary electrons and mass filtered secondary sons) in a correlative approach. Since its development HIM-SIMS has been successfully applied to a large variety of scientific and technological topics. Here, we will present and summarise recent applications of nanoscale imaging in materials research, life sciences and geology.

4D Surface Reconstructions to Study Microscale Structures and Functions in Soil Biogeochemistry

The development of high-resolution microscopy and spectroscopy techniques has allowed the analysis of microscopic 3D objects in fields like nanotechnology and life and soil sciences. Soils have the ability to incorporate and store large amounts of organic carbon. To study this organic matter (OM) sequestration, it is essential to analyze its association with soil minerals at the relevant microaggregate scale. This has been previously studied in 2D. However, 3D surface representations would allow a variable angle and magnification analysis, providing detailed insight on their architecture. Here we illustrate a 4D surface reconstruction workflow able to locate preferential sites for OM deposition with respect to microaggregate topography. We used Helium Ion Microscopy to acquire overlapping Secondary Electron (SE) images to reconstruct the soil topography in 3D. Then we used nanoscale Secondary Ion Mass Spectrometry imaging to chemically differentiate between the OM and mineral constituents forming the microaggregates. This image was projected onto the 3D SE model to create a 4D surface reconstruction. Our results show that organo-mineral associations mainly form at medium curvatures while flat and highly curved surfaces are avoided. This method presents an important step forward to survey the 3D physical structure and chemical composition of microscale biogeochemical systems correlatively.

Single cell imaging reveals cisplatin regulating interactions between transcription (co)factors and DNA

Cisplatin is an extremely successful anticancer drug, and is commonly thought to target DNA. However, the way in which cisplatin-induced DNA lesions regulate interactions between transcription factors/cofactors and genomic DNA remains unclear. Herein, we developed a dual-modal microscopy imaging strategy to investigate, in situ, the formation of ternary binding complexes of the transcription cofactor HMGB1 and transcription factor Smad3 with cisplatin crosslinked DNA in single cells. We utilized confocal microscopy imaging to map EYFP-fused HMGB1 and fluorescent dye-stained DNA in single cells, followed by the visualization of cisplatin using high spatial resolution (200-350 nm) time of flight secondary ion mass spectrometry (ToF-SIMS) imaging of the same cells. The superposition of the fluorescence and the mass spectrometry (MS) signals indicate the formation of HMGB1-Pt-DNA ternary complexes in the cells. More significantly, for the first time, similar integrated imaging revealed that the cisplatin lesions at Smad-binding elements, for example GGC(GC)/(CG) and AGAC, disrupted the interactions of Smad3 with DNA, which was evidenced by the remarkable reduction in the expression of Smad-specific luciferase reporters subjected to cisplatin treatment. This finding suggests that Smad3 and its related signalling pathway are most likely involved in the intracellular response to cisplatin induced DNA damage.

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.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach

We present a subspace method that accelerates data acquisition using Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). For MSI of biological tissue samples, there is a finite number of heterogeneous tissue types with distinct chemical profiles that introduce redundancy in the high-dimensional measurements. Our subspace model exploits the redundancy in data measured from whole-slice tissue samples by decomposing the transient signals into linear combinations of a set of basis transients with the desired spectral resolution. This decomposition allowed us to design a strategy that acquires a subset of long transients for basis determination and short transients for the remaining pixels, drastically reducing the acquisition time. The computational reconstruction strategy can maintain high-mass-resolution and spatial-resolution MSI while providing a 10-fold improvement in throughput. We validated the capability of the subspace model using a rat sagittal brain slice imaging data set. Comprehensive evaluation of the quality of the mass spectral and ion images demonstrated that the reconstructed data produced by the reported method required only 15% of the typical acquisition time and exhibited both qualitative and quantitative consistency when compared to the original data. Our method enables either higher sample throughput or higher-resolution images at similar acquisition lengths, providing greater flexibility in obtaining FT-ICR MSI measurements.

Correlia: an ImageJ plug-in to co-register and visualise multimodal correlative micrographs

The correlation of different microscopic imaging techniques alongside with microanalytical methods is crucial to better understand biological processes on a subcellular level. For that, micrographs and chemical maps exhibiting both, very different spatial resolution and field-of-view but also a highly multimodal content has to be co-registered. We developed the ImageJ/Fiji plug-in Correlia that provides an environment for handling multimodal correlative microscopy data. Several linear and nonlinear registration methods using either feature or area-based similarity measures can flexibly be cascaded to align and warp 2D microscopy data sets. The registration of data sets containing light- and electron micrographs as well as chemical maps acquired by secondary-ion mass spectroscopy and energy-dispersive X-ray spectroscopy is demonstrated. Correlia is an open-source tool developed particularly for the registration and analysis of highly multimodal 2D correlative microscopy data. LAY DESCRIPTION: If a microscopic object is imaged correlatively by two or more different microscopes the acquired micrographs will have to be overlaid accurately using an image-registration software. In cases of relatively similar image content creating such an overlay is straight-forward but what if the fields-of-view and resolutions of the micrographs differ significantly? What if there are distortions in a micrograph which have to be corrected before creating an overlay? What if furthermore a chemical map shall be overlaid that merely shows regions in which a certain chemical element is present? The rapidly increasing number of applications in correlative microscopy is calling for an easy-to-use and flexible image registration software that can deal with these challenges. Having that in mind, we developed Correlia, an ImageJ/Fiji plug-in that provides an environment for handling multimodal 2D correlative microscopy data-sets. It allows for creating overlays using different registration algorithms that can flexibly be cascaded. In this paper we describe what is happening 'under the hood' and give two example data-sets from microbiology which were registered using Correlia. Correlia is open source software and available from www.ufz.de/correlia - including introductory examples, as the authors would like to encourage other scientists to process their individual correlative microscopy data using Correlia.

Photoluminescence of ZnO/ZnMgO heterostructure nanobelts grown by MBE

ZnO nanobelts may grow with their polar axis perpendicular to growth direction. Heterostructured nanobelts therefore contain hetero-interfaces along the polar axis of ZnO where polarisation mismatch may induce electron confinement. These interfaces run along the length of the nanobelts. Such heterostructure nanobelts are grown by molecular beam epitaxy and TEM images confirm the core-shell structure. The effects of shell-growth temperature on nano-heterostructures is investigated using photoluminescence and secondary ion mass spectrometry in a focussed ion-beam microscope with Ne⁺ as the primary ion beam. We perform low temperature photoluminescence on ensembles of such heterostructures and single nanostructures. We show how single nanobelts have photoluminescence spectra rich in features and attribute these to band misalignment at ZnO/ZnMgO interfaces embedded within nano-heterostructures.

Surface cleaning and sample carrier for complementary high-resolution imaging techniques

Nowadays, high-resolution imaging techniques are extensively applied in a complementary way to gain insights into complex phenomena. For a truly complementary analytical approach, a common sample carrier is required that is suitable for the different preparation methods necessary for each analytical technique. This sample carrier should be capable of accommodating diverse analytes and maintaining their pristine composition and arrangement during deposition and preparation. In this work, a new type of sample carrier consisting of a silicon wafer with a hydrophilic polymer coating was developed. The robustness of the polymer coating toward solvents was strengthened by cross-linking and stoving. Furthermore, a new method of UV-ozone cleaning was developed that enhances the adhesion of the polymer coating to the wafer and ensures reproducible surface-properties of the resulting sample carrier. The hydrophilicity of the sample carrier was recovered applying the new method of UV-ozone cleaning, while avoiding UV-induced damages to the polymer. Noncontact 3D optical profilometry and contact angle measurements were used to monitor the hydrophilicity of the coating. The hydrophilicity of the polymer coating ensures its spongelike behavior so that upon the deposition of an analyte suspension, the solvent and solutes are separated from the analyte by absorption into the polymer. This feature is essential to limit the coffee-ring effect and preserve the native identity of an analyte upon deposition. The suitability of the sample carrier for various sample types was tested using nanoparticles from suspension, bacterial cells, and tissue sections. To assess the homogeneity of the analyte distribution and preservation of sample integrity, optical and scanning electron microscopy, helium ion microscopy, laser ablation inductively coupled plasma mass spectrometry, and time-of-flight secondary ion mass spectrometry were used. This demonstrates the broad applicability of the newly developed sample carrier and its value for complementary imaging.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Precision biomarker discovery powered by microscopy image fusion-assisted high spatial resolution ambient ionization mass spectrometry imaging

Mass spectrometry imaging (MSI) using the ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry were employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion were verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-μm spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI was ∼100 μm. In this regard, sharpened molecular histology of tissue sections was achieved, providing complementary references to the pathology. Such a multi-modal integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen potential biomarkers on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section were identified.

Stationary beam full-field transmission helium ion microscopy using sub-50 keV He+: Projected images and intensity patterns

A dedicated transmission helium ion microscope (THIM) for sub-50 keV helium has been constructed to investigate ion scattering processes and contrast mechanisms, aiding the development of new imaging and analysis modalities. Unlike a commercial helium ion microscope (HIM), the in-house built instrument allows full flexibility in experimental configuration. Here, we report projection imaging and intensity patterns obtained from powder and bulk crystalline samples using stationary broad-beam as well as convergent-beam illumination conditions in THIM. The He⁺ ions formed unexpected spot patterns in the far field for MgO, BN and NaCl powder samples, but not for Au-coated MgO. The origin of the spot patterns in these samples was investigated. Surface diffraction of ions was excluded as a possible cause because the recorded scattering angles do not correspond to the predicted Bragg angles. Complementary secondary electron (SE) imaging in the HIM revealed that these samples charge significantly under He⁺ ion irradiation. The spot patterns obtained in the THIM experiments are explained as artefacts related to sample charging. The results presented here indicate that factors other than channeling, blocking and surface diffraction of ions have an impact on the final intensity distribution in the far field. Hence, the different processes contributing to the final intensities will need to be understood in order to decouple and study the relevant ion-beam scattering and deflection phenomena.

Imaging and Analytics on the Helium Ion Microscope

The helium ion microscope (HIM) has emerged as an instrument of choice for patterning, imaging and, more recently, analytics at the nanoscale. Here, we review secondary electron imaging on the HIM and the various methodologies and hardware components that have been developed to confer analytical capabilities to the HIM. Secondary electron-based imaging can be performed at resolutions down to 0.5 nm with high contrast, with high depth of field, and directly on insulating samples. Analytical methods include secondary electron hyperspectral imaging (SEHI), scanning transmission ion microscopy (STIM), backscattering spectrometry and, in particular, secondary ion mass spectrometry (SIMS). The SIMS system that was specifically designed for the HIM allows the detection of all elements, the differentiation between isotopes, and the detection of trace elements. It provides mass spectra, depth profiles, and 2D or 3D images with lateral resolutions down to 10 nm.

Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues

Over the last two decades, mass spectrometry imaging (MSI) has been increasingly employed to investigate the spatial distribution of a wide variety of molecules in complex biological samples. MSI has demonstrated its potential in numerous applications from drug discovery, disease state evaluation through proteomic and/or metabolomic studies. Significant technological and methodological advancements have addressed natural limitations of the techniques, i.e., increased spatial resolution, increased detection sensitivity especially for large molecules, higher throughput analysis and data management. One of the next major evolutions of MSI is linked to the introduction of imaging mass cytometry (IMC). IMC is a multiplexed method for tissue phenotyping, imaging signalling pathway or cell marker assessment, at sub-cellular resolution (1 μm). It uses MSI to simultaneously detect and quantify up to 30 different antibodies within a tissue section. The combination of MSI with other molecular imaging techniques can also provide highly relevant complementary information to explore new scientific fields. Traditionally, classical histology (especially haematoxylin and eosin–stained sections) is overlaid with molecular profiles obtained by MSI. Thus, MSI-based molecular histology provides a snapshot of a tissue microenvironment and enables the correlation of drugs, metabolites, lipids, peptides or proteins with histological/pathological features or tissue substructures. Recently, many examples combining MSI with other imaging modalities such as fluorescence, confocal Raman spectroscopy and MRI have emerged. For instance, brain pathophysiology has been studied using both MRI and MSI, establishing correlations between in and ex vivo molecular imaging techniques. Endogenous metabolite and small peptide modulation were evaluated depending on disease state. Here, we review advanced ‘hot topics’ in MSI development and explore the combination of MSI with established molecular imaging techniques to improve our understanding of biological and pathophysiological processes.

Multi-modal Image Fusion for Multispectral Super-resolution in Microscopy

Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectral channels, existing technology cannot capture spectral images at the same spatial resolution as structural microscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpret and analyze. This highlights a need to upsample the low-resolution spectral image by using spatial information contained in the high-resolution image, thereby creating a fused representation with high specificity both spatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural and spectral microscopy images to create super-resolved representations of spectral images. As a first application, we super-resolve spectral images of retinal tissue imaged with confocal laser scanning microscopy, by using spatial information from structured illumination microscopy. Second, we super-resolve mass spectroscopic images of mouse brain tissue, by using spatial information from high-resolution histology images. We present a systematic validation of model assumptions crucial towards maintaining the original nature of spectra and the applicability of super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R ² values, and the spatial quality of the super-resolved images are evaluated using normalized mutual information.

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.

ColorEM: analytical electron microscopy for element-guided identification and imaging of the building blocks of life

Nanometer-scale identification of multiple targets is crucial to understand how biomolecules regulate life. Markers, or probes, of specific biomolecules help to visualize and to identify. Electron microscopy (EM), the highest resolution imaging modality, provides ultrastructural information where several subcellular structures can be readily identified. For precise tagging of (macro)molecules, electron-dense probes, distinguishable in gray-scale EM, are being used. However, practically these genetically-encoded or immune-targeted probes are limited to three targets. In correlated microscopy, fluorescent signals are overlaid on the EM image, but typically without the nanometer-scale resolution and limited to visualization of few targets. Recently, analytical methods have become more sensitive, which has led to a renewed interest to explore these for imaging of elements and molecules in cells and tissues in EM. Here, we present the current state of nanoscale imaging of cells and tissues using energy dispersive X-ray analysis (EDX), electron energy loss spectroscopy (EELS), cathodoluminescence (CL), and touch upon secondary ion mass spectroscopy at the nanoscale (NanoSIMS). ColorEM is the term encompassing these analytical techniques the results of which are then displayed as false-color at the EM scale. We highlight how ColorEM will become a strong analytical nano-imaging tool in life science microscopy.