Fourier Transform Infrared Microscopy Enables Guidance of Automated Mass Spectrometry Imaging to Predefined Tissue Morphologies

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.

Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.

Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow

The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

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.

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.

MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives

Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.

Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the effects at the molecular level in organisms is of increasing interest due to the environmental exposure to numerous contaminants. The analysis of tissues can be performed by histological examination, which is still time-consuming and restricted to target-specific staining methods. The histological approaches can be complemented with chemical imaging analysis. Chemical imaging of tissue sections is typically performed using a single imaging approach. However, for toxicological testing of environmental pollutants, a multimodal approach combined with improved data acquisition and evaluation is desirable, since it may allow for more rapid tissue characterization and give further information on ecotoxicological effects at the tissue level. Therefore, using the soil model organism Eisenia fetida as a model, we developed a sequential workflow combining Fourier transform infrared spectroscopy (FTIR) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for chemical analysis of the same tissue sections. Data analysis of the FTIR spectra via random decision forest (RDF) classification enabled the rapid identification of target tissues (e.g., digestive tissue), which are relevant from an ecotoxicological point of view. MALDI imaging analysis provided specific lipid species which are sensitive to metabolic changes and environmental stressors. Taken together, our approach provides a fast and reproducible workflow for label-free histochemical tissue analyses in E. fetida, which can be applied to other model organisms as well.

SALDI-MS and SERS Multimodal Imaging: One Nanostructured Substrate to Rule Them Both

Imaging techniques based on mass spectrometry or spectroscopy methods inform in situ about the chemical composition of biological tissues or organisms, but they are sometimes limited by their specificity, sensitivity, or spatial resolution. Multimodal imaging addresses these limitations by combining several imaging modalities; however, measuring the same sample with the same preparation using multiple imaging techniques is still uncommon due to the incompatibility between substrates, sample preparation protocols, and data formats. We present a multimodal imaging approach that employs a gold-coated nanostructured silicon substrate to couple surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced Raman spectroscopy (SERS). Our approach integrates both imaging modalities by using the same substrate, sample preparation, and data analysis software on the same sample, allowing the coregistration of both images. We transferred molecules from clean fingertips and fingertips covered with plasticine modeling clay onto our nanostructure and analyzed their chemical composition and distribution by SALDI-MS and SERS. Multimodal analysis located the traces of plasticine on fingermarks and provided chemical information on the composition of the clay. Our multimodal approach effectively combines the advantages of mass spectrometry and vibrational spectroscopy with the signal enhancing abilities of our nanostructured substrate.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Probing the Metabolic Landscape of Plant Vascular Bundles by Infrared Fingerprint Analysis, Imaging and Mass Spectrometry

Fingerprint analysis is a common technique in forensic and criminal investigations. Similar techniques exist in the field of infrared spectroscopy to identify biomolecules according to their characteristic spectral fingerprint features. These unique markers are located in a wavenumber range from 1800 to 600 cm-1 in the mid infrared region. Here, a novel bioanalytical concept of correlating these spectral features with corresponding mass spectrometry datasets to unravel metabolic clusters within complex plant tissues was applied. As proof of concept, vascular bundles of oilseed rape (Brassica napus) were investigated, one of the most important and widely cultivated temperate zone oilseed crops. The link between mass spectrometry data and spectral data identified features that co-aligned within both datasets. Regions of origin were then detected by searching for these features in hyperspectral images of plant tissues. This approach, based on co-alignment and co-localization, finally enabled the detection of eight distinct metabolic clusters, reflecting functional and structural arrangements within the vascular bundle. The proposed analytical concept may assist future synergistic research approaches and may lead to biotechnological innovations with regard to crop yield and sustainability.

Image fusion of IR and optical microscopy for mapping of biomolecules in tissue

It is shown that a pixel-level image fusion technique can produce images that combine the spatial resolution of optical microscopy images of haematoxylin and eosin (H&E) stained tissue with the chemical information in Fourier transform infrared (FTIR) images. The fused images show minimal distortion and the higher spatial resolution of the H&E images overcomes the diffraction limit on the spatial resolution of the FTIR images. A consideration of the FTIR spectra of nucleic acids and collagen can explain the changes in contrast between non-cancerous oral epithelium and underlying stroma within fused images formed by combining an H&E stain of oral tissue with FTIR images of the tissue obtained at a number of wavenumbers.

Rapid brain structure and tumour margin detection on whole frozen tissue sections by fast multiphotometric mid-infrared scanning

Frozen section analysis is a frequently used method for examination of tissue samples, especially for tumour detection. In the majority of cases, the aim is to identify characteristic tissue morphologies or tumour margins. Depending on the type of tissue, a high number of misdiagnoses are associated with this process. In this work, a fast spectroscopic measurement device and workflow was developed that significantly improves the speed of whole frozen tissue section analyses and provides sufficient information to visualize tissue structures and tumour margins, dependent on their lipid and protein molecular vibrations. That optical and non-destructive method is based on selected wavenumbers in the mid-infrared (MIR) range. We present a measuring system that substantially outperforms a commercially available Fourier Transform Infrared (FT-IR) Imaging system, since it enables acquisition of reduced spectral information at a scan field of 1 cm² in 3 s, with a spatial resolution of 20 µm. This allows fast visualization of segmented structure areas with little computational effort. For the first time, this multiphotometric MIR system is applied to biomedical tissue sections. We are referencing our novel MIR scanner on cryopreserved murine sagittal and coronal brain sections, especially focusing on the hippocampus, and show its usability for rapid identification of primary hepatocellular carcinoma (HCC) in mouse liver.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

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.

A combined high-throughput and high-content platform for unified on-chip synthesis, characterization and biological screening

Acceleration and unification of drug discovery is important to reduce the effort and cost of new drug development. Diverse chemical and biological conditions, specialized infrastructure and incompatibility between existing analytical methods with high-throughput, nanoliter scale chemistry make the whole drug discovery process lengthy and expensive. Here, we demonstrate a chemBIOS platform combining on-chip chemical synthesis, characterization and biological screening. We developed a dendrimer-based surface patterning that enables the generation of high-density nanodroplet arrays for both organic and aqueous liquids. Each droplet (among > 50,000 droplets per plate) functions as an individual, spatially separated nanovessel, that can be used for solution-based synthesis or analytical assays. An additional indium-tin oxide coating enables ultra-fast on-chip detection down to the attomole per droplet by matrix-assisted laser desorption/ionization mass spectrometry. The excellent optical properties of the chemBIOS platform allow for on-chip characterization and in-situ reaction monitoring in the ultraviolet, visible (on-chip UV-Vis spectroscopy and optical microscopy) and infrared (on-chip IR spectroscopy) regions. The platform is compatible with various cell-biological screenings, which opens new avenues in the fields of high-throughput synthesis and drug discovery.

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

Physicians diagnose subjectively the etiology of inaccessible infections where sampling is not feasible (such as, pneumonia, sinusitis, cholecystitis, peritonitis), as bacterial or viral. The diagnosis is based on their experience with some medical markers like blood counts and medical symptoms since it is harder to obtain swabs and reliable laboratory results for most cases. In this study, infrared spectroscopy with machine learning algorithms was used for the rapid and objective diagnosis of the etiology of inaccessible infections and enables an assessment of the error for the subjective diagnosis of the etiology of these infections by physicians. Our approach allows for diagnoses of the etiology of both accessible and inaccessible infections as based on an analysis of the innate immune system response through infrared spectroscopy measurements of white blood cell (WBC) samples. In the present study, we examined 343 individuals involving 113 controls, 89 inaccessible bacterial infections, 54 accessible bacterial infections, 60 inaccessible viral infections, and 27 accessible viral infections. Using our approach, the results show that it is possible to differentiate between controls and infections (combined bacterial and viral) with 95% accuracy, and enabling the diagnosis of the etiology of accessible infections as bacterial or viral with >94% sensitivity and > 90% specificity within one hour after the collection of the blood sample with error rate <6%. Based on our approach, the error rate of the physicians' subjective diagnosis of the etiology of inaccessible infections was found to be >23%.

Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format

BACKGROUND

Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand.

RESULTS

Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications.

CONCLUSION

Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

Morphological and Biochemical Properties of Human Astrocytes, Microglia, Glioma, and Glioblastoma Cells Using Fourier Transform Infrared Spectroscopy

BACKGROUND With infiltration, high-grade glioma easily causes the boundary between tumor tissue and adjacent tissue to become unclear and results in tumor recurrence at or near the resection margin according to the incomplete surgical resection. Fourier transform infrared spectroscopy (FTIR) technique has been demonstrated to be a useful tool that yields a molecular fingerprint and provides rapid, nondestructive, high-throughput and clinically relevant diagnostic information. MATERIAL AND METHODS FTIR was used to investigate the morphological and biochemical properties of human astrocytes (HA), microglia (HM1900), glioma cells (U87), and glioblastoma cells (BT325) cultured in vitro to simulate the infiltration area, with the use of multi-peak fitting and principal component analysis (PCA) of amide I of FTIR spectra and the use of hierarchical cluster analysis (HCA). RESULTS We found that the secondary structures of the 4 types of cells were significantly different. The contents of a-helix structure in glial cells was significantly higher than in the glioma cells, but the levels of ß-sheet, ß-turn, and random coil structures were lower. The 4 types of cells could be clearly separated with 85% for PC1 and 12.2% for PC2. CONCLUSIONS FTIR can be used to distinguish between human astrocytes, microglia, glioma, and glioblastoma cells in vitro. The protein secondary structure can be used as an indicator to distinguish tumor cells from glial cells. Further tissue-based and in vivo studies are needed to determine whether FTIR can identify cerebral glioma.

Mass spectrometry imaging and LC/MS reveal decreased cerebellar phosphoinositides in Niemann-Pick type C1-null mice

Niemann-Pick disease type C1 (NPC1) is a lipid storage disorder in which cholesterol and glycosphingolipids accumulate in late endosomal/lysosomal compartments because of mutations in the NPC1 gene. A hallmark of NPC1 is progressive neurodegeneration of the cerebellum as well as visceral organ damage; however, the mechanisms driving this disease pathology are not fully understood. Phosphoinositides are phospholipids that play distinct roles in signal transduction and vesicle trafficking. Here, we utilized a consensus spectra analysis of MS imaging data sets and orthogonal LC/MS analyses to evaluate the spatial distribution of phosphoinositides and quantify them in cerebellar tissue from Npc1-null mice. Our results suggest significant depletion of multiple phosphoinositide species, including PI, PIP, and PIP₂, in the cerebellum of the Npc1-null mice in both whole-tissue lysates and myelin-enriched fractions. Additionally, we observed altered levels of the regulatory enzyme phosphatidylinositol 4-kinase type 2α in Npc1-null mice. In contrast, the levels of related kinases, phosphatases, and transfer proteins were unaltered in the Npc1-null mouse model, as observed by Western blot analysis. Our discovery of phosphoinositide lipid biomarkers for NPC1 opens new perspectives on the pathophysiology underlying this fatal neurodegenerative disease.

Molecule Sensitive Optical Imaging and Monitoring Techniques-A Review of Applications in Micro-Process Engineering

This paper provides an overview of how molecule-sensitive, spatially-resolved technologies can be applied for monitoring and measuring in microchannels. The principles of elastic light scattering, fluorescence, near-infrared, mid-infrared, and Raman imaging, as well as combination techniques, are briefly presented, and their advantages and disadvantages are explained. With optical methods, images can be acquired both scanning and simultaneously as a complete image. Scanning technologies require more acquisition time, and fast moving processes are not easily observable. On the other hand, molecular selectivity is very high, especially in Raman and mid-infrared (MIR) scanning. For near-infrared (NIR) images, the entire measuring range can be simultaneously recorded with indium gallium arsenide (InGaAs) cameras. However, in this wavelength range, water is the dominant molecule, so it is sometimes necessary to use complex learning algorithms that increase the preparation effort before the actual measurement. These technologies excite molecular vibrations in a variety of ways, making these methods suitable for specific products. Besides measurements of the fluid composition, technologies for particle detection are of additional importance. With scattered light techniques and evaluation according to the Mie theory, particles in the range of 0.2–1 µm can be detected, and fast growth processes can be observed. Local multispectral measurements can also be carried out with fiber optic-coupled systems through small probe heads of approximately 1 mm diameter.

Rapid diagnosis of infection etiology in febrile pediatric oncology patients using infrared spectroscopy of leukocytes

Rapid diagnosis of the etiology of infection is highly important for an effective treatment of the infected patients. Bacterial and viral infections are serious diseases that can cause death in many cases. The human immune system deals with many viral and bacterial infections that cause no symptoms and pass quietly without treatment. However, oncology patients undergoing chemotherapy have a very weak immune system caused by leukopenia, and even minor pathogen infection threatens their lives. For this reason, physicians tend to prescribe immediately several types of antibiotics for febrile pediatric oncology patients (FPOPs). Uncontrolled use of antibiotics is one of the major contributors to the development of resistant bacteria. Therefore, for oncology patients, a rapid and objective diagnosis of the etiology of the infection is extremely critical. Current identification methods are time-consuming (>24 h). In this study, the potential of midinfrared spectroscopy in tandem with machine learning algorithms is evaluated for rapid and objective diagnosis of the etiology of infections in FPOPs using simple peripheral blood samples. Our results show that infrared spectroscopy enables the diagnosis of the etiology of infection as bacterial or viral within 70 minutes after the collection of the blood sample with 93% sensitivity and 88% specificity.

Contrast enhancement of surface layers with fast middle-infrared scanning

In this study, we present an efficient and innovative method to visualize absorption differences in the mid-infrared range with spatial resolution using laser technology. We focus on only two lasers with wavelengths between 3.4 μm and 3.6 μm and a spatial resolution of 20 μm and thus achieve a scanning speed up to 300 kS/s for fast image generation. In this article, we focus especially on the detection of C–H bands in this region of the absorption spectrum. Concealed structures are examined by calculating the measured structures with both wavelengths. In our results, we demonstrate exemplary measurements on 130-μm-thick polyvinyl chloride layers. In turn, these structures are suitable for further processing in rapid quantitative quality control.

Genetically Encoded Fluorescent Proteins Enable High-Throughput Assignment of Cell Cohorts Directly from MALDI-MS Images

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides a unique in situ chemical profile that can include drugs, nucleic acids, metabolites, lipids, and proteins. MSI of individual cells (of a known cell type) affords a unique insight into normal and disease-related processes and is a prerequisite for combining the results of MSI and other single-cell modalities (e.g. mass cytometry and next-generation sequencing). Technological barriers have prevented the high-throughput assignment of MSI spectra from solid tissue preparations to their cell type. These barriers include obtaining a suitable cell-identifying image (e.g. immunohistochemistry) and obtaining sufficiently accurate registration of the cell-identifying and MALDI-MS images. This study introduces a technique that overcame these barriers by assigning cell type directly from mass spectra. We hypothesized that, in MSI from mice with a defined fluorescent protein expression pattern, the fluorescent protein's molecular ion could be used to identify cell cohorts. A method was developed for the purification of enhanced yellow fluorescent protein (EYFP) from mice. To determine EYFP's molecular mass for MSI studies, we performed intact mass analysis and characterized the protein's primary structure and post-translational modifications through various techniques. MALDI-MSI methods were developed to enhance the detection of EYFP in situ, and by extraction of EYFP's molecular ion from MALDI-MS images, automated, whole-image assignment of cell cohorts was achieved. This method was validated using a well-characterized mouse line that expresses EYFP in motor and sensory neurons and should be applicable to hundreds of commercially available mice (and other animal) strains comprising a multitude of cell-specific fluorescent labels.

Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy

Histology-directed imaging mass spectrometry (IMS) is a spatially targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations onto the IMS tissue section. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational monomodal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (∼3-10 μm based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1% of the tissue section area) malaria infected mouse hepatocytes and glomeruli in the human kidney case study.

Mass spectrometry imaging of lipids: untargeted consensus spectra reveal spatial distributions in Niemann-Pick disease type C1

Mass spectrometry imaging (MSI) is a tool to rapidly map the spatial location of analytes without the need for tagging or a reporter system. Niemann-Pick disease type C1 (NPC1) is a neurodegenerative, lysosomal storage disorder characterized by accumulation of unesterified cholesterol and sphingolipids in the endo-lysosomal system. Here, we use MSI to visualize lipids including cholesterol in cerebellar brain tissue from the NPC1 symptomatic mouse model and unaffected controls. To complement the imaging studies, a data-processing pipeline was developed to generate consensus mass spectra, thereby using both technical and biological image replicates to assess differences. The consensus spectra are used to determine true differences in lipid relative abundance; lipid distributions can be determined in an unbiased fashion without prior knowledge of location. We show the cerebellar distribution of gangliosides GM1, GM2, and GM3, including variants of lipid chain length. We also performed MALDI-MSI of cholesterol. Further analysis of lobules IV/V and X of the cerebellum gangliosides indicates regional differences. The specificity achieved highlights the power of MSI, and this new workflow demonstrates a universal approach for addressing reproducibility in imaging experiments applied to NPC1.