Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.

Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue.

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

High-Resolution Imaging Mass Spectrometry of Human Donor Eye: Photoreceptors Cells and Basal Laminar Deposit of Age-Related Macular Degeneration.

Pathologies of the retina are clinically visualized in vivo with OCT and ex vivo with immunohistochemistry. Although both techniques provide valuable information on prognosis and disease state, a comprehensive method for fully elucidating molecular constituents present in locations of interest is desirable. The purpose of this work was to use multimodal imaging technologies to localize the vast number of molecular species observed with matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) in aged and diseased retinal tissues. Herein, MALDI IMS was utilized to observe molecular species that reside in photoreceptor cells and also a basal laminar deposit from two human donor eyes. The molecular species observed to accumulate in these discrete regions can be further identified and studied to attempt to gain a greater understanding of biological processes occurring in debilitating eye diseases such as age-related macular degeneration (AMD).

Highly multiplexed immunofluorescence of the human kidney using co-detection by indexing.

The human kidney is composed of many cell types that vary in their abundance and distribution from normal to diseased organ. As these cell types perform unique and essential functions, it is important to confidently label each within a single tissue to accurately assess tissue architecture and microenvironments. Towards this goal, we demonstrate the use of co-detection by indexing (CODEX) multiplexed immunofluorescence for visualizing 23 antigens within the human kidney. Using CODEX, many of the major cell types and substructures, such as collecting ducts, glomeruli, and thick ascending limb, were visualized within a single tissue section. Of these antibodies, 19 were conjugated in-house, demonstrating the flexibility and utility of this approach for studying the human kidney using custom and commercially available antibodies. We performed a pilot study that compared both fresh frozen and formalin-fixed paraffin-embedded healthy non-neoplastic and diabetic nephropathy kidney tissues. The largest cellular differences between the two groups was observed in cells labeled with aquaporin 1, cytokeratin 7, and α-smooth muscle actin. Thus, our data show the power of CODEX multiplexed immunofluorescence for surveying the cellular diversity of the human kidney and the potential for applications within pathology, histology, and building anatomical atlases.

α-Cyano-4-hydroxycinnamic Acid and Tri-Potassium Citrate Salt Pre-Coated Silicon Nanopost Array Provides Enhanced Lipid Detection for High Spatial Resolution MALDI Imaging Mass Spectrometry.

We have developed a pre-coated substrate for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) that enables high spatial resolution mapping of both phospholipids and neutral lipid classes in positive ion mode as metal cation adducts. The MALDI substrates are constructed by depositing a layer of α-cyano-4-hydroxycinnamic acid (CHCA) and potassium salts onto silicon nanopost arrays (NAPA) prior to tissue mounting. The matrix/salt pre-coated NAPA substrate significantly enhances all detected lipid signals allowing lipids to be detected at lower laser energies than bare NAPA. The improved sensitivity at lower laser energy enabled ion images to be generated at 10 µm spatial resolution from rat retinal tissue. Optimization of matrix pre-coated NAPA consisted of testing lithium, sodium, and potassium salts along with various matrices to investigate the increased sensitivity toward lipids for MALDI IMS experiments. It was determined that pre-coating NAPA with CHCA and potassium salts before thaw-mounting of tissue resulted in a signal intensity increase of at least 5.8 ± 0.1-fold for phospholipids and 2.0 ± 0.1-fold for neutral lipids compared to bare NAPA. Pre-coating NAPA with matrix and salt also reduced the necessary laser power to achieve desorption/ionization by ∼35%. This reduced the effective diameter of the ablation area from 13 ± 2 µm down to 8 ± 1 µm, enabling high spatial resolution MALDI IMS. Using pre-coated NAPA with CHCA and potassium salts offers a MALDI IMS substrate with broad molecular coverage of lipids in a single polarity that eliminates the need for extensive sample preparation after sectioning.

Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model.

BACKGROUND: The definitive diagnosis of melanocytic neoplasia using solely histopathologic evaluation can be challenging. Novel techniques that objectively confirm diagnoses are needed. This study details the development and validation of a melanoma prediction model from spatially resolved multivariate protein expression profiles generated by imaging mass spectrometry (IMS). METHODS: Three board-certified dermatopathologists blindly evaluated 333 samples. Samples with triply concordant diagnoses were included in this study, divided into a training set (n = 241) and a test set (n = 92). Both the training and test sets included various representative subclasses of unambiguous nevi and melanomas. A prediction model was developed from the training set using a linear support vector machine classification model. RESULTS: We validated the prediction model on the independent test set of 92 specimens (75 classified correctly, 2 misclassified, and 15 indeterminate). IMS detects melanoma with a sensitivity of 97.6% and a specificity of 96.4% when evaluating each unique spot. IMS predicts melanoma at the sample level with a sensitivity of 97.3% and a specificity of 97.5%. Indeterminate results were excluded from sensitivity and specificity calculations. CONCLUSION: This study provides evidence that IMS-based proteomics results are highly concordant to diagnostic results obtained by careful histopathologic evaluation from a panel of expert dermatopathologists.

An orthogonal methods assessment of topical drug concentrations in skin and the impact for risk assessment in the viable epidermis.

Systemic toxicity assessments for oral or parenteral drugs often utilize the concentration of drug in plasma to enable safety margin calculations for human risk assessment. For topical drugs, there is no standard method for measuring drug concentrations in the stratum basale of the viable epidermis. This is particularly important since the superficial part of the epidermis, the stratum corneum (SC), is nonviable and where most of a topically applied drug remains, never penetrating deeper into the skin. We investigated the relative concentrations of a prototype kinase inhibitor using punch biopsy, laser capture microdissection, and imaging mass spectrometry methods in the SC, stratum basale, and dermis of minipig skin following topical application as a cream formulation. The results highlight the value of laser capture microdissection and mass spectrometry imaging in quantifying the large difference in drug concentration across the skin and even within the epidermis, and supports use of these methods for threshold-based toxicity risk assessments in specific anatomic locations of the skin, like of the stratum basale.

Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometry.

Identifying the spatial distributions of biomolecules in tissue is crucial for understanding integrated function. Imaging mass spectrometry (IMS) allows simultaneous mapping of thousands of biosynthetic products such as lipids but has needed a means of identifying specific cell-types or functional states to correlate with molecular localization. We report, here, advances starting from identity marking with a genetically encoded fluorophore. The fluorescence emission data were integrated with IMS data through multimodal image processing with advanced registration techniques and data-driven image fusion. In an unbiased analysis of spleens, this integrated technology enabled identification of ether lipid species preferentially enriched in germinal centers. We propose that this use of genetic marking for microanatomical regions of interest can be paired with molecular information from IMS for any tissue, cell-type, or activity state for which fluorescence is driven by a gene-tracking allele and ultimately with outputs of other means of spatial mapping.

Co-registration and analysis of multiple imaging mass spectrometry datasets targeting different analytes.

MOTIVATION: MALDI imaging mass spectrometry (IMS) has been successfully used to image a variety of biomolecules. Imaging of the many classes of biomolecules is often achieved through several incompatible sample preparations. Thus, multiple datasets must be acquired from multiple tissue sections to obtain a total molecular overview of a single sample. Addressing the need for single datasets from multiple IMS analyses, we developed the R package RegCombIMS as an extension of R package Cardinal to co-register, combine and create single IMS datasets acquired from serial sections of tissue. RESULTS: Dataset recombination and analysis is achieved by registration of the IMS datasets to a single coordinate space. The workflow allows for correlation of ions from IMS acquisitions that require incompatible sample preparations as well as multivariate analysis to mine the combined dataset for rapid and more thorough molecular query. AVAILABILITY AND IMPLEMENTATION: The source code and example data are freely available at https://github.com/NHPatterson/RegCombIMS. All code was implemented in R. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining Salt Doping and Matrix Sublimation for High Spatial Resolution MALDI Imaging Mass Spectrometry of Neutral Lipids.

The combination of sodium salt doping of a tissue section along with the sublimation of the matrix 2,5-dihydrobenzoic acid (DHB) was found to be an effective coating for the simultaneous detection of neutral lipids and phospholipids using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry in positive ionization mode. Lithium, sodium, and potassium acetate were initially screened for their ability to cationize difficult to analyze neutral lipids such as cholesterol esters, cerebrosides, and triglycerides directly from a tissue section. The combination of sodium salt and DHB sublimation was found to be an effective cation/matrix combination for detection of neutral lipids. Further experimental optimizations revealed that sodium carbonate or sodium phosphate followed by DHB sublimation increases the signal intensity of the neutral lipids studied depending on the specific lipid family and tissue type by 10-fold to 140-fold compared with that of previously published methods. Application of sodium carbonate tissue doping and DHB sublimation resulted in crystal sizes ≤2 µm. We were thus able to image a mouse brain cerebellum at a high spatial resolution and detected 37 cerebrosides in a single run using a MALDI-TOF instrument. The combination of sodium doping and DHB sublimation offer a targeted and sensitive approach for the detection of neutral lipids that do not typically ionize well under normal MALDI conditions.

MicroLESA: Integrating Autofluorescence Microscopy, In Situ Micro-Digestions, and Liquid Extraction Surface Analysis for High Spatial Resolution Targeted Proteomic Studies.

The ability to target discrete features within tissue using liquid surface extractions enables the identification of proteins while maintaining the spatial integrity of the sample. Here, we present a liquid extraction surface analysis (LESA) workflow, termed microLESA, that allows proteomic profiling from discrete tissue features of ∼110 µm in diameter by integrating nondestructive autofluorescence microscopy and spatially targeted liquid droplet micro-digestion. Autofluorescence microscopy provides the visualization of tissue foci without the need for chemical stains or the use of serial tissue sections. Tryptic peptides are generated from tissue foci by applying small volume droplets (∼250 pL) of enzyme onto the surface prior to LESA. The microLESA workflow reduced the diameter of the sampled area almost 5-fold compared to previous LESA approaches. Experimental parameters, such as tissue thickness, trypsin concentration, and enzyme incubation duration, were tested to maximize proteomics analysis. The microLESA workflow was applied to the study of fluorescently labeled Staphylococcus aureus infected murine kidney to identify unique proteins related to host defense and bacterial pathogenesis. Proteins related to nutritional immunity and host immune response were identified by performing microLESA at the infectious foci and surrounding abscess. These identifications were then used to annotate specific proteins observed in infected kidney tissue by MALDI FT-ICR IMS through accurate mass matching.

Mapping the triglyceride distribution in NAFLD human liver by MALDI imaging mass spectrometry reveals molecular differences in micro and macro steatosis.

Hepatic lipid accumulation, mainly in the form of triglycerides (TGs), is the hallmark of non-alcoholic fatty liver disease (NAFLD). To date, the spatial distribution of individual lipids in NAFLD-affected livers is not well characterized. This study aims to map the triglyceride distribution in normal human liver samples and livers with NAFLD and cirrhosis with imaging mass spectrometry (MALDI IMS). Specifically, whether individual triglyceride species differing by fatty acid chain length and degree of saturation correlate with the histopathological features of NAFLD as identified with classical H&E. Using a recently reported sodium-doped gold-assisted laser desorption/ionization IMS sample preparation, 20 human liver samples (five normal livers, five samples with simple steatosis, five samples with steatohepatitis, and five samples with cirrhosis) were analyzed at 10-µm lateral resolution. A total of 24 individual lipid species, primarily neutral lipids, were identified (22 TGs and two phospholipids). In samples with a low level of steatosis, TGs accumulated around the pericentral zone. In all samples, TGs with different degrees of side-chain saturation and side-chain length demonstrated differential distribution. Furthermore, hepatocytes containing macro lipid droplets were highly enriched in fully saturated triglycerides. This enrichment was also observed in areas of hepatocyte ballooning in samples with steatohepatitis and cirrhosis. In conclusion, macro lipid droplets in NAFLD are enriched in fully saturated triglycerides, indicating a possible increase in de novo lipogenesis that leads to steatohepatitis and cirrhosis.

Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy.

The correlation of imaging mass spectrometry (IMS) with histopathology can help relate novel molecular findings obtained through IMS to the well-characterized and validated histopathology knowledge base. The quality of correlation between these two modalities is limited by the quality of the spatial mapping that is obtained by registration of the two image types. In this work, we develop novel workflows for MALDI IMS-to-microscopy data registration and analysis using nondestructive IMS-compatible wide field autofluorescence (AF) microscopy combined with computational image registration. First, a substantially automated procedure for high-accuracy registration between IMS and microscopy data of the same section is described that explicitly links the MALDI laser ablation pattern imaged by microscopy to its corresponding IMS pixel. Subsequent examination of the registered data allows for high-confidence colocalization of image features between the two modalities, down to single-cell scales within tissue. Building on this IMS-microscopy spatial mapping, we furthermore demonstrate the automated spatial correlation between IMS measurements from serial sections. This AF-registration-driven inter-section analysis, using a combination of nonlinear AF-to-AF and IMS-to-AF image registrations, can be applied to tissue sections that are prepared and imaged with different sample preparations (e.g., lipids vs proteins) and/or that are measured using different spatial resolutions. Importantly, all registrations, whether within a single section or across serial sections, are entirely independent of the IMS intensity signal content and thus unbiased by it.

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.

Multimodal detection of GM2 and GM3 lipid species in the brain of mucopolysaccharidosis type II mouse by serial imaging mass spectrometry and immunohistochemistry.

Mucopolysaccharidosis type II (Hunter's disease) mouse model (IdS-KO) was investigated by both imaging mass spectrometry (IMS) and immunohistochemistry (IHC) performed on the same tissue sections. For this purpose, IdS-KO mice brain sections were coated with sublimated 1,5-diaminonaphtalene and analyzed by high spatial resolution IMS (5 µm) and anti-GM3 IHC on the same tissue sections to characterize the ganglioside monosialated ganglioside (GM) deposits found in Hunter's disease. IMS analysis have found that two species of GM3 and GM2 that are only different due to the length of their fatty acid residue (stearic or arachidic residue) were overexpressed in the IdS-KO mice compared to a control mouse. GM3 and GM2 were characterized by on-tissue exact mass and MS/MS compared to a GM3 standard. Realignment of both IMS and IHC data sets further confirmed the observed regioselective signal previously detected by providing direct correlation of the IMS image for the two GM3 overly expressed MS signals with the anti-GM3 IHC image. Furthermore, these regioselective GM MS signals were also found to have highly heterogeneous distributions within the GM3-IHC staining. Some deposits showed high content in GM3 and GM2 stearic species (r = 0.74) and others had more abundant GM3 and GM2 arachidic species (r = 0.76). Same-section analysis of Hunter's disease mouse model by both high spatial resolution IMS and IHC provides a more in-depth analysis of the composition of the GM aggregates while providing spatial distribution of the observed molecular species. Graphical Abstract Ganglioside imaging mass spectrometry followed by immunohistochemistry performed on the same tissue section.

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.

Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.