Spatial Metabolomics Identifies LPC(18:0) and LPA(18:1) in Advanced Atheroma With Translation to Plasma for Cardiovascular Risk Estimation

BACKGROUND

The metabolic alterations occurring within the arterial architecture during atherosclerosis development remain poorly understood, let alone those particular to each arterial tunica. We aimed first to identify, in a spatially resolved manner, the specific metabolic changes in plaque, media, adventitia, and cardiac tissue between control and atherosclerotic murine aortas. Second, we assessed their translatability to human tissue and plasma for cardiovascular risk estimation.

METHODS

In this observational study, mass spectrometry imaging (MSI) was applied to identify region-specific metabolic differences between atherosclerotic (n=11) and control (n=11) aortas from low-density lipoprotein receptor-deficient mice, via histology-guided virtual microdissection. Early and advanced plaques were compared within the same atherosclerotic animals. Progression metabolites were further analyzed by MSI in 9 human atherosclerotic carotids and by targeted mass spectrometry in human plasma from subjects with elective coronary artery bypass grafting (cardiovascular risk group, n=27) and a control group (n=27).

RESULTS

MSI identified 362 local metabolic alterations in atherosclerotic mice (log2 fold-change ≥1.5; P≤0.05). The lipid composition of cardiac tissue is altered during atherosclerosis development and presents a generalized accumulation of glycerophospholipids, except for lysolipids. Lysolipids (among other glycerophospholipids) were found at elevated levels in all 3 arterial layers of atherosclerotic aortas. LPC(18:0) (lysophosphatidylcholine; P=0.024) and LPA(18:1) (lysophosphatidic acid; P=0.025) were found to be significantly elevated in advanced plaques as compared with mouse-matched early plaques. Higher levels of both lipid species were also observed in fibrosis-rich areas of advanced- versus early-stage human samples. They were found to be significantly reduced in human plasma from subjects with elective coronary artery bypass grafting (P<0.001 and P=0.031, respectively), with LPC(18:0) showing significant association with cardiovascular risk (odds ratio, 0.479 [95% CI, 0.225-0.883]; P=0.032) and diagnostic potential (area under the curve, 0.778 [95% CI, 0.638-0.917]).

CONCLUSIONS

An altered phospholipid metabolism occurs in atherosclerosis, affecting both the aorta and the adjacent heart tissue. Plaque-progression lipids LPC(18:0) and LPA(18:1), as identified by MSI on tissue, reflect cardiovascular risk in human plasma.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

Tumor ratio of unsaturated to saturated sulfatide species is associated with disease-free survival in intrahepatic cholangiocarcinoma

PURPOSE

Cholangiocarcinoma (CCA) is a malignancy arising from the bile duct epithelium and has a poor outcome. Sulfatides are lipid components of lipid rafts, and are implicated in several cancer types. In the liver, sulfatides are specifically present in the bile ducts. Here, sulfatide abundance and composition were analyzed using mass spectrometry imaging in intrahepatic CCA (iCCA) tumor tissue, and correlated with tumor biology and clinical outcomes.

METHODS

Sulfatides were analyzed in iCCA (n = 17), hepatocellular carcinoma (HCC, n = 10) and colorectal liver metastasis (CRLM, n = 10) tumor samples, as well as tumor-distal samples (control, n = 16) using mass spectrometry imaging. Levels of sulfatides as well as the relative amount in structural classes were compared between groups, and were correlated with clinical outcomes for iCCA patients.

RESULTS

Sulfatide localization was limited to the respective tumor areas and the bile ducts. Sulfatide abundance was similar in iCCA and control tissue, while intensities were notably higher in CRLM in comparison with control (18-fold, P < 0.05) and HCC tissue (47-fold, P < 0.001). Considerable variation in sulfatide abundance was observed in iCCA tumors. A high ratio of unsaturated to saturated sulfatides was associated with reduced disease-free survival (10 vs. 20 months) in iCCA. The sulfatide pattern in HCC deviated from the other groups, with a higher relative abundance of odd- versus even-chain sulfatides.

CONCLUSION

Sulfatides were found in tumor tissue of patients with iCCA, with sulfatide abundance per pixel being similar to bile ducts. In this explorative study, sulfatide abundance was not related to overall survival of iCCA patients. A high ratio of unsaturated to saturated sulfatides was associated with earlier tumor recurrence in patients with iCCA.

An optimized MALDI MSI protocol for spatial detection of tryptic peptides in fresh frozen prostate tissue

MALDI MS imaging (MSI) is a powerful analytical tool for spatial peptide detection in heterogeneous tissues. Proper sample preparation is crucial to achieve high quality, reproducible measurements. Here we developed an optimized protocol for spatially resolved proteolytic peptide detection with MALDI time‐of‐flight MSI of fresh frozen prostate tissue sections. The parameters tested included four different tissue washes, four methods of protein denaturation, four methods of trypsin digestion (different trypsin densities, sprayers, and incubation times), and five matrix deposition methods (different sprayers, settings, and matrix concentrations). Evaluation criteria were the number of detected and excluded peaks, percentage of high mass peaks, signal‐to‐noise ratio, spatial localization, and average intensities of identified peptides, all of which were integrated into a weighted quality evaluation scoring system. Based on these scores, the optimized protocol included an ice‐cold EtOH+H₂O wash, a 5 min heating step at 95°C, tryptic digestion incubated for 17h at 37°C and CHCA matrix deposited at a final amount of 1.8 μg/mm². Including a heat‐induced protein denaturation step after tissue wash is a new methodological approach that could be useful also for other tissue types. This optimized protocol for spatial peptide detection using MALDI MSI facilitates future biomarker discovery in prostate cancer and may be useful in studies of other tissue types.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

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Mass spectrometry imaging (MSI) is a powerful molecular imaging technique.

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Integration with other imaging modalities is essential in clinical MSI.

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Image integration is performed by image registration techniques.

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Technical potential of image registration in MSI has not been fully exploited.

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Roadmap proposed to improve registration accuracy.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Combined Quantitative (Phospho)proteomics and Mass Spectrometry Imaging Reveal Temporal and Spatial Protein Changes in Human Intestinal Ischemia-Reperfusion

Intestinal ischemia–reperfusion (IR) injury is a severe clinical condition, and unraveling its pathophysiology is crucial to improve therapeutic strategies and reduce the high morbidity and mortality rates. Here, we studied the dynamic proteome and phosphoproteome in the human intestine during ischemia and reperfusion, using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to gain quantitative information of thousands of proteins and phosphorylation sites, as well as mass spectrometry imaging (MSI) to obtain spatial information. We identified a significant decrease in abundance of proteins related to intestinal absorption, microvillus, and cell junction, whereas proteins involved in innate immunity, in particular the complement cascade, and extracellular matrix organization increased in abundance after IR. Differentially phosphorylated proteins were involved in RNA splicing events and cytoskeletal and cell junction organization. In addition, our analysis points to mitogen-activated protein kinase (MAPK) and cyclin-dependent kinase (CDK) families to be active kinases during IR. Finally, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI presented peptide alterations in abundance and distribution, which resulted, in combination with Fourier-transform ion cyclotron resonance (FTICR) MSI and LC-MS/MS, in the annotation of proteins related to RNA splicing, the complement cascade, and extracellular matrix organization. This study expanded our understanding of the molecular changes that occur during IR in the human intestine and highlights the value of the complementary use of different MS-based methodologies.

Examination of lipid profiles in abdominal fascial healing using MALDI-TOF to identify potential therapeutic targets

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Lipids change overtime in normal fascial healing in the early post-surgery period.

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Specific lipid species are correlated with the changes of inflammation cells and fibroblasts.

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Lipid species in the present study are considered as predictive markers for the formation of incisional hernia.

Background

Failure of fascial healing in the abdominal wall can result in incisional hernia, which is one of the most common complications after laparotomy. Understanding the molecular healing process of abdominal fascia may provide lipid markers of incisional hernia or therapeutic targets that allow prevention or treatment of incisional hernias.

Purpose

This study aims to investigate temporal and in situ changes of lipids during the normal healing process of abdominal fascia in the first postoperative week.

Methods

Open hemicolectomy was performed in a total of 35 Wistar rats. The midline fascia was closed identically for all rats using a single continuous suturing technique. These animals were sacrificed with equal numbers (n = 5) at each of 7-time points (6, 12, 24, 48, 72, 120, and 168 h. The local and temporal changes of lipids were examined with mass spectrometry imaging and correlated to histologically scored changes during healing using hematoxylin and eosin staining.

Results

Two phosphatidylcholine lipid species (PC O-38:5 and PC 38:4) and one phosphatidylethanolamine lipid (PE O-16:1_20:4) were found to significantly correlate with temporal changes of inflammation. A phosphatidylcholine (PC 32:0) and a monosialodihexosylganglioside (GM3 34:1;2) were found to correlate with fibroblast cell growth.

Conclusion

Glycerophospholipids and gangliosides are strongly involved in the normal healing process of abdominal fascia and their locally fluctuating concentrations are considered as potential lipid markers and therapeutic targets of fascial healing.

Clinical importance of high-mannose, fucosylated and complex N-glycans in breast cancermetastasis

BACKGROUND. Although aberrant glycosylation is recognized as a hallmark of cancer, glycosylation in clinical breast cancer (BC) metastasis has not yet been studied. While preclinical studies show that the glycocalyx coating of cancer cells is involved in adhesion, migration, and metastasis, glycosylation changes from primary tumor (PT) to various metastatic sites remain unknown in patients.

METHODS. We investigated N-glycosylation profiles in 17 metastatic BC patients from our rapid autopsy program. Primary breast tumor, lymph node metastases, multiple systemic metastases, and various normal tissue cores from each patient were arranged on unique single-patient tissue microarrays (TMAs). We performed mass spectrometry imaging (MSI) combined with extensive pathology annotation of these TMAs, and this process enabled spatially differentiated cell-based analysis of N-glycosylation patterns in metastatic BC.

RESULTS. N-glycan abundance increased during metastatic progression independently of BC subtype and treatment regimen, with high-mannose glycans most frequently elevated in BC metastases, followed by fucosylated and complex glycans. Bone metastasis, however, displayed increased core-fucosylation and decreased high-mannose glycans. Consistently, N-glycosylated proteins and N-glycan biosynthesis genes were differentially expressed during metastatic BC progression, with reduced expression of mannose-trimming enzymes and with elevated EpCAM, N-glycan branching, and sialyation enzymes in BC metastases versus PT.

CONCLUSION. We show in patients that N-glycosylation of breast cancer cells undergoing metastasis occurs in a metastatic site–specific manner, supporting the clinical importance of high-mannose, fucosylated, and complex N-glycans as future diagnostic markers and therapeutic targets in metastatic BC.

FUNDING. NIH grants R01CA213428, R01CA213492, R01CA264901, T32CA193145, Dutch Province Limburg “LINK”, European Union ERA-NET TRANSCAN2-643638.

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared to histopathological response assessment which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9 to 93.3% using the metabolic classifier and from 62.2 to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI = 1.40-8.12, P = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI = 0.67-1.53, P = 0.968) or stromal classifier (HR 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

BACKGROUND

Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE.

METHODS

Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 × 50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset.

RESULTS

ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)).

CONCLUSIONS

In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949.

Mass spectrometry imaging of L-[ring-13C6]-labeled phenylalanine and tyrosine kinetics in non-small cell lung carcinoma

BACKGROUND

Metabolic reprogramming is a common phenomenon in tumorigenesis and tumor progression. Amino acids are important mediators in cancer metabolism, and their kinetics in tumor tissue are far from being understood completely. Mass spectrometry imaging is capable to spatiotemporally trace important endogenous metabolites in biological tissue specimens. In this research, we studied L-[ring-¹³C₆]-labeled phenylalanine and tyrosine kinetics in a human non-small cell lung carcinoma (NSCLC) xenografted mouse model using matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI).

METHODS

We investigated the L-[ring-¹³C₆]-Phenylalanine (¹³C₆-Phe) and L-[ring-¹³C₆]-Tyrosine (¹³C₆-Tyr) kinetics at 10 min (n = 4), 30 min (n = 3), and 60 min (n = 4) after tracer injection and sham-treated group (n = 3) at 10 min in mouse-xenograft lung tumor tissues by MALDI-FTICR-MSI.

RESULTS

The dynamic changes in the spatial distributions of 19 out of 20 standard amino acids are observed in the tumor tissue. The highest abundance of ¹³C₆-Phe was detected in tumor tissue at 10 min after tracer injection and decreased progressively over time. The overall enrichment of ¹³C₆-Tyr showed a delayed temporal trend compared to ¹³C₆-Phe in tumor caused by the Phe-to-Tyr conversion process. Specifically, ¹³C₆-Phe and ¹³C₆-Tyr showed higher abundances in viable tumor regions compared to non-viable regions.

CONCLUSIONS

We demonstrated the spatiotemporal intra-tumoral distribution of the essential aromatic amino acid ¹³C₆-Phe and its de-novo synthesized metabolite ¹³C₆-Tyr by MALDI-FTICR-MSI. Our results explore for the first time local phenylalanine metabolism in the context of cancer tissue morphology. This opens a new way to understand amino acid metabolism within the tumor and its microenvironment.

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

Batch Effects in MALDI Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has become an indispensible tool for spatially resolved molecular investigation of tissues. One of the key application areas is biomedical research, where matrix-assisted laser desorption/ionization (MALDI) MSI is predominantly used due to its high-throughput capability, flexibility in the molecular class to investigate, and ability to achieve single cell spatial resolution. While many of the initial technical challenges have now been resolved, so-called batch effects, a phenomenon already known from other omics fields, appear to significantly impede reliable comparison of data from particular midsized studies typically performed in translational clinical research. This critical insight will discuss at what levels (pixel, section, slide, time, and location) batch effects can manifest themselves in MALDI-MSI data and what consequences this might have for biomarker discovery or multivariate classification. Finally, measures are presented that could be taken to recognize and/or minimize these potentially detrimental effects, and an outlook is provided on what is still needed to ultimately overcome these effects.

Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI

Background

Prostate cancer tissues are inherently heterogeneous, which presents a challenge for metabolic profiling using traditional bulk analysis methods that produce an averaged profile. The aim of this study was therefore to spatially detect metabolites and lipids on prostate tissue sections by using mass spectrometry imaging (MSI), a method that facilitates molecular imaging of heterogeneous tissue sections, which can subsequently be related to the histology of the same section.

Methods

Here, we simultaneously obtained metabolic and lipidomic profiles in different prostate tissue types using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MSI. Both positive and negative ion mode were applied to analyze consecutive sections from 45 fresh-frozen human prostate tissue samples (N = 15 patients). Mass identification was performed with tandem MS.

Results

Pairwise comparisons of cancer, non-cancer epithelium, and stroma revealed several metabolic differences between the tissue types. We detected increased levels of metabolites crucial for lipid metabolism in cancer, including metabolites involved in the carnitine shuttle, which facilitates fatty acid oxidation, and building blocks needed for lipid synthesis. Metabolites associated with healthy prostate functions, including citrate, aspartate, zinc, and spermine had lower levels in cancer compared to non-cancer epithelium. Profiling of stroma revealed higher levels of important energy metabolites, such as ADP, ATP, and glucose, and higher levels of the antioxidant taurine compared to cancer and non-cancer epithelium.

Conclusions

This study shows that specific tissue compartments within prostate cancer samples have distinct metabolic profiles and pinpoint the advantage of methodology providing spatial information compared to bulk analysis. We identified several differential metabolites and lipids that have potential to be developed further as diagnostic and prognostic biomarkers for prostate cancer. Spatial and rapid detection of cancer-related analytes showcases MALDI-TOF MSI as a promising and innovative diagnostic tool for the clinic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00242-z.

Multilabel Per-Pixel Quantitation in Mass Spectrometry Imaging

In quantitative mass spectrometry imaging (MSI), the gold standard adds a single structural homologue of the target compound at a known concentration to the sample. This internal standard enables to map the detected intensity of the target molecule against an external calibration curve. This approach, however, ignores local noise levels and disproportional ion suppression effects, which might depend on the concentration of the target compound. To overcome these issues, we propose a novel approach that applies several isotopically labeled versions, each at a different concentration, to the sample. This allows creating individual internal calibration curves for every MSI pixel. As proof of principle, we have quantified an endogenous peptide of histone H4 by matrix-assisted laser desorption/ionization-Q-MSI (MALDI-Q-MSI), using a mixture of three isotopically labeled versions. The usage of a fourth label allowed us to compare the gold standard to our multilabel approach. We observed substantial heterogeneity in ion suppression across the tissue, which disclosed itself as varying slopes in the per-pixel regression analyses. These slopes were histology-dependent and differed from each other by up to a factor of 4. The results were validated by liquid chromatography-mass spectrometry (LC-MS), exhibiting a high agreement between LC-MS and MALDI-Q-MSI (Pearson correlation r = 0.87). A comparison between the multilabel and single-label approaches revealed a higher accuracy for the multilabel method when the local target compound concentration differed too much from the concentration of the single label. In conclusion, we show that the multilabel approach provides superior quantitation compared to a single-label approach, in case the target compound is inhomogeneously distributed at a wide concentration range in the tissue.

Morphometric Cell Classification for Single-Cell MALDI-Mass Spectrometry Imaging

The large-scale and label-free molecular characterization of single cells in their natural tissue habitat remains a major challenge in molecular biology. We present a method that integrates morphometric image analysis to delineate and classify individual cells with their single-cell-specific molecular profiles. This approach provides a new means to study spatial biological processes such as cancer field effects and the relationship between morphometric and molecular features.

Atheroma-Specific Lipids in ldlr-/- and apoe-/- Mice Using 2D and 3D Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Atherosclerosis is the major contributor to cardiovascular diseases. It is a spatially and temporally complex inflammatory disease, in which intravascular accumulation of a plethora of lipids is considered to play a crucial role. To date, both the composition and local distribution of the involved lipids have not been thoroughly mapped yet. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) enables analyzing and visualizing hundreds of lipid molecules within the plaque while preserving each lipid's specific location. In this study, we aim to identify and verify aortic plaque-specific lipids with high-spatial-resolution 2D and 3D MALDI-MSI common to high-fat-diet-fed low-density lipoprotein receptor deficient (ldlr^-/-) mice and chow-fed apolipoprotein E deficient (apoe^-/-) mice, the two most widely used animal models for atherosclerosis. A total of 11 lipids were found to be significantly and specifically colocalized to the plaques in both mouse models. These were identified and belong to one sphingomyelin (SM), three lysophosphatidic acids (LPA), four lysophosphatidylcholines (LPC), two lysophosphatidylethanolamines (LPE), and one lysophosphatidylinositol (LPI). While these lysolipids and SM 34:0;2 were characteristic of the atherosclerotic aorta plaque itself, LPI 18:0 was mainly localized in the necrotic core of the plaque.

Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges

Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.

Simultaneous Detection of Zinc and Its Pathway Metabolites Using MALDI MS Imaging of Prostate Tissue

Levels of zinc, along with its mechanistically related metabolites citrate and aspartate, are widely reported as reduced in prostate cancer compared to healthy tissue and are therefore pointed out as potential cancer biomarkers. Previously, it has only been possible to analyze zinc and metabolites by separate detection methods. Through matrix-assisted laser desorption/ionization mass spectrometry imaging (MSI), we were for the first time able to demonstrate, in two different sample sets (n = 45 and n = 4), the simultaneous spatial detection of zinc, in the form of ZnCl₃^–, together with citrate, aspartate, and N-acetylaspartate on human prostate cancer tissues. The reliability of the ZnCl₃^– detection was validated by total zinc determination using laser ablation inductively coupled plasma MSI on adjacent serial tissue sections. Zinc, citrate, and aspartate were correlated with each other (range r = 0.46 to 0.74) and showed a significant reduction in cancer compared to non-cancer epithelium (p < 0.05, log₂ fold change range: −0.423 to −0.987), while no significant difference between cancer and stroma tissue was found. Simultaneous spatial detection of zinc and its metabolites is not only a valuable tool for analyzing the role of zinc in prostate metabolism but might also provide a fast and simple method to detect zinc, citrate, and aspartate levels as a biomarker signature for prostate cancer diagnostics and prognostics.

Precise co-registration of mass spectrometry imaging, histology, and laser microdissection-based omics

Mass spectrometry imaging (MSI) is an analytical technique for the unlabeled and multiplex imaging of molecules in biological tissue sections. It therefore enables the spatial and molecular annotations of tissues complementary to histology. It has already been shown that MSI can guide subsequent material isolation technologies such as laser microdissection (LMD) to enable a more in-depth molecular characterization of MSI-highlighted tissue regions. However, with MSI now reaching spatial resolutions at the single-cell scale, there is a need for a precise co-registration between MSI and the LMD. As proof-of-principle, MSI of lipids was performed on a breast cancer tissue followed by a segmentation of the data to detect molecularly distinct segments within its tumor areas. After image processing of the segmentation results, the coordinates of the MSI-detected segments were passed to the LMD system by three co-registration steps. The errors of each co-registration step were quantified and the total error was found to be less than 13 μm. With this link established, MSI data can now accurately guide LMD to excise MSI-defined regions of interest for subsequent extract-based analyses. In our example, the excised tissue material was then subjected to ultrasensitive microproteomics in order to determine predominant molecular mechanisms in each of the MSI-highlighted intratumor segments. This work shows how the strengths of MSI, histology, and extract-based omics can be combined to enable a more comprehensive molecular characterization of in situ biological processes.

Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification

SCOPE

In biomedical research, mass spectrometry imaging (MSI) can obtain spatially-resolved molecular information from tissue sections. Especially matrix-assisted laser desorption/ionization (MALDI) MSI offers, depending on the type of matrix, the detection of a broad variety of molecules ranging from metabolites to proteins, thereby facilitating the collection of multilevel molecular data. Lately, integrative clustering techniques have been developed that make use of the complementary information of multilevel molecular data in order to better stratify patient cohorts, but which have not yet been applied in the field of MSI.

MATERIALS AND METHODS

In this study, the potential of integrative clustering is investigated for multilevel molecular MSI data to subdivide cancer patients into different prognostic groups. Metabolomic and peptidomic data are obtained by MALDI-MSI from a tissue microarray containing material of 46 esophageal cancer patients. The integrative clustering methods Similarity Network Fusion, iCluster, and moCluster are applied and compared to non-integrated clustering.

CONCLUSION

The results show that the combination of multilevel molecular data increases the capability of integrative algorithms to detect patient subgroups with different clinical outcome, compared to the single level or concatenated data. This underlines the potential of multilevel molecular data from the same subject using MSI for subsequent integrative clustering.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Strategies for managing multi-patient 3D mass spectrometry imaging data

Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample. SIGNIFICANCE: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.

Round robin study of formalin-fixed paraffin-embedded tissues in mass spectrometry imaging

Mass spectrometry imaging (MSI) has provided many results with translational character, which still have to be proven robust in large patient cohorts and across different centers. Although formalin-fixed paraffin-embedded (FFPE) specimens are most common in clinical practice, no MSI multicenter study has been reported for FFPE samples. Here, we report the results of the first round robin MSI study on FFPE tissues with the goal to investigate the consequences of inter- and intracenter technical variation on masking biological effects. A total of four centers were involved with similar MSI instrumentation and sample preparation equipment. A FFPE multi-organ tissue microarray containing eight different types of tissue was analyzed on a peptide and metabolite level, which enabled investigating different molecular and biological differences. Statistical analyses revealed that peptide intercenter variation was significantly lower and metabolite intercenter variation was significantly higher than the respective intracenter variations. When looking at relative univariate effects of mass signals with statistical discriminatory power, the metabolite data was more reproducible across centers compared to the peptide data. With respect to absolute effects (cross-center common intensity scale), multivariate classifiers were able to reach on average > 90% accuracy for peptides and > 80% for metabolites if trained with sufficient amount of cross-center data. Overall, our study showed that MSI data from FFPE samples could be reproduced to a high degree across centers. While metabolite data exhibited more reproducibility with respect to relative effects, peptide data-based classifiers were more directly transferable between centers and therefore more robust than expected. Graphical abstract ᅟ.

Mass Spectrometry Imaging of Metabolites

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

Detection of Localized Hepatocellular Amino Acid Kinetics by using Mass Spectrometry Imaging of Stable Isotopes

Mass spectrometry imaging (MSI) simultaneously detects and identifies the spatial distribution of numerous molecules throughout tissues. Currently, MSI is limited to providing a static and ex vivo snapshot of highly dynamic systems in which molecules are constantly synthesized and consumed. Herein, we demonstrate an innovative MSI methodology to study dynamic molecular changes of amino acids within biological tissues by measuring the dilution and conversion of stable isotopes in a mouse model. We evaluate the method specifically on hepatocellular metabolism of the essential amino acid l-phenylalanine, associated with liver diseases. Crucially, the method reveals the localized dynamics of l-phenylalanine metabolism, including its in vivo hydroxylation to l-tyrosine and co-localization with other liver metabolites in a time course of samples from different animals. This method thus enables the dynamics of localized biochemical synthesis to be studied directly from biological tissues.

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival (m/z 180.9436 and 241.0118) and metastasis-free survival (m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine. Graphical Abstract ᅟ.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

More from less: high-throughput dual polarity lipid imaging of biological tissues

Here, we reveal the increased biochemical and spatial information acquired using high-speed MALDI-MSI and sequential acquisitions of positive and negative lipid-MSI data from single tissue sections.

Lipid and protein maps defining arterial layers in atherosclerotic aorta

Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. The molecular anatomy of healthy and atherosclerotic tissue is pursued to identify ongoing molecular changes in atherosclerosis development. Mass Spectrometry Imaging (MSI) accounts with the unique advantage of analyzing proteins and metabolites (lipids) while preserving their original localization; thus two dimensional maps can be obtained. Main molecular alterations were investigated in a rabbit model in response to early development of atherosclerosis. Aortic arterial layers (intima and media) and calcified regions were investigated in detail by MALDI-MSI and proteins and lipids specifically defining those areas of interest were identified. These data further complement main findings previously published in J Proteomics (M. Martin-Lorenzo et al., J. Proteomics. (In press); M. Martin-Lorenzo et al., J. Proteomics 108 (2014) 465-468.) [1,2].

Molecular anatomy of ascending aorta in atherosclerosis by MS Imaging: Specific lipid and protein patterns reflect pathology

The molecular anatomy of healthy and atherosclerotic tissue is pursued here to identify ongoing molecular changes in atherosclerosis development. Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. Mass spectrometry imaging (MSI) is a novel unexplored ex vivo imaging approach in CVD able to provide in-tissue molecular maps. A rabbit model of early atherosclerosis was developed and high-spatial-resolution MALDI-MSI was applied to comparatively analyze histologically-based arterial regions of interest from control and early atherosclerotic aortas. Specific protocols were applied to identify lipids and proteins significantly altered in response to atherosclerosis. Observed protein alterations were confirmed by immunohistochemistry in rabbit tissue, and additionally in human aortas. Molecular features specifically defining different arterial regions were identified. Localized in the intima, increased expression of SFA and lysolipids and intimal spatial organization showing accumulation of PI, PG and SM point to endothelial dysfunction and triggered inflammatory response. TG, PA, SM and PE-Cer were identified specifically located in calcified regions. Thymosin β4 (TMSB4X) protein was upregulated in intima versus media layer and also in response to atherosclerosis. This overexpression and localization was confirmed in human aortas. In conclusion, molecular histology by MS Imaging identifies spatial organization of arterial tissue in response to atherosclerosis.

Large-scale mass spectrometry imaging investigation of consequences of cortical spreading depression in a transgenic mouse model of migraine

Cortical spreading depression (CSD) is the electrophysiological correlate of migraine aura. Transgenic mice carrying the R192Q missense mutation in the Cacna1a gene, which in patients causes familial hemiplegic migraine type 1 (FHM1), exhibit increased propensity to CSD. Herein, mass spectrometry imaging (MSI) was applied for the first time to an animal cohort of transgenic and wild type mice to study the biomolecular changes following CSD in the brain. Ninety-six coronal brain sections from 32 mice were analyzed by MALDI-MSI. All MSI datasets were registered to the Allen Brain Atlas reference atlas of the mouse brain so that the molecular signatures of distinct brain regions could be compared. A number of metabolites and peptides showed substantial changes in the brain associated with CSD. Among those, different mass spectral features showed significant (t-test, P < 0.05) changes in the cortex, 146 and 377 Da, and in the thalamus, 1820 and 1834 Da, of the CSD-affected hemisphere of FHM1 R192Q mice. Our findings reveal CSD- and genotype-specific molecular changes in the brain of FHM1 transgenic mice that may further our understanding about the role of CSD in migraine pathophysiology. The results also demonstrate the utility of aligning MSI datasets to a common reference atlas for large-scale MSI investigations.

De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry

An essential and so far unresolved factor influencing the evolution of cancer and the clinical management of patients is intratumour clonal and phenotypic heterogeneity. However, the de novo identification of tumour subpopulations is so far both a challenging and an unresolved task. Here we present the first systematic approach for the de novo discovery of clinically detrimental molecular tumour subpopulations. In this proof-of-principle study, spatially resolved, tumour-specific mass spectra were acquired, using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, from tissues of 63 gastric carcinoma and 32 breast carcinoma patients. The mass spectra, representing the proteomic heterogeneity within tumour areas, were grouped by a corroborated statistical clustering algorithm in order to obtain segmentation maps of molecularly distinct regions. These regions were presumed to represent different phenotypic tumour subpopulations. This was confirmed by linking the presence of these tumour subpopulations to the patients' clinical data. This revealed several of the detected tumour subpopulations to be associated with a different overall survival of the gastric cancer patients (p = 0.025) and the presence of locoregional metastases in patients with breast cancer (p = 0.036). The procedure presented is generic and opens novel options in cancer research, as it reveals microscopically indistinct tumour subpopulations that have an adverse impact on clinical outcome. This enables their further molecular characterization for deeper insights into the biological processes of cancer, which may finally lead to new targeted therapies.

Automatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embedding

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.