Spatial segmentation of MALDI FT-ICR MSI data: a powerful tool to explore the head and neck tumor in situ lipidome

Targeted Desorption Electrospray Ionization Mass Spectrometry Imaging for Drug Distribution, Toxicity, and Tissue Classification Studies

With increased use of mass spectrometry imaging (MSI) in support of pharmaceutical research and development, there are opportunities to develop analytical pipelines that incorporate exploratory high-performance analysis with higher capacity and faster targeted MSI. Therefore, to enable faster MSI data acquisition we present analyte-targeted desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) utilizing a triple-quadrupole (TQ) mass analyzer. The evaluated platform configuration provided superior sensitivity compared to a conventional time-of-flight (TOF) mass analyzer and thus holds the potential to generate data applicable to pharmaceutical research and development. The platform was successfully operated with sampling rates up to 10 scans/s, comparing positively to the 1 scan/s commonly used on comparable DESI-TOF setups. The higher scan rate enabled investigation of the desorption/ionization processes of endogenous lipid species such as phosphatidylcholines and a co-administered cassette of four orally dosed drugs—erlotininb, moxifloxacin, olanzapine, and terfenadine. This was used to enable understanding of the impact of the desorption/ionization processes in order to optimize the operational parameters, resulting in improved compound coverage for olanzapine and the main olanzapine metabolite, hydroxy-olanzapine, in brain tissue sections compared to DESI-TOF analysis or matrix-assisted laser desorption/ionization (MALDI) platforms. The approach allowed reducing the amount of recorded information, thus reducing the size of datasets from up to 150 GB per experiment down to several hundred MB. The improved performance was demonstrated in case studies investigating the suitability of this approach for mapping drug distribution, spatially resolved profiling of drug-induced nephrotoxicity, and molecular–histological tissue classification of ovarian tumors specimens.

MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues

Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies.

Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo

Metabolism is at the cornerstone of all cellular functions and mounting evidence of its deregulation in different diseases emphasizes the importance of a comprehensive understanding of metabolic regulation at the whole-organism level. Stable-isotope measurements are a powerful tool for probing cellular metabolism and, as a result, are increasingly used to study metabolism in in vivo settings. The additional complexity of in vivo metabolic measurements requires paying special attention to experimental design and data interpretation. Here, we review recent work where in vivo stable-isotope measurements have been used to address relevant biological questions within an in vivo context, summarize different experimental and data interpretation approaches and their limitations, and discuss future opportunities in the field.

Microscopy and Mass Spectrometry Imaging Reveals the Distributions of Curcumin Species in Dried Turmeric Root

Plants contain many secondary metabolites, which are sometimes used as spices and herbal medicines. However, the three-dimensional distribution of metabolites is usually unknown. In this study, the spatial distribution of curcumin, one of the main components of dried turmeric root, was examined. Because dried turmeric samples are extremely hard and impossible to section with existing cryomicrotomes, we introduced a new sectioning method and analyzed the two-dimensional distribution of curcumin in turmeric sections cut in different directions. The geometrical analysis of the imaging results suggested that curcumin forms tubular components inside turmeric. The wide-target analysis showed that the spatial distribution of curcumin analogues was similar to that of curcumin. Thus, mass spectrometry imaging was successfully implemented for clarifying the distribution of secondary metabolites in dry plant samples. Understanding the distribution of metabolites inside the plant body might contribute to improving their production processes, including the methods for extraction of active ingredients.

Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration

Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]^-, m/z 835.53 [PI(34:1) - H]^- and m/z 863.56 [PI(18:1)(18:0) - H]^- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.

Identification of Proteomic Markers in Head and Neck Cancer Using MALDI-MS Imaging, LC-MS/MS, and Immunohistochemistry

PURPOSE

The heterogeneity of squamous cell carcinoma tissue greatly complicates diagnosis and individualized therapy. Therefore, characterizing the heterogeneity of tissue spatially and identifying appropriate biomarkers is crucial. MALDI-MS imaging (MSI) is capable of analyzing spatially resolved tissue biopsies on a molecular level.

EXPERIMENTAL DESIGN

MALDI-MSI is used on snap frozen and formalin-fixed and paraffin-embedded (FFPE) tissue samples from patients with head and neck cancer (HNC) to analyze m/z values localized in tumor and nontumor regions. Peptide identification is performed using LC-MS/MS and immunohistochemistry (IHC).

RESULTS

In both FFPE and frozen tissue specimens, eight characteristic masses of the tumor's epithelial region are found. Using LC-MS/MS, the peaks are identified as vimentin, keratin type II, nucleolin, heat shock protein 90, prelamin-A/C, junction plakoglobin, and PGAM1. Lastly, vimentin, nucleolin, and PGAM1 are verified with IHC.

CONCLUSIONS AND CLINICAL RELEVANCE

The combination of MALDI-MSI, LC-MS/MS, and subsequent IHC furnishes a tool suitable for characterizing the molecular heterogeneity of tissue. It is also suited for use in identifying new representative biomarkers to enable a more individualized therapy.

Lipidomes of lung cancer and tumour-free lung tissues reveal distinct molecular signatures for cancer differentiation, age, inflammation, and pulmonary emphysema

Little is known about the human lung lipidome, its variability in different physiological states, its alterations during carcinogenesis and the development of pulmonary emphysema. We investigated how health status might be mirrored in the lung lipidome. Tissues were sampled for both lipidomic and histological analysis. Using a screening approach, we characterised lipidomes of lung cancer tissues and corresponding tumour-free alveolar tissues. We quantified 311 lipids from 11 classes in 43 tissue samples from 26 patients. Tumour tissues exhibited elevated levels of triacylglycerols and cholesteryl esters, as well as a significantly lower abundance of phosphatidylglycerols, which are typical lung surfactant components. Adenocarcinomas and squamous cell carcinomas were distinguished with high specificity based on lipid panels. Lipidomes of tumour biopsy samples showed clear changes depending on their histology and, in particular, their proportion of active tumour cells and stroma. Partial least squares regression showed correlations between lipid profiles of tumour-free alveolar tissues and the degree of emphysema, inflammation status, and the age of patients. Unsaturated long-chain phosphatidylserines and phosphatidylinositols showed a positive correlation with a worsened emphysema status and ageing. This work provides a resource for the human lung lipidome and a systematic data analysis strategy to link clinical characteristics and histology.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

INTRODUCTION

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

OBJECTIVES

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

METHODS

A literature search of the current relevant primary research was performed.

RESULTS

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

CONCLUSION

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

Pancreatic Cancer Surgical Resection Margins: Molecular Assessment by Mass Spectrometry Imaging

BACKGROUND

Surgical resection with microscopically negative margins remains the main curative option for pancreatic cancer; however, in practice intraoperative delineation of resection margins is challenging. Ambient mass spectrometry imaging has emerged as a powerful technique for chemical imaging and real-time diagnosis of tissue samples. We applied an approach combining desorption electrospray ionization mass spectrometry imaging (DESI-MSI) with the least absolute shrinkage and selection operator (Lasso) statistical method to diagnose pancreatic tissue sections and prospectively evaluate surgical resection margins from pancreatic cancer surgery.

METHODS AND FINDINGS

Our methodology was developed and tested using 63 banked pancreatic cancer samples and 65 samples (tumor and specimen margins) collected prospectively during 32 pancreatectomies from February 27, 2013, to January 16, 2015. In total, mass spectra for 254,235 individual pixels were evaluated. When cross-validation was employed in the training set of samples, 98.1% agreement with histopathology was obtained. Using an independent set of samples, 98.6% agreement was achieved. We used a statistical approach to evaluate 177,727 mass spectra from samples with complex, mixed histology, achieving an agreement of 81%. The developed method showed agreement with frozen section evaluation of specimen margins in 24 of 32 surgical cases prospectively evaluated. In the remaining eight patients, margins were found to be positive by DESI-MSI/Lasso, but negative by frozen section analysis. The median overall survival after resection was only 10 mo for these eight patients as opposed to 26 mo for patients with negative margins by both techniques. This observation suggests that our method (as opposed to the standard method to date) was able to detect tumor involvement at the margin in patients who developed early recurrence. Nonetheless, a larger cohort of samples is needed to validate the findings described in this study. Careful evaluation of the long-term benefits to patients of the use of DESI-MSI for surgical margin evaluation is also needed to determine its value in clinical practice.

CONCLUSIONS

Our findings provide evidence that the molecular information obtained by DESI-MSI/Lasso from pancreatic tissue samples has the potential to transform the evaluation of surgical specimens. With further development, we believe the described methodology could be routinely used for intraoperative surgical margin assessment of pancreatic cancer.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium - application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data

Intra-tumor heterogeneity is a vivid problem of molecular oncology that could be addressed by imaging mass spectrometry. Here we aimed to assess molecular heterogeneity of oral squamous cell carcinoma and to detect signatures discriminating normal and cancerous epithelium. Tryptic peptides were analyzed by MALDI-IMS in tissue specimens from five patients with oral cancer. Novel algorithm of IMS data analysis was developed and implemented, which included Gaussian mixture modeling for detection of spectral components and iterative k-means algorithm for unsupervised spectra clustering performed in domain reduced to a subset of the most dispersed components. About 4% of the detected peptides showed significantly different abundances between normal epithelium and tumor, and could be considered as a molecular signature of oral cancer. Moreover, unsupervised clustering revealed two major sub-regions within expert-defined tumor areas. One of them showed molecular similarity with histologically normal epithelium. The other one showed similarity with connective tissue, yet was markedly different from normal epithelium. Pathologist's re-inspection of tissue specimens confirmed distinct features in both tumor sub-regions: foci of actual cancer cells or cancer microenvironment-related cells prevailed in corresponding areas. Hence, molecular differences detected during automated segmentation of IMS data had an apparent reflection in real structures present in tumor.

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

BACKGROUND

There is a clinical need for new technologies that would enable rapid disease diagnosis based on diagnostic molecular signatures. Ambient ionization mass spectrometry has revolutionized the means by which molecular information can be obtained from tissue samples in real time and with minimal sample pretreatment. New developments in ambient ionization techniques applied to clinical research suggest that ambient ionization mass spectrometry will soon become a routine medical tool for tissue diagnosis.

CONTENT

This review summarizes the main developments in ambient ionization techniques applied to tissue analysis, with focus on desorption electrospray ionization mass spectrometry, probe electrospray ionization, touch spray, and rapid evaporative ionization mass spectrometry. We describe their applications to human cancer research and surgical margin evaluation, highlighting integrated approaches tested for ex vivo and in vivo human cancer tissue analysis. We also discuss the challenges for clinical implementation of these tools and offer perspectives on the future of the field.

SUMMARY

A variety of studies have showcased the value of ambient ionization mass spectrometry for rapid and accurate cancer diagnosis. Small molecules have been identified as potential diagnostic biomarkers, including metabolites, fatty acids, and glycerophospholipids. Statistical analysis allows tissue discrimination with high accuracy rates (>95%) being common. This young field has challenges to overcome before it is ready to be broadly accepted as a medical tool for cancer diagnosis. Growing research in new, integrated ambient ionization mass spectrometry technologies and the ongoing improvements in the existing tools make this field very promising for future translation into the clinic.

Mass spectrometry imaging for plant biology: a review

Mass spectrometry imaging (MSI) is a developing technique to measure the spatio-temporal distribution of many biomolecules in tissues. Over the preceding decade, MSI has been adopted by plant biologists and applied in a broad range of areas, including primary metabolism, natural products, plant defense, plant responses to abiotic and biotic stress, plant lipids and the developing field of spatial metabolomics. This review covers recent advances in plant-based MSI, general aspects of instrumentation, analytical approaches, sample preparation and the current trends in respective plant research.

MALDI mass spectrometric imaging meets “omics”: recent advances in the fruitful marriage

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a method that allows the investigation of the molecular content of surfaces, in particular, tissues, within its morphological context.