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.

Lipidomic Phenotyping Of Human Small Intestinal Organoids Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

In the past decade, interest in organoids for biomedical research has surged, resulting in a higher demand for advanced imaging techniques. Traditional specimen embedding methods pose challenges, such as analyte delocalization and histological assessment. Here, we present an optimized sample preparation approach utilizing an Epredia M-1 cellulose-based embedding matrix, which preserves the structural integrity of fragile small intestinal organoids (SIOs). Additionally, background interference (delocalization of analytes, nonspecific (histological) staining, matrix ion clusters) was minimized, and we demonstrate the compatibility with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). With our approach, we can conduct label-free lipid imaging at the single-cell level, thereby yielding insights into the spatial distribution of lipids in both positive and negative ion modes. Moreover, M-1 embedding allows for an improved coregistration with histological and immunohistochemical (IHC) stainings, including MALDI-IHC, facilitating combined untargeted and targeted spatial information. Applying this approach, we successfully phenotyped crypt-like (CL) and villus-like (VL) SIOs, revealing that PE 36:2 [M - H]- (m/z 742.5) and PI 38:4 [M - H]- (m/z 885.5) display higher abundance in CL organoids, whereas PI 36:1 [M - H]- (m/z 863.6) was more prevalent in VL organoids. Our findings demonstrate the utility of M-1 embedding for advancing organoid research and unraveling intricate biological processes within these in vitro models.

Key regulator PNPLA8 drives phospholipid reprogramming induced proliferation and migration in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and leads to the poorest patient outcomes despite surgery and chemotherapy treatment. Exploring new molecular mechanisms of TNBC that could lead to the development of novel molecular targets are critically important for improving therapeutic options for treating TNBC.

METHODS

We sought to identify novel therapeutic targets in TNBC by combining genomic and functional studies with lipidomic analysis, which included mechanistic studies to elucidate the pathways that tie lipid profile to critical cancer cell properties. Our studies were performed in a large panel of human breast cancer cell lines and patient samples.

RESULTS

Comprehensive lipid profiling revealed that phospholipid metabolism is reprogrammed in TNBC cells. We discovered that patatin-like phospholipase domain-containing lipase 8 (PNPLA8) is overexpressed in TNBC cell lines and tissues from breast cancer patients. Silencing of PNPLA8 disrupted phospholipid metabolic reprogramming in TNBC, particularly affecting the levels of phosphatidylglycerol (PG), phosphatidylcholine (PC), lysophosphatidylcholine (LPC) and glycerophosphocholine (GPC). We showed that PNPLA8 is essential in regulating cell viability, migration and antioxidation in TNBC cells and promoted arachidonic acid and eicosanoid production, which in turn activated PI3K/Akt/Gsk3β and MAPK signaling.

CONCLUSIONS

Our study highlights PNPLA8 as key regulator of phospholipid metabolic reprogramming and malignant phenotypes in TNBC, which could be further developed as a novel molecular treatment target.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) reveals potential lipid markers between infrapatellar fat pad biopsies of osteoarthritis and cartilage defect patients

The incidence of osteoarthritis (OA) has been expected to increase due to an aging population, as well as an increased incidence of intra-articular (osteo-) chondral damage. Lipids have already been shown to be involved in the inflammatory process of OA. This study aims at revealing region-specific lipid profiles of the infrapatellar fat pad (IPFP) of OA or cartilage defect patients by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which could be used as biomarkers for early OA detection. A higher presence of phospholipids was found in OA patients compared with cartilage defect patients. In addition, a higher abundance of ether-linked phosphatidylethanolamines (PE O-s) containing arachidonic acid was specifically found in OA patients compared with cartilage defect patients. These lipids were mainly found in the connective tissue of the IPFP. Specific lipid species were associated to OA patients compared with cartilage defect patients. PE O-s have been suggested as possible biomarkers for OA. As these were found more abundantly in the connective tissue, the IPFP's intra-tissue heterogeneity might play an important role in biomarker discovery, implying that the amount of fibrous tissue is associated with OA.

Sample preparation for lipid analysis of intra-articular adipose tissue by using matrix-assisted laser desorption/ionization imaging

Mass spectrometry imaging (MSI) is a powerful technique enabling the visualization of the spatial distribution of different molecules in tissue biopsies with different pathologies. Sample handling and preparing adipose tissue for MSI is challenging and prone to molecular delocalization due to tissue melting. In this work, we developed a method for matrix-assisted laser desorption/ionization (MALDI)-MSI to study lipids in human infrapatellar fat pad (IPFP), a biomarker source in musculoskeletal pathologies, while preserving molecular spatial distribution. Cryosectioning at 15 μm with a temperature below -30 °C, thaw-mounting, and sublimation, was demonstrated to preserve IPFP's heterogeneous appearance and spatial distribution of lipids.

Fast Mass Microscopy: Mass Spectrometry Imaging of a Gigapixel Image in 34 Minutes

Mass spectrometry imaging (MSI) maps the spatial distributions of chemicals on surfaces. MSI requires improvements in throughput and spatial resolution, and often one is compromised for the other. In microprobe-mode MSI, improvements in spatial resolution increase the imaging time quadratically, thus limiting the use of high spatial resolution MSI for large areas or sample cohorts and time-sensitive measurements. Here, we bypass this quadratic relationship by combining a Timepix3 detector with a continuously sampling secondary ion mass spectrometry mass microscope. By reconstructing the data into large-field mass images, this new method, fast mass microscopy, enables orders of magnitude higher throughput than conventional MSI albeit yet at lower mass resolution. We acquired submicron, gigapixel images of fingerprints and rat tissue at acquisition speeds of 600,000 and 15,500 pixels s-1, respectively. For the first image, a comparable microprobe-mode measurement would take more than 2 months, whereas our approach took 33.3 min.

'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging

The molecular pathology of breast cancer is challenging due to the complex heterogeneity of cellular subtypes. The ability to directly identify and visualize cell subtype distribution at the single-cell level within a tissue section enables precise and rapid diagnosis and prognosis. Here, we applied mass spectrometry imaging (MSI) to acquire and visualize the molecular profiles at the single-cell and subcellular levels of 14 different breast cancer cell lines. We built a molecular library of genetically well-characterized cell lines. Multistep processing, including deep learning, resulted in a breast cancer subtype, the cancer's hormone status, and a genotypic recognition model based on metabolic phenotypes with cross-validation rates of up to 97%. Moreover, we applied our single-cell-based recognition models to complex tissue samples, identifying cell subtypes in tissue context within seconds during measurement. These data demonstrate "on the spot" digital pathology at the single-cell level using MSI, and they provide a framework for fast and accurate high spatial resolution diagnostics and prognostics.

Lipid Analysis of Fracture Hematoma With MALDI-MSI: Specific Lipids are Associated to Bone Fracture Healing Over Time

Background: Fracture healing is a complex process, involving cell-cell interactions, various cytokines, and growth factors. Although fracture treatment improved over the last decades, a substantial part of all fractures shows delayed or absent healing. The fracture hematoma (fxh) is known to have a relevant role in this process, while the exact mechanisms by which it influences fracture healing are poorly understood. To improve strategies in fracture treatment, regulatory pathways in fracture healing need to be investigated. Lipids are important molecules in cellular signaling, inflammation, and metabolism, as well as key structural components of the cell. Analysis of the lipid spectrum in fxh may therefore reflect important events during the early healing phase. This study aims to develop a protocol for the determination of lipid signals over time, and the identification of lipids that contribute to these signals, with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) in fxh in healthy fracture healing.

Methods: Twelve fxh samples (6 porcine; 6 human) were surgically removed, snap frozen, sectioned, washed, and analyzed using MALDI-MSI in positive and negative ion mode at different time points after fracture (porcine: 72 h; human samples: range 1–19 days). A tissue preparation protocol for lipid analysis in fxh has been developed with both porcine and human fxh. Data were analyzed through principal component- and linear discriminant analyses.

Results: A protocol for the preparation of fxh sections was developed and optimized. Although hematoma is a heterogeneous tissue, the intra-variability within fxh was smaller than the inter-variability between fxh. Distinctive m/z values were detected that contributed to the separation of three different fxh age groups: early (1–3 days), middle (6–10 days), and late (12–19 days). Identification of the distinctive m/z values provided a panel of specific lipids that showed a time dependent expression within fxh.

Conclusion: This study shows that MALDI-MSI is a suitable analytical tool for lipid analysis in fxh and that lipid patterns within fxh are time-dependent. These lipid patterns within fxh may serve as a future diagnostic tool. These findings warrant further research into fxh analysis using MALDI-MSI and its possible clinical implications in fracture treatment.

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.

Isomer-Resolved Imaging of Prostate Cancer Tissues Reveals Specific Lipid Unsaturation Profiles Associated With Lymphocytes and Abnormal Prostate Epithelia

Prostate cancer is the fourth most common cancer worldwide with definitive diagnosis reliant on biopsy and human-graded histopathology. As with other pathologies, grading based on classical haematoxylin and eosin (H&E) staining of formalin fixed paraffin-embedded material can be prone to variation between pathologists, prompting investigation of biomolecular markers. Comprising around 50% of cellular mass, and with known metabolic variations in cancer, lipids provide a promising target for molecular pathology. Here we apply isomer-resolved lipidomics in combination with imaging mass spectrometry to interrogate tissue sections from radical prostatectomy specimens. Guided by the histopathological assessment of adjacent tissue sections, regions of interest are investigated for molecular signatures associated with lipid metabolism, especially desaturation and elongation pathways. Monitoring one of the most abundant cellular membrane lipids within these tissues, phosphatidylcholine (PC) 34:1, high positive correlation was observed between the n-9 isomer (site of unsaturation 9-carbons from the methyl terminus) and epithelial cells from potential pre-malignant lesions, while the n-7 isomer abundance was observed to correlate with immune cell infiltration and inflammation. The correlation of lipid isomer signatures with human disease states in tissue suggests a future role for isomer-resolved mass spectrometry imaging in assisting pathologists with prostate cancer diagnoses and patient stratification.

Heterogeneity of Lipid and Protein Cartilage Profiles Associated with Human Osteoarthritis with or without Type 2 Diabetes Mellitus

Osteoarthritis (OA) is a multifactorial pathology and comprises a wide range of distinct phenotypes. In this context, the characterization of the different molecular profiles associated with each phenotype can improve the classification of OA. In particular, OA can coexist with type 2 diabetes mellitus (T2DM). This study investigates lipidomic and proteomic differences between human OA/T2DM- and OA/T2DM+ cartilage through a multimodal mass spectrometry approach. Human cartilage samples were obtained after total knee replacement from OA/T2DM- and OA/T2DM+ patients. Label-free proteomics was employed to study differences in protein abundance and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for spatially resolved-lipid analysis. Label-free proteomic analysis showed differences between OA/T2DM- and OA/T2DM+ phenotypes in several metabolic pathways such as lipid regulation. Interestingly, phospholipase A2 protein was found increased within the OA/T2DM+ cohort. In addition, MALDI-MSI experiments revealed that phosphatidylcholine and sphingomyelin species were characteristic of the OA/T2DM- group, whereas lysolipids were more characteristic of the OA/T2DM+ phenotype. The data also pointed out differences in phospholipid content between superficial and deep layers of the cartilage. Our study shows distinctively different lipid and protein profiles between OA/T2DM- and OA/T2DM+ human cartilage, demonstrating the importance of subclassification of the OA disease for better personalized treatments.

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.

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.

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.

LipostarMSI: Comprehensive, Vendor-Neutral Software for Visualization, Data Analysis, and Automated Molecular Identification in Mass Spectrometry Imaging

Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.

Imaging Isomers on a Biological Surface: A Review

Mass spectrometry imaging is an imaging technology that allows the localization and identification of molecules on (biological) sample surfaces. Obtaining the localization of a compound in tissue is of great value in biological research. Yet, the identification of compounds remains a challenge. Mass spectrometry alone, even with high-mass resolution, cannot always distinguish between the subtle structural differences of isomeric compounds. This review discusses recent advances in mass spectrometry imaging of lipids, steroid hormones, amino acids and proteins that allow imaging with isomeric resolution. These improvements in detailed identification can give new insights into the local biological activity of isomers.

Specific Lipid and Metabolic Profiles of R-CHOP-Resistant Diffuse Large B-Cell Lymphoma Elucidated by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging and in Vivo Imaging

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell non-Hodgkin lymphoma. To treat this aggressive disease, R-CHOP, a combination of immunotherapy (R; rituximab) and chemotherapy (CHOP; cyclophosphamide, doxorubicin, vincristine, and prednisone), remains the most commonly used regimen for newly diagnosed DLBCLs. However, up to one-third of patients ultimately becomes refractory to initial therapy or relapses after treatment, and the high mortality rate highlights the urgent need for novel therapeutic approaches based upon selective molecular targets. In order to understand the molecular mechanisms underlying relapsed DLBCL, we studied differences in the lipid and metabolic composition of nontreated and R-CHOP-resistant tumors, using a combination of in vivo DLBCL xenograft models and mass spectrometry imaging. Together, these techniques provide information regarding analyte composition and molecular distributions of therapy-resistant and sensitive areas. We found specific lipid and metabolic profiles for R-CHOP-resistant tumors, such as a higher presence of phosphatidylinositol and sphingomyelin fragments. In addition, we investigated intratumor heterogeneity and identified specific lipid markers of viable and necrotic areas. Furthermore, we could monitor metabolic changes and found reduced adenosine triphosphate and increased adenosine monophosphate in the R-CHOP-resistant tumors. This work highlights the power of combining in vivo imaging and MSI to track molecular signatures in DLBCL, which has potential application for other diseases.