Imaging mass spectrometry (IMS) is becoming an essential technique in lipidomics. Still, many questions remain open, precluding it from achieving its full potential. Among them, identification of species directly from the tissue is of paramount importance. However, it is not an easy task, due to the abundance and variety of lipid species, their numerous fragmentation pathways, and the formation of a significant number of adducts, both with the matrix and with the cations present in the tissue. Here, we explore the fragmentation pathways of 17 lipid classes, demonstrating that in-source fragmentation hampers identification of some lipid species. Then, we analyze what type of adducts each class is more prone to form. Finally, we use that information together with data from on-tissue MS/MS and MS 3 to refine the peak assignment in a real experiment over sections of human nevi, to demonstrate that statistical analysis of the data is significantly more robust if unwanted peaks due to fragmentation, matrix, and other species that only introduce noise in the analysis are excluded.
For many years, traditional histology has been the gold standard for the diagnosis of many diseases. However, alternative and powerful techniques have appeared in recent years that complement the information extracted from a tissue section. One of the most promising techniques is imaging mass spectrometry applied to lipidomics. Here, we demonstrate the capabilities of this technique to highlight the architectural features of the human kidney at a spatial resolution of 10 μm. Our data demonstrate that up to seven different segments of the nephron and the interstitial tissue can be readily identified in the sections according to their characteristic lipid fingerprints and that such fingerprints are maintained among different individuals ( n = 32). These results set the foundation for further studies on the metabolic bases of the diseases affecting the human kidney.
Sublimation is a widely used method for matrix deposition in imaging mass spectrometry experiments. Still, most of the time, standard glass sublimators are used for this purpose, which do not enable optimal matrix deposition reproducibility, compromising inter-experiment comparison of the results. Here, we present an in-house designed stainless steel sublimator in which the parameters that have the strongest influence over matrix deposition reproducibility can be easily monitored. Using sections of human colon biopsies, we demonstrate the capabilities of this new prototype.
Here, we present a simple and cost-effective procedure to improve the spatial resolution of the commercial MALDI source of a LTQ Orbitrap. Based in spatial filtering techniques, we demonstrate that, with minimal modifications of the original setup, the system resolution can be pushed forward to <10 μm. The improved system performance is demonstrated by means of MALDI imaging of human colon biopsies.
Ferroptosis, a form of regulated necrosis characterized by peroxidation of lipids such as arachidonic acid‐containing phosphatidylethanolamine (PE), contributes to the pathogenesis of acute kidney injury (AKI). We have characterized the kidney lipidome in an experimental nephrotoxic AKI induced in mice using folic acid and assessed the impact of the ferroptosis inhibitor Ferrostatin‐1. Matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) was used to assess kidney lipidomics and it discriminated between glomeruli, medulla, and cortex in control kidneys, AKI kidneys, and AKI + Ferrostatin‐1 kidneys. Out of 139 lipid species from 16 classes identified, 29 (20.5%) showed significant differences between control and AKI at 48 h. Total PE and lyso‐sulfatide species decreased, while phosphatidylinositol (PI) species increased in AKI. Dysregulated mRNA levels for Pemt, Pgs1, Cdipt, and Tamm41, relevant to lipid metabolism, were in line with the lipid changes observed. Ferrostatin‐1 prevented AKI and some AKI‐associated changes in lipid levels, such as the decrease in PE and lyso‐sulfatide species, without changing the gene expression of lipid metabolism enzymes. In conclusion, changes in the kidney lipid composition during nephrotoxic AKI are associated with differential gene expression of lipid metabolism enzymes and are partially prevented by Ferrostatin‐1. © 2022 The Pathological Society of Great Britain and Ireland.
Lipid imaging mass spectrometry (LIMS) has been tested in several pathological contexts, demonstrating its ability to segregate and isolate lipid signatures in complex tissues, thanks to the technique's spatial resolution. However, it cannot yet compete with the superior identification power of high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS), and therefore, very often, the latter is used to refine the assignment of the species detected by LIMS. Also, it is not clear if the differences in sensitivity and spatial resolution between the two techniques lead to a similar panel of biomarkers for a given disease. Here, we explore the capabilities of LIMS and HPLC-MS to produce a panel of lipid biomarkers to screen nephrectomy samples from 40 clear cell renal cell carcinoma patients. The same set of samples was explored by both techniques, and despite the important differences between them in terms of the number of detected and identified species (148 by LIMS and 344 by HPLC-MS in negative-ion mode) and the presence/absence of image capabilities, similar conclusions were reached: using the lipid fingerprint, it is possible to set up classifiers that correctly identify the samples as either healthy or tumor samples. The spatial resolution of LIMS enables extraction of additional information, such as the existence of necrotic areas or the existence of different tumor cell populations, but such information does not seem determinant for the correct classification of the samples, or it may be somehow compensated by the higher analytical power of HPLC-MS. Similar conclusions were reached with two very different techniques, validating their use for the discovery of lipid biomarkers.
Glioblastoma (GBM) represents one of the deadliest tumors owing to a lack of effective treatments. The adverse outcomes are worsened by high rates of treatment discontinuation, caused by the severe side effects of temozolomide (TMZ), the reference treatment. Therefore, understanding TMZ’s effects on GBM and healthy brain tissue could reveal new approaches to address chemotherapy side effects. In this context, we have previously demonstrated the membrane lipidome is highly cell type-specific and very sensitive to pathophysiological states. However, little remains known as to how membrane lipids participate in GBM onset and progression. Hence, we employed an ex vivo model to assess the impact of TMZ treatment on healthy and GBM lipidome, which was established through imaging mass spectrometry techniques. This approach revealed that bioactive lipid metabolic hubs (phosphatidylinositol and phosphatidylethanolamine plasmalogen species) were altered in healthy brain tissue treated with TMZ. To better understand these changes, we interrogated RNA expression and DNA methylation datasets of the Cancer Genome Atlas database. The results enabled GBM subtypes and patient survival to be linked with the expression of enzymes accounting for the observed lipidome, thus proving that exploring the lipid changes could reveal promising therapeutic approaches for GBM, and ways to ameliorate TMZ side effects.
Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation tools should consider in-source fragmentation such as rMSIfragment to increase annotation confidence and reduce the number of false positives.
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