SALDI-MS and SERS Multimodal Imaging: One Nanostructured Substrate to Rule Them Both.

Imaging techniques based on mass spectrometry or spectroscopy methods inform in situ about the chemical composition of biological tissues or organisms, but they are sometimes limited by their specificity, sensitivity, or spatial resolution. Multimodal imaging addresses these limitations by combining several imaging modalities; however, measuring the same sample with the same preparation using multiple imaging techniques is still uncommon due to the incompatibility between substrates, sample preparation protocols, and data formats. We present a multimodal imaging approach that employs a gold-coated nanostructured silicon substrate to couple surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced Raman spectroscopy (SERS). Our approach integrates both imaging modalities by using the same substrate, sample preparation, and data analysis software on the same sample, allowing the coregistration of both images. We transferred molecules from clean fingertips and fingertips covered with plasticine modeling clay onto our nanostructure and analyzed their chemical composition and distribution by SALDI-MS and SERS. Multimodal analysis located the traces of plasticine on fingermarks and provided chemical information on the composition of the clay. Our multimodal approach effectively combines the advantages of mass spectrometry and vibrational spectroscopy with the signal enhancing abilities of our nanostructured substrate.

Perspective on Multimodal Imaging Techniques Coupling Mass Spectrometry and Vibrational Spectroscopy: Picturing the Best of Both Worlds.

Studies on complex biological phenomena often combine two or more imaging techniques to collect high-quality comprehensive data directly in situ, preserving the biological context. Mass spectrometry imaging (MSI) and vibrational spectroscopy imaging (VSI) complement each other in terms of spatial resolution and molecular information. In the past decade, several combinations of such multimodal strategies arose in research fields as diverse as microbiology, cancer, and forensics, overcoming many challenges toward the unification of these techniques. Here we focus on presenting the advantages and challenges of multimodal imaging from the point of view of studying biological samples as well as giving a perspective on the upcoming trends regarding this topic. The latest efforts in the field are discussed, highlighting the purpose of the technique for clinical applications.

Characterization of 1H NMR Plasma Glycoproteins as a New Strategy To Identify Inflammatory Patterns in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease associated with a high index of morbidity and mortality from cardiovascular diseases. We used 1H NMR to characterize the plasma glycoprotein and lipoprotein profiles of a cohort of patients with RA ( n = 210) versus healthy individuals ( n = 203) to associate them with the RA disease and its severity. Using 1H NMR, we developed a line-shape method to characterize the two peaks associated with glycoproteins (GlycA and GlycB) and its derived variables: areas of GlycB (Area GlycB) and GlycA (Area GlycA), shape factors of these two peaks (H/W = height/width), and the distance between them (Distance GlycB-GlycA). We also used the advanced lipoprotein test Liposcale (CE) to characterize the lipoprotein subclasses. The standard lipid panel and traditional inflammatory markers such as C-reactive protein, the erythrocyte sedimentation rate, fibrinogen, the rheumatoid factor, anticitrullinated peptide antibodies, and the DAS28 index have also been determined. RA patients presented a significant 10.65% increase in the GlycA associated area compared with the control group ( p = 2.21 × 10-10). They also presented significantly higher H/W GlycA and GlycB ratios than the control population (H/W GlycB p = 7.88 × 10-8; H/W GlycA p = 5.61 × 10-8). The prediction model that uses the traditional inflammatory variables and the 1H NMR-derived parameters presented an AUC that was almost 10% higher than the model that only uses the traditional inflammatory variables (from 0.7 to 0.79 AUC). We have demonstrated that GlycA and GlycB variables derived from 1H NMR, along with classic inflammatory parameters, help to improve the classification of individuals with high RA disease activity.

Hypothalamic JNK1-hepatic fatty acid synthase axis mediates a metabolic rewiring that prevents hepatic steatosis in male mice treated with olanzapine via intraperitoneal: Additional effects of PTP1B inhibition.

Olanzapine (OLA), a widely used second-generation antipsychotic (SGA), causes weight gain and metabolic alterations when administered orally to patients. Recently, we demonstrated that, contrarily to the oral treatment which induces weight gain, OLA administered via intraperitoneal (i.p.) in male mice resulted in body weight loss. This protection was due to an increase in energy expenditure (EE) through a mechanism involving the modulation of hypothalamic AMPK activation by higher OLA levels reaching this brain region compared to those of the oral treatment. Since clinical studies have shown hepatic steatosis upon chronic treatment with OLA, herein we further investigated the role of the hypothalamus-liver interactome upon OLA administration in wild-type (WT) and protein tyrosine phosphatase 1B knockout (PTP1B-KO) mice, a preclinical model protected against metabolic syndrome. WT and PTP1B-KO male mice were fed an OLA-supplemented diet or treated via i.p. Mechanistically, we found that OLA i.p. treatment induces mild oxidative stress and inflammation in the hypothalamus in a JNK1-independent and dependent manner, respectively, without features of cell dead. Hypothalamic JNK activation up-regulated lipogenic gene expression in the liver though the vagus nerve. This effect concurred with an unexpected metabolic rewiring in the liver in which ATP depletion resulted in increased AMPK/ACC phosphorylation. This starvation-like signature prevented steatosis. By contrast, intrahepatic lipid accumulation was observed in WT mice treated orally with OLA; this effect being absent in PTP1B-KO mice. We also demonstrated an additional benefit of PTP1B inhibition against hypothalamic JNK activation, oxidative stress and inflammation induced by chronic OLA i.p. treatment, thereby preventing hepatic lipogenesis. The protection conferred by PTP1B deficiency against hepatic steatosis in the oral OLA treatment or against oxidative stress and neuroinflammation in the i.p. treatment strongly suggests that targeting PTP1B might be also a therapeutic strategy to prevent metabolic comorbidities in patients under OLA treatment in a personalized manner.

rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios.

Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.

Acute-phase glycoprotein profile responses to different oral macronutrient challenges: Influence of sex, functional hyperandrogenism and obesity.

Acute-phase glycoprotein 1H-NMR spectroscopy profiles serve as surrogate markers of chronic inflammation in metabolic disorders such as obesity, diabetes and polycystic ovary syndrome (PCOS). The latter is associated with increased height-to-width (H/W) ratios of GlycA and GlycB after fasting, but not to glycoprotein areas, regardless of obesity. We studied the responses to separate glucose, lipid and protein oral challenges of five glycoprotein variables (GlycA, GlycB, and GlycF areas and the GlycA and GlycB H/W ratios) in 17 women with PCOS, 17 control women, and 19 healthy men. Glucose and protein ingestion resulted into decreases in all glycoprotein variables, whereas lipid ingestion increased GlycA, GlycF and induced minimal changes in GlycB and GlycB H/W. We found no effects of obesity or group of subjects on postprandial glycoprotein variables regardless of the macronutrient being ingested. However, a statistically significant interaction indicated that obesity blunted the decrease in some of these variables in control women and men, whereas obese women with PCOS showed larger changes when compared with their non-obese counterparts. In conclusion, acute-phase glycoprotein profiles indicate an anti-inflammatory response during postprandial phase that is less pronounced after lipid ingestion, and is counteracted by the chronic inflammatory background associated with obesity and PCOS.

Gold Nanoparticle-Assisted Black Silicon Substrates for Mass Spectrometry Imaging Applications.

Mass spectrometry imaging (MSI) based on matrix-assisted laser desorption ionization (MALDI) is widely used in proteomics. However, matrix-free technologies are gaining popularity for detecting low molecular mass compounds. Small molecules were analyzed with nanostructured materials as ionization promoters, which produce low-to-no background signal, and facilitate enhanced specificity and sensitivity through functionalization. We investigated the fabrication and the use of black silicon and gold-coated black silicon substrates for surface-assisted laser desorption/ionization mass spectrometry imaging (SALDI-MSI) of animal tissues and human fingerprints. Black silicon was created using dry etching, while gold nanoparticles were deposited by sputtering. Both methods are safe for the user. Physicochemical characterization and MSI measurements revealed the optimal properties of the substrates for SALDI applications. The gold-coated black silicon worked considerably better than black silicon as the LDI-MSI substrate. The substrate was also compatible with imprinting, as a sample-simplification method that allows efficient transference of metabolites from the tissues to the substrate surface, without compound delocalization. Moreover, by modifying the surface with hydrophilic and hydrophobic groups, specific interactions were stimulated between surface and sample, leading to a selective analysis of molecules. Thus, our substrate facilitates targeted and/or untargeted in situ metabolomics studies for various fields such as clinical, environmental, forensics, and pharmaceutical research.