Ascorbate deficiency increases progression of shigellosis in guinea pigs and mice infection models.

Shigella spp. are the causative agents of bacterial dysentery and shigellosis, mainly in children living in developing countries. The study of Shigella entire life cycle in vivo and the evaluation of vaccine candidates' protective efficacy have been hampered by the lack of a suitable animal model of infection. None of the studies evaluated so far (rabbit, guinea pig, mouse) allowed the recapitulation of full shigellosis symptoms upon Shigella oral challenge. Historical reports have suggested that dysentery and scurvy are both metabolic diseases associated with ascorbate deficiency. Mammals, which are susceptible to Shigella infection (humans, non-human primates and guinea pigs) are among the few species unable to synthesize ascorbate. We optimized a low-ascorbate diet to induce moderate ascorbate deficiency, but not scurvy, in guinea pigs to investigate whether poor vitamin C status increases the progression of shigellosis. Moderate ascorbate deficiency increased shigellosis symptom severity during an extended period of time (up to 48 h) in all strains tested (Shigella sonnei, Shigella flexneri 5a, and 2a). At late time points, an important influx of neutrophils was observed both within the disrupted colonic mucosa and in the luminal compartment, although Shigella was able to disseminate deep into the organ to reach the sub-mucosal layer and the bloodstream. Moreover, we found that ascorbate deficiency also increased Shigella penetration into the colon epithelium layer in a Gulo-/- mouse infection model. The use of these new rodent models of shigellosis opens new doors for the study of both Shigella infection strategies and immune responses to Shigella infection.

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways.

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging: Diagnostic Pathways and Metabolites for Renal Tumor Entities.

BACKGROUND: Correct tumor subtyping of primary renal tumors is essential for treatment decision in daily routine. Most of the tumors can be classified based on morphology alone. Nevertheless, some diagnoses are difficult, and further investigations are needed for correct tumor subtyping. Besides histochemical investigations, high-mass-resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can detect new diagnostic biomarkers and hence improve the diagnostic. PATIENTS AND METHODS: Formalin-fixed paraffin embedded tissue specimens from clear cell renal cell carcinoma (ccRCC, n = 552), papillary renal cell carcinoma (pRCC, n = 122), chromophobe renal cell carcinoma (chRCC, n = 108), and renal oncocytoma (rO, n = 71) were analyzed by high-mass-resolution MALDI fourier-transform ion cyclotron resonance (FT-ICR) MSI. The SPACiAL pipeline was executed for automated co-registration of histological and molecular features. Pathway enrichment and pathway topology analysis were performed to determine significant differences between RCC subtypes. RESULTS: We discriminated the four histological subtypes (ccRCC, pRCC, chRCC, and rO) and established the subtype-specific pathways and metabolic profiles. rO showed an enrichment of pentose phosphate, taurine and hypotaurine, glycerophospholipid, amino sugar and nucleotide sugar, fructose and mannose, glycine, serine, and threonine pathways. ChRCC is defined by enriched pathways including the amino sugar and nucleotide sugar, fructose and mannose, glycerophospholipid, taurine and hypotaurine, glycine, serine, and threonine pathways. Pyrimidine, amino sugar and nucleotide sugar, glycerophospholipids, and glutathione pathways are enriched in ccRCC. Furthermore, we detected enriched phosphatidylinositol and glycerophospholipid pathways in pRCC. CONCLUSION: In summary, we performed a classification system with a mean accuracy in tumor discrimination of 85.13%. Furthermore, we detected tumor-specific biomarkers for the four most common primary renal tumors by MALDI-MSI. This method is a useful tool in differential diagnosis and biomarker detection.

Spatial metabolomics reveals upregulation of several pyrophosphate-producing pathways in cortical bone of Hyp mice.

Patients with the renal phosphate-wasting disease X-linked hypophosphatemia (XLH) and Hyp mice, the murine homolog of XLH, are characterized by loss-of-function mutations in phosphate-regulating endopeptidase homolog X-linked (PHEX), leading to excessive secretion of the bone-derived phosphotropic hormone FGF23. The mineralization defect in patients with XLH and Hyp mice is caused by a combination of hypophosphatemia and local accumulation of mineralization-inhibiting molecules in bone. However, the mechanism by which PHEX deficiency regulates bone cell metabolism remains elusive. Here, we used spatial metabolomics by employing matrix-assisted laser desorption/ionization (MALDI) Fourier-transform ion cyclotron resonance mass spectrometry imaging (MSI) of undecalcified bone cryosections to characterize in situ metabolic changes in bones of Hyp mice in a holistic, unbiased manner. We found complex changes in Hyp bone metabolism, including perturbations in pentose phosphate, purine, pyrimidine, and phospholipid metabolism. Importantly, our study identified an upregulation of several biochemical pathways involved in intra- and extracellular production of the mineralization inhibitor pyrophosphate in the bone matrix of Hyp mice. Our data emphasize the utility of MSI-based spatial metabolomics in bone research and provide holistic in situ insights as to how Phex deficiency-induced changes in biochemical pathways in bone cells are linked to impaired bone mineralization.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients.

BACKGROUND: The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning. METHODS: Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naïve patients with NSCLC (n = 85). RESULTS: The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716-8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004-4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445-1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977-1.550) were not independent prognostic factors. Using Kaplan-Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non-responders (P < 0.001). CONCLUSIONS: Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response.

Spatial Metabolomics Identifies Distinct Tumor-Specific Subtypes in Gastric Cancer Patients.

PURPOSE: Current systems of gastric cancer molecular classification include genomic, molecular, and morphological features. Gastric cancer classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct gastric cancer subtypes and identify their clinicopathological and molecular characteristics. EXPERIMENTAL DESIGN: Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 patients with gastric cancer. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive gastric cancer from a multicenter observational study. RESULTS: Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however, linked to distinct metabolic pathways and molecular features. CONCLUSIONS: Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing gastric cancer molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.

A simple preparation step to remove excess liquid lipids in white adipose tissue enabling improved detection of metabolites via MALDI-FTICR imaging MS.

Matrix-assisted laser desorption ionization (MALDI) Fourier transform ion cyclotron resonance (FTICR) imaging mass spectrometry (MS) is a powerful technology used to analyze metabolites in various tissues. However, it faces significant challenges in studying adipose tissues. Poor matrix distribution and crystallization caused by excess liquid lipids on the surface of tissue sections hamper m/z species detection, an adverse effect that particularly presents in lipid-rich white adipose tissue (WAT). In this study, we integrated a simple and low-cost preparation step into the existing MALDI-FTICR imaging MS pipeline. The new method-referred to as filter paper application-is characterized by an easy sample handling and high reproducibility. The aforementioned filter paper is placed onto the tissue prior to matrix application in order to remove the layer of excess liquid lipids. Consequently, MALDI-FTICR imaging MS detection was significantly improved, resulting in a higher number of detected m/z species and higher ion intensities. After analyzing various durations of filter paper application, 30 s was found to be optimal, resulting in the detection of more than 3700 m/z species. Apart from the most common lipids found in WAT, other molecules involved in various metabolic pathways were detected, including nucleotides, carbohydrates, and amino acids. Our study is the first to propose a solution to a specific limitation of MALDI-FTICR imaging MS in investigating lipid-rich WAT. The filter paper approach can be performed quickly and is particularly effective for achieving uniform matrix distribution on fresh frozen WAT while maintaining tissue integrity. It thus helps to gain insight into the metabolism in WAT.

MALDI Mass Spectrometry Imaging-Prognostic Pathways and Metabolites for Renal Cell Carcinomas.

High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan-Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.

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 with 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 1.86, 95% CI 0.81-4.25, p = 0.143). The classifier even allowed us to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment have 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 the tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Patterns of Carbon-Bound Exogenous Compounds in Patients with Lung Cancer and Association with Disease Pathophysiology.

Asymptomatic anthracosis is the accumulation of black carbon particles in adult human lungs. It is a common occurrence, but the pathophysiologic significance of anthracosis is debatable. Using in situ high mass resolution matrix-assisted laser desorption/ionization (MALDI) fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging analysis, we discovered noxious carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAH), tobacco-specific nitrosamines, or aromatic amines, in a series of 330 patients with lung cancer in highly variable and unique patterns. The characteristic nature of carbon-bound exogenous compounds had a strong association with patient outcome, tumor progression, the tumor immune microenvironment, programmed death-ligand 1 (PD-L1) expression, and DNA damage. Spatial correlation network analyses revealed substantial differences in the metabolome of tumor cells compared with tumor stroma depending on carbon-bound exogenous compounds. Overall, the bioactive pool of exogenous compounds is associated with several changes in lung cancer pathophysiology and correlates with patient outcome. Given the high prevalence of anthracosis in the lungs of adult humans, future work should investigate the role of carbon-bound exogenous compounds in lung carcinogenesis and lung cancer therapy. SIGNIFICANCE: This study identifies a bioactive pool of carbon-bound exogenous compounds in patient tissues associated with several tumor biological features, contributing to an improved understanding of drivers of lung cancer pathophysiology.

Optimized protocol for metabolomic and lipidomic profiling in formalin-fixed paraffin-embedded kidney tissue by LC-MS.

Formalin-fixed and paraffin-embedded (FFPE) tissue represents a valuable resource to examine cancer metabolic alterations and to identify potential markers of disease. Protocols commonly used for liquid-chromatography mass spectrometry (LC-MS)-based FFPE metabolomics have not been optimized for lipidomic analysis and pre-analytical factors, that potentially affect metabolite levels, were scarcely investigated. We here demonstrate the assessment and optimization of sample preparation procedures for comprehensive metabolomic and lipidomic profiling in FFPE kidney tissue by LC-QTOF-MS. The optimized protocol allows improved monitoring of lipids including ceramides (Cer), glycosphingolipids (GSL) and triglycerides (TAGs) while the profiling capability for small polar molecules is maintained. Further, repeatable sample preparation (CVs < 20%) along with high analytical (CVs < 10%) and inter-day precision (CVs < 20%) is achieved. As proof of concept, we analyzed a set of clear cell renal cell carcinoma (ccRCC) and corresponding non-tumorous FFPE tissue samples, achieving phenotypic distinction. Investigation of the impact of tissue fixation time (6 h, 30 h and 54 h) on FFPE tissue metabolic profiles revealed metabolite class-dependent differences on their detection abundance. Whereas specific lipids (e.g. phosphatidylinositoles, GSLs, saturated fatty acids and saturated lyso-phosphatidytlethanolamines [LPE]) remained largely unaffected (CVs < 20% between groups of fixation time), neutral lipids (e.g. Cer and TAGs) exhibited high variability (CVs > 80%). Strikingly, out of the lipid classes assigned as unaffected, fatty acids 18:0, 16:0 and LPE 18:0 were detectable by high-resolution MALDI-FT-ICR MS imaging in an independent cohort of ccRCC tissues (n = 64) and exhibited significant differences between tumor and non-tumor regions.

Light sheet fluorescence microscopy guided MALDI-imaging mass spectrometry of cleared tissue samples.

Light sheet fluorescence microscopy (LSFM) of optically cleared biological samples represents a powerful tool to analyze the 3-dimensional morphology of tissues and organs. Multimodal combinations of LSFM with additional analyses of the identical sample help to limit the consumption of restricted specimen and reduce inter-sample variation. Here, we demonstrate the proof-of-concept that LSFM of cleared brain tissue samples can be combined with Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) for detection and quantification of proteins. Samples of freshly dissected murine brain and of archived formalin-fixed paraffin-embedded (FFPE) human brain tissue were cleared (3DISCO). Tissue regions of interest were defined by LSFM and excised, (re)-embedded in paraffin, and sectioned. Mouse sections were coated with sinapinic acid matrix. Human brain sections were pre-digested with trypsin and coated with α-cyano-4-hydroxycinnamic acid matrix. Subsequently, sections were subjected to MALDI-time-of-flight (TOF)-MSI in mass ranges between 0.8 to 4 kDa (human tissue sections), or 2.5-25 kDa (mouse tissue sections) with a lateral resolution of 50 µm. Protein- and peptide-identities corresponding to acquired MALDI-MSI spectra were confirmed by parallel liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. The spatial abundance- and intensity-patterns of established marker proteins detected by MALDI-MSI were also confirmed by immunohistochemistry.

De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry.

BACKGROUND: Imaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging. METHODS: We developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features. RESULTS: To demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells. CONCLUSIONS: We developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

Derangements of amino acids in cachectic skeletal muscle are caused by mitochondrial dysfunction.

BACKGROUND: Cachexia is the direct cause of at least 20% of cancer-associated deaths. Muscle wasting in skeletal muscle results in weakness, immobility, and death secondary to impaired respiratory muscle function. Muscle proteins are massively degraded in cachexia; nevertheless, the molecular mechanisms related to this process are poorly understood. Previous studies have reported conflicting results regarding the amino acid abundances in cachectic skeletal muscle tissues. There is a clear need to identify the molecular processes of muscle metabolism in the context of cachexia, especially how different types of molecules are involved in the muscle wasting process. METHODS: New in situ -omics techniques were used to produce a more comprehensive picture of amino acid metabolism in cachectic muscles by determining the quantities of amino acids, proteins, and cellular metabolites. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging, we determined the in situ concentrations of amino acids and proteins, as well as energy and other cellular metabolites, in skeletal muscle tissues from genetic mouse cancer models (n = 21) and from patients with cancer (n = 6). Combined results from three individual MALDI mass spectrometry imaging methods were obtained and interpreted. Immunohistochemistry staining for mitochondrial proteins and myosin heavy chain expression, digital image analysis, and transmission electron microscopy complemented the MALDI mass spectrometry imaging results. RESULTS: Metabolic derangements in cachectic mouse muscle tissues were detected, with significantly increased quantities of lysine, arginine, proline, and tyrosine (P = 0.0037, P = 0.0048, P = 0.0430, and P = 0.0357, respectively) and significantly reduced quantities of glutamate and aspartate (P = 0.0008 and P = 0.0124). Human skeletal muscle tissues revealed similar tendencies. A majority of altered amino acids were released by the breakdown of proteins involved in oxidative phosphorylation. Decreased energy charge was observed in cachectic muscle tissues (P = 0.0101), which was related to the breakdown of specific proteins. Additionally, expression of the cationic amino acid transporter CAT1 was significantly decreased in the mitochondria of cachectic mouse muscles (P = 0.0133); this decrease may play an important role in the alterations of cationic amino acid metabolism and decreased quantity of glutamate observed in cachexia. CONCLUSIONS: Our results suggest that mitochondrial dysfunction has a substantial influence on amino acid metabolism in cachectic skeletal muscles, which appears to be triggered by diminished CAT1 expression, as well as the degradation of mitochondrial proteins. These findings provide new insights into the pathobiochemistry of muscle wasting.

Multimodal analysis of formalin-fixed and paraffin-embedded tissue by MALDI imaging and fluorescence in situ hybridization for combined genetic and metabolic analysis.

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

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.

Chronic d-serine supplementation impairs insulin secretion.

OBJECTIVE: The metabolic role of d-serine, a non-proteinogenic NMDA receptor co-agonist, is poorly understood. Conversely, inhibition of pancreatic NMDA receptors as well as loss of the d-serine producing enzyme serine racemase have been shown to modulate insulin secretion. Thus, we aim to study the impact of chronic and acute d-serine supplementation on insulin secretion and other parameters of glucose homeostasis. METHODS: We apply MALDI FT-ICR mass spectrometry imaging, NMR based metabolomics, 16s rRNA gene sequencing of gut microbiota in combination with a detailed physiological characterization to unravel the metabolic action of d-serine in mice acutely and chronically treated with 1% d-serine in drinking water in combination with either chow or high fat diet feeding. Moreover, we identify SNPs in SRR, the enzyme converting L-to d-serine and two subunits of the NMDA receptor to associate with insulin secretion in humans, based on the analysis of 2760 non-diabetic Caucasian individuals. RESULTS: We show that chronic elevation of d-serine results in reduced high fat diet intake. In addition, d-serine leads to diet-independent hyperglycemia due to blunted insulin secretion from pancreatic beta cells. Inhibition of alpha 2-adrenergic receptors rapidly restores glycemia and glucose tolerance in d-serine supplemented mice. Moreover, we show that single nucleotide polymorphisms (SNPs) in SRR as well as in individual NMDAR subunits are associated with insulin secretion in humans. CONCLUSION: Thus, we identify a novel role of d-serine in regulating systemic glucose metabolism through modulating insulin secretion.

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.