Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer.

BACKGROUND: Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear. METHODS: Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients. RESULTS: Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance. CONCLUSIONS: This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy. CLINICAL TRIAL REGISTRATION: The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma.

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

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.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes.

Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.

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.

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.

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.

Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis.

Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism.

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.

Native glycan fragments detected by MALDI mass spectrometry imaging are independent prognostic factors in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies to date. The impressively developed stroma that surrounds and modulates the behavior of cancer cells is one of the main factors regulating the PDAC growth, metastasis and therapy resistance. Here, we postulate that stromal and cancer cell compartments differentiate in protein/lipid glycosylation patterns and analyze differences in glycan fragments in those compartments with clinicopathologic correlates. RESULTS: We analyzed native glycan fragments in 109 human FFPE PDAC samples using high mass resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR-MSI). Our method allows detection of native glycan fragments without previous digestion with PNGase or any other biochemical reaction. With this method, 8 and 18 native glycans were identified as uniquely expressed in only stromal or only cancer cell compartment, respectively. Kaplan-Meier survival model identified glycan fragments that are expressed in cancer cell or stromal compartment and significantly associated with patient outcome. Among cancer cell region-specific glycans, 10 predicted better and 6 worse patient survival. In the stroma, 1 glycan predicted good and 4 poor patient survival. Using factor analysis as a dimension reduction method, we were able to group the identified glycans in 2 factors. Multivariate analysis revealed that these factors can be used as independent survival prognostic elements with regard to the established Union for International Cancer Control (UICC) classification both in tumor and stroma regions. CONCLUSION: Our method allows in situ detection of naturally occurring glycans in FFPE samples of human PDAC tissue and highlights the differences among glycans found in stromal and cancer cell compartment offering a basis for further exploration on the role of specific glycans in cancer-stroma communication.

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.

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.

Mass Spectrometry Imaging Establishes 2 Distinct Metabolic Phenotypes of Aldosterone-Producing Cell Clusters in Primary Aldosteronism.

Aldosterone-producing adenomas (APAs) are one of the main causes of primary aldosteronism and the most prevalent surgically correctable form of hypertension. Aldosterone-producing cell clusters (APCCs) comprise tight nests of zona glomerulosa cells, strongly positive for CYP11B2 (aldosterone synthase) in immunohistochemistry. APCCs have been suggested as possible precursors of APAs because they frequently carry driver mutations for constitutive aldosterone production, and a few adrenal lesions with histopathologic features of both APCCs and APAs have been identified. Our objective was to investigate the metabolic phenotypes of APCCs (n=27) compared with APAs (n=6) using in situ matrix-assisted laser desorption/ionization mass spectrometry imaging of formalin-fixed paraffin-embedded adrenals from patients with unilateral primary aldosteronism. Specific distribution patterns of metabolites were associated with APCCs and classified 2 separate APCC subgroups (subgroups 1 and 2) indistinguishable by CYP11B2 immunohistochemistry. Metabolic profiles of APCCs in subgroup 1 were tightly clustered and distinct from subgroup 2 and APAs. Multiple APCCs from the same adrenal displayed metabolic profiles of the same subgroup. Metabolites of APCC subgroup 2 were highly similar to the APA group and indicated enhanced metabolic pathways favoring cell proliferation compared with APCC subgroup 1. In conclusion, we demonstrate specific subgroups of APCCs with strikingly divergent distribution patterns of metabolites. One subgroup displays a metabolic phenotype convergent with APAs and may represent the progression of APCCs to APAs.

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.