bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022‒04‒03
seventeen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

  1. Nat Metab. 2022 Mar 31.
      The alteration of metabolic pathways is a critical strategy for cancer cells to attain the traits necessary for metastasis in disease progression. Here, we find that dysregulation of propionate metabolism produces a pro-aggressive signature in breast and lung cancer cells, increasing their metastatic potential. This occurs through the downregulation of methylmalonyl coenzyme A epimerase (MCEE), mediated by an extracellular signal-regulated kinase 2-driven transcription factor Sp1/early growth response protein 1 transcriptional switch driven by metastatic signalling at its promoter level. The loss of MCEE results in reduced propionate-driven anaplerotic flux and intracellular and intratumoral accumulation of methylmalonic acid, a by-product of propionate metabolism that promotes cancer cell invasiveness. Altogether, we present a previously uncharacterized dysregulation of propionate metabolism as an important contributor to cancer and a valuable potential target in the therapeutic treatment of metastatic carcinomas.
  2. iScience. 2022 Apr 15. 25(4): 104056
      Castration-resistant prostate cancer (CRPC) is incurable and remains a significant worldwide challenge (Oakes and Papa, 2015). Matched untargeted multi-level omic datasets may reveal biological changes driving CRPC, identifying novel biomarkers and/or therapeutic targets. Untargeted RNA sequencing, proteomics, and metabolomics were performed on xenografts derived from three independent sets of hormone naive and matched CRPC human cell line models of local, lymph node, and bone metastasis grown as murine orthografts. Collectively, we tested the feasibility of muti-omics analysis on models of CRPC in revealing pathways of interest for future validation investigation. Untargeted metabolomics revealed NAA and NAAG commonly accumulating in CRPC across three independent models and proteomics showed upregulation of related enzymes, namely N-acetylated alpha-linked acidic dipeptidases (FOLH1/NAALADL2). Based on pathway analysis integrating multiple omic levels, we hypothesize that increased NAA in CRPC may be due to upregulation of NAAG hydrolysis via NAALADLases providing a pool of acetyl Co-A for upregulated sphingolipid metabolism and a pool of glutamate and aspartate for nucleotide synthesis during tumor growth.
    Keywords:  Cell biology; Metabolomics; Proteomics
  3. Sci Data. 2022 Mar 30. 9(1): 126
      In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).
  4. Anal Chim Acta. 2022 Apr 15. pii: S0003-2670(22)00238-0. [Epub ahead of print]1202 339667
      This research reports on the development of a comprehensive two-dimensional liquid chromatography (2D-LC) method hyphenated to inline DAD-UV and ESI-QTOF-MS/MS-detection for the separation of conjugated polyunsaturated fatty acid isomers and structurally related (saturated, unconjugated, oxidized) compounds. In pharmaceutical lipid formulations conjugated fatty acids can be found as impurities, generated by oxidation of polyunsaturated fatty acids. Due to the structural complexity of resultant multi-component samples one dimensional liquid chromatography may be suboptimal for quality control and impurity profiling. The screened reversed-phase columns showed a lack of selectivity for the conjugated fatty acid isomers but the resolutions improved with the shape selectivity of the stationary phases (C18- < C30- < cholesteryl-ether-bonded). Further enhanced selectivity for the non-chiral conjugated FAs could be achieved with amylose/cellulose-based chiral stationary phases (CSPs) which harbor cavities for selective inclusion depending on E/Z configurations of the double bonds of the analytes. Amylose-based CSPs showed higher selectivity for conjugated fatty acids than the cellulose-based polysaccharide CSPs. Hyphenating the chiral and reversed-phase columns in a comprehensive 2D-LC-setup was favorable since they showed orthogonality and good compatibility, because both were operated under RP-conditions. The chiral dimension (1D) mainly separated the different isomers, while the reversed-phase dimension (2D) separated according to number of double bonds and degree of oxidation. Using this setup, advanced structural annotation of unknowns was possible based on UV-, MS1- and MS2-spectra. Data-independent acquisition (by SWATH) enabled differentiation of positional isomers of oxidized lipids by characteristic MS2-fragments and elucidation of co-eluted compounds by selective extracted ion chromatograms of fragment ions (MS2 EICs).
    Keywords:  Data-independent acquisition; Food analysis; LC×LC; Lipidomics; Oxylipins; Pharmaceutical analysis
  5. J Chromatogr A. 2022 Mar 15. pii: S0021-9673(22)00150-9. [Epub ahead of print]1670 462952
      LC-MS metabolomic analysis in complex biological matrices may be complicated by degeneracy when using large-bore columns. Degeneracy is the detection of multiple mass spectral peaks from the same analyte due to adduction of salts to the metabolite, dimerization, or loss of neutrals. This introduces interferences to the MS spectra, diminishes quantification, and increases the rate of false identifications. Analysis using 2.1 mm inner diameter (i.d.) columns typically leads to degenerate peaks whereas nanospray using capillary columns (25, 50, and 75 µm i.d.) reduces degeneracy. Optimization of chromatographic parameters of capillary LC for amino acid standards showed the lowest HETP at 1.25 mm/sec across all capillary i.d. columns. Results suggest mass-sensitive detection below the optimum velocity. At faster velocities, concentration-dependent detection occurred across all capillaries. The 2.1 mm i.d. analytical scale column showed the greatest level of degeneracy, particularly in the low signal intensity range. 25 µm i.d. columns showed higher levels of metabolite annotation for the same signal intensity range. It also provided the lowest level of degeneracy, making it best suited for untargeted analysis. The 25 µm i.d. column achieved a peak capacity (nc) of 144 in a 30-minute gradient method with nc decreasing as the column i.d. increased. 75 µm i.d. capillary columns showed the highest signal intensity, which is beneficial for targeted analysis. These effects of chromatographic performance, resolution, and degeneracy profile of capillary and analytical scale columns were compared for metabolomic analyses in complex serum and cell lysate matrices.
    Keywords:  Capillary LC; Degeneracy; E. coli; HPLC; Human serum; Ion trap; MS; Metabolomics; Nanospray; Optimization; Orbitrap
  6. Int J Biol Sci. 2022 ;18(5): 1912-1932
      Patients with peritoneal metastasis (PM) of colorectal cancer (CRC) have poorer overall survival outcomes than those without PM. Cancer-associated fibroblasts (CAFs) are a major component of the tumor microenvironment and mediate CRC progression and PM. It is imperative to identify and develop novel therapeutic targets for PM-CRC driven by CAFs. Using lipidomics, we reveal that the abundance of phosphatidylcholine (PC) with unsaturated acyl chains was increased in clinical PM-CRC specimens. Additionally, we found that CAFs were present at a higher relative abundance in primary PM-CRC tumors and that membrane fluidity in CRC cells was increased after incubation with CAF-conditioned medium (CM) through three independent methods: lipidomics, fluorescence recovery after photobleaching (FRAP), and generalized polarization. Then, we found that increased membrane fluidity can enhance glucose uptake and metabolism, as supported by real-time bioenergetics analysis and U-13C glucose labeling. Interestingly, stearoyl-CoA desaturase 1 (SCD), the rate-limiting enzyme in the biosynthesis of unsaturated fatty acids (uS-FAs), was expressed at low levels in PM and associated with poor prognosis in CRC patients. Importantly, by untargeted metabolomics analysis and fatty acid ([U-13C]-stearic acid) tracing analyses, we found that CRC cells take up lipids and lipid-like metabolites secreted from CAFs, which may compensate for low SCD expression. Both in vitro and in vivo experiments demonstrated that sodium palmitate (C16:0) treatment could decrease the CAF-induced change in cell membrane fluidity, limit glucose metabolism, suppress cell invasiveness, and impair tumor growth and intraperitoneal dissemination. An increased C16:0 concentration was shown to induce apoptosis linked to lipotoxicity. Furthermore, C16:0 effectively enhanced the antitumor activity of 5-fluorouracil (5-FU) in vitro and was well tolerated in vivo. Taken together, these findings suggest that adding the saturated fatty acid (S-FA) C16:0 to neoadjuvant chemotherapy may open new opportunities for treating PM-CRC in the future.
    Keywords:  C16:0; Lipidomics; cancer-associated fibroblasts (CAFs); colorectal cancer peritoneal metastasis; glucose metabolism; metabolomics
  7. Front Mol Biosci. 2022 ;9 841373
      Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
    Keywords:  experimental network; graph-based analysis; knowledge network; metabolic network; metabolism; systems biology; untargeted metabolomics
  8. Compr Rev Food Sci Food Saf. 2022 Mar 29.
      Food fraud is currently a growing global concern with far-reaching consequences. Food authenticity attributes, including biological identity, geographical origin, agricultural production, and processing technology, are susceptible to food fraud. Metabolic markers and their corresponding authentication methods are considered as a promising choice for food authentication. However, few metabolic markers were available to develop robust analytical methods for food authentication in routine control. Untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS) is increasingly used to discover metabolic markers. This review summarizes the general workflow, recent applications, advantages, advances, limitations, and future needs of untargeted metabolomics by LC-MS for identifying metabolic markers in food authentication. In conclusion, untargeted metabolomics by LC-MS shows great efficiency to discover the metabolic markers for the authenticity assessment of biological identity, geographical origin, agricultural production, processing technology, freshness, cause of animals' death, and so on, through three main steps, namely, data acquisition, biomarker discovery, and biomarker validation. The application prospects of the selected markers by untargeted metabolomics require to be valued, and the selected markers need to be eventually applicable at targeted analysis assessing the authenticity of unknown food samples.
    Keywords:  authentication; food; fraud; liquid chromatography; marker; mass spectrometry; metabolomics
  9. FASEB J. 2022 May;36(5): e22296
      Metabolic reprogramming is a hallmark of cancer characterized by global changes in metabolite levels. However, compared with the study of gene expression, profiling of metabolites in cancer samples remains relatively understudied. We obtained metabolomic profiling and gene expression data from 454 human solid cancer cell lines across 24 cancer types from the Cancer Cell Line Encyclopedia (CCLE) database, to evaluate the feasibility of inferring metabolite levels from gene expression data. For each metabolite, we trained multivariable LASSO regression models to identify gene sets that are most predictive of the level of each metabolite profiled. Even when accounting for cell culture conditions or cell lineage in the model, few metabolites could be accurately predicted. In some cases, the inclusion of the upstream and downstream metabolites improved prediction accuracy, suggesting that gene expression is a poor predictor of steady-state metabolite levels. Our analysis uncovered a single robust relationship between the expression of nicotinamide N-methyltransferase (NNMT) and 1-methylnicotinamide (MNA), however, this relationship could only be validated in cancer samples with high purity, as NNMT is not expressed in immune cells. Together, we have trained models that use gene expression profiles to predict the level of individual metabolites. Our analysis suggests that inferring metabolite levels based on the expression of genes is generally challenging in cancer.
    Keywords:  cell line; gene expression; machine learning; metabolite; pan-cancer
  10. Bio Protoc. 2022 Feb 20. 12(4): e4321
      Three-dimensional (3D) cell culture models are widely used in tumor studies to more accurately reflect cell-cell interactions and tumor growth conditions in vivo. 3D anchorage-independent spheroids derived by culturing cells in ultra-low attachment (ULA) conditions is particularly relevant to ovarian cancer, as such cell clusters are often observed in malignant ascites of late-stage ovarian cancer patients. We and others have found that cells derived from anchorage-independent spheroids vary widely in gene expression profiles, proliferative state, and metabolism compared to cells maintained under attached culture conditions. This includes changes in mitochondrial function, which is most commonly assessed in cultured live cells by measuring oxygen consumption in extracellular flux assays. To measure mitochondrial function in anchorage-independent multicellular aggregates, we have adapted the Agilent Seahorse extracellular flux assay to optimize measurements of oxygen consumption and extracellular acidification of ovarian cancer cell spheroids generated by culture in ULA plates. This protocol includes: (i) Methods for culturing tumor cells as uniform anchorage-independent spheroids; (ii) Optimization for the transfer of spheroids to the Agilent Seahorse cell culture plates; (iii) Adaptations of the mitochondrial and glycolysis stress tests for spheroid extracellular flux analysis; and (iv) Suggestions for optimization of cell numbers, spheroid size, and normalization of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) values. Using this method, we have found that ovarian cancer cells cultured as anchorage-independent spheroids display altered mitochondrial function compared to monolayer cultures attached to plastic dishes. This method allows for the assessment of mitochondrial function in a more relevant patho/physiological culture condition and can be adapted to evaluate mitochondrial function of various cell types that are able to aggregate into multicellular clusters in anchorage-independence. Graphic abstract: Workflow of the Extracellular Flux Assay to Measure Respiration of Anchorage-independent Tumor Cell Spheroids.
    Keywords:  Anchorage independence; Cancer metabolism; Extracellular flux assay; Live-cell metabolic assay; Ovarian cancer; Respiration; Seahorse XFp; Tumor spheroids
  11. Bioinformatics. 2022 Mar 31. pii: btac197. [Epub ahead of print]
      MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility (IM) increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross section (CCS).RESULTS: We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.
    AVAILABILITY: All relevant source code is available at
    SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.
  12. STAR Protoc. 2022 Jun 17. 3(2): 101189
      Ferroptosis is a non-apoptotic iron-dependent cell death. Here we present a protocol for stratifying ferroptosis sensitivity in cells and mouse tissues. This protocol uses photochemical activation of lipid peroxidation (PALP) coupled with fluorescent imaging to assess the relative sensitivity to ferroptosis. Using commercial reagents and common equipment, PALP is readily accessible to most laboratories. One remaining challenge is the inability to multiplex this technique in analyzing multiple tissues or regions simultaneously. This protocol may have applications in developing ferroptosis-targeted therapies. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).
    Keywords:  Cancer; Cell Biology; Cell Membrane; Metabolism; Microscopy
  13. Anal Chem. 2022 Mar 28.
      Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.
  14. Horm Metab Res. 2022 Mar 29.
      Estrogens and androgens are important regulators of sexual development and physiological processes in men and women, acting on numerous organs throughout the body. Moreover, they can contribute to a variety of pathologies, including osteoporosis, cancer, and cardiovascular and neurologic diseases. Analysis of estrogens and androgens in biological samples has been commonly performed using immunoassays for many years. However, these assays are suboptimal, as there is cross-reactivity with similar analytes, and they have moderate specificity and sensitivity. Thus, there is a clinical need to develop highly sensitive and specific methods for the accurate measurement of estrogen and androgen concentrations. Herein, we describe the development of three liquid chromatography coupled tandem mass spectrometry-based methods that incorporate the use of a Triple Quadrupole Mass Spectrometer for quantitative measurement of endogenous concentrations of various steroid hormones in human serum samples: (1) the simultaneous measurement of testosterone, androstenedione, and cortisol, (2) dehydroepiandrosterone (DHEA), and (3) 17β-estradiol (E2). The use of derivatizing reagents, Girard's reagent P and dansyl chloride, allowed for significant gains in sensitivity in the analysis of DHEA and E2, respectively, relative to the underivatized analyte. These procedures proved efficient and adequately sensitive for steroid hormone analysis in extracted patient sera samples from older men and postmenopausal women, providing reliable data down to low nanogram/ml and sub-nanogram/ml levels. Moreover, utilizing the combination of highly specific mass transitions associated with these analytes and their respective internal deuterated standards provided a high degree of specificity to the identity of these hormones.
  15. Anal Chem. 2022 Mar 31.
      Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at
  16. J Am Soc Mass Spectrom. 2022 Mar 31.
      RNA is dynamically modified in cells by a plethora of chemical moieties to modulate molecular functions and processes. Over 140 modifications have been identified across species and RNA types, with the highest density and diversity of modifications found in tRNA (tRNA). The methods used to identify and quantify these modifications have developed over recent years and continue to advance, primarily in the fields of next-generation sequencing (NGS) and mass spectrometry (MS). Most current NGS methods are limited to antibody-recognized or chemically derivatized modifications and have limitations in identifying multiple modifications simultaneously. Mass spectrometry can overcome both of these issues, accurately identifying a large number of modifications in a single run. Here, we present advances in MS data acquisition for the purpose of RNA modification identification and quantitation. Using this approach, we identified multiple tRNA wobble position modifications in Arabidopsis thaliana that are upregulated in salt-stressed growth conditions and may stabilize translation of salt stress induced proteins. This work presents improvements in methods for studying RNA modifications and introduces a possible regulatory role of wobble position modifications in A. thaliana translation.