bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024‒09‒01
sixteen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics.
  2. Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.
  3. Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation.
  4. Lactate helps cancer cells resist chemotherapy.
  5. Immune suppression by human thymus-derived effector Tregs relies on glucose/lactate-fueled fatty acid synthesis.
  6. Immunometabolism of ferroptosis in the tumor microenvironment.
  7. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics.
  8. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.
  9. The unique catalytic properties of PSAT1 mediate metabolic adaptation to glutamine blockade.
  10. A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.
  11. Simultaneous detection of 10 cancer-associated amino acids and polyamines by high-performance liquid chromatography-high resolution mass spectrometry in pleural effusion cells obtained from lung adenocarcinoma patients.
  12. Amino acid metabolism in kidney health and disease.
  13. Gas Chromatography-Mass Spectrometry-Based Analyses of Fecal Short-Chain Fatty Acids (SCFAs): A Summary Review and Own Experience.
  14. Metabolite signaling in the heart.
  15. Phosphoenolpyruvate carboxykinase 2-mediated metabolism promotes lung tumorigenesis by inhibiting mitochondrial-associated apoptotic cell death.
  16. Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.
  1. ACS Meas Sci Au. 2024 Aug 21. 4(4): 315-337
    A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics.
    Zicong Wang, Peng-Kai Liu, Lingjun Li.
      Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.
    DOI:  https://doi.org/10.1021/acsmeasuresciau.4c00007
  2. Metabolites. 2024 Aug 19. pii: 461. [Epub ahead of print]14(8):
    Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.
    Johanna von Gerichten, Kyle Saunders, Melanie J Bailey, Lee A Gethings, Anthony Onoja, Nophar Geifman, Matt Spick.
      Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.
    Keywords:  bioinformatics; lipidomics; machine learning; mass spectrometry; retention time; separation science
    DOI:  https://doi.org/10.3390/metabo14080461
  3. ACS Meas Sci Au. 2024 Aug 21. 4(4): 442-451
    Establishing Quality Control Metrics for Large-Scale Plasma Proteomic Sample Preparation.
    Nekesa C Oliver, Min Ji Choi, Albert B Arul, Marsalas D Whitaker, Renã A S Robinson.
      Large-scale plasma proteomics studies have been transformed due to the multiplexing and automation of sample preparation workflows. However, these workflows can suffer from reproducibility issues, a lack of standardized quality control (QC) metrics, and the assessment of variation before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The incorporation of robust QC metrics in sample preparation workflows ensures better reproducibility, lower assay variation, and better-informed decisions for troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of patient samples (N = 808) using tandem mass tag (TMT) 16-plex batches (N = 58). The proteomic workflow consisted of protein depletion, protein digestion, TMT labeling, and fractionation. Five QC sample types (QCstd, QCdig, QCpool, QCTMT, and QCBSA) were created to measure the performance of sample preparation prior to the final LC-MS/MS analysis. We measured <10% CV for individual sample preparation steps in the proteomic workflow based on data from various QC sample steps. The establishment of robust measures for QC of sample preparation steps allowed for greater confidence in prepared samples for subsequent LC-MS/MS analysis. This study also provides recommendations for standardized QC metrics that can assist with future large-scale cohort sample preparation workflows.
    DOI:  https://doi.org/10.1021/acsmeasuresciau.3c00070
  4. Nature. 2024 Aug 23.
    Lactate helps cancer cells resist chemotherapy.
      
    Keywords:  Cancer; Metabolism
    DOI:  https://doi.org/10.1038/d41586-024-02731-9
  5. Cell Rep. 2024 Aug 23. pii: S2211-1247(24)01032-5. [Epub ahead of print]43(9): 114681
    Immune suppression by human thymus-derived effector Tregs relies on glucose/lactate-fueled fatty acid synthesis.
    Sander de Kivit, Mark Mensink, Sarantos Kostidis, Rico J E Derks, Esther A Zaal, Marieke Heijink, Lotte J Verleng, Evert de Vries, Ellen Schrama, Niek Blomberg, Celia R Berkers, Martin Giera, Jannie Borst.
      Regulatory T cells (Tregs) suppress pro-inflammatory conventional T cell (Tconv) responses. As lipids impact cell signaling and function, we compare the lipid composition of CD4+ thymus-derived (t)Tregs and Tconvs. Lipidomics reveal constitutive enrichment of neutral lipids in Tconvs and phospholipids in tTregs. TNFR2-co-stimulated effector tTregs and Tconvs are both glycolytic, but only in tTregs are glycolysis and the tricarboxylic acid (TCA) cycle linked to a boost in fatty acid (FA) synthesis (FAS), supported by relevant gene expression. FA chains in tTregs are longer and more unsaturated than in Tconvs. In contrast to Tconvs, tTregs effectively use either lactate or glucose for FAS and rely on this process for proliferation. FASN and SCD1, enzymes responsible for FAS and FA desaturation, prove essential for the ability of tTregs to suppress Tconvs. These data illuminate how effector tTregs can thrive in inflamed or cancerous tissues with limiting glucose but abundant lactate levels.
    Keywords:  (regulatory) T cell; CP: Immunology; CP: Metabolism; costimulation; fatty acids; glycolysis; immune suppression; isotopologue tracing; lactate; lipidomics; mass spectrometry; metabolism
    DOI:  https://doi.org/10.1016/j.celrep.2024.114681
  6. Front Oncol. 2024 ;14 1441338
    Immunometabolism of ferroptosis in the tumor microenvironment.
    Gian Luca Lupica-Tondo, Emily N Arner, Denis A Mogilenko, Kelsey Voss.
      Ferroptosis is an iron-dependent form of cell death that results from excess lipid peroxidation in cellular membranes. Within the last decade, physiological and pathological roles for ferroptosis have been uncovered in autoimmune diseases, inflammatory conditions, infection, and cancer biology. Excitingly, cancer cell metabolism may be targeted to induce death by ferroptosis in cancers that are resistant to other forms of cell death. Ferroptosis sensitivity is regulated by oxidative stress, lipid metabolism, and iron metabolism, which are all influenced by the tumor microenvironment (TME). Whereas some cancer cell types have been shown to adapt to these stressors, it is not clear how immune cells regulate their sensitivities to ferroptosis. In this review, we discuss the mechanisms of ferroptosis sensitivity in different immune cell subsets, how ferroptosis influences which immune cells infiltrate the TME, and how these interactions can determine epithelial-to-mesenchymal transition (EMT) and metastasis. While much focus has been placed on inducing ferroptosis in cancer cells, these are important considerations for how ferroptosis-modulating strategies impact anti-tumor immunity. From this perspective, we also discuss some promising immunotherapies in the field of ferroptosis and the challenges associated with targeting ferroptosis in specific immune cell populations.
    Keywords:  TME; ferroptosis; immunometabolism; iron; metastasis
    DOI:  https://doi.org/10.3389/fonc.2024.1441338
  7. Biomedicines. 2024 Aug 06. pii: 1786. [Epub ahead of print]12(8):
    Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics.
    Darcy Cochran, Robert Powers.
      Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.
    Keywords:  FT-ICR-MS; imaging; mass spectrometry; metabolomics
    DOI:  https://doi.org/10.3390/biomedicines12081786
  8. ACS Meas Sci Au. 2024 Aug 21. 4(4): 338-417
    Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.
    Yuming Jiang, Devasahayam Arokia Balaya Rex, Dina Schuster, Benjamin A Neely, Germán L Rosano, Norbert Volkmar, Amanda Momenzadeh, Trenton M Peters-Clarke, Susan B Egbert, Simion Kreimer, Emma H Doud, Oliver M Crook, Amit Kumar Yadav, Muralidharan Vanuopadath, Adrian D Hegeman, Martín L Mayta, Anna G Duboff, Nicholas M Riley, Robert L Moritz, Jesse G Meyer.
      Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
    DOI:  https://doi.org/10.1021/acsmeasuresciau.3c00068
  9. Nat Metab. 2024 Aug;6(8): 1529-1548
    The unique catalytic properties of PSAT1 mediate metabolic adaptation to glutamine blockade.
    Yijian Qiu, Olivia T Stamatatos, Qingting Hu, Jed Ruiter Swain, Suzanne Russo, Ava Sann, Ana S H Costa, Sara Violante, David L Spector, Justin R Cross, Michael J Lukey.
      Cultured cancer cells frequently rely on the consumption of glutamine and its subsequent hydrolysis by glutaminase (GLS). However, this metabolic addiction can be lost in the tumour microenvironment, rendering GLS inhibitors ineffective in the clinic. Here we show that glutamine-addicted breast cancer cells adapt to chronic glutamine starvation, or GLS inhibition, via AMPK-mediated upregulation of the serine synthesis pathway (SSP). In this context, the key product of the SSP is not serine, but α-ketoglutarate (α-KG). Mechanistically, we find that phosphoserine aminotransferase 1 (PSAT1) has a unique capacity for sustained α-KG production when glutamate is depleted. Breast cancer cells with resistance to glutamine starvation or GLS inhibition are highly dependent on SSP-supplied α-KG. Accordingly, inhibition of the SSP prevents adaptation to glutamine blockade, resulting in a potent drug synergism that suppresses breast tumour growth. These findings highlight how metabolic redundancy can be context dependent, with the catalytic properties of different metabolic enzymes that act on the same substrate determining which pathways can support tumour growth in a particular nutrient environment. This, in turn, has practical consequences for therapies targeting cancer metabolism.
    DOI:  https://doi.org/10.1038/s42255-024-01104-w
  10. J Proteome Res. 2024 Aug 28.
    A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.
    Mengtian Shi, Chiyuan Huang, Renhui Chen, David Da Yong Chen, Binjun Yan.
      Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.
    Keywords:  data-independent acquisition; evaluation metric; mass spectrometry; proteomics; quantitative accuracy
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00088
  11. J Pharm Biomed Anal. 2024 Aug 14. pii: S0731-7085(24)00458-8. [Epub ahead of print]251 116418
    Simultaneous detection of 10 cancer-associated amino acids and polyamines by high-performance liquid chromatography-high resolution mass spectrometry in pleural effusion cells obtained from lung adenocarcinoma patients.
    Tingting Wang, Yina Pan, Xiuhong Hu, Wenbo Ren, Li Li, Jing Huang.
      The deregulation of amino acid and polyamine metabolism is a hallmark of malignancy that regulates cancer cell proliferation, angiogenesis, and invasion. A sensitive mass spectrometry method was developed to simultaneously quantify 10 cancer-associated metabolites in pleural effusion cells for the diagnosis of malignancy and to complement conventional pleural cytology. Analytes were detected by high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) using C8-reversed-phase HPLC for separation and sequential window acquisition of all theoretical fragment ion spectra (SWATH) acquisition for obtaining high-resolution quantitative MS/MS chromatograms. This method was validated and applied to pleural effusion cells from patients with lung adenocarcinoma (LUAD, n = 48) and those from benign controls (n = 23). The range of the above metabolites was 2-200 ng/mL for proline, aspartate, ornithine, creatine, glutamine, glutamate, arginine, citrulline, and spermine and 10-1000 ng/mL for putrescine. The intra-assay and inter-assay coefficient of variation was below 13.70 % for all analytes. The joint detection of these metabolites in pleural effusion cells achieved a clinical sensitivity of 75.0 % and specificity of 95.7 % differentiating LUAD patients from benign controls. This assay enabled the detection of 10 cancer-associated metabolites in pleural effusion cells, and the increased concentration of these metabolites was correlated with the presence of LUAD.
    Keywords:  Cancer-associated metabolites; High-performance liquid chromatography-high resolution mass spectrometry; Lung adenocarcinoma; Pleural effusion
    DOI:  https://doi.org/10.1016/j.jpba.2024.116418
  12. Nat Rev Nephrol. 2024 Aug 28.
    Amino acid metabolism in kidney health and disease.
    Martine G E Knol, Vera C Wulfmeyer, Roman-Ulrich Müller, Markus M Rinschen.
      Amino acids form peptides and proteins and are therefore considered the main building blocks of life. The kidney has an important but under-appreciated role in the synthesis, degradation, filtration, reabsorption and excretion of amino acids, acting to retain useful metabolites while excreting potentially harmful and waste products from amino acid metabolism. A complex network of kidney transporters and enzymes guides these processes and moderates the competing concentrations of various metabolites and amino acid products. Kidney amino acid metabolism contributes to gluconeogenesis, nitrogen clearance, acid-base metabolism and provision of fuel for tricarboxylic acid cycle and urea cycle intermediates, and is thus a central hub for homeostasis. Conversely, kidney disease affects the levels and metabolism of a variety of amino acids. Here, we review the metabolic role of the kidney in amino acid metabolism and describe how different diseases of the kidney lead to aberrations in amino acid metabolism. Improved understanding of the metabolic and communication routes that are affected by disease could provide new mechanistic insights into the pathogenesis of kidney diseases and potentially enable targeted dietary or pharmacological interventions.
    DOI:  https://doi.org/10.1038/s41581-024-00872-8
  13. Biomedicines. 2024 Aug 20. pii: 1904. [Epub ahead of print]12(8):
    Gas Chromatography-Mass Spectrometry-Based Analyses of Fecal Short-Chain Fatty Acids (SCFAs): A Summary Review and Own Experience.
    Paweł Czarnowski, Michał Mikula, Jerzy Ostrowski, Natalia Żeber-Lubecka.
      The gut microbiome, crucial to human health, changes with age and disease, and influences metabolic profiles. Gut bacteria produce short-chain fatty acids (SCFAs), essential for maintaining homeostasis and modulating inflammation. Dysbiosis, commonly due to poor diet or lifestyle, disrupts the integrity of the intestinal barrier and may contribute to conditions such as obesity, diabetes, and non-alcoholic fatty liver disease (NAFLD). Analytical methods such as gas chromatography-mass spectrometry (GC/MS) are vital for SCFA analysis, with various preparation and storage techniques improving the accuracy. Advances in these methods have improved the reliability and sensitivity of SCFA quantification, which is crucial for the identification of disease biomarkers. Evidence from GC/MS-based studies has revealed that accurate SCFA quantification requires meticulous sample preparation and handling. The process begins with the extraction of SCFAs from biological samples using methods such as direct solvent extraction or solid-phase microextraction (SPME), both of which require optimization for maximum recovery. Derivatization, which chemically modifies SCFAs to enhance volatility and detectability, is a crucial step, typically involving esterification or silylation. Following this, the cleanup process removes impurities that might interfere with the analysis. Although recent advances in GC/MS technology have significantly improved SCFA-detection sensitivity and specificity, proper sample storage, with acid preservatives and the avoidance of repeated thawing, is essential for maintaining SCFA integrity.
    Keywords:  GC/MS; SCFA; stool metabolites
    DOI:  https://doi.org/10.3390/biomedicines12081904
  14. Nat Cardiovasc Res. 2023 Jun;2(6): 504-516
    Metabolite signaling in the heart.
    Emily Flam, Zolt Arany.
      The heart is the most metabolically active organ in the body, sustaining a continuous and high flux of nutrient catabolism via oxidative phosphorylation. The nature and relative contribution of these fuels have been studied extensively for decades. By contrast, less attention has been placed on how intermediate metabolites generated from this catabolism affect intracellular signaling. Numerous metabolites, including intermediates of glycolysis and the tricarboxylic acid (TCA) cycle, nucleotides, amino acids, fatty acids and ketones, are increasingly appreciated to affect signaling in the heart, via various mechanisms ranging from protein-metabolite interactions to modifying epigenetic marks. We review here the current state of knowledge of intermediate metabolite signaling in the heart.
    DOI:  https://doi.org/10.1038/s44161-023-00270-6
  15. Front Pharmacol. 2024 ;15 1434988
    Phosphoenolpyruvate carboxykinase 2-mediated metabolism promotes lung tumorigenesis by inhibiting mitochondrial-associated apoptotic cell death.
    Jing Zhang, Wenjuan He, Dongmei Liu, Wenyu Zhang, Huan Qin, Song Zhang, Ailan Cheng, Qiang Li, Feilong Wang.
      Background: It is unknown how cancer cells override apoptosis and maintain progression under nutrition-deprived conditions within the tumor microenvironment. Phosphoenolpyruvate carboxykinase (PEPCK or PCK) catalyzes the first rate-limiting reaction in gluconeogenesis, which is an essential metabolic alteration that is required for the proliferation of cancer cells under glucose-limited conditions. However, if PCK-mediated gluconeogenesis affects apoptotic cell death of non small cell lung cancer (NSCLC) and its potential mechanisms remain unknown.Methods: RNA-seq, Western blot and RT-PCR were performed in A549 cell lines cultured in medium containing low or high concentrations of glucose (1 mM vs. 20 mM) to gain insight into how cancer cells rewire their metabolism under glucose-restriction conditions. Stable isotope tracing metabolomics technology (LC-MS) was employed to allow precise quantification of metabolic fluxes of the TCA cycle regulated by PCK2. Flow Cytometry was used to assess the rates of early and later apoptosis and mitochondrial ROS in NSCLC cells. Transwell assays and luciferase-based in vivo imaging were used to determine the role of PCK2 in migration and invasion of NSCLC cells. Xenotransplants on BALB/c nude mice to evaluate the effects of PCK2 on tumor growth in vivo. Western blot, Immunohistochemistry and TUNEL assays to evaluate the protein levels of mitochondrial apoptosis.
    Results: This study report that the mitochondrial resident PCK (PCK2) is upregulated in dependent of endoplasmic reticulum stress-induced expression of activating transcription factor 4 (ATF4) upon glucose deprivation in NSCLC cells. Further, the study finds that PCK2-mediated metabolism is required to decrease the burden of the TCA cycles and oxidative phosphorylation as well as the production of mitochondrial reactive oxygen species. These metabolic alterations in turn reduce the activation of Caspase9-Caspase3-PARP signal pathway which drives apoptotic cell death. Importantly, silencing PCK2 increases apoptosis of NSCLC cells under low glucose condition and inhibits tumor growth both in vitro and in vivo.
    Conclusion: In summary, PCK2-mediated metabolism is an important metabolic adaptation for NSCLC cells to acquire resistance to apoptosis under glucose deprivation.
    Keywords:  lung tumorigenesis; metabolic reprogramming; mitochondrial apoptosis; phosphoenolpyruvate carboxykinase 2; reactive oxygen species
    DOI:  https://doi.org/10.3389/fphar.2024.1434988
  16. Rapid Commun Mass Spectrom. 2024 Oct 30. 38(20): e9876
    Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.
    Francesco F Russo, Yannek Nowatzky, Carsten Jaeger, Maria K Parr, Phillipp Benner, Thilo Muth, Jan Lisec.
      Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.
    DOI:  https://doi.org/10.1002/rcm.9876
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