bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022–06–05
twenty-one papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh
  1. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape.
  2. Targeting lipid metabolism in the treatment of ovarian cancer.
  3. Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.
  4. Noninvasive proteomic biomarkers for alcohol-related liver disease.
  5. Nutritional and Metabolic Control of Ferroptosis.
  6. Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility.
  7. Quantitation of non-derivatized free amino acids for detecting inborn errors of metabolism by incorporating mixed-mode chromatography with tandem mass spectrometry.
  8. Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.
  9. Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics.
  10. Proteoform Analysis and Construction of Proteoform Families in Proteoform Suite.
  11. Metabolic Reprogramming and Lipophagy Mediates Survival of Ascites Derived Metastatic Ovarian Cancer Cells.
  12. MS-based technologies for untargeted single-cell proteomics.
  13. Mass Fabrication of Capillary Columns Based on Centrifugal Packing.
  14. High-throughput and high-sensitivity biomarker monitoring in body fluid by FAIMS-enhanced fast LC SureQuant™ IS targeted quantitation.
  15. Correction: Targeting KDM6A Suppresses SREBP1c-Dependent Lipid Metabolism and Prostate Tumorigenesis.
  16. Top-Down Mass Spectrometry Data Analysis Using TopPIC Suite.
  17. Targeting metabolism: A potential strategy for hematological cancer therapy.
  18. Targeted Mass Spectrometry Analysis of Protein Phosphorylation by Selected Ion Monitoring Coupled to Parallel Reaction Monitoring (tSIM/PRM).
  19. Optimization of Microflow LC Coupled with Scanning SWATH and Its Application in Hepatocellular Carcinoma Tissues.
  20. Lipidomic profiling of Pleurotus ostreatus by LC/MS Q-TOF analysis.
  21. Fatty acid oxidation protects cancer cells from apoptosis by increasing mitochondrial membrane lipids.
  1. Mass Spectrom Rev. 2022 May 29. e21781
    Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape.
    Reta Birhanu Kitata, Jhih-Ci Yang, Yu-Ju Chen.
      The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
    Keywords:  data-independent acquisition (DIA); mass spectrometry; proteomics
    DOI:  https://doi.org/10.1002/mas.21781
  2. Oncotarget. 2022 ;13 768-783
    Targeting lipid metabolism in the treatment of ovarian cancer.
    Saliha Chaudhry, Stefani N Thomas, Glenn E Simmons.
      Cancer cells undergo alterations in lipid metabolism to support their high energy needs, tumorigenesis and evade an anti-tumor immune response. Alterations in fatty acid production are controlled by multiple enzymes, chiefly Acetyl CoA Carboxylase, ATP-Citrate Lyase, Fatty Acid Synthase, and Stearoyl CoA Desaturase 1. Ovarian cancer (OC) is a common gynecological malignancy with a high rate of aggressive carcinoma progression and drug resistance. The accumulation of unsaturated fatty acids in ovarian cancer supports cell growth, increased cancer cell migration, and worse patient outcomes. Ovarian cancer cells also expand their lipid stores via increased uptake of lipids using fatty acid translocases, fatty acid-binding proteins, and low-density lipoprotein receptors. Furthermore, increased lipogenesis and lipid uptake promote chemotherapy resistance and dampen the adaptive immune response needed to eliminate tumors. In this review, we discuss the role of lipid synthesis and metabolism in driving tumorigenesis and drug resistance in ovarian cancer conferring poor prognosis and outcomes in patients. We also cover some aspects of how lipids fuel ovarian cancer stem cells, and how these metabolic alterations in intracellular lipid content could potentially serve as biomarkers of ovarian cancer.
    Keywords:  biomarkers; fatty acid; lipid metabolism; microenvironment; ovarian cancer
    DOI:  https://doi.org/10.18632/oncotarget.28241
  3. Mass Spectrom Rev. 2022 May 29. e21785
    Recent advances in LC-MS-based metabolomics for clinical biomarker discovery.
    Chao-Jung Chen, Der-Yen Lee, Jiaxin Yu, Yu-Ning Lin, Tsung-Min Lin.
      The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.
    Keywords:  LC-MS; derivatization; disease marker; machine learning; metabolomics
    DOI:  https://doi.org/10.1002/mas.21785
  4. Nat Med. 2022 Jun 02.
    Noninvasive proteomic biomarkers for alcohol-related liver disease.
    Lili Niu, Maja Thiele, Philipp E Geyer, Ditlev Nytoft Rasmussen, Henry Emanuel Webel, Alberto Santos, Rajat Gupta, Florian Meier, Maximilian Strauss, Maria Kjaergaard, Katrine Lindvig, Suganya Jacobsen, Simon Rasmussen, Torben Hansen, Aleksander Krag, Matthias Mann.
      Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease-fibrosis, inflammation and steatosis-remains incomplete. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic-area under the curve (ROC-AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC-AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong's test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell's C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing.
    DOI:  https://doi.org/10.1038/s41591-022-01850-y
  5. Annu Rev Nutr. 2022 Jun 01.
    Nutritional and Metabolic Control of Ferroptosis.
    Eikan Mishima, Marcus Conrad.
      Ferroptosis is a type of regulated cell death characterized by an excessive lipid peroxidation of cellular membranes caused by the disruption of the antioxidant defense system and/or an imbalanced cellular metabolism. Ferroptosis differentiates from other forms of regulated cell death in that several metabolic pathways and nutritional aspects, including endogenous antioxidants (such as coenzyme Q10, vitamin E, and di/tetrahydrobiopterin), iron handling, energy sensing, selenium utilization, amino acids, and fatty acids, directly regulate the cells' sensitivity to lipid peroxidation and ferroptosis. As hallmarks of ferroptosis have been documented in a variety of diseases, including neurodegeneration, acute organ injury, and therapy-resistant tumors, the modulation of ferroptosis using pharmacological tools or by metabolic reprogramming holds great potential for the treatment of ferroptosis-associated diseases and cancer therapy. Hence, this review focuses on the regulation of ferroptosis by metabolic and nutritional cues and discusses the potential of nutritional interventions for therapy by targeting ferroptosis. Expected final online publication date for the Annual Review of Nutrition, Volume 42 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    DOI:  https://doi.org/10.1146/annurev-nutr-062320-114541
  6. J Am Soc Mass Spectrom. 2022 Jun 02.
    Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility.
    Bailey S Rose, Jody C May, Allison R Reardon, John A McLean.
      Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.
    DOI:  https://doi.org/10.1021/jasms.2c00067
  7. J Mass Spectrom Adv Clin Lab. 2022 Aug;25 1-11
    Quantitation of non-derivatized free amino acids for detecting inborn errors of metabolism by incorporating mixed-mode chromatography with tandem mass spectrometry.
    Patrick D DeArmond, Dustin R Bunch.
       Introduction: Amino acids are critical biomarkers for many inborn errors of metabolism, but amino acid analysis is challenging due to the range of chemical properties inherent in these small molecules. Techniques are available for amino acid analysis, but they can suffer from long run times, laborious derivatization, and/or poor resolution of isobaric compounds.
    Objective: To develop and validate a method for the quantitation of a non-derivatized free amino acid profile in both plasma and urine samples using mixed-mode chromatography and tandem mass spectrometry.
    Methods: Chromatographic conditions were optimized to separate leucine, isoleucine, and allo-isoleucine and maintain analytical runtime at less than 15 min. Sample preparation included a quick protein precipitation followed by LC-MS/MS analysis. Matrix effects, interferences, linearity, carryover, acceptable dilution limits, precision, accuracy, and stability were evaluated in both plasma and urine specimen types.
    Results: A total of 38 amino acids and related compounds were successfully quantitated with this method. In addition, argininosuccinic acid was qualitatively analyzed. A full clinical validation was performed that included method comparison to a reference laboratory for plasma and urine with Deming regression slopes ranging from 0.38 to 1.26.
    Conclusion: This method represents an alternative to derivatization-based methods, especially in urine samples where interference from metabolites and medications is prevalent.
    Keywords:  AMR, analytical measurement range; AQC, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate; ASA, argininosuccinic acid; Allo-isoleucine; Amino acid; CEX, cation exchange; CS, Fcerebrospinal fluid; CV, coefficient of variation; GABA, gamma-aminobutyric acid; GC/MS, gas chromatography-mass spectrometry; HCl, hydrochloric acid; HILIC, hydrophilic interaction liquid chromatography; IS, internal standard; Inborn errors of metabolism; LC-MS/MS; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LLOQ, lower limit of quantitation; MM, mixed-mode; MS/MS, tandem mass spectrometry; MSU, Dmaple syrup urine disease; Maple syrup urine disease; QC, quality control; RPL, Creversed phase liquid chromatography; ULO, Qupper limit of quantitation
    DOI:  https://doi.org/10.1016/j.jmsacl.2022.05.002
  8. Environ Sci Technol. 2022 Jun 02.
    Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning.
    MaKayla Foster, Markace Rainey, Chandler Watson, James N Dodds, Kaylie I Kirkwood, Facundo M Fernández, Erin S Baker.
      The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.
    Keywords:  PFAS; ion mobility spectrometry; machine learning; mass defect; mass spectrometry; per- and polyfluoroalkyl substances; xenobiotics
    DOI:  https://doi.org/10.1021/acs.est.2c00201
  9. Proteomics. 2022 Jun 02. e2100328
    Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics.
    María Moran-Garrido, Sandra M Camunas-Alberca, Alberto Gil-de-la Fuente, Antonio Mariscal, Ana Gradillas, Coral Barbas, Jorge Sáiz.
      Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.
    Keywords:  Acquisition; Data; Databases ; Ion Mobility; Lipidomics; Mass Spectrometry
    DOI:  https://doi.org/10.1002/pmic.202100328
  10. Methods Mol Biol. 2022 ;2500 67-81
    Proteoform Analysis and Construction of Proteoform Families in Proteoform Suite.
    Leah V Schaffer, Michael R Shortreed, Lloyd M Smith.
      Proteoform Suite is an interactive software program for the identification and quantification of intact proteoforms from mass spectrometry data. Proteoform Suite identifies proteoforms observed by intact-mass (MS1) analysis. In intact-mass analysis, unfragmented experimental proteoforms are compared to a database of known proteoform sequences and to one another, searching for mass differences corresponding to well-known post-translational modifications or amino acids. Intact-mass analysis enables proteoforms observed in the MS1 data without MS/MS (MS2) fragmentation to be identified. Proteoform Suite further facilitates the construction and visualization of proteoform families, which are the sets of proteoforms derived from individual genes. Bottom-up peptide identifications and top-down (MS2) proteoform identifications can be integrated into the Proteoform Suite analysis to increase the sensitivity and accuracy of the analysis. Proteoform Suite is open source and freely available at https://github.com/smith-chem-wisc/proteoform-suite .
    Keywords:  Mass spectrometry; Post-translational modification; Proteoform; Proteoform family; Top-down proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-2325-1_7
  11. Asian Pac J Cancer Prev. 2022 May 01. pii: 90121. [Epub ahead of print]23(5): 1699-1709
    Metabolic Reprogramming and Lipophagy Mediates Survival of Ascites Derived Metastatic Ovarian Cancer Cells.
    Sandeep Kumar S, Shalini N Swamya, Varadahally Rangaiah Devaraj, Chennagiri S Premalathac, V Rd Pallavi, Bk Chandrashekar Sagar, Dhananjay D Shinde, Ramesh Gawari.
       OBJECTIVE: The study was aimed at understanding the survival of metastatic ovarian cancer spheroids in the malignant ascites microenvironment.
    METHODS: All the assays were performed using aseptically collected patient samples. The cells were characterized for the expression of ovarian and cancer stem cell markers using immunocytochemistry. The presence of lipid in the primary metastatic cancer spheroids were confirmed by neutral fat staining using Oil Red-O and transmission electron microscopy. The mRNA expression of autophagy and lipid metabolism genes was analyzed using RT-PCR. The lipid content was analyzed using lipidomics analysis. Etomoxir and chloroquine were used to study the effect of inhibition of autophagy in the metastatic cells. The data were analyzed using appropriate statistical tools and a p-value <0.05 was considered to be statistically significant.
    RESULTS: Metastatic ovarian cancer spheroids exhibit cancer stem like properties and undergo a metabolic reprogramming when they disseminate from the primary tumor. We report here the accumulation of numerous cytoplasmic lipid droplets and lipophagic vesicles in the metastatic cells in contrast to their primary tumors. In addition we also report that these cells depend on lipophagy for the utilization of lipids rather than the conventional lipolytic pathway. The lipidomics analysis data reveals that the metastatic cells possess high levels of unsaturated fatty acids. We have also reported the occurrence of distinct accumulation of multiple nuclei in the patient derived metastatic cells. Inhibition of beta-oxidation and autophagic machinery using etomoxir and chloroquine resulted in cell death suggesting a potential mode to suppress metastatic cancer cells.
    CONCLUSION:  Metabolic reprogramming is a characteristic feature of the metastatic ovarian cancer cells that are persisting in the malignant ascites. Targeting of the metastatic by gaining an insight into the various metabolic and molecular changes that occur in the metastatic niche provides a promising therapeutic approach in management of the disease.
    Keywords:  Autophagy; CPT-1a; Multinucleated cancer cells; Unsaturated fatty acids; lipid droplets
    DOI:  https://doi.org/10.31557/APJCP.2022.23.5.1699
  12. Curr Opin Biotechnol. 2022 May 26. pii: S0958-1669(22)00070-2. [Epub ahead of print]76 102736
    MS-based technologies for untargeted single-cell proteomics.
    Lir Beck, Tamar Geiger.
      Single-cell analyses characterize individual cells, allowing their clustering and characterization in an unsupervised manner. Single-cell genomics and transcriptomics dominate the field of single-cell analysis, however, these often do not accurately reflect cellular functions. In contrast, single-cell protein analyses were until recently only performed using antibody-based approaches. This review aims to highlight the recent developments in mass spectrometry-based single-cell proteomics and discuss the challenges and opportunities. Advances in the field hold the promise to impact biomedical research and contribute to the understanding of complex biological systems.
    DOI:  https://doi.org/10.1016/j.copbio.2022.102736
  13. Anal Chem. 2022 Jun 01.
    Mass Fabrication of Capillary Columns Based on Centrifugal Packing.
    Ya Liu, Hanrong Wen, Shiyi Chen, Xiaojuan Wang, Xudong Zhu, Lianzhong Luo, Xiaofei Wang, Bo Zhang.
      Packed capillary columns have become the standard front-end separation device for mass spectrometry-based proteomics. The development of simple, fast, and robust capillary column technology, especially that with mass-fabrication capacity, can greatly improve analytical throughput and reproducibility in omics research. In this technical note, we report a centrifugal packing technology, which has the capability to mass fabricate high quality capillary columns with a 2886 columns/day fabrication throughput. The centrifugally packed columns presented significantly improved efficiency (reduced plate height hmin = 1.6, 37%-40% improvement compared with slurry packed columns), advanced kinetic performance limit, and excellent column-to-column reproducibility (2.0% RSD for retention time, 50 columns). Such columns enabled ∼5300 HeLa proteins identified in single-shot proteomic analysis, displaying both intercolumn and inter-run retention time stability (retention time RSD = 0.94% between nine replicates on three columns for probing peptide sequence). The mass-fabrication technology reported in this technical note may support disposable use of high quality chromatographic columns in large-scale bioanalysis.
    DOI:  https://doi.org/10.1021/acs.analchem.2c00442
  14. Mol Cell Proteomics. 2022 May 26. pii: S1535-9476(22)00059-7. [Epub ahead of print] 100251
    High-throughput and high-sensitivity biomarker monitoring in body fluid by FAIMS-enhanced fast LC SureQuant™ IS targeted quantitation.
    Konstantinos Kalogeropoulos, Simonas Savickas, Aleksander M Haack, Cathrine A Larsen, Jacek Mikosiński, Erwin M Schoof, Hans Smola, Louise Bundgaard, Ulrich Auf dem Keller.
      Targeted proteomics methods have been greatly improved and refined over the last decade and are becoming increasingly the method of choice in protein and peptide quantitative assays. Despite the tremendous progress, targeted proteomics assays still suffer from inadequate sensitivity for lower abundant proteins and throughput, especially in complex biological samples. These attributes are essential for establishing targeted proteomics methods at the forefront of clinical use. Here, we report an assay utilizing the SureQuantTM internal standard triggered targeted method on a newest generation mass spectrometer coupled with a FAIMS (high-field asymmetric waveform ion mobility spectrometry) interface ion mobility device and an EvoSep One liquid chromatography platform, which displays markedly enhanced sensitivity and a high throughput of 100 samples per day. We demonstrate the robustness of this method by quantifying proteins ranging six orders of magnitude in human wound fluid exudates, a biological fluid that exhibits sample complexity and composition similar to plasma. Among the targets quantified were low-abundance proteins such at TNFA and IL1B, highlighting the value of this method in the quantification of trace amounts of invaluable biomarkers that were until recently hardly accessible by targeted proteomics methods. Taken together, this method extends the toolkit of targeted proteomics assays and will help to drive forward mass spectrometry-based proteomics biomarker quantification.
    DOI:  https://doi.org/10.1016/j.mcpro.2022.100251
  15. Cancer Res. 2022 Jun 02. OF1
    Correction: Targeting KDM6A Suppresses SREBP1c-Dependent Lipid Metabolism and Prostate Tumorigenesis.
    Donge Tang, Yong Dai, Songhui Xu.
      
    DOI:  https://doi.org/10.1158/0008-5472.CAN-22-1463
  16. Methods Mol Biol. 2022 ;2500 83-103
    Top-Down Mass Spectrometry Data Analysis Using TopPIC Suite.
    In Kwon Choi, Xiaowen Liu.
      With the advances of mass spectrometry (MS) techniques, top-down MS-based proteomics has gained increasing attention because of its advantages over bottom-up MS in studying complex proteoforms. TopPIC Suite is a widely used software package for top-down MS-based proteoform identification and quantification. Here, we present the methods for top-down MS data analysis using TopPIC Suite.
    Keywords:  Proteoform identification; Proteoform quantification; Proteomics; Spectral deconvolution; Top-down mass spectrometry
    DOI:  https://doi.org/10.1007/978-1-0716-2325-1_8
  17. World J Clin Cases. 2022 Apr 06. 10(10): 2990-3004
    Targeting metabolism: A potential strategy for hematological cancer therapy.
    Xue Tang, Fen Chen, Li-Chun Xie, Si-Xi Liu, Hui-Rong Mai.
      Most hematological cancer-related relapses and deaths are caused by metastasis; thus, the importance of this process as a target of therapy should be considered. Hematological cancer is a type of cancer in which metabolism plays an essential role in progression. Therefore, we are required to block fundamental metastatic processes and develop specific preclinical and clinical strategies against those biomarkers involved in the metabolic regulation of hematological cancer cells, which do not rely on primary tumor responses. To understand progress in this field, we provide a summary of recent developments in the understanding of metabolism in hematological cancer and a general understanding of biomarkers currently used and under investigation for clinical and preclinical applications involving drug development. The signaling pathways involved in cancer cell metabolism are highlighted and shed light on how we could identify novel biomarkers involved in cancer development and treatment. This review provides new insights into biomolecular carriers that could be targeted as anticancer biomarkers.
    Keywords:  Anticancer; Biomarker; Cancer; Hematological cancer; Metabolism; Metastasis
    DOI:  https://doi.org/10.12998/wjcc.v10.i10.2990
  18. Methods Mol Biol. 2022 ;2526 227-240
    Targeted Mass Spectrometry Analysis of Protein Phosphorylation by Selected Ion Monitoring Coupled to Parallel Reaction Monitoring (tSIM/PRM).
    Jesús Pascual, Saijaliisa Kangasjärvi.
      Recent developments in targeted mass spectrometry-based proteomics have provided new methodological solutions for accurate and quantitative analysis of proteins and their posttranslational control, which has significantly advanced our understanding of stress responses in different plant species. Instrumentation allowing high-resolution, accurate-mass (HR/AM) analysis has provided new acquisition strategies for targeted quantitative proteomic analysis by targeted selected ion monitoring (tSIM) and parallel reaction monitoring (PRM). Here we report a sensitive and accurate method for targeted analysis of protein phosphorylation by tSIM coupled to PRM (tSIM/PRM). The tSIM/PRM method takes advantage of HR/AM mass spectrometers and benefits from the combination of highly sensitive precursor ion quantification by tSIM and highly confident peptide identification by spectral library matching in PRM. The detailed protocol describes tSIM/PRM analysis of Arabidopsis thaliana foliar proteins, from the building of a spectral library to sample preparation, mass spectrometry, and data analysis, and provides a methodological approach for specifying the molecular mechanisms of interest.
    Keywords:  Parallel reaction monitoring; Protein phosphorylation; Selected ion monitoring; Targeted mass spectrometry
    DOI:  https://doi.org/10.1007/978-1-0716-2469-2_17
  19. J Proteome Res. 2022 Jun 02.
    Optimization of Microflow LC Coupled with Scanning SWATH and Its Application in Hepatocellular Carcinoma Tissues.
    Huanhuan Gao, Youqi Liu, Vadim Demichev, Stephen Tate, Chen Chen, Jiang Zhu, Cong Lu, Markus Ralser, Tiannan Guo, Yi Zhu.
      Scanning SWATH coupled with normal-flow LC has been recently introduced for high-content, high-throughput proteomics analysis, which requires a relatively large amount of sample injection. Here we established the microflow LC coupled with Scanning SWATH for samples with relatively small quantities. First, we optimized several key parameters of the LC and MS settings, including C18 particle size for the analytical column, LC gradient and flow rate, as well as effective ion accumulation time and isolation window width for MS acquisition. We then compared the optimized Scanning SWATH method with the conventional variable window SWATH (referred to as SWATH) method. Results showed that the total ion chromatogram signals in Scanning SWATH were 10 times higher than that of SWATH, and Scanning SWATH identified 12.2-22.2% more peptides than SWATH. Finally, we employed 120 min Scanning SWATH to acquire the proteomes of 62 formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 patients with hepatocellular carcinoma (HCC). Altogether, 92 334 peptides and 8516 proteins were quantified. Besides the reported biomarkers, including ANXA2, MCM7, SUOX, and AKR1B10, we identified new potential HCC biomarkers such as CST5, TP53, CEBPB, and E2F4. Taken together, we present an optimal workflow integrating microflow LC and Scanning SWATH that effectively improves the protein identification and quantitation.
    Keywords:  SWATH; hepatocellular carcinoma; microflow LC; scanning SWATH
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00078
  20. Food Res Int. 2022 Jun;pii: S0963-9969(22)00392-1. [Epub ahead of print]156 111335
    Lipidomic profiling of Pleurotus ostreatus by LC/MS Q-TOF analysis.
    Roberto Maria Pellegrino, Federica Ianni, Francesca Blasi, Paola Angelini, Carla Emiliani, Roberto Venanzoni, Lina Cossignani.
      In this paper, for the first time a lipidomic analysis on Pleurotus ostreatus species was performed by liquid chromatography quadrupole time-of-flight mass spectrometry (LC/MS Q-TOF). Twenty-seven lipid classes, including polar and non-polar lipid classes, were detected. Free fatty acids (FA) were the predominant fraction (>57%), followed by fatty acid ester of hydroxyl fatty acid and ceramide. C18 chain length and two double bonds were the main structural characteristics for FA. Phosphatydilcholine, phosphatydiletanolamine, and glycerophosphates showed high percentages of polyunsaturated fatty acids. Unconventional fatty acids, such as odd and oxygenated chains, were detected. The highest odd/even ratio was found in hexosylceramides and sphingomyelin, while oxygenated chains were mainly represented in ceramides. As a preliminary approach, the results of lipid molecular species, subjected to principal component analysis and discriminant analysis, were able to differentiate P. ostreatus samples on the base of grown substrate. The results of the comprehensive analysis of P. ostreatus lipids are useful to evaluate the lipid nutritional value and could facilitate exploitation of P. ostreatus consumption.
    Keywords:  Discriminant analysis; LC/MS Q-TOF; Lipid classes; Lipid molecular species; Lipidomics; Pleurotus ostreatus mushroom; Principal component analysis
    DOI:  https://doi.org/10.1016/j.foodres.2022.111335
  21. Cell Rep. 2022 May 31. pii: S2211-1247(22)00645-3. [Epub ahead of print]39(9): 110870
    Fatty acid oxidation protects cancer cells from apoptosis by increasing mitochondrial membrane lipids.
    Yi-Jia Li, Johannes Francois Fahrmann, Maryam Aftabizadeh, Qianqian Zhao, Satyendra C Tripathi, Chunyan Zhang, Yuan Yuan, David Ann, Samir Hanash, Hua Yu.
      Overcoming resistance to chemotherapies remains a major unmet need for cancers, such as triple-negative breast cancer (TNBC). Therefore, mechanistic studies to provide insight for drug development are urgently needed to overcome TNBC therapy resistance. Recently, an important role of fatty acid β-oxidation (FAO) in chemoresistance has been shown. But how FAO might mitigate tumor cell apoptosis by chemotherapy is unclear. Here, we show that elevated FAO activates STAT3 by acetylation via elevated acetyl-coenzyme A (CoA). Acetylated STAT3 upregulates expression of long-chain acyl-CoA synthetase 4 (ACSL4), resulting in increased phospholipid synthesis. Elevating phospholipids in mitochondrial membranes leads to heightened mitochondrial integrity, which in turn overcomes chemotherapy-induced tumor cell apoptosis. Conversely, in both cultured tumor cells and xenograft tumors, enhanced cancer cell apoptosis by inhibiting ASCL4 or specifically targeting acetylated-STAT3 is associated with a reduction in phospholipids within mitochondrial membranes. This study demonstrates a critical mechanism underlying tumor cell chemoresistance.
    Keywords:  ACSL; CP: Cancer; CP: Metabolism; STAT3 acetylation; anti-apoptosis; chemoresistance; fatty acid oxidation; mitochondrial membrane potential; phospholipids
    DOI:  https://doi.org/10.1016/j.celrep.2022.110870
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