bims-mascan 2024-01-28 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–01–28
eightteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Mass spectrometry-based proteomics data from thousands of HeLa control samples.
Metabolic heterogeneity in cancer.
Intramolecular Ring-Chain Equilibrium Elimination Strategy for Pinpointing C═C Positional and Geometric Isomers of N-Alkylpyridinium Unsaturated Fatty Acid Derivatives via Ion Mobility-Mass Spectrometry.
Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.
Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review.
Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches.
ETD-Based Proteomic Profiling Improves Arginine Methylation Identification and Reveals Novel PRMT5 Substrates.
Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling.
In Silico-Predicted Dynamic Oxlipidomics MS/MS Library: High-Throughput Discovery and Characterization of Unknown Oxidized Lipids.
Data-Independent Acquisition Proteomics and N-Terminomics Methods Reveal Alterations in Mitochondrial Function and Metabolism in Ischemic-Reperfused Hearts.
Specific changes in amino acid profiles in monocytes of patients with breast, lung, colorectal and ovarian cancers.
Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.
Deciphering metabolic crosstalk in context: lessons from inflammatory diseases.
Identification of Core-Fucosylated Glycoproteins by Single-Step Truncation of N-Glycans.
Single cell proteomics by mass spectrometry reveals deep epigenetic insight and new targets of a class specific histone deacetylase inhibitor.
Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine.
Lipids as mediators of cancer progression and metastasis.
Challenges and perspectives for naming lipids in the context of lipidomics.

Sci Data. 2024 Jan 23. 11(1): 112

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Henry Webel, Yasset Perez-Riverol, Annelaura Bach Nielsen, Simon Rasmussen.

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

DOI: https://doi.org/10.1038/s41597-024-02922-z
Nat Metab. 2024 Jan 24.

Metabolic heterogeneity in cancer.

Margherita Demicco, Xiao-Zheng Liu, Katharina Leithner, Sarah-Maria Fendt.

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

DOI: https://doi.org/10.1038/s42255-023-00963-z
Anal Chem. 2024 Jan 23.

Intramolecular Ring-Chain Equilibrium Elimination Strategy for Pinpointing C═C Positional and Geometric Isomers of N-Alkylpyridinium Unsaturated Fatty Acid Derivatives via Ion Mobility-Mass Spectrometry.

Yuling Li, Jiahui Bai, Kuofeng Tseng, Xiaoqiang Zhang, Li Zhang, Jing Zhang, Wenjian Sun, Yinlong Guo.

Free unsaturated fatty acids (UFA) are key intermediates of lipid metabolism and participate in many metabolic pathways with specific biological functions. Although various fragmentation-based methods for pinpointing C═C locations in UFA were developed, the current mass spectrometry methods are difficult to simultaneously differentiate geometric isomers and positional isomers in trace samples due to low ionization efficiency, low conversion, and low resolution. Herein, an intramolecular ring-chain equilibrium elimination strategy via 4-plex stable isotope labeling dual derivatization-assisted ion mobility-mass spectrometry was developed, thereby one-pot specifically labeling C═C and carboxyl groups among the trace and unstable UFA with high sensitivity, high efficiency, and good substrate generality. It achieved fast separation of both C═C positional and geometric isomers with high resolution, which benefited from eliminating the intramolecular ring-chain equilibrium by suppressing the formation of salt bridges between free carboxyl groups and pyridine cations. 4-plex stable isotope labeling reagents showed similar reactivity, enabling high-throughput quantitative analysis of omics. This method was successfully applied for accurate and rapid identification of the UFA composition in olive oil extract. These results suggest that the developed method provides new insight for rapid characterization of UFA C═C positional and geometric isomers in complex samples to explore disease biomarkers and food quality control indicators.

DOI: https://doi.org/10.1021/acs.analchem.3c04320
J Proteome Res. 2024 Jan 26.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

Klemens Fröhlich, Regula Furrer, Christian Schori, Christoph Handschin, Alexander Schmidt.

  In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Keywords:  DIA; PGC-1α; WikiPathWay enrichments; deep proteome profiling; murine skeletal muscle; narrow mass range; proteomics; robust quantitation

DOI:  https://doi.org/10.1021/acs.jproteome.3c00736
Metabolites. 2024 Jan 14. pii: 54. [Epub ahead of print]14(1):

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review.

Ekaterina Demicheva, Vladislav Dordiuk, Fernando Polanco Espino, Konstantin Ushenin, Saied Aboushanab, Vadim Shevyrin, Aleksey Buhler, Elena Mukhlynina, Olga Solovyova, Irina Danilova, Elena Kovaleva.

  Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Keywords:  GC×GC-MS; biomarkers; blood profiling; glycomics; lipidomics; mass spectrometry; metabolomics; non-cancer diseases; review

DOI:  https://doi.org/10.3390/metabo14010054
Exp Cell Res. 2024 Jan 24. pii: S0014-4827(24)00026-0. [Epub ahead of print] 113936

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches.

Raziyeh Abooshahab, Fatemeh Razavi, Fatemeh Ghorbani, Koroush Hooshmand, Maryam Zarkesh, Mehdi Hedayati.

  Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Keywords:  Cell line models; Metabolic pathways; Metabolism; Metabolomics; Thyroid cancer

DOI:  https://doi.org/10.1016/j.yexcr.2024.113936
J Proteome Res. 2024 Jan 25.

ETD-Based Proteomic Profiling Improves Arginine Methylation Identification and Reveals Novel PRMT5 Substrates.

Lingzi Lu, Zilu Ye, Rou Zhang, Jesper V Olsen, Yanqiu Yuan, Yang Mao.

  Protein arginine methylations are important post-translational modifications (PTMs) in eukaryotes, regulating many biological processes. However, traditional collision-based mass spectrometry methods inevitably cause neutral losses of methylarginines, preventing the deep mining of biologically important sites. Herein we developed an optimized mass spectrometry workflow based on electron-transfer dissociation (ETD) with supplemental activation for proteomic profiling of arginine methylation in human cells. Using symmetric dimethylarginine (sDMA) as an example, we show that the ETD-based optimized workflow significantly improved the identification and site localization of sDMA. Quantitative proteomics identified 138 novel sDMA sites as potential PRMT5 substrates in HeLa cells. Further biochemical studies on SERBP1, a newly identified PRMT5 substrate, confirmed the coexistence of sDMA and asymmetric dimethylarginine in the central RGG/RG motif, and loss of either methylation caused increased the recruitment of SERBP1 to stress granules under oxidative stress. Overall, our optimized workflow not only enabled the identification and localization of extensive, nonoverlapping sDMA sites in human cells but also revealed novel PRMT5 substrates whose sDMA may play potentially important biological functions.

Keywords:  PRMT5; arginine methylation; electron transfer dissociation; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.3c00724
Genome Biol. 2024 Jan 24. 25(1): 34

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling.

Naixin Zhang, Qiaochu Chen, Peipei Zhang, Kejun Zhou, Yaqing Liu, Haiyan Wang, Shumeng Duan, Yongming Xie, Wenxiang Yu, Ziqing Kong, Luyao Ren, Wanwan Hou, Jingcheng Yang, Xiaoyun Gong, Lianhua Dong, Xiang Fang, Leming Shi, Ying Yu, Yuanting Zheng.

   BACKGROUND: Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.
RESULTS: As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.
CONCLUSIONS: Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Keywords:  LC–MS; Metabolomics; Quality control; Reference material; Signal-to-noise ratio (SNR); Standardization

DOI:  https://doi.org/10.1186/s13059-024-03168-z
Anal Chem. 2024 Jan 26.

In Silico-Predicted Dynamic Oxlipidomics MS/MS Library: High-Throughput Discovery and Characterization of Unknown Oxidized Lipids.

Zheng Zhou, Xuhui Huang, Yu-Ying Zhang, Shuang Cui, Ying Wang, Meng Dong, Dayong Zhou, Beiwei Zhu, Lei Qin.

Nontargeted lipidomics using liquid chromatography-tandem mass spectrometry can detect thousands of molecules in biological samples. However, the annotation of unknown oxidized lipids is limited to the structures present in libraries, restricting the analysis and interpretation of experimental data. Here, we describe Doxlipid, a computational tool for oxidized lipid annotation that predicts a dynamic MS/MS library for every experiment. Doxlipid integrates three key simulation algorithms to predict libraries and covers 32 subclasses of oxidized lipids from the three main classes. In the evaluation, Doxlipid achieves very high prediction and characterization performance and outperforms the current oxidized lipid annotation methods. Doxlipid, combined with a molecular network, further annotates unknown chemical analogs in the same reaction or pathway. We demonstrate the broad utility of Doxlipid by analyzing oxidized lipids in ferroptosis hepatocellular carcinoma, tissue samples, and other biological samples, substantially advancing the discovery of biological pathways at the trace oxidized lipid level.

DOI: https://doi.org/10.1021/acs.analchem.3c04459
J Proteome Res. 2024 Jan 24.

Data-Independent Acquisition Proteomics and N-Terminomics Methods Reveal Alterations in Mitochondrial Function and Metabolism in Ischemic-Reperfused Hearts.

Bridgette Hartley, Wesam Bassiouni, Andrej Roczkowsky, Richard Fahlman, Richard Schulz, Olivier Julien.

  Myocardial ischemia-reperfusion (IR) (stunning) injury triggers changes in the proteome and degradome of the heart. Here, we utilize quantitative proteomics and comprehensive degradomics to investigate the molecular mechanisms of IR injury in isolated rat hearts. The control group underwent aerobic perfusion, while the IR injury group underwent 20 min of ischemia and 30 min of reperfusion to induce a stunning injury. As MMP-2 activation has been shown to contribute to myocardial injury, hearts also underwent IR injury with ARP-100, an MMP-2-preferring inhibitor, to dissect the contribution of MMP-2 to IR injury. Using data-independent acquisition (DIA) and mass spectroscopy, we quantified 4468 proteins in ventricular extracts, whereby 447 proteins showed significant alterations among the three groups. We then used subtiligase-mediated N-terminomic labeling to identify more than a hundred specific cleavage sites. Among these protease substrates, 15 were identified following IR injury. We identified alterations in numerous proteins involved in mitochondrial function and metabolism following IR injury. Our findings provide valuable insights into the biochemical mechanisms of myocardial IR injury, suggesting alterations in reactive oxygen/nitrogen species handling and generation, fatty acid metabolism, mitochondrial function and metabolism, and cardiomyocyte contraction.

Keywords:  data-independent acquisition; degradomics; intracellular protease; ischemic heart disease; mass spectrometry; matrix metalloproteinase; proteolysis

DOI:  https://doi.org/10.1021/acs.jproteome.3c00754
Front Immunol. 2023 ;14 1332043

Specific changes in amino acid profiles in monocytes of patients with breast, lung, colorectal and ovarian cancers.

Vitaliy Chagovets, Natalia Starodubtseva, Alisa Tokareva, Anastasia Novoselova, Marina Patysheva, Irina Larionova, Elizaveta Prostakishina, Militsa Rakina, Anna Kazakova, Evgenii Topolnitskiy, Nikolay Shefer, Julia Kzhyshkowska, Vladimir Frankevich, Gennadiy Sukhikh.

   Introduction: Immunometabolism is essential factor of tumor progression, and tumor-associated macrophages are characterized by substantial changes in their metabolic status. In this study for the first time, we applied targeted amino acid LC-MS/MS analysis to compare amino acid metabolism of circulating monocytes isolated from patients with breast, ovarian, lung, and colorectal cancer.
Methods: Monocyte metabolomics was analyzed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/ MS) analysis of amino acid extracts. The targeted analysis of 26 amino acids was conducted by LCMS/MS on an Agilent 6460 triple quadrupole mass spectrometer equipped with an electrospray ionization source and an Agilent 1260 II liquid chromatograph.
Results: Comparison of monocytes of cancer patients with monocytes of healthy control individuals demonstrated that in breast cancer most pronounced changes were identified for tryptophan (AUC = 0.76); for ovarian cancer, aminobutyric acid was significantly elevated (AUC= 1.00); for lung cancer significant changes we indented for citrulline (AUC = 0.70). In order to identify key amino acids that are characteristic for monocytes in specific cancer types, we compared each individual cancer with other 3 types of cancer. We found, that aspartic acid and citrulline are specific for monocytes of patients with colorectal cancer (p<0.001, FC = 1.40 and p=0.003, FC = 1.42 respectively). Citrulline, sarcosine and glutamic acid are ovarian cancer-specific amino acids (p = 0.003, FC = 0.78, p = 0.003, FC = 0.62, p = 0.02, FC = 0.78 respectively). Glutamine, methionine and phenylalanine (p = 0.048, FC = 1.39. p = 0.03, FC = 1.27 and p = 0.02, FC = 1.41) are lung cancer-specific amino acids. Ornithine in monocytes demonstrated strong positive correlation (r = 0.63) with lymph node metastasis incidence in breast cancer patients. Methyl histidine and cysteine in monocytes had strong negative correlation with lymph node metastasis in ovarian cancer patients (r = -0.95 and r = -0.95 respectively). Arginine, citrulline and ornithine have strong negative correlation with tumor size (r = -0.78, citrulline) and lymph node metastasis (r = -0.63 for arginine and r = -0.66 for ornithine).
Discussion: These alterations in monocyte amino acid metabolism can reflect the reaction of systemic innate immunity on the growing tumor. Our data indicate that this metabolic programming is cancer specific and can be inhibiting cancer progression. Cancer-specific differences in citrulline, as molecular link between metabolic pathways and epigenetic programing, provide new option for the development and validation of anti-cancer therapies using inhibitors of enzymes catalyzing citrullination.

Keywords:  mass spectrometry; metabolomics; monocytes; oncology; tumor-associated macrophages

DOI:  https://doi.org/10.3389/fimmu.2023.1332043
J Proteome Res. 2024 Jan 20.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

Tobias Jumel, Andrej Shevchenko.

  We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Keywords:  DIA-NN; LC-MS/MS; accuracy; benchmark; data-independent acquisition (DIA); differential proteomics analysis; label-free quantification (LFQ)

DOI:  https://doi.org/10.1021/acs.jproteome.3c00531
Mol Oncol. 2024 Jan 26.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases.

Fenne W M Verheijen, Thi N M Tran, Jung-Chin Chang, Femke Broere, Esther A Zaal, Celia R Berkers.

  Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Keywords:  advanced metabolomics methods; immunometabolism; inflammatory diseases; metabolic crosstalk; metabolic heterogeneity; microenvironment

DOI:  https://doi.org/10.1002/1878-0261.13588
Curr Protoc. 2024 Jan;4(1): e982

Identification of Core-Fucosylated Glycoproteins by Single-Step Truncation of N-Glycans.

Yao Min, Xinyuan Zhao, Wantao Ying.

  Alpha-1,6 core fucosylation (CF) is a unique glycoform of N-glycans, and studies showed that CF modifications are involved in the occurrence and progression of various diseases and may provide potential disease biomarkers. Current strategies for the CF glycoproteome are often based on multistep enrichment of glycoproteins or glycopeptides and sequential cleavage with different glycosidases to truncate the N-glycans. Although the detection ability of low-abundance glycoproteins is improved, sample loss, high cost, and the time-consuming multistep operation also affect the reproducibility of results and the practicality of the method. Here we developed a single-step truncation (SST) strategy and evaluated its potential for the CF glycoproteome of human serum. The SST strategy has the advantages of fewer operational steps, lower cost, higher number of identifications, and better quantitative stability compared with previous approaches and provides an efficient solution for large-scale quantitative analysis of the CF glycoproteome. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Single-step truncation strategy for core fucosylation glycoproteome analysis in human serum Basic Protocol 2: Liquid chromatography-tandem mass spectrometry quantification of site-specific core fucosylation glycopeptides Alternate Protocol: Pretreatment of cellular samples of core fucosylation glycoproteome with single-step truncation strategy.

Keywords:  Endo F3; core fucosylation; glycoproteome; mass spectrometry; pancreatic ductal adenocarcinoma

DOI:  https://doi.org/10.1002/cpz1.982
bioRxiv. 2024 Jan 06. pii: 2024.01.05.574437. [Epub ahead of print]

Single cell proteomics by mass spectrometry reveals deep epigenetic insight and new targets of a class specific histone deacetylase inhibitor.

Benjamin C Orsburn.

Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than ever described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.
Abstract graphic:

DOI: https://doi.org/10.1101/2024.01.05.574437
Talanta. 2024 Jan 19. pii: S0039-9140(24)00078-X. [Epub ahead of print]271 125699

Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine.

Eliška Ivanovová, Barbora Piskláková, Dana Dobešová, Hana Janečková, Hana Foltenová, Aleš Kvasnička, Matúš Prídavok, Kateřina Bouchalová, Julie de Sousa, David Friedecký.

   OBJECTIVE: The laboratory diagnosis of inherited metabolic disorders (IMD) has undergone significant development in recent decades, mainly due to the use of mass spectrometry, which allows rapid multicomponent analysis of a wide range of metabolites. Combined with advanced software tools, the diagnosis becomes more efficient as a benefit for both physicians and patients.
METHODS: A hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry assay for determination of urinary purines, pyrimidines, N-acylglycines, N-acetylated amino acids, sugars, sugar alcohols and other diagnostically important biomarkers was developed and validated. Evaluation of the results consisting of utilisation of robust scaling and advanced visualization tools is simple and even suitable for urgent requirements.
RESULTS: The developed method, covering 65 biomarkers, provides a comprehensive diagnostic platform for 51 IMD. For most analytes, linearity with R2 > 0.99, intra and inter-day accuracy between 80 and 120 % and precision lower than 20 % were achieved. Diagnostic workflow was evaluated on 47 patients and External Quality Assurance samples involving a total of 24 different IMD. Over seven years, more than 2300 urine samples from patients suspected for IMD have been routinely analysed.
CONCLUSIONS: This method offers the advantage of a broad coverage of intermediate metabolites of interest and therefore may be a potential alternative and simplification for clinical laboratories that use multiple methods for screening these markers.

Keywords:  Diagnosis; Hydrophilic interaction chromatography; Inherited metabolic disorders; Liquid chromatography; Mass spectrometry

DOI:  https://doi.org/10.1016/j.talanta.2024.125699
Nat Cancer. 2024 Jan 25.

Lipids as mediators of cancer progression and metastasis.

Felix C E Vogel, Adriano B Chaves-Filho, Almut Schulze.

Metastasis formation is a complex process, involving multiple crucial steps, which are controlled by different regulatory mechanisms. In this context, the contribution of cancer metabolism to the metastatic cascade is being increasingly recognized. This Review focuses on changes in lipid metabolism that contribute to metastasis formation in solid tumors. We discuss the molecular mechanisms by which lipids induce a pro-metastatic phenotype and explore the role of lipids in response to oxidative stress and as signaling molecules. Finally, we reflect on potential avenues to target lipid metabolism to improve the treatment of metastatic cancers.

DOI: https://doi.org/10.1038/s43018-023-00702-z
Metabolomics. 2024 Jan 24. 20(1): 15

Challenges and perspectives for naming lipids in the context of lipidomics.

Michael Witting, Adnan Malik, Andrew Leach, Alan Bridge, Lucila Aimo, Matthew J Conroy, Valerie B O'Donnell, Nils Hoffmann, Dominik Kopczynski, Franck Giacomoni, Nils Paulhe, Amaury Cazenave Gassiot, Nathalie Poupin, Fabien Jourdan, Justine Bertrand-Michel.

   INTRODUCTION: Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied.
OBJECTIVES: In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized.
METHODS & RESULTS: In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results.
CONCLUSION: Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks.

Keywords:  Anotation; Identification; Interoperability; Lipidomic

DOI:  https://doi.org/10.1007/s11306-023-02075-x