bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022‒07‒17
eighteen papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. Comprehensive metabolome analysis of intracellular metabolites in cultured cells.
  2. Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions.
  3. Lipid profiling using Raman and a modified support vector machine algorithm.
  4. Increasing the throughput of sensitive proteomics by plexDIA.
  5. Dynamic Progress in Technological Advances to Study Lipids in Aging: Challenges and Future Directions.
  6. Tumor metabolic reprogramming in lung cancer progression.
  7. Mass Spectrometry Multiplexed Detection of SARS-CoV-2.
  8. Optimized data-independent acquisition approach for proteomic analysis at single-cell level.
  9. Tumor cells dictate anti-tumor immune responses by altering pyruvate utilization and succinate signaling in CD8+ T cells.
  10. Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology.
  11. Metabolic analysis as a driver for discovery, diagnosis, and therapy.
  12. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging.
  13. Context-dependent regulation of ferroptosis sensitivity.
  14. Metabolite profiling of abalone (Haliotis iris) energy metabolism: a Chatham Islands case study.
  15. Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension.
  16. Pan-cancer proteomic map of 949 human cell lines.
  17. Bias caused by incomplete metabolite extraction and matrix effect: Evaluation of critical factors for plasma sample preparation prior to metabolomics.
  18. Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery.
  1. STAR Protoc. 2022 Jul 11. pii: S2666-1667(22)00411-7. [Epub ahead of print]3(3): 101531
    Comprehensive metabolome analysis of intracellular metabolites in cultured cells.
    Ryuhei Kudo, Kaori Igarashi, Tomoyoshi Soga, Takamasa Ishikawa, Yasuhiro Saito.
      Capillary electrophoresis mass spectrometry (CE-MS) can measure the intracellular amount of highly polar and charged metabolites; liquid chromatography mass spectrometry (LC-MS) can quantify hydrophobic metabolites. A comprehensive metabolome analysis requires independent sample preparation for LC-MS and CE-MS. Here, we present a protocol to prepare for sequentially analyzing the metabolites from one sample. Here we describe the steps for breast cancer cell lines, MCF-7 cells, but the protocol can be applied to other cell types.
    Keywords:  Cell Biology; Cell culture; Mass Spectrometry; Metabolism; Metabolomics
    DOI:  https://doi.org/10.1016/j.xpro.2022.101531
  2. Nat Protoc. 2022 Jul 13.
    Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions.
    Kaylie I Kirkwood, Brian S Pratt, Nicholas Shulman, Kaipo Tamura, Michael J MacCoss, Brendan X MacLean, Erin S Baker.
      Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline ( https://skyline.ms/skyline.url ) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline's recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.
    DOI:  https://doi.org/10.1038/s41596-022-00714-6
  3. J Raman Spectrosc. 2021 Nov;52(11): 1910-1922
    Lipid profiling using Raman and a modified support vector machine algorithm.
    Mariana C Potcoava, Gregory L Futia, Emily A Gibson, Isabel R Schlaepfer.
      Lipid droplets are dynamic organelles that play important cellular roles. They are composed of a phospholipid membrane and a core of triglycerides and sterol esters. Fatty acids have important roles in phospholipid membrane formation, signaling, and synthesis of triglycerides as energy storage. Better non-invasive tools for profiling and measuring cellular lipids are needed. Here we demonstrate the potential of Raman spectroscopy to determine with high accuracy the composition changes of the fatty acids and cholesterol found in the lipid droplets of prostate cancer cells treated with various fatty acids. The methodology uses a modified least squares fitting (LSF) routine that uses highly discriminatory wavenumbers between the fatty acids present in the sample using a support vector machine algorithm. Using this new LSF routine, Raman micro-spectroscopy can become a better non-invasive tool for profiling and measuring fatty acids and cholesterol for cancer biology.
    Keywords:  LNCaP cells; Raman spectroscopy; cholesterol; fatty acids; lipid droplets; support vector machine
    DOI:  https://doi.org/10.1002/jrs.6238
  4. Nat Biotechnol. 2022 Jul 14.
    Increasing the throughput of sensitive proteomics by plexDIA.
    Jason Derks, Andrew Leduc, Georg Wallmann, R Gray Huffman, Matthew Willetts, Saad Khan, Harrison Specht, Markus Ralser, Vadim Demichev, Nikolai Slavov.
      Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.
    DOI:  https://doi.org/10.1038/s41587-022-01389-w
  5. Front Aging. 2022 ;3 851073
    Dynamic Progress in Technological Advances to Study Lipids in Aging: Challenges and Future Directions.
    Fangyuan Gao, Emily Tom, Dorota Skowronska-Krawczyk.
      Lipids participate in all cellular processes. Diverse methods have been developed to investigate lipid composition and distribution in biological samples to understand the effect of lipids across an organism's lifespan. Here, we summarize the advanced techniques for studying lipids, including mass spectrometry-based lipidomics, lipid imaging, chemical-based lipid analysis and lipid engineering and their advantages. We further discuss the limitation of the current methods to gain an in-depth knowledge of the role of lipids in aging, and the possibility of lipid-based therapy in aging-related diseases.
    Keywords:  aging; lipidomic; lipids; review; technology
    DOI:  https://doi.org/10.3389/fragi.2022.851073
  6. Oncol Lett. 2022 Aug;24(2): 287
    Tumor metabolic reprogramming in lung cancer progression.
    Xin Li, Minghui Liu, Hongyu Liu, Jun Chen.
      Metabolic reprogramming is an important characteristic of tumor cells. Tumor cells reprogram their metabolic pathways to meet the material, energy and redox force needs for rapid proliferation. Metabolic reprogramming changes the level or type of specific metabolites inside and outside cells, and promotes tumor growth by affecting gene expression, cell state and the tumor microenvironment. Glucose metabolism, glutamine metabolism and lipid metabolism are significant metabolic pathways in tumors. Targeting metabolic reprogramming can significantly inhibit tumor growth and induce apoptosis. Metabolic reprogramming also plays an important role in maintaining the growth advantage of tumor cells and enhancing the chemotherapy tolerance of lung cancer. This review summarizes abnormal changes in the metabolism of glucose, fat and amino acids in lung cancer, and the underlying molecular mechanism, with the aim of providing novel ideas for the prevention, early diagnosis and treatment of lung cancer.
    Keywords:  lung cancer; metabolic reprogramming
    DOI:  https://doi.org/10.3892/ol.2022.13407
  7. Methods Mol Biol. 2022 ;2511 161-174
    Mass Spectrometry Multiplexed Detection of SARS-CoV-2.
    Luciana Godoy Viana, Adriana Lebkuchen, Rodrigo Andrade Schuch, Guilherme Gonçalves Okai, Jessica Silva Salgueiro, Karina Helena Morais Cardozo, Valdemir Melechco Carvalho.
      Testing of large populations for virus infection is now a reality worldwide due to the coronavirus (SARS-CoV-2) pandemic. The demand for SARS-CoV-2 testing using alternatives other than PCR led to the development of mass spectrometry (MS)-based assays. However, MS for SARS-CoV-2 large-scale testing have some downsides, including complex sample preparation and slow data analysis. Here, we describe a high-throughput targeted proteomics method to detect SARS-CoV-2 directly from nasopharyngeal and oropharyngeal swabs. This strategy employs fully automated sample preparation mediated by magnetic particles, followed by detection of SARS-CoV-2 nucleoprotein peptides by turbulent flow chromatography coupled with tandem mass spectrometry.
    Keywords:  Liquid chromatography tandem mass spectrometry; Multiplexed turbulent flow chromatography; SARS-CoV-2; Targeted proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-2395-4_12
  8. Clin Proteomics. 2022 Jul 09. 19(1): 24
    Optimized data-independent acquisition approach for proteomic analysis at single-cell level.
    Yuefan Wang, Tung-Shing Mamie Lih, Lijun Chen, Yuanwei Xu, Morgan D Kuczler, Liwei Cao, Kenneth J Pienta, Sarah R Amend, Hui Zhang.
      BACKGROUND: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis.METHODS: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow.
    RESULTS: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number.
    CONCLUSIONS: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.
    DOI:  https://doi.org/10.1186/s12014-022-09359-9
  9. Cell Metab. 2022 Jul 07. pii: S1550-4131(22)00228-5. [Epub ahead of print]
    Tumor cells dictate anti-tumor immune responses by altering pyruvate utilization and succinate signaling in CD8+ T cells.
    Ilaria Elia, Jared H Rowe, Sheila Johnson, Shakchhi Joshi, Giulia Notarangelo, Kiran Kurmi, Sarah Weiss, Gordon J Freeman, Arlene H Sharpe, Marcia C Haigis.
      The tumor microenvironment (TME) is a unique metabolic niche that can inhibit T cell metabolism and cytotoxicity. To dissect the metabolic interplay between tumors and T cells, we establish an in vitro system that recapitulates the metabolic niche of the TME and allows us to define cell-specific metabolism. We identify tumor-derived lactate as an inhibitor of CD8+ T cell cytotoxicity, revealing an unexpected metabolic shunt in the TCA cycle. Metabolically fit cytotoxic T cells shunt succinate out of the TCA cycle to promote autocrine signaling via the succinate receptor (SUCNR1). Cytotoxic T cells are reliant on pyruvate carboxylase (PC) to replenish TCA cycle intermediates. By contrast, lactate reduces PC-mediated anaplerosis. The inhibition of pyruvate dehydrogenase (PDH) is sufficient to restore PC activity, succinate secretion, and the activation of SUCNR1. These studies identify PDH as a potential drug target to allow CD8+ T cells to retain cytotoxicity and overcome a lactate-enriched TME.
    Keywords:  T cells; cancer metabolism; lactate; pyruvate; succinate; tumor immunity
    DOI:  https://doi.org/10.1016/j.cmet.2022.06.008
  10. Biofactors. 2022 Jul 11.
    Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology.
    Tingting Han, Hailin Cong, Bing Yu, Youqing Shen.
      Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.
    Keywords:  biomarker; identification; liquid chromatography-mass spectrometry; peptide; quantification
    DOI:  https://doi.org/10.1002/biof.1875
  11. Cell. 2022 Jul 11. pii: S0092-8674(22)00781-4. [Epub ahead of print]
    Metabolic analysis as a driver for discovery, diagnosis, and therapy.
    Ralph J DeBerardinis, Kayvan R Keshari.
      Metabolic anomalies contribute to tissue dysfunction. Current metabolism research spans from organelles to populations, and new technologies can accommodate investigation across these scales. Here, we review recent advancements in metabolic analysis, including small-scale metabolomics techniques amenable to organelles and rare cell types, functional screening to explore how cells respond to metabolic stress, and imaging approaches to non-invasively assess metabolic perturbations in diseases. We discuss how metabolomics provides an informative phenotypic dimension that complements genomic analysis in Mendelian and non-Mendelian disorders. We also outline pressing challenges and how addressing them may further clarify the biochemical basis of human disease.
    Keywords:  genomics; magnetic resonance; metabolism; metabolomics; molecular imaging; positron emission tomography; stable isotopes
    DOI:  https://doi.org/10.1016/j.cell.2022.06.029
  12. Mass Spectrom Rev. 2022 Jul 13. e21794
    What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging.
    Gerard Baquer, Lluc Sementé, Toufik Mahamdi, Xavier Correig, Pere Ràfols, María García-Altares.
      Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
    Keywords:  identification confidence levels; mass spectrometry imaging; metabolomics; molecular annotation; molecular identification; software
    DOI:  https://doi.org/10.1002/mas.21794
  13. Cell Chem Biol. 2022 Jun 29. pii: S2451-9456(22)00236-7. [Epub ahead of print]
    Context-dependent regulation of ferroptosis sensitivity.
    Leslie Magtanong, Grace D Mueller, Kevin J Williams, Maximilian Billmann, Katherine Chan, David A Armenta, Jason Moffat, Charles Boone, Chad L Myers, James A Olzmann, Steven J Bensinger, Scott J Dixon.
      Ferroptosis is an important mediator of pathophysiological cell death and an emerging target for cancer therapy. Whether ferroptosis sensitivity is governed by a single regulatory mechanism is unclear. Here, based on the integration of 24 published chemical genetic screens combined with targeted follow-up experimentation, we find that the genetic regulation of ferroptosis sensitivity is highly variable and context-dependent. For example, the lipid metabolic gene acyl-coenzyme A (CoA) synthetase long chain family member 4 (ACSL4) appears far more essential for ferroptosis triggered by direct inhibition of the lipid hydroperoxidase glutathione peroxidase 4 (GPX4) than by cystine deprivation. Despite this, distinct pro-ferroptotic stimuli converge upon a common lethal effector mechanism: accumulation of lipid peroxides at the plasma membrane. These results indicate that distinct genetic mechanisms regulate ferroptosis sensitivity, with implications for the initiation and analysis of this process in vivo.
    Keywords:  ACSL4; PUFA; ROS; cancer; ether lipid; ferroptosis; iron
    DOI:  https://doi.org/10.1016/j.chembiol.2022.06.004
  14. Metabolomics. 2022 Jul 13. 18(7): 52
    Metabolite profiling of abalone (Haliotis iris) energy metabolism: a Chatham Islands case study.
    Leonie Venter, Andrea C Alfaro, Thao Van Nguyen, Jeremie Zander Lindeque.
      INTRODUCTION: The Chatham Islands has some of the most prized black-footed abalone (Haliotis iris) beds in New Zealand. This well-managed fishery includes restrictions on catch and size limits, selective fishing methods, and shellfish management. However, recent declines in biomass and growth parameters have prompted omics research to characterise the biological responses of abalone, potentially contributing towards animal management strategies.OBJECTIVES: The aim of this study was to characterise the metabolite profiles of slow and fast growing, juvenile and adult abalone, relating to metabolites supporting energy metabolism.
    METHODS: A gas chromatography-mass spectrometry metabolite profiling, applying methyl chloroformate alkylation, was performed on juvenile and adult abalone samples collected from Point Durham and Wharekauri sites, Chatham Islands, New Zealand.
    RESULTS: The results obtained from haemolymph and muscle samples indicated that abalone from the fast-growing area, Wharekauri, fuelled metabolic functions via carbohydrate sources, providing energy for fatty acid and amino acid synthesis. Conversely, higher amino acid levels were largely utilised to promote growth in this population. The metabolism of juvenile abalone favoured anabolism, where metabolites were diverted from glycolysis and the tricarboxylic acid cycle, and used for the production of nucleotides, amino acids and fatty acids.
    CONCLUSIONS: This research provides unique physiological insights towards abalone populations supporting the use of metabolomics as a tool to investigate metabolic processes related to growth. This work sets the stage for future work aimed at developing biomarkers for growth and health monitoring to support a growing and more sustainably abalone fishery.
    Keywords:  Abalone; Anabolism; Chatham Islands; Metabolomics; Populations
    DOI:  https://doi.org/10.1007/s11306-022-01907-6
  15. Nat Commun. 2022 Jul 14. 13(1): 4099
    Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension.
    Markus M Rinschen, Oleg Palygin, Ashraf El-Meanawy, Xavier Domingo-Almenara, Amelia Palermo, Lashodya V Dissanayake, Daria Golosova, Michael A Schafroth, Carlos Guijas, Fatih Demir, Johannes Jaegers, Megan L Gliozzi, Jingchuan Xue, Martin Hoehne, Thomas Benzing, Bernard P Kok, Enrique Saez, Markus Bleich, Nina Himmerkus, Ora A Weisz, Benjamin F Cravatt, Marcus Krüger, H Paul Benton, Gary Siuzdak, Alexander Staruschenko.
      Hypertension and kidney disease have been repeatedly associated with genomic variants and alterations of lysine metabolism. Here, we combined stable isotope labeling with untargeted metabolomics to investigate lysine's metabolic fate in vivo. Dietary 13C6 labeled lysine was tracked to lysine metabolites across various organs. Globally, lysine reacts rapidly with molecules of the central carbon metabolism, but incorporates slowly into proteins and acylcarnitines. Lysine metabolism is accelerated in a rat model of hypertension and kidney damage, chiefly through N-alpha-mediated degradation. Lysine administration diminished development of hypertension and kidney injury. Protective mechanisms include diuresis, further acceleration of lysine conjugate formation, and inhibition of tubular albumin uptake. Lysine also conjugates with malonyl-CoA to form a novel metabolite Nε-malonyl-lysine to deplete malonyl-CoA from fatty acid synthesis. Through conjugate formation and excretion as fructoselysine, saccharopine, and Nε-acetyllysine, lysine lead to depletion of central carbon metabolites from the organism and kidney. Consistently, lysine administration to patients at risk for hypertension and kidney disease inhibited tubular albumin uptake, increased lysine conjugate formation, and reduced tricarboxylic acid (TCA) cycle metabolites, compared to kidney-healthy volunteers. In conclusion, lysine isotope tracing mapped an accelerated metabolism in hypertension, and lysine administration could protect kidneys in hypertensive kidney disease.
    DOI:  https://doi.org/10.1038/s41467-022-31670-0
  16. Cancer Cell. 2022 Jul 13. pii: S1535-6108(22)00274-4. [Epub ahead of print]
    Pan-cancer proteomic map of 949 human cell lines.
    Emanuel Gonçalves, Rebecca C Poulos, Zhaoxiang Cai, Syd Barthorpe, Srikanth S Manda, Natasha Lucas, Alexandra Beck, Daniel Bucio-Noble, Michael Dausmann, Caitlin Hall, Michael Hecker, Jennifer Koh, Howard Lightfoot, Sadia Mahboob, Iman Mali, James Morris, Laura Richardson, Akila J Seneviratne, Rebecca Shepherd, Erin Sykes, Frances Thomas, Sara Valentini, Steven G Williams, Yangxiu Wu, Dylan Xavier, Karen L MacKenzie, Peter G Hains, Brett Tully, Phillip J Robinson, Qing Zhong, Mathew J Garnett, Roger R Reddel.
      The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.
    Keywords:  CRISPR-Cas9; cancer; cancer vulnerability; cell line; drug response; gene essentiality; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1016/j.ccell.2022.06.010
  17. J Pharm Biomed Anal. 2022 Jul 06. pii: S0731-7085(22)00351-X. [Epub ahead of print]219 114930
    Bias caused by incomplete metabolite extraction and matrix effect: Evaluation of critical factors for plasma sample preparation prior to metabolomics.
    Hsiao-Wei Liao, Yu-Wen Cheng, Sung-Chun Tang, Ching-Hua Kuo.
      Metabolomics is an omics strategy to study the metabolite alteration in the biological system. Unbiased observation of the metabolite level is essential for targeted metabolite quantification and untargeted metabolic profiling. State-of-the-art instruments and versatile tools have been developed for accurate observation of metabolic alterations in various studies. Several analytical pitfalls, such as sample overloading and signal-saturation-induced bias, have been revealed and addressed. In this study, we proposed incomplete-metabolite-extraction-caused bias is also an important issue that results in biased observation when performing metabolomics. In the demonstration example, numerous metabolites exhibited no significant difference between extracted plasma samples with different plasma contents, which is attributed to incomplete-metabolite-extraction-caused bias and matrix effect. Matrix effect is a well-known factor that result in biased observation, it can be reduced by sample dilution and compensated by using stable isotope labelled internal standards. The detection of metabolite signals in the following consecutive extractions provided further evidence of incomplete metabolite extraction. The completeness of metabolite extraction is crucial for unbiased observation of metabolic profile changes. To address this issue, we optimized the extraction time and methanol volume to reduce the incomplete-metabolite-extraction-caused bias and evaluated the metabolite signals in consecutive extractions. Methanol extraction performed with a plasma-to-methanol ratio of 1:14 resulted in metabolite responses of less than 18.1 % in the second extractions observed by metabolomic profiling. Finally, the optimized sample preparation procedure and untargeted profiling platform were applied to detect metabolite alterations associated with patients with cerebrovascular diseases and several features with significant difference were successfully identified. This study revealed and evaluated the bias caused by incomplete metabolite extraction and matrix effect in the commonly used methanol extraction method for human plasma sample preparation for metabolomics. We anticipate the proposed metabolite extraction evaluation method could benefit more clinical and biological metabolomics studies.
    Keywords:  Cerebrovascular diseases; Incomplete metabolite extraction; Liquid chromatography electrospray ionization-mass spectrometry; Metabolomics; Plasma
    DOI:  https://doi.org/10.1016/j.jpba.2022.114930
  18. Nat Commun. 2022 Jul 13. 13(1): 4043
    Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery.
    Pierre C Havugimana, Raghuveera Kumar Goel, Sadhna Phanse, Ahmed Youssef, Dzmitry Padhorny, Sergei Kotelnikov, Dima Kozakov, Andrew Emili.
      Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.
    DOI:  https://doi.org/10.1038/s41467-022-31809-z
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