bims-mascan 2024-10-13 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–10–13
twenty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity.
Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies.
Single Cell mass spectrometry: Towards quantification of small molecules in individual cells.
Deep Spectral Library of Mice Retina for Myopia Research: Proteomics Dataset generated by SWATH and DIA-NN.
Development and Validation of RoboCap, a Robotic Capillary Platform to Automate Capillary Electrophoresis Mass Spectrometry En Route to High-Throughput Single-Cell Proteomics.
Determination of double bond positions in unsaturated fatty acids by pre-column derivatization with dimethyl and dipyridyl disulfide followed by LC-SWATH-MS analysis.
Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.
Peroxisomes and PPARs: Emerging role as master regulators of cancer metabolism.
Combination of low glucose and SCD1 inhibition impairs cancer metabolic plasticity and growth in MCF-7 cancer cells: a comprehensive metabolomic and lipidomic analysis.
An economically viable stable isotope-enhanced multiple reaction monitoring method for total fatty acid analysis in a mouse model of non-alcoholic fatty liver disease.
Insights into predicting small molecule retention times in liquid chromatography using deep learning.
Selective Enrichment via TiO2 Magnetic Nanoparticles Enables Deep Profiling of Circulating Neutral Glycosphingolipids.
End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.
GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.
Deep Proteome Analysis of Cerebrospinal Fluid from Pediatric Patients with Central Nervous System Cancer.
Achieving a 35-Plex Tandem Mass Tag Reagent Set through Deuterium Incorporation.
Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery.
Native Digestion and Shotgun Proteomics for Host Cell Protein Profiling of Adeno-Associated Viruses.
Divergent Metabolic Fates of Aromatic Amino Acid-Derived Isomers: Insights from Ex Vivo Metabolomics and HDX-HRMS/MS-Based Resolution of Tautomers.
Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries.
Metabolite regulation of epigenetics in cancer.

bioRxiv. 2024 Oct 05. pii: 2024.09.25.615060. [Epub ahead of print]

Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity.

Yun Xiong, Lin Tan, Wai-Kin Chan, Eric S Yin, Sri Ramya Donepudi, Jibin Ding, Bo Wei, Bao Tran, Sara Martinez, Iqbal Mahmud, Hamish I Stewart, Daniel J Hermanson, John N Weinstein, Philip L Lorenzi.

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.

DOI: https://doi.org/10.1101/2024.09.25.615060
J Am Soc Mass Spectrom. 2024 Oct 07.

Mobile Phase Contaminants Affect Neutral Lipid Analysis in LC-MS-Based Lipidomics Studies.

Joshua A Roberts, Angela S Radnoff, Aleksandra Bushueva, Jocelyn A Menard, Karl V Wasslen, Meaghan Harley, Jeffrey M Manthorpe, Jeffrey C Smith.

  Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LC-MS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex, and most spectral features in LC-MS lipidomics data sets remain unidentified. In-depth analyses of commercial triacylglycerol, diacylglycerol, and cholesterol ester standards revealed the expected ammoniated and sodiated ions as well as five additional unidentified higher mass peaks with relatively high intensities. The identities and origin of these unknown peaks were investigated by modifying the chromatographic mobile-phase components and LC-MS source parameters. Tandem MS (MS/MS) of each unknown adduct peak yielded no lipid structural information, producing only an intense ion of the adducted species. The unknown adducts were identified as low-mass contaminants originating from methanol and isopropanol in the mobile phase. Each contaminant was determined to be an alkylated amine species using their monoisotopic masses to calculate molecular formulas. Analysis of bovine liver extract identified 33 neutral lipids with an additional 73 alkyl amine adducts. Analysis of LC-MS-grade methanol and isopropanol from different vendors revealed substantial alkylated amine contamination in one out of three different brands that were tested. Substituting solvents for ones with lower levels of alkyl amine contamination increased lipid annotations by 36.5% or 27.4%, depending on the vendor, and resulted in >2.5-fold increases in peak area for neutral lipid species without affecting polar lipid analysis. These findings demonstrate the importance of solvent selection and disclosure for lipidomics protocols and highlight some of the major challenges when comparing data between experiments.

Keywords:  HPLC; LC-MS-grade solvents; contaminants; informatics; lipidomics; mass spectrometry; neutral lipids; untargeted lipidomics

DOI:  https://doi.org/10.1021/jasms.4c00320
Trends Analyt Chem. 2024 May;pii: 117657. [Epub ahead of print]174

Single Cell mass spectrometry: Towards quantification of small molecules in individual cells.

Yunpeng Lan, Zhu Zou, Zhibo Yang.

  Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Keywords:  Ambient techniques; Cell heterogeneity; High throughput; Quantitative analysis; Single cell mass spectrometry; Single cell metabolomics; Vacuum-based techniques

DOI:  https://doi.org/10.1016/j.trac.2024.117657
Sci Data. 2024 Oct 10. 11(1): 1115

Deep Spectral Library of Mice Retina for Myopia Research: Proteomics Dataset generated by SWATH and DIA-NN.

Ying Hon Sze, Dennis Yan Yin Tse, Bing Zuo, King Kit Li, Qian Zhao, Xiaoyan Jiang, Toshihide Kurihara, Kazuo Tsubota, Thomas Cheun Lam.

The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that the use of a predicted retinal-specific spectral library combined with the robust quantification achieved within this dataset will contribute to a better understanding of the proteome complexity. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.

DOI: https://doi.org/10.1038/s41597-024-03958-x
Anal Chem. 2024 Oct 09.

Development and Validation of RoboCap, a Robotic Capillary Platform to Automate Capillary Electrophoresis Mass Spectrometry En Route to High-Throughput Single-Cell Proteomics.

Dashuang Jia, Peter Nemes.

Current developments in single-cell mass spectrometry (MS) aim to deepen proteome coverage while enhancing analytical speed to study entire cell populations, one cell at a time. Custom-built microanalytical capillary electrophoresis (μCE) played a critical role in the foundation of discovery single-cell MS proteomics. However, requirements for manual operation, substantial expertise, and low measurement throughput have so far hindered μCE-based single-cell studies on large numbers of cells. Here, we design and construct a robotic capillary (RoboCap) platform that grants single-cell CE-MS with automation for proteomes limited to less than ∼100 nL. RoboCap remotely controls precision actuators to translate the sample to the fused silica separation capillary, using vials in this work. The platform is hermetically enclosed and actively pressurized to inject ∼1-250 nL of the sample into a CE separation capillary, with errors below ∼5% relative standard deviation (RSD). The platform and supporting equipment were operated and monitored remotely on a custom-written Virtual Instrument (LabView). Detection performance was validated empirically on ∼5-250 nL portions of the HeLa proteome digest using a trapped ion mobility mass spectrometer (timsTOF PRO). RoboCap improved CE-ESI sample utilization to ∼20% from ∼3% on the manual μCE, the closest reference technology. Proof-of-principle experiments found proteome identification and quantification to robustly return ∼1,800 proteins (∼13% RSD) from ∼20 ng of the HeLa proteome digest on this earlier-generation detector. RoboCap automates CE-MS for limited sample amounts, paving the way to electrophoresis-based high-throughput single-cell proteomics.

DOI: https://doi.org/10.1021/acs.analchem.4c04353
Anal Bioanal Chem. 2024 Oct 05.

Determination of double bond positions in unsaturated fatty acids by pre-column derivatization with dimethyl and dipyridyl disulfide followed by LC-SWATH-MS analysis.

Matthias Olfert, Cornelius Knappe, Adrian Sievers-Engler, Benedikt Masberg, Michael Lämmerhofer.

  Comprehensive in-depth structural characterization of free mono-unsaturated and polyunsaturated fatty acids often requires the determination of carbon-carbon double bond positions due to their impact on physiological properties and relevance in biological samples or during impurity profiling of pharmaceuticals. In this research, we report on the evaluation of disulfides as suitable derivatization reagents for the determination of carbon-carbon double bond positions of unsaturated free fatty acids by UHPLC-ESI-QTOF-MS/MS analysis and SWATH (sequential windowed acquisition of all theoretical mass spectra) acquisition. Iodine-catalyzed derivatization of C = C double bonds with dimethyl disulfide (DMDS) enabled detection of characteristic carboxy-terminal MS2 fragments for various fatty acids in ESI negative mode. The determination of double bond positions of fatty acids with up to three double bonds, the transfer of the method to plasma samples, and its limitations have been shown. To achieve charge-switching for positive ion mode MS-detection, derivatization with 2,2'-dipyridyldisulfide (DPDS) was investigated. It enabled detection of both corresponding characteristic omega-end- and carboxy-end-fragments for fatty acids with up to two double bonds in positive ion mode. It provides a straightforward strategy for designing MRM transitions for targeted LC-MS/MS assays. Both derivatization techniques represent a simple and inexpensive way for the determination of double bond positions in fatty acids with low number of double bonds. No adaptation of MS hardware is required and the specific isotopic pattern of resulting sulfur-containing products provides additional structural confirmation. This reaction scheme opens up the avenue of structural tuning of disulfide reagents beyond DMDS and DPDS using reagents like cystine and analogs to achieve enhanced performance and sensitivity.

Keywords:  2,2′-Dipyridyldisulfide (DPDS); Collision-induced dissociation (CID); Data-independent acquisition (DIA); Dimethyl disulfide (DMDS); Isomer; Lipidomics

DOI:  https://doi.org/10.1007/s00216-024-05542-z
J Proteins Proteom. 2024 ;15(3): 281-298

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.

David L Tabb, Mohammed Hanzala Kaniyar, Omar G Rosas Bringas, Heaji Shin, Luciano Di Stefano, Martin S Taylor, Shaoshuai Xie, Omer H Yilmaz, John LaCava.

  Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.
Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

Keywords:  Affinity enrichment; Bioinformatics; Co-immunoprecipitation; Data-independent acquisition; Label-free quantitation

DOI:  https://doi.org/10.1007/s42485-024-00166-4
Mol Metab. 2024 Oct 04. pii: S2212-8778(24)00175-3. [Epub ahead of print] 102044

Peroxisomes and PPARs: Emerging role as master regulators of cancer metabolism.

Anggi Muhtar Pratama, Mansi Sharma, Srivatsava Naidu, Heike Bömmel, Samudyata C Prabhuswamimath, Madhusudhan Thati, Hevi Wihadmadyatami, Akash Bachhuka, Srikanth Karnati.

  Cancer is a disease characterized by the acquisition of a multitude of unique traits. It has long been understood that cancer cells divert significantly from normal cell metabolism. The most obvious of metabolic changes is that cancer cells strongly rely on glucose conversion by aerobic glycolysis. In addition, they also regularly develop mechanisms to use lipids and fatty acids for their energy needs. Peroxisomes lie central to these adaptive changes of lipid metabolism. Peroxisomes are metabolic organelles that take part in over 50 enzymatic reactions crucial for cellular functioning. Thus, they are essential for an effective and comprehensive use of lipids' energy supplied to cells. Cancer cells display a substantial increase in the biogenesis of peroxisomes and an increased expression of proteins necessary for the enzymatic functions provided by peroxisomes. Moreover, the enzymatic conversion of FAs in peroxisomes is a significant source of reactive oxygen and nitrogen species (ROS/RNS) that strongly impact cancer malignancy. Important regulators in peroxisomal FA oxidation and ROS/RNS generation are the transcription factors of the peroxisome proliferator-activated receptor (PPAR) family. This review describes the metabolic changes in tumorigenesis and cancer progression influenced by peroxisomes. We will highlight the ambivalent role that peroxisomes and PPARs play in the different stages of tumor development and summarize our current understanding of how to capitalize on the comprehension of peroxisomal biology for cancer treatment.

Keywords:  Breast cancer; Lung cancer; PPAR; Peroxisomes; Tumor

DOI:  https://doi.org/10.1016/j.molmet.2024.102044
Metabolomics. 2024 Oct 05. 20(5): 112

Combination of low glucose and SCD1 inhibition impairs cancer metabolic plasticity and growth in MCF-7 cancer cells: a comprehensive metabolomic and lipidomic analysis.

Wentao Zhu, John A Lusk, Vadim Pascua, Danijel Djukovic, Daniel Raftery.

   BACKGROUND: Cancer cells exhibit remarkable metabolic plasticity, enabling them to adapt to fluctuating nutrient conditions. This study investigates the impact of a combination of low glucose levels and inhibition of stearoyl-CoA desaturase 1 (SCD1) using A939572 on cancer metabolic plasticity and growth.
METHODS: A comprehensive metabolomic and lipidomic analysis was conducted to unravel the intricate changes in cellular metabolites and lipids. MCF-7 cells were subjected to low glucose conditions, and SCD1 was inhibited using A939572. The resulting alterations in metabolic pathways and lipid profiles were explored to elucidate the synergistic effects on cancer cell physiology.
RESULTS: The combination of low glucose and A939572-induced SCD1 inhibition significantly impaired cancer cell metabolic plasticity. Metabolomic analysis highlighted shifts in key glycolytic and amino acid pathways, indicating the cells' struggle to adapt to restricted glucose availability. Lipidomic profiling revealed alterations in lipid composition, implying disruptions in membrane integrity and signaling cascades.
CONCLUSION: Our findings underscore the critical roles of glucose availability and SCD1 activity in sustaining cancer metabolic plasticity and growth. Simultaneously targeting these pathways emerges as a promising strategy to impede cancer progression. The comprehensive metabolomic and lipidomic analysis provides a detailed roadmap of molecular alterations induced by this combination treatment, that may help identify potential therapeutic targets.

Keywords:  Cancer metabolism; Glucose deprivation; Lipidomics; Metabolic plasticity; Metabolomics; Stearoyl-CoA desaturase 1

DOI:  https://doi.org/10.1007/s11306-024-02179-y
J Chromatogr A. 2024 Sep 29. pii: S0021-9673(24)00780-5. [Epub ahead of print]1736 465406

An economically viable stable isotope-enhanced multiple reaction monitoring method for total fatty acid analysis in a mouse model of non-alcoholic fatty liver disease.

Zijia Zhang, Yawen Liu, Gaohan Li, Xiaoling Chen, Min Lei, Yang Zhou, Huali Long, Qinhua Chen, Jinjun Hou, Wanying Wu.

  The complex pathological mechanisms of non-alcoholic fatty liver disease (NAFLD) are closely related to dysregulated lipid metabolism, and the therapeutic effects of the traditional Chinese medicine Zexie-Baizhu Decoction (AA) on NAFLD have been gaining increasing attention. However, research into altered lipid metabolism, especially fatty acids, in NAFLD and the intervention of AA faces technical challenges, especially in the precise quantitative analysis of fatty acids in biological samples. The high complexity of biological matrices, particularly after drug intervention, greatly increases the difficulty of detection. Therefore, this study innovatively developed a simple and economical stable isotope derivatization technique by synthesizing d6N,N-dimethylethylenediamine (d6-DMED) in the laboratory, establishing a simple and economical method for fatty acid quantification. This method employs a chemical reaction under low-temperature conditions to ensure the efficient synthesis of d6-DMED. Using ultra-high performance liquid chromatography-triple quadrupole mass spectrometry technique (UHPLC-MS/MS), combined with optimized chromatographic separation conditions and dynamic multiple reaction monitoring mode, the study established a highly sensitive detection method for 35 fatty acid derivatives. Methodological evaluation showed that the limits of quantification ranged from 0.002 to 0.060 μM, with high linearity of R² > 0.995. Additionally, the relative recovery rates were between 93.14% and 106.63%. To further demonstrate the feasibility of this method for fatty acid quantification, it was applied to measure fatty acids in multiple tissues in a mouse NAFLD model, as well as the effects of AA intervention on fatty acid metabolism. This rapid, simple, and cost-effective detection method not only enhances the understanding of NAFLD mechanisms but also provides a new strategy for evaluating the biological complex system after drug intervention.

Keywords:  Fatty acid; Multiple reaction monitoring; N,N-dimethylethylenediamine; NAFLD; Stable isotope

DOI:  https://doi.org/10.1016/j.chroma.2024.465406
J Cheminform. 2024 Oct 07. 16(1): 113

Insights into predicting small molecule retention times in liquid chromatography using deep learning.

Yuting Liu, Akiyasu C Yoshizawa, Yiwei Ling, Shujiro Okuda.

  In untargeted metabolomics, structures of small molecules are annotated using liquid chromatography-mass spectrometry by leveraging information from the molecular retention time (RT) in the chromatogram and m/z (formerly called ''mass-to-charge ratio'') in the mass spectrum. However, correct identification of metabolites is challenging due to the vast array of small molecules. Therefore, various in silico tools for mass spectrometry peak alignment and compound prediction have been developed; however, the list of candidate compounds remains extensive. Accurate RT prediction is important to exclude false candidates and facilitate metabolite annotation. Recent advancements in artificial intelligence (AI) have led to significant breakthroughs in the use of deep learning models in various fields. Release of a large RT dataset has mitigated the bottlenecks limiting the application of deep learning models, thereby improving their application in RT prediction tasks. This review lists the databases that can be used to expand training datasets and concerns the issue about molecular representation inconsistencies in datasets. It also discusses the application of AI technology for RT prediction, particularly in the 5 years following the release of the METLIN small molecule RT dataset. This review provides a comprehensive overview of the AI applications used for RT prediction, highlighting the progress and remaining challenges. SCIENTIFIC CONTRIBUTION: This article focuses on the advancements in small molecule retention time prediction in computational metabolomics over the past five years, with a particular emphasis on the application of AI technologies in this field. It reviews the publicly available datasets for small molecule retention time, the molecular representation methods, the AI algorithms applied in recent studies. Furthermore, it discusses the effectiveness of these models in assisting with the annotation of small molecule structures and the challenges that must be addressed to achieve practical applications.

Keywords:  Deep learning; Liquid chromatography; MassBank; PredRet; QSRR; RepoRT; Retention time prediction; SMRT; Small molecules; Untargeted metabolomics

DOI:  https://doi.org/10.1186/s13321-024-00905-1
Anal Chem. 2024 Oct 11.

Selective Enrichment via TiO2 Magnetic Nanoparticles Enables Deep Profiling of Circulating Neutral Glycosphingolipids.

Zidan Wang, Yu Xia.

Circulating neutral glycosphingolipids (neutral GSLs (nGSLs)) are a unique subset of nGSLs that detach from organs or cell membranes and enter the bloodstream. Altered molecular distribution of circulating nGSL is increasingly associated with diseases. However, profiling of circulating nGSLs presents a lasting challenge due to their low abundances and structural complexity. Although TiO2 magnetic nanoparticles (TiO2 MNPs) were effective for the enrichment of nGSLs in brain tissue, the protocol showed limited selectivity for circulating nGSLs because their abundances were 100-times lower in human plasma than in brain tissue. In this work, we optimized the key parameters of selective enrichment by TiO2 MNPs and achieved 1:10,000 selectivity for nGSLs over interfering phospholipids, while maintaining ∼70% recovery for different subclasses of nGSLs. By integrating TiO2 MNP-based selective enrichment with reversed-phase liquid chromatography mass spectrometry and charge-tagging Paternò-Büchi derivatization, we achieved deep profiling of over 300 structures of nGSLs and sulfatides across 5 orders of magnitude in relative abundances, a significant leap regarding lipid coverage. We also depicted the structural atlas of nGSLs with defined headgroup, long-chain base, N-acyl chain, the location of desaturation, and 2-hydroxylation. Such information provides a valuable resource for lipidomic studies concerning the roles of circulating nGSLs in health and diseases.

DOI: https://doi.org/10.1021/acs.analchem.4c04094
J Proteome Res. 2024 Oct 07.

End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.

Sheldon T Cheung, Yongkang Kim, Ji-Hoon Cho, Kristoffer R Brandvold, Brahma Ghosh, Amanda M Del Rosario, Harris Bell-Temin.

  Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.

Keywords:  automation; chemical proteomics; photoaffinity liganding; sample preparation; target deconvolution; target engagement; throughput

DOI:  https://doi.org/10.1021/acs.jproteome.4c00442
Genomics Proteomics Bioinformatics. 2024 Oct 08. pii: qzae069. [Epub ahead of print]

GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.

Zheng Fang, Mingming Dong, Hongqiang Qin, Mingliang Ye.

  Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.

Keywords:  Glycoproteomics; Glycosylation; Proteomics; Software; Visualization

DOI:  https://doi.org/10.1093/gpbjnl/qzae069
J Proteome Res. 2024 Oct 09.

Deep Proteome Analysis of Cerebrospinal Fluid from Pediatric Patients with Central Nervous System Cancer.

Christian Mirian, Ole Østergaard, Maria Thastrup, Signe Modvig, Jon Foss-Skiftesvik, Jane Skjøth-Rasmussen, Marianne Berntsen, Josefine Britze, Alex Christian Yde Nielsen, René Mathiasen, Kjeld Schmiegelow, Jesper Velgaard Olsen.

  The cerebrospinal fluid (CSF) is a key matrix for discovery of biomarkers relevant for prognosis and the development of therapeutic targets in pediatric central nervous system malignancies. However, the wide range of protein concentrations and age-related differences in children makes such discoveries challenging. In addition, pediatric CSF samples are often sparse and first prioritized for clinical purposes. The present work focused on optimizing each step of the proteome analysis workflow to extract the most detailed proteome information possible from the limited CSF resources available for research purposes. The strategy included applying sequential ultracentrifugation to enrich for extracellular vesicles (EV) in addition to analysis of a small volume of raw CSF, which allowed quantification of 1351 proteins (+55% relative to raw CSF) from 400 μL CSF. When including a spectral library, a total of 2103 proteins (+240%) could be quantified. The workflow was optimized for CSF input volume, tryptic digestion method, gradient length, mass spectrometry data acquisition method and database search strategy to quantify as many proteins a possible. The fully optimized workflow included protein aggregation capture (PAC) digestion, paired with data-independent acquisition (DIA, 21 min gradient) and allowed 2989 unique proteins to be quantified from only 400 μL CSF, which is a 340% increase in proteins compared to analysis of a tryptic digest of raw CSF.

Keywords:  CSF input volume; biomarker; cerebrospinal fluid; extracellular vesicle; protein aggregation capture

DOI:  https://doi.org/10.1021/acs.jproteome.4c00471
J Proteome Res. 2024 Oct 08.

Achieving a 35-Plex Tandem Mass Tag Reagent Set through Deuterium Incorporation.

Nathan R Zuniga, Dustin C Frost, Karsten Kuhn, Myungsun Shin, Rebecca L Whitehouse, Ting-Yu Wei, Yuchen He, Shane L Dawson, Ian Pike, Ryan D Bomgarden, Steven P Gygi, Joao A Paulo.

  Mass spectrometry-based sample multiplexing with isobaric tags permits the development of high-throughput and precise quantitative biological assays with proteome-wide coverage and minimal missing values. Here, we nearly doubled the multiplexing capability of the TMTpro reagent set to a 35-plex through the incorporation of one deuterium isotope into the reporter group. Substituting deuterium frequently results in suboptimal peak coelution, which can compromise the accuracy of reporter ion-based quantification. To counteract the deuterium effect on quantitation, we implemented a strategy that necessitated the segregation of nondeuterium and deuterium-containing channels into distinct subplexes during normalization procedures, with reassembly through a common bridge channel. This multiplexing strategy of "design independent sub-plexes but acquire together" (DISAT) was used to compare protein expression differences between human cell lines and in a cysteine-profiling (i.e., chemoproteomics) experiment to identify compounds binding to cysteine-113 of Pin1.

Keywords:  ABPP; Astral; TMTpro; TMTproD; deuterium; isobaric tagging

DOI:  https://doi.org/10.1021/acs.jproteome.4c00668
Biomed Chromatogr. 2024 Oct 07. e6019

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery.

Nikhil Titkare, Sachin Chaturvedi, Sapan Borah, Nitish Sharma.

  Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Keywords:  biomarker; disease; mass spectrometry; metabolomics

DOI:  https://doi.org/10.1002/bmc.6019
Anal Chem. 2024 Oct 08.

Native Digestion and Shotgun Proteomics for Host Cell Protein Profiling of Adeno-Associated Viruses.

Jean M Lodge, Lihua Huang, Zhirui Lian, Jun Qian, Yuwei Tian.

Host cell proteins (HCPs) are contaminants of biotherapeutics produced from engineered living systems; they can influence the product's quality, efficacy, and toxicity. Liquid chromatography coupled to mass spectrometry can detect HCPs thereby mitigating their risks. However, highly abundant biotherapeutics hamper the detection of low-level HCPs. Sample preparation termed native digestion has proven effective to preferentially digest and draw out HCPs from intact antibodies. Here, we adapted native digestion to adeno-associated viruses (AAV), which is a vector gaining popularity for gene therapy. We leveraged quantitative proteomics using capillary-flow liquid chromatography-mass spectrometry (LC-MS) and demonstrated that native digestion was more effective than applying denaturing conditions to extract the HCPs associated with different AAV serotypes.

DOI: https://doi.org/10.1021/acs.analchem.4c00893
Anal Chem. 2024 Oct 07.

Divergent Metabolic Fates of Aromatic Amino Acid-Derived Isomers: Insights from Ex Vivo Metabolomics and HDX-HRMS/MS-Based Resolution of Tautomers.

Michael W Christopher, Aiden C Ericson, Alexander C Klug, Rhoel R Dinglasan, Boone M Prentice, Timothy J Garrett.

Tautomers are one of the many types of isomers, and differences in tautomeric structures confer altered chemical and biological properties. Using ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) ex vivo metabolomics, we investigate, in whole blood, the divergent metabolism of enol and keto forms of indole-3-pyruvate (IPyA), a tautomeric product of aromatic amino acid metabolism. Two new compounds resulting from IPyA metabolism were discovered, 3-(1H-indol-3-yl)-2,3-dioxopropanoic acid or "indole-3-oxopyruvic acid" and glutathionyl-indole pyruvate (GSHIPyA), which were characterized via ultraviolet photodissociation (UVPD) and higher-energy collisional dissociation (HCD). Computational calculations support the hypothesis that GSHIPyA forms specifically through the enol form of IPyA. GSHIPyA is also hypothesized to be tautomeric, and a hydrogen-deuterium exchange-high-resolution tandem mass spectrometry (HDX-HRMS/MS) approach is developed to prove the presence of an enol and keto tautomer. HDX of GSHIPyA labels the keto form with an additional deuterium, relative to the enol form. HRMS/MS of the labeled isomers is employed to leverage the relationship of resolving power scaling inversely with the square root of m/z, for Orbitrap mass analyzers. HRMS/MS yields a smaller-molecular-weight deuterated tautomeric product ion, reducing the analyte ion m/z and thus lowering the resolving power necessary to separate the deuterated keto tautomer product ion from the [13]C product ion.

DOI: https://doi.org/10.1021/acs.analchem.4c03862
Chemosphere. 2024 Oct 05. pii: S0045-6535(24)02389-0. [Epub ahead of print]366 143489

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries.

Peter Tarábek, Nataliia Leonova, Olga Konovalova, Michal Kirchner.

  Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.

Keywords:  Emerging contaminants; Environmental analysis; LC-HRMS; Non-target screening; Tandem mass spectral libraries; Water quality

DOI:  https://doi.org/10.1016/j.chemosphere.2024.143489
Cell Rep. 2024 Oct 04. pii: S2211-1247(24)01166-5. [Epub ahead of print]43(10): 114815

Metabolite regulation of epigenetics in cancer.

Pu Wang, Lei-Lei Chen, Yue Xiong, Dan Ye.

  The catalytic activity of most epigenetic enzymes requires a metabolite produced by central carbon metabolism as a cofactor or (co-)substrate. The concentrations of these metabolites undergo dynamic changes in response to nutrient levels and environmental conditions, reprogramming metabolic processes and epigenetic landscapes. Abnormal accumulations of epigenetic modulatory metabolites resulting from mutations in metabolic enzymes contribute to tumorigenesis. In this review, we first present the concept that metabolite regulation of gene expression represents an evolutionarily conserved mechanism from prokaryotes to eukaryotes. We then review how individual metabolites affect epigenetic enzymes and cancer development. Lastly, we discuss the advancement of and opportunity for therapeutic targeting of metabolite-epigenetic regulation in cancer therapy.

Keywords:  CP: Cancer; CP: Metabolism

DOI:  https://doi.org/10.1016/j.celrep.2024.114815