bims-mascan 2024-07-07 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–07–07
twenty-two papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Proteomic Analysis of Dictyostelium discoideum by Mass Spectrometry.
Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples.
Challenges and recent advances in quantitative mass spectrometry-based metabolomics.
msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease.
ATP-dependent citrate lyase Drives Left Ventricular Dysfunction by Metabolic Remodeling of the Heart.
PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.
Interactions of Fatty Acid and Cholesterol Metabolism with Cellular Stress Response Pathways in Cancer.
Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.
A genome-wide CRISPR screen reveals that antagonism of glutamine metabolism sensitizes head and neck squamous cell carcinoma to ferroptotic cell death.
Advances in targeted liquid chromatography-tandem mass spectrometry methods for endocannabinoid and N-acylethanolamine quantification in biological matrices: A systematic review.
[Microbial metaproteomics--From sample processing to data acquisition and analysis].
Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.
Efficient Strategy for Characterization and Quantification of Polyunsaturated Lipids by Microwave-Assisted MMPP Epoxidation.
Repurposed 3D Printer Allows Economical and Programmable Fraction Collection for Proteomics of Nanogram Scale Samples.
quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data.
A proximity proteomics pipeline with improved reproducibility and throughput.
Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network.
A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.
A practical approach to supported liquid extraction and measurement of 18 steroids in plasma and serum by targeted liquid chromatography tandem mass spectrometry.
Hyphenation of microflow chromatography with electrospray ionization mass spectrometry for bioanalytical applications focusing on low molecular weight compounds: A tutorial review.
Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.
Functional multi-organelle units control inflammatory lipid metabolism of macrophages.

Methods Mol Biol. 2024 ;2814 247-255

Proteomic Analysis of Dictyostelium discoideum by Mass Spectrometry.

Ramesh Rijal, Richard H Gomer.

  The large-scale proteomic analysis of Dictyostelium discoideum has contributed to our understanding of intracellular as well as secreted proteins in this versatile model eukaryote. Mass spectrometry-based proteomic analysis is a robust, sensitive, and rapid analytical method for identification and characterization of proteins extracted from tissues, cells, cell fractions, or pull-down assays. The availability of core facilities which make proteomics inexpensive and easy to do has facilitated a wide range of research projects. In this chapter, we present a simple standard methodology to extract proteins and prepare samples from D. discoideum for mass spectrometry and methods to analyze the identified proteins.

Keywords:  Dictyostelium discoideum; Mass spectrometry; Proteomics

DOI:  https://doi.org/10.1007/978-1-0716-3894-1_17
J Proteome Res. 2024 Jun 29.

Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples.

Ryan M Riley, Gian Luca Negri, S-W Grace Cheng, Sandra E Spencer Miko, Ryan D Morin, Gregg B Morin.

  Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Keywords:  HPLC; TMT; ion acquisition; mass spectrometry; offline fractionation

DOI:  https://doi.org/10.1021/acs.jproteome.4c00014
Anal Sci Adv. 2024 Jun;5(5-6): e2400007

Challenges and recent advances in quantitative mass spectrometry-based metabolomics.

Nathan Ghafari, Lekha Sleno.

  The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Keywords:  internal standards; mass spectrometry; quantitative metabolomics; stable isotope labelling

DOI:  https://doi.org/10.1002/ansa.202400007
Bioinformatics. 2024 Jun 29. pii: btae432. [Epub ahead of print]

msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease.

Thang V Pham, Alex A Henneman, Nam X Truong, Connie R Jimenez.

   SUMMARY: Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools.
AVAILABILITY AND IMPLEMENTATION: sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Keywords:  DIA-MS; MaxLFQ quantification; identification; mass spectrometry; phosphopeptide; phosphoproteomics; phosphosite

DOI:  https://doi.org/10.1093/bioinformatics/btae432
bioRxiv. 2024 Jun 21. pii: 2024.06.21.600099. [Epub ahead of print]

ATP-dependent citrate lyase Drives Left Ventricular Dysfunction by Metabolic Remodeling of the Heart.

Shijie Liu, Seth T Gammon, Lin Tan, Yaqi Gao, Kyoungmin Kim, Ian K Williamson, Janet Pham, Angela Davidian, Radhika Khanna, Benjamin D Gould, Rebecca Salazar, Heidi Vitrac, An Dinh, Evan C Lien, Francisca N de L Vitorino, Joanna M Gongora, Sara A Martinez, Czer S C Lawrence, Evan P Kransdorf, David Leffer, Blake Hanson, Benjamin A Garcia, Matthew G Vander Heiden, Philip L Lorenzi, Heinrich Taegtmeyer, David Piwnica-Worms, James F Martin, Anja Karlstaedt.

Background: Metabolic remodeling is a hallmark of the failing heart. Oncometabolic stress during cancer increases the activity and abundance of the ATP-dependent citrate lyase (ACL, Acly ), which promotes histone acetylation and cardiac adaptation. ACL is critical for the de novo synthesis of lipids, but how these metabolic alterations contribute to cardiac structural and functional changes remains unclear.
Methods: We utilized human heart tissue samples from healthy donor hearts and patients with hypertrophic cardiomyopathy. Further, we used CRISPR/Cas9 gene editing to inactivate Acly in cardiomyocytes of MyH6-Cas9 mice. In vivo, positron emission tomography and ex vivo stable isotope tracer labeling were used to quantify metabolic flux changes in response to the loss of ACL. We conducted a multi-omics analysis using RNA-sequencing and mass spectrometry-based metabolomics and proteomics. Experimental data were integrated into computational modeling using the metabolic network CardioNet to identify significantly dysregulated metabolic processes at a systems level.
Results: Here, we show that in mice, ACL drives metabolic adaptation in the heart to sustain contractile function, histone acetylation, and lipid modulation. Notably, we show that loss of ACL increases glucose oxidation while maintaining fatty acid oxidation. Ex vivo isotope tracing experiments revealed a reduced efflux of glucose-derived citrate from the mitochondria into the cytosol, confirming that citrate is required for reductive metabolism in the heart. We demonstrate that YAP inactivation facilitates ACL deficiency. Computational flux analysis and integrative multi-omics analysis indicate that loss of ACL induces alternative isocitrate dehydrogenase 1 flux to compensate.
Conclusions: This study mechanistically delineates how cardiac metabolism compensates for suppressed citrate metabolism in response to ACL loss and uncovers metabolic vulnerabilities in the heart.

DOI: https://doi.org/10.1101/2024.06.21.600099
Cell Rep Methods. 2024 Jun 26. pii: S2667-2375(24)00159-0. [Epub ahead of print] 100803

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

Weiwei Lin, Fatemeh Mousavi, Benjamin C Blum, Christian F Heckendorf, Matthew Lawton, Noah Lampl, Ryan Hekman, Hongbo Guo, Mark McComb, Andrew Emili.

  High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Keywords:  CP: biotechnology; CP: metabolism; SPME; integrated omics; metabolomics; nLC-MS; proteomics

DOI:  https://doi.org/10.1016/j.crmeth.2024.100803
Cold Spring Harb Perspect Med. 2024 Jul 01. pii: a041548. [Epub ahead of print]

Interactions of Fatty Acid and Cholesterol Metabolism with Cellular Stress Response Pathways in Cancer.

Alina M Winkelkotte, Kamal Al-Shami, Adriano B Chaves-Filho, Felix C E Vogel, Almut Schulze.

Lipids have essential functions as structural components of cellular membranes, as efficient energy storage molecules, and as precursors of signaling mediators. While deregulated glucose and amino acid metabolism in cancer have received substantial attention, the roles of lipids in the metabolic reprogramming of cancer cells are less well understood. However, since the first description of de novo fatty acid biosynthesis in cancer tissues almost 70 years ago, numerous studies have investigated the complex functions of altered lipid metabolism in cancer. Here, we will summarize the mechanisms by which oncogenic signaling pathways regulate fatty acid and cholesterol metabolism to drive rapid proliferation and protect cancer cells from environmental stress. The review also discusses the role of fatty acid metabolism in metabolic plasticity required for the adaptation to changing microenvironments during cancer progression and the connections between fatty acid and cholesterol metabolism and ferroptosis.

DOI: https://doi.org/10.1101/cshperspect.a041548
Bioinformatics. 2024 Jul 03. pii: btae417. [Epub ahead of print]

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

Yichi Zhang, Chenghui Hu, Xiaohui Wu, Jian Song.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits.
RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms.
AVAILABILITY: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btae417
Cancer Lett. 2024 Jul 02. pii: S0304-3835(24)00484-1. [Epub ahead of print] 217089

A genome-wide CRISPR screen reveals that antagonism of glutamine metabolism sensitizes head and neck squamous cell carcinoma to ferroptotic cell death.

Michael M Allevato, Sally Trinh, Keiichi Koshizuka, Daniela Nachmanson, Thien-Tu C Nguyen, Yumi Yokoyama, Xingyu Wu, Allen Andres, Zhiyong Wang, Jeramie Watrous, Alfredo A Molinolo, Prashant Mali, Olivier Harismendy, Mohit Jain, Robert Wild, J Silvio Gutkind.

  Glutamine is a conditionally essential amino acid for the growth and survival of rapidly proliferating cancer cells. Many cancers are addicted to glutamine, and as a result, targeting glutamine metabolism has been explored clinically as a therapeutic approach. Glutamine-catalyzing enzymes are highly expressed in primary and metastatic head and neck squamous cell carcinoma (HNSCC). However, the nature of the glutamine-associated pathways in this aggressive cancer type has not been elucidated. Here, we explored the therapeutic potential of a broad glutamine antagonist, DRP-104 (sirpiglenastat), in HNSCC tumors and aimed at shedding light on glutamine-dependent pathways in this disease. We observed a potent antitumoral effect of sirpiglenastat in HPV- and HPV+ HNSCC xenografts. We conducted a whole-genome CRISPR screen and metabolomics analyses to identify mechanisms of sensitivity and resistance to glutamine metabolism blockade. These approaches revealed that glutamine metabolism blockade results in the rapid buildup of polyunsaturated fatty acids (PUFAs) via autophagy nutrient-sensing pathways. Finally, our analysis demonstrated that GPX4 mediates the protection of HNSCC cells from accumulating toxic lipid peroxides; hence, glutamine blockade sensitizes HNSCC cells to ferroptosis cell death upon GPX4 inhibition. These findings demonstrate the therapeutic potential of sirpiglenastat in HNSCC and establish a novel link between glutamine metabolism and ferroptosis, which may be uniquely translated into targeted glutamine-ferroptosis combination therapies.

Keywords:  Glutamine; autophagy; ferroptosis; head and neck squamous cell carcinoma; poly-unsaturated fatty acids; precision medicine; targeted therapy

DOI:  https://doi.org/10.1016/j.canlet.2024.217089
Mass Spectrom Rev. 2024 Jul 03.

Advances in targeted liquid chromatography-tandem mass spectrometry methods for endocannabinoid and N-acylethanolamine quantification in biological matrices: A systematic review.

Khalisa Amir Hamzah, Natalie Turner, David Nichols, Luke J Ney.

  Liquid chromatography paired with tandem mass spectrometry (LC-MS/MS) is the gold standard in measurement of endocannabinoid concentrations in biomatrices. We conducted a systematic review of literature to identify advances in targeted LC-MS/MS methods in the period 2017-2024. We found that LC-MS/MS methods for endocannabinoid quantification are relatively consistent both across time and across biomatrices. Recent advances have primarily been in three areas: (1) sample preparation techniques, specific to the chosen biomatrix; (2) the range of biomatrices tested, recently favoring blood matrices; and (3) the breadth of endocannabinoid and endocannabinoid-like analytes incorporated into assays. This review provides a summary of the recent literature and a guide for researchers looking to establish the best methods for quantifying endocannabinoids in a range of biomatrices.

Keywords:  LC‐MS; anandamide; endocannabinoids; liquid chromatography; mass spectrometry

DOI:  https://doi.org/10.1002/mas.21897
Se Pu. 2024 Jul;42(7): 658-668

[Microbial metaproteomics--From sample processing to data acquisition and analysis].

En-Hui Wu, Liang Qiao.

  Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Keywords:  data analysis strategy; database; metaproteomics; sample pretreatment

DOI:  https://doi.org/10.3724/SP.J.1123.2024.02009
Metabolomics. 2024 Jul 02. 20(4): 70

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.

Stuart Mires, Eduardo Sommella, Fabrizio Merciai, Emanuela Salviati, Vicky Caponigro, Manuela Giovanna Basilicata, Federico Marini, Pietro Campiglia, Mai Baquedano, Tim Dong, Clare Skerritt, Kelly-Ann Eastwood, Massimo Caputo.

   INTRODUCTION: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.
OBJECTIVE: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.
METHODS: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.
RESULTS: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.
CONCLUSIONS: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Keywords:  ALSPAC; Congenital heart disease; Metabolomics; Prediction models; Risk factors

DOI:  https://doi.org/10.1007/s11306-024-02129-8
Anal Chem. 2024 Jul 04.

Efficient Strategy for Characterization and Quantification of Polyunsaturated Lipids by Microwave-Assisted MMPP Epoxidation.

Longhui Huang, Minhan Huang, Ting Zhou.

Lipids play integral roles in biological processes, with carbon-carbon double bonds (C═C) markedly influencing their structure and function. Precise characterization and quantification of unsaturated lipids are crucial for understanding lipid physiology and discovering disease biomarkers. However, using mass spectrometry for these purposes presents significant challenges. In this study, we developed a microwave-assisted magnesium monoperoxyphthalate hexahydrate (MMPP) epoxidation reaction, coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS), to analyze unsaturated lipids. Microwave irradiation expedited the MMPP epoxidation, achieving complete derivatization in 10 min without byproducts. A diagnostic ion pair, displaying a 16 Da mass difference, effectively identified the location of the C═C bond in mass spectra. Microwave irradiation also significantly facilitated the epoxidation reaction of polyunsaturated lipids, achieving yields greater than 85% and yielding a complete epoxidation product. This simplifies chromatographic separation and aids in accurate quantification. Additionally, a purification process was implemented to remove excess derivatization reagents, significantly reducing mass spectrometry response suppression and enhancing analytical reproducibility. The method's effectiveness was validated by analyzing unsaturated lipids in rat plasma from a type I diabetes model. We quantified nine unsaturated lipids and characterized 42 fatty acids and glycerophospholipids. The results indicated that unsaturated fatty acids increased in diabetic plasma while unsaturated glycerophospholipids decreased. Furthermore, the relative abundances of Δ9/Δ11 isomer pairs also exhibited a close association with diabetes. In conclusion, microwave-assisted MMPP epoxidation coupled with LC-MS/MS provides an effective strategy for characterization and quantification of polyunsaturated lipids, offering deeper insight into the physiological impact of unsaturated lipids in related diseases.

DOI: https://doi.org/10.1021/acs.analchem.4c00410
Anal Chem. 2024 Jul 05.

Repurposed 3D Printer Allows Economical and Programmable Fraction Collection for Proteomics of Nanogram Scale Samples.

Eduardo S Kitano, Gareth Nisbet, Yana Demyanenko, Katarzyna M Kowalczyk, Louisa Iselin, Stephen Cross, Alfredo Castello, Shabaz Mohammed.

In this work, we describe the construction and application of a repurposed 3D-printer as a fraction collector. We utilize a nano-LC to ensure minimal volumes and surfaces although any LC can be coupled. The setup operates as a high-pH fractionation system capable of effectively working with nanogram scales of lysate digests. The 2D RP-RP system demonstrated superior proteome coverage over single-shot data-dependent acquisition (DDA) analysis using only 5 ng of human cell lysate digest with performance increasing with increasing amounts of material. We found that the fractionation system allowed over 60% signal recovery at the peptide level and, more importantly, we observed improved protein level intensity coverage, which indicates the complexity reduction afforded by the system outweighs the sample losses endured. The application of data-independent acquisition (DIA) and wide window acquisition (WWA) to fractionated samples allowed nearly 8000 proteins to be identified from 50 ng of the material. The utility of the 2D system was further investigated for phosphoproteomics (>21 000 phosphosites from 50 μg starting material) and pull-down type experiments and showed substantial improvements over single-shot experiments. We show that the 2D RP-RP system is a highly versatile and powerful tool for many proteomics workflows.

DOI: https://doi.org/10.1021/acs.analchem.4c01731
Nat Methods. 2024 Jul 04.

quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data.

Chengxin Dai, Julianus Pfeuffer, Hong Wang, Ping Zheng, Lukas Käll, Timo Sachsenberg, Vadim Demichev, Mingze Bai, Oliver Kohlbacher, Yasset Perez-Riverol.

The volume of public proteomics data is rapidly increasing, causing a computational challenge for large-scale reanalysis. Here, we introduce quantms ( https://quant,ms.org/ ), an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 83 public ProteomeXchange datasets, comprising 29,354 instrument files from 13,132 human samples, to quantify 16,599 proteins based on 1.03 million unique peptides. quantms is based on standard file formats improving the reproducibility, submission and dissemination of the data to ProteomeXchange.

DOI: https://doi.org/10.1038/s41592-024-02343-1
Mol Syst Biol. 2024 Jul 01.

A proximity proteomics pipeline with improved reproducibility and throughput.

Xiaofang Zhong, Qiongyu Li, Benjamin J Polacco, Trupti Patil, Aaron Marley, Helene Foussard, Prachi Khare, Rasika Vartak, Jiewei Xu, Jeffrey F DiBerto, Bryan L Roth, Manon Eckhardt, Mark von Zastrow, Nevan J Krogan, Ruth Hüttenhain.

  Proximity labeling (PL) via biotinylation coupled with mass spectrometry (MS) captures spatial proteomes in cells. Large-scale processing requires a workflow minimizing hands-on time and enhancing quantitative reproducibility. We introduced a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. Combining this with optimized quantitative MS based on data-independent acquisition (DIA), we increased sample throughput and improved protein identification and quantification reproducibility. We applied this pipeline to delineate subcellular proteomes across various compartments. Using the 5HT2A serotonin receptor as a model, we studied temporal changes of proximal interaction networks induced by receptor activation. In addition, we modified the pipeline for reduced sample input to accommodate CRISPR-based gene knockout, assessing dynamics of the 5HT2A network in response to perturbation of selected interactors. This PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, enhancing throughput and reproducibility of standard protocols.

Keywords:  APEX2-based Proximity Labeling; G Protein-Coupled Receptor; Protein–Protein Interaction; Proximity Proteomics; Subcellular Proteomics

DOI:  https://doi.org/10.1038/s44320-024-00049-2
J Chem Inf Model. 2024 Jul 03.

Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network.

Cheng Wang, Chuang Yuan, Yahui Wang, Yuying Shi, Tao Zhang, Gary J Patti.

Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.

DOI: https://doi.org/10.1021/acs.jcim.3c01934
Clin Proteomics. 2024 Jul 05. 21(1): 49

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.

Hagen M Gegner, Thomas Naake, Karim Aljakouch, Aurelien Dugourd, Georg Kliewer, Torsten Müller, Dustin Schilling, Marc A Schneider, Nina Kunze-Rohrbach, Thomas G P Grünewald, Rüdiger Hell, Julio Saez-Rodriguez, Wolfgang Huber, Gernot Poschet, Jeroen Krijgsveld.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

DOI: https://doi.org/10.1186/s12014-024-09501-9
MethodsX. 2024 Jun;12 102728

A practical approach to supported liquid extraction and measurement of 18 steroids in plasma and serum by targeted liquid chromatography tandem mass spectrometry.

Scott G Denham, Joanna P Simpson, Federico Diez, Patricia Lee, Catriona Kyle, Ruth Morgan, Natalie Zm Homer.

  Chromatography combined with mass spectrometry is a gold standard technique for steroid measurement, however the type of sample preparation, the dynamic range and reliability of the calibration curve, the chromatographic separation and mass spectrometry settings ultimately determine the success of the method. The steroid biosynthetic pathway is conserved in higher mammals and literature demonstrates that the concentration ranges of different steroid groups are relatively comparable across species. We sought to develop a robust and reliable multi steroid targeted analysis method for blood that would have wide application across higher mammals. The method was developed following bioanalytical method validation guidelines to standards typically applied to human clinical studies, including isotopically labelled internal standards where at all possible. Here we describe the practical approach to a 96-well supported liquid extraction (SLE) method of extraction from plasma (200 µL) using an Extrahera liquid handling robot (Biotage, Sweden), including quality control samples, followed by a comprehensive separation and targeted LC-MS/MS analysis of 18 steroids in plasma (pregnenolone, progesterone, 17α-hydroxyprogesterone, 11-deoxycorticosterone, corticosterone, 11-dehydrocorticosterone, aldosterone, 11-deoxycortisol, 21-deoxycortisol, cortisol, cortisone, androstenedione, testosterone, 5α-dihydrotestosterone, dehydroepiandrosterone, estrone, 17β-estradiol and estriol). •SLE in a 96-well format of up to 74 biological plasma samples, enriched with multiple isotopically labelled internal standards, a 12-point aqueous calibration curve, and 6 serum quality controls, designed to monitor long-term performance of the method•Chromatographic separation of multiple steroids along the gradient, with ammonium fluoride mobile phase additive to improve sensitivity, followed by electrospray ionisation and constant polarity switching•Aqueous calibration standards that cover physiologically relevant ranges - high nanomolar glucocorticoids, low nanomolar androgens and picomolar ranges for estrogens and steroid intermediates.

Keywords:  Automation; Chromatography; Comparative endocrinology; Mass spectrometry; Steroid profiling; Supported liquid extraction and LC-MS/MS analysis of multiple steroids in 200 µL plasma; Targeted

DOI:  https://doi.org/10.1016/j.mex.2024.102728
Mass Spectrom Rev. 2024 Jul 01.

Hyphenation of microflow chromatography with electrospray ionization mass spectrometry for bioanalytical applications focusing on low molecular weight compounds: A tutorial review.

Sergey Girel, Isabel Meister, Gaetan Glauser, Serge Rudaz.

  Benefits of miniaturized chromatography with various detection modes, such as increased sensitivity, chromatographic efficiency, and speed, were recognized nearly 50 years ago. Over the past two decades, this approach has experienced rapid growth, driven by the emergence of mass spectrometry applications serving -omics sciences and the need for analyzing minute volumes of precious samples with ever higher sensitivity. While nanoscale liquid chromatography (flow rates <1 μL/min) has gained widespread recognition in proteomics, the adoption of microscale setups (flow rates ranging from 1 to 100 μL/min) for low molecular weight compound applications, including metabolomics, has been surprisingly slow, despite the inherent advantages of the approach. Highly heterogeneous matrices and chemical structures accompanied by a relative lack of options for both selective sample preparation and user-friendly equipment are usually reported as major hindrances. To facilitate the wider implementation of microscale analyses, we present here a comprehensive tutorial encompassing important theoretical and practical considerations. We provide fundamental principles in micro-chromatography and guide the reader through the main elements of a microflow workflow, from LC pumps to ionization devices. Finally, based on both our literature overview and experience, illustrated by some in-house data, we highlight the critical importance of the ionization source design and its careful optimization to achieve significant sensitivity improvement.

Keywords:  bioanalysis; electrospray; mass spectrometry; microflow chromatography

DOI:  https://doi.org/10.1002/mas.21898
Anal Sci Adv. 2024 Jun;5(5-6): e2400002

Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.

Pauline Couacault, Dennisse Avella, Sara Londoño-Osorio, Ana S Lorenzo, Ana Gradillas, Olli Kärkkäinen, Elizabeth Want, Michael Witting.

  Blood microsampling (BµS) offers an alternative to conventional methods that use plasma or serum for profiling human health, being minimally invasive and cost effective, especially beneficial for vulnerable populations. We present a non-systematic review that offers a synopsis of the analytical methods, applications and perspectives related to dry blood microsampling in targeted and untargeted metabolomics and lipidomics research in the years 2022 and 2023. BµS shows potential in neonatal and paediatric studies, therapeutic drug monitoring, metabolite screening, biomarker research, sports supervision, clinical disorders studies and forensic toxicology. Notably, dried blood spots and volumetric absorptive microsampling options have been more extensively studied than other volumetric technologies. Therefore, we suggest that a further investigation and application of the volumetric technologies will contribute to the use of BµS as an alternative to conventional methods. Conversely, we support the idea that harmonisation of the analytical methods when using BµS would have a positive impact on its implementation.

Keywords:  LC–MS analysis; blood microsampling; dried blood; lipidomics; metabolomics; targeted and untargeted

DOI:  https://doi.org/10.1002/ansa.202400002
Nat Cell Biol. 2024 Jul 05.

Functional multi-organelle units control inflammatory lipid metabolism of macrophages.

Julia A Zimmermann, Kerstin Lucht, Manuel Stecher, Chahat Badhan, Katharina M Glaser, Maximilian W Epple, Lena R Koch, Ward Deboutte, Thomas Manke, Klaus Ebnet, Frauke Brinkmann, Olesja Fehler, Thomas Vogl, Ev-Marie Schuster, Anna Bremser, Joerg M Buescher, Angelika S Rambold.

Eukaryotic cells contain several membrane-separated organelles to compartmentalize distinct metabolic reactions. However, it has remained unclear how these organelle systems are coordinated when cells adapt metabolic pathways to support their development, survival or effector functions. Here we present OrgaPlexing, a multi-spectral organelle imaging approach for the comprehensive mapping of six key metabolic organelles and their interactions. We use this analysis on macrophages, immune cells that undergo rapid metabolic switches upon sensing bacterial and inflammatory stimuli. Our results identify lipid droplets (LDs) as primary inflammatory responder organelle, which forms three- and four-way interactions with other organelles. While clusters with endoplasmic reticulum (ER) and mitochondria (mitochondria-ER-LD unit) help supply fatty acids for LD growth, the additional recruitment of peroxisomes (mitochondria-ER-peroxisome-LD unit) supports fatty acid efflux from LDs. Interference with individual components of these units has direct functional consequences for inflammatory lipid mediator synthesis. Together, we show that macrophages form functional multi-organellar units to support metabolic adaptation and provide an experimental strategy to identify organelle-metabolic signalling hubs.

DOI: https://doi.org/10.1038/s41556-024-01457-0