bims-mascan 2024-03-17 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–03–17
23 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics.
MALDI-MSI-LC-MS/MS Workflow for Single-Section Single Step Combined Proteomics and Quantitative Lipidomics.
AlphaPept: a modern and open framework for MS-based proteomics.
Neat plasma proteomics: getting the best out of the worst.
13 C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer.
Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology.
Mass Spectrometry Strategies for O-Glycoproteomics.
The underappreciated diversity of bile acid modifications.
Quantification of metabolic activity from isotope tracing data using automated methodology.
SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics.
Microfluidic Impedance Cytometry Enabled One-Step Sample Preparation for Efficient Single-Cell Mass Spectrometry.
Targeted Metabolomics on Rare Primary Cells.
Mass Spectrometric Proteomics 2.0.
Reprogramming of Lipid Metabolism Mediates Crosstalk, Remodeling, and Intervention of Microenvironment Components in Breast Cancer.
Quantitative mass spectrometry imaging (qMSI): A tutorial.
A microfluidic chip-based capillary zone electrophoresis-mass spectrometry method for measuring adenosine 5'-Triphosphate and its similar nucleotide analogues.
Hypoxia rewires glucose and glutamine metabolism in different sources of skeletal stem and progenitor cells similarly, except for pyruvate.
Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).
Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.
Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model.
Metabolic phenotyping of healthy and diseased human RPE cells.
One-pot method for preparing DNA, RNA, and protein for multiomics analysis.
Proteomics of prostate cancer serum and plasma using low and high throughput approaches.

Int J Mol Sci. 2024 Mar 01. pii: 2899. [Epub ahead of print]25(5):

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics.

Tomas Cajka, Jiri Hricko, Stanislava Rakusanova, Kristyna Brejchova, Michaela Novakova, Lucie Rudl Kulhava, Veronika Hola, Michaela Paucova, Oliver Fiehn, Ondrej Kuda.

  Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

Keywords:  hydrogen/deuterium exchange; liquid chromatography; mass spectrometry; metabolomics; unknown identification

DOI:  https://doi.org/10.3390/ijms25052899
Anal Chem. 2024 Mar 12. 96(10): 4266-4274

MALDI-MSI-LC-MS/MS Workflow for Single-Section Single Step Combined Proteomics and Quantitative Lipidomics.

Tim F E Hendriks, Kasper K Krestensen, Ronny Mohren, Michiel Vandenbosch, Steven De Vleeschouwer, Ron M A Heeren, Eva Cuypers.

We introduce a novel approach for comprehensive molecular profiling in biological samples. Our single-section methodology combines quantitative mass spectrometry imaging (Q-MSI) and a single step extraction protocol enabling lipidomic and proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis on the same tissue area. The integration of spatially correlated lipidomic and proteomic data on a single tissue section allows for a comprehensive interpretation of the molecular landscape. Comparing Q-MSI and Q-LC-MS/MS quantification results sheds new light on the effect of MSI and related sample preparation. Performing MSI before Q-LC-MS on the same tissue section led to fewer protein identifications and a lower correlation between lipid quantification results. Also, the critical role and influence of internal standards in Q-MSI for accurate quantification is highlighted. Testing various slide types and the evaluation of different workflows for single-section spatial multiomics analysis emphasized the need for critical evaluation of Q-MSI data. These findings highlight the necessity for robust quantification methods comparable to current gold-standard LC-MS/MS techniques. The spatial information from MSI allowed region-specific insights within heterogeneous tissues, as demonstrated for glioblastoma multiforme. Additionally, our workflow demonstrated the efficiency of a single step extraction for lipidomic and proteomic analyses on the same tissue area, enabling the examination of significantly altered proteins and lipids within distinct regions of a single section. The integration of these insights into a lipid-protein interaction network expands the biological information attainable from a tissue section, highlighting the potential of this comprehensive approach for advancing spatial multiomics research.

DOI: https://doi.org/10.1021/acs.analchem.3c05850
Nat Commun. 2024 Mar 09. 15(1): 2168

AlphaPept: a modern and open framework for MS-based proteomics.

Maximilian T Strauss, Isabell Bludau, Wen-Feng Zeng, Eugenia Voytik, Constantin Ammar, Julia P Schessner, Rajesh Ilango, Michelle Gill, Florian Meier, Sander Willems, Matthias Mann.

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

DOI: https://doi.org/10.1038/s41467-024-46485-4
Clin Proteomics. 2024 Mar 12. 21(1): 22

Neat plasma proteomics: getting the best out of the worst.

Ines Metatla, Kevin Roger, Cerina Chhuon, Sara Ceccacci, Manuel Chapelle, Pierre-Olivier Schmit, Vadim Demichev, Ida Chiara Guerrera.

  Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.

Keywords:  DIA-NN; Extracellular vesicles (EVs); Neat plasma; Plasma proteomics; diaPASEF

DOI:  https://doi.org/10.1186/s12014-024-09477-6
bioRxiv. 2024 Feb 28. pii: 2023.08.18.553810. [Epub ahead of print]

13 C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer.

Elena Buglakova, Måns Ekelöf, Michaela Schwaiger-Haber, Lisa Schlicker, Martijn R Molenaar, Shahraz Mohammed, Lachlan Stuart, Andreas Eisenbarth, Volker Hilsenstein, Gary J Patti, Almut Schulze, Marteinn T Snaebjornsson, Theodore Alexandrov.

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13 C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13 C 6 -glucose-derived 13 C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13 C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13 C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13 C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

DOI: https://doi.org/10.1101/2023.08.18.553810
Nat Commun. 2024 Mar 11. 15(1): 2200

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology.

Frantisek Filandr, Vladimir Sarpe, Shaunak Raval, D Alex Crowder, Morgan F Khan, Pauline Douglas, Stephen Coales, Rosa Viner, Aleem Syed, John A Tainer, Susan P Lees-Miller, David C Schriemer.

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS2) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS1-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

DOI: https://doi.org/10.1038/s41467-024-46610-3
Cells. 2024 Feb 25. pii: 394. [Epub ahead of print]13(5):

Mass Spectrometry Strategies for O-Glycoproteomics.

Amanda Helms, Jennifer S Brodbelt.

  Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Keywords:  analytical workflow; enrichment; glycans; glycoprotease; glycoproteomics; tandem mass spectrometry

DOI:  https://doi.org/10.3390/cells13050394
Cell. 2024 Mar 06. pii: S0092-8674(24)00185-5. [Epub ahead of print]

The underappreciated diversity of bile acid modifications.

Ipsita Mohanty, Helena Mannochio-Russo, Joshua V Schweer, Yasin El Abiead, Wout Bittremieux, Shipei Xing, Robin Schmid, Simone Zuffa, Felipe Vasquez, Valentina B Muti, Jasmine Zemlin, Omar E Tovar-Herrera, Sarah Moraïs, Dhimant Desai, Shantu Amin, Imhoi Koo, Christoph W Turck, Itzhak Mizrahi, Penny M Kris-Etherton, Kristina S Petersen, Jennifer A Fleming, Tao Huan, Andrew D Patterson, Dionicio Siegel, Lee R Hagey, Mingxun Wang, Allegra T Aron, Pieter C Dorrestein.

  The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.

Keywords:  GABA; MassQL; agmatine; bile acids; diet; fastMASST; microbial; polyamines; putrescine; spectral resource

DOI:  https://doi.org/10.1016/j.cell.2024.02.019
bioRxiv. 2024 Feb 28. pii: 2024.02.25.581907. [Epub ahead of print]

Quantification of metabolic activity from isotope tracing data using automated methodology.

Shiyu Liu, Xiaojing Liu, Jason W Locasale.

Isotope tracing is a widely used technique to study metabolic activities by introducing heavy labeled nutrients into living cells and organisms. However, interpreting isotope tracing data is often heuristic, and application of automated methods using artificial intelligence is limited due to the paucity of evaluative knowledge. Our study developed a new pipeline that efficiently predicts metabolic activity in expansive metabolic networks and systematically quantifies flux uncertainty of traditional computational methods. We further developed an algorithm adept at significantly reducing this uncertainty, enabling robust evaluations of metabolic activity with limited data. Using this technology, we discovered highly reprogrammed mitochondria-cytosol exchange cycles in tumor tissue of patients, and observed similar metabolic patterns influenced by nutritional conditions in cancer cells. Thus, our refined methodology provides robust automated quantification of metabolism allowing for new insight into metabolic network activity.

DOI: https://doi.org/10.1101/2024.02.25.581907
Interdiscip Sci. 2024 Mar 12.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics.

Qingzu He, Huan Guo, Yulin Li, Guoqiang He, Xiang Li, Jianwei Shuai.

  Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

Keywords:  Data-independent acquisition; Deep learning; Manual screening peptides; Mass spectrometry; Proteomics analysis; Squeeze-and-excitation networks

DOI:  https://doi.org/10.1007/s12539-024-00611-4
Small. 2024 Mar 14. e2310700

Microfluidic Impedance Cytometry Enabled One-Step Sample Preparation for Efficient Single-Cell Mass Spectrometry.

Junwen Zhu, Siyuan Pan, Huichao Chai, Peng Zhao, Yongxiang Feng, Zhen Cheng, Sichun Zhang, Wenhui Wang.

  Single-cell mass spectrometry (MS) is significant in biochemical analysis and holds great potential in biomedical applications. Efficient sample preparation like sorting (i.e., separating target cells from the mixed population) and desalting (i.e., moving the cells off non-volatile salt solution) is urgently required in single-cell MS. However, traditional sample preparation methods suffer from complicated operation with various apparatus, or insufficient performance. Herein, a one-step sample preparation strategy by leveraging label-free impedance flow cytometry (IFC) based microfluidics is proposed. Specifically, the IFC framework to characterize and sort single-cells is adopted. Simultaneously with sorting, the target cell is transferred from the local high-salinity buffer to the MS-compatible solution. In this way, one-step sorting and desalting are achieved and the collected cells can be directly fed for MS analysis. A high sorting efficiency (>99%), cancer cell purity (≈87%), and desalting efficiency (>99%), and the whole workflow of impedance-based separation and MS analysis of normal cells (MCF-10A) and cancer cells (MDA-MB-468) are verified. As a standalone sample preparation module, the microfluidic chip is compatible with a variety of MS analysis methods, and envisioned to provide a new paradigm in efficient MS sample preparation, and further in multi-modal (i.e., electrical and metabolic) characterization of single-cells.

Keywords:  impedance flow cytometry; mass spectrometry; sample preparation; single-cell analysis; sorting and desalting

DOI:  https://doi.org/10.1002/smll.202310700
J Vis Exp. 2024 Feb 23.

Targeted Metabolomics on Rare Primary Cells.

Katharina M Glaser, Mirijam Egg, Sebastian Hobitz, Michael Mitterer, Dominik Schain-Zota, Katharina Schönberger, Konrad Schuldes, Nina Cabezas-Wallscheid, Tim Lämmermann, Angelika Rambold, Joerg M Buescher.

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule intermediates. However, obtaining accurate and reliable measurements of cellular metabolism, particularly in rare cell types like hematopoietic stem cells, has traditionally required pooling cells from multiple animals. A protocol now enables researchers to measure metabolites in rare cell types using only one mouse per sample while generating multiple replicates for more abundant cell types. This reduces the number of animals that are required for a given project. The protocol presented here involves several key differences over traditional metabolomics protocols, such as using 5 g/L NaCl as a sheath fluid, sorting directly into acetonitrile, and utilizing targeted quantification with rigorous use of internal standards, allowing for more accurate and comprehensive measurements of cellular metabolism. Despite the time required for the isolation of single cells, fluorescent staining, and sorting, the protocol can preserve differences among cell types and drug treatments to a large extent.

DOI: https://doi.org/10.3791/65690
Int J Mol Sci. 2024 Mar 04. pii: 2960. [Epub ahead of print]25(5):

Mass Spectrometric Proteomics 2.0.

Paolo Iadarola, Simona Viglio.

This Special Issue, "Mass Spectrometric Proteomics 2 [...].

DOI: https://doi.org/10.3390/ijms25052960
Int J Biol Sci. 2024 ;20(5): 1884-1904

Reprogramming of Lipid Metabolism Mediates Crosstalk, Remodeling, and Intervention of Microenvironment Components in Breast Cancer.

Jia Wang, Wenfeng Zhang, Cun Liu, LongYun Wang, Jibiao Wu, Changgang Sun, Qibiao Wu.

  Due to the unique characteristics of breast cancer initiation sites and significant alterations in tumor metabolism, breast cancer cells rely on lipid metabolic reprogramming to effectively regulate metabolic programs during the disease progression cascade. This adaptation enables them to meet the energy demands required for proliferation, invasion, metastasis, and responses to signaling molecules in the breast cancer microenvironment. In this review, we comprehensively examined the distinctive features of lipid metabolic reprogramming in breast cancer and elucidated the underlying mechanisms driving aberrant behavior of tumor cells. Additionally, we emphasize the potential role and adaptive changes in lipid metabolism within the breast cancer microenvironment, while summarizing recent preclinical studies. Overall, precise control over lipid metabolism rewiring and understanding of plasticity within the breast cancer microenvironment hold promising implications for developing targeted treatment strategies against this disease. Therefore, interventions targeting the lipid metabolism in breast cancer may facilitate innovative advancements in clinical applications.

Keywords:  Breast cancer; Lipid metabolism; Targeted intervention; Tumor microenvironment

DOI:  https://doi.org/10.7150/ijbs.92125
J Mass Spectrom. 2024 Apr;59(4): e5009

Quantitative mass spectrometry imaging (qMSI): A tutorial.

Russell R Kibbe, David C Muddiman.

  Mass spectrometry imaging (MSI) is an analytical technique that enables the simultaneous detection of hundreds to thousands of chemical species while retaining their spatial information; usually, MSI is applied to biological tissues. Combining these elements can create ion images, which allows for the identification and localization of multiple chemical species within the sample. Being able to produce molecular images of biological tissues has already impacted the study of health and disease; however, the next logical step is being able to combine MSI with quantitative mass spectrometry methods to both quantify and determine the localization of disease progression or drug action. In this tutorial, we will detail the main factors to consider when designing a qMSI experiment and highlight the methods that have been developed to overcome these added complexities, specifically for those newer to the field of MSI.

Keywords:  absolute quantification; mass spectrometry imaging; qMSI; relative quantification

DOI:  https://doi.org/10.1002/jms.5009
Anal Chim Acta. 2024 Apr 15. pii: S0003-2670(24)00201-0. [Epub ahead of print]1298 342400

A microfluidic chip-based capillary zone electrophoresis-mass spectrometry method for measuring adenosine 5'-Triphosphate and its similar nucleotide analogues.

Cristina Di Poto, Xiang Tian, Scott Mellors, Sanna Rosengren, Sabina Issop, Sara J Bonvini, Sonja Hess, Erik L Allman.

   BACKGROUND: Extracellular ATP is involved in disorders that cause inflammation of the airways and cough, thus limiting its release has therapeutic benefits. Standard luminescence-based ATP assays measure levels indirectly through enzyme degradation and do not provide a simultaneous readout for other nucleotide analogues. Conversely, mass spectrometry can provide direct ATP measurements, however, common RPLC and HILIC methods face issues because these molecules are unstable, metal-sensitive analytes which are often poorly retained. These difficulties have traditionally been overcome using passivation or ion-pairing chromatography, but these approaches can be problematic for LC systems. As a result, more effective analytical methods are needed.
RESULTS: Here, we introduce a new application that uses microfluidic chip-based capillary zone electrophoresis-mass spectrometry (μCZE-MS) to measure ATP and its analogues simultaneously in biofluids. The commercially available ZipChip Interface and a High-Resolution Bare-glass microchip (ZipChip, HRB, 908 Devices Inc.) coupled to a Thermo Scientific Tribrid Orbitrap, were successfully used to separate and detect various nucleotide standards, as well as ATP, ADP, AMP, and adenosine in plasma and BALF obtained from naïve Brown Norway rats. The findings demonstrate that this approach can rapidly and directly detect ATP and its related nucleotide analogues, while also highlighting the need to preserve these molecules in biofluids with chelators like EDTA. In addition, we demonstrate that this μCZE-MS method is also suitable for detecting a variety of metabolites, revealing additional potential future applications.
SIGNIFICANCE: This innovative μCZE-MS approach provides a robust new tool to directly measure ATP and other nucleotide analogues in biofluids. This can enable the study of eATP in human disease and potentially contribute to the creation of ATP-targeting therapies for airway illnesses.

Keywords:  Adenosine 5′-triphosphate; Bronchoalveolar lavage fluid; Capillary zone electrophoresis mass spectrometry; Chronic cough; Ethylenediaminetetraacetic acid; Metabolomics

DOI:  https://doi.org/10.1016/j.aca.2024.342400
J Bone Miner Res. 2024 Jan 11. pii: zjad016. [Epub ahead of print]

Hypoxia rewires glucose and glutamine metabolism in different sources of skeletal stem and progenitor cells similarly, except for pyruvate.

Shauni Loopmans, Guillaume Tournaire, Ingrid Stockmans, Steve Stegen, Geert Carmeliet.

  Skeletal stem and progenitor cells (SSPCs) are crucial for bone development, homeostasis, and repair. SSPCs are considered to reside in a rather hypoxic niche in the bone, but distinct SSPC niches have been described in different skeletal regions, and they likely differ in oxygen and nutrient availability. Currently it remains unknown whether the different SSPC sources have a comparable metabolic profile and respond in a similar manner to hypoxia. In this study, we show that cell proliferation of all SSPCs was increased in hypoxia, suggesting that SSPCs can indeed function in a hypoxic niche in vivo. In addition, low oxygen tension increased glucose consumption and lactate production, but affected pyruvate metabolism cell-specifically. Hypoxia decreased tricarboxylic acid (TCA) cycle anaplerosis and altered glucose entry into the TCA cycle from pyruvate dehydrogenase to pyruvate carboxylase and/or malic enzyme. Finally, a switch from glutamine oxidation to reductive carboxylation was observed in hypoxia, as well as cell-specific adaptations in the metabolism of other amino acids. Collectively, our findings show that SSPCs from different skeletal locations proliferate adequately in hypoxia by rewiring glucose and amino acid metabolism in a cell-specific manner.

Keywords:  cell metabolism; chondrocyte; hypoxia; proliferation; skeletal progenitor

DOI:  https://doi.org/10.1093/jbmr/zjad016
J Proteome Res. 2024 Mar 14.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

Luke Whiley, Nathan G Lawler, Annie Xu Zeng, Alex Lee, Sung-Tong Chin, Maider Bizkarguenaga, Chiara Bruzzone, Nieves Embade, Julien Wist, Elaine Holmes, Oscar Millet, Jeremy K Nicholson, Nicola Gray.

  To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Keywords:  COVID-19; LC-MS; SARS-CoV-2; TCA cycle; bile acids; hypoxia; metabolic phenotyping; metabolic phenotyping array; organic acids; oxidative stress; validation

DOI:  https://doi.org/10.1021/acs.jproteome.3c00797
Metabolomics. 2024 Mar 13. 20(2): 41

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

Goncalo Jorge Gouveia, Thomas Head, Leo L Cheng, Chaevien S Clendinen, John R Cort, Xiuxia Du, Arthur S Edison, Candace C Fleischer, Jeffrey Hoch, Nathaniel Mercaldo, Wimal Pathmasiri, Daniel Raftery, Tracey B Schock, Lloyd W Sumner, Panteleimon G Takis, Valérie Copié, Hamid R Eghbalnia, Robert Powers.

   BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.
AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.
KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Keywords:  Best practices; Mass spectrometry; Metabolomics; NMR; Use and reuse of metabolomics datasets

DOI:  https://doi.org/10.1007/s11306-024-02090-6
Nat Commun. 2024 Mar 13. 15(1): 2288

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model.

David Gomez-Zepeda, Danielle Arnold-Schild, Julian Beyrle, Arthur Declercq, Ralf Gabriels, Elena Kumm, Annica Preikschat, Mateusz Krzysztof Łącki, Aurélie Hirschler, Jeewan Babu Rijal, Christine Carapito, Lennart Martens, Ute Distler, Hansjörg Schild, Stefan Tenzer.

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

DOI: https://doi.org/10.1038/s41467-024-46380-y
bioRxiv. 2024 Mar 01. pii: 2024.02.28.582405. [Epub ahead of print]

Metabolic phenotyping of healthy and diseased human RPE cells.

Saira Rizwan, Beverly Toothman, Bo Li, Abbi J Engel, Rayne R Lim, Jinyu Lu, Jennifer R Chao, Jianhai Du.

Purpose: Metabolic defects in retinal pigment epithelium (RPE) are underlying many retinal degenerative diseases. This study aims to identify the nutrient requirements of healthy and diseased human RPE cells.
Methods: We profiled the utilization of 183 nutrients in human RPE cells: 1) differentiated and dedifferentiated fetal RPE (fRPE), 2) induced pluripotent stem cell derived-RPE (iPSC RPE), 3) Sorsby fundus dystrophy (SFD) patient-derived iPSC RPE and its CRISPR-corrected isogenic SFD (cSFD) iPSC RPE, and 5) ARPE-19 cell lines cultured under different conditions.
Results: Differentiated fRPE cells and healthy iPSC RPE cells can utilize 51 and 48 nutrients respectively, including sugars, intermediates from glycolysis and tricarboxylic acid (TCA) cycle, fatty acids, ketone bodies, amino acids, and dipeptides. However, when fRPE cells lose epithelial phenotype through dedifferentiated, they can only utilize 17 nutrients, primarily sugar and glutamine-related amino acids. SFD RPE cells can utilize 37 nutrients; however, Compared to cSFD RPE and healthy iPSC RPE, they are unable to utilize lactate, some TCA cycle intermediates, and short-chain fatty acids. Nonetheless, they show increased utilization of branch-chain amino acids (BCAAs) and BCAA-containing dipeptides. The dedifferentiated ARPE-19 cells in traditional culture media cannot utilize lactate and ketone bodies. In contrast, nicotinamide supplementation promotes differentiation into epithelial phenotype, restoring the ability to use these nutrients.
Conclusions: Epithelial phenotype confers metabolic flexibility to the RPE for utilizing various nutrients. SFD RPE cells have reduced metabolic flexibility, relying on the oxidation of BCAAs. Our findings highlight the importance of nutrient availability and utilization in RPE differentiation and diseases.

DOI: https://doi.org/10.1101/2024.02.28.582405
Commun Biol. 2024 Mar 14. 7(1): 324

One-pot method for preparing DNA, RNA, and protein for multiomics analysis.

Stephanie Biedka, Duah Alkam, Charity L Washam, Svitlana Yablonska, Aaron Storey, Stephanie D Byrum, Jonathan S Minden.

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

DOI: https://doi.org/10.1038/s42003-024-05993-1
Clin Proteomics. 2024 Mar 12. 21(1): 21

Proteomics of prostate cancer serum and plasma using low and high throughput approaches.

Ghaith M Hamza, Rekha Raghunathan, Stephanie Ashenden, Bairu Zhang, Eric Miele, Andrew F Jarnuczak.

  Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Keywords:  BOXCAR; Biofluids; Biomarkers; Diagnostic; FAIMS; Mass spectrometry; Plasma; Prostate cancer; Proteomics; Serum; TMT

DOI:  https://doi.org/10.1186/s12014-024-09461-0