bims-mascan 2023-03-19 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–03–19
twenty-one papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.
A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics.
DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.
Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis.
Metabolomics meets systems immunology.
Flavonoids attenuate cancer metabolism by modulating Lipid metabolism, amino acids, ketone bodies and redox state mediated by Nrf2.
Epithelial-Mesenchymal Plasticity: Implications for Ferroptosis Vulnerability and Cancer Therapy.
Transcription factor NKX2-1 drives serine and glycine synthesis addiction in cancer.
Screening in serum-derived medium reveals differential response to compounds targeting metabolism.
Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants.
Hyperplexing Approaches for up to 45-Plex Quantitative Proteomic Analysis.
Quantification of Glycosaminoglycans in Urine by Isotope-Dilution Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry.
The effect of prions on cellular metabolism: The metabolic impact of the [RNQ+] prion and potential role of native Rnq1p.
Localization of cyclopropyl groups and alkenes within glycerophospholipids using gas-phase ion/ion chemistry.
Medium depth influences O 2 availability and metabolism in cultured RPE cells.
A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.
Statistical and Computational Methods for Proteogenomic Data Analysis.
NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples.
Expanding the Epitranscriptomic RNA Sequencing and Modification Mapping Mass Spectrometry Toolbox with Field Asymmetric Waveform Ion Mobility and Electrochemical Elution Liquid Chromatography.
SLC27A4-mediated selective uptake of mono-unsaturated fatty acids promotes ferroptosis defense in hepatocellular carcinoma.
Advancing wide implementation of precision oncology: A liquid nitrogen-free snap freezer preserves molecular profiles of biological samples.

J Proteome Res. 2023 Mar 15.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.

Ashley Phetsanthad, Austin V Carr, Lauren Fields, Lingjun Li.

  Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Keywords:  DIA; data-independent acquisition; design of experiments; label-free quantitation; neuropeptides; peptidomics

DOI:  https://doi.org/10.1021/acs.jproteome.3c00088
Proteomics. 2023 Mar 12. e2200041

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics.

Thang V Pham, Vinh V Nguyen, Duong Vu, Alex A Henneman, Robin A Richardson, Sander R Piersma, Connie R Jimenez.

  Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Keywords:  DIA-MS; deep learning; retention time prediction; spectral library; transformer architecture

DOI:  https://doi.org/10.1002/pmic.202200041
Anal Chem. 2023 Mar 12.

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

Jinghan Yang, Zhiyuan Cheng, Fuzhou Gong, Yan Fu.

In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of methods to predict peptide digestibility and detectability. Here, we propose a bidirectional long short-term memory (BiLSTM)-based algorithm, named DeepDetect, for the prediction of peptide detectability enhanced by peptide digestibility. Compared with existing algorithms, DeepDetect is featured by its improved prediction accuracy for a wide range of commonly used proteases, covering trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. On 11 test data sets from E. coli, yeast, mouse, and human samples, DeepDetect achieved higher prediction accuracies than PepFormer, a state-of-the-art deep-learning-based peptide detectability prediction algorithm. The results further demonstrated that peptide digestibility can substantially enhance the performance of peptide detectability predictors. As an application, DeepDetect was used to reduce the in silico predicted spectral libraries in data-independent acquisition mass spectrometry data analysis. Experiments using DIA-NN software showed that DeepDetect can significantly accelerate the library search without loss of peptide and protein identification sensitivity.

DOI: https://doi.org/10.1021/acs.analchem.2c03662
Methods Mol Biol. 2023 ;2629 247-269

Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis.

Sili Fan, Christopher M Wilson, Brooke L Fridley, Qian Li.

  In this chapter, we review the cutting-edge statistical and machine learning methods for missing value imputation, normalization, and downstream analyses in mass spectrometry metabolomics studies, with illustration by example datasets. The missing peak recovery includes simple imputation by zero or limit of detection, regression-based or distribution-based imputation, and prediction by random forest. The batch effect can be removed by data-driven methods, internal standard-based, and quality control sample-based normalization. We also summarize different types of statistical analysis for metabolomics and clinical outcomes, such as inference on metabolic biomarkers, clustering of metabolomic profiles, metabolite module building, and integrative analysis with transcriptome.

Keywords:  Imputation; Integrative analysis; Mass spectrometry; Metabolomics; Normalization; Statistical and machine learning

DOI:  https://doi.org/10.1007/978-1-0716-2986-4_12
EMBO Rep. 2023 Mar 14. e55747

Metabolomics meets systems immunology.

Jianbo Fu, Feng Zhu, Cheng-Jian Xu, Yang Li.

  Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Keywords:  infection; metabolomics; multiomics; statistical analysis; systems immunology

DOI:  https://doi.org/10.15252/embr.202255747
Eur J Pharmacol. 2023 Mar 13. pii: S0014-2999(23)00166-8. [Epub ahead of print] 175655

Flavonoids attenuate cancer metabolism by modulating Lipid metabolism, amino acids, ketone bodies and redox state mediated by Nrf2.

Marek Samec, Alena Mazurakova, Vincent Lucansky, Lenka Koklesova, Renata Pecova, Martin Pec, Olga Golubnitschaja, Raghad Khalid Al-Ishaq, Martin Caprnda, Ludovit Gaspar, Robert Prosecky, Katarina Gazdikova, Mariusz Adamek, Dietrich Büsselberg, Peter Kruzliak, Peter Kubatka.

  Metabolic reprogramming of cancer cells is a common hallmark of malignant transformation. The preference for aerobic glycolysis over oxidative phosphorylation in tumors is a well-studied phenomenon known as the Warburg effect. Importantly, metabolic transformation of cancer cells also involves alterations in signaling cascades contributing to lipid metabolism, amino acid flux and synthesis, and utilization of ketone bodies. Also, redox regulation interacts with metabolic reprogramming during malignant transformation. Flavonoids, widely distributed phytochemicals in plants, exert various beneficial effects on human health through modulating molecular cascades altered in the pathological cancer phenotype. Recent evidence has identified numerous flavonoids as modulators of critical components of cancer metabolism and associated pathways interacting with metabolic cascades such as redox balance. Flavonoids affect lipid metabolism by regulating fatty acid synthase, redox balance by modulating nuclear factor-erythroid factor 2-related factor 2 (Nrf2) activity, or amino acid flux and synthesis by phosphoglycerate mutase 1. Here, we discuss recent preclinical evidence evaluating the impact of flavonoids on cancer metabolism, focusing on lipid and amino acid metabolic cascades, redox balance, and ketone bodies.

Keywords:  Cancer cells; Carcinogenesis; Flavonoids; Metabolic reprogramming; Metabolism

DOI:  https://doi.org/10.1016/j.ejphar.2023.175655
Crit Rev Oncol Hematol. 2023 Mar 15. pii: S1040-8428(23)00052-5. [Epub ahead of print] 103964

Epithelial-Mesenchymal Plasticity: Implications for Ferroptosis Vulnerability and Cancer Therapy.

Jaewang Lee, Jong-Lyel Roh.

  Cancers polarized to a mesenchymal or poorly differentiated state can often evade cell death induced by conventional therapies. The epithelial-mesenchymal transition is involved in lipid metabolism and increases polyunsaturated fatty acid levels in cancer cells, contributing to chemo- and radio-resistance. Altered metabolism in cancer enables invasion and metastasis but is prone to lipid peroxidation under oxidative stress. Cancers with mesenchymal rather than epithelial signatures are highly vulnerable to ferroptosis. Therapy-resistant persister cancer cells show a high mesenchymal cell state and dependence on the lipid peroxidase pathway, which can respond more sensitively to ferroptosis inducers. Cancer cells may survive under specific metabolic and oxidative stress conditions, and targeting this unique defense system can selectively kill only cancer cells. Therefore, this article summarizes the core regulatory mechanisms of ferroptosis in cancer, the relationship between ferroptosis and epithelial-mesenchymal plasticity, and the implications of epithelial-mesenchymal transition for ferroptosis-based cancer therapy.

Keywords:  Ferroptosis; cancer; epithelial-mesenchymal transition; plasticity; vulnerability

DOI:  https://doi.org/10.1016/j.critrevonc.2023.103964
Br J Cancer. 2023 Mar 17.

Transcription factor NKX2-1 drives serine and glycine synthesis addiction in cancer.

Elien Heylen, Paulien Verstraete, Linde Van Aerschot, Shauni L Geeraerts, Tom Venken, Kalina Timcheva, David Nittner, Jelle Verbeeck, Jonathan Royaert, Marion Gijbels, Anne Uyttebroeck, Heidi Segers, Diether Lambrechts, Jan Cools, Kim De Keersmaecker, Kim R Kampen.

BACKGROUND: One-third of cancers activate endogenous synthesis of serine/glycine, and can become addicted to this pathway to sustain proliferation and survival. Mechanisms driving this metabolic rewiring remain largely unknown.
METHODS: NKX2-1 overexpressing and NKX2-1 knockdown/knockout T-cell leukaemia and lung cancer cell line models were established to study metabolic rewiring using ChIP-qPCR, immunoblotting, mass spectrometry, and proliferation and invasion assays. Findings and therapeutic relevance were validated in mouse models and confirmed in patient datasets.
RESULTS: Exploring T-cell leukaemia, lung cancer and neuroendocrine prostate cancer patient datasets highlighted the transcription factor NKX2-1 as putative driver of serine/glycine metabolism. We demonstrate that transcription factor NKX2-1 binds and transcriptionally upregulates serine/glycine synthesis enzyme genes, enabling NKX2-1 expressing cells to proliferate and invade in serine/glycine-depleted conditions. NKX2-1 driven serine/glycine synthesis generates nucleotides and redox molecules, and is associated with an altered cellular lipidome and methylome. Accordingly, NKX2-1 tumour-bearing mice display enhanced tumour aggressiveness associated with systemic metabolic rewiring. Therapeutically, NKX2-1-expressing cancer cells are more sensitive to serine/glycine conversion inhibition by repurposed anti-depressant sertraline, and to etoposide chemotherapy.
CONCLUSION: Collectively, we identify NKX2-1 as a novel transcriptional regulator of serine/glycine synthesis addiction across cancers, revealing a therapeutic vulnerability of NKX2-1-driven cancers. Transcription factor NKX2-1 fuels cancer cell proliferation and survival by hyperactivating serine/glycine synthesis, highlighting this pathway as a novel therapeutic target in NKX2-1-positive cancers.

DOI: https://doi.org/10.1038/s41416-023-02216-y
bioRxiv. 2023 Feb 27. pii: 2023.02.25.529972. [Epub ahead of print]

Screening in serum-derived medium reveals differential response to compounds targeting metabolism.

Keene L Abbott, Ahmed Ali, Dominick Casalena, Brian T Do, Raphael Ferreira, Jaime H Cheah, Christian K Soule, Amy Deik, Tenzin Kunchok, Daniel R Schmidt, Steffen Renner, Sophie E Honeder, Michelle Wu, Sze Ham Chan, Tenzin Tseyang, Daniel Greaves, Peggy P Hsu, Christopher W Ng, Chelsea J Zhang, Ali Farsidjani, Iva Monique T Gramatikov, Nicholas J Matheson, Caroline A Lewis, Clary B Clish, Matthew G Rees, Jennifer A Roth, Lesley Mathews Griner, Alexander Muir, Douglas S Auld, Matthew G Vander Heiden.

A challenge for screening new candidate drugs to treat cancer is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels to propagate cells. Which nutrients are available can influence how cancer cells use metabolism to proliferate and impact sensitivity to some drugs, but a general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To enable screening of compounds to determine how the nutrient environment impacts drug efficacy, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We used this system to screen several small molecule libraries and found that compounds targeting metabolic enzymes were enriched as having differential efficacy in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.

DOI: https://doi.org/10.1101/2023.02.25.529972
Metabolomics. 2023 Mar 15. 19(3): 18

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants.

Anza-Tshilidzi Ramabulana, Daniel Petras, Ntakadzeni E Madala, Fidele Tugizimana.

   INTRODUCTION: Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS1) and MS2 spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.
OBJECTIVES: This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.
METHODS: Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.
RESULTS: We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS2 spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.
CONCLUSION: Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

Keywords:  Data-dependent acquisition; MS parameter optimisation; MS/MS spectra; Molecular networking; Momordica cardiospermoides; Natural products

DOI:  https://doi.org/10.1007/s11306-023-01981-4
Anal Chem. 2023 Mar 14.

Hyperplexing Approaches for up to 45-Plex Quantitative Proteomic Analysis.

Zhen Wu, Weirong Xiang, Lin Huang, Shuwei Li, Xumin Zhang.

Isobaric labeling has emerged as an indispensable quantitative proteomic approach for its unprecedented multiplexing capacity in a single analysis. Currently, different hyperplexing approaches have been developed to meet the demand for the increasing sample size in large-scale cohort analysis. In this report, we present a tribrid hyperplexing approach by the combinatorial use of three types of isobaric reagents, a novel isobaric tag 16-plex (IBT16) reagent and the widely used tandem mass tag (TMT; TMT11) and TMTpro (TMT18) reagents. After the determination of labeling efficiency and the optimization of testing conditions, we systematically evaluated the identification and quantification performance of the three labeling reagents in both independent and combinatorial manners using the mixtures of E. coli and HeLa peptides with different ratios. Our results reveal that the three reagents are quite similar in all testing aspects despite some differences, and the combination use of the three reagents could expand the multiplexing capacity to up to 45-plex. Furthermore, we conclude the advantages of IBT16 in the combination use and the preferred combinations for different practical applications. Data are available via ProteomeXchange with identifier PXD037498.

DOI: https://doi.org/10.1021/acs.analchem.3c00237
Curr Protoc. 2023 Mar;3(3): e701

Quantification of Glycosaminoglycans in Urine by Isotope-Dilution Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry.

Haoyue Zhang, Sarah P Young, David S Millington.

  Mucopolysaccharidoses (MPSs) are complex lysosomal storage disorders that result in the accumulation of glycosaminoglycans (GAGs) in urine, blood, and tissues. Lysosomal enzymes responsible for GAG degradation are defective in MPSs. GAGs including chondroitin sulfate (CS), dermatan sulfate (DS), heparan sulfate (HS), and keratan sulfate (KS) are disease-specific biomarkers for MPSs. This article describes a stable isotope dilution-tandem mass spectrometric method for quantifying CS, DS, and HS in urine samples. The GAGs are methanolyzed to uronic or iduronic acid-N-acetylhexosamine or iduronic acid-N-sulfo-glucosamine dimers and mixed with internal standards derived from deuteriomethanolysis of GAG standards. Specific dimers derived from HS, DS, and CS are separated by ultra-performance liquid chromatography (UPLC) and analyzed by electrospray ionization tandem mass spectrometry (MS/MS) using selected reaction monitoring for each targeted GAG product and its corresponding internal standard. This UPLC-MS/MS GAG assay is useful for identifying patients with MPS types I, II, III, VI, and VII. © 2023 Wiley Periodicals LLC. Basic Protocol: Urinary GAG analysis by ESI-MS/MS Support Protocol 1: Prepare calibration samples Support Protocol 2: Preparation of stable isotope-labeled internal standards Support Protocol 3: Preparation of quality controls for GAG analysis in urine Support Protocol 4: Optimization of the methanolysis time Support Protocol 5: Measurement of the concentration of methanolic HCl.

Keywords:  LC-ESI-MS/MS; dermatan sulfate; glycosaminoglycans; heparan sulfate; isotope dilution; mucopolysaccharidosis

DOI:  https://doi.org/10.1002/cpz1.701
Res Sq. 2023 Feb 28. pii: rs.3.rs-2511186. [Epub ahead of print]

The effect of prions on cellular metabolism: The metabolic impact of the [RNQ+] prion and potential role of native Rnq1p.

Tyler Howell-Bray, Lee Byrne.

Within the field of amyloid and prion disease there is a need for a more comprehensive understanding of the fundamentals of disease biology. In order to facilitate the progression treatment and underpin comprehension of toxicity, fundamental understanding of the disruption to normal cellular biochemistry and trafficking is needed. Here, by removing the complex biochemistry of the brain, we have utilised known prion forming strains of Saccharomyces cerevisiae carrying different conformational variants of the Rnq1p to obtain Liquid Chromatography-Mass Spectrometry (LC-MS) metabolic profiles and identify key perturbations of prion presence. These studies reveal that prion containing [ RNQ + ] cells display a significant reduction in amino acid biosynthesis and distinct perturbations in sphingolipid metabolism, with significant downregulation in metabolites within these pathways. Moreover, that native Rnq1p appears to downregulate ubiquinone biosynthesis pathways within cells, suggesting that Rnq1p may play a lipid/mevalonate-based cytoprotective role as a regulator of ubiquinone production. These findings contribute to the understanding of how prion proteins interact in vivo in both their prion and non-prion confirmations and indicate potential targets for the mitigation of these effects. We demonstrate specific sphingolipid centred metabolic disruptions due to prion presence and give insight into a potential cytoprotective role of the native Rnq1 protein. This provides evidence of metabolic similarities between yeast and mammalian cells as a consequence of prion presence and establishes the application of metabolomics as a tool to investigate prion/amyloid-based phenomena.

DOI: https://doi.org/10.21203/rs.3.rs-2511186/v1
J Mass Spectrom. 2023 Apr;58(4): e4913

Localization of cyclopropyl groups and alkenes within glycerophospholipids using gas-phase ion/ion chemistry.

De'Shovon M Shenault, Scott A McLuckey, Elissia T Franklin.

  Shotgun lipid analysis using electrospray ionization tandem mass spectrometry (ESI-MS/MS) is a common approach for the identification and characterization of glycerophohspholipids GPs. ESI-MS/MS, with the aid of collision-induced dissociation (CID), enables the characterization of GP species at the headgroup and fatty acyl sum compositional levels. However, important structural features that are often present, such as carbon-carbon double bond(s) and cyclopropane ring(s), can be difficult to determine. Here, we report the use of gas-phase charge inversion reactions that, in combination with CID, allow for more detailed structural elucidation of GPs. CID of a singly deprotonated GP, [GP - H]- , generates FA anions, [FA - H]- . The fatty acid anions can then react with doubly charged cationic magnesium tris-phenanthroline complex, [Mg(Phen)3 ]2+ , to form charge inverted complex cations of the form [FA - H + MgPhen2 ]+ . CID of the complex generates product ion spectral patterns that allow for the identification of carbon-carbon double bond position(s) as well as the sites of cyclopropyl position(s) in unsaturated lipids. This approach to determining both double bond and cyclopropane positions is demonstrated with GPs for the first time using standards and is applied to lipids extracted from Escherichia coli.

Keywords:  cyclopropane location; double bond location; glycerophospholipids; ion/ion reactions; shotgun lipidomics

DOI:  https://doi.org/10.1002/jms.4913
bioRxiv. 2023 Mar 02. pii: 2023.03.01.530623. [Epub ahead of print]

Medium depth influences O 2 availability and metabolism in cultured RPE cells.

Daniel T Hass, Qitao Zhang, Gillian Autterson, Richard Bryan, James B Hurley, Jason Ml Miller.

Purpose: RPE oxidative metabolism is critical for normal retinal function and is often studied in cell culture systems. Here, we show that conventional culture media volumes dramatically impact O 2 availability, limiting oxidative metabolism. We suggest optimal conditions to ensure cultured RPE is in a normoxic environment permissive to oxidative metabolism.
Methods: We altered the availability of O 2 to human primary RPE cultures directly via a hypoxia chamber or indirectly via the amount of medium over cells. We measured oxygen consumption rates (OCR), glucose consumption, lactate production, 13 C 6 -glucose flux, hypoxia inducible factor (HIF-1α) stability, intracellular lipid droplets after a lipid challenge, trans-epithelial electrical resistance, cell morphology, and pigmentation.
Results: Medium volumes commonly employed during RPE culture limit diffusion of O 2 to cells, triggering hypoxia, activating HIF-1α, limiting OCR, and dramatically altering cell metabolism, with only minor effects on typical markers of RPE health. Media volume effects on O 2 availability decrease acetyl-CoA utilization, increase glycolysis, and alter the size and number of intracellular lipid droplets under lipid-rich conditions.
Conclusions: Despite having little impact on visible and typical markers of RPE culture health, media volume dramatically affects RPE physiology "under the hood". As RPE-centric diseases like age-related macular degeneration (AMD) involve oxidative metabolism, RPE cultures need to be optimized to study such diseases. We provide guidelines for optimal RPE culture volumes that balance ample nutrient availability from larger media volumes with adequate O 2 availability seen with smaller media volumes.

DOI: https://doi.org/10.1101/2023.03.01.530623
Anal Chim Acta. 2023 Apr 22. pii: S0003-2670(23)00259-3. [Epub ahead of print]1251 341038

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.

Wei Fang, Zhuokun Du, Linlin Kong, Bin Fu, Guibin Wang, Yangjun Zhang, Weijie Qin.

  Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Keywords:  Cellular heterogeneity; MS1-only acquisition; Retention time prediction; Single-cell proteomics

DOI:  https://doi.org/10.1016/j.aca.2023.341038
Methods Mol Biol. 2023 ;2629 271-303

Statistical and Computational Methods for Proteogenomic Data Analysis.

Xiaoyu Song.

  Proteins are the functional molecules for almost all cellular and biological processes. They are also the targets of most drugs. Proteins employ complex, multilevel regulations, so their abundance levels do not well correlated with their mRNA expression levels. The structure, activity, and functional roles of proteins are affected by posttranslational modifications (PTM), which are even less correlated with mRNA expression levels than protein abundances. Comprehensive characterization of the proteomics data is critical for understanding the molecular and cellular mechanisms of biological systems and developing news therapeutics. Current large-scale proteomic profiling technologies, such as mass spectrometry, provide relative identification of peptides and proteins, with data vulnerable to outliers, batch effects, and nonrandom missingness. In order to perform high-quality proteomic data analysis, we will first introduce a data preprocessing and quality control pipeline that includes normalization, outlier detection and removal, batch effect identification and handling, and missing data imputation. Then, we will describe several statistical methods that leverage well-processed proteomic data to generate scientific discoveries, especially with an integration with genomics and transcriptomics. These methods cover topics like association analysis, network construction, clustering, and cell-type deconvolution. To demonstrate these methods, we will use the proteogenomic data from the lung squamous cell carcinoma study of the Clinical Proteomic Tumor Analysis Consortium and provide sample codes for data access and analyses.

Keywords:  Integrative proteogenomic analysis; Mass spectrometry; Preprocessing and quality control; Proteomics

DOI:  https://doi.org/10.1007/978-1-0716-2986-4_13
bioRxiv. 2023 Mar 02. pii: 2023.03.01.530642. [Epub ahead of print]

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples.

Wanli Wang, Li-Hua Ma, Mirjana Maletic-Savatic, Zhandong Liu.

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

DOI: https://doi.org/10.1101/2023.03.01.530642
Anal Chem. 2023 Mar 14.

Expanding the Epitranscriptomic RNA Sequencing and Modification Mapping Mass Spectrometry Toolbox with Field Asymmetric Waveform Ion Mobility and Electrochemical Elution Liquid Chromatography.

Richard Lauman, Hee Jong Kim, Lindsay K Pino, Alessandro Scacchetti, Yixuan Xie, Faith Robison, Simone Sidoli, Roberto Bonasio, Benjamin A Garcia.

Post-transcriptional modifications of RNA strongly influence the RNA structure and function. Recent advances in RNA sequencing and mass spectrometry (MS) methods have identified over 140 of these modifications on a wide variety of RNA species. Most next-generation sequencing approaches can only map one RNA modification at a time, and while MS can assign multiple modifications simultaneously in an unbiased manner, MS cannot accurately catalog and assign RNA modifications in complex biological samples due to limitations in the fragment length and coverage depth. Thus, a facile method to identify novel RNA modifications while simultaneously locating them in the context of their RNA sequences is still lacking. We combined two orthogonal modes of RNA ion separation before MS identification: high-field asymmetric ion mobility separation (FAIMS) and electrochemically modulated liquid chromatography (EMLC). FAIMS RNA MS increases both coverage and throughput, while EMLC LC-MS orthogonally separates RNA molecules of different lengths and charges. The combination of the two methods offers a broadly applicable platform to improve the length and depth of MS-based RNA sequencing while providing contextual access to the analysis of RNA modifications.

DOI: https://doi.org/10.1021/acs.analchem.2c04114
Free Radic Biol Med. 2023 Mar 14. pii: S0891-5849(23)00119-3. [Epub ahead of print]

SLC27A4-mediated selective uptake of mono-unsaturated fatty acids promotes ferroptosis defense in hepatocellular carcinoma.

Ziwen Li, Xinyi Liao, Yameng Hu, Man Li, Miaoling Tang, Shuxia Zhang, Shuang Mo, Xincheng Li, Suwen Chen, Wanying Qian, Rongni Feng, Ruyuan Yu, Yingru Xu, Shuanghu Yuan, Chan Xie, Jun Li.

  Aberrant lipid metabolism mediated by the selective transport of fatty acids plays vital roles in cancer initiation, progression, and therapeutic failure. However, the biological function and clinical significance of abnormal fatty acid transporters in human cancer remain unclear. In the present study, we reported that solute carrier family 27 member 4 (SLC27A4) is significantly overexpressed in 21 types of human cancer, especially in the fatty acids-enriched microenvironment surrounding hepatocellular carcinoma (HCC), breast cancer, and ovarian cancer. Upregulated SLC27A4 expression correlated with shorter overall and relapse-free survival of patients with HCC, breast cancer, or ovarian cancer. Lipidomic analysis revealed that overexpression of SLC27A4 significantly promoted the selective uptake of mono-unsaturated fatty acids (MUFAs), which induced a high level of MUFA-containing phosphatidylcholine and phosphatidylethanolamine in HCC cells, consequently resulting in resistance to lipid peroxidation and ferroptosis. Importantly, silencing SLC27A4 significantly promoted the sensitivity of HCC to sorafenib treatment, both in vitro and in vivo. Our findings revealed a plausible role for SLC27A4 in ferroptosis defense via lipid remodeling, which might represent an attractive therapeutic target to increase the effectiveness of sorafenib treatment in HCC.

Keywords:  Ferroptosis; Hepatocellular carcinoma; Lipid remodeling; SLC27A4; Uptake of fatty acids

DOI:  https://doi.org/10.1016/j.freeradbiomed.2023.03.013
Cancer Med. 2023 Mar 14.

Advancing wide implementation of precision oncology: A liquid nitrogen-free snap freezer preserves molecular profiles of biological samples.

Hanneke van der Wijngaart, Sahil Jagga, Henk Dekker, Richard de Goeij, Sander R Piersma, Thang V Pham, Jaco C Knol, Babs M Zonderhuis, Harry J Holland, Connie R Jiménez, Henk M W Verheul, Srinivas Vanapalli, Mariette Labots.

   PURPOSE: In precision oncology, tumor molecular profiles guide selection of therapy. Standardized snap freezing of tissue biospecimens is necessary to ensure reproducible, high-quality samples that preserve tumor biology for adequate molecular profiling. Quenching in liquid nitrogen (LN2 ) is the golden standard method, but LN2 has several limitations. We developed a LN2 -independent snap freezer with adjustable cold sink temperature. To benchmark this device against the golden standard, we compared molecular profiles of biospecimens.
METHODS: Cancer cell lines and core needle normal tissue biopsies from five patients' liver resection specimens were used to compare mass spectrometry (MS)-based global phosphoproteomic and RNA sequencing profiles and RNA integrity obtained by both freezing methods.
RESULTS: Unsupervised cluster analysis of phosphoproteomic and transcriptomic profiles of snap freezer versus LN2 -frozen K562 samples and liver biopsies showed no separation based on freezing method (with Pearson's r 0.96 (range 0.92-0.98) and >0.99 for K562 profiles, respectively), while samples with +2 h bench-time formed a separate cluster. RNA integrity was also similar for both snap freezing methods. Molecular profiles of liver biopsies were clearly identified per individual patient regardless of the applied freezing method. Two to 25 s freezing time variations did not induce profiling differences in HCT116 samples.
CONCLUSION: The novel snap freezer preserves high-quality biospecimen and allows identification of individual patients' molecular profiles, while overcoming important limitations of the use of LN2 . This snap freezer may provide a useful tool in clinical cancer research and practice, enabling a wider implementation of (multi-)omics analyses for precision oncology.

Keywords:  biopsies; cancer; cooling; multi-omics; precision oncology; snap freezing

DOI:  https://doi.org/10.1002/cam4.5781