bims-mascan 2024-11-24 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–11–24
ten papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Development and application of an LC-MS/MS method for the combined quantification of oxysterols and bile acids.
An alternative route for β-hydroxybutyrate metabolism supports fatty acid synthesis in cancer cells.
The microbiome diversifies N -acyl lipid pools - including short-chain fatty acid-derived compounds.
Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.
Review and Practical Guide for Getting Started With Single-Cell Proteomics.
Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics.
MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.
Utilizing two-dose difference combined with stable isotope tracing for effective and comprehensive metabolite identification of pioglitazone via ultra-high-performance liquid chromatography-mass spectrometry.
Systematic Optimization of Activity-Based Protein Profiling for Identification of Polysorbate-Degradative Enzymes in Biotherapeutic Drug Substance down to 10 ppb.
Simultaneous Quantitation of Multiple Biological Thiols Using Reactive Ionization and Derivatization with Charged Mass Tags.

J Lipid Res. 2024 Nov 16. pii: S0022-2275(24)00202-5. [Epub ahead of print] 100697

Development and application of an LC-MS/MS method for the combined quantification of oxysterols and bile acids.

Martin Roumain, Giulio G Muccioli.

  Oxysterols and bile acids are interconnected bioactive lipids playing pivotal roles in diverse physiological and pathological processes. For this reason, they are increasingly studied together for their implications in various diseases. However, due to analytical challenges inherent to the nature of these analytes, very few methods have been developed for the simultaneous analysis of these lipids. We here report the development of a sensitive LC-MS/MS method for the combined quantification of 18 oxysterols, 11 unconjugated, 15 conjugated bile acids, and 1 bile acid precursor, using 8 isotope-labeled internal standards, addressing the need for a more comprehensive analysis of these interesting lipid families. During the method development, we investigated different extraction protocols, set up a purification step and achieved chromatographic separation for these lipids, overcoming challenges such as the large number of analytes, isomers, and wide range of polarity across the analytes. Finally, the method was successfully applied to the analysis of preclinical and clinical samples, quantifying 12 oxysterols and 14 bile acids in human plasma, 10 oxysterols and 18 bile acids in mouse plasma from the vena cava, and 10 oxysterols and 20 bile acids in mouse plasma from the portal vein within a single chromatographic run.

Keywords:  25-hydroxycholesterol; 4β-hydroxycholesterol; Lipidomics; cholesterol metabolism; cytochrome P450; deoxycholic acid; lipids; liquid chromatography mass spectrometry; sterols; ursodeoxycholic acid

DOI:  https://doi.org/10.1016/j.jlr.2024.100697
bioRxiv. 2024 Nov 03. pii: 2024.10.31.621317. [Epub ahead of print]

An alternative route for β-hydroxybutyrate metabolism supports fatty acid synthesis in cancer cells.

Faith C Kaluba, Thomas J Rogers, Yu-Jin Jeong, Althea Waldhart, Kelly H Sokol, Cameron J Lee, Samuel R Daniels, Joseph Longo, Amy Johnson, Ryan D Sheldon, Russell G Jones, Evan C Lien.

Cancer cells are exposed to diverse metabolites in the tumor microenvironment that are used to support the synthesis of nucleotides, amino acids, and lipids needed for rapid cell proliferation 1-3 . Recent work has shown that ketone bodies such as β-hydroxybutyrate (β-OHB), which are elevated in circulation under fasting conditions or low glycemic diets, can serve as an alternative fuel that is metabolized in the mitochondria to provide acetyl-CoA for the tricarboxylic acid (TCA) cycle in some tumors 4-7 . Here, we discover a non-canonical route for β-OHB metabolism, in which β-OHB can bypass the TCA cycle to generate cytosolic acetyl-CoA for de novo fatty acid synthesis in cancer cells. We show that β-OHB-derived acetoacetate in the mitochondria can be shunted into the cytosol, where acetoacetyl-CoA synthetase (AACS) and thiolase convert it into acetyl-CoA for fatty acid synthesis. This alternative metabolic routing of β-OHB allows it to avoid oxidation in the mitochondria and net contribute to anabolic biosynthetic processes. In cancer cells, β-OHB is used for fatty acid synthesis to support cell proliferation under lipid-limited conditions in vitro and contributes to tumor growth under lipid-limited conditions induced by a calorie-restricted diet in vivo . Together, these data demonstrate that β-OHB is preferentially used for fatty acid synthesis in cancer cells to support tumor growth.

DOI: https://doi.org/10.1101/2024.10.31.621317
bioRxiv. 2024 Nov 02. pii: 2024.10.31.621412. [Epub ahead of print]

The microbiome diversifies N -acyl lipid pools - including short-chain fatty acid-derived compounds.

Helena Mannochio-Russo, Vincent Charron-Lamoureux, Martijn van Faassen, Santosh Lamichhane, Wilhan D Gonçalves Nunes, Victoria Deleray, Abubaker Patan, Kyle Vittali, Prajit Rajkumar, Yasin El Abiead, Haoqi Nina Zhao, Paulo Wender Portal Gomes, Ipsita Mohanty, Carlynda Lee, Aidan Sund, Meera Sharma, Yuanhao Liu, David Pattynama, Gregory T Walker, Grant J Norton, Lora Khatib, Mohammadsobhan S Andalibi, Crystal X Wang, Ronald J Ellis, David J Moore, Jennifer E Iudicello, Donald Franklin, Scott Letendre, Loryn Chin, Corinn Walker, Simone Renwick, Jasmine Zemlin, Michael J Meehan, Xinyang Song, Dennis Kasper, Zachary Burcham, Jane J Kim, Sejal Kadakia, Manuela Raffatellu, Lars Bode, Karsten Zengler, Mingxun Wang, Dionicio Siegel, Rob Knight, Pieter C Dorrestein.

N -acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N -acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N -acyl lipid patterns from 2,700 public datasets, identifying 851 N -acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N -acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

DOI: https://doi.org/10.1101/2024.10.31.621412
Anal Bioanal Chem. 2024 Nov 18.

Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.

Mélina Ramos, Valérie Camel, Even Le Roux, Soha Farah, Mathieu Cladiere.

  Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice, there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting, respectively, for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection; in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.

Keywords:  Inter-batch correction; Liquid chromatography (LC); Mass spectrometry (MS); Non-targeted analyses; Quality control (QC); Tea leaves (dry)

DOI:  https://doi.org/10.1007/s00216-024-05646-6
Proteomics. 2024 Nov 16. e202400021

Review and Practical Guide for Getting Started With Single-Cell Proteomics.

Hsien-Jung L Lin, Kei G I Webber, Andikan J Nwosu, Ryan T Kelly.

  Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

Keywords:  cellular heterogeneity; data acquisition methods; liquid handling automation; sample preparation; single‐cell proteomics (SCP)

DOI:  https://doi.org/10.1002/pmic.202400021
J Proteome Res. 2024 Nov 22.

Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics.

Alejandro J Brenes.

  The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.

Keywords:  CV; coefficient of variation; precision; proteomics; quantification

DOI:  https://doi.org/10.1021/acs.jproteome.4c00461
Anal Chem. 2024 Nov 21.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.

Songyuan Yao, Tra D Nguyen, Yunpeng Lan, Wen Yang, Dan Chen, Yihan Shao, Zhibo Yang.

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

DOI: https://doi.org/10.1021/acs.analchem.4c02038
Talanta. 2024 Nov 15. pii: S0039-9140(24)01588-1. [Epub ahead of print]284 127209

Utilizing two-dose difference combined with stable isotope tracing for effective and comprehensive metabolite identification of pioglitazone via ultra-high-performance liquid chromatography-mass spectrometry.

Hui-I Yu, Yi-Shiou Chiou, Jie-Huei Wang, Meng-Chi Yen, Zih-Han Wu, Chia-Lung Shih.

  Drug metabolite identification is an essential characterization process spanning multiple phases of drug discovery and development. Various data processing techniques have been employed in metabolite identification using high-resolution mass spectrometry. However, metabolite identification is not consistent among approaches. Thus, a more comprehensive approach to drug metabolite identification is required. This paper proposes two-dose difference in conjunction with stable isotope tracing (SIT) to identify pioglitazone (PIO) metabolites. The results of this study revealed thatincubating both native and isotope-labeled PIOs in the same tube led to more stable metabolite identification compared with separated incubation. Our approach offers a high accuracy rate in metabolite identification, with approximately 70 % of metabolites validated as potential PIO metabolites. We compared our developed approach with other 3 approaches, namely the dose-response technique coupled with SIT, mass defect filter coupled with SIT, and orthogonal partial least squares-discriminant analysis. The results revealed that our developed approach was able to identify not only all the potential PIO metabolites identified by the other 3 approaches but also additional metabolites. These results suggest that two-dose difference coupled with SIT is an effective and comprehensive approach for drug metabolite identification.

Keywords:  Dose–response technique; Mass defect filter; Stable isotope tracing; Two-dose difference; orthogonal partial least squares–discriminant analysis

DOI:  https://doi.org/10.1016/j.talanta.2024.127209
J Am Soc Mass Spectrom. 2024 Nov 21.

Systematic Optimization of Activity-Based Protein Profiling for Identification of Polysorbate-Degradative Enzymes in Biotherapeutic Drug Substance down to 10 ppb.

Taku Tsukidate, Anita P Liu, Shannon Rivera, Alyssa Q Stiving, Jonathan Welch, Xuanwen Li.

The identification and control of high-risk host cell proteins (HCPs) in biotherapeutics development are crucial for ensuring product quality and shelf life. Specifically, HCPs with hydrolase activity can cause the degradation of excipient polysorbates (PS), leading to a decrease in the shelf life of the drug product. In this study, we systematically optimized every step of an activity-based protein profiling (ABPP) workflow to identify trace amounts of active polysorbate-degradative enzymes (PSDEs) in biotherapeutic process intermediates. Evaluation of various parameters during sample preparation pinpointed the optimal pH level and fluorophosphonate (FP)-biotin concentration. Moreover, the combined use of a short liquid chromatography gradient and the fast-scanning parallel accumulation-serial fragmentation (PASEF) methodology increased sample throughput without compromising identification coverage. Tuning the trapped ion mobility spectrometry (TIMS) parameters further enhanced sensitivity. In addition, we evaluated various data acquisition modes, including PASEF combined with data-dependent acquisition (DDA PASEF), data-independent acquisition (diaPASEF), or parallel reaction monitoring (prm-PASEF). By employing the newly optimized ABPP workflow, we successfully identified PSDEs at a concentration as low as 10 ppb in a drug substance sample. Finally, the new workflow enabled us to detect a PSDE that could not be detected with the original workflow during a PS degradation root-cause investigation.

DOI: https://doi.org/10.1021/jasms.4c00387
Anal Chem. 2024 Nov 21.

Simultaneous Quantitation of Multiple Biological Thiols Using Reactive Ionization and Derivatization with Charged Mass Tags.

Mousumi Saha, Lingqi Qiu, Yu Han-Hallett, Christopher J Welch, R Graham Cooks.

The biologically important thiols (cysteine, homocysteine, N-acetyl cysteine, and glutathione) are key species in redox homeostasis, and there is a clinical need to measure them rapidly, accurately, and simultaneously at low levels in complex biofluids. The solution to the challenge presented here is based on a new derivatizing reagent that combines a thiol-selective unit to optimize the chemical transformation and a precharged pyridinium unit chosen to maximize sensitivity in mass spectrometry. Derivatization is performed simultaneously with ionization ("reactive ionization"), and mass spectrometry is used to record and characterize the thiol reaction products. The method is applicable over the concentration range from 1 μM to 10 mM and is demonstrated for 25 blood serum, 1 plasma, and 3 types of tissue samples. The experiment is characterized by limited sample preparation (<4 min) and short analysis time (<1 min). High precision and accuracy (both better than 8%) are validated using independent HPLC-MS analysis. Cystine-cysteine redox homeostasis can be monitored by introducing an additional reduction step, and the accuracy and precision of these results are also validated by HPLC-MS.

DOI: https://doi.org/10.1021/acs.analchem.4c03807