bims-mascan 2024-08-18 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–08–18
fourteen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Proteomic Identification and Label-Free Quantification of Proteins Implicated in Neurite and Spine Formation.
MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis.
Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics.
Metabolomics and cellular altered pathways in cancer biology: A review.
Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer.
Network Topology Evaluation and Transitive Alignments for Molecular Networking.
Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics.
Ordering molecular diversity in untargeted metabolomics via molecular community networking.
dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples.
Protocol for the simultaneous quantification of oxidative purine lesions in DNA using LC-MS/MS analysis.
Advances in the Clinical Application of High-throughput Proteomics.
A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.
Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose.
Automated Immunoprecipitation, Sample Preparation, and Individual Ion Mass Spectrometry Platform for Proteoforms.

Methods Mol Biol. 2024 ;2831 133-143

Proteomic Identification and Label-Free Quantification of Proteins Implicated in Neurite and Spine Formation.

Kanika Sharma, Prashant Kaushal, Vikas Kumar.

  The molecular mechanisms underlying neurite formation include multiple crosstalk between pathways such as membrane trafficking, intracellular signaling, and actin cytoskeletal rearrangement. To study the proteins involved in such complex pathways, we present a detailed workflow of the sample preparation for mass spectrometry-based proteomics and data analysis. We have also included steps to perform label-free quantification of proteins that will help researchers quantify changes in the expression levels of key regulators of neuronal morphogenesis on a global scale.

Keywords:  Dendritic spine; Differentially expressed proteins; Label-free quantitative analysis; Liquid chromatography; Mass spectrometry; Neurite; Proteomics

DOI:  https://doi.org/10.1007/978-1-0716-3969-6_10
Anal Chem. 2024 Aug 11.

MRMQuant: Automated MRM Data Quantitation for Large-Scale Targeted Metabolomics Analysis.

Ke-Shiuan Lynn, Hsiang-Yu Tang, Chi-Jen Lo, Cheng-Hung Yang, Yi-Ting Tseng, Mei-Ling Cheng.

Multiple reaction monitoring (MRM) is a powerful and popular technique used for metabolite quantification in targeted metabolomics. Accurate and consistent quantitation of metabolites from the MRM data is essential for subsequent analyses. Here, we developed an automated tool, MRMQuant, for targeted metabolomic quantitation using high-throughput liquid chromatography-tandem mass spectrometry MRM data to provide users with an easy-to-use tool for accurate MRM data quantitation with minimal human intervention. This tool has many user-friendly functions and features to inspect and correct the quantitation results as required. MRMQuant possesses the following features to ensure accurate quantitation: (1) dynamic signal smoothing, (2) automatic deconvolution of coeluted peaks, (3) absolute quantitation via standard curves and/or internal standards, (4) visualized inspection and correction, (5) corrections applicable to multiple samples, and (6) batch-effect correction.

DOI: https://doi.org/10.1021/acs.analchem.4c02462
J Am Soc Mass Spectrom. 2024 Aug 13.

Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics.

Saki Kiuchi, Yasuhiro Otoguro, Tomoaki Nitta, Mi Hwa Chung, Taiki Nakaya, Yuki Matsuzawa, Katsuya Ohbuchi, Kazunori Sasaki, Hiroyuki Yamamoto, Hiroshi Tsugawa.

Capillary electrophoresis coupled with tandem mass spectrometry (CE-MS/MS) offers advantages in peak capacity and sensitivity for metabolic profiling owing to the electroosmotic flow-based separation. However, the utilization of data-independent MS/MS acquisition (DIA) is restricted due to the absence of an optimal procedure for analytical chemistry and its related informatics framework. We assessed the mass spectral quality using two DIA techniques, namely, all-ion fragmentation (AIF) and variable DIA (vDIA), to isolate 60-800 Da precursor ions with respect to annotation rates. Our findings indicate that vDIA, coupled with the updated MS-DIAL chromatogram deconvolution algorithm, yields higher spectral matching scores and annotation rates compared to AIF. Additionally, we evaluated a linear migration time (MT) correction method using internal standards to accurately align chromatographic peaks in a data set. Postcorrection, the data set exhibited less than 0.1 min MT drifts, a difference mostly equivalent to that of conventional reverse-phase liquid chromatography techniques. Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. Our platform provides a peak annotation platform utilizing MT information, accurate precursor m/z, and the MS/MS spectrum recommended by the metabolomics standards initiative. Applying this procedure, we investigated metabolic alterations in lipopolysaccharide (LPS)-induced macrophages, characterizing 170 metabolites. Furthermore, we assigned metabolite information to unannotated peaks using an in silico structure elucidation tool, MS-FINDER. The results were integrated into the nodes in the molecular spectrum network based on the MS/MS similarity score. Consequently, we identified significantly altered metabolites in the LPS-administration group, where glycinamide ribonucleotide, not present in any spectral libraries, was newly characterized. Additionally, we retrieved metabolites of false-negative hits during the initial spectral annotation procedure. Overall, our study underscores the potential of CE-MS/MS with DIA and computational mass spectrometry techniques for metabolic profiling.

DOI: https://doi.org/10.1021/jasms.4c00132
J Biochem Mol Toxicol. 2024 Sep;38(9): e23807

Metabolomics and cellular altered pathways in cancer biology: A review.

Jitender Kumar Bhardwaj, Anshu Siwach, Som Nath Sachdeva.

  Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Keywords:  bioinformatics; biomarker; cancer; metabolic reprogramming; metabolomics

DOI:  https://doi.org/10.1002/jbt.23807
Nat Commun. 2024 Aug 15. 15(1): 7016

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer.

Noah M Lancaster, Pavel Sinitcyn, Patrick Forny, Trenton M Peters-Clarke, Caroline Fecher, Andrew J Smith, Evgenia Shishkova, Tabiwang N Arrey, Anna Pashkova, Margaret Lea Robinson, Nicholas Arp, Jing Fan, Juli Hansen, Andrea Galmozzi, Lia R Serrano, Julie Rojas, Audrey P Gasch, Michael S Westphall, Hamish Stewart, Christian Hock, Eugen Damoc, David J Pagliarini, Vlad Zabrouskov, Joshua J Coon.

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology.

DOI: https://doi.org/10.1038/s41467-024-51274-0
J Am Soc Mass Spectrom. 2024 Aug 12.

Network Topology Evaluation and Transitive Alignments for Molecular Networking.

Xianghu Wang, Michael Strobel, Allegra T Aron, Vanessa V Phelan, Deepa D Acharya, Christopher J Brown, Ken Clevenger, Jie Hu, Ashley Kretsch, Elizabeth H Mahood, Carla Menegatti, Quanbo Xiong, Mingxun Wang.

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.

DOI: https://doi.org/10.1021/jasms.4c00208
J Proteome Res. 2024 Aug 14.

Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics.

Nidhi Sharma, Silvia Angori, AnnSofi Sandberg, Georgios Mermelekas, Janne Lehtiö, Oscar P B Wiklander, André Görgens, Samir El Andaloussi, Hanna Eriksson, Maria Pernemalm.

  Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

Keywords:  extracellular vesicles; lung adenocarcinoma; mass spectrometry; melanoma; plasma; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00490
bioRxiv. 2024 Aug 07. pii: 2024.08.02.606356. [Epub ahead of print]

Ordering molecular diversity in untargeted metabolomics via molecular community networking.

Elizabeth A Coler, Alexey Melnik, Ali Lotfi, Dana Moradi, Ben Ahiadu, Paulo Wender Portal Gomes, Abubaker Patan, Pieter C Dorrestein, Stephen Barnes, Vladimir Boginski, Alexander Semenov, Alexander A Aksenov.

Nature's molecular diversity is not random but displays intricate organization stemming from biological necessity. Molecular networking connects metabolites with structural similarity, enabling molecular discoveries from mass spectrometry data using arbitrary similarity thresholds that can fracture natural metabolite families. We present molecular community networking (MCN), that optimizes connectivity for each metabolite, rescuing lost relationships and capturing otherwise "hidden" metabolite connections. Using MCN, we demonstrate the discovery of novel dipeptide-conjugated bile acids.

DOI: https://doi.org/10.1101/2024.08.02.606356
bioRxiv. 2024 Aug 10. pii: 2024.08.09.607341. [Epub ahead of print]

dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples.

Aswini Panigrahi, Allison L Hunt, Diego Assis, Matthew Willetts, Bhaskar V Kallakury, Bruce Davidson, Thomas P Conrads, Radoslav Goldman.

We employed laser microdissection to selectively harvest tumor cells and stroma from the microenvironment of formalin-fixed, paraffin-embedded head and neck squamous cell carcinoma (HNSCC) tissues. The captured HNSCC tissue fractions were analyzed by quantitative mass spectrometry-based proteomics using a data independent analysis approach. In paired samples, we achieved excellent proteome coverage having quantified 6,668 proteins with a median quantitative coefficient of variation under 10%. We observed significant differences in relevant functional pathways between the spatially resolved tumor and stroma regions. Our results identified extracellular matrix (ECM) as a major component enriched in the stroma, including many cancer associated fibroblast signature proteins in this compartment. We demonstrate the potential for comparative deep proteome analysis from very low starting input in a scalable format that is useful to decipher the alterations in tumor and the stromal microenvironment. Correlating such results with clinical features or disease progression will likely enable identification of novel targets for disease classification and interventions.

DOI: https://doi.org/10.1101/2024.08.09.607341
STAR Protoc. 2024 Aug 14. pii: S2666-1667(24)00356-3. [Epub ahead of print]5(3): 103191

Protocol for the simultaneous quantification of oxidative purine lesions in DNA using LC-MS/MS analysis.

Chryssostomos Chatgilialoglu, Marios G Krokidis, Michael A Terzidis.

  Most DNA damages induced through oxidative metabolism are single lesions which can accumulate in tissues. Here, we present a protocol for the simultaneous quantification of oxidative purine lesions (cPu and 8-oxo-Pu) in DNA. We describe steps for enzymatic digestion of DNA and sample pre-purification, followed by quantification through liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. We optimized this protocol in commercially available calf thymus DNA and used genomic and mitochondrial DNA extracted from cell cultures and animal and human tissues.

Keywords:  Chemistry; Mass Spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/j.xpro.2024.103191
Explor Res Hypothesis Med. 2024 Jul-Sep;9(3):9(3): 209-220

Advances in the Clinical Application of High-throughput Proteomics.

Miao Cui, Fei Deng, Mary L Disis, Chao Cheng, Lanjing Zhang.

  High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Keywords:  Biomarkers; Chromatography; Ion-mobility spectrometry; Liquid; Mass spectrometry; Neoplasms; Protein-protein interaction domain; Proteomics

DOI:  https://doi.org/10.14218/erhm.2024.00006
J Mass Spectrom. 2024 Sep;59(9): e5078

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Dylan H Ross, Erin L Bredeweg, Josie G Eder, Daniel J Orton, Meagan C Burnet, Jennifer E Kyle, Ernesto S Nakayasu, Xueyun Zheng.

  Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

Keywords:  deep learning; double bond position; lipidomics; mass spectrometry; ozone‐induced dissociation

DOI:  https://doi.org/10.1002/jms.5078
J Chromatogr A. 2024 Aug 06. pii: S0021-9673(24)00604-6. [Epub ahead of print]1732 465230

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose.

Sietske Grijseels, Terje Vasskog, Pia J Heinsvig, Torbjørn N Myhre, Terkel Hansen, Marie Mardal.

  Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

Keywords:  Fit-for-purpose; HILIC; LC-HRMS; Untargeted metabolomics; Validation; Validation reports

DOI:  https://doi.org/10.1016/j.chroma.2024.465230
Anal Chem. 2024 Aug 14.

Automated Immunoprecipitation, Sample Preparation, and Individual Ion Mass Spectrometry Platform for Proteoforms.

Benjamin J Des Soye, John P McGee, Michael A R Hollas, Eleonora Forte, Ryan T Fellers, Rafael D Melani, John T Wilkins, Philip D Compton, Jared O Kafader, Neil L Kelleher.

Charge detection mass spectrometry (CDMS) is a well-established technique that provides direct mass spectral outputs regardless of analyte heterogeneity or molecular weight. Over the past few years, it has been demonstrated that CDMS can be multiplexed on Orbitrap analyzers utilizing an integrated approach termed individual ion mass spectrometry (I2MS). To further increase adaptability, robustness, and throughput of this technique, here, we present a method that utilizes numerous integrated equipment components including a Kingfisher system, SampleStream platform, and Q Exactive mass spectrometer to provide a fully automated workflow for immunoprecipitation, sample preparation, injection, and subsequent I2MS acquisition. This automated workflow has been applied to a cohort of 58 test subjects to determine individualized patient antibody responses to SARS-CoV-2 antigens. Results from a range of serum donors include 37 subject I2MS spectra that contained a positive COVID-19 antibody response and 21 I2MS spectra that contained a negative COVID-19 antibody response. This high-throughput automated I2MS workflow can currently process over 100 samples per week and is general for making immunoprecipitation-MS workflows achieve proteoform resolution.

DOI: https://doi.org/10.1021/acs.analchem.4c01962