bims-mascan 2024-06-23 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–06–23
eleven papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Spatial Proteomics of Single Hepatocytes with Multiplexed Data-Independent Acquisition (mDIA).
Single-Cell Proteomics Analysis with Tecan Uno and SCREEN Workflow.
EquiCP: Targeted Single-Cell Proteomics by Mass Spectrometry with Isobaric Labeled Multiplexing.
An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.
Optimal transport for automatic alignment of untargeted metabolomic data.
Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.
Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.
Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics.
Metabolic Analysis of Intracellular Pathogenic Bacteria Using NMR.
Thiol Redox Proteomics for Identifying Redox-Sensitive Cysteine Residues Within the Protein of Interest During Stress.
A targeted LC-MRM3 proteomic approach for the diagnosis of SARS-CoV-2 infection in nasopharyngeal swabs.

Methods Mol Biol. 2024 ;2817 97-113

Spatial Proteomics of Single Hepatocytes with Multiplexed Data-Independent Acquisition (mDIA).

Marvin Thielert, Caroline A M Weiss, Matthias Mann, Florian A Rosenberger.

  Spatially resolved mass spectrometry-based proteomics at single-cell resolution promises to provide insights into biological heterogeneity. We describe a protocol based on multiplexed data-independent acquisition (mDIA) with dimethyl labeling to enhance proteome depth, accuracy, and throughput while minimizing costs. It enables high-quality proteome analysis of single isolated hepatocytes and utilizes liver zonation for single-cell proteomics benchmarking. This adaptable, modular protocol will promote the use of single-cell proteomics in spatial biology.

Keywords:  DIA; Hepatocyte; Liver; Mass spectrometry; Multiplexing; Single-cell proteomics; Spatial proteomics

DOI:  https://doi.org/10.1007/978-1-0716-3934-4_9
Methods Mol Biol. 2024 ;2817 157-175

Single-Cell Proteomics Analysis with Tecan Uno and SCREEN Workflow.

Michael Lewandowski, Shad Morton, Matthew Blake, Erica Squires, Rushdy Ahmad, David R Walt, Bogdan Budnik.

  With advances in sample preparation, small-volume liquid dispensing technologies, high-resolution MS/MS instrumentation, and data acquisition methodologies, it has become increasingly possible to confidently investigate the heterogeneous proteome found within individual cells. In this chapter, we present an automated high-throughput sample preparation workflow based on the Tecan Uno instrument for quantitative single-cell mass spectrometry-based proteomics. Cells are analyzed by the Single-Cell Proteome Analysis platform (SCREEN), which was introduced earlier and provides deeper proteome coverage across single cells.

Keywords:  Automation; High-throughput sample preparation; SCREEN method; Tecan Uno

DOI:  https://doi.org/10.1007/978-1-0716-3934-4_13
Methods Mol Biol. 2024 ;2817 133-143

EquiCP: Targeted Single-Cell Proteomics by Mass Spectrometry with Isobaric Labeled Multiplexing.

Prajakta Naval, Laura Pérez Pañeda, Maria Athanasopoulou, Jaakko S Teppo, Roman A Zubarev, Ákos Végvári.

  Nontargeted single-cell proteomics analysis by mass spectrometry with sample multiplexing utilizing isobaric labeling is often performed using a carrier proteome. The presented protocol describes a targeted approach that replaces the carrier proteome with a set of synthetic peptides from selected proteins, which improves the identification and quantification of these proteins in single human cells.

Keywords:  Mass spectrometry; Sample preparation; Single-cell proteomics; Synthetic peptides; Tandem mass tag; Targeted proteomics

DOI:  https://doi.org/10.1007/978-1-0716-3934-4_11
Int J Mol Sci. 2024 May 28. pii: 5901. [Epub ahead of print]25(11):

An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.

Shreyans K Jain, Shivani Bansal, Sunil Bansal, Baldev Singh, William Klotzbier, Khyati Y Mehta, Amrita K Cheema.

  Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Keywords:  4-Chloro-o-phenylenediamine; CCMs; LC–MRM; pancreatic cancer; quantification

DOI:  https://doi.org/10.3390/ijms25115901
Elife. 2024 Jun 18. pii: RP91597. [Epub ahead of print]12

Optimal transport for automatic alignment of untargeted metabolomic data.

Marie Breeur, George Stepaniants, Pekka Keski-Rahkonen, Philippe Rigollet, Vivian Viallon.

  Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Keywords:  Gromov-Wasserstein; LC-MS; cancer biology; cancer metabolism; computational biology; data integration; human; optimal transport; systems biology; untargeted metabolomics

DOI:  https://doi.org/10.7554/eLife.91597
bioRxiv. 2024 Jun 04. pii: 2024.06.01.596967. [Epub ahead of print]

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.

Bo Wen, Jack Freestone, Michael Riffle, Michael J MacCoss, William S Noble, Uri Keich.

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

DOI: https://doi.org/10.1101/2024.06.01.596967
Methods Mol Biol. 2024 ;2817 19-31

Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.

Alan J Zimmerman, Michal Greguš, Alexander R Ivanov.

  Clinical and biological samples are often scarce and precious (e.g., rare cell isolates, microneedle tissue biopsies, small-volume liquid biopsies, and even single cells or organelles). Typical large-scale proteomic methods, where significantly higher protein amounts are analyzed, are not directly transferable to the analysis of limited samples due to their incompatibility with pg-, ng-, and low-μg-level protein sample amounts. Here, we report the on-microsolid-phase extraction tip (OmSET)-based sample preparation workflow for sensitive analysis of limited biological samples to address this challenge. The developed platform was successfully tested for the analysis of 100-10,000 typical mammalian cells and is scalable to allow for lower and larger protein amounts and more samples to be analyzed (i.e., higher throughput of analysis).

Keywords:  BUP (bottom-up proteomics); DIA (data-independent acquisition); Limited samples; MS (mass spectrometry); Micro-SPE (micro-solid phase extraction); OmSET (on-microsolid-phase extraction tip); On-membrane digestion; Sample preparation; nanoLC-MS (nanoflow liquid chromatography coupled to mass spectrometry)

DOI:  https://doi.org/10.1007/978-1-0716-3934-4_3
Mol Cell Proteomics. 2024 Jun 14. pii: S1535-9476(24)00090-2. [Epub ahead of print] 100800

Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics.

Klemens Fröhlich, Matthias Fahrner, Eva Brombacher, Adrianna Seredynska, Maximilian Maldacker, Clemens Kreutz, Alexander Schmidt, Oliver Schilling.

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

DOI: https://doi.org/10.1016/j.mcpro.2024.100800
Methods Mol Biol. 2024 ;2813 95-105

Metabolic Analysis of Intracellular Pathogenic Bacteria Using NMR.

Ke-Chuan Wang, Pernille Rose Jensen.

  Pathogen proliferation and virulence depend on available nutrients, and these vary when the pathogen moves from outside of the host cell (extracellular) to the inside of the host cell (intracellular). Nuclear Magnetic Resonance (NMR) is a versatile analytical method, which lends itself for metabolic studies. In this chapter, we describe how 1H NMR can be combined with a cellular infection model to study the metabolic crosstalk between a bacterial pathogen and its host both in the extracellular and intracellular compartments. Central carbon metabolism is highlighted by using glucose labeled with the stable isotope 13C.

Keywords:  Cellular infection model; Intracellular pathogens; NMR; Shigella; Tracer-based metabolism

DOI:  https://doi.org/10.1007/978-1-0716-3890-3_6
Methods Mol Biol. 2024 ;2832 99-113

Thiol Redox Proteomics for Identifying Redox-Sensitive Cysteine Residues Within the Protein of Interest During Stress.

Lara Vogelsang, Jürgen Eirich, Iris Finkemeier, Karl-Josef Dietz.

  Redox modulation is a common posttranslational modification to regulate protein activity. The targets of oxidizing agents are cysteine residues (Cys), which have to be exposed at the surface of the proteins and are characterized by an environment that favors redox modulation. This includes their protonation state and the neighboring amino acids. The Cys redox state can be assessed experimentally by redox titrations to determine the midpoint redox potential in the protein. Exposed cysteine residues and putative intramolecular disulfide bonds can be predicted by alignments with structural data using dedicated software tools and information on conserved cysteine residues. Labeling with light and heavy reagents, such as N-ethylmaleimide (NEM), followed by mass spectrometric analysis, allows for the experimental determination of redox-responsive cysteine residues. This type of thiol redox proteomics is a powerful approach to assessing the redox state of the cell, e.g., in dependence on environmental conditions and, in particular, under abiotic stress.

Keywords:  Cysteine; Disulfide; Mass spectrometry; NEM; Redox titration

DOI:  https://doi.org/10.1007/978-1-0716-3973-3_7
Mol Cell Proteomics. 2024 Jun 17. pii: S1535-9476(24)00095-1. [Epub ahead of print] 100805

A targeted LC-MRM3 proteomic approach for the diagnosis of SARS-CoV-2 infection in nasopharyngeal swabs.

Nicolas Drouin, Hyung L Elfrink, Stefan A Boers, Sam van Hugten, Els Wessels, Jutte J C de Vries, Geert H Groeneveld, Paul Miggiels, Bart Van Puyvelde, Maarten Dhaenens, Andries E Budding, Leonie Ran, Roy Masius, Zoltan Takats, Arjen Boogaerds, Markus Bulters, Wouter Muurlink, Paul Oostvogel, Amy C Harms, Mariken van der Lubben, Thomas Hankemeier.

  Since its first appearance, SARS-CoV-2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein (NCAP). A highly selective multiple reaction monitoring MRM3 strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral NCAP peptides from nasopharyngeal swabs samples presenting a cycle threshold (Ct) value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immuno-purification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with Ct-values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted mass spectrometry.

Keywords:  MRM(3); SARS-CoV-2; clinical validation; diagnostic test; high throughput proteomics; mass spectrometry

DOI:  https://doi.org/10.1016/j.mcpro.2024.100805