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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Study of cellular oncometabolism via multidimensional protein identification technology
Claire Aukim-Hastie1, Spiros D Garbis2
1Faculty of Health & Medical Sciences, University of Surrey, Guildford, United Kingdom; Faculty of Medicine, Cancer Sciences and CES Units, Institute for Life Sciences, University of Southampton, Southampton, United Kingdom.
Abstract:
Cellular proteomics is becoming a widespread clinical application, matching the definition of bench-to-bedside translation. Among various fields of investigation, this approach can be applied to the study of the metabolic alterations that accompany oncogenesis and tumor progression, which are globally referred to as oncometabolism. Here, we describe a multidimensional protein identification technology (MuDPIT)-based strategy that can be employed to study the cellular proteome of malignant cells and tissues. This method has previously been shown to be compatible with the reproducible, in-depth analysis of up to a thousand proteins in clinical samples. The possibility to employ this technique to study clinical specimens demonstrates its robustness. MuDPIT is advantageous as compared to other approaches because it is direct, highly sensitive, and reproducible, it provides high resolution with ultra-high mass accuracy, it allows for relative quantifications, and it is compatible with multiplexing (thus limiting costs).This method enables the direct assessment of the proteomic profile of neoplastic cells and tissues and could be employed in the near future as a high-throughput, rapid, quantitative, and cost-effective screening platform for clinical samples.
Insights
Cellular proteomics, using multidimensional protein identification technology (MuDPIT), offers a robust method for analyzing cancer cell metabolism. This technique enables direct, quantitative proteomic profiling of clinical samples for oncometabolism research.
Area of Science:
- Biochemistry
- Oncology
- Proteomics
Background:
- Cellular proteomics is a key component of bench-to-bedside translation in clinical applications.
- Oncogenesis and tumor progression involve significant metabolic alterations, collectively termed oncometabolism.
Purpose of the Study:
- To describe a multidimensional protein identification technology (MuDPIT)-based strategy for studying the cellular proteome of malignant cells and tissues.
- To highlight the utility of MuDPIT for analyzing clinical specimens in cancer research.
Main Methods:
- Utilized a multidimensional protein identification technology (MuDPIT) strategy.
- Applied the method to analyze the cellular proteome of malignant cells and tissues, including clinical samples.
- Demonstrated compatibility with reproducible, in-depth analysis of up to a thousand proteins.
Main Results:
- The MuDPIT strategy proved robust and compatible with the analysis of clinical specimens.
- The method offers direct, highly sensitive, and reproducible proteomic analysis.
- MuDPIT provides high resolution, ultra-high mass accuracy, relative quantification, and multiplexing capabilities, limiting costs.
Conclusions:
- MuDPIT enables direct assessment of the proteomic profile in neoplastic cells and tissues.
- This technique holds promise as a high-throughput, rapid, quantitative, and cost-effective screening platform for clinical samples in cancer research.
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