Journal of Systems Biology & Proteome Research

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Perspective - Journal of Systems Biology & Proteome Research (2023) Volume 4, Issue 2

Unraveling the proteome landscape of cancer cells using mass spectrometry

Hasan Abosbah*

Department of Chemistry, Faculty of Science, Thaksin University, Phatthalung, Thailand

Corresponding Author:
Hasan Abosbah
Department of Chemistry
Faculty of Science, Thaksin University
Phatthalung, Thailand
Email: abos012@gmail.com

Received: 24-Feb-2023, Manuscript No. AASBPR-23-91180; Editor assigned: 25-Feb-2023, PreQC No. AASBPR-23-91180(PQ); Reviewed: 09-Mar-2023, QC No. AASBPR-23-91180; Revised: 13-Mar-2023, Manuscript No. AASBPR-23-91180(R); Published: 20-Mar-2023, DOI: 10.35841/aasbpr-4.2.140

Citation: Abosbah H. Unraveling the proteome landscape of cancer cells using mass spectrometry. J Syst Bio Proteome Res. 2023;4(2):140

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Keywords

Proteome, Cancer cells, Mass spectrometry, Biomarkers, Protein profiling, Protein identification, Tumor microenvironment, Quantitative proteomics.

Introduction

Mass spectrometry (MS) is a powerful analytical technique used to identify and quantify the constituents of a sample. In the field of cancer research, MS is used to unravel the complex proteome landscape of cancer cells. The proteome refers to the complete set of proteins expressed by a cell, tissue, or organism. Understanding the proteome of cancer cells is important for developing new therapies and improving existing treatments. In this essay, I will explain how MS is used to analyze the proteome of cancer cells [1].

The first step in analysing the proteome of cancer cells using MS is to extract the proteins from the cells. This can be done using a variety of methods, such as homogenization, sonication, or detergent lysis. The extracted proteins are then digested into smaller peptides using an enzyme such as trypsin. These peptides are then separated using liquid chromatography (LC) and introduced into the MS instrument.

The most common type of MS used to analyze the proteome of cancer cells is called tandem MS (MS/MS). In MS/MS, peptides are ionized and fragmented into smaller ions in the first stage of MS (MS1). These fragment ions are then analyzed in the second stage of MS (MS2), allowing for the identification and quantification of the peptides. There are several different methods of fragmentation used in MS/MS, such as collision-induced dissociation (CID), electron transfer dissociation (ETD), and higher-energy collisional dissociation (HCD). Each fragmentation method has its own advantages and disadvantages, and the choice of method depends on the goals of the experiment [2].

After fragmentation, the resulting peptides are identified using a database search algorithm. The most commonly used database for peptide identification is the UniProt database, which contains information on millions of proteins from a wide range of organisms. The database search algorithm compares the experimental spectra of the peptides to the theoretical spectra of peptides in the database to identify the most likely peptide sequence.

Once the peptides have been identified, the next step is to quantify their abundance. This can be done using labelfree quantification or stable isotope labeling. In label-free quantification, the abundance of each peptide is determined based on the intensity of its MS signal. In stable isotope labeling, two samples (e.g. cancer vs. normal tissue) are labeled with different isotopes (e.g. heavy and light), mixed together, and analyzed by MS. The ratio of heavy to light peptides is used to quantify the abundance of each peptide [3].

After peptide identification and quantification, the final step is to interpret the data to gain insights into the proteome of cancer cells. This can be done using bioinformatics tools that allow for the analysis of large datasets. For example, Gene Ontology (GO) analysis can be used to identify biological pathways that are overrepresented in the dataset. Protein-protein interaction networks can also be constructed to identify proteins that are likely to be functionally related [4].

Using MS to analyze the proteome of cancer cells has many applications. One application is biomarker discovery, which involves identifying proteins that are differentially expressed in cancer cells compared to normal cells. Biomarkers can be used for early cancer detection, diagnosis, and monitoring treatment response. MS has also been used to identify novel drug targets by identifying proteins that are essential for cancer cell survival. MS has also been used to analyze the proteome of cancer cells at the single-cell level. Single-cell proteomics is a rapidly growing field that allows for the analysis of the proteome of individual cells, which can reveal heterogeneity within a tumor. This can provide insights into the mechanisms of tumor progression and resistance to therapy [5].

Conclusion

In conclusion, mass spectrometry has emerged as a powerful tool for unraveling the proteome landscape of cancer cells. This technique has allowed researchers to identify and quantify proteins and post-translational modifications that are important in cancer development and progression. By comparing the proteome of cancer cells to normal cells, researchers can identify proteins that are specifically dysregulated in cancer, providing insights into potential therapeutic targets. Additionally, mass spectrometry has the potential to be used for cancer diagnosis and prognosis, as well as for monitoring treatment response. As mass spectrometry technology continues to improve, it is likely that we will gain even deeper insights into the complex proteome landscape of cancer cells, ultimately leading to improved cancer treatment and patient outcomes.

References

  1. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nat. 2003;422(6928):198-207.

    2. Indexed at, Google Scholar, Cross Ref

  2. Altelaar AM, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet. 2013;14(1):35-48.

    3. Indexed at, Google Scholar, Cross Ref

  3. Mertins P, Tang LC, Krug K, et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry. Nat Protoc. 2018;13(7):1632-61.

    4. Indexed at, Google Scholar, Cross Ref

  4. Han X, Aslanian A, Yates III JR. Mass spectrometry for proteomics. Curr Opin Chem Biol. 2008;12(5):483-90.

    5. Indexed at, Google Scholar, Cross Ref

  5. Meehan KL, Holland JW, Dawkins HJ. Proteomic analysis of normal and malignant prostate tissue to identify novel proteins lost in cancer. Prostate. 2002;50(1):54-63.

    6. Indexed at, Google Scholar, Cross Ref

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