Arbaz Raza Khan,
- Student, Department of Biotechnology, Amity University Gurgaon, Haryana, India
Abstract
Comparative proteomics is a powerful tool for understanding the molecular differences between various biological samples. It entails identifying and measuring proteins in complex biological samples to assess their abundance, modifications, and interactions under various conditions. This approach plays a crucial role in advancing biomedical research, especially in disease understanding, biomarker discovery, and therapeutic development. While cell lines are widely used for proteomic studies due to their controlled environments and reproducibility, the transition to clinical samples offers a more accurate reflection of the in vivo state. However, the comparison between these two sources – cell lines and clinical samples – presents both opportunities and challenges due to differences in protein expression, post-translational modifications, and sample complexity. Proteomic technologies, including mass spectrometry (MS) and protein microarrays, are central to comparative proteomics. These technologies allow the identification of differentially expressed proteins, their interactions, and modifications, contributing to the discovery of potential biomarkers for diseases like cancer, neurodegenerative disorders, and cardiovascular diseases. However, clinical samples introduce substantial variability due to the heterogeneity of patient conditions, sample collection methods, and individual genetic differences, making the analysis of clinical proteomes more complex than that of cell lines. In this manuscript, we will explore the principles of comparative proteomics, focusing on the transition from cell lines to clinical samples. We will examine the strengths and limitations of each approach, the technological advancements facilitating proteomic studies, and how these can contribute to a deeper understanding of human health and disease. Furthermore, we will address the challenges that researchers face when translating findings from model systems to clinical settings, including issues of reproducibility, variability, and the need for more standardized protocols. Finally, we will discuss the future directions of comparative proteomics, including the incorporation of systems biology and multi-omics approaches to create more comprehensive and accurate models for disease understanding. By integrating cell line data with clinical samples, proteomics can provide insights into the molecular mechanisms of disease and contribute to the development of personalized medicine. This manuscript aims to highlight the potential of comparative proteomics in bridging the gap between preclinical and clinical research, ultimately advancing our ability to diagnose, monitor, and treat a variety of diseases
Keywords: Proteomics, mass spectrometry, cell lines, clinical samples, disease biomarkers
[This article belongs to Research & Reviews : Journal of Computational Biology ]
Arbaz Raza Khan. Comparative Proteomics: From Cell Lines to Clinical Samples. Research & Reviews : Journal of Computational Biology. 2024; 13(03):30-34.
Arbaz Raza Khan. Comparative Proteomics: From Cell Lines to Clinical Samples. Research & Reviews : Journal of Computational Biology. 2024; 13(03):30-34. Available from: https://journals.stmjournals.com/rrjocb/article=2024/view=190353
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Research and Reviews : Journal of Computational Biology
| Volume | 13 |
| Issue | 03 |
| Received | 10/12/2024 |
| Accepted | 17/12/2024 |
| Published | 19/12/2024 |
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