Enabling more sophisticated proteomic profile analysis
Mass spectrometry (MS)-based proteomics is a powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g., biomarker discovery in diseases and study of drug responses. However, MS-based proteomics tend to have consistency (poor reproducibility and inter-sample agreement) and coverage (inability to detect the entire proteome) issues that need to be urgently addressed. The former implies that multiple analytical runs of the same sample under constant experimental conditions will result in the detection of different but overlapping sets of proteins. Intuitively, this means more LC-MS/MS runs are required to identify a sufficiently large portion of any proteome and is intricately linked to the second issue of inadequate proteome coverage.
Experimental methods to overcome these issues are technically challenging, resource heavy or place an unreasonable heavy dependency on the quality of the initial data set. These include exhaustive fractionation of samples, repeated MS runs of the same sample to reach saturation and compilation of MS data specific to a sample type generated and archived from different laboratories. The problems are particularly exemplified in a large-scale collaborative study to assess the extent of reproducibility across different laboratories. The results were striking: only 7 out of 27 laboratories correctly reported all 20 proteins, and only 1 laboratory successfully reported all 22 unique peptides. Therefore, alternative approaches are needed to complement existing experimental approaches to circumvent the stochastic sampling of peptides by MS and increase the comprehensiveness of proteome coverage.
- Wilson Goh
- Limsoon Wong
- Zhao Yaxing
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- Wilson Wen Bin Goh, Yie Hou Lee, Zubaidah M. Ramdzan, Maxey Chung, Limsoon Wong, Marek Sergot. A network-based maximum link approach towards MS identifies potentially important roles for undetected ARRB1/2 and ACTB in liver cancer progression. International Journal of Bioinformatics Research and Applications, 8(3/4):155–170, August 2012.
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- Wilson Wen Bin Goh, Marek Sergot, Judy Sng, Limsoon Wong. Comparative network-based recovery analysis and proteomic profiling of neurological changes in valporic acid-treated mice. Journal of Proteome Research, 12(5):2116–2127, April 2013.
- Yie Hou Lee, Wilson Wen Bin Goh, Choon Keow Ng, Manfred Raida, Limsoon Wong, Qingsong Lin, Urs A. Boelsterli, Maxey Chung. Integrative toxicoproteomics implicates impaired mitochondrial glutathione import as off-target effect of troglitazone. Journal of Proteome Research, 12(6):2933–2945, May 2013.
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- Wilson Wen Bin Goh, Limsoon Wong. NetProt: Complex-based feature selection. Journal of Proteome Research, 16(8):3102–3112, June 2017. NetProt v0.1
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- Wilson Wen Bin Goh, Yaxing Zhao, Andrew Chi-Hau Sue, Tiannan Guo, Limsoon Wong. Proteomic investigation of intra-tumor heterogeneity using network-based contextualization—a case study on prostate cancer. Journal of Proteomics, 206:103446, August 2019