论文信息 - A new paradigm for clinical biomarker discovery and screening with Mass Spectrometry through biomedical image analysis principles

A new paradigm for clinical biomarker discovery and screening with Mass Spectrometry through biomedical image analysis principles

Biomarker discovery in amenably sampled body fluids has the potential to empower clinical screening programs for the early detection of disease. Liquid Chromatography interfaced to Mass Spectrometry (LC-MS) has emerged as a central technique for sensitive and automated analysis of proteins and metabolites from these clinical samples. However, the potential of LC-MS as a precise and reliable platform for discovery and screening is dependent on robust, sensitive and specific signal extraction and interpretation. The output of LC-MS is formed as a set of quantifiable images containing thousands of biochemical signals regulated in disease and treatment. We propose to tackle this problem for the first time with a biomedical image analysis paradigm. A novel workflow of image reconstruction, groupwise image registration and Bayesian functional mixed-effects modeling is presented. Poisson counting noise and lognormal biological variation are modeled in the raw image domain, resulting in markedly improved detection limit for differential analysis.

[1] Jeffrey S. Morris,et al. Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches. , 2012, Statistics and its interface.

[2] D S Biggs,et al. Acceleration of iterative image restoration algorithms. , 1997, Applied optics.

[3] Oleg V. Michailovich,et al. Regularized Richardson-Lucy algorithm for reconstruction of Poissonian medical images , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4] R. Aebersold,et al. Mass spectrometry-based proteomics , 2003, Nature.

[5] Frank Suits,et al. A noise model for mass spectrometry based proteomics , 2008, Bioinform..

[6] Guang-Zhong Yang,et al. Image analysis tools and emerging algorithms for expression proteomics , 2010, Proteomics.

[7] Sudhir Srivastava,et al. The early detection research network: 10-year outlook. , 2013, Clinical chemistry.

[8] Bernhard Y. Renard,et al. NITPICK: peak identification for mass spectrometry data , 2008, BMC Bioinformatics.

[9] Jim Graham,et al. Robust and Accurate Registration of 2-D Electrophoresis Gels Using Point-Matching , 2007, IEEE Transactions on Image Processing.