Bayesian estimation for molecular profile reconstruction in proteomics based on liquid chromatography and mass spectrometry.

There is a need to precisely measure concentration of proteins in biological substance for early diagnosis of disease or knowledge of fundamental biological processes. Many apparatus have been proposed, and now data processing methods have to be investigated. This paper focuses on data processing of proteomic experiments combining nano liquid chromatography and mass spectrometry. Experimental fluctuations of this process raise an interest for robust methods. Consequently, we propose a model of this acquisition system and a probabilistic Bayesian method to estimate the proteins' concentrations and system parameters.

Chromatographic alignment combined with chemometrics profile reconstruction approaches applied to LC-MS data.

This paper presents a full proteomics analysis LC-MS (Liquid Chromatography-Mass Spectrometry) chain combining bio, nano and information technologies in order to quantify targeted proteins in blood sample. The objective is to enable an early detection of pancreatic cancer. We focus on the data processing step which estimates the proteins' concentration. First, we pre-process the data in order to correct time shift between the experiments. We propose to use block matching algorithm. Second, quantification of protein is performed using chemometrics approaches and more precisely CLS, PLS, N-PLS and PARAFAC algorithms. Performances of the various methods have been compared on cytochrome c protein LC-MS analyses.

Functional specifications of an integrated proteomics information management and analysis platform.

Detecting proteins in human blood holds the promise of a revolution in cancer diagnosis. Also, the ability to perform laboratory operations on small scales using miniaturized (lab-on-a-chip) devices has many benefits. Designing and fabricating such systems is extremely challenging, but physicists and engineers are beginning to construct such highly integrated and compact labs on chips with exciting functionality. This paper focuses on the presentation of the requirements of the information technology layer in such an integrated platform been developed in the LOCCANDIA project. LOCCANDIA is a Specific Targeted Research project (STREP) funded under the 6th Framework program of the EC. Its ultimate objective is to develop an innovative nano-technology based (lab-on-a-chip) platform for the medical-proeomics field. The paper presents the main engineering aspects, challenges and architecture for creating an Integrated Clinico-Proteomic Environment. The environment will be used to monitor and document the analysis and discovery chain and to allow the physician to interpret the digital spectrogram data delivered by the mass spectrometer, for diagnostic purposes.

A new method for x-ray scatter correction: first assessment on a cone-beam CT experimental setup.

Cone-beam computed tomography (CBCT) enables three-dimensional imaging with isotropic resolution and a shorter acquisition time compared to a helical CT scanner. Because a larger object volume is exposed for each projection, scatter levels are much higher than in collimated fan-beam systems, resulting in cupping artifacts, streaks and quantification inaccuracies. In this paper, a general method to correct for scatter in CBCT, without supplementary on-line acquisition, is presented. This method is based on scatter calibration through off-line acquisition combined with on-line analytical transformation based on physical equations, to adapt calibration to the object observed. The method was tested on a PMMA phantom and on an anthropomorphic thorax phantom. The results were validated by comparison to simulation for the PMMA phantom and by comparison to scans obtained on a commercial multi-slice CT scanner for the thorax phantom. Finally, the improvements achieved with the new method were compared to those obtained using a standard beam-stop method. The new method provided results that closely agreed with the simulation and with the conventional CT scanner, eliminating cupping artifacts and significantly improving quantification. Compared to the beam-stop method, lower x-ray doses and shorter acquisition times were needed, both divided by a factor of 9 for the same scatter estimation accuracy.