Using a spike-in experiment to evaluate analysis of LC-MS data

BACKGROUND: Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. RESULTS: The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose. CONCLUSIONS: The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins

Metabolic transistor strategy for controlling electron transfer chain activity in Escherichia coli

A novel strategy to finely control a large metabolic flux by using a “metabolic transistor” approach was established. In this approach a small change in the level or availability of an essential component for the process is controlled by adding a competitive reaction that affects a precursor or an intermediate in its biosynthetic pathway. The change of the basal level of the essential component, considered as a base current in a transistor, has a large effect on the flux through the major pathway. In this way, the fine-tuning of a large flux can be accomplished. The “metabolic transistor” strategy was applied to control electron transfer chain function by manipulation of the quinone synthesis pathway in Escherichia coli. The achievement of a theoretical yield of lactate production under aerobic conditions via this strategy upon manipulation of the biosynthetic pathway of the key participant, ubiquinone-8 (Q8), in an E. coli strain provides an in vivo, genetically tunable means to control the activity of the electron transfer chain and manipulate the production of reduced products while limiting consumption of oxygen to a defined amount

Using a spike-in experiment to evaluate analysis of LC-MS data

Abstract Background Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. Results The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose. Conclusions The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.</p