著書

Capillary electrophoresis–mass spectrometry (CE–MS) is an ideal method for analyzing various metabolites in biological samples. CE–MS can simultaneously identify and quantify hundreds of charged metabolites using only two acquisition methods for positively and negatively charged metabolites. Furthermore, CE–MS is commonly used for analyzing biological samples to understand the pathology of diseases at the metabolic level and biofluid samples, such as blood and urine, to explore biomarkers. Here, we introduce a protocol that delineates the handling of clinical samples to ensure that the CE–MS analysis yields reproducible quantified data. We have focused on sample collection, storage, processing, and measurement. Although the implementation of rigorous standard operating protocols is preferred for enhancing the quality of the samples, various limitations in an actual clinical setting make it difficult to adhere to strict rules. Therefore, the effect of each process on the quantified metabolites needs to be evaluated to design a protocol with acceptable tolerances. Furthermore, quality controls and assessments to handle clinical samples are introduced.

Metabolomics is defined as the comprehensive identification and quantification of small molecules that provide a holistic view of cellular metabolism. The metabolomic network is downstream of the central dogma that transfers regulatory information from the genome. The metabolomic profile, that is, concentration patterns of metabolites on metabolic pathways, can be expected to directly reflect cellular phenotype. Thus, metabolomics has been used for many biomarker discoveries for metabolic diseases, such as diabetes, myocardial infarctions, and cancers. However, there are large limitations in the measurement technologies, compared to the other omics. Currently, a single analytical methodology cannot profile whole metabolites and various methods should be used to monitor the wide range of metabolic pathways. Here, we introduce metabolomics using capillary electrophoresis-mass spectrometry which is suitable for hydrophilic metabolite quantifications.

Cancer cells require higher oxygen levels and nutrition than normal cells. Cancer cells induce angiogenesis (the development of new blood vessels) from preexisting vessels. This biological process depends on the special, chemical, and physical properties of the microenvironment surrounding tumor tissues. The complexity of these properties hinders an understanding of their mechanisms. Various mathematical models have been developed to describe quantitative relationships related to angiogenesis. We developed a three-dimensional mathematical model that incorporates angiogenesis and tumor growth. We examined angiopoietin, which regulates the spouting and branching events in angiogenesis. The simulation successfully reproduced the transient decrease in new vessels during vascular network formation. This chapter describes the protocol used to perform the simulations.

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.