Masterclass Certificate in Metabolomic Sample Preparation
-- ViewingNowThe Masterclass Certificate in Metabolomic Sample Preparation is a comprehensive course that equips learners with critical skills in metabolomics, a rapidly growing field in biomedical research. This course emphasizes the importance of proper sample preparation, a crucial step in metabolomics research, and highlights its role in enabling accurate data interpretation and driving impactful findings.
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⢠Sample Collection: Proper collection and storage of metabolomic samples is crucial for accurate analysis. This unit will cover best practices for collecting various types of samples, including biofluids and tissue, and will discuss the importance of proper sample labeling, storage conditions, and transport.
⢠Sample Extraction: This unit will delve into the various methods used to extract metabolites from samples, such as liquid-liquid extraction, solid-phase extraction, and protein precipitation. Emphasis will be placed on selecting the most appropriate extraction method based on the sample type and the metabolites of interest.
⢠Derivatization: Derivatization is often used in metabolomics to enhance the detection and quantification of specific metabolites. This unit will discuss various derivatization techniques, their advantages and limitations, and will provide practical guidance on how to choose and perform derivatization reactions.
⢠Sample Cleanup and Fractionation: To minimize matrix effects and improve the separation of metabolites, sample cleanup and fractionation techniques are often employed. This unit will explore solid-phase extraction, liquid-liquid extraction, and other cleanup methods, as well as fractionation techniques such as normal-phase and reversed-phase chromatography.
⢠Quality Control and Assurance: Ensuring the quality and reproducibility of metabolomic data is essential. This unit will discuss strategies for quality control and assurance, including the use of internal standards, quality control samples, and data normalization techniques.
⢠Data Analysis and Interpretation: Once metabolomic data has been generated, it must be analyzed and interpreted to extract meaningful insights. This unit will cover various data analysis techniques, including multivariate statistical analysis, univariate analysis, and pathway analysis, and will provide guidance on how to interpret the results in the context of the research question.
⢠Ethics and Regulations: This unit will discuss the ethical and regulatory considerations surrounding metabolomic research, including data privacy, informed consent, and the responsible use of biobanked samples.
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