Blood Sample Preparation (for metabolomics)

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By carefully considering these blood-specific aspects at each stage of the metabolomics workflow, researchers can obtain high-quality data and generate meaningful insights into human health and disease

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• Goals:​ For blood samples, metabolomics can be used for a wide range of applications, including:

  • Disease diagnostics: Identifying biomarkers for early disease detection and monitoring disease progression.

  • Personalized medicine: Tailoring treatments based on individual metabolic profiles.

  • Drug discovery and development: Understanding drug mechanisms of action and identifying potential drug targets.

  • Nutritional studies: Investigating the impact of diet on metabolic health.

  • Population health studies: Exploring metabolic differences across populations and identifying risk factors for disease.

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• Sample Prep Steps (Blood): Blood sample preparation is crucial and requires careful attention to detail due to the complexity of blood.

  • Collection:

    • Standardized Protocol: Use a standardized blood collection protocol. Consistency is key.

    • Tube Type: Different tube types (e.g., serum, plasma, whole blood) may be required depending on the specific metabolites of interest. Plasma is often preferred as it contains anticoagulants that prevent clotting, preserving the metabolic profile. Serum, collected after clotting, may have different metabolite levels due to ongoing metabolic processes.

    • Quenching: Immediately quench metabolism to prevent ex vivo changes in metabolite levels. This is typically done by adding the blood sample to a pre-chilled quenching solution (e.g., methanol, acetonitrile, or a mixture) or by snap-freezing in liquid nitrogen. The choice of quenching method depends on the metabolites of interest and downstream processing.

  • Sample Processing:

    • Deproteination: Blood contains high levels of proteins that can interfere with LC-MS analysis. These proteins must be removed. Common methods include:

      • Protein Precipitation: Adding an organic solvent (e.g., methanol, acetonitrile) to precipitate proteins, followed by centrifugation.

      • Ultrafiltration: Using a filter to separate proteins from smaller metabolites.

    • Extraction: After deproteination, further extraction may be necessary to isolate specific classes of metabolites. This often involves liquid-liquid extraction using different organic solvents.

  • Drying and Reconstitution: The extracted metabolites are usually dried under nitrogen or by lyophilization and then reconstituted in a suitable solvent compatible with LC-MS analysis. Volatile metabolites may require different handling.

  • Internal Standards: Add internal standards (known compounds at known concentrations) to the samples before analysis. These are used for normalization and quality control.

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• LC-MS (Blood): The LC-MS setup for blood metabolomics is similar to general metabolomics, but some considerations are specific to blood:

  • Chromatographic Separation: Reversed-phase LC is commonly used for blood samples due to the diverse range of metabolites present. HILIC chromatography may be used for more polar metabolites.

  • Mass Spectrometry: Triple quadrupole MS is often used for targeted metabolomics of specific metabolites in blood. High-resolution MS (e.g., Orbitrap) is useful for untargeted metabolomics and metabolite identification.

  • Data Acquisition: Both untargeted and targeted approaches can be used, depending on the study goals. Targeted methods are often preferred for clinical applications where specific biomarkers are being measured.

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• Informatics (Blood): Informatics for blood metabolomics follows the general workflow but with some specific considerations:

  • Database Matching: Blood metabolomics studies often rely on databases specific to human metabolites (e.g., HMDB, Metlin) for metabolite identification.

  • Pathway Analysis: Pathway analysis tools can be used to map altered metabolites onto metabolic pathways relevant to human physiology and disease. Focus on pathways relevant to blood-related processes.

  • Clinical Data Integration: For clinical studies, metabolomics data should be integrated with other clinical data (e.g., patient demographics, medical history, laboratory test results) for a more comprehensive analysis.

  • Machine Learning: Machine learning methods can be used to identify patterns in blood metabolomics data and develop diagnostic or prognostic models for disease. This is particularly relevant for handling the complexity and large datasets often generated in blood metabolomics studies.

  • Longitudinal Analysis: In studies involving repeated blood samples over time, longitudinal analysis methods are used to track changes in metabolite levels and understand how they relate to disease progression or treatment response.