Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery.

Area of Science:

• Immunology
• Biotechnology
• Clinical Diagnostics

Background:

• Multi-color flow cytometry (MCFC) is vital for diagnosing immune deficiencies and inflammatory disorders.
• Conventional manual data analysis of MCFC is complex and prone to errors, leading to significant variability.
• This variability impacts diagnostic accuracy and therapeutic monitoring.

Purpose of the Study:

• To provide standardized strategies for multi-color flow cytometric staining.
• To introduce guidelines for unsupervised data analysis of whole blood patient samples.
• To reduce inter-analyst and inter-laboratory variability in MCFC.

Main Methods:

• Development of standardized protocols for multi-color flow cytometric staining.
• Implementation of unsupervised machine learning algorithms for data analysis.
• Application of methods to whole blood patient samples.

Main Results:

• Established reproducible staining procedures for MCFC.
• Demonstrated the effectiveness of unsupervised analysis in reducing data variability.
• Provided a framework for consistent interpretation of MCFC data.

Conclusions:

• Standardized MCFC staining and unsupervised analysis significantly improve data consistency.
• These guidelines enhance the reliability of MCFC for diagnosing and monitoring immune-related conditions.
• The proposed approach facilitates more accurate and reproducible patient sample analysis.