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Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium.

Greg Finak1, Marc Langweiler2, Maria Jaimes3

  • 1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, 98109, WA.

Scientific Reports
|February 11, 2016
PubMed

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Summary
This summary is machine-generated.

Standardizing immunophenotyping with pre-configured reagent panels and automated gating significantly reduces cross-laboratory variability. This approach enhances data comparability across studies and centers for immune cell analysis.

Area of Science:

  • Immunology
  • Biotechnology
  • Data Science

Background:

  • Standardization of immunophenotyping is crucial for reliable cross-study and cross-center data comparison.
  • Variability in reagents, sample handling, instrument setup, and data analysis impacts data consistency.

Purpose of the Study:

  • To evaluate standardized, pre-configured immunophenotyping panels and automated gating for reducing cross-center variability.
  • To compare the performance of centralized manual gating versus site-specific manual and automated gating.

Main Methods:

  • Development and use of five standardized, eight-color immunophenotyping panels in lyophilized, 96-well plates.
  • Coordinated analysis across nine laboratories using standardized operating procedures (SOPs).
  • Comparison of manual gating (site-specific and central) with automated gating algorithms.

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Main Results:

  • Within-site variability was consistently low across all experiments.
  • Cross-site variability was reduced with centralized manual gating compared to site-specific analysis.
  • Automated gating demonstrated performance comparable to central manual analysis, with minimal bias and variability.

Conclusions:

  • Standardized staining, data collection, and automated gating effectively reduce variability in immunophenotyping.
  • Automated methods can streamline analysis and improve the power of immunophenotyping studies.
  • Implementation of these standardized approaches enhances data reliability for immune cell subset identification.