Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model.

Area of Science:

• Hematology
• Immunology
• Biotechnology

Background:

• Flow cytometry is crucial for enumerating hematopoietic stem cells (SC) in various biological samples.
• The ISHAGE single-platform gating method is the established standard for CD34+ SC enumeration.
• Challenges in uniform ISHAGE application necessitate advanced analytical approaches.

Purpose of the Study:

• To introduce and evaluate an automated, multivariate classification method, Probability State Modeling (PSM), for SC analysis.
• To compare the performance of automated PSM analysis against manual ISHAGE gating.

Main Methods:

• 258 samples were analyzed using BD FACSCanto II flow cytometers.
• Stain-lyse-no-wash technique with CD34, CD45, 7-AAD, and light scatter for population definition.
• Automated PSM was developed to classify events (beads, debris, cells) and quantify intact-live SC.

Main Results:

• Excellent agreement between ISHAGE and PSM for SC concentration (r² = 0.999).
• Bland-Altman analysis showed minimal bias (2.018 SC/μL) with a wide 95% confidence interval.
• PSM demonstrated perfect operator-to-operator reproducibility (r² = 1.000).

Conclusions:

• Automated PSM analysis of SC data strongly correlates with ISHAGE gating results.
• PSM offers superior reproducibility, objectivity, and speed compared to manual gating.
• The PSM approach achieves accuracy at least equivalent to the established ISHAGE method.