Automated Analysis of Flow Cytometric Data for CD34+ Stem Cell Enumeration Using a Probability State Model

Background: Flow Cytometry is widely used for enumeration of hematopoietic stem cell (SC) levels in bone marrow, cord blood, peripheral blood, and apheresis products. The ISHAGE single-platform gating method is considered by many to be the standard for CD34+ SC enumeration. However, attempts at uniform application of this ISHAGE method have met with only partial success. We propose an automated, multivariate classification approach for SC analysis based on Probability State Modeling™ (PSM). In this study, we compare the results from automated PSM analysis with manual ISHAGE gating analysis as performed by a trained analyst.

Methods: A total of 258 samples were assayed on BD FACSCanto II flow cytometers using a stain-lyse-no-wash technique. Populations were defined using CD34, CD45, 7-AAD, and light scatter. BD TruCount™ bead tubes were used for absolute SC concentrations. A PSM was designed to classify events into beads, debris, intact-dead cells, and intact-live SC; run unattended and record results.

Results: The ISHAGE and PSM methods show excellent agreement in estimating the concentration of #SC/μL: slope = 1.009, r² = 0.999. Bland-Altman Analysis for the SC concentration has an average difference (bias) of 2.018 SC/μL. The 95% confidence interval is from -59.350 to 63.380 SC/μL. The operator-to-operator agreement using PSM is perfect: r² = 1.000.

Conclusions: Automated PSM analysis of SC listmode data produces results that agree strongly with ISHAGE gate-based results. The PSM approach provides higher reproducibility, objectivity, and speed with accuracy at least equivalent to the ISHAGE method.