How to cite this article: Herbert DJ, Miller DT, Bagwell CB. Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model. Cytometry Part B 2012; 82B: 313–318.
Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model†
Article first published online: 11 JUL 2012
Copyright © 2012 International Clinical Cytometry Society
Cytometry Part B: Clinical Cytometry
Volume 82B, Issue 5, pages 313–318, September 2012
How to Cite
Herbert, D. J., Miller, D. T. and Bruce Bagwell, C. (2012), Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model. Cytometry, 82B: 313–318. doi: 10.1002/cyto.b.21032
- Issue published online: 6 SEP 2012
- Article first published online: 11 JUL 2012
- Accepted manuscript online: 21 JUN 2012 03:00PM EST
- Manuscript Accepted: 25 MAY 2012
- Manuscript Revised: 10 MAY 2012
- Manuscript Received: 12 MAR 2012
- Verity Software House (VSH)
Vol. 84, Issue 6, 427, Article first published online: 11 FEB 2013
Additional Supporting Information may be found in the online version of this article, and contains: 1) Supplemental Figure 1: Stem Cell Probability State Model Design (CYTO_21032_sm_SuppFig1.tif); 2) GemStone Model template that can be used in the GemStone versions 1.64 and above to analyze sample data files (StemCellTemplate_v16_Supp.zip); 3) Sample file 1: FCS data file from study that can be analyzed by the GemStone model template to show how the analysis works (SAMPLE-1.zip); 4) Sample file 2: FCS data file from study (SAMPLE-2.zip); 5) Narrated video describing model components and how analysis works (Supplemental Video -Automated Analysis of Stem Cell Enumeration.mp4).
|CYTO_21032_sm_SuppFig1.tif||3354K||Supplemental Figure 1: Stem Cell Probability State Model Design. The stem cell model uses probabilities to classify events into one of five categories or “Cell Types”: Beads (B), Debris (X), Intact Dead (ID), Stem Cells (SC), and Unclassified. A “Cell Type” category is defined by a set of expression profiles (EPs). In the figure, EPs are denoted as rectangles, where x-axis units are cumulative percentage of events and y-axis units are relative intensity for a specific measurement. When the model is applied to simple mixture problems as in this case, the intensity does not vary with cumulative percentage and is referred to as a “constant” EP. EPs also define the line-spread of the measurement as 95% confidence limits. During the modeling process, the EPs are moved vertically to nearest detected peaks (see black triangles) which compensates for intensity changes from case to case. The Beads category is defined by finding the population with highest intensity for TruCount measurement and then modeling it with a constant EP. The Debris category excludes Beads with an inverted TruCount EP and captures Debris in a similar manner as the Beads classification but with low intensity constant EP for FSC. Inverted EPs, similar in concept to NOT gates, avoid classifying events into inappropriate categories. The Intact Dead category excludes Beads and Debris with inverted TruCount and FSC EPs. Because it is easier to detect a live population than a dead one, the Intact Dead category includes an inverted 7AAD EP as well. The Stem Cells category excludes Beads and Debris; and includes low 7AAD , low SSC, high CD34, and moderate CD45 EPs. Events not selected into any of these four defined categories are considered Unclassified.|
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