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NIH Grant
#1 R43 RR024094-01A1

Table of Contents
Introduction
Project Plan
Methods
 Use Cases
 Algorithms
 Metrics
 Infrastructure
Results
Discussion
References

3.2.2 GvHD Results
4.2.2 GvHD Discussion

Acute graft versus host disease (GvHD) is one of the most significant clinical problems in allogeneic blood and bone marrow transplantation. [45] The potential now exists for combining the advantages of flow cytometry with the power of modern bioinformatics and statistical techniques to determine if there are patterns of cells in the peripheral blood that correlate with a variety of physiologic or disease states, including GvHD. Because GvHD is mediated by donor T-cells and other immune effector cells, lymphocyte populations detectable by flow cytometry can predict development of GvHD.

GvHD Use Case Experiment Description using MiFlowCyt Standard
1. Experiment Overview
2. Flow Sample/Specimen Details
3. Instrument Details - BD FACSCalibur #E3597
4. Data Analysis Details

31 runs(patients) each with 8-18 timepoints representing PBMCs drawn over a timecourse and frozen for sample prep with acquisition done on the same day. = 422 samples x 10 staining patterns = 4220 fcs files. Ryan Brinkman's group evaluated 121 populations of cells from 10 panels of antibody stains.

Each timepoint has a panel of fcs files prepared with different reagents shown in table 1

For the first iteration of the analysis of this dataset, Tree Star chose to evaluate a biologically relevant subset of data, panel 2 FCS files from the first patient, and we chose CD3+/CD4+/CD8b+ as the only target population because it was shown to be predictive/correlative of acquired GvHD by Brinkman, et al. in Biol Blood Marrow Transplant 13 (6): 691-700, 2007. Our first goal was to have Tree Star application scientists perform the gating on this set of files and look at the inter-gater variability.

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Standard Operating Procedure (SOP) given to Tree Star Application Scientists in April 2009

To look for populations that correlate to GvHD development, peripheral blood samples from patients undergoing allogeneic blood and marrow transplant are analyzed and values are compared to results from the same patient over time.

Your Goal is to select the CD3+ CD4+ CD8β+ cells in 12 samples.

Please adjust the gates to be as accurate as you would normally gate these populations. If you want to use different types of gates to get to the final populations, please create your own gating tree, but also adjust the existing ones.

You will be saving your workspace with this nomenclature:   Yourfirstname_TATSIS_GvHD.wsp

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Preliminary Results of the GvHD Gate-a-thon

Challenges and potential solutions: Workspaces sent in by Ryan Brinkman (contributor workspaces) represent each patient with all fcs files for each patient's timecourse and they are are .jo workspaces.

1. Inter-gater variability is very high in the initial iteration of analysis.  Need to confirm the gating strategy from Clay Smith and Ryan Brinkman, or Maura (who did the gating).  Potentially have Clay Smith's group do the manual gating for consensus.
   
2. Contributor Workspaces need to be pared down to just panel 2 stained FCS files.
   
3. Parse out non-essential gates from gating tree and rename the target population as "CD4+, CD8b+" These step done manually by Jill for runs 1, 3, 4, 5 GvHD_E1_simple.zip through GvHD_E5_simple.zip Time required was 3.0 hours for 4 .wsps; an additional 20 hours would be required to update the remaining workspaces.
   
4. Contributor Workspaces need to have the target populations exported as popmask files for comparison using the utilities constructed for the database and evaluation tools.
5. Workspaces need to be constructed in FlowJo 7.6 to serve as "seed" workspaces in the DB/repository Research FlowDx Tools for more manual gating and automated classifications such as ANN, SVM, Magnetic, and Probability Bin Clustering.
6. Should we be looking at the biological outcome (diagnosis) to see if our computational methods can find the same answers as the manual gating?  Then we need to be able to do spline fitting and Functional Linear Discriminant Analysis (FLDA) as was done in the above referenced publication. We do not know how to do the mathmatical/statistical analysis for the Functional Linear Discriminant Analysis to get the biological relevance http://en.wikipedia.org/wiki/Linear_discriminant_analysis
7. Are Clay and Ryan going to approve of our change the goal of the dataset? Original plan of evaluating 121 target populations for possible correlation of a cellular signal of GvHD to our updated goal of finding just the CD3+/CD4+/CD8b+ as the sole target population.