Abstract
Purpose :
To develop, test and apply a multidimensional flow cytometry-based protein expression analysis approach allowing classification and grouping of states of health and disease in human ocular autoimmune disorders.
Methods :
We utilized, developed and applied a data analysis strategy taking into account all mathematically possible combinations of protein markers in a given flow cytometry panel for the analysis of selected mined flow cytometry data generated using peripheral blood samples derived from human healthy, sarcoidosis or Behcet’s uveitis patients. Original FACS data files were mined from Dryad Digital Repository http://dx.doi.org/10.5061/dryad.v6ste with reference to http://dx.doi.org/10.1371/journal.pcbi.1003215, gated utilizing FlowJo software
according to population partitioning in bivariate plots. We used combinatory mathematics to generate a matrix quantifying the representation of all possible cell populations using a given set of staining antibodies (markers) within the respective starting population and coded the algorithm in Java to create a platform enabling the computation of input values derived from the measurements of a theoretically unlimited number of markers. The resulting data sets were visualized in a heat map approach using R to classify patient samples according to states of health and disease.
Results :
Our approach clustered healthy vs diseased subjects with minimal error using only 4 common markers (CD3, CD8, CD197 and CD45), and allowed differential clustering of uveitis patients with sarcoidosis and Behcet’s disease.
Conclusions :
Multi-dimensional analysis of flow cytometry data allows meaningful large-scale screening of biologically relevant markers at the protein
level enabling classification and characterization of states of health and autoimmune disease, using measurement of only a few common markers. The approach is unbiased as all mathematically possible marker combinations enter analysis, thus enabling the discovery of cell populations with relevance as potential biomarkers or biological research targets.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.