Date: Monday, November 6, 2017
Session Title: Miscellaneous Rheumatic and Inflammatory Diseases Poster I
Session Type: ACR Poster Session B
Session Time: 9:00AM-11:00AM
Background/Purpose: The information content of multi-parametric flow cytometry-based immune-phenotyping experiments is routinely underexploited given the paucity of adequate tools and strategies for large-scale unbiased data analysis.
Methods: We developed and applied a data analysis approach taking into account all mathematically possible combinations of markers in a given flow cytometry panel. We analyzed mined (dx.doi.org/10.5061/dryad.v6st3) flow cytometry data generated from peripheral blood samples of healthy humans and subjects with a uniform ocular patho-phenotype (pan-uveitis) caused by 2 different autoimmune diseases: Behcet’s disease and sarcoidosis. Flow data were gated in bivariate plots. We employed combinatory mathematics to generate a matrix quantifying the representation of all possible cell populations using given sets of markers within the respective starting populations and coded the algorithm in Java. Computed metadata were tested against primary data sets of manually gated cell populations with known markers and immunological properties. External variables defined by biological characteristics of the subjects, i.e., ‘healthy vs diseased’ and ‘Behcet’s uveitis vs Sarcoid uveitis’ were used as independent measures of truth for sample classification.
Results: Our method increased the retrievable information content from each panel exponentially in relation to the number of markers used instead of linearly as in conventional approaches. Computed cell populations matched those in primary data sets. The method enabled clustering of healthy vs diseased subjects using only 4 common markers (CD3, C8, CD197, and CD45), and of sarcoid vs Behcet’s uveitis subjects with minimal error.
Conclusion: Our approach demonstrates that multi-dimensional analysis of flow cytometry data allows meaningful screening of biologically relevant markers enabling classification and characterization of states of health and autoimmune disease. The approach is unbiased and has the potential to facilitate the discovery of cell populations with relevance as potential biomarkers or biological research targets.
To cite this abstract in AMA style:Nowatzky J, Resnick E, Manasson J, Stagnar C, Manches O. High Output Flow Cytometry Array Classifies Subjects with Uveitis Due to Behcet’s Disease and Sarcoidosis [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/high-output-flow-cytometry-array-classifies-subjects-with-uveitis-due-to-behcets-disease-and-sarcoidosis/. Accessed May 8, 2021.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/high-output-flow-cytometry-array-classifies-subjects-with-uveitis-due-to-behcets-disease-and-sarcoidosis/