June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
High Output Flow Cytometry Array Protein Expression Profiling Facilitates Discriminant Phenotyping of Behcet’s and Sarcoidosis Patient-derived Peripheral Whole Blood Cells Revealing Distinct Immunophenotypes of Autoimmune Uveitides in the Context of Systemic Autoimmunity
Author Affiliations & Notes
  • Johannes Nowatzky
    Medicine-Rheumatology, NYU School of Medicine, New York, New York, United States
  • Julia Manasson
    Medicine-Rheumatology, NYU School of Medicine, New York, New York, United States
  • Ezra Resnick
    Google Inc., New York, New York, United States
  • Cristy Stagnar
    Medicine-Rheumatology, NYU School of Medicine, New York, New York, United States
  • Olivier Manches
    L'EFS en Rhône-Alpes-AuvergneRecherche et Développement "Immunobiology and Immunotherapy in Chronic Diseases", French National Institute of Health and Medical Research, Beynost, France
  • Footnotes
    Commercial Relationships   Johannes Nowatzky, None; Julia Manasson, None; Ezra Resnick, None; Cristy Stagnar, None; Olivier Manches, None
  • Footnotes
    Support  NIH K08 EY025324-02
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 558. doi:
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      Johannes Nowatzky, Julia Manasson, Ezra Resnick, Cristy Stagnar, Olivier Manches; High Output Flow Cytometry Array Protein Expression Profiling Facilitates Discriminant Phenotyping of Behcet’s and Sarcoidosis Patient-derived Peripheral Whole Blood Cells Revealing Distinct Immunophenotypes of Autoimmune Uveitides in the Context of Systemic Autoimmunity. Invest. Ophthalmol. Vis. Sci. 2017;58(8):558.

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      © ARVO (1962-2015); The Authors (2016-present)

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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.

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