Extracting a High-Dimensional Single Cell Data through Flow Cytometry Analysis of Eye Immune Cells with Novel Bioinformatics Tools

Authors

• B Stroud-Williams
• R Villasmil
• J Laux
• R Caspi

Abstract

Current methods of single cell analysis in cytometry use rudimentary tools like threshold and polygonal gates and may require prior knowledge or assumptions of biological information. Newly developed high dimensional analytic tools are available to understand the complex differences between immune cells. Cytobank is a platform that allows examination and interpretation of the data. Here we test two of these tools, Spanning-tree Progression Analysis of Density-normalized Events (SPADE) and Visualization of Stochastic Neighbor Embedding (viSNE). We use SPADE and viSNE to analyze lacrimal gland cells. These tools coordinate more than two measurements concurrently, reduce errors from gating, and do not require prior knowledge or assumptions. Both bioinformatics tools are effective cytometric analysis for eye immune cells. These tools allow identification and analysis of rare cell types, gate single cell events in different samples, and use gating from viSNE to run SPADE analysis.

Scientific Focus Area: Research Support Services

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