Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

Abstract

Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system under investigation. To objectively uncover cellular heterogeneity from single-cell measurements, we present a novel computational approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE). We applied SPADE to cytometry data of mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. In addition, SPADE produced a map of intracellular signal activation across the landscape of human hematopoietic development. SPADE revealed a functionally distinct cell population, natural killer (NK) cells, without using any NK-specific parameters. SPADE is a versatile method that facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations.