CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.Author Summary: Sorting out homogenous subpopulations in a heterogeneous population of single cells enables downstream characterization of specific cell types, such as cell-type specific genomic profiling. This study proposes a data-driven gating strategy, CCAST, for sorting out homogeneous subpopulations from a heterogeneous population of single cells without relying on expert knowledge thereby removing human bias and variability. In a fully automated manner, CCAST identifies the relevant gating markers, gating hierarchy and partitions that isolate homogeneous cell subpopulations. CCAST is optimized for cell sorting but can be applied to the identification and analysis of homogeneous subpopulations. CCAST is shown to identify more homogeneous breast cancer subpopulations in SUM159 compared to prior sorting strategies. When applied to normal bone marrow single cell data, CCAST proposes an efficient strategy for gating out T-cells without relying on expert knowledge; on B-cells, it reveals a new characterization of mature B-cell subtypes not revealed by manual gating.