title: Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE).
abstract: Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual "gating." Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8(+) T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8(+) T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.
authors.affiliation: Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
authors.forename: Karthik
authors.initials: K
authors.lastname: Shekhar
authors.forename: Petter
authors.initials: P
authors.lastname: Brodin
authors.forename: Mark M
authors.initials: MM
authors.lastname: Davis
authors.forename: Arup K
authors.initials: AK
authors.lastname: Chakraborty
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001331:Automation; D018414:CD8-Positive T-Lymphocytes / Q000166:cytology; D003198:Computer Simulation; D005434:Flow Cytometry / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D005819:Genetic Markers; D007156:Immunologic Memory; D016130:Immunophenotyping; D051379:Mice; D010641:Phenotype; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D005819:Genetic Markers
keywords: CyTOF; FACS; class discovery; immunophenotyping; machine learning