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Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

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Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE).

Karthik Shekhar¹, Petter Brodin, Mark M Davis

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|December 18, 2013

Summary

This summary is machine-generated.

Mass cytometry generates complex data, limiting analysis. ACCENSE, a new tool, uses nonlinear dimensionality reduction and density-based partitioning to reveal novel CD8(+) T-cell subpopulations, enhancing single-cell data interpretation.

Keywords:

CyTOF FACS class discovery immunophenotyping machine learning

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Area of Science:

Immunology
Computational Biology
Single-cell analysis

Background:

Mass cytometry offers high-dimensional single-cell analysis but faces challenges with data interpretation due to large parameter numbers.
Manual gating and traditional clustering methods struggle with the complexity and scale of mass cytometry data, often losing single-cell resolution.
Identifying unknown cellular subpopulations is crucial for understanding immune cell heterogeneity.

Purpose of the Study:

To introduce ACCENSE, a novel computational tool for analyzing high-dimensional mass cytometry data.
To demonstrate ACCENSE's ability to combine nonlinear dimensionality reduction with density-based partitioning for improved cellular phenotype visualization.
To identify and characterize novel CD8(+) T-cell subpopulations using ACCENSE.

Main Methods:

Development and application of ACCENSE, a tool integrating nonlinear dimensionality reduction and density-based partitioning.
Analysis of 35-parameter mass cytometry data from CD8(+) T cells from specific pathogen-free and germ-free mice.
Stratification of cells into phenotypic subpopulations based on multivariate analysis.

Main Results:

ACCENSE successfully visualizes multivariate cellular phenotypes on a 2D plot, overcoming limitations of manual gating.
Significant heterogeneity was observed within known CD8(+) T-cell subpopulations.
A large, novel subpopulation with a distinct phenotypic signature was identified in both specific pathogen-free and germ-free mice, undetectable by standard biaxial plots.

Conclusions:

ACCENSE enables automated identification of cellular subpopulations from comprehensive proteomic data.
The tool facilitates the full utilization of high-throughput single-cell technologies like mass cytometry.
Discovery of a novel CD8(+) T-cell subpopulation highlights the power of advanced computational approaches in uncovering biological insights.