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Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Journal article
Authors Soheila Dolatabadi
J. Candia
Nina Akrap
Christoffer Vannas
Tajana Tesan Tomic
W. Losert
Göran Landberg
Pierre Åman
Anders Ståhlberg
Published in Frontiers in Genetics
Volume 8
ISSN 1664-8021
Publication year 2017
Published at Sahlgrenska Cancer Center
Institute of Biomedicine, Department of Pathology
Language en
Links dx.doi.org/10.3389/fgene.2017.00001
Keywords cell cycle, cell size, single-cell gene expression, machine learning, variable selection, random forests, cell subpopulations, cell transitions, mammalian-cells, transcription, breast, roles, dna, Genetics & Heredity
Subject categories Medical Biotechnology

Abstract

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (GO/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

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