Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level.
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 (G0/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.
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Soheila
S
Dolatabadi
Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of HealthBethesda, MD, USA; Department of Physics, University of MarylandCollege Park, MD, USA.
Julián
J
Candia
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Nina
N
Akrap
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Christoffer
C
Vannas
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Tajana
T
Tesan Tomic
Department of Physics, University of Maryland College Park, MD, USA.
Wolfgang
W
Losert
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Göran
G
Landberg
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Pierre
P
Åman
Department of Pathology and Genetics, Sahlgrenska Cancer Center, Institute of Biomedicine, University of Gothenburg Gothenburg, Sweden.
Anders
A
Ståhlberg
cell cycle; cell size; cell subpopulations; cell transitions; machine learning; random forests; single-cell gene expression; variable selection