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

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 (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.

Automated cell cycle and cell size measurements for single-cell gene expression studies

“…The uncorrelated cell size with the total transcript levels has been already shown using flow cytometry sorting of individual cells combined with Biomark system [29]. This method can be used to measure the influence of cell cycle and cell size on single-cell gene expression analysis without any potential misleading cell state effects induced by cell cycle synchronization methods [29,30]. It could be also an alternative method to avoid artificial cell sorting according to their size or their cell cycle phase, which could be interesting for low amount of cells.…”

“…In T2EC, correlation tests between gene expression and these two morphological factors were negative. The uncorrelated cell size with the total transcript levels has been already shown using flow cytometry sorting of individual cells combined with Biomark system [29]. This method can be used to measure the influence of cell cycle and cell size on single-cell gene expression analysis without any potential misleading cell state effects induced by cell cycle synchronization methods [29,30].…”

“…In various transcriptome analyses, the question of the influence of cell proliferation on measures have to be assessed [29]. In this study, we propose a way to measure the influence of these factors on a panel of 92 genes without affecting their transcription.…”

Aldehyde dehydrogenase activity of Wharton jelly mesenchymal stromal cells: isolation and characterization

“…The global gene expression profiles of a variety of non-hematopoietic progenitors are likely to have an important impact on their properties as well as on their cellular therapeutic application (De Kock et al 2012). Both populations of WJ-MSCs demonstrated a distinct transcriptome profile (related to their ALDH activity) for genes associated with MSC major properties (stemness, cell-cycle, proliferation, phenotype, response to hypoxia, angiogenesis, multilineage capacities, immunomodulation and hematopoiesis support) (Kang et al 2016;Stanko et al 2014;Dolatabadi et al 2017;Heo et al 2016;Torensma et al 2013). These differences between WJ-MSC populations may indicate biological features with high specific therapeutic relevance.…”

“…cells seem to have more potential for proliferation, important aspect that should envisaged for therapeutic use. ALDH activity could influence the cell cycle and modulate properties of cellular population (Dolatabadi et al 2017;Meng et al 2014;Hegab et al 2014). Mechanistically, ALDH throughout the catabolism of some endogenous substrates have the capacity to either stimulate or inhibit the expression of genes involved in cell cycle and therefore actively involved in controlling cell proliferation (Muzio et al 2012).…”