Quantitative analysis of dynamic phenotypes as determined by time-lapse data.

<p>(A) Association between the morphology, switch frequency, cell cycle length, and the type of cell divisions of second- and third-generation cells. Each point represents a single cell. Siblings with different dynamic behaviour and morphology (in green) are usually characterised by high switch frequencies. Siblings with similar dynamic behaviour and morphologies are shown in blue. The morphology is given as a ratio of time spent in round/polarised shape by a cell during the cell cycle. Switch frequency is given in number of morphological transformations per hour. Cell cycle length is in hours. (B) Dynamic phenotype change during the first 2 cell divisions as determined on the basis of time-lapse records. Three different dynamic phenotypes were identified: stable polarised, frequent switchers, and stable round. Cells tended to transmit dynamic phenotypes to daughter cells during cell division. Polarised and frequent switchers produced round cells, and frequent switchers were always produced by polarised mothers. Phenotypic change is not associated with asymmetric division; it can occur at any time in the cell cycle. Since round cells always produce round daughters, the whole process is biased and the proportion of this phenotype increases. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s012" target="_blank">S2 Data</a>.)</p

Single-cell gene expression in ‘high’, ‘medium’, and ‘low’ CD133 cells.

<p>(A) t-stochastic neighbour embedding (t-SNE) map of single-cell transcriptional data. Each point represents a single cell highlighted in a different colour for ‘high’, ‘medium’, and ‘low’ CD133 cells. ‘High’ and ‘low’ cells are in separated clusters corresponding to cluster #1 and #2 in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1B</a>. ‘Medium’ CD133 cells are distributed in and between these 2 clusters, indicating their intermediate character. (B) Scatter plot representation of PU1 and GATA1 expression in individual cells of the ‘high’, ‘medium’, and ‘low’ CD133 fraction. Note that GATA1 is not expressed in ‘high’ cells. Coexpression of the 2 genes is observed only in some ‘medium’ and ‘low’ cells. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p

Transcriptional profile of cord blood-derived CD34+ cells treated with valproic acid (VPA) at t = 0 h, t = 24 h, t = 48 h, and t = 72 h after the beginning of the experiment as compared to untreated, normal control cells.

<p>(A) A cytometric analysis of the effect of VPA on cord blood CD34+ cells shows an increase in the CD90 protein in most cells, while the CD34 and CD38 markers remain essentially unchanged. (B) Heat map representation of the expression levels of 90 genes as determined by single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR) in VPA-treated cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h. The colour codes for the time points of cells are indicated on the right; the colour codes for expression levels are indicated below the heat map. Note the high heterogeneity and lack of clear clustering of the expression patterns. (C) t-distributed stochastic neighbour embedding (t-SNE) plot representation of transcription data obtained for VPA-treated cells compared to untreated normal cells (data for these cells are the same as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1</a>). The gene expression data obtained in the 2 experiments were mapped together. Each point represents a single cell, and the cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h are highlighted separately in the 4 panels. The colour codes for VPA-treated (+VPA) and VPA-untreated (−VPA) are indicated below the panels. Clusters #1 and #2, identified at t = 48 h and t = 72 h in −VPA cells (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.g001" target="_blank">Fig 1</a>), are indicated on the t = 72 h panel. Note the clear separation of the +VPA and −VPA cells at every time point except t = 24 h. Note also that +VPA cells do not contribute to clusters #1 and #2, indicating that they do not acquire expression profiles typical of these cells. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p

Transcriptional profile of cord blood-derived CD34+ cells at t = 0 h, t = 24 h, t = 48 h, and t = 72 h after the beginning of the experiment.

<p>(A) CD34+ cells were isolated from human cord blood and cultured in serum-free medium with early acting cytokines. Single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to analyse single-cell transcription at 0 h, 24 h, 48 h and 72 h. At the same time, individual clones were continuously monitored using time-lapse microscopy. (B) t-distributed stochastic neighbour embedding (t-SNE) map of single-cell transcription data. The 4 panels show analysis of the same data set, with each point representing a single cell highlighted in different colours depending on the given time point. The 2 clusters identified by gap statistics at t = 48 h and t = 72 h are surrounded by an ellipse and numbered #1 and #2 for multipotent and common myeloid progenitor (CMP)-like cells. Note the rapid evolution of the expression profiles. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.) (C) A heat map representation of the expression levels of a subset of genes that strongly contributed to the differentiation of the different groups (as detected by principal component analysis [PCA]; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s002" target="_blank">S2 Fig</a>) and cluster analysis of expression profiles at the different time points show the rapid evolution of gene expression. The list of the genes used (shown on the right) includes well-known genes acting during hematopoietic differentiation but also many randomly selected genes. The colour code for expression levels is indicated below. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.) (D) Pairwise correlations between the genes analysed using single-cell quantitative reverse transcription polymerase chain reaction (qRT-PCR). Only the gene pairs with a Pearson correlation coefficient higher than 0.8 are indicated for each time point. The 2 clusters identified at t = 48 h and t = 72 h are represented separately. Note the transient increase of the average correlation in cluster #2 at t = 48 h, indicating a state transition. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s011" target="_blank">S1 Data</a>.)</p

Time-lapse tracking of individual clones.

<p>(A) These frames, extracted from a representative time-lapse record, show a microwell containing a single founder cell, which divides to produce a clone. Each individual cell was tracked, and their morphological characteristics were recorded. (B) Two representative lineage pedigrees obtained from the time-lapse record. The strong difference in clone size observed at the end of the record is established gradually after the third cell division. (C) Box plot representation of cell cycle lengths obtained from the time-lapse records of every clone. Note the long first cell cycle. Subsequent cell cycles have comparable lengths, with a slight tendency to become shorter. (Underlying data can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001867#pbio.2001867.s012" target="_blank">S2 Data</a>.)</p

Cell-to-cell heterogeneity measurement using Shannon entropy.

<p>(A) A Shannon entropy was calculated for each time-point for each gene. Boxplots represent the distribution of the entropy values; (B) Gene entropy variation: for each gene (i.e., lines), we represented the difference between entropy values at two consecutive time-points (Δ-entropy) using a color gradient code. Negative and null delta entropies (i.e., for a given time-point, the entropy value for these genes decreased or does not change, compared to the earlier time-point) are colored in blue and green. Positive delta entropies are colored in orange or red; (C) We assessed the significance of the differences between any pair of time-point through a Wilcoxon test. The robustness of the result was assessed by performing subsampling. The barplot shows the results as the percentage of 1,000 iterations for which a significant difference (<i>p</i>-value < 0.05) was detected. Data for this figure can be found at osf.io/k2q5b.</p

Analysis of single-cell data averaged over pseudo-populations.

<p>We separated single-cells into three pseudo-populations with around one-third of single cells for each time-point. We then calculated the average gene expression over each pseudo-population, and analyzed the resulting averaged data using multivariate statistical methods. (A) Principal component analysis (PCA); (B) Hierarchical cluster analysis (HCA).</p

Analysis of bulk-cell gene expression during the differentiation process.

<p>Gene expression data were produced by RT-qPCR in triplicate from three independent T2EC populations collected at five differentiation time-points (0 h, 8 h, 24 h, 48 h, 72 h). The expression level of 110 genes (18 invariants, 50 down-regulated and 42 up-regulated) was analyzed by two different multivariate statistical methods: (A) Principal component analysis (PCA), and (B) Dendogram resulting from hierarchical cluster analysis (HCA). The dots in (A) and leaves in (B) indicate the different cell populations and the colors indicate the differentiation time-points at which they were collected.</p

Exploration of potential cofounding factors.

<p>(A) Correlation of the CTCF gene with the rest of the 91 genes, at all six time-points. (B) FACS analysis of the cell cycle repartition at 0 and 8 h of differentiation. The difference between the two distributions was found not to be statistically significant (<i>p</i> = 0.18 using a Wilcoxon test). (C and D): calculation of the entropy content per cluster of cells re-organized using either WANDERLUST (C) or TSCAN algorithm (D). (E and F) In silico comparison of the effect of a synchronous versus an asynchronous differentiation process on the evolution of entropy. Data for this figure (C to F) can be found at osf.io/k2q5b.</p

Evolution of physiological differentiation parameters.

<p>(A) T2EC were induced to differentiate for 24 and 48 h and subsequently seeded back in self-renewal conditions. Cells were then counted every day for 5 d. The green curve represents the growth of cells induced to differentiate for 24 h and the orange curve indicates the growth of cells induced to differentiate for 48 h. The data shown are the mean +/− standard deviation calculated on the basis of three independent experiments for the time-points 72 h and 96 h and four experiments for all other time-points. The growth ratio was computed as the cell number divided by the total cells at day 0. The significance of the difference between growth ratios at 24 h and 48 h was calculated using a Wilcoxon test. (B) The boxplots of the mean size observed were based on four independent experiments, each using 50,000 cells, using FSC_A as a proxy for cell size. All of the variances were compared by pairs using the F test and the * indicates when the variances were significantly different. Data for this figure can be found at osf.io/k2q5b.</p