Abstract
Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.
Introduction
Hematopoietic stem and progenitor cells (HSPC) give rise to all the cellular components of blood. The major stages of differentiation and the key genes participating in this process are now well characterized [1]. According to the classical view, haematopoiesis is a hierarchically organised process of successive fate commitments, where differentiation potential is progressively restricted in an orderly way over cell divisions. There are several variants of the model [2-6]. In all cases the first fate decision is a binary choice taken by multipotent progenitors (MPP), which leads to two different committed progenitors (for the purpose of simplicity, these progenitors are designed here as common myeloid (CMP) and common lymphoid progenitors (CLP)). In molecular terms, the choice is believed to be the result of the strictly regulated activation of master regulator genes and their underlying transcriptional network [7]. However, the strict hierarchical logic of classical models has recently been challenged by a number of in vivo and in vitro studies [8,9]. Single cell gene expression studies have revealed a much higher heterogeneity of cell subtypes that can be detected using a combination of surface markers [10]. It is not surprising that the number of identifiable cell types increases with the resolution of the detection method. Although correct cell type classification is a key step in understanding the cell fate decision issue, it cannot reveal the dynamic features of the fate commitment process and leaves a number of unanswered questions. Do different phenotypic forms represent different cell types or different stages of the same process? How does the transition between the forms occur? How long does it take?
Until recently, fully deterministic explanations were predominant, but recent studies have suggested other alternatives. Two different possibilities have been put forward. According to the first, the commitment process starts with the sporadic, independent activation of genes within the same cell. The simultaneous stochastic expression of regulatory genes specifying different lineages creates a multiprimed intermediate state that enables these cells to choose one of the lineages [11-15]. A coherent lineage-specific expression profile would then emerge from this metastable state. According to the second, commitment is preceded by transcriptome fluctuations between different lineage-biased states [16-18]. Surprisingly the time scale of transformations related to the cellular fate decision process remains largely unexplored. The transcriptome of the same cell can be analysed only once, because the cell is destroyed by RNA extraction. Therefore, indirect approaches are required to identify trends and patterns in time series.
We addressed the issue of the dynamics and the time-scale of the commitment process by integrating single-cell quantitative RT-PCR, cell division history and morphological changes determined by time-lapse analysis. Contrary to the common strategy consisting in isolating defined cell subpopulations with the help of specific surface markers and characterizing their gene expression profiles at the single-cell level [19], we used an alternative approach. Individual cells were randomly isolated from the heterogeneous cord blood CD34+ cell fraction at different time points after cytokine stimulation and their gene expression profiles were determined using single-cell quantitative RT-PCR. The data provided a series of snapshots, showing the actual statistical distribution of single-cell gene expression patterns across the whole population. The structure of the population at the successive time points was revealed by unsupervised classification of the expression profiles according to their similarity using multiparametric approaches. The progression of the fate commitment process was deduced from the evolution of the population structure. At the same time, using time-lapse microscopy we tracked randomly isolated individual CD34+ cells and their progeny for several days after cytokine stimulation. We recorded the division history and the morphological changes of each cell in the clones. The population structure was deduced on the basis of the statistical analysis of these observations. The efficiency of the time-lapse approach in investigating cell fate decisions has been recently shown [20]. To reinforce this approach, the time-lapse and gene expression data were integrated into a coherent scenario. This was done by using CD133 protein expression levels to isolate cells with one or the other transcription profiles and record directly their dynamic phenotype, thereby providing a direct link between dynamic phenotype and transcription profile.
Altogether, our results revealed that fate decision is a dynamic, fluctuating process that is more complex than a simple binary switch between two options as it is usually envisioned.
Results
Single cell gene expression
The transcriptional profile of individual cord-blood CD34+ cells was determined at 0, 24, 48 and 72 hours after the beginning of cytokine stimulation (Fig.1A). Single-cell quantitative RT-PCR was used to quantify the mRNA levels of 90 different genes. A set of 32 genes was selected for their known function in the early differentiation of hematopoietic cells and were expected to inform on the functional state of the cells (see Supplemental Table 1.). A second set of 54 genes was chosen randomly from a list of genes known to be expressed in the hematopoietic lineage [21,22]. These genes provided an assessment of the overall transcriptional activity of the genome. Five additional genes were added to the list for their role in maintaining the pluripotent state in embryonic stem cells. A heat-map of all data and a violin plot of the expression profile of each gene at the four separate time points are shown in the supplemental information section (Fig S1). The normalized single-cell quantitative gene expression data obtained for the different time points were merged in a single database and screened for subpopulations by k-means clustering. The number of statistically distinguishable groups was inferred using gap statistics [23]. The groups were visualized on heat maps and on a two-dimensional plot using t-stochastic neighbour embedding (tSNE) [24]. Although every cell had a unique gene expression pattern, this approach enabled us to clearly identify sub-groups of cells in the population on the basis of the statistical similarity of their gene expression patterns (Fig 1B).
Non-stimulated CD34+ cells isolated from cord blood represented the t=0h time point. A heat-map of the single-cell transcriptional profiles of genes contributing significantly to the identification of subgroups (Fig S2) showed that this population of cells was heterogeneous. Several genes reported to play a role in self-renewal, quiescence and other stem cell functions (CD71, CD133, CXCR4, GATA2 and FLT3) were expressed sporadically and at variable levels in a fraction of cells. Genuine pluripotent stem cell genes were also expressed at low level in a fraction of cells (Nanog, OCT4, KLF4). Nevertheless, no correlation was found between these genes (Fig.1D) and the statistical analysis did not reveal distinguishable expression patterns that could define cell types. The only detectable differences were donor-associated and probably reflected differences related to the processing of individual blood samples. Donor-specific differences disappeared at later stages.
The gene expression profile 24 hours after the onset of cytokine stimulation was found to be fundamentally different to t=0h cells. Almost every cell responded to cytokine stimulation by increasing transcript levels and generating a unique gene expression pattern (Fig 1C). When represented on the two-dimensional tSNE (Fig 1B) and PCA plots (Fig.S2A) the cells formed a single but dispersed cluster, well separated from the t=0h cells. In a fraction of cells moderate to high transcription of previously non-expressed hematopoietic regulator genes was observed in addition to that already seen at t=0h. For example, the expression of GATA1, GATA2, PU1, CD71, FOG1, CD133 or EPOR increased or was more frequent than at t=0h. In some cells all these genes were expressed simultaneously. Nevertheless, no distinct subpopulations could be identified at the resolution of our approach. The pairwise correlation coefficients between genes remained low (Fig 1D). Strong correlations were only between housekeeping genes (for example, GAPDH with ACTB). It is therefore likely that the patterns observed at 24 hours resulted from essentially uncoordinated up-regulation of gene transcription and led to a highly heterogeneous cell population. This is a transition state reminiscent of the reported multi-lineage primed state with simultaneous expression of lineage-affiliated genes specifying alternative cell fates [11,14].
The first signs of coordinated differential gene expression appeared at t=48h after cytokine stimulation. At this stage, two distinct gene expression patterns emerged from the highly variable background of earlier stages. The two clusters are clearly distinguishable on the tSNE plot (Fig.1B) and identified by gap statistics. They are also easily seen on the heat-map representing gene expression levels (Fig1C). Cluster#2 comprised cells with simultaneous expression of genes characteristic of erythro-myeloid progenitors (CMP) such as GATA1 and EPOR [7]. The expression profile of the cells in cluster#1 was characterized by the strong expression of genes reported for multipotent cells, CD133, GFI1, KLF4 or FLT3 and the lack of expression of GATA1 and EPOR. Although this pattern is reminiscent of a self-renewing, multipotent profile, it is difficult to determine the exact identity of these cells at the level of resolution used in our study [25]. Typical genes for pluripotent stem cells NANOG and OCT4 were expressed at moderate level in many cells from both clusters (Fig.1C and Fig.S1). Randomly selected genes were also good predictors for the two groups of cells. Only a small fraction of cells could not be classified in one of the two main clusters at t=48h (Fig.1B). The tendency observed at t=48h was further reinforced by t=72h. The cells in cluster #2 with CMP-like profile represented more than the half of all cells (Fig.1B-C). We observed a strong but transient increase in the number of highly correlated genes in this group (Fig.1D) indicating a state transition with the coordinated increase of the expression of some genes leading to a committed cell state. Indirectly, this suggested that the cluster#1 profile was more in continuity with the previous profile observed at t=24h and that the cluster#2 profile at t=48h represented a transition to a new pattern.
Taken together, these single-cell gene expression observations revealed that the cell-fate decision process in cytokine stimulated CD34+ cord blood cells occurred during the first two days. Initially, each cell responds to cytokine stimulation by an uncoordinated change in gene expression, which is followed by the emergence of two distinct gene expression patterns reminiscent of the two known major types of hematopoietic progenitor cells. Although indications of this second change may appear as early as 24 hours after stimulation, the two distinct gene expression patterns are clearly distinguishable at 48 hours and consolidated by 72 hours. By this stage almost every cell seems to have adopted one profile or the other.
Time-lapse tracking studies
In order to integrate the gene expression snapshots into a dynamic scenario, we made time-lapse records of individual CD34+ cells under in vitro conditions identical to those in the single-cell gene expression studies. We imaged individual cells in microwells at a rate of 60 frames per hour for 7 days (Fig.2A). Using a semi-automatic image analysis approach we established individual clonal pedigrees, and recorded cell cycle durations and major morphological changes. As shown in Fig.2B,C, the pedigrees of individual clones were highly variable but shared some general features. Some clones produced only a few cells during the observation period, while others proliferated faster and produced up to 30-40 siblings. We focused our attention on the first three generations. As reported for cells cultured in early acting cytokines [26], the first cell cycle was exceptionally long in all clones. The division of the founder cell occurred between 35 to 80 hours after the start of culturing with the median cell cycle length being 58 hours (Fig.2). We questioned whether the culturing of isolated cells in microwells where direct contact with the other cells was not possible - influenced cell cycle length. To measure the division rate in a population context, the cells cultured together were labelled using Cell Trace Violet. The results (Fig.S3) showed that the cells had similar division profile regardless of whether they were cultured individually or in population. The unusually long first cell cycle was particularly important when interpreting results. It implied that the transition from the initial to the multi-lineage primed transcription profile followed by one of the two types of progenitor-like profiles observed at 24 and 48 hours after CD34+ cell stimulation occurred during the life of the founder cell, before the first mitosis.
Previous studies have demonstrated that there is a connection between cell morphology and the differentiation potential of CD34+ cells. Two major morphological forms have been described in the CD34+ cord blood cell fraction. Polarized cells are capable of active motion with the help of lamellipodia and possess, on their opposite end, large protrusions called uropods. These cells have been found to retain primitive self-renewing and stem cell functions [27,28]. The second morphological type is round. These cells have been considered as already engaged in differentiation [27,28].
Time-lapse records revealed that the two cell morphologies were not permanent; most cells were able to switch between forms several times during the cell cycle. After recovering from the stress of isolation and manipulation, founder cells acquired polarized morphologies within a few hours, developing uropod and starting to move actively (see Movies 1 to 3 in the Supplemental information section). During the first cell cycle, cells mostly conserved the polarized form and switches between the two morphologies were rare. As indicated above, the first cell division occurred on average at 58 hours and the average lengths of subsequent cell cycles were around 20 to 22 hours. The daughter (second generation) and granddaughter (third generation) cells were able to switch between the two morphologies at much higher frequency compared to the founder cells. In order to quantify these events, we manually tracked each cell and recorded each switch. Representative profiles are presented on Fig.S4.
In order to compare quantitatively the dynamic phenotype of cells we calculated three parameters based on their dynamic profile. The first parameter was calculated as a ratio of the time a given cell spent as a round shape compared to the time spent as a polarized shape. This parameter was close to 0 for stable polarized cells, and 1 for stable round cells. Intermediate values correspond to the fraction of time cells spent in round shape. The second parameter was the frequency of morphological switches during the cell cycle. This parameter expressed the cell's ability to maintain a stable morphology. The third parameter was the cell cycle length. When cells were represented as individual points in the space determined by the three parameters, we identified three major categories (Fig.3A). The first category included cells with mainly polarized shapes; the second category was composed of cells with predominantly round shapes. The cells in the third category switched shape frequently, generally fluctuating between both morphologies (Fig.3A)
When sister cell pairs were examined, it became obvious that many displayed very similar dynamic phenotypes. In some cases, periods of stable morphology and switching events coincided almost perfectly (Fig.S4). We considered these cells were produced by symmetric cell division. In other cases, division was asymmetric and the two sister cells behaved differently. In the most extreme cases, one sister cell adopted a stable round form and the other a stable polarized form immediately after division. However, most of the asymmetric divisions produced siblings with obviously different dynamic phenotypes. Based on the above-mentioned parameters, we used k-means clustering to classify cells derived from a symmetric or asymmetric division (Fig.3A). This classification confirmed that most asymmetric divisions produced cells with highly fluctuating dynamic phenotypes.
We calculated the frequency with which a cell with a given dynamic phenotype was produced by a mother cell with similar or dissimilar phenotype (Fig.3B). Maternal cells clearly tended to transmit the dynamic phenotype to daughter cells. We also observed regularity with which phenotype changes occurred in daughter cells. Polarized cells were systematically produced by polarized cells. At lower probabilities, both polarized and fluctuating cells could produce stable round phenotype cells. Round cells always gave rise to round siblings (Fig.3B). Following these simple rules, the cumulative outcome of the process was the gradual increase of round cells in the population. Cells with fluctuating morphologies appeared to be intermediate form between polarized and round cells. Since 25% of daughter cells conserved this phenotype, the fluctuating intermediate cells persisted in the population. On static snapshots however this category remained undetectable: only polarized and round cells were observed. A polarized form was considered to be a feature of multipotent cells and the round form a committed myeloid progenitor phenotype [27,28].
Coupling the molecular and cellular scales
The dynamically fluctuating behaviour we have described here for the first time represent a transition between the two states and reflect a “hesitant” but incomplete fate determination process. Since we detected only two major transcription profiles but observed three different dynamic behaviours, it is possible that “hesitant” cells are not characterized by a clearly distinct transcription pattern. Morphology fluctuations may be accompanied by fluctuations in the transcript or protein levels of at least some key genes.
To test this assumption, we took advantage of the observation that the gene coding for the CD133 cell surface protein was expressed preferentially in one of the two transcription patterns detected at 48 hours (Fig.1B and C). Previous reports have established that CD133 protein is typically present in cells with polarized forms and accumulates in the uropod [27-29]. We confirmed this using image cytometry and immunohistochemistry on fixed cells (Fig.S5). Cells expressing high levels of CD133 were mostly polarized, while those with low levels of CD133 were round (Fig.S5). This observation explicitly established the link between the cell morphology and the transcription patterns detected by single-cell RT-PCR.
We used the CD133 protein as a proxy for the isolation of a cell fraction enriched either in polarized or round cells and recorded their dynamic phenotype. The “high” and “low/negative” CD133-expressing cell fractions were sorted 48 hours after cytokine stimulation, put in culture and tracked using time-lapse microscopy for an additional 48 hours. The cell-cycle length, switch frequency and division asymmetry were calculated. The “high CD133-expressing" cells and their progeny reproduced the three types of cells observed previously but in different proportions (Fig.4). Most of the cells displayed stable polarized morphologies or were frequent switchers; only a few cells displayed stable round morphologies (Fig.4). By contrast, the “low/negative" cells produced either stable round progeny or cells with fluctuating morphologies (Fig.4). The frequency of asymmetric division (in terms of dynamic behaviour) was higher in the “high CD133" population than in the “low CD133" population (78% versus 59%).
These observations confirmed the idea that cells with stable round shape were derived from cells with polarized shapes and high CD133 levels, following a gradual transformation process that involved a transition period with fluctuating phenotype. The process of transformation did not correlate with the cell cycle; some cells reached a stable morphology rapidly while others fluctuated over several cycles. The process was accompanied by a gradual decrease in CD133 protein level in cells. We found no evidence that asymmetric divisions played a direct role in this process.
Single-cell transcription profile of the multipotent stage
In order to determine which of the observed phenotypes correspond to the multipotent stage, we took advantage of recent observations demonstrating that the inhibition of histone deacetylase (HDAC) activity with a pharmacologic agent resulted in a substantial increase in their incidence in the cord blood CD34+ population [12,30,31]. We anticipated that this would increase the proportion of cells with transcription profile typical of the multipotential phenotype. Since valproic acid (VPA) was shown to be the most efficient [30], we used this agent to treat CD34+ cord blood cells stimulated by cytokines as above, before sampling transcription profiles. The increase of the CD90 marker, as analysed by flow cytometry confirmed that the VPA effect was already visible after 24 hours and gradually grew stronger during subsequent steps (Fig.5A and Fig.S6). The expression of CD34 and CD38 markers remained unchanged (Fig.S6). Although we did not analyse the in vivo potential of these cells, based on previous reports, we considered them enriched for bona fide multipotent cells. We performed single-cell RT-PCR 0, 24, 48 and 72 hours after the start of the experiment, as in control cells. At all four time points, cell populations were very heterogeneous. At each time point, the cells displayed a unique transcription profile (Fig.5B) and no identifiable transcription patterns appeared during the 72 hours of the experiment despite slight profile evolutions. Overall, transcription patterns in individual cells were reminiscent of the uncoordinated multi-lineage primed profile detected in control cells at 24 hours, but the two groups clustered separately on tSNE maps (Fig.5C). Since the cells did not divide during the first 48 hours, the increase observed in the multipotent cell fraction could not result from the selective proliferation of an initially small subpopulation of cells. Instead, this occurred because cells already present in the population changed the expression of many genes in response to the valproic acid.
Discussion
In the present study we aimed to identify the initial stages of fate commitment in the CD34+ cell fraction of human cord blood and determine the typical time scale for these events. Without cytokine stimulation, CD34+ cells remain quiescent and die after a few days in culture. Early-acting cytokines allow these cells to survive, become metabolically active and enter the cell cycle [32] without showing overt signs of differentiation during the first few days. This creates ideal conditions for studying early events. Our experimental design combined continuous time-lapse observations with snapshots of high-resolution single-cell transcriptome analysis. The data can be integrated in a dynamic fate decision process scenario. Fate decision is necessarily accompanied by a change in the gene expression pattern. This is a multistep process. Firstly, upon stimulation, cells rapidly reach the multiprimed state, which is characterized by promiscuous gene expression pattern and predominantly polarized morphologies. This is an unstable phase and two distinct transcription profiles start to emerge before the end of the first cell cycle. The process by which cells relax from a multiprimed to more stable state is continuous and of variable length. Some cells reach stable morphology and coherent lineage-affiliated transcription profile by the end of the first cell cycle, which they transmit to daughter cells. Other cells divide into unstable daughter cells with dynamic “hesitant" behaviour. This behaviour is characterized by fluctuations between polarized, actively moving, amoeboid and round morphologies over several cell cycles, suggesting that instability can be transmitted mitotically. Although we have no formal evidence that the transcriptome of these cells also fluctuates, two observations suggest that this could be the case. Firstly, we only found two established transcription profiles that presumably correspond to polarized and round morphologies with high and low CD133 expression levels (Fig.1C). However, we observe three dynamic phenotypes, one of which is fluctuating. Secondly, cells isolated on the basis of high CD133 protein level were either polarized or fluctuating and low expressing cells were either round or fluctuating. This suggests that fluctuating cells may have intermediate levels of the CD133 protein and represent a transition between the stable polarized morphology and round morphology. This dynamic scenario reconciles assumption of a stochastic multi-lineage primed state with the idea of fluctuating transcriptomes [14,16] by suggesting that relaxation from the metastable multi-lineage primed state to the stable lineage committed state involves an uncertain “hesitant" phase of variable length.
Increased stochastic variation in gene expression may be responsible for the rapid shift away from the initial quiescent state and lead to the uncommitted multi-lineage primed state [11,14,15]. Cell division is not required for this process; it occurs during the first cell cycle following stimulation. Cells engaged on the path toward the new phenotype represent the committed state. The critical moment in this process is the transition between the two phenotypes when the old gene network has broken down but the new network is not yet assembled. We consider that cells with fluctuating morphologies represent this transition state. The rapidity of the transition may be dependent on the time required for the new gene expression network to settle to a stable state. Since phenotypic stability of a cell lineage largely depends on the frequency of transcription initiation and the stability of the resulting mRNA-s and proteins [33,34], the observed “hesitant” phenotype might be the consequence of rapid mRNA and protein turnover. The consolidation of the chromatin structure appears to be an essential element in this process, because, as shown in single cell transcription studies the HDAC inhibitor valproic acid delays the transition and blocks cells in a promiscuous gene expression pattern typical of multi-lineage primed state. Indeed, HDAC inhibitors have been shown to increase gene expression stochasticity by increasing chromatin acetylation [35].
In summary, in the current study we identified the earliest phases of fate commitment in human cord blood CD34+ cells and assigned a time scale to this process. We demonstrated that the rapid initiation of the process occurs within a single cell cycle and is followed by a dynamic transition state of variable length that may span several cell cycles. Since experimental conditions were constant, the changes observed are likely to reflect cell-intrinsic processes whereas the convergence toward a similar endpoint may reflect the constraints imposed by these conditions. From this perspective and in accordance with earlier theoretical and experimental work [36-40], fate decision appears to be a process of spontaneous variation/selective stabilisation reminiscent of trial-error learning, where each cell explores many different possibilities at its own rhythm by expressing a large variety of genes before finding a stable combination corresponding to the actual environment.
Materials and methods
Human sample and cell culture
Human cord blood (UCB) was collected from placentas and/or umbilical cords obtained from Etablissement Français du Sang (EFS), Saint Louis Hospital, France or from Centre Hospitalier Sud Francilien, Evry, France in accordance with international ethical principles and French national law (bioethics law n°2011-814) under declaration N° DC-201-1655 to the French Ministry of Research and Higher Studies. Human CD34+ cells were isolated from the mononuclear fraction of UCB samples using the autoMACSpro (Miltenyi Biotec, Paris, France) immunomagnetic cell separation system. They were then cryopreserved in Cryostor (StemCell, Paris, France) and stored in liquid nitrogen or used directly without freezing.
Cells were cultured at 37°C in a humidified atmosphere containing 5% CO2 in a 24-well plate in X-VIVO (Lonza) supplemented with 100 U/ml penicillin, 100 μg/ml streptomycin (Gibco, Thermo Scientific), 50 ng/ml h-FLT3, 25 ng/ml h-SCF, 25 ng/ml h-TPO, 10 ng/ml h-IL3 (Miltenyi Biotec, Paris, France) final concentration. Valproic acid (VPA) (Sigma Aldrich) was used at final concentration of 1,25mM.
Single cell qRT-PCR
Single-cell qRT-PCR was carried out using the Fluidigm BioMark platform. DELTAgenes assays (life technologies) were used at a final concentration of 500nM for each of the 96 assays. Individual cells were sorted directly into a RT mix solution and spikes (life technologies) in a 96-well plate. RNA was denatured and reverse transcribed. of Premplification (20 cycles) of 96 specific cDNA was performed by denaturing the cDNA at 96°C for 5 secondes followed by annealing and extension at 60°C for 4 minutes. Unincorporated primers were removed by Exonuclease I and the preamplified products were diluted 5-fold. Amplification was performed with EvaGreen Supermix with Low ROX and inventoried DELTAGenes assays in 96.96 Dynamic Arrays on a Biomark System (Fluidigm). Ct values were calculated from the system’s software (BioMark Real-Time PCR Analysis, Fluidigm).
Single cell Data Normalization
Ct values obtained from the Biomark System were normalized with the help of two externally added controls (spike 1 and spike 4, life technologies) according to a set of rules provided below. For each gene, inconsistent readings or “failed” quality control readings were removed. Cells with failed or inconsistent detection of spikes were removed. Expression values were calculated by subtracting the gene Ct value from the geometric average of Ct values from spike 1 and spike 4 in the corresponding cell. An arbitrary dCt value of −17 was assigned for all the genes with a dCt value of less than −17.
Single cell qRT-PCR data analysis
Analyses of qRT-PCR single cell data were performed with R software (R Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/) using Heatmap3, factomineR, kmeans, ggplot2 packages. Correlation calculations were performed using custom R scripts. t-SNE and gap statistics calculations were performed as described by Grun et al. [41]
Confocal microscopy
Images were obtained with a spectral confocal LEICA SP8 scanning microscope (Leica Microsystems, Germany). 5.104 cells were cultured in a 48-well plate in 200μL prestimulation medium. After 72h, 100μL of 3% glutaraldehyde was added to the cell-containing well (1% final) for 15 minutes. Cells were washed twice with PBS 1X and incubated two hours with 2mg/mL NaBH4 at room temperature. Fcɤ receptors were saturated with Gamma Immune (Sigma Aldrich) for 5 min at 4°C (1:2 dilution). The cells were permeabilized with the fix/perm kit (BD-Biosciences), labeled for 20 minutes at 4°C with a 1:10 dilution of the mouse anti-humanCD133-APC antibody (clone Ac133 – Miltenyi Biotec), a 1:1000 dilution of phalloidin-TRITC (Sigma Aldrich) and stained with DAPI.
The images were acquired using a 63X PL APO CS2 1.40 NA oil immersion objective (Leica Microsystems, Germany). DAPI was excited with a 405nm, TRITC with a 552nm and APC with a 635nm laser. Finally, images were processed with a contrast enhancement algorithm (histogram equalization) and a home-designed background substraction algorithm.
Micro-grid cell culture
A polydimethylsiloxane (PDMS) micro-grid array (Microsurfaces, Australia) of 1024 micro-wells (125 μm width, 60 μm-depth) was placed in specialized culture dish divided into 4 parts (Hi-Q4, Ibidi, Germany). Each part of the dish was filled with cell culture medium. A suspension of 5 × 103 cells per case was added at a concentration likely to lead to a high number of wells with a single cell.
Time-lapse microscopy
The time-lapse microscopy protocol was previously described [42]. Time-lapse acquisitions were performed with the Biostation IM time-lapse microscope (Nikon Instruments, Europe). 20 field positions were recorded covering 4 micro-wells each. Images were acquired every minute for 7 days using a 20X magnitude phase contrast objective. Only micro-wells containing a single cell were considered in the analyses.
Image analyses
Images were analysed using ImageJ 1.47g 64-bits software (Rasband, W.S., ImageJ, U.S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997–2014.). Cell tracking was performed manually using the ImageJ TrackMate plugin. The morphologies of first, second and third generation cells were analyzed semi automatically with Fiji (ImageJ 1.50e). A cell counter plugin was used to identify the moment when the cell switches from a round to a polarized morphology.
Time-lapse data analyses
Analyses of time-lapse data were performed using R software. Cell lineage representations, cycle length, roundness and switch frequency were calculated with custom R made scripts. Euclidean distances of the last three parameters (cycle length, roundness and switch frequency) between the two sister cells were calculated. Cells were classified into 2 groups using the kmeans algorithm: derived from symmetric or asymmetric division. Boxplot representation combined with individual points was calculated with the beeswarm package (Aron Eklund (2016). beeswarm: The Bee Swarm Plot, an Alternative to Stripchart. R package version 0.2.3. https://CRAN.R-project.org/package=beeswarm).
Proliferation assay
CD34+ cells were labeled with 2,5μM of CellTrace Violet (CTV) (Life technologies) at t=0h and analyzed using flow cytometry (LSRII – BD biosciences, France) after 24h, 48h and 72h with ModFit LT software as described previously by Neildez et al. [43]
Image flow cytometry assay
Image flow cytometry analysis was performed using Image Stream MKII (Amnis, Proteigen, Merk Millipore). 5.104 cells were cultured in a 48-wells plate in 200μL prestimulation medium. After 72h, 100μL of 3% glutaraldehyde was added to the cell-containing well (1% final) for 15 minutes. Glutaraldehyde offers good preservation of cell shape. Cells were washed twice with PBS 1X and incubated two hours with 2mg/mL NaBH4 at room temperature. Fcɤ receptors were saturated with Gamma Immune (Sigma Aldrich) for 5 min at 4°C (1:2 dilution). Cells were labeled for 20 minutes at 4°C with a 1:10 dilution of mouse anti-human CD133-APC antibody (clone AC133 – Miltenyi Biotec). Cell were then suspended in PBS and analysed with the image flow cytometer. Bright Field and APC channels were recorded (Bright Field: 745nm laser – APC: 642nm laser) with the 40X magnitude objective. Analyses of image stream data were performed with IDEAS® (Amnis, Proteigen, Merk Millipore).
Cell sorting analysis
The CD34+CD133high and CD34+CD133low/neg cells were sorted at t=48h. Prior to labeling, Fc receptors were saturated with Gamma Immune (Sigma Aldrich). The CD34+ cells were labeled with CD34-PE (Miltenyi Biotec), CD45-APC-H7 (Beckman Coulter) and CD133-APC (clone AC133, Miltenyi Biotec) antibodies and 7-AAD marker (Sigma Aldrich). Isotype controls were used for the gating strategy. Cells were purified using a MoFlo® Astrios cell sorter (Beckman Coulter, France) and analysed with Kaluza software.
Flow cytometric assay
The CD34+ cells were labeled using the following cell-surface markers: CD34-PE (Miltenyi Biotec), CD38-Pacific Blue (Beckman Coulter) and CD90-APC-Cy7 (Beckman Coulter) antibodies and 7-AAD marker (Sigma Aldrich). Isotype controls were used for gating strategies. Cells were analysed at 72h after prestimulation by flow cytomety (LSRII – BD biosciences, France) and analysed with FlowJo (v10.1) software.
Author contribution
AP, DS, AM and OG designed the study
AM, AR, JC, CS and RP conducted the experiments
AM, DS, GC, JC and AP analysed the results and performed statistical analysis
AM, JC and DS prepared the figures
AP wrote the paper with the help of his colleagues
Acknowledgements
This work was supported by EPHE (11REC/BIMO), ANR grant n° BSV6 014 02 « Stochagene » and Genethon. The authors are grateful to Jean-Jacques Kupiec and François Delhommeau for helpful discussions, to Peggy Sanatine for the precious help with cell sorting and Rhonda Campbell for the language revision of the manuscript. The authors declare no competing interest.