Presented here is a flow cytometry-based technique that allows for simultaneously measuring the number of cell divisions, surface cell phenotype, and cellular kinship. Those properties can be tested statistically using a permutation-based framework.
Few techniques can assess phenotype and fate for the same cell simultaneously. Most of the current protocols used to characterize phenotype, although able to generate large datasets, necessitate the destruction of the cell of interest, making it impossible to assess its functional fate. Heterogeneous biological differentiating systems like hematopoiesis are therefore difficult to describe. Building on cell division tracking dyes, we further developed a protocol to simultaneously determine kinship, division number, and differentiation status for many single hematopoietic progenitors. This protocol allows the assessment of the ex vivo differentiation potential of murine and human hematopoietic progenitors, isolated from various biological sources. Moreover, as it is based on flow cytometry and a limited number of reagents, it can quickly generate a large amount of data, at the single-cell level, in a relatively inexpensive manner. We also provide the analytical pipeline for single-cell analysis, combined with a robust statistical framework. As this protocol allows the linking of cell division and differentiation at the single-cell level, it can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. Altogether, this protocol can be used in experimental designs aiming to unravel the biological differences between hematopoietic progenitors, from a single-cell perspective.
The past decade was marked by the worldwide spreading of single-cell approaches to cellular and molecular biology. Following in the steps of single-cell genomics1,2, nowadays it is possible to study many components of a single cell (e.g., DNA, RNA, proteins), with novel single cell -omics techniques burgeoning every year. These techniques have shed light on old and new questions for the fields of immunology, neurobiology, oncology, and others, both using human and model organism cells3. By highlighting the differences between individual cells, single cell -omics prompted the definition of a new model of hematopoiesis, centered on the heterogeneity of hematopoietic stem and progenitor cells (HSPCs) and moving away from the classical model of discrete homogeneous populations4,5.
One of the few drawbacks of all the -omics techniques is the destruction of the cell of interest, precluding the possibility to assess its functionality. Conversely, other single-cell methods, such as single-cell transplantation assay and lineage tracing technologies, provide a readout of the functionality of the ancestor cell by assessing the fate of individual cells in vivo6,7. Lineage tracing technologies involves labeling the cell of interest with a heritable genetic7 or a fluorescent label8,9, allowing the fate of multiple single cells to be followed at the same time. However, the characterization of the starting cells is typically limited to a restricted number of parameters, such as the expression of a few surface proteins assessed by flow cytometry10. In addition, single-cell lineage tracing technologies require laborious detection of the cellular label, typically via DNA/RNA sequencing or imaging. This last point in particular limits the number of conditions that can be tested in a single experiment.
Another class of methods that are used to study the functionality of single cells are ex vivo cell culturing systems of single HSPCs. Easy to perform, these gold standard assays involve the sorting of individual cells into 96-wells cell culture vessels, and after culture, characterizing the cell progeny phenotype, typically by flow cytometry or morphological analysis. These assays have mostly been used to characterize the long-term differentiation of HSPCs into mature cells, typically after 2-3 weeks of culture11,12. Alternatively, they have been used to try to maintain and expand ex vivo HSPCs13,14,15,16,17,18, with the promise of medical benefit for human stem cell transplantation19. Lastly, they have been used to study the early commitment of HSPCs using short-term culture20, with the low number of cells generated in this culture being the main limiting factor. One drawback of these different kinds of ex vivo assays is that they only partially reflect the in vivo complexity; still, they are one of the rare ways to study human HSPC differentiation.
One missing piece of information from existing single-cell methods (single cell-omics, lineage tracing, and ex vivo culture) is the accurate detection of cell divisions, an essential parameter to consider when studying HSPC dynamics21. A simple way to assess the number of divisions via flow cytometry is the use of soluble "protein dyes", like the 5-(and 6)-carboxyfluorescein diacetate succinimidyl ester (CFSE)22. These division dyes diffuse inside the cytoplasm of the stained cells, and diluted by half and passed to the two daughter cells at every cell division, allowing to enumerate up to 10 divisions. Combining several division dyes, it is possible to seed multiple individual progenitors in the same well, as each individual dye allows separation of the different descendants. This is the principle behind the use of cell dyes for multiplex clonal and division-tracking that was first introduced for murine lymphocytes23,24.
Here, we present the development of the MultiGen assay for use with murine and human HSPCs. It allows the testing of many single cells simultaneously for their properties of differentiation, division, and kinship ex vivo. This high-throughput, easy to perform, and inexpensive assay allows to measure the cellular phenotype, the number of divisions performed, and the cell kinship and clonal relationship with the other cells in the well, all at the same time. It can be used to quantitatively assess symmetric and asymmetric fate commitment, the balance between self-renewal and differentiation, and the number of divisions necessary for a given commitment fate. The protocol requires a fluorescence activated cell sorter (FACS) and a flow cytometer with a plate reader, plus the equipment necessary to perform cell culture. In addition to the technical protocol for execution of the assay on human HSPCs, we also provide the detailed analysis framework, including the statistical testing necessary to assess cellular properties related to the concept of cell family25. This protocol has already been used successfully to describe the murine HSPC compartment26,27.
The following protocol uses magnetically enriched CD34+ cells as the starting material28. In this way, it is possible to efficiently stain and isolate the human HSPCs from different blood sources (e.g., cord blood, bone marrow, peripheral blood). It is important not to discard the CD34– fraction, as it will be used as part of the protocol to set different types of experimental controls. The mentioned cell amounts and volumes can be scaled up or down, according to the experimental workflow and necessities. Similarly, the protocol can be adapted to the study of different types of progenitors, simply by modifying the antibodies used for the cell sorting and flow cytometry steps.
For the following protocol, de-identified umbilical cord blood was used as the HSPC source and collected in accordance with the guidelines defined by the Saint-Louis Hospital cord blood biobank (authorization AC-2016-2759) and with the Declaration of Helsinki.
NOTE: Before starting, ensure that all reagents and equipment necessary for this protocol are available, as listed in the Table of Materials and mentioned in the protocol. Prepare the relevant reagents fresh and do not store them, unless explicitly mentioned.
1. Cell dye staining
NOTE: This section describes staining with four combinations of CFSE and violet dye (CTV) cell division dyes. Process all the tubes simultaneously, even if no cell dye solution is added. All steps are performed in sterile conditions to allow the following cell culture step. Time required: approximately 100 min.
2. Antibody staining
NOTE: Antibody staining can be customized according to the experimental needs. Only the CD34+ fractions undergo antibody staining; the CD34– fractions are used as a single staining control for the cell division dye combinations (CV, VC, CF, and VI fractions). The following panel is tailored for the detection of four types of HSPCs: hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), lymphoid-primed multipotent progenitors (LMPPs), and hematopoietic progenitor cells (HPCs)12. However, identification of HSCs and MPPs is presented. Time required: 75 min.
Table 1: Template to prepare the antibody mastermix for a cell sorting experiment. Please click here to download this Table.
3. Cell sorting
NOTE: The sorted cell numbers could vary according to the total amount of cells available. In the protocol, a minimum cell number for each control is provided. Time required (for a single plate): 100 min.
Table 2: Template for a cell sorting 96-well plate, based on the specific requirements for the successive flow cytometry analysis. Please click here to download this Table.
4. Cell sorting data analysis
NOTE: To validate the quality of cell sorting, the FACS data analysis is necessary before moving further. The main output of this step is the generation of a spreadsheet containing the marker intensities of each sorted individual cell.
5. After culture antibody staining
NOTE: Perform this part of the protocol in sterile conditions; several reagents are shared with the former steps, and need to stay sterile. For the flow cytometry analysis, use a flow cytometer with a plate reader. This allows performing the staining directly in the tissue culture plate, reducing the cell loss to a minimum by limiting the amount of pipetting and spinning. Prepare the surface marker single color staining using the compensation beads, except for wells A1-A4, which represent the single staining for the CF/CV/VC/VI colors and are already present in the 96-well plate. The bulk populations sorted according to the cell dye help setting the gating strategy for the number of divisions and the general gating. Time required: 120 min.
Table 3: Antibody mastermix for a flow cytometry experiment, specifically for the identification of HSPCs from human cord blood. Please click here to download this Table.
6. Post-culture flow cytometry data analysis
NOTE: The data analysis described is specific for the software mentioned in the Table of Materials. The main output is the generation of a spreadsheet containing information for surface marker intensity, number of divisions, and kinship per each analyzed cell. Included in this part of the protocol is a script written in R, necessary to this workflow to generate the final analysis spreadsheet.
Table 4: Gating matrix for the cell fate assignment, before the statistical analysis. The CD45h refers to the intensity of CD45RA for the HPC subset gating, while CD45l refers to the intensity of CD45RA for the CD34+CD38– subsets. Please click here to download this Table.
7. Statistical analysis
NOTE: The statistical testing of the data generated involves a custom-made analysis pipeline, coded using the programming language Python (Supplemental File 2, Supplemental File 3, and Supplemental File 4). The script is organized in three blocks: the first one for processing the spreadsheet, the second block to generate the heatmap for data visualization, and the last block to generate multiple histograms to analyze and test differentiation and division properties.
FACS sorting
The sorting gating strategies presented in this protocol are based on widely accepted strategies12,30,31. For the gating strategy presented in Figure 1, the starting material is cord blood progenitors previously purified via CD34+ magnetic enrichment, which explains the negligible percentage of Lineage positive cells. It is essential to use tight gates for the four intracellular dye combinations (e.g., the CTV in the figure), to improve the peaks' resolution during the following analysis and to gate the correct cell population (Figure 1D). In the case displayed in the figure, the gates select for the largest and better defined population. The presence of multiple, close populations for each cell division dye combination is, in our experience, not representative of biological differences. Instead, it could indicate a) a non-optimal staining procedure, or b) a large heterogeneity (especially in size) in the starting pool of cells. This is not unexpected when starting from cord blood or other complex biological sources (e.g., bone marrow aspirates, peripheral blood). If the gate is not tightly defined, the progressive dilution of the different dye combinations can lead to merging of the later peaks, specifically for the conditions CV and VC (Figure 2D). Another negative consequence of a suboptimal gating is the inability to efficiently distinguish different peaks after cell culture, as a heterogeneous starting population can lead to shallow peaks.
Figure 1: Gating strategy for cell sorting. (A) FSC-A versus SSC-A, to exclude debris and contaminating cells. (B) FSC-A versus FSC-H, to exclude doublets and cell clumps. (C) Lin versus FSC-H, to exclude cells that are Lin+. (D) CTV versus CFSE, to univocally identify the cells stained with the dye combinations CF, CV, VC, and VI. The gates should be strict enough to include a homogeneous population. (E) CD34 versus CD38, to separate the restricted progenitors CD34+CD38+ (also called HPCs) from the multipotent compartment CD34+CD38–. (F) CD45RA versus CD90, from the CD34+CD38– population, to separate between the most immature progenitors enriched in the HSC (CD90+CD45RA–), the LMPP (CD90midCD45RA+), and the more committed MPP (CD90–CD45RA–). (G) Index sorted events, represented here for their cell dye combination staining and (H) the expression of surface markers CD90 and CD45RA. Please click here to view a larger version of this figure.
Flow cytometry analysis after cell culture
The data in Figure 2 is representative of human cord blood HSCs, kept in culture for 72 h, in the presence of multiple cytokines able to support a range of myeloid progenitors and precursors. Panels 2A a 2D represent the gating necessary to establish the kinship of each of the individual cells, while panels 2E a 2G allow cellular phenotyping. The reduced presence of MEPs in the figure is probably the consequence of the culture conditions used for this representative experiment (Figure 2F). Using different cytokines and culture conditions alter the relative percentage of each subset, similarly to selecting different starting cells for the experiment.
Figure 2: Gating strategy for the flow cytometry analysis. (A) FSC-A versus SSC-A, to exclude debris and contaminating cells. (B) FSC-A versus FSC-H, to exclude doublets and cell clumps. (C) CTV versus CFSE, the gate Labeled allows to exclude any auto-fluorescent event that could affect the data resolution. (D) CTV versus CFSE. It is extremely important to stringently gate the four populations, based on the cell division dye dilutions. (E) CD34 versus CD38, to distinguish between committed precursors (CD34–), restricted progenitors (HPC) (CD34+CD38+), and immature progenitors (CD34+CD38–). (F) CD45RA versus CD123, to distinguish three types of restricted progenitors: CMP (CD123+CD45RA–), MEP (CD123–CD45RA–), and GMP (CD123+CD45RA+). (G) CD45RA versus CD90, from the CD34+CD38–, to identify HSCs, LMPPs, and MPPs. Please click here to view a larger version of this figure.
The peak definition and assignment steps (Figure 3 and Figure 4) are crucial aspects of the protocol and require the definition of strict gates. For the peak definition (Figure 3), at least 1,000 events are necessary for a reliable identification. In this sense, it could be beneficial to isolate more cells during the cell sorting step for the "Bulk" wells. Figure 4 describes four examples of single wells containing multiple families. This figure clarifies the importance of Figure 2D and Figure 3 gating, especially for the identification of each family and each peak. Figure 4A illustrates a straightforward example, as all the cells in the gate CF are very close to each other and can be easily assigned to a single peak. Figure 4C shows another example of a family distributed univocally on two well separated peaks, as it is clearly displayed in the histogram of Figure 4D. Figure 4E,G reveal the importance of strict gating based on a large number of events; they both display few events that are close, but outside the dye combination gates. Those events could be incorrectly included in the VI and CF gates, based exclusively on the single-well analysis. Finally, Figure 4F,H display two different examples of families spread on multiple peaks, with one example of two similar intensity peaks (Figure 4F) and one with two unequal intensity peaks (Figure 4H).
Figure 3: Peak definition for the flow cytometry analysis. (A–D) Peaks should be defined registering at least 500 events, to assure a good representation for every individual peak. (A) Histogram for the CFSE-A intensity. Several peaks can be identified, each one corresponding to a different population of dividing cells. (B,C) Histograms for the intensity of the derived parameter, representing the CFSE-CTV mixture, CV (B) and VC (C), respectively. (D) Histogram for the CTV-A intensity. Please click here to view a larger version of this figure.
Figure 4: Peak assignment. (A,B) One peak only can be detected for this well, in the CF gate. (C,D) Two peaks of almost equal intensity can be detected in this well, in the VI gate. The peaks are well-resolved. (E,F) Two peaks of comparable intensity can be detected in this well, in the VI gate. Only the events in the gate have been considered, based on the strategy set using the bulk wells. (G-H) Two peaks of unequal intensity can be detected in this well, in the gate CF. Please click here to view a larger version of this figure.
Data representation and statistical testing
Figure 5 shows different types of data representation of two separate experiments, both performed after 72 h of cell culture. HSCs and MPPs have been cultured in two different cell culture media, supposed to alter the cell division and differentiation properties. These media are named "Diff" (Differentiation)32 and "GT"33; the first promote myeloid and erythroid differentiation, as it contains erythropoietin (EPO) and granulo-monocytic colony stimulating factor (GM-CSF), while the second has been developed in the context of gene therapy clinical trials, with the goal to maintain and amplify a high percentage of HSPCs. Figure 5A is a representative heatmap for the condition "Diff", representing a variety of cell families, both in cell fates and divisions. In this heatmap, each row represents an individual family, each square an individual cell, and the columns are grouping all cells that are in the same generation (e.g., cells in generation 2 divided at least twice). It is possible to distinguish highly homogeneous families, composed of a single cell type and displaying the same number of divisions (e.g., family #63), and heterogeneous families, including three cell types over two generations (e.g., family #84). Because the cellular recovery rate for this analysis is approximately 70%, complete families, which are defined by having all their cells recovered in possibly different generations (e.g., a family of one cell in generation 1 and two cells in generation 2), are rarely observed (displaying a hashtag next to their ID number in Figure 5A). There are multiple explanations accounting for the incomplete detection, which could be technical (staining issue, cell loss due to the protocol) or biological (cell death and/or apoptosis). Technical limitations can be overcome using an analyzer designed to reduce the dead volume associated with the individual sample, and by performing the cell staining directly in the cell culture plate to reduce the volume pipetting. Conversely, orthogonal methods to determine the amount of cell death (e.g., via live-cell imaging experiments) can help to distinguish the technical and biological factors resulting in incomplete detection.
Figure 5Bi shows how to visualize the effect of the culture condition on the cell type composition, as if one had performed a bulk assay. Here, the Diff condition promotes a larger number of fates, and a higher percentage of CD34+ cells (defined as all the cell types except CD34–). The confidence intervals are calculated in the script via basic bootstrap, with 250,000 bootstrapped datasets34. It is worth noting that all the other histograms in Figure 5 display confidence intervals calculated in the same way. Table 5 recapitulates all the information about the number of families and number of cells in each generation.
Figure 5Bii graphically represents the output of the statistical testing performed in the script "2_bar_plot". A formal description of the statistical framework is available26. In brief, this framework enables statistical hypothesis testing while assuming that cells from the same family are dependent (an assumption that is itself testable), contrary to classical statistics that would require independence between all observed cells. In the specific case presented in the figure, the statistical test challenges the hypothesis that the cell fate choices of MPPs, measured as the frequencies of the different cell types present in the culture, are independent of the cell culture conditions used. First, the G-test statistic is used to evaluate the discrepancy between cell type frequencies from different cell media (for the example in Bii, this statistic is represented with the red bar). Then, a randomization of the data is performed via permutation, swapping entire families of cells between the two cell culture conditions. This is to preserve the dependence between family-related cells, while keeping the number of families in each set consistent with the original data. The G-test statistic is calculated from the randomized data set. The blue values represented in 5Bii are the G-test statistic for 250,000 permutations. Finally, the p value is computed to assess the extent to which the original dataset deviates from the distribution of the permuted ones. In the example, the original statistic largely deviates from the distribution, resulting in a small p value and thus rejecting the hypothesis that the cell fate of MPPs is independent of the culture conditions.
Figure 5C represents the percentage of cell families per maximum generation, to explore how different conditions change cell division per cell family. This data plot shows that at 72 h, the cells cultured in the Diff condition complete a larger number of divisions than cells in the GT condition. Represented is the number of maximum generations per each family, so one family that displays cells in generations 1 and 2 is considered as generation 2. The same statistical framework used for Figure 5B can be used to statistically test the independence between cellular division and culture condition.
Figure 5D explores the type of symmetry/asymmetry of the first division for the different ancestor types (HSCs or MPPs). For the complete cell families in generation 1-the only generation where it is possible to definitively establish the two daughter cells as sister cells-four different types of symmetry/asymmetry can be defined: the label "Sym Undiff" describes families where both daughters retain the phenotype of the cell of origin. "Sym Diff" means that both daughters have the same phenotype, and it is different from the cell of origin. "Asym Undiff" means that one daughter only retains the phenotype of the cell of origin. Finally, "Asym Diff" describes families where both daughters have different phenotypes, and none of them are the same as the cell of origin. To gain statistical power in assessing these symmetric/asymmetric fates, performing the MultiGen analysis at early time points is desirable, in order to observe more families whose offspring are found in generation 1.
Finally, Figure 5E represents the percentages of cell types as a function of the number of divisions, to gain insights about the differentiation pattern progression across divisions. For example, the data displayed in the figure suggest that cells progress to the CD34– state, with over 50% of detected cells in this class after only three divisions. Moreover, it is possible to infer that MPPs do not favor self-renewal division, as a small percentage of cells retain the original phenotype. Some of these conclusions can then be tested using the statistical framework presented in the previous figures.
Figure 5: Example of data representation for one 72 h experiment using cord blood HSPCs. (A) Heatmaps for a selected dataset (HSC, in "Diff" medium, after 72 h of culture). The plots represent all individual cells (squares) according to their kinship (rows), number of divisions performed (columns, called generation), and phenotype (colors). (Bi) Histogram comparing proportions of the cell types of the cell progenies of HSCs and MPPs, between the condition GT and the condition Diff. (Bii) The graph represents the statistical tests performed in the "2_bar_plot" script for MPPs at 72h of culture, comparing between the "Diff" and the "GT" cytokine cocktails. The experimental value is displayed in red, and the values generated via 250,000 permutations in blue. The p. value of the G-test is indicated in the top right corner with the number of families used for the test. (C) Histogram comparing the percentage of families (314 families in total) in each generation (color coded), for HSCs and MPPs per culture condition. The confidence intervals are calculated with 250,000 bootstrapped datasets. (D) Histogram representing the type of symmetry/asymmetry between the fate of the daughter cells for the families with two cells in generation 1: Sym Undiff (both daughters retain the phenotype of the cell of origin), Sym Diff (both daughters have the same phenotype, and it is different from the cell of origin), Asym Undiff (only one daughter retains the phenotype of the cell of origin), and Asym Diff (both daughters have different phenotypes and none of them resemble the cell of origin). (E) Histograms of the contribution of cell types classified per generation for MPPs cultured with the "Diff" cocktail; n = 204 cells and 97 families. Please click here to view a larger version of this figure.
Table 5: Description of the number of families and cells analyzed per each experimental condition (cell of origin and cell culture medium). Please click here to download this Table.
Supplemental File 1: Please click here to download this File.
Supplemental File 2: Please click here to download this File.
Supplemental File 3: Please click here to download this File.
Supplemental File 4: Please click here to download this File.
The MultiGen assay is a high-throughput, easy to perform, and unexpensive assay, that has been instrumental to study lymphocyte23,24,35 and murine hematopoietic cells26,27. Here, we present a new development of the approach that allows to decipher ex vivo the early phase of human HSPC commitment, at the single-cell level using short term culture (Figure 6). Single-cell ex vivo culture systems are typically used to assess the long-term fate of HSPCs into mature cells, but some fates occur earlier than others36, potentially biasing the analysis toward fewer fates. In addition, these culture systems usually miss information about divisions during fate commitment. The first commitment steps have been shown to occur as early as at the start of the culture, sometimes without division26,37, making short term culture and tracking division essential to study early fate commitment. By simultaneously following the fate, division, and kinship, this assay allows to understand the role of the first division and fate decision in human HSPCs. Using the assay, it is possible to infer after how many divisions the commitment process occurs, the balance between self-renewal and differentiation for those early progenitors, and how those properties are inherited across generations. To our knowledge, this is the only assay that allows these types of measurements for human HSPCs, at single-cell resolution. In addition, using different combinations of cell division dyes, we increased the throughput of the analysis, making this assay a valuable tool for generating large datasets quickly. The dye combinations allow to follow several families in the same wells, increasing the number of cells available for analysis in short-term culture. The number of combinations could potentially be increased even more, via the addition of other dyes (e.g., yellow dye) or modifying the ratio of CFSE and CTV. However this reduces the number of other parameters that can be analyzed.
Figure 6: Schematic representation of the protocol. Please click here to view a larger version of this figure.
To perform the analysis successfully, due to the large number of wells and the reduced number of cells to analyze, it is necessary to run the flow cytometry analysis on an analyzer equipped with a plate reader. The new generation of bench analyzers is particularly adapted to this assay, as most of them have a smaller dead volume to reduce the percentage of cell loss. This in turn guarantees a higher efficiency in recovering the entirety of each well, prompting an efficiency estimated in the 70% range26. Estimating the cell loss during the flow cytometry acquisition is crucial for the analysis of each individual family. For example, assuming no cell death and counting the number of divisions, it is possible to estimate the number of cells per each family. Nevertheless, it is desirable to run some confirmatory experiments, particularly in the estimation of cell death in the tested culture conditions and measuring the recovery rate experimentally using a defined numbers of cells.
One of the crucial steps of this protocol is the peak assignment. As already mentioned, a good quality peak distribution is strongly dependent from the isolation of very narrow peaks at cell sorting. Nevertheless, it is still difficult to assign the correct number of divisions based uniquely on the distribution. As cell sorting and flow cytometry analysis are performed on two different machines, it is not possible to directly compare the intensity of each signal, so it could be difficult to know if the first peak observed on the right end of the histogram is peak 0 or peak 1. In this regard, few solutions are possible; one way is to perform an orthogonal experiment to accurately measure the number of divisions performed by these cells (e.g., live-cell imaging). Another possibility is to simply count the number of cells in the well under an inverted brightfield microscope, before running the flow cytometry analysis. This will infer an average number of divisions (assuming no cell death). Finally, a post-hoc solution for peak assignment is the detection of an unusual number of "impossible families"; these families are composed of a larger than possible number of cells per generation (e.g., five cells in generation 2, or two cells in generation 1 and one cell in generation 2). The possibility to exclude impossible families is coded in the statistical analysis step, and flags the impossible family. If the occurrence of these errors is too high, it is reasonable to assume that the peak assignment needs to be revised.
In this protocol, we included a few examples of data representation and analysis for the assay, as this has become an essential step in the generation and interpretation of large datasets38. The first example is the heatmap showing the totality of all analyzed cells, organized per family. This is an efficient tool to explore the general properties of the data and potential conclusions: are families composed by multiple cell types or do they tend to be homogeneous in composition? Are families spread over multiple generations, or are they mostly dividing the same number of times? This exploratory analysis needs to then be complemented with more specific plots and statistical testing. It can be used to quantitatively assess symmetric and asymmetric fate commitment, differentiation without division, the balance between self-renewal and differentiation, and the number of divisions for a given commitment fate. It is fundamental, during the experimental planning, to set the cell culture length accordingly to the type of question asked; for example, for the first two questions (symmetric/asymmetric balance and differentiation without division), planning very short cultures steps allows the isolation of a large number of families that have performed only one or no divisions at all26. Conversely, longer experiments allow the exploration of the number of divisions required for a specific cell commitment, as they sample families at different stages of differentiation. Nevertheless, this method is not designed for long-term cultures (2-3 weeks), as cell dye dilution is not able to accurately track more than seven or eight divisions22. As a consequence, this tool is mostly adapted to study the early commitment of hematopoietic progenitors, and is not designed to make robust conclusions on the long term differentiation properties of these cells.
The statistical framework was developed specifically for the analysis of this type of data and based on the concept of permutations26. This was necessary because of the observation of a familial dependance on the cell type distribution and on the number of divisions performed. In other words, cells that are part of the same family are also more likely to display similar phenotypes and divide the same number of times. While an in-depth analysis is beyond the scope of this work, the provided set of statistical tests should be sufficient when assessing different conditions.
In conclusion, this protocol constitutes a valuable tool for assessing the cellular dynamics of hematopoietic stem and progenitor cells ex vivo, in a rapid and inexpensive way. Due to its flexibility and versatility regarding time point, culture conditions, and type of HSPCs analyzed, it allows to test a variety of experimental conditions. As a flow cytometry-based assay, it can be implemented in most labs, and it does not require extensive prior knowledge, making it a good candidate for screenings and pilot experiments.
The authors have nothing to disclose.
We would like to thank the members of the Institut Curie Flow Facility for their help with setting up the flow cytometry experiments. We also want to acknowledge the contributions of the other members of the Team Perié, during multiple discussions. We thank Dr. Julia Marchingo and Prof. Phil Hodgkin (Walter end Eliza Hall Institute of Medical Research) for sharing their protocol of the multiplexing of cell division dyes on lymphocytes. We thank the Saint Louis hospital cord blood biobank for providing the biological resources necessary for the development of this protocol. The study was supported by an ATIP-Avenir grant from CNRS and Bettencourt-Schueller Foundation (to L.P.), grants from the Labex CelTisPhyBio (ANR-10-LBX-0038) (to L.P. and A.D.), Idex Paris-Science-Lettres Program (ANR-10-IDEX-0001-02 PSL) (to L.P.), the Canceropole INCA Emergence (2021-1-EMERG-54b-ICR-1, to L.P.), and the ITMO MIIC grant (21CM044, to L.P.). As well as funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program ERC StG 758170-Microbar (to L.P.), A.D. was supported by a fellowship from Fondation de France.
1.5 mL polypropylene microcentrifuge tubes | vWR | 87003-294 | |
15-mL polypropylene tubes | vWR | 734-0451 | |
50-mL polypropylene tubes | vWR | 734-0448 | |
96-well U-bottom culture plate | Falcon | 353077 | |
Anti-human Lin APC | Thermo Fisher | 22-7776-72 | Dilution 1/40 |
ARIA III | BD | Can be replaced with any FACS sorter able to sort individual cells in 96-wells plate | |
Carboxyfluorescein succinimidyl ester (CFSE) | Life Technologies | C34570 | |
Cell Trace Violet (CTV) | Life Technologies | C34571 | |
Compensation beads | BD | 552843 | |
Dulbecco’s modified Eagle medium (DMEM) | Life Technologies | 11320033 | |
Ethylenediaminetetraacetic acid (EDTA) | Thermo Scientific | J62948-36 | Prepare a solution 0.5 M, in sterilised water |
FC block Fc1.3216 | BD | 564220 | Dilution 1/50 |
Fetal Bovine Serum (FBS) | Dutscher | S1900-500C | Batch S00CH |
FlowJo v10.8.1 | BD | ||
Mouse anti-human CD10 PerCP-5.5, clone HI10a | Biolegend | 312216 | Dilution 1/20 |
Mouse anti-human CD123 BUV395, clone 7G3 | BD | 564195 | Dilution 1/15 |
Mouse anti-human CD34 APC-Cy7, clone 581 | Biolegend | 343513 | Dilution 1/40 |
Mouse anti-human CD38 BV650, clone HB7 | Biolegend | 356620 | Dilution 1/40 |
Mouse anti-human CD45RA AF700, clone HI100 | BD | 560673 | Dilution 1/20 |
Mouse anti-human CD45RA PE-Cy7, clone HI100 | BD | 560675 | Dilution 1/20 |
Mouse anti-human CD90 PE, clone 5E10 | Biolegend | 328110 | Dilution 1/20 |
Phosphate Buffered Saline (PBS) 1X | Life Technologies | 10010001 | |
Python | |||
R | |||
Sterile 12×75 mm conical polypropylene tubes | Falcon | ||
ZE5 | Biorad | Can be replaced with any flow cytometry analyzer equipped with a plate reader | |
Laboratory prepared | |||
Cell culture media | Depends from the specific experiment. Prepare fresh daily and store at +4 °C until use | ||
DMEM + 10% FBS | Can be stored in sterile conditions, at +4 °C up to 1 year. To prepare 500 mL, add 50 mL of FBS to 450 mL DMEM | ||
PBS 1X + EDTA 0.1% | Can be stored in sterile conditions, at room temperature, up to 1 year. To prepare 500 mL, add 3.42 mL of EDTA 0.5 M to 500 mL PBS 1X | ||
Staining buffer | Can be stored in sterile conditions, at +4 °C up to 1 year. To prepare 500 mL, add 2 mL of EDTA 0.5 M and 1 mL FBS to 500 mL PBS 1X |