You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#sec1}
============

Hematopoiesis ensures that blood demand is met under homeostatic and stress conditions through tightly controlled regulation of hematopoietic stem cells (HSCs) and their progeny. HSCs are historically identified by the unique capacity to self-renew, providing long-term, serial reconstitution of the entire hematopoietic system upon their transplantation into myeloablated hosts. Functional self-renewal of HSCs is associated with reduced cell cycle activity. Seminal papers demonstrated that cell cycle becomes more frequent as HSCs gradually differentiate into lineage-restricted progenitors ([@bib3; @bib20; @bib26; @bib35; @bib37]). Although the HSC compartment was thought to be heterogeneous in cycling ability ([@bib18]) 40 years ago, this has only recently been supported by experimental evidence as follows. (1) Label retaining studies ([@bib7; @bib28; @bib36; @bib40]) conclusively established that the HSC pool comprises at least two compartments differing in their frequency of division. (2) The most dormant cells have the highest repopulation capacity and can be reversibly brought into cell cycle through extrinsic cues, especially upon injury ([@bib7; @bib40]). (3) The HSC pool has been fractionated into long-term (LT-), intermediate-term (IT-), short-term (ST-) HSCs and multipotent progenitors (MPPs) and is hierarchically organized based on progressively reduced repopulation capacity and increased cycling properties ([@bib2; @bib5; @bib6; @bib7; @bib22; @bib24; @bib28; @bib40]). While the hierarchically organized HSC subsets are widely thought to prevent HSCs exhaustion and preserve lifelong blood production, knowledge of the molecular mechanisms that govern the variable cycling properties of each HSC subset is lacking.

Quiescence, defined as a reversible absence of cycling, also called G~0~, is a defining feature of HSCs first described in [@bib14]. Most transgenic and knockout mouse models altering HSC function decrease quiescence, leading to HSC exhaustion (reviewed in [@bib25; @bib32]). Quiescence and infrequent cycling of HSCs are considered to protect against damage accumulation, and impaired maintenance of HSC quiescence is thought to contribute to aging and leukemia. However, understanding how HSCs switch from quiescence to cycling and how division, self-renewal, and differentiation are integrated is lacking.

Upon reception of mitogenic signals, multiple processes must occur: HSCs must exit quiescence to enter the cell cycle, which then must be traversed to complete a division. This requires reactivating all the necessary metabolic and cell cycle machinery. Doubling time analysis at homeostasis has shown that ST-HSCs and MPPs divide more frequently than LT-HSCs ([@bib7; @bib22; @bib40]). Little is known about quiescence exit. It is unclear if and how it is differentially regulated among distinct HSC subsets and if the duration of this exit affects HSC function. We recently showed that the duration of a division starting from G~0~ after stimulation by a mitogenic signal is shorter in IT-HSCs than in LT-HSCs ([@bib2]). The unknown mechanism underlying increased cycling in IT/ST-HSCs could theoretically be due to (1) easier activation from external stimuli, (2) less time in G~0~, (3) faster exit from quiescence, (4) faster completion of divisions, or (5) a combination of these. An integrated view is necessary to ascertain how these properties in HSC subsets are molecularly regulated. Here, we establish that the duration of HSC exit from quiescence upon mitogenic stimulation is differentially regulated within the human HSC pool by a CDK6-primed quiescence state in ST-HSCs. Tight control of quiescence exit length via CDK6 levels plays an important role in HSC pool dynamics, preserving integrity and preventing LT-HSCs clonal expansion.

Results {#sec2}
=======

Heterogeneity in the Human HSC Pool {#sec2.1}
-----------------------------------

The cycling properties of mouse HSC subpopulations are described, but they have not been validated in the human HSC hierarchy. Human LT-HSCs, isolated from umbilical cord blood (CB) as Lin^−^ CD34^+^ CD38^−^ CD45RA^−^ CD90^+^ CD49f^+^ ([@bib21]), provide robust multilineage repopulation beyond 30 weeks in the NSG mouse xenograft assay with about 10% frequency ([@bib21]) and efficiently engraft upon secondary transplantation ([Table S1](#app3){ref-type="sec"}). In contrast, Lin^−^ CD34^+^ CD38^−^ CD45RA^−^ CD90^−^ CD49f^−^ cells generate multilineage grafts over intermediate time periods ([@bib21]), but they lack serial transplantation ability and thus have limited self-renewal ([Table S1](#app3){ref-type="sec"}). According to the criteria used in mouse, this population corresponds to ST-HSCs. Importantly, LT- and ST-HSC-enriched populations can be purified with the cell surface markers indicated above from NSG mice repopulated with human cells ([Table S1](#app3){ref-type="sec"}). Similar to transplantation models in mice, phenotypic human LT- and ST-HSCs expand in the first 4 weeks after xenotransplant (with \>50% that actively cycle) then regain quiescence by 20 weeks when a transient equilibrium phase is reached ([Figures 1](#fig1){ref-type="fig"}A, [S1](#app3){ref-type="sec"}A, and S1B). In our model, functionally repopulating LT-HSCs expanded ≈30-fold in 20 weeks ([Figure 1](#fig1){ref-type="fig"}B).

To estimate the division frequency of human HSC subsets, we tracked BrdU incorporation kinetics of phenotypic LT- and ST-HSCs in xenografts and observed, similarly to mouse models, that phenotypic LT-HSCs divide less frequently (1.5- to 1.9-fold) than phenotypic ST-HSCs ([Figure 1](#fig1){ref-type="fig"}C), whether in expansion or at equilibrium. Although both subsets highly proliferate after transplantation, we detected fewer ST-HSCs, possibly because of their higher drive to produce differentiated cells ([Figure 1](#fig1){ref-type="fig"}A). To capture a core signature of genes distinguishing LT- from ST-HSCs, we subjected LT- and ST-HSCs isolated from CB and at different times after xenotransplantation to transcriptome analysis. Using the Bayesian Estimation of Temporal Regulation algorithm ([@bib1]), we identified 241 genes showing sustained differential expression between LT- and ST-HSCs independent of environmental effects and changes in proliferation ([Table S2](#mmc2){ref-type="supplementary-material"} and [Figures S1](#app3){ref-type="sec"}C--S1E). This was significantly more than in our previous static analyses of CB ([@bib16; @bib21]). This signature contains genes important in murine HSC function ([@bib8; @bib12; @bib13; @bib15]) ([Figure 1](#fig1){ref-type="fig"}D) and is enriched for gene ontology terms related to the immune/inflammatory response, chromatin remodeling, and most significantly, cell cycle regulation ([Figure 1](#fig1){ref-type="fig"}E). Thus, human ST-HSCs, similar to mouse, have lower repopulation capacity, more frequent divisions, and a distinct transcriptional profile compared to LT-HSCs.

LT-HSCs and ST-HSCs Are Equally Quiescent {#sec2.2}
-----------------------------------------

The increased frequency of ST-HSC divisions may be due to (1) more cells actively cycling at any time, (2) increased sensitivity, or (3) faster response to mitogenic stimulation. To resolve the basis for these increased divisions, we investigated the proportions of LT- and ST-HSCs in each cell cycle phase and found them to be identical at all points during the xenotransplantation process ([Figure S1](#app3){ref-type="sec"}B). Freshly isolated from CB, both HSC subsets had more than 90% of cells in G~0~ (Ki67^−^ 2n DNA content, [Figures 2](#fig2){ref-type="fig"}A and 2B). Importantly, no cell was found in S-G~2~-M as determined by DNA content ([Figures 2](#fig2){ref-type="fig"}A and 2B) and by complete absence of the mitotic marker phosphoH3 ([Figure S2](#app3){ref-type="sec"}A). Cell diameters were equally small in LT- and ST-HSCs ([Figure 2](#fig2){ref-type="fig"}C), with both lacking in cytoplasm. Metabolically, both LT- and ST-HSCs showed low mitochondrial activity ([Figure 2](#fig2){ref-type="fig"}D) and similar levels of mTOR activation (assessed by phosphoS6 staining; [Figure 2](#fig2){ref-type="fig"}E). All these parameters indicate a G~0~ quiescent state. To exclude a possible differential G~1~ arrest state for LT- and ST-HSCs, we analyzed the phosphorylation state of retinoblastoma protein (RB) at S807/S811, a marker upregulated in G~0~ cells before entry into G~1~ ([@bib29]). Both cell types were negative ([Figures 2](#fig2){ref-type="fig"}F and [S2](#app3){ref-type="sec"}B). In contrast, granulocyte-monocyte progenitors (GMPs) were largely in G~1~, as most cells were Ki67^+^ with 2n DNA content ([Figure 2](#fig2){ref-type="fig"}B) and had a larger diameter ([Figure 2](#fig2){ref-type="fig"}C), visible cytoplasm, increased mitochondrial activity ([Figure 2](#fig2){ref-type="fig"}D), and RB phosphorylation on S807/S811 ([Figure 2](#fig2){ref-type="fig"}F). Collectively, these data establish that both human LT- and ST-HSCs freshly isolated from CB reside in a G~0~ quiescent state lacking all markers of G~1~.

Distinct Cell Division Durations in HSC Subsets {#sec2.3}
-----------------------------------------------

Since the proportions of LT- and ST-HSCs in G~0~ were identical ([Figures 2](#fig2){ref-type="fig"}B and [S1](#app3){ref-type="sec"}B), we hypothesized that the differences in expression of cell cycle genes and frequency of division observed between these two subsets reflected differences in their capacity to exit quiescence upon mitogenic stimulation. Therefore, we measured the duration of single divisions occurring upon activation by a mitogenic signal. Such studies need to be performed with single cells using in vitro assays. We sorted 576 single LT- and ST-HSCs from CB and monitored their divisions over 140 hr in serum-free conditions. As expected, proliferation was higher in ST-HSCs ([Figure S2](#app3){ref-type="sec"}C). The mean time to first division (t~FirstDiv~) varied between CB samples, but on average it was 9 hr shorter in ST-HSCs compared to LT-HSCs ([Figures 2](#fig2){ref-type="fig"}G and 2H). The mean time to second division (t~SecondDiv~ = t~G1-S-G2-M~) was also significantly shorter in ST-HSCs than in LT-HSCs ([Figures 2](#fig2){ref-type="fig"}I and [S2](#app3){ref-type="sec"}D). Importantly the second division was always shorter than the first, identifying a latency restricted to HSCs that transition out from a non-stimulated quiescent state. In contrast to in vivo repopulation, cells in this assay do not return to G~0~ after division ([Figure S2](#app3){ref-type="sec"}E). Therefore, the latency phenomenon observed in the first division encompasses the events pertaining to the G~0~ to G~1~ transition but may also include portions of early G~1~. For simplicity, it will be hereafter called "G~0~ exit," and calculated as t~FirstDiv~ -- t~SecondDiv~. By this analysis, LT-HSCs egressed from G~0~ less rapidly than ST-HSCs on average by 5.8 hr ([Figure 2](#fig2){ref-type="fig"}J). Similar results with slower kinetics were obtained when single LT- and ST-HSCs were cultured in a medium with lower cytokine and nutrient concentrations ([Figures S2](#app3){ref-type="sec"}F and S2G). These parameters are not unique to CB: fully quiescent LT-HSCs isolated from adult bone marrow also displayed a significant delay in G~0~ exit compared to ST-HSCs ([Figure S2](#app3){ref-type="sec"}H). Collectively, upon stimulation by mitogenic signals, the duration of a division is consistently shorter in human ST-HSCs than in LT-HSCs, whether cells need to transition out of quiescence or continuously cycle.

Distinct Expression of CDK6 Protein in the Quiescent HSC Pool {#sec2.4}
-------------------------------------------------------------

To identify the molecular determinants underlying differences in the duration of LT- and ST-HSC divisions, we screened the 241-gene core signature for genes known to be involved in either G~0~ exit or G~1~ progression. *CDK6* was selected because its mRNA was consistently upregulated in ST-HSCs both in CB and upon xenotransplantation ([Figures 3](#fig3){ref-type="fig"}A, [S3](#app3){ref-type="sec"}A, and S3B), and CDK6/CyclinD complexes regulate G~0~ exit and early G~1~ ([@bib34]). Importantly, the CDK6 protein was undetectable in most of the quiescent CB LT-HSCs, but it was upregulated after 4 days of culture when all HSC subsets actively cycle ([Figures 3](#fig3){ref-type="fig"}B and 3C). In sharp contrast, before culture, ST-HSCs already expressed high levels of CDK6 protein, similar to that found in G~1~ GMPs, despite being quiescent ([Figures 3](#fig3){ref-type="fig"}B, 3C, and [S3](#app3){ref-type="sec"}C). Similarly, adult ST-HSCs isolated from either bone marrow or mobilized peripheral blood also expressed high levels of CDK6, though it was undetectable in adult LT-HSCs ([Figures S3](#app3){ref-type="sec"}D--S3F). To gain insight into how ST-HSCs can remain quiescent with high levels of CDK6 protein, and because CDK6 kinase activity depends on its association with CyclinD proteins, we examined the levels of CyclinD1 and CyclinD3 protein in sorted LT- and ST-HSC subsets from purified CB at day 0. We found that neither one expressed CyclinD1 nor CyclinD3; as expected, both were expressed in G~1~ GMPs ([Figures S3](#app3){ref-type="sec"}G, S3H, [3](#fig3){ref-type="fig"}D, and 3E). Therefore the CDK6 in ST-HSCs is not part of an active complex, which explains the absence of RB phosphorylation in these cells ([Figure 2](#fig2){ref-type="fig"}F) and their quiescence. To gain insight into how the CyclinD-CDK6 complexes integrate proliferative signals once HSCs are activated, we did a time course analysis of CDK6, CyclinD1, and CyclinD3 protein expression. After 2 days of culture, less than 5% of LT- and ST-HSCs divided ([Figure 2](#fig2){ref-type="fig"}G). Interestingly, about 54% of LT-HSCs expressed CDK6 and 44% and 25% express CyclinD1 and CyclinD3, respectively ([Figures S3](#app3){ref-type="sec"}H and [3](#fig3){ref-type="fig"}F). In contrast, almost all ST-HSCs had upregulated CyclinD3 protein by day 2, and 52% express CyclinD1. By day 4, when all LT- and ST-HSCs actively cycle ([Figure 2](#fig2){ref-type="fig"}G), each HSC subset expresses both CDK6 and CyclinD3 proteins ([Figure 3](#fig3){ref-type="fig"}F). These data indicate that, upon activation by mitogens, the assembly of the CDK/CyclinD complex is more rapid and more robust in ST-HSCs than in LT-HSCs. Overall, these data reveal two unexpected findings. First, ST-HSCs exist in a G~0~ state, yet they express a known driver of G~1~ progression (CDK6) while lacking the cognate partners (CyclinD1 and CyclinD3) of CDK6. Second, the hierarchical organization based on functional repopulation properties also exhibits a hierarchy of CDK6/CyclinD complex components reflecting distinct cycling properties: LT-HSCs are negative for both CDK6 and CyclinD; ST-HSCs express exclusively CDK6; and lineage-restricted progenitors, e.g., GMPs, express both.

CDK6 Levels Regulate the Duration of G~0~ Exit {#sec2.5}
----------------------------------------------

To gain mechanistic insight into the correlation between CDK6 protein levels and cell division duration within HSC subsets, we altered CDK6 levels and investigated the effect on the kinetics of the first HSC division. To examine loss of function, we measured the duration of cell division when single LT- and ST-HSCs are exposed to a mitogenic stimulus in the presence of the highly specific CDK4-CDK6 inhibitor PD033299. The majority of LT-HSCs never divided in the presence of PD033299 ([Figure 4](#fig4){ref-type="fig"}A). Similarly there was a strong reduction in the number of ST-HSCs that could divide ([Figure 4](#fig4){ref-type="fig"}A). However, for those ST-HSCs that divided, inhibition of CDK6 brought the length of the first division to that of LT-HSCs ([Figures 4](#fig4){ref-type="fig"}B and 4C). Intriguingly, those 10% LT-HSCs dividing in the presence of PD033299 were not further delayed, potentially representing a subset of more "activated" cells within the LT-HSC phenotypic compartment. To examine the consequences of CDK6 gain of function, we enforced expression of CDK6 protein (CDK6 EE) with lentiviral vectors in LT- and ST-HSCs before their first division ([Figures S4](#app3){ref-type="sec"}A and S4B). CDK6 EE did not change any ST-HSC cell cycle parameters ([Figures 4](#fig4){ref-type="fig"}D--4F, [S4](#app3){ref-type="sec"}C, and S4E). By contrast, a division starting from G~0~ (first division) of CDK6 EE LT-HSCs was significantly shortened to values similar to those of ST-HSCs ([Figures 4](#fig4){ref-type="fig"}D and 4E); control transduced LT-HSCs (LUC) showed no such changes. CDK6 EE did not decrease the duration of a division starting from G~1~ that remained significantly longer than that of ST-HSCs ([Figures 4](#fig4){ref-type="fig"}F, [S4](#app3){ref-type="sec"}C, and S4F). Also, CDK6 EE did not affect the long-term proliferative output of LT-HSCs in vitro in conditions where cells do not return to G~0~ ([Figure 4](#fig4){ref-type="fig"}G). These approaches show that CDK6 shortens divisions starting from G~0~, but not divisions starting from G~1~. Moreover, variation in CDK6 protein levels between HSC subsets results in active regulation of the duration of the latency that is unique to HSCs transiting out of G~0~, a process we define as G~0~ exit. Importantly, our data also establish that pre-existent CDK6 in ST-HSCs primes them for earlier cell division upon mitogenic stimuli.

CDK6 EE Confers a Competitive Advantage to LT-HSCs without Exhaustion {#sec2.6}
---------------------------------------------------------------------

In mouse models, failure to maintain quiescence and/or increased cycling are mostly associated with decreased self-renewal and eventual HSC exhaustion ([@bib23; @bib25; @bib32]). To examine the long-term effect of exclusively accelerating the duration of exit from quiescence upon reception of mitogenic stimuli, we enforced CDK6 expression in LT-HSCs in vivo, where HSCs return to G~0~ after most divisions under homeostatic conditions ([@bib40] and [Figure S1](#app3){ref-type="sec"}B). Competitive xenotransplantation experiments showed that CDK6 EE does not confer a proliferative advantage within the first 4 weeks post-transplantation, during which HSCs are actively cycling ([Figure 5](#fig5){ref-type="fig"}A). However, by 20 weeks post-transplantation, most HSCs have regained quiescence. At this point, CDK6 EE cells, unlike LUC cells, significantly outcompete untransduced cells (median GFP^+^ percentage: LUC, 59.4%; CDK6 EE, 76.2%, p = 0.007, [Figure 5](#fig5){ref-type="fig"}A) without displaying any lineage bias ([Figures 5](#fig5){ref-type="fig"}B and [S5](#app3){ref-type="sec"}A). This expansion originated from LT-HSCs ([Figures 5](#fig5){ref-type="fig"}C and 5D) and extended to all progenitor populations ([Figure S5](#app3){ref-type="sec"}B). To determine whether the CDK6 EE in phenotypic LT-HSCs altered serial repopulating capacity, secondary transplantation was performed. There was no significant difference in the graft size at 12 weeks after secondary transplantation when high numbers of control (LUC/GFP^−^) and CDK6 EE LT-HSCs were transplanted ([Figures 5](#fig5){ref-type="fig"}E and [S5](#app3){ref-type="sec"}C). However, limiting dilution analysis revealed a 4-fold increase in the frequency of repopulating LT-HSCs in the CDK6 EE group compared to controls ([Figure 5](#fig5){ref-type="fig"}F), confirming LT-HSC expansion over two rounds of transplantation. Importantly, like our in vitro results, CDK6 EE did not change the rate of cell cycle transit of LT- or ST-HSCs ([Figure S5](#app3){ref-type="sec"}D) but accelerated the first division of LT-HSCs ([Figure S5](#app3){ref-type="sec"}E). These data show that the unique shortening in the duration of G~0~ exit conferred by CDK6 EE gives LT-HSCs a competitive advantage without altering self-renewal or differentiation abilities.

Simulating the Impact of G~0~ Exit Durations on Hematopoietic Homeostasis {#sec2.7}
-------------------------------------------------------------------------

Our data show that upon activation, LT-HSCs are delayed in their quiescence exit. Because LT-HSCs have been estimated to divide very infrequently, approximately once every 135 and 280 days in mouse and human, respectively ([@bib4; @bib40]), we sought to quantify consequences of this delay to cell cycle entry in homeostatic conditions. Because it is impossible to experimentally examine homeostatic human HSC pool dynamics over long periods, we turned to computational modeling. Our data strongly suggest that control of cell division is achieved through regulation of quiescence exit and cell cycle transit as two discrete steps. We established an agent-based model to investigate (1) the consequences of independent control of the duration of quiescence exit and (2) the effect of the 5.8 hr delay in LT-HSC quiescence exit. In this model, the maintenance of the number of cells in the system is controlled in a closed loop, and dynamic properties of the model---how often cells divide and how quick the response to injury---arise purely from the different durations of the stages of cell division ([Figure S6](#app3){ref-type="sec"}A). All parameters and assumptions of the model are reported in the [Supplemental Experimental Procedures](#app3){ref-type="sec"}. Most parameters, in particular the division times (mean ± SD), were measured experimentally. When not possible (i.e., HSC pool exit rate and noise), we tested the full range of possible values (discussed in the [Supplemental Experimental Procedures](#app3){ref-type="sec"}) and chose those predicting a number of HSC divisions per year that is in the range reported in the literature for human HSCs ([@bib4]) ([Figures S6](#app3){ref-type="sec"}B--S6G). With this set of physiologically relevant parameters, we investigated the outcome of (1) a control situation in which division is controlled with one kinetic parameter (from reception of signal to the generation of two daughter cells, with cells committed to divide upon sensing the signal), and (2) a situation in which quiescence exit and cell cycle transit are controlled independently, and where commitment to division happens only once the cell has transitioned out of the quiescence exit phase ([Figure 6](#fig6){ref-type="fig"}A). We invariably found that the number of LT-HSC divisions is lower when the duration of a cell division starting from G~0~ is defined by two independent kinetic parameters (quiescence exit and cell cycle transit) rather than a single parameter describing the average division time ([Figures 6](#fig6){ref-type="fig"}B and 6C). Furthermore, the overall number of LT-HSC divisions simulated to occur over 1 year was again decreased with a 5.8 hr delay in LT-HSC t~G0\ exit~ ([Figures 6](#fig6){ref-type="fig"}B and 6C). In fact, a delay as short as 2.6 hr was sufficient to significantly spare the number of LT-HSC divisions ([Figure 6](#fig6){ref-type="fig"}D). In addition, in response to perturbation such as might be experienced under hematopoietic stress, the rate of recovery in the HSC and progenitor pools was considerably improved by regulation through two kinetic parameters and even further when the delay in t~G0\ exit~ in LT-HSCs was included ([Figures 6](#fig6){ref-type="fig"}E, [S6](#app3){ref-type="sec"}H, and S6I). Our model thus demonstrates that the ability to modulate the length of G~0~ exit independently of changes in duration of cell cycle transit provides better robustness to homeostatic and stress response hematopoiesis. Importantly, a delay in the duration of G~0~ exit in LT-HSCs compared to ST-HSCs leads to further optimization, indicating that regulation of the duration of the G~0~ exit phase rather than that of a whole division is key to controlling HSC pool maintenance and hematopoietic system responses.

Discussion {#sec3}
==========

Our study provides key insights into the regulation of cycling within the HSC pool and furthers our understanding of quiescence. We establish that the level of CDK6 functions as a master regulator of the duration of quiescence exit. CDK6 is differentially regulated at the transcriptional and post-transcriptional level in HSC subsets. In particular, the absence of CDK6 protein in LT-HSCs results in a 5--6 hr delay to G~0~ exit. The cumulative effect of this delay limits LT-HSC divisions and ultimately preserves HSC pool integrity in the long term. Because human HSC possess unique mechanisms to prevent propagation when damaged ([@bib38; @bib19]), we speculate that delayed G~0~ exit may also be crucial to coordinate repair and LT-HSC fate choices upon their exposure to stress.

In line with the importance of the relative levels of the CyclinD-CDK partners in mediating cell cycle entry and progression ([@bib34]), our data indicate that the presence of CDK6 in ST-HSC is sufficient to place these cells in the "starting blocks" for division upon mitogenic signaling. Production and activation of CyclinD-CDK complexes is gradual and involves many levels of regulation including gene transcription, protein stability, assembly, and nuclear import ([@bib34]). Consistent with our results a recent study found that a constitutive knockout of CDK6 does not affect HSCs in homeostasis, but their activation in vivo by mitogenic signals such as 5-FU or IFN is prevented ([@bib33]). In line with what is seen in HSCs, memory T cells segregate fully formed CyclinD3/CDK6 complexes in their cytoplasm, which, upon antigen stimulation, allow them to enter cell cycle faster than naive T cells wherein both CDK6 and CyclinD3 are expressed at much lower levels ([@bib39]). Together, these findings support a model in which any molecular configuration that puts cells closer to an active CDK/CyclinD complex is likely to result in faster cell cycle entry/G~0~ exit. Our data further indicate that deeper (LT-HSCs) and shallower (ST-HSCs) states of quiescence are an embedded feature of the hematopoietic hierarchy at homeostasis. Furthermore, the CDK6-primed G~0~ state of ST-HSCs does not overlap with G~alert~, a recently described injury-stimulus-induced adaptive mechanism that positions stem cells to rapidly respond to further stress by activating the mTORC1 pathway ([@bib30]). We found that homeostatic ST-HSCs display similar levels of mitochondrial and mTORC activity to that of LT-HSCs, indicating that they are not in G~alert~. Rather, the injury-independent pre-existent diversity in quiescent states that we report coexists with, and is upstream of, G~alert~.

In a high output system like blood, which is sustained by a limited number of active HSCs, a number of theoretical frameworks describe how HSC heterogeneity, notably in division rates, contributes to lifelong maintenance of hematopoiesis. Previous modeling strategies ([@bib9; @bib10; @bib31]) describe the division properties of HSCs by a single, unique parameter that is usually derived from average division frequencies and thus includes the time spent in G~0~ plus the time from the reception of the signal to the end of division. In contrast to these prior studies, we explicitly model the control of the duration of cell division from the time of the mitogenic signal, using a computational model where the signal is automatically generated depending on needs. This framework allows investigation of which division duration control strategy better preserves HSC pool integrity and maximizes system responsiveness. In this context, "quiescence exit" is a phase during which cells receive and accumulate signals prior to committing to division. Our simulations show that when cell division controlled by two independent characteristic times (one before and one after the commitment point), it is far more efficient than if cells are committed to divide within a fixed period after sensing a signal. Control is further optimized when quiescence exit is differentially regulated between LT- and ST-HSCs. Thus, we propose that, even though its molecular boundaries remain to be defined, the duration of quiescence exit is a biologically relevant time interval and the regulation of this duration inherently determines the dynamics of blood formation. Overall our data point to a model of homeostasis where the deeply quiescent state of LT-HSCs with a long duration of exit from quiescence and the CDK6-primed G~0~ state of ST-HSCs together provide a means to achieve efficient production of cells from ST-HSCs while limiting the number of divisions that LT-HSCs undergo.

Our data show that when the G~0~ exit delay is abolished due to CDK6-enforced expression, the CDK6 EE LT-HSCs divide more in repopulation assays, due to repeated rounds of accelerated G~0~ exits. Accelerating exit from quiescence (at least via CDK6 EE) does not alter the balance between self-renewal and differentiation or impair LT-HSC maintenance; rather, LT-HSCs acquire a competitive advantage. This result is in striking contrast with most situations of increased cycling presented in the literature (reviewed in [@bib25]) that cause impaired HSC function. Interestingly, two of the most notable examples that increase HSC division without damaging their long-term function are p18 knockout ([@bib41]) and miR-126 knockdown ([@bib17]). Because p18 is an Ink4 family member known to repress CDK6, and because our recent data suggest that miR-126 also targets CDK6 (E. Lechman and J.E.D., unpublished data), it will be interesting to verify if the delay in G~0~ exit of LT-HSCs is also suppressed in these models. Similarly, it needs to be addressed how G~0~ exit duration is affected in cases where increased cycling leads to HSC exhaustion. Indeed, why increased cycling is generally associated with impaired LT-HSC maintenance remains hypothetical ([@bib23; @bib25; @bib32]). In view of our own results, we speculate that LT-HSCs may be pushed toward differentiation at the expense of self-renewal if they shorten or bypass phases of the cell cycle other than G~0~ exit, undergo several rounds of division without returning to G~0~, or show imbalances in key differentiation genes. Importantly, our experimental and computational modeling data establish that fully functional LT-HSCs can acquire competitive advantages in a purely kinetic way. Such a phenomenon may be crucial during aging or in the initial steps of leukemia, where clonal dominance may uniquely arise as a consequence of the accelerated duration of G~0~ exit of LT-HSCs. Furthermore, recent work indicates that PD033299, which selectively inhibits CDK6, might be efficacious against multiple myeloma ([@bib11]) and MLL-rearranged AML ([@bib27]), malignancies where the pre-leukemic cell of origin is thought to be an HSC. Overall, the finding that the duration of G~0~ exit is a highly relevant biological parameter that controls stem cell pool dynamics warrants further investigation of whether perturbation of stem cell-specific quiescence exit mechanisms represents an early step of malignancy.

Experimental Procedures {#sec4}
=======================

CB Lineage Depletion {#sec4.1}
--------------------

All CB samples were obtained with informed consent according to procedures approved by the institutional review boards of the University Health Network, Trillium, and Credit Valley Hospital. Mononuclear cells were obtained by centrifugation on Lymphoprep medium (Stem Cell Technologies) and were depleted of Lin^+^ cells (lineage depletion) by negative selection with the StemSep Human Progenitor Cell Enrichment Kit according to the manufacturer's protocol (Stem Cell Technologies). Lin^−^ CB cells were stored at −150°C.

Cell Preparation for Cell Sorting {#sec4.2}
---------------------------------

Lin^--^ cells were thawed by drop-wise addition of IMDM/DNase (100 μg/ml, Roche) and were resuspended at 1 × 10^6^ cells /ml. Cells were then stained with the following (with all antibodies from BD, unless stated otherwise): FITC---anti-CD45RA (1:50, 555488), PE---anti-CD90 (1:50, 555596), PECy5---anti-CD49f (1:50, 551129), V450---anti-CD7 (1:33.3, 642916), PECy7---anti-CD38 (1:100, 335790), APC---anti-CD10 (1:50, 340923), and APCCy7---anti-CD34 (1:100, custom made by BD). Cells were sorted on FACS Aria III (Becton Dickinson) or MoFlo (Beckman Coulter) sorters, consistently yielding \>95% purity. LT-HSCs were sorted based on the following markers: CD34^+^ CD38^−^ CD45RA^−^ CD90^+^ CD49f^+^; ST-HSCs, based on CD34^+^ CD38^−^ CD45RA^−^ CD90^−^ CD49f^−^; and GMPs, based on CD34^+^ CD38^−^ CD10^−^ CD7^−^ CD45RA^+^.

Single-Cell Experiments {#sec4.3}
-----------------------

Single LT-HSCs or ST-HSCs were sorted into 96-well round-bottom Nunc plates in 100 μl of either high or low cytokines media, using FACS Aria III (Becton Dickinson). Cells were centrifuged 5 min at 400 × *g* and incubated at 37°C for 1 week. Cells were visualized and counted in each well twice a day using an inverted microscope. High cytokine condition medium was by StemPro (Stem Cell Technologies) supplemented with StemPro nutrients (Stem Cell Technologies), L-glutamine (GIBCO), Pen/Strep (GIBCO), human LDL (Stem Cell Technologies, 50 ng/ml), and the following cytokines (all from Miltenyi): SCF (100 ng/ml), Flt3L (20 ng/ml), TPO (100 ng/ml), EPO (3 units/ml), IL-6 (50 ng/ml), IL-3 (10 ng/ml), and GM-CSF (20 ng/ml). Low cytokine condition medium was composed of X-VIVO 10 medium (BioWhittaker) supplemented with 1% BSA (Roche), L-glutamine (GIBCO), Pen/Strep (GIBCO), and the following cytokines (all from Miltenyi): SCF (100 ng/ml), Flt3L (100 ng/ml), TPO (50 ng/ml), and IL7 (IL-7; 10 ng/ml).

Immunofluorescence {#sec4.4}
------------------

5 × 10^3^ sorted LT-HSCs, ST-HSCs, or GMPs sorted by flow cytometry were fixed ovre 10 min at room temperature (RT) in PBS and 2% paraformaldehyde, washed in PBS, distributed in 150 μl of PBS on polylysine-coated slides, and incubated overnight in a humidified chamber at RT. Cells were then permeabilized over 10 min in 0.2% Triton (SIGMA), washed twice in PBS, and blocked over 20 min using 150 μl of PBS and 10% Goat Serum (Life Technologies). Cells were stained over 1 hr at RT in 150 μl of primary antibody solution in PBS and 10% Goat Serum with appropriate concentrations (CDK6, mouse monoclonal ab54576, Abcam, or CDK6 B-10, Santa Cruz sc7961; mouse IgG, Santa Cruz sc-2025; Cyclin D3 (C-16), Santa Cruz: sc-182; rabbit IgG, Santa Cruz sc-2027). After cells were washed twice in PBS, secondary antibody solution (goat anti-mouse Alexa 488, Life Technologies, A11001) was added over 45 min at RT in the dark in 150 μl PBS (10% Goat Serum) with the appropriate concentration (usually 1:500). Slides were visualized on an Axioimager microscope and fluorescence quantification and cell diameter measurements were performed with ImageJ software.

Cell cycle analysis assay, xenotransplantation, the derivation of cell cycle parameters, modeling, transcriptome studies, bioinformatics, qPCR, lentiviral transduction, and mitochondrial mass measurements are reported in the [Supplemental Experimental Procedures](#app3){ref-type="sec"}.

Author Contributions {#sec5}
====================

E.L. and J.E.D designed the study; E.L., S.X., C.F. and C.F. analyzed and interpreted the data; E.L., S.X., C.F., R.F, S.Z., and SD performed experiments; C.F.D. performed computational modeling; A.N. and I.P. cloned lentiviral vector plasmids and did RT-PCR; J.C. and C.A. did gene-expression profiling experiments;, J.B.F. and N.I. supervised specific experiments; E.L. wrote the manuscript; E.L., S.X., C.F., R.F, C.F.D, N.I., and J.E.D edited the manuscript; and J.E.D. supervised the study. E.L., C.F., and S.X. contributed equally to this study.

Accession Numbers {#app1}
=================

The GEO database accession number for the gene expression data reported in this paper is [GSE58299](ncbi-p:GSE58299){#intref0010}.

Supplemental Information {#app3}
========================

Document S1. Figures S1--S6, Table S1, and Supplemental Experimental ProceduresTable S2. List of 241 Genes Differentially Expressed between LT- and ST-HSCs as Determined by BETR Algorithm, Related to Figure 1Document S2. Article plus Supplemental Information

We thank the obstetrics unit of Trillium Health Partners (Missisauga and Credit Valley sites) for cord blood samples; N. Simard, P.A. Penttilä, A. Khandi, L. Jamieson, and S. Zhao at the UHN-SickKids Flow Cytometry Facility for cell sorting; V. Voisin and G. Bader for advice on bioinformatics; and M. Doedens for help with intrafemoral injections. This work was supported by the Swiss National Science Foundation (E.L.), Roche (E.L.), the Fondation Suisse pour les Bourses en Médecine et Biologie (E.L.), the Swedish Research Council (S.Z.); and a Canadian Institutes of Health Research (CIHR) fellowship in partnership with the Aplastic Anemia and Myelodysplasia Association of Canada (S.Z.). Work in J.E.D.'s laboratory is supported by grants from the CIHR, Canadian Cancer Society, Terry Fox Foundation, Genome Canada through the Ontario Genomics Institute, Ontario Institute for Cancer Research with funds from the province of Ontario, a Canada Research Chair, the Princess Margaret Hospital foundation, and the Ontario Ministry of Health and Long Term Care (OMOHLTC). Research in E.L.'s laboratory is currently supported by a recruitment support from the Wellcome Trust and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust -- Medical Research Council Cambridge Stem Cell Institute.

This is an open access article under the CC BY license (<http://creativecommons.org/licenses/by/4.0/>).

![Human HSC Subsets in the Xenograft Divide with Distinct Frequencies and Display Distinct Transcriptional Profiles\
(A) Number of cells per mouse of indicated populations in the bone marrow of the mice at indicated time points post-transplantation of 70,000 Lin^−^ CB (saturating number of LT-HSCs). Median and interquantile ranges are shown. ^∗∗∗^p \< 0.01 by one-way ANOVA and Tukey test.\
(B) The number of repopulating LT-HSCs per mouse at indicated time points post-transplantation were calculated by multiplying the number of phenotypic LT-HSCs shown in (A) by the frequency of long-term repopulating cells indicated in [Table S1](#app3){ref-type="sec"}.\
(C) BrdU incorporation kinetics over 12 days of LT-HSC (black) and ST-HSC (red) enriched populations isolated from pools of two to five mice engrafted with 70,000 Lin^−^ CB cells. BrdU was started either at 4 (left panel, expanding phase) or 20 weeks post-transplantation (right panel, equilibrium phase). n = 1--4 pools of three to five mice from six (4 weeks) or one (20 weeks) independent CB samples. Curve is least-squares fit. Left panel: R^2^ \> 0.96; right panel: R^2^ \> 0.98. Doubling times (half times of fit) in hours are shown in the insert. ^∗∗∗^p \< 0.01 by extra-sum of squares test.\
(D and E) Derivation of a 241-gene signature distinguishing LT- and ST-HSCs in unperturbed CB over 20 weeks in a xenotransplant. (D) Examples of five expression profiles of genes with known HSC function over the course of 20 weeks of xenotransplant (black: LT-HSCs, red: ST-HSCs), mean ± S.E.M shown, n = 3 per time point. (E) Selected gene ontology terms significantly enriched in the 241-gene LT-HSC/ST-HSC core signature. Shown is the −log~10~ of the Benjamini-Hochberg adjusted p value.\
See also [Figure S1](#app3){ref-type="sec"}.](gr1){#fig1}

![LT- and ST-HSCs Are Equally Quiescent, but upon Mitogenic Stimulation They Differ in the Duration of Divisions Starting from G~0~ or G~1~\
(A and B) Proportion of human CB HSC and progenitor cells in each phase of the cell cycle. Parameters were assessed by flow cytometry using Ki67 and Hoechst (Ki67^−^ 2n DNA content, G~0~; Ki67^+^ 2n DNA content, G~1~; Ki67^+^ \> 2n DNA content, S-G~2~-M). (A) Representative flow cytometry cell cycle profiles of CB LT- and ST-HSCs and the percentage of cells in each gate. Event count: LT-HSCs (top panel), 1,320 cells; ST-HSCs (bottom panel), 1,143 cells. (B) Mean ± SEM is shown; n = 3 CB samples.\
(C) Cell diameter of indicated populations measured with ImageJ from microscopy pictures. n \> 323 cells from four independent CB samples.\
(D) Mitochondrial mass as measured by flow cytometry with MitoGreen. MFI, Mean Fluorescence Intensity; mean ± S.E.M shown, n = 2 independent CB samples.\
(E) PhosphoS6 protein levels as measured by flow cytometry. Left panels: representative flow cytometry plots; black line, LT-HSCs; red line, ST-HSCs. Right panel: median fluorescence intensity of phosphoS6 staining. Mean ± SEM is shown. n = 2 CB samples.\
(F) Percentage of cells positive for phosphoRB (S807/S811) as measured by flow cytometry; mean ± S.E.M shown, n = 2 independent CB samples. GMP, granulocyte-monocyte progenitors.\
(G) Cumulative first division kinetics (excluding dead cells) of LT-HSCs (black) and ST-HSCs (red) from a representative CB example. Curve is least-squares sigmoid fit. R^2^ \> 0.99. Arrowheads represent time to first division as estimated from sigmoid fit (t~FirstDiv~ = logEC~50~). Time 0 is the time of exposure to mitogenic stimulus.\
(H) Mean time to first division (in hours).\
(I) Mean time of cell cycle transit (t~SecondDiv~ = logEC~50~ of sigmoid fit of cumulative second division kinetics; see [Figure S2](#app3){ref-type="sec"}E).\
(J) Mean time of G~0~ exit (in hours) (t~G0exit~ = t~FirstDiv~ -- t~SecondDiv~).\
In (H)--(J), individual CB samples are shown; gray lines connect LT-HSC and ST-HSC parameters from the same CB. ^∗∗^p \< 0.05, ^∗∗∗^p \< 0.01 by paired t test. See also [Figure S2](#app3){ref-type="sec"}.](gr2){#fig2}

![Distinct CDK6 Levels Govern the G~0~ Exit Kinetics of LT- and ST-HSCs\
(A) Log2 signal intensity for *CDK6* mRNA probe. Shown are individual measures (black circles: LT-HSCs, red squares: ST-HSCs, green triangles: GMPs) and the median and interquantile ranges (horizontal bars); n = 3. All multiple comparisons have been tested.\
(B) Immunofluorescence for CDK6 protein in LT- and ST-HSCs sorted from CB (left panel) or cultured for 4 days (right panel). Representative pictures and histograms of CDK6 fluorescence density are normalized to the fluorescence density of the IgG control in the same population. Positivity threshold was set over the median + 1 SD of the IgG control distribution and the percetnage of positive cells is indicated. 100--570 cells are analyzed with n = 3 CB samples. Scale bar represents 10 μM.\
(C) Normalized median CDK6 fluorescence density. Mean ± SEM is shown; n = 3 CB samples. GMPs, granulocyte-monocyte progenitors.\
(D) Immunofluorescence for CyclinD3 protein in LT-HSCs, ST-HSCs, and GMPs from freshly isolated CB (Day 0, left panel) or after 4 days of culture (Day 4, right panel). Shown are histograms of CyclinD3 fluorescence density normalized to the fluorescence density of the IgG control in the same population. Positivity threshold (dotted line) was set over the median + 1 SD of the IgG control distribution. n = 135--315 cells analyzed for day 0 and n = 49--245 cells for day 4.\
(E) Normalized median CyclinD3 fluorescence density at the indicated time points. Mean ± SEM is shown; n = 3 CB samples. ^∗∗^p \< 0.05 by paired t test.\
(F) Time course analysis of CDK6 and CyclinD3 upon stimulation by mitogenic signals. Percentages of CDK6^+^ (top panel) or CyclinD3^+^ (bottom panel) cells in each of the indicated populations at the indicated time points after isolation from CB are shown. Mean ± S.E.M shown. n = 3 CB samples, except for day 2, where n = 2 CB samples. ^∗∗^p \< 0.05 by paired t test.\
See also [Figure S3](#app3){ref-type="sec"}.](gr3){#fig3}

![CDK6 Levels Determine the Duration of Quiescence Exit in the HSC Pool\
(A--C) Cell division duration of single LT- and ST-HSCs after exposure to mitogenic signal in the presence or absence of PD033299 (50 nM). (A) Percentage of cells from the indicated populations that divided after 100 hr in culture. (B) Cumulative first division kinetics (excluding dead cells). Data from a representative CB example are shown. Curve is least-squares sigmoid fit. R^2^ \> 0.99. (C) Mean time to first division (hours) (t~firstDiv~ = logEC~50~).\
(D--G) Cell division duration of single LT- and ST-HSCs after exposure to mitogenic signal with or without CDK6 EE. (D) Cumulative first division kinetics (excluding dead cells) of indicated populations transduced with indicated lentiviral vectors. Data from a representative CB are shown. Curve is least-squares sigmoid fit. R^2^ \> 0.99. (E) Mean time to first division (hours) (t~firstDiv~ = logEC~50~). (F) Time of cell cycle transit of indicated populations in hours.\
(G) Expansion curves of LT- and ST-HSCs in culture. Shown is the average number of cells per single cell plated at the indicated time points after culture initiation. Data are from one representative experiment out of three. Time 0 represents the time of exposure to mitogenic stimulus.\
In (A), (C), (E), and (F), individual CB samples are shown; gray lines connect parameters from the same condition. ^∗∗^p \< 0.05 by paired t test. See also [Figure S4](#app3){ref-type="sec"}.](gr4){#fig4}

![CDK6 EE LT-HSCs Outcompete Wild-Type HSCs without Exhaustion\
(A--D) NSG mice were injected with sorted Lin^−^ CD34^+^ CD38^−^ cells transduced with CDK6 EE or control (LUC) lentiviral vectors (GFP^+^ cells) and untransduced competitive cells (GFP^−^). Bone marrow was harvested at indicated time points post-transplantation and analyzed by flow cytometry. (A) Percentage of GFP^+^ cells among engrafted human hematopoietic cells (CD45^+^). Time 0 corresponds to percentage of GFP^+^ cells before injection in four independent CB samples. 4 weeks post-transplantation: n = 13 LUC and 14 CDK6 EE mice; 20 weeks post-transplantation: n = 25 LUC and 23 CDK6 EE mice. (B) Lymphoid to myeloid ratio (percentage of CD19^+^/CD33^+^) among GFP^+^ cells at 20 weeks post-transplantation. n = 25 LUC and 23 CDK6 EE mice. In (A) and (B), boxplots represent median, 25^th^, and 75^th^ percentiles and whiskers represent min and max. Gray boxes, LUC; white boxes, CDK6 EE. (C) Percentage of GFP^+^ cells among LT-HSCs at 20 weeks post-transplantation. (D) Absolute number of LT-HSCs at 20 weeks post-transplantation. In (C) and (D), n = 6 mice from two CB samples. Individual mice, median, and interquantile range are shown. In (A)--(D), ^∗^p \< 0.1, ^∗∗^p \< 0.05, ^∗∗∗^p \< 0.01 by Mann-Whitney test.\
(E and F) CDK6 EE LT-HSCs expand over serial transplantation. LUC, CDK6 EE (GFP^+^), or untransduced (GFP^−^) LT-HSCs were sorted from primary transplanted mice (n = 2 pools of three to five mice) and injected at four different doses into secondary NSG mice. (E) Engraftment levels (percentage of CD45^+^ cells) 12 weeks after secondary transplantation (\>0.01% CD45^+^ GFP^+^ or CD45^+^ GFP^−^) at the two highest doses (200 and 400 cells/mouse). Individual mice, median, and interquantile range are shown. (F) Summary table of number of mice engrafted at each dose tested and estimation of LT-HSC frequencies in each group by the ELDA statistical method. See also [Figure S5](#app3){ref-type="sec"}.](gr5){#fig5}

![Simulation of the Impact of Delayed G~0~ Exit in LT-HSCs on the HSC Pool with an Agent-Based Model\
(A) Comparison between the three hypotheses tested by the modeling strategy. Earlier modeling strategies of homeostasis assumed that all HSCs started division upon receiving a mitogenic signal with one characteristic cycling time per HSC subtype (HYP. 1). Rather, we propose that G~0~ exit and cell cycle progression are differentially and independently regulated. This results in two characteristic cycling times per HSC subtype without (HYP. 2.1) or with (HYP. 2.2) a delay in G~0~ exit between LT- and ST-HSCs. Cycling times indicated are as measured in [Figures 2](#fig2){ref-type="fig"}G--2J. (B and C) Simulated number of LT-HSC divisions per year in the HSC pool at homeostasis (B) and after perturbation (C). (D) Effect of changing the LT-HSC G~0~ exit delay parameter on the number of LT-HSC divisions. Shown is the number of divisions in the LT-HSC pool per year when the delay of G~0~ exit of LT-HSCs (compared to ST-HSCs) is inputted at 0 (no delay), 2.9, 5.8 (experimental value), or 11.6 hr. (E) Perturbation model: 1% of the progenitor compartment was eliminated at the time indicated by an arrow to simulate injury. Number of progenitor cells (left panel), ST-HSCs (middle panel), and LT-HSCs (right panel) are displayed as a function of time. In (B)--(E), data represent the mean ± SD of 256 runs. The simulations were run with a noise parameter of 5% and an HSC pool exit rate of 24 cells per day. How parameters were chosen and results with different parameters are shown in [Figure S6](#app3){ref-type="sec"} and discussed in the [Supplemental Experimental Procedures](#app3){ref-type="sec"}.](gr6){#fig6}

[^1]: Co-first author

[^2]: Present address: Wellcome Trust -- Medical Research Council Cambridge Stem Cell Institute, Department of Haematology, University of Cambridge, Cambridge, CB2 0AH, UK