Search tips
Search criteria 


Logo of lwwopenLippincott Williams & WilkinsThis article
Current Opinion in Hematology
Curr Opin Hematol. 2016 July; 23(4): 295–303.
Published online 2016 April 28. doi:  10.1097/MOH.0000000000000250
PMCID: PMC4900429

Unperturbed vs. post-transplantation hematopoiesis: both in vivo but different


Purpose of review

Hematopoietic stem cell (HSC) transplantation has yielded tremendous information on experimental properties of HSCs. Yet, it remains unclear whether transplantation reflects the physiology of hematopoiesis. A limitation is the difficulty in accessing HSC functions without isolation, in-vitro manipulation and readout for potential. New genetic fate mapping and clonal marking techniques now shed light on hematopoiesis under physiological conditions.

Recent findings

Transposon-based genetic marks were introduced across the entire hematopoietic system to follow the clonal dynamics of these tags over time. A polyclonal source downstream from stem cells was found responsible for the production of at least granulocytes. In independent experiments, HSCs were genetically marked in adult mice, and the kinetics of label emergence throughout the system was followed over time. These experiments uncovered that during physiological steady-state hematopoiesis large numbers of HSCs yield differentiated progeny. Individual HSCs were active only rarely, indicating their very slow periodicity of differentiation rather than quiescence.


Noninvasive genetic experiments in mice have identified a major role of stem and progenitor cells downstream from HSCs as drivers of adult hematopoiesis, and revealed that post-transplantation hematopoiesis differs quantitatively from normal steady-state hematopoiesis.

Keywords: clonal marking, fate mapping, mathematical modeling, stem cell transplantation, unperturbed hematopoiesis


Compared to solid tissues that are maintained by stem cells such as the gut or the skin, the hematopoietic system is particularly accessible. This is because of the fact that the mostly migratory cells can readily be retrieved from the blood and from lymphoid organs. A detailed phenotypic map of cells, cell populations, tissues, and organs under resting, immune-responding, or pathological conditions is now available. Multicolor phenotyping by flow cytometry has evolved into mass cytometry that allows the detection of tens of markers simultaneously on the cell surface and inside of single cells. Unlike in flow cytometry, the cells are destroyed in the process and cannot further be examined functionally. mRNA sequencing and DNA mutation analyses in single cells have become powerful tools. Collectively, the immune system can now be deconvoluted to the resolution of single cells, and at large scales, that is, for thousands of individual cells [14].

In vitro, precursor–product relationships starting from single cells have been examined since the development of hematopoietic colony assays. This area has been much refined by high-resolution tracking of single cells undergoing colony formation, and by simultaneous observation of gene expression associated with, or driving lineage commitment [57]. These assays read out the possible potential, and not necessarily the potential that is realized in vivo. Cells may be exposed to ‘supra-physiological’ conditions, often aiming at maximal cloning efficiencies or burst sizes, which can result in developmental skewing, or even impose a potential on cells. For example, ectopic expression of Notch-ligands, Delta-like-1 or 4 on stromal cells can persuade undecided progenitors toward a T-cell lineage program [8]. Notch signals are essential for T-cell development but forcing progenitors down the T-cell path in vitro does not necessarily identify those cells that would normally take this route [9,10].

Because in-vitro readouts may or may not reveal the physiology of hematopoiesis, transplantation of stem and progenitor cells into myeloablated recipients has been the mainstay of in-vivo hematopoiesis research [11]. Transplantation is a robust assay, the conditions are well established, it works experimentally in animals and clinically in humans, and the reconstituted blood and immune systems are functional long term. Successful reconstitution from a single hematopoietic stem cell [(HSC), in a fraction of the mice] [1217,18[filled square][filled square],19[filled square][filled square],20[filled square]], proves within this system two postulated key properties of the HSC true:

  1. Self-renewal: One HSC needs to reconstitute thousands of cells in the HSC compartment, which can only be achieved by massive proliferation without obvious loss of HSC identity.
  2. Multipotency: One HSC generates mature progeny for essentially all hematopoietic lineages, demonstrating, by definition, multipotency of transplanted HSC in vivo.

Genetic barcoding prior to transplantation is a sophisticated approach to track individually tagged cells and their progeny in mice transplanted with bulk populations of stem cells [2123]. After transplantation, high-throughput sequencing of DNA barcoding tags has demonstrated self-renewal and multipotency, and revealed that only few, that is, in the order of tens, HSCs contribute to hematopoiesis [21,22].

Self-renewal and multipotency are fascinating but experimentally challenging to demonstrate for normal hematopoiesis. HSC transplantation represents an artificial situation, and little has been known about the degree of self-renewal and multipotency in the normal bone marrow. In-vivo lineage tracing has now provided evidence for self-renewal (maintenance of few labeled HSCs throughout the lifespan of the mouse) and multipotency (full lineage spectrum generated from few HSCs in vivo, although not at clonal level). HSC proliferation [2426] as such is no indicator of differentiation or self-renewal because proliferation can feed either process. Finally, the relative roles of HSCs vs. downstream stem and progenitor cells for the maintenance of steady-state hematopoiesis, or the differentiation flow through the system remained enigmatic.

Collectively, soon there will be complete structural information of all cellular components of the immune system at very high resolution. Yet, as an outlook, noninvasive experimental methods have to be developed to address key open questions on the operation of hematopoiesis under physiological conditions. Here, we will review recent studies on unperturbed hematopoiesis. It now appears that, while the hematopoietic system can be reconstituted by bone marrow transplantation, HSC engraftment after transplantation does not recapitulate the system as it originally operated in the donor. Their enormous adaptability to demand and huge regenerative capacity allow HSCs to perform under both conditions, albeit with differing division of labor between stem cells. 

Box 1
no caption available


Phenotypic analysis leads to the description of cells based on parameters that include expression of cell surface markers, intracellular proteins, protein modifications, RNA expression, epigenetic marks, and sequencing of genomic mutations. Phenotyping of living cells plays a key role for the purification of rare cells, including HSCs [18[filled square][filled square],19[filled square][filled square],27,28,29[filled square],30], for prospective analysis in adoptive transfer experiments. Owing to technological advances, phenotyping approaches unprecedented depth and resolution. Mass cytometry enables the simultaneous identification of in the order of 40 phenotypic antigens (including, for example, intracellular signaling cues such as protein phosphorylation) at the single cell level [31,32]. In conjunction with large-scale single-cell RNA sequencing, proteomics and epigenetic analyses, these new tools enable deep (detailed biochemical or genetic ‘status’ of a cell) and broad (degree of heterogeneity within populations) biological information on stem cells and progenitors [3337]. These experiments provide a deconvoluted (yet not easily comprehensible) view on complex cell ensembles [32,34,38]. Recent reviews have covered these developments [1,3,4,39,40].

Most single-cell approaches require the retrieval of cell suspensions from biological samples, and artifacts in the wake of cell isolation, sorting and further manipulations cannot fully be excluded. In this regard, the combination of mass spectrometry with immunohistochemistry is not only offering new avenues in pathology but may also bring some of these new technologies closer (or back) to the tissues [39,41]. Still also here, fixation and other procedures may not be always neutral [2].

Ultimately, an important question is whether this holistic phenotypic approach leads to a detailed ‘structural’ map of the immune system (a highly valuable resource), or whether also developmental routes can be recapitulated, or even be predicted, from these data. Several groups have developed mathematical models to extract developmental trajectories (akin to a movie) from a (standing) picture based on data obtained from large bodies of single cells [31,37,42,43]. To what extent these new approaches will help to unravel developmental pathways in previously uncharted territory remains to be determined. It appears, currently, that the obtained trajectories are either fitted to, or tested against known pathways. This is of course important for validation, yet it does not prove that navigation will be safe with no land in sight. It seems likely that the ‘running machine’ is not readily deducible from stills of its parts. Consider a progenitor cell in which a set of transcription factors turns on erythropoietin receptor mRNA transcription; is this cell always destined to become a committed erythrocyte progenitor, or can it turn off the erythropoietin receptor gene and choose an alternative fate? In-vivo fate mapping shows that Cre recombinase drivers inserted into lineage-specific gene loci can indeed separate branches in vivo; however, this separation is not absolute (e.g., in IL7rαCre mice, some myeloid cells are marked, implying that not every cell expressing the IL7rα during its ontogeny will become a lymphocyte) [44]. In summary, fate-mapping experiments are likely necessary to complement the phenotypic picture functionally.


Transplantation of HSCs and progenitors has compelling experimental advantages: it is feasible, and the conditions (e.g., dose of radiation; radioprotective cell doses; and engraftment kinetics) are established. Transplantation can separate durable engraftment (long-term reconstitution; by convention referring to HSC activity for at least 4 months) from transient reconstitution (for about 6 weeks and often important for radioprotection). HSC exhaustion can be tested by serial transplantation. Transplantation can reveal competitive advantages or disadvantages when test and competitor cells are coinjected, it can yield functional information on mutant stem and progenitor cells from knockout mice, it can facilitate the analysis of lethal mutants from which at least fetal liver cells can be isolated, and it can be crucial to distinguish hematopoietic intrinsic vs. extrinsic (systemic) phenotypes.

The race for the world record in enrichment and purity in HSCs has been based on repopulating frequencies starting from single or few cells (or graded numbers of cells in limiting dilution experiments). This was the guiding principle in the original descriptions of phenotypically enriched HSC populations [13,14,16,4547]. It is interesting to observe the reported engraftment frequencies over time. One report claimed near absolute engraftment [48] whereas others reported limitations, for instance because of seeding inefficiencies (e.g., cell loss in the lungs on intravenous transfer or imperfect niche homing) [49]. Repopulating HSCs (also termed long-term [LT] HSCs) are highly enriched in a rare subset (about 0.006%) of cells in bone marrow that have a lineage (Lin)Kit+Sca1+CD150+CD48 phenotype; sometimes further markers (e.g., CD34−/low, CD41, and Flt3) are included. However, some combinations appear redundant because, when tightly gated, some markers virtually exclude cells bearing other markers (e.g., CD34 and CD150 are mutually exclusive).

HSC transcriptomes have been analyzed in the search for new HSC markers and to deduce information on HSC biology [18[filled square][filled square],28,30,33,50]. HSCs have successfully been identified in vivo using reporter mice for Hoxb4 [51], and Fgd5 [29[filled square]]. HSC reporter mice have also been used for the in-situ localization of the stem cell niche in the bone marrow [18[filled square][filled square],30]. In α-catulinGFP knock-in mice, HSC could be enriched by using only this marker from a frequency of 1/37 000 (unfractionated bone marrow) to 1/7 (α-catulinGFP + cells) [18[filled square][filled square]]. Conversely, depletion of α-catulinGFP + cells from bone marrow reduced the repopulating frequency almost 100-fold over total bone marrow. The highest HSC repopulating efficiency (1/3) was achieved by combining α-catulinGFP + cells with the LinKit+Sca1+CD150+CD48 phenotype [18[filled square][filled square]]. One report used Hoxb5 expression to further dissect the HSC compartment [30]. In Hoxb5-triple-mCherry reporter mice, about 20% of phenotypically defined HSCs (LinKit+Sca1+CD150+CD48CD34−/lowFlt3 cells) were mCherry+, and only this fraction was highly enriched for long-term repopulating HSCs (at a frequency of 1/2 compared with 1/16 for mCherry cells). It appears difficult, however, to reconcile low frequencies in the Hoxb5 population with frequencies in the range of 20–40% that others measured previously for the entire phenotypically defined HSC population [13,14,16,17,18[filled square][filled square]].

The procedures underlying transplantation are not standardized. Details of cell purification and cell sorting (e.g., setting of lineage and other gates in the flow cytometer), choice of recipient strains, conditions of myeloablation, efficiency of cell injections, as well as mouse keeping conditions may matter, effectively precluding direct comparisons of repopulating frequencies. Still, HSC populations can be purified to harbor repopulating frequencies of up to 50% [14,18[filled square][filled square],30,52]. Finally, based on transplantation there is evidence for heterogeneity among HSCs based on expression levels of Kit [53] and CD150 [52,54].

In transplantation experiments, the lineage output has been analyzed at various time points for at least lymphocytes (mostly T and B cells) and myeloid cells (mostly granulocytes). The notion that a myeloid bias is a hallmark of aging hematopoiesis is largely based on the transplantation of young and old HSCs. Moreover, transplanted HSCs have fallen into distinct lineage and kinetic repopulation patterns, suggesting functional heterogeneity with the total HSC compartment [16,17,20[filled square],52,54,55]. It is unknown whether such HSC subsets exist in situ.

Genetic barcoding of HSCs and progenitors in vitro, followed by transplantation, has been used to track the clonal output of tagged cells in vivo [2123]. The identification of DNA barcodes in sorted cell populations can uncover the types of lineages and the relative amounts of cells that have been generated in vivo from those tagged HSCs that successfully engrafted. These experiments demonstrated that the number of contributing HSCs (i.e., the number of unique barcodes) is very low (in the order of tens). By extrapolation, this suggested that only few HSCs might contribute also to normal hematopoiesis.

Recently, a barcoding strategy has also been applied to obtain a high-resolution view of the myeloid-erythroid progenitor compartments [56], which is in agreement with a parallel study based on single-cell RNA expression data [37]. This marked heterogeneity within common myeloid progenitors is in keeping with the identification of committed erythrocyte progenitors within the common myeloid progenitor compartment [57].

From this brief update on transplant hematopoiesis, it is evident that much of the current understanding of the system is based on studies of the fate and functions of HSCs following adoptive cell transfer and engraftment. The flipside is that our knowledge of hematopoiesis under nonperturbed conditions in the bone marrow is limited.


In-vivo tracing methods have been developed to ‘visualize’ hematopoietic development from HSCs under native conditions (see [58] for underlying considerations). Inducible lineage tracing of cells emerging from HSCs was reported in mice expressing tamoxifen-dependent versions of Cre recombinase from the Scl locus [27], or from the Runx1 locus [59]. Key for HSC fate mapping is whether or not Cre expression can be as specifically as possible restricted to long-term HSCs in vivo [19[filled square][filled square]]. Other suitable candidate loci include the aforementioned genes Hoxb4 [51], Fgd5 [29[filled square]], α-catulin [18[filled square][filled square]], and Hoxb5 [30]. Busch et al. [19[filled square][filled square]] generated a knock-in mouse expressing from the Tie2 locus Cre recombinase flanked on both ends by a modified estrogen receptor domain; this fusion protein, termed MerCreMer (MCM), is not leaky in the absence of tamoxifen [60]. In Tie2MCM mice, a small fraction (in the order of 1%) of LinKit+Sca1+CD150+CD48 HSCs could be induced to express the inheritable fluorescent marker YFP [19[filled square][filled square],61]. In this system, kinetic cell tracing experiments in a large cohort of mice, combined with limiting dilution analysis and mathematical modeling, revealed several unexpected quantitative properties of hematopoiesis from stem cells in the bone marrow [19[filled square][filled square],58]:

  1. The number of HSCs that contribute to normal hematopoiesis is in the order of 5000 cells (or more, given that this was a lower estimate) per mouse. Considering that mice have in the order of 17 000 HSCs, this implies that at least 30% of all HSCs participate over time in hematopoiesis.
  2. Estimates on the flux of differentiation through the system (between phenotypically-defined stem and progenitor compartments) could be obtained. The by far slowest differentiation step was found between Kit+Sca1+CD150+CD48 phenotype HSCs and short-term (Kit+Sca1+CD150CD48) phenotype HSCs, which takes almost 1 year. This implies that those HSCs, most apical in the pyramid, are only rarely involved in the daily generation of new blood and immune cells. Steady-state hematopoiesis is thus expected to be largely independent of the activity of HSCs for extended periods of time, which may have implications for time between bone marrow injury and bone marrow failure or hypoplasia.
  3. An individual HSC is giving rise to differentiated progeny (cell divisions leading to differentiation) approximately once per 110 days. The rate of proliferation (one division per 110 cells and day, or approximately 1% of HSC per day) is lower than actually measured proliferation rates in HSCs based on the literature [26], which are in the order of a few percent. This difference should reflect those cell divisions that compensate for cell loss.
  4. The observation of many mice over long periods of time with low HSC labeling frequencies indicates very slow periodicity of HSCs (averaged over the labeled HSC population) and no obvious signs of random or stochastic HSC activation. The relative homogeneity of the output from few labeled HSCs argues for one HSC pool with rare individual HSC activity rather than two pools, one active and one dormant, within the labeled cells.
  5. Under steady-state conditions, the contribution of individual HSCs (output) to the hematopoietic system is low. This would imply relatively small clone sizes of mature immune cells, and ensure that many different HSCs contribute to the maintenance of hematopoiesis.
  6. Hematopoiesis in adult mice (not only aged mice) is characterized by a very strong myeloid bias, i.e. the flow from HSCs into myeloid lineages is several 100-fold larger compared with the lymphoid lineages. Because, in general, myeloid cells are shorter-lived than lymphoid cells, these different production rates could be explained by the higher demand for de novo myelopoiesis compared with lymphopoiesis.
  7. 5-FU-induced leukopenia enhances the flux from HSCs.
  8. In-situ labeled HSCs, when grafted using conventional bone marrow transplantation, fail to reestablish their original representation (i.e., clonal composition) in the host bone marrow. Differences in HSC contributions over time under steady state and post-transplantation are depicted in Fig. Fig.1.1. Quantitative hallmarks that distinguish steady-state and post-transplant hematopoiesis are summarized in Table Table11.
    FIGURE 1
    Schematic depiction of HSC diversity and contribution to hematopoiesis tested in transplantation or by fate mapping. (a) Transfer of HSC leads to a transient multiclonal contribution in the first weeks after injection. The majority of HSC clones is lost ...
    Table 1
    Quantitative and qualitative hallmarks of hematopoietic stem cells, and experimental advantages and limitations (shown on top) vs. experimental categories discussed in this review (shown on left)

Genetic barcoding is a powerful tool to study precursor product and inter-lineage relationships at very high, possibly clonal resolution. The generation of barcodes in vivo without the use of viral vectors possesses an experimental challenge. Sun and colleagues [62[filled square][filled square]] developed a new mouse model in which they labeled cells with unique genetic barcodes in vivo. The system was driven from the ubiquitously expressed Rosa26 locus, doxycycline-inducible transposon mobilization occurred in approximately 30% of cells, and the labeling included stem or progenitor cells as well as fully differentiated cells. At early time points, the presence of tags in the periphery was unrelated to development, but following chase periods up to 40 weeks, the turnover of label revealed data on the dynamics of native hematopoiesis under steady-state conditions.

Kinetic resolution of peripheral blood samples at 4–6 week intervals revealed highly polyclonal granulopoiesis that originates from successive, distinct sets of progenitors during steady-state granulopoiesis [62[filled square][filled square]]. Because the residence time (time cells spend in a compartment) for multipotent progenitors (MPP) is on average 70 days [19[filled square][filled square]], these results are compatible with the idea that a given set of MPP generates (at least) granulocytes over several weeks before another set takes over. Sun et al. found only very few overlapping barcodes comparing LT-HSC in the bone marrow and mature cells in the periphery, and concluded ‘that LT-HSC have limited lineage output under unperturbed conditions for at least 40 weeks’ [62[filled square][filled square]]. However, the output from HSCs is not zero, but an estimated number of 150 HSCs still contribute per day [19[filled square][filled square]]. The absence of common tags could in theory also be explained by loss of tags from the HSC pool because of stem cell consumption during differentiation (in that case, proliferation would always lead to two differentiating daughter cells that leave the HSC compartment, causing loss of the original barcode from the HSC pool).

This barcoding system has also been used to compare hematopoiesis in situ and post-transplant. Akin to the model depicted in Fig. Fig.1,1, many clones contributed early and transiently after transplantation but, with time, an oligoclonal pattern arose, providing strong support for the notion that upon transplantation the population of donor HSCs fails to reestablish its original composition, clone sizes or activity rhythm in the host. Finally, this barcoding system also provided hints into lineage relationships. Only 10% of MPP clones showed bipotent development into myeloid and lymphoid cells. Although different lifespans of myeloid and lymphoid lineage could mask bipotency, the experiments indicate that multipotency exists at this progenitor level in vivo.


Until recently, information on the functions of HSCs and progenitors was largely based on transplantation experiments. New experimental systems have been developed to explore the functions of normal steady-state hematopoiesis in the bone marrow in mice. These noninvasive experiments have uncovered major differences comparing normal and post-transplantation hematopoiesis; hence both are in vivo but different. Following transplantation, hematopoiesis is initially driven by many transient clones, which fade over time, resulting in hematopoiesis that is maintained by few HSCs (oligoclonal hematopoiesis). By contrast, new studies now show that normally very large proportions of HSCs (at least 30% or about 5000 HSCs per mouse) are active over time. HSCs have a very slow periodicity, for example, long lag times between two incidents of activity with no evidence for distinct dormant vs. active HSC compartments. Tracking of the emergence of the label from HSCs via differentiation into mature cells led to estimates on rates of differentiation flux, rates of differentiation-associated proliferation, and residence times in compartments. In vivo barcoding data gave insights into the clonal dynamics (number and diversity of barcodes distributed over time) of hematopoiesis, and suggest that at least at the MPP stage most in-vivo potential is myeloid rather than bipotent lymphoid plus myeloid. This is in line with the several 100-fold myeloid over lymphoid bias at the MPP stage deduced from HSC fate mapping data.

Collectively, these developments provide a quantitative framework for the dynamics of normal hematopoiesis (whereas refining the structure and relatedness of the lineage pathways under normal conditions will require even more sophisticated tools). The regulation of the HSC output and the hematopoietic flow under stress conditions or during pathology will be key questions in the future. Moreover, massive single-cell analyses will provide an unprecedented depth of the structure of the hematopoietic system. Eventually, these routes of investigation should merge in a detailed functional description of the physiology and pathology of hematopoiesis.


We thank Thomas Höfer and Melania Barile for ongoing collaborations, and we are grateful to Nikolaus Dietlein, Thorsten Feyerabend, Kay Klapproth, and Thomas Höfer for critically reading of this manuscript.

Financial support and sponsorship

Work on stem cells in the Rodewald laboratory is funded by a grant from the DFG (SFB 873 project B11) and by DKFZ core funding.

Conflicts of interest

There are no conflicts of interest.


Papers of particular interest, published within the annual period of review, have been highlighted as:

  • [filled square] of special interest
  • [filled square][filled square] of outstanding interest


1. Bjornson ZB, Nolan GP, Fantl WJ. Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 2013; 25:484–494. [PMC free article] [PubMed]
2. Rimm DL. Next-gen immunohistochemistry. Nat Methods 2014; 11:381–383. [PubMed]
3. Jaitin DA, Keren-Shaul H, Elefant N, Amit I. Each cell counts: hematopoiesis and immunity research in the era of single cell genomics. Semin Immunol 2015; 27:67–71. [PubMed]
4. Proserpio V, Lonnberg T. Single-cell technologies are revolutionizing the approach to rare cells. Immunol Cell Biol 2015; 94:225–229. [PMC free article] [PubMed]
5. Mossadegh-Keller N, Sarrazin S, Kandalla PK, et al. M-CSF instructs myeloid lineage fate in single haematopoietic stem cells. Nature 2013; 497:239–243. [PMC free article] [PubMed]
6. Rieger MA, Hoppe PS, Smejkal BM, et al. Hematopoietic cytokines can instruct lineage choice. Science 2009; 325:217–218. [PubMed]
7. Etzrodt M, Endele M, Schroeder T. Quantitative single-cell approaches to stem cell research. Cell Stem Cell 2014; 15:546–558. [PubMed]
8. Schmitt TM, Zuniga-Pflucker JC. T-cell development, doing it in a dish. Immunol Rev 2006; 209:95–102. [PubMed]
9. Schlenner SM, Rodewald HR. Early T cell development and the pitfalls of potential. Trends Immunol 2010; 31:303–310. [PubMed]
10. Richie Ehrlich LI, Serwold T, Weissman IL. In vitro assays misrepresent in vivo lineage potentials of murine lymphoid progenitors. Blood 2011; 117:2618–2624. [PubMed]
11. Till JE, McCullouch EA. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat Res 1961; 14:213–222. [PubMed]
12. Smith LG, Weissman IL, Heimfeld S. Clonal analysis of hematopoietic stem-cell differentiation in vivo. Proc Natl Acad Sci U S A 1991; 88:2788–2792. [PubMed]
13. Osawa M, Hanada K, Hamada H, Nakauchi H. Long-term lymphohematopoietic reconstitution by a single CD34-low/negative hematopoietic stem cell. Science 1996; 273:242–245. [PubMed]
14. Kiel MJ, Yilmaz OH, Iwashita T, et al. SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 2005; 121:1109–1121. [PubMed]
15. Sieburg HB, Cho RH, Dykstra B, et al. The hematopoietic stem compartment consists of a limited number of discrete stem cell subsets. Blood 2006; 107:2311–2316. [PubMed]
16. Dykstra B, Kent D, Bowie M, et al. Long-term propagation of distinct hematopoietic differentiation programs in vivo. Cell Stem Cell 2007; 1:218–229. [PubMed]
17. Benz C, Copley MR, Kent DG, et al. Hematopoietic stem cell subtypes expand differentially during development and display distinct lymphopoietic programs. Cell Stem Cell 2012; 10:273–283. [PubMed]
18[filled square][filled square]. Acar M, Kocherlakota KS, Murphy MM, et al. Deep imaging of bone marrow shows nondividing stem cells are mainly perisinusoidal. Nature 2015; 526:126–130. [PMC free article] [PubMed]Comprehensive analysis of repopulating frequencies in total bone marrow and specific HSC phenotypes.
19[filled square][filled square]. Busch K, Klapproth K, Barile M, et al. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 2015; 518:542–546. [PubMed]Comprehensive lineage tracing data in combination with mathematical modeling allowed determination of proliferation and flux rates in hematopoiesis from stem cells. This study reveals polyclonal character of steady-state hematopoiesis with the short-term HSC as the main driver. Also elucidates fundamental differences to post-transplantation hematopoiesis, also shown in [62[filled square][filled square]].
20[filled square]. Luchsinger LL, de Almeida MJ, Corrigan DJ, et al. Mitofusin 2 maintains haematopoietic stem cells with extensive lymphoid potential. Nature 2016; 529:528–531. [PMC free article] [PubMed]The study shows that in transplantation assays Mfn2 is essentiel to maintain lymphoid potential of HSCs indicating heterogeneity among HSCs.
21. Gerrits A, Dykstra B, Kalmykowa OJ, et al. Cellular barcoding tool for clonal analysis in the hematopoietic system. Blood 2010; 115:2610–2618. [PubMed]
22. Lu R, Neff NF, Quake SR, Weissman IL. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat Biotechnol 2011; 29:928–933. [PMC free article] [PubMed]
23. Naik SH, Perie L, Swart E, et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 2013; 496:229–232. [PubMed]
24. Wilson A, Laurenti E, Oser G, et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 2008; 135:1118–1129. [PubMed]
25. Foudi A, Hochedlinger K, Van Buren D, et al. Analysis of histone 2B-GFP retention reveals slowly cycling hematopoietic stem cells. Nat Biotechnol 2009; 27:84–90. [PMC free article] [PubMed]
26. Oguro H, Ding L, Morrison SJ. SLAM family markers resolve functionally distinct subpopulations of hematopoietic stem cells and multipotent progenitors. Cell Stem Cell 2013; 13:102–116. [PMC free article] [PubMed]
27. Gothert JR, Gustin SE, Hall MA, et al. In vivo fate-tracing studies using the Scl stem cell enhancer: embryonic hematopoietic stem cells significantly contribute to adult hematopoiesis. Blood 2005; 105:2724–2732. [PubMed]
28. Gazit R, Garrison BS, Rao TN, et al. Transcriptome analysis identifies regulators of hematopoietic stem and progenitor cells. Stem Cell Rep 2013; 1:266–280. [PMC free article] [PubMed]
29[filled square]. Gazit R, Mandal PK, Ebina W, et al. Fgd5 identifies hematopoietic stem cells in the murine bone marrow. J Exp Med 2014; 211:1315–1331. [PMC free article] [PubMed]In this study expression analysis identified several HSC-restricted genes that led to the generation of novel reporter knock-in mice (Fgd5 reporter mice).
30. Chen JY, Miyanishi M, Wang SK, et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 2016; 530:223–227. [PMC free article] [PubMed]
31. Bendall SC, Davis KL, Amir el AD, et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 2014; 157:714–725. [PMC free article] [PubMed]
32. Newell EW, Sigal N, Bendall SC, et al. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 2012; 36:142–152. [PMC free article] [PubMed]
33. Cabezas-Wallscheid N, Klimmeck D, Hansson J, et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 2014; 15:507–522. [PubMed]
34. Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014; 343:776–779. [PMC free article] [PubMed]
35. Lipka DB, Wang Q, Cabezas-Wallscheid N, et al. Identification of DNA methylation changes at cis-regulatory elements during early steps of HSC differentiation using tagmentation-based whole genome bisulfite sequencing. Cell Cycle 2014; 13:3476–3487. [PMC free article] [PubMed]
36. Wilson NK, Kent DG, Buettner F, et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 2015; 16:712–724. [PMC free article] [PubMed]
37. Paul F, Arkin Y, Giladi A, et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 2015; 163:1663–1677. [PubMed]
38. Becher B, Schlitzer A, Chen J, et al. High-dimensional analysis of the murine myeloid cell system. Nat Immunol 2014; 15:1181–1189. [PubMed]
39. Giesen C, Wang HA, Schapiro D, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 2014; 11:417–422. [PubMed]
40. Woodhouse S, Moignard V, Gottgens B, Fisher J. Processing, visualising and reconstructing network models from single-cell data. Immunol Cell Biol 2015; 94:256–265. [PubMed]
41. Angelo M, Bendall SC, Finck R, et al. Multiplexed ion beam imaging of human breast tumors. Nat Med 2014; 20:436–442. [PMC free article] [PubMed]
42. Haghverdi L, Buettner F, Theis FJ. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 2015; 31:2989–2998. [PubMed]
43. Moignard V, Woodhouse S, Haghverdi L, et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 2015; 33:269–276. [PMC free article] [PubMed]
44. Schlenner SM, Madan V, Busch K, et al. Fate mapping reveals separate origins of T cells and myeloid lineages in the thymus. Immunity 2010; 32:426–436. [PubMed]
45. Spangrude GJ, Heimfeld S, Weissman IL. Purification and characterization of mouse hematopoietic stem cells. Science 1988; 242:58–62. [PubMed]
46. Okada S, Nakauchi H, Nagayoshi K, et al. In vivo and in vitro stem cell function of c-kit- and Sca-1-positive murine hematopoietic cells. Blood 1992; 80:3044–3050. [PubMed]
47. Goodell MA, Brose K, Paradis G, et al. Isolation and functional properties of murine hematopoietic stem cells that are replicating in vivo. J Exp Med 1996; 183:1797–1806. [PMC free article] [PubMed]
48. Matsuzaki Y, Kinjo K, Mulligan RC, Okano H. Unexpectedly efficient homing capacity of purified murine hematopoietic stem cells. Immunity 2004; 20:87–93. [PubMed]
49. Camargo FD, Chambers SM, Drew E, et al. Hematopoietic stem cells do not engraft with absolute efficiencies. Blood 2006; 107:501–507. [PubMed]
50. Seita J, Sahoo D, Rossi DJ, et al. Gene expression commons: an open platform for absolute gene expression profiling. PLoS One 2012; 7:e40321. [PMC free article] [PubMed]
51. Hills D, Gribi R, Ure J, et al. Hoxb4-YFP reporter mouse model: a novel tool for tracking HSC development and studying the role of Hoxb4 in hematopoiesis. Blood 2011; 117:3521–3528. [PubMed]
52. Morita Y, Ema H, Nakauchi H. Heterogeneity and hierarchy within the most primitive hematopoietic stem cell compartment. J Exp Med 2010; 207:1173–1182. [PMC free article] [PubMed]
53. Grinenko T, Arndt K, Portz M, et al. Clonal expansion capacity defines two consecutive developmental stages of long-term hematopoietic stem cells. J Exp Med 2014; 211:209–215. [PMC free article] [PubMed]
54. Beerman I, Bhattacharya D, Zandi S, et al. Functionally distinct hematopoietic stem cells modulate hematopoietic lineage potential during aging by a mechanism of clonal expansion. Proc Natl Acad Sci U S A 2010; 107:5465–5470. [PubMed]
55. Eaves CJ. Hematopoietic stem cells: concepts, definitions, and the new reality. Blood 2015; 125:2605–2613. [PubMed]
56. Perie L, Duffy KR, Kok L, et al. The branching point in erythro-myeloid differentiation. Cell 2015; 163:1655–1662. [PubMed]
57. Terszowski G, Waskow C, Conradt P, et al. Prospective isolation and global gene expression analysis of the erythrocyte colony-forming unit (CFU-E). Blood 2005; 105:1937–1945. [PubMed]
58. Hofer T, Busch K, Klapproth K, Rodewald HR. Fate mapping and quantitation of hematopoiesis in vivo. Annu Rev Immunol 2016; 34:449–478. [PubMed]
59. Samokhvalov IM, Samokhvalova NI, Nishikawa S. Cell tracing shows the contribution of the yolk sac to adult haematopoiesis. Nature 2007; 446:1056–1061. [PubMed]
60. Verrou C, Zhang Y, Zurn C, et al. Comparison of the tamoxifen regulated chimeric Cre recombinases MerCreMer and CreMer. Biol Chem 1999; 380:1435–1438. [PubMed]
61. Gomez Perdiguero E, Klapproth K, Schulz C, et al. Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors. Nature 2015; 518:547–551. [PubMed]
62[filled square][filled square]. Sun J, Ramos A, Chapman B, et al. Clonal dynamics of native haematopoiesis. Nature 2014; 514:322–327. [PMC free article] [PubMed]The authors developed a transposon-based experimental model that allows genetic barcoding of individual hematopoietic cells in situ. Analyses of clonal dynamics showed highly polyclonal output of long-lived progenitors to granulopoiesis which is in stark contrast to post-transplant hematopoiesis.

Articles from Lippincott Williams & Wilkins Open Access are provided here courtesy of Wolters Kluwer Health