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Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche

Nature volume 530, pages 223–227 (2016)Cite this article

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Abstract

Haematopoietic stem cells (HSCs) are arguably the most extensively characterized tissue stem cells. Since the identification of HSCs by prospective isolation1, complex multi-parameter flow cytometric isolation of phenotypic subsets has facilitated studies on many aspects of HSC biology, including self-renewal2,3,4, differentiation, ageing, niche5, and diversity6,7,8. Here we demonstrate by unbiased multi-step screening, identification of a single gene, homeobox B5 (Hoxb5, also known as Hox-2.1), with expression in the bone marrow that is limited to long-term (LT)-HSCs in mice. Using a mouse single-colour tri-mCherry reporter driven by endogenous Hoxb5 regulation, we show that only the Hoxb5+ HSCs exhibit long-term reconstitution capacity after transplantation in primary transplant recipients and, notably, in secondary recipients. Only 7–35% of various previously defined immunophenotypic HSCs are LT-HSCs. Finally, by in situ imaging of mouse bone marrow, we show that >94% of LT-HSCs (Hoxb5+) are directly attached to VE-cadherin+ cells, implicating the perivascular space as a near-homogenous location of LT-HSCs.

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Figure 1: Multi-step unbiased screening identifies Hoxb5 as a candidate LT-HSC marker.
Figure 2: Hoxb5 distinguishes between LT-HSC and non-LT-HSC.
Figure 3: Previously defined HSC immunophenotypes contain Hoxb5− cells.
Figure 4: LT-HSCs exhibit near-homogenous attachment to VE-cadherin+ cells.

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Gene Expression Omnibus

Data deposits

Microarray data was deposited at GEO under accession number GSE77078.

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Acknowledgements

We would like to acknowledge N. Neff and G. Mantalas for advice regarding RNA sequencing; B. Yu and A. Beel for providing critical input on imaging data; H. Nishikii for advice regarding imaging data; S. Karten for help in editing the manuscript; L. Jerabek and T. Storm for laboratory management; A. McCarty and C. Wang for animal care; P. Lovelace and J. Ho for FACS facility management; H. Zeng, Y. Li, and C. Wang for collaboration in generating the mouse model; and Y. Sato for technical assistance in Imaris software analysis. The authors would like to acknowledge ongoing support for this work: NCI and NHLBI of the NIH under award numbers R01 CA086065, U01 HL099999, and R01 HL058770, and by the Virginia and D. K. Ludwig Fund for Cancer Research (I.L.W.); Stanford University Medical Scientist Training Program (T32 GM007365) and NHLBI Ruth L. Kirschstein National Research Service Award (F30-HL122096) (J.Y.C.); and Human Frontier Science Program Long-Term Fellowships, the Uehara Memorial Foundation Research Fellowship, Toyobo Biotechnology Foundation Research Fellowship, and Kanzawa Medical Research Foundation Overseas study grants (M.M.). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

Author information

Author notes

  1. Debashis Sahoo

    Present address: †Present address: Department of Pediatrics and Department of Computer Science and Engineering, University of California San Diego, San Diego, California 92123, USA.,

  2. James Y. Chen and Masanori Miyanishi: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, 94305, California, USA

    James Y. Chen, Masanori Miyanishi, Sean K. Wang, Rahul Sinha, Kevin S. Kao, Jun Seita, Debashis Sahoo, Hiromitsu Nakauchi & Irving L. Weissman

  2. Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, 94305, California, USA

    James Y. Chen, Masanori Miyanishi, Sean K. Wang, Rahul Sinha, Kevin S. Kao, Debashis Sahoo & Irving L. Weissman

  3. Division of Stem Cell Therapy, Center for Stem Cell Biology and Regenerative Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan

    Satoshi Yamazaki & Hiromitsu Nakauchi

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Contributions

J.Y.C. and M.M. contributed equally to this work, and either has the right to list himself first in bibliographic documents. M.M. and J.Y.C. conceived, performed, analysed, and oversaw the experiments, with suggestions from I.L.W. M.M. and J.Y.C. identified Hoxb5 as a LT-HSC marker, and made and characterized the Hoxb5–tri-mCherry mouse. S.K.W. and K.S.K. performed experiments and prepared figures under the supervision of M.M. and J.Y.C. S.Y. generated CUBIC data and evaluated the association with VE-cadherin vasculature. R.S. designed and performed RNA-seq and associated data analysis. J.S. and D.S. designed the gene expression commons for microarray analysis. D.S. provided critical advice regarding combined analysis of microarray and RNA-seq data. M.M., J.Y.C., S.K.W., K.S.K., and I.L.W. wrote the manuscript. H.N. and R.S. provided comments on the manuscript.

Corresponding authors

Correspondence to Masanori Miyanishi or Irving L. Weissman.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 GEXC expression of previously reported HSC markers in mouse bone marrow.

a, Ideal expression pattern of HSC-specific genes (pink represents increased expression, blue represents decreased expression). b, Relative expression of Hoxb5 (top left), α-catulin/Ctnnal1 (top middle), Fgd5 (top right), CD150/Slamf1 (bottom left), Hoxb4 (bottom middle), Gfi-1 (bottom right) in haematopoietic and stromal populations as determined by microarray analysis.

Extended Data Figure 2 Gating scheme for HSC and progenitors.

a, Representative flow cytometry gating to isolate pHSCs, MPPs, and oligopotent progenitors from mouse bone marrow. Panels gated as shown after exclusion of doublets and dead cells. b, Hoxb5 reporter expression (red) in Flk2+ MPPs, megakaryocyte erythrocyte progenitor (MEP), granulocyte monocyte progenitor (GMP), common myeloid progenitor (CMP), and common lymphoid progenitor (CLP) populations compared to wild-type controls (blue). Values indicate the percentage of mCherry+ cells ± s.d. in each fraction for n = 3 mice.

Extended Data Figure 3 Hoxb5 is not expressed in CD45− bone marrow.

Hoxb5 reporter expression in the CD45− compartment within bone marrow of wild-type (red) and three Hoxb5–tri-mCherry mice (blue, orange, and green, n = 3 mice).

Extended Data Figure 4 FMO gating for Hoxb5+ signal.

Representative flow cytometry gating to separate mCherry (Hoxb5)-high, -low, and -negative populations in both wild-type and Hoxb5–tri-mCherry mice.

Extended Data Figure 5 Hoxb5 distinguishes between LT-HSCs and non-LT-HSCs.

a, Reconstitution kinetics in primary recipients 4, 8, and 12 weeks after receiving ten Hoxb5neg (n = 9 mice), Hoxb5lo (n = 13 mice), or Hoxb5hi (n = 18 mice) pHSCs. Each column represents an individual mouse. b, Reconstitution kinetics 4, 8, and 12 weeks after whole bone marrow secondary transplant. c, Reconstitution kinetics in primary recipients receiving three Hoxb5neg (n = 11 mice), Hoxb5lo (n = 12 mice), or Hoxb5hi (n = 14 mice) pHSCs. Each column represents an individual mouse. d, Reconstitution kinetics following secondary transplant of 100 sorted LSK Hoxb5− (n = 14 mice) or Hoxb5+ (n = 9 mice) cells and 2 × 105 supporting cells.

Extended Data Figure 6 Limiting dilution analysis of Hoxb5+ and Hoxb5− pHSCs.

Limiting dilution results of ten- and three-cell transplants of Hoxb5hi (red, n = 18 mice for ten-cell and n = 14 mice for three-cell), Hoxb5lo (green, n = 13 mice for ten-cell and n = 12 mice for three-cell), and Hoxb5neg (blue, n = 9 mice for ten-cell and n = 11 mice for three-cell). Frequency of LT/ST-HSCs by limiting dilution analysis is 1 in 2.1 for Hoxb5hi, 1 in 2.4 for Hoxb5lo, and 1 in 16.1 for Hoxb5neg cells.

Extended Data Figure 7 Previously defined HSC immunophenotypes contain Hoxb5− cells.

Representative HSC gating strategy for various HSC definitions after exclusion of doublets and dead cells. a, CD11a− (LSK CD150+CD34−/loCD11a−)21. b, HSC-1 (LSK CD150+CD48−/loCD229−/loCD244−)20. c, Fraction I (LSK CD150+CD34−/loCD41−)18. d, CD150+CD48−CD41− cells22 (n = 5 mice).

Extended Data Figure 8 Specificity of Hoxb5 as a single marker for LT-HSCs.

a, Flow cytometry plots of wild type (top row) and Hoxb5–tri-mCherry (bottom row) after excluding doublets, dead cells, autofluorescence, and gating on Hoxb5+ events. Frequencies shown are percentage in gate ± s.d. in each fraction (n = 3 mice).

Extended Data Figure 9 Comparison of processing methods on pHSC and Hoxb5+ LT-HSC yield.

a, b, Relative frequency of pHSCs (a) and Hoxb5+ LT-HSCs (b) in tibial plugs (flushed) (n = 6 mice) compared to tibial plugs plus bones (crushed) (n = 6 mice).

Extended Data Figure 10 Hoxb5+ HSCs are evenly distributed in the tibia.

a, Distribution of Hoxb5+ cells (red and arrows) in bone marrow in 3D-reconstructed images. Nuclei are counterstained with DAPI (blue) wild-type (top panel) Hoxb5–tri-mCherry (middle and bottom panel). Scale bar, 100 μm. b, Cartoon representing the location of the proximal, medial, and distal sampling. c, Representative 3D-reconstructed images of Hoxb5+ cells (red) in proximal (left column), medial (middle column), and distal (right column) regions of the tibia. Scale bar, 150 μm. Nuclei are counterstained with DAPI (blue); n = 3 mice.

Supplementary information

Supplementary Table 1

The file shows the marker definitions of the cell populations used in gene microarray profiling. (XLSX 30 kb)

Supplementary Table 2

This file contains a list of HSC-specific genes found by screening the haematopoietic sub-fractions in the adult mouse bone marrow. (XLSX 33 kb)

Supplementary Table 3

This file contains the RNA expression of α-catulin in Hoxb5 subsets within the pHSC gate. (XLSX 9 kb)

Supplementary Table 4

Tis file contains a list of flow cytometry reagents used in this study, including, antigen, clone, fluorophore, and manufacturer. (XLSX 10 kb)

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Chen, J., Miyanishi, M., Wang, S. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016). https://doi.org/10.1038/nature16943

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