Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Gene replacement of α-globin with β-globin restores hemoglobin balance in β-thalassemia-derived hematopoietic stem and progenitor cells

Abstract

β-Thalassemia pathology is due not only to loss of β-globin (HBB), but also to erythrotoxic accumulation and aggregation of the β-globin-binding partner, α-globin (HBA1/2). Here we describe a Cas9/AAV6-mediated genome editing strategy that can replace the entire HBA1 gene with a full-length HBB transgene in β-thalassemia-derived hematopoietic stem and progenitor cells (HSPCs), which is sufficient to normalize β-globin:α-globin messenger RNA and protein ratios and restore functional adult hemoglobin tetramers in patient-derived red blood cells. Edited HSPCs were capable of long-term and bilineage hematopoietic reconstitution in mice, establishing proof of concept for replacement of HBA1 with HBB as a novel therapeutic strategy for curing β-thalassemia.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: sgRNA and AAV6 design for editing at the α-globin locus.
Fig. 2: WGR of α-globin using a promoterless reporter.
Fig. 3: WGR of α-globin with β-globin in SCD HSPCs.
Fig. 4: Engraftment of α-globin-targeted human HSPCs into NSG mice.
Fig. 5: WGR of α-globin with β-globin in β-thalassemia HSPCs.
Fig. 6: Engraftment of α-globin-targeted β-thalassemia HSPCs into NSG mice.

Data availability

High-throughput sequencing data generated in the Cas9 off-target activity assessment have been uploaded to the public NCBI data repository: BioProject ID PRJNA691350, available at http://www.ncbi.nlm.nih.gov/bioproject/691350. All other data supporting the findings of this study are either included in the published article and/or Supplementary information files or (if too large to be included therein) are available from the corresponding author on reasonable request. These include, but are not limited to, Sanger sequencing, ddPCR, flow cytometry and HPLC data that were used to generate the figures and conclusions in this study. Source data are provided with this paper.

Code availability

No previously unreported computer code or algorithm was generated during the course of this study.

References

  1. 1.

    Galanello, R. & Origa, R. Beta-thalassemia. Orphanet J. Rare Dis. 5, 11 (2010).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Mentzer, W. C. & Kan, Y. W. Prospects for research in hematologic disorders: sickle cell disease and thalassemia. JAMA 285, 640–642 (2001).

    CAS  PubMed  Google Scholar 

  3. 3.

    Ehlers, K. H., Giardina, P. J., Lesser, M. L., Engle, M. A. & Hilgartner, M. W. Prolonged survival in patients with beta-thalassemia major treated with deferoxamine. J. Pediatr. 118, 540–545 (1991).

    CAS  PubMed  Google Scholar 

  4. 4.

    Mettananda, S., Gibbons, R. J. & Higgs, D. R. alpha-Globin as a molecular target in the treatment of beta-thalassemia. Blood 125, 3694–3701 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Dye, D. E., Brameld, K. J., Maxwell, S., Goldblatt, J. & O’Leary, P. The impact of single gene and chromosomal disorders on hospital admissions in an adult population. J. Community Genet. 2, 81–90 (2011).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Fleischhauer, K. et al. Graft rejection after unrelated donor hematopoietic stem cell transplantation for thalassemia is associated with nonpermissive HLA-DPB1 disparity in host-versus-graft direction. Blood 107, 2984–2992 (2006).

    CAS  PubMed  Google Scholar 

  7. 7.

    Puthenveetil, G. et al. Successful correction of the human beta-thalassemia major phenotype using a lentiviral vector. Blood 104, 3445–3453 (2004).

    CAS  PubMed  Google Scholar 

  8. 8.

    Negre, O. et al. Gene therapy of the beta-hemoglobinopathies by lentiviral transfer of the beta(A(T87Q))-globin gene. Hum. Gene Ther. 27, 148–165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Thompson, A. A. et al. Gene therapy in patients with transfusion-dependent beta-thalassemia. N. Engl. J. Med. 378, 1479–1493 (2018).

    CAS  PubMed  Google Scholar 

  10. 10.

    Breda, L. et al. Therapeutic hemoglobin levels after gene transfer in beta-thalassemia mice and in hematopoietic cells of beta-thalassemia and sickle cells disease patients. PLoS ONE 7, e32345 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Cavazzana-Calvo, M. et al. Transfusion independence and HMGA2 activation after gene therapy of human beta-thalassaemia. Nature 467, 318–322 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Marktel, S. et al. Intrabone hematopoietic stem cell gene therapy for adult and pediatric patients affected by transfusion-dependent ss-thalassemia. Nat. Med. 25, 234–241 (2019).

    CAS  PubMed  Google Scholar 

  13. 13.

    Xu, L. et al. CRISPR-edited stem cells in a patient with HIV and acute lymphocytic leukemia. N. Engl. J. Med. 381, 1240–1247 (2019).

    CAS  PubMed  Google Scholar 

  14. 14.

    Stadtmauer, E. A. et al. CRISPR-engineered T cells in patients with refractory cancer. Science 367, eaba7365 (2020).

  15. 15.

    Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Alter, B. P. Fetal erythropoiesis in stress hematopoiesis. Exp. Hematol. 7, 200–209 (1979).

    PubMed  Google Scholar 

  17. 17.

    Stamatoyannopoulos, G., Veith, R., Galanello, R. & Papayannopoulou, T. Hb F production in stressed erythropoiesis: observations and kinetic models. Ann. N. Y. Acad. Sci. 445, 188–197 (1985).

    CAS  PubMed  Google Scholar 

  18. 18.

    Bak, R. O., Dever, D. P. & Porteus, M. H. CRISPR/Cas9 genome editing in human hematopoietic stem cells. Nat. Protoc. 13, 358–376 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Martin, R. M. et al. Highly efficient and marker-free genome editing of human pluripotent stem cells by CRISPR-Cas9 RNP and AAV6 donor-mediated homologous recombination. Cell Stem Cell 24, 821–828 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Pavel-Dinu, M. et al. Gene correction for SCID-X1 in long-term hematopoietic stem cells. Nat. Commun. 10, 1634 (2019).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Gomez-Ospina, N. et al. Human genome-edited hematopoietic stem cells phenotypically correct mucopolysaccharidosis type I. Nat. Commun. 10, 4045 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Schiroli, G. et al. Precise gene editing preserves hematopoietic stem cell function following transient p53-mediated DNA damage response. Cell Stem Cell 24, 551–565 e558 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Schiroli, G. et al. Preclinical modeling highlights the therapeutic potential of hematopoietic stem cell gene editing for correction of SCID-X1. Sci. Transl. Med. 9, eaan0820 (2017).

    PubMed  Google Scholar 

  24. 24.

    Dever, D. P. et al. CRISPR/Cas9 beta-globin gene targeting in human haematopoietic stem cells. Nature 539, 384–389 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Pattabhi, S. et al. In vivo outcome of homology-directed repair at the hbb gene in hsc using alternative donor template delivery methods. Mol. Ther. Nucleic Acids 17, 277–288 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    DeWitt, M. A. et al. Selection-free genome editing of the sickle mutation in human adult hematopoietic stem/progenitor cells. Sci. Transl. Med. 8, 360ra134 (2016).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    De Ravin, S. S. et al. CRISPR-Cas9 gene repair of hematopoietic stem cells from patients with X-linked chronic granulomatous disease. Sci. Transl. Med. 9, eaah3480 (2017).

    PubMed  Google Scholar 

  28. 28.

    Thein, S. L. The molecular basis of beta-thalassemia. Cold Spring Harb. Perspect. Med. 3, a011700 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Weatherall, D. 2003 William Allan Award address. The thalassemias: the role of molecular genetics in an evolving global health problem. Am. J. Hum. Genet. 74, 385–392 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Hendel, A. et al. Chemically modified guide RNAs enhance CRISPR-Cas genome editing in human primary cells. Nat. Biotechnol. 33, 985–989 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cradick, T. J., Qiu, P., Lee, C. M., Fine, E. J. & Bao, G. COSMID: a Web-based tool for identifying and validating CRISPR/Cas off-target sites. Mol. Ther. Nucleic Acids 3, e214 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Charlesworth, C. T. et al. Priming human repopulating hematopoietic stem and progenitor cells for Cas9/sgRNA gene targeting. Mol. Ther. Nucleic Acids 12, 89–104 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Dulmovits, B. M. et al. Pomalidomide reverses gamma-globin silencing through the transcriptional reprogramming of adult hematopoietic progenitors. Blood 127, 1481–1492 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Hu, J. et al. Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo. Blood 121, 3246–3253 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Bak, R. O. et al. Multiplexed genetic engineering of human hematopoietic stem and progenitor cells using CRISPR/Cas9 and AAV6. eLlife 6, e27873 (2017).

    Google Scholar 

  37. 37.

    Andreani, M. et al. Persistence of mixed chimerism in patients transplanted for the treatment of thalassemia. Blood 87, 3494–3499 (1996).

    CAS  PubMed  Google Scholar 

  38. 38.

    Andreani, M. et al. Long-term survival of ex-thalassemic patients with persistent mixed chimerism after bone marrow transplantation. Bone Marrow Transpl. 25, 401–404 (2000).

    CAS  Google Scholar 

  39. 39.

    Ferrari, S. et al. Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking. Nat. Biotechnol. 38, 1298–1308 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Sharma, R. et al. The TRACE-Seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells. Nat. Commun. 12, 472 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    McDermott, S. P., Eppert, K., Lechman, E. R., Doedens, M. & Dick, J. E. Comparison of human cord blood engraftment between immunocompromised mouse strains. Blood 116, 193–200 (2010).

    CAS  PubMed  Google Scholar 

  43. 43.

    Wunderlich, M. et al. AML xenograft efficiency is significantly improved in NOD/SCID-IL2RG mice constitutively expressing human SCF, GM-CSF and IL-3. Leukemia 24, 1785–1788 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Khan, I. F., Hirata, R. K. & Russell, D. W. AAV-mediated gene targeting methods for human cells. Nat. Protoc. 6, 482–501 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Aurnhammer, C. et al. Universal real-time PCR for the detection and quantification of adeno-associated virus serotype 2-derived inverted terminal repeat sequences. Hum. Gene Ther. Methods 23, 18–28 (2012).

    CAS  PubMed  Google Scholar 

  46. 46.

    Cromer, M. K. et al. Global transcriptional response to CRISPR/Cas9-AAV6-based genome editing in CD34(+) hematopoietic stem and progenitor cells. Mol. Ther. 26, 2431–2442 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Bak, R. O. & Porteus, M. H. CRISPR-mediated integration of large gene cassettes using aav donor vectors. Cell Rep. 20, 750–756 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Park, S. H. et al. Highly efficient editing of the beta-globin gene in patient-derived hematopoietic stem and progenitor cells to treat sickle cell disease. Nucleic Acids Res. 47, 7955–7972 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Nemati, H., Bahrami, G. & Rahimi, Z. Rapid separation of human globin chains in normal and thalassemia patients by RP-HPLC. Mol. Biol. Rep. 38, 3213–3218 (2011).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the following funding sources that made this work possible: Doris Duke Charitable Trust (no. 22399) to M.H.P., NIH NHLBI (no. R01-HL135607) to M.H.P. and D.P.D., and NIH NHLBI (no. T32-HL120824) to M.K.C.

Author information

Affiliations

Authors

Contributions

D.P.D. and M.H.P. supervised the project. M.K.C., J.C., R.M., V.A.S., J.F.T., D.P.D. and M.H.P. designed experiments. M.K.C., J.C., R.M.M., B.J.L., C.A.V., V.T.L., Y.Z., A.G., F.Z., E.P., W.S., R.O.B. and N.U. carried out experiments. M.K.C., J.C., D.P.D. and M.H.P. wrote the manuscript.

Corresponding authors

Correspondence to Daniel P. Dever or Matthew H. Porteus.

Ethics declarations

Competing interests

M.H.P. is a member of the scientific advisory board of Allogene Therapeutics. M.H.P. is on the Board of Directors of Graphite Bio. M.H.P. has equity in CRISPR Tx. C.A.V., N.M.B., G.K., M.A.C., G.R.R. and M.A.B. are employees of Integrated DNA Technologies. D.P.D. is an employee of Graphite Bio.

Additional information

Peer review information Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Kate Gao and Joao Monteiro were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Analysis of Cas9 sgRNAs targeting α-globin loci.

a, Table with guide RNA sequences. PAM shown in grey, and differences between HBA1 and HBA2 are highlighted in red for each guide. b, Schematic depicting locations of all five guide sequences at genomic loci. c, Representative indel spectrum of HBA1-specific sg5 generated by TIDE software.

Extended Data Fig. 2 Off-target analysis of HBA1-specific sg5.

a, Summary of rhAmpSeq targeted sequencing results at on-target and 40 most highly predicted off-target sites by COSMID for HBA1 sg5. Values are indel frequency for RNP treatment after subtraction of indel frequency for Mock treatment at each locus for each experimental replicate. N = 3 biologically independent HSPC donors, though not all values are displayed since some were <0.01% after subtraction of Mock indel frequencies. Bars represent median. b, List of genomic coordinates for forty most highly predicted off-target sites by COSMID for HBA1 sg5.

Source data

Extended Data Fig. 3 Targeting α-globin with GFP integration vectors.

a, Timeline for editing and analysis of HSPCs targeted with SFFV-GFP integration vectors. b, Depicted are representative flow cytometry images for human HSPCs 14d post-editing. This indicates that WGR integration yields a greater MFI per GFP+ cell than CS integration at the HBA1 locus. Analysis was performed on BD Accuri C6 platform. Median MFI across all replicates is shown below each flow cytometry image, and schematics of integration vectors are shown above.

Extended Data Fig. 4 Staining and gating scheme used to analyze editing and differentiation rates of RBCs.

a, Representative flow cytometry staining and gating scheme for human HSPCs targeted at HBA1 with HBB-T2A-YFP (HBA1 UTRs) and differentiated into RBCs. This indicates that only RBCs (CD34/CD45/CD71+/GPA+) are able to express the promoterless YFP marker. Analysis was performed on BD FACS Aria II platform. b, Representative flow cytometry images of RBCs (CD34/CD45/CD71+/GPA+) derived from HSPCs targeted with HBA1 UTRs, HBA2 UTRs, and HBB UTRs vector. AAV only controls were used for each vector to establish gating scheme, leading to slight variation in positive/negative cut-offs across images.

Extended Data Fig. 5 Viability of HSPCs post-editing.

HSPC viability was quantified 2-4d post-editing by flow cytometry. Depicted are the percentage of cells that stained negative for GhostRed viability dye. All cells were edited with our optimized HBB WGR vector using standard conditions (that is electroporation of Cas9 RNP + sg5, 5 K MOI of AAV, and no AAV wash at 24 h). Bars represent median ± interquartile range. WT: N = 5 for mock, N = 3 for RNP only, N = 1 for AAV only, and N = 6 for RNP + AAV treatment group; SCD: N = 2 for each treatment group with exception of RNP + AAV with N = 4; β-thal: N = 3 for mock, N = 1 for RNP only, and N = 7 for RNP + AAV treatment group.

Source data

Extended Data Fig. 6 Relationship between % edited alleles and % edited cells.

a, Representative flow cytometry plots of HSPCs simultaneously targeted at HBA1 with GFP (shown in Fig. 1c) and mPlum integration cassettes. b, Table showing % of populations targeted with GFP only, mPlum only, and both colors. Percent of edited cells was then converted to % edited alleles by the following equation: (total % targeted cells + (% dual color)*2)/2 = total % targeted alleles. c, Percent edited cells is plotted against % edited alleles for data shown in panel B. A polynomial regression (R2 = 0.9981) was used to determine an equation to convert between % edited alleles and % edited cells.

Extended Data Fig. 7 Colony-forming ability of edited HSPCs.

a, Distribution of genotypes of methylcellulose colonies displayed in Panels B and D. Numbers of clones corresponding to each category are included in the pie chart. b, In vitro (pre-engraftment) live CD34+ HSPCs from healthy donors were single-cell sorted into 96-well plates containing semisolid methylcellulose media for colony forming assays. 14d post-sorting cells were analyzed for morphology. Depicted are number of colonies formed for each lineage (CFU-E = erythroid lineage; CFU-GEMM = multi-lineage; or CFU-GM = granulocyte/macrophage lineage) divided by the total number of wells available for colonies. Columns represent median ± interquartile range. N = 3 experimental replicates with a minimum of 3 96-well methylcellulose-coated plates for mock, RNP only, and WGR-GFP AAV6 treatment groups; N = 2 for AAV only and HBB-HBA1 AAV6 treatment groups. c, Percent distribution of each lineage among all colonies for each treatment for Panel B. d, As above, in vitro (pre-engraftment) live CD34+ β-thalassemia HSPCs were sorted into 96-well plates for colony forming assays. Depicted are number of colonies formed for each lineage (B = BFU-E and C = CFU-E (erythroid lineage); GE = CFU-GEMM (multi-lineage); or GM = CFU-GM (granulocyte/macrophage lineage)) divided by the total number of wells available for colonies. Columns represent median ± interquartile range. For Mock and RNP + AAV, N = 2 experimental replicates with a minimum of 3 96-well methylcellulose-coated plates for each treatment; N = 1 experimental replicate with 3 plates for RNP only treatment. e, Percent distribution of each lineage among all colonies for each treatment for Panel D.

Extended Data Fig. 8 Engraftment into NSG mice of human HSPCs targeted with GFP at α-globin locus.

a, Timeline for targeting of HSPCs with UbC-GFP integration vector, transplantation into mice (both 1o and 2o engraftment), and subsequent analysis. b, AAV6 DNA repair donor design schematic to introduce a UbC-GFP-BGH integration is depicted at the HBA1 locus. c, 16 weeks after bone marrow transplantation of targeted human CD34+ HSPCs into NSG mice, bone marrow was harvested and rates of engraftment were determined (1°). Depicted is the percentage of mTerr119 cells (non-RBCs) that were hHLA+ from the total number of cells that were either mCd45+ or hHLA+. Bars represent median ± interquartile range. N = 8 biologically independent NSG mouse transplantations. d, Among engrafted human cells, the distribution among CD19+ (B-cell), CD33+ (myeloid), or other (that is HSPC/RBC/T/NK/Pre-B) lineages are indicated. Bars represent median ± interquartile range. N = 8 biologically independent NSG mouse transplantations. e, Percentage of GFP+ cells among pre-transplantation (in vitro, post-sorting) and successfully engrafted populations, both bulk HSPCs and among lineages. Bars represent median ± interquartile range. N = 3 independent HSPC donors from in vitro experiments that were transplanted into N = 6 individual NSG mice, from which N = 4 individual mice were lineage sorted and analyzed. Various green shades correspond to each particular HSPC donor. b, Following primary engraftments, engrafted human cells were transplanted a second time into the bone marrow of NSG mice. 16 weeks post-transplantation, bone marrow was harvested and rates of engraftment were determined (2°). Depicted is the percentage of mTerr119 cells (nonRBCs) that were hHLA+ from the total number of cells that were either mCd45+ or hHLA+. N = 1 NSG mouse transplantation. g, Percentage of GFP+ cells among successfully engrafted population from the secondary transplant depicted in Panel F. N = 1 NSG mouse transplantation.

Source data

Extended Data Fig. 9 Characterization of targeted β-thalassemia HSPCs.

a, Following differentiation of targeted HSPCs into RBCs, mRNA was harvested and converted into cDNA. Expression of HBA (does not distinguish between HBA1 and HBA2) and HBB transgene were normalized to HBG expression. Bars represent median ± interquartile range. N = 3 biologically independent editing experiments for all treatment groups with exception of HBA1 UTRs with N = 1. b, Summary of reverse-phase globin chain HPLC results showing % AUC of β-globin and α-globin. Bars represent median ± interquartile range. Bars represent median ± interquartile range. N = 3 biologically independent erythroid differentiation experiments for all treatment groups with exception of RNP only with N = 5. **P < 0.005; ***P < 0.0001 using unpaired two-tailed t test without adjustment for multiple comparisons.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cromer, M.K., Camarena, J., Martin, R.M. et al. Gene replacement of α-globin with β-globin restores hemoglobin balance in β-thalassemia-derived hematopoietic stem and progenitor cells. Nat Med 27, 677–687 (2021). https://doi.org/10.1038/s41591-021-01284-y

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing