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Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking

Nature Biotechnology volume 38, pages 1298–1308 (2020)Cite this article

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Abstract

Targeted gene editing in hematopoietic stem cells (HSCs) is a promising treatment for several diseases. However, the limited efficiency of homology-directed repair (HDR) in HSCs and the unknown impact of the procedure on clonal composition and dynamics of transplantation have hampered clinical translation. Here, we apply a barcoding strategy to clonal tracking of edited cells (BAR-Seq) and show that editing activates p53, which substantially shrinks the HSC clonal repertoire in hematochimeric mice, although engrafted edited clones preserve multilineage and self-renewing capacity. Transient p53 inhibition restored polyclonal graft composition. We increased HDR efficiency by forcing cell-cycle progression and upregulating components of the HDR machinery through transient expression of the adenovirus 5 E4orf6/7 protein, which recruits the cell-cycle controller E2F on its target genes. Combined E4orf6/7 expression and p53 inhibition resulted in HDR editing efficiencies of up to 50% in the long-term human graft, without perturbing repopulation and self-renewal of edited HSCs. This enhanced protocol should broaden applicability of HSC gene editing and pave its way to clinical translation.

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Fig. 1: BAR-Seq enables clonal tracking of human HDR-edited HSPCs.
Fig. 2: Combined transient expression of Ad5-E4orf6/7 and GSE56 improves editing efficiency in human HSPCs.
Fig. 3: Ad5-E4orf6/7 forces cell-cycle progression and upregulates HDR machinery via the E2F pathway.
Fig. 4: Editing enhancers enable high proportion of HDR-edited HSPCs and stable reconstitution in xenograft model.
Fig. 5: Editing enhancers allow polyclonal composition of the human edited graft without perturbing clonal dynamics.

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Data availability

All relevant data are included in the manuscript. BAR-Seq and RNA-Seq data are deposited in the Gene Expression Omnibus with the following access codes: GSE143995 (for RNA-Seq) and GSE144340 (BAR-Seq). The reagents described in this manuscript are available under a material transfer agreement with IRCCS Ospedale San Raffaele and Fondazione Telethon; requests for materials should be addressed to L.N.

Code availability

The software for BAR-Seq analysis is freely available at https://bitbucket.org/bereste/bar-seq.

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Acknowledgements

We thank all members of L.N.’s laboratory for discussion, the IRCCS San Raffaele Hospital Flow Cytometry facility, the IRCCS San Raffaele Center for Omics Sciences (COSR), A. Auricchio and M. Doria (Telethon Institute of GEnetics and Medicine; TIGEM Vector Core, Pozzuoli, Italy) for providing AAV6 vectors, E. Ayuso (INSERM UMR1089, Nantes, France) for providing comments on AAV biology, L. Periè (Institute Curie, Paris, France) and J. Urbanus (the Netherlands Cancer Institute, Amsterdam, the Netherlands) for advice on the BAR cloning strategy, G. Schiroli for initial help with the design of the BAR-Seq strategy, R. Di Micco and B. Gentner for critical reading of the manuscript. We thank T. Plati for technical support in ddPCR analyses, T. Di Tomaso and G. Desantis for purifying mPB HSPCs, F. Benedicenti for helping in library preparation for NHEJ clonal tracking, L. Sergi Sergi, I. Cuccovillo, M. Biffi and M. Soldi for IDLV production and purification (SR-TIGET, IRCCS San Raffaele Scientific Institute), C. Di Serio for coordinating CUSSB support (Vita-Salute San Raffaele University). This work was supported by grants to: L.N. from Telethon (TIGET grant no. E4), the Italian Ministry of Health (grant nos. PE-2016-02363691; E-Rare-3 JTC 2017), the Italian Ministry of University and Research (PRIN 2017 Prot. no. 20175XHBPN), the EU Horizon 2020 Program (UPGRADE) and from the Louis-Jeantet Foundation through the 2019 Jeantet-Collen Prize for Translational Medicine; P.G. from Telethon (TIGET grant no. E3) and the Italian Ministry of Health (grant no. GR-2013-02358956); A.K.R. from the ERC (ImmunoStem, grant no. 819815). S.F., V.V. and G.U. conducted this study as partial fulfillment of their PhD in Molecular Medicine, International PhD School, Vita-Salute San Raffaele University (Milan, Italy). A.J. conducted this study as partial fulfillment of his PhD in Translational and Molecular Medicine DIMET, Milano-Bicocca University (Monza, Italy) with M. Serafini acting as the university tutor.

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Author notes

  1. Pietro Genovese

    Present address: Gene Therapy Program, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Department of Pediatric Oncology, Harvard Medical School, Boston, MA, USA

  2. These authors contributed equally: Samuele Ferrari, Aurelien Jacob.

  3. These authors jointly supervised this work: Pietro Genovese, Luigi Naldini.

Authors and Affiliations

  1. San Raffaele Telethon Institute for Gene Therapy (SR-TIGET), IRCCS San Raffaele Scientific Institute, Milan, Italy

    Samuele Ferrari, Aurelien Jacob, Stefano Beretta, Giulia Unali, Luisa Albano, Valentina Vavassori, Anna Kajaste-Rudnitski, Ivan Merelli, Pietro Genovese & Luigi Naldini

  2. Vita-Salute San Raffaele University, Milan, Italy

    Samuele Ferrari, Giulia Unali, Valentina Vavassori & Luigi Naldini

  3. Milano-Bicocca University, Monza, Italy

    Aurelien Jacob

  4. Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Davide Cittaro & Dejan Lazarevic

  5. CUSSB—University Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy

    Chiara Brombin & Federica Cugnata

  6. National Research Council, Institute for Biomedical Technologies, Segrate, Italy

    Ivan Merelli

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Contributions

S.F. and A.J. performed research, interpreted data and wrote the manuscript. G.U. performed experiments with IDLV-based repair template supervised by A.K.R. L.A. provided technical help with mouse experiments. V.V. performed CD40LG and T cell experiments. S.B., D.C., D.L. and I.M. performed bioinformatic analysis. C.B. and F.C. performed statistical analyses. P.G. and L.N. designed the study, interpreted data, supervised research and wrote the manuscript. L.N. coordinated the work.

Corresponding author

Correspondence to Luigi Naldini.

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Competing interests

L.N., P.G. and A.K.R. are inventors of patents on applications of gene editing in HSPCs owned and managed by the San Raffaele Scientific Institute and the Telethon Foundation, including a patent application on improved gene editing filed by S.F., A.J., P.G. and L.N. L.N. is founder and quota holder and P.G. is quota holder of GeneSpire, a startup company aiming to develop ex vivo gene editing in genetic diseases. All other authors declare no conflict of interest.

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Extended data

Extended Data Fig. 1 BAR-Seq dissects clonal dynamics of HDR-edited cells.

a, Percentage of GFP+ cells within subpopulations 96 h after AAVS1 editing with the barcoded or non-barcoded AAV6 (3 HSPC donors; n = 4). Median. b, Number of unique BARs and relative abundances in bulk cultured HSPCs 72 h after editing. One representative sample out of two is shown. c, Experimental scheme. d, e, Culture composition (d) and percentage of GFP+ cells within subpopulations (e) of AAVS1 edited HSPCs with the indicated treatments at the time of transplant and 96 h after editing, respectively (10 HSPC donors; n = 1). f, g, Percentage of hCD45+ cells (f) and GFP+ cells within human graft (g) in BM or spleen (SPL) of mice from Fig. 1c (n = 9, 10, 6, 3). Median. Kruskal-Wallis test. h, i, Abundance of ranked BARs from PBMCs collected at 8 (h) and 12 (i) weeks after transplant, as in Fig. 1e. j, Heatmap as in Fig. 1f for ‘w/o S/U (+4 days)’-transplanted mice. k, Number of dominant unique BARs in sorted hCD45+ cell lineages and HSPCs of mice from Fig. 1c. Mice with % of circulating hCD45+GFP+ cells at 18 weeks timepoints < 0.1 were plotted with BAR count = 0 (n = 9, 10, 10, 10). Median. l, Correlation between the percentage of GFP+ cells (within hCD45+) and the number of dominant unique BARs in “w/o S/U”, “RNP + AAV6” and “+ GSE56” mice of this study (n = 71). Each dot represents one mouse. Mice with number of dominant unique BARs ≥6 (arbitrary threshold) are shown in magenta (coefficient of variation (CV) = 0.51); mice with number of dominant unique BARs <5 are shown in yellow (CV = 0.87). Dashed line indicates the median percentage of GFP+ cells within CD90+ HSPCs in the in vitro outgrown of transplanted edited cells. m, Longitudinal PBMC analysis as in Fig. 1k but including in the analysis >95% of total BAR reads (n = 4, 5). Median. n, Correlation as in Fig. 1l at 8 weeks after transplant (n = 28). Spearman correlation coefficient was calculated. All statistical tests are two-tailed. n indicate independent animals.

Extended Data Fig. 2 Identification of Ad protein variants improving HDR efficiency.

a, b, Multiple sequences alignment of E4orf1 (a) and E4orf6/7 (b) Ad variants. Sequences were collected from online RCSB Protein Data Bank and UniProt. c, d, Percentage of HDR-edited alleles (c) and GFP+ cells within subpopulations (d) 96 h after AAVS1 editing in bulk CB HSPCs with indicated treatments (n = 4, 4, 4, 4; other treatments: n = 2). Median. e, FACS plots of untreated (UT) and AAV6-transduced HSPCs in absence (“Mock AAV6”) or presence of Ad5-E1B55K+Ad5-E4orf6 measured 24 h after treatments. The results of one representative experiment out of three is shown. f, Number of colonies from bulk edited HSPCs in the indicated treatments (n = 2). Mean. g, h, Fold change expansion of live HSPCs after indicated treatments from Extended Data Fig. 2c (n = 2). Median. i, Number of colonies from bulk edited HSPCs with the indicated treatments (n = 2). Mean. j, CD90 MFI in edited HSPCs measured 96 h after editing with indicated treatments (n = 6). Median with IQR. Friedman test with two-tailed Dunn’s multiple comparisons. k, Percentage of live, early/late apoptotic and necrotic bulk HSPCs 24 h after editing with the indicated treatments (7 HSPC donors; n = 3). Mean ± SEM. l-m, Percentage of HDR/NHEJ-edited alleles (l) and culture composition (m) 96 h after editing of bulk mPB HSPCs from Fig. 2h (n = 3). Mean ± SEM. n) Percentage of GFP+ T cells 14 days after AAVS1 editing with indicated treatments (n = 3). Median. o-p, Percentage of HDR/NHEJ-edited alleles (o) and culture composition (p) 96 h after IDLV-based editing of bulk CB HSPCs from Fig. 2i (n = 3). Mean ± SEM. Red arrows indicate Ad protein variants selected for further investigation. n indicate independent experiments.

Extended Data Fig. 3 Investigating the transcriptional response upon enhanced editing.

a, b, Fold change expression of cell cycle related genes relative to UT 24 h after AAV-based editing with the indicated treatments in CB (a) or mPB (b) HSPCs (CB: n = 8, 5, 7, 6, 3, 3, 3, 3; mPB: n = 4, 4, 4, 3). Median. c, Fold change expression of CDKN1A relative to UT 24 h after IDLV-based editing with indicated treatments in CB HSPCs (n = 3). Median. d, Fold change expression of CDKN1A relative to UT at 24 h after AAV-based editing with indicated treatments in CB HSPCs (n = 5). Median. e) MA plots showing significant down- (green) and up- (red) regulated genes after AAVS1 editing in mock electroporated (left) and standard edited (right) compared to UT (n = 3). PPP1R12C, the AAVS1 hosting gene appears among the downregulated genes, concordantly with previous reports showing transient transcriptional repression at the site of DNA DSB15. f) Random walk plots for the indicated Reactome categories. Relative adjusted p-values and NES are shown. g) Venn diagram showing the number of genes related to the “Allograft rejection” category upregulated upon standard editing and downregulated in presence of “+ Ad5-E4orf6/7” treatment. h) Venn diagram showing the number of HDR genes (“Homology directed repair” category from Reactome database) shared with E2F pathway target genes (Hallmark gene set) from cluster 1 or other clusters from Fig. 3e. i) Schematic of “cell cycle” and “p53 pathway” KEGG gene ontologies highlighting genes (red) belonging to clusters 1 (top) and 3 (bottom) of Fig. 3e. For all panels with statistical analysis: Friedman test with two-tailed Dunn’s multiple comparisons. n indicate independent experiments, except for Extended Data Fig. 3e where n indicates independent samples.

Extended Data Fig. 4 Transplantation of enhancer-edited HSPCs in NSG mice.

a, Experimental workflow. b, Percentage of hCD45+ cells in SPL and BM of mice from Figs. 4a, b (n = 23, 11, 15, 16). LME followed by post-hoc analysis. Mean ± SEM. c, BM cell composition in mice from Fig. 4a, b. LME followed by post-hoc analysis for HSPCs (n = 23, 11, 15, 16). Mean ± SEM. d, Percentage of cells harboring monoallelic or biallelic integration(s) in SPL of mice from Fig. 4a, b (n = 23, 11, 15, 16). Mean ± SEM. e, Percentage of circulating hCD45+ cells in mice transplanted with CB HSPCs IL2RG-edited in presence of GSE56 and Ad5 E4orf6/7 (n = 4). Comparison with the previously published results for “RNP+AAV6” and “+ GSE56” groups22 is shown (n = 5, 6). All statistical tests are two-tailed. n indicate independent animals.

Extended Data Fig. 5 Enhanced editing preserves multilineage repopulation capacity and self-renewing potential of individual edited HSPC clones.

a, Heatmap showing the abundance (red-scaled palette) of dominant unique BARs (rows) retrieved in PBMCs at indicated times after transplant and sorted hCD45+ cell lineages of mice from one experiment of Fig. 4a (separated columns). b, Clonal diversity within sorted hCD45+ cell lineages in mice from Extended Data Fig. 5a (B cells: n = 5, 3, 5; Myeloid and T cells: n = 6, 3, 3). Median. Two-tailed Friedman test with Dunn’s multiple comparisons. Experimental groups were unified for statistical analysis. c, Number of dominant unique BARs in PBMCs or BM of mice from one experiment in Fig. 4a (PBMCs: n = 5, 4; BM: n = 3, 3). Median. d, Percentage of NHEJ-edited alleles within the non-HDR edited fraction from Fig. 5g (n = 6, 3, 6). Median. e, Heatmaps as in Extended Data Fig. 5a showing the dominant unique BARs in 9-weeks PBMCs and in sorted hCD45+ cell lineages (15 weeks) of secondary recipients. n indicate independent animals.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables 1–4

This file includes the following items. Supplementary Table 1, complete gene list from Fig. 3e. Supplementary Table 2, complete gene list and fold change expression from Fig. 3f. Supplementary Table 3, list of primers and probes. Supplementary Table 4, details on mRNA-expressing vectors and the final concentration used.

Supplementary Table 5

Detailed statistical analyses.

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Ferrari, S., Jacob, A., Beretta, S. et al. Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking. Nat Biotechnol 38, 1298–1308 (2020). https://doi.org/10.1038/s41587-020-0551-y

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