The efficiency of targeted knock-in for cell therapeutic applications is generally low, and the scale is limited. In this study, we developed CLASH, a system that enables high-efficiency, high-throughput knock-in engineering. In CLASH, Cas12a/Cpf1 mRNA combined with pooled adeno-associated viruses mediate simultaneous gene editing and precise transgene knock-in using massively parallel homology-directed repair, thereby producing a pool of stably integrated mutant variants each with targeted gene editing. We applied this technology in primary human T cells and performed time-coursed CLASH experiments in blood cancer and solid tumor models using CD3, CD8 and CD4 T cells, enabling pooled generation and unbiased selection of favorable CAR-T variants. Emerging from CLASH experiments, a unique CRISPR RNA (crRNA) generates an exon3 skip mutant of PRDM1 in CAR-Ts, which leads to increased proliferation, stem-like properties, central memory and longevity in these cells, resulting in higher efficacy in vivo across multiple cancer models, including a solid tumor model. The versatility of CLASH makes it broadly applicable to diverse cellular and therapeutic engineering applications.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
All data generated or analyzed during this study are included in this article and its supplementary information files. Specifically, source data and statistics for non-high-throughput experiments, such as flow cytometry, qPCR, protein experiments and other molecular or cellular assays, are provided in an Excel file of source data and statistics. Processed data for genomic sequencing (for example, CLASH, RNA-seq, amplicon sequencing, MIPS and AAV off-target) and other forms of high-throughput experiments are provided as processed quantifications in Supplementary Datasets. Genomic sequencing raw data are being deposited to the Gene Expression Omnibus (GEO), with the following accession numbers: GSE207143 for all CLASH screens; GSE219061 for MIPS, Nextera and AAV integration off-target; GSE207404 for RNA-seq; and GSE201997 for CUT&RUN107,108,109,110. CLASH vectors and libraries are available via material transfer agreements. All other data and materials that support the findings of this research are available either via public repositories or from the corresponding author upon reasonable request to the academic community. Source data are provided with this paper.
Analytic codes used to generate figures that support the findings of this study will be available from the corresponding author upon reasonable request.
Lim, W. A. & June, C. H. The principles of engineering immune cells to treat cancer. Cell 168, 724–740 (2017).
Porter, D. L., Levine, B. L., Kalos, M., Bagg, A. & June, C. H. Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia. N. Engl. J. Med. 365, 725–733 (2011).
Neelapu, S. S. et al. Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N. Engl. J. Med. 377, 2531–2544 (2017).
Raje, N. et al. Anti-BCMA CAR T-cell therapy bb2121 in relapsed or refractory multiple myeloma. N. Engl. J. Med. 380, 1726–1737 (2019).
Tang, J., Pearce, L., O’Donnell-Tormey, J. & Hubbard-Lucey, V. M. Trends in the global immuno-oncology landscape. Nat. Rev. Drug Discov. 17, 783–784 (2018).
June, C. H., O’Connor, R. S., Kawalekar, O. U., Ghassemi, S. & Milone, M. C. CAR T cell immunotherapy for human cancer. Science 359, 1361–1365 (2018).
Weber, E. W., Maus, M. V. & Mackall, C. L. The emerging landscape of immune cell therapies. Cell 181, 46–62 (2020).
Porter, D. L. et al. Chimeric antigen receptor T cells persist and induce sustained remissions in relapsed refractory chronic lymphocytic leukemia. Sci. Transl. Med. 7, 303ra139 (2015).
Gardner, R. et al. Acquisition of a CD19-negative myeloid phenotype allows immune escape of MLL-rearranged B-ALL from CD19 CAR-T-cell therapy. Blood 127, 2406–2410 (2016).
Sadelain, M., Rivière, I. & Riddell, S. Therapeutic T cell engineering. Nature 545, 423–431 (2017).
Kosti, P. et al. Hypoxia-sensing CAR T cells provide safety and efficacy in treating solid tumors. Cell Rep. Med. 2, 100227 (2021).
Ying, Z. et al. A safe and potent anti-CD19 CAR T cell therapy. Nat. Med. 25, 947–953 (2019).
Schneider, D. et al. Trispecific CD19-CD20-CD22–targeting duoCAR-T cells eliminate antigen-heterogeneous B cell tumors in preclinical models. Sci. Transl. Med. 13, eabc6401 (2021).
Lynn, R. C. et al. c-Jun overexpression in CAR T cells induces exhaustion resistance. Nature 576, 293–300 (2019).
Ma, L. et al. Enhanced CAR-T cell activity against solid tumors by vaccine boosting through the chimeric receptor. Science 365, 162–168 (2019).
Ghorashian, S. et al. Enhanced CAR T cell expansion and prolonged persistence in pediatric patients with ALL treated with a low-affinity CD19 CAR. Nat. Med. 25, 1408–1414 (2019).
Savoldo, B. et al. CD28 costimulation improves expansion and persistence of chimeric antigen receptor–modified T cells in lymphoma patients. J. Clin. Invest. 121, 1822–1826 (2011).
Weber, E. W. et al. Transient rest restores functionality in exhausted CAR-T cells through epigenetic remodeling. Science 372, eaba1786 (2021).
Liu, X. et al. Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction. Nature 567, 525–529 (2019).
Seo, H. et al. TOX and TOX2 transcription factors cooperate with NR4A transcription factors to impose CD8+ T cell exhaustion. Proc. Natl Acad. Sci. USA 116, 12410–12415 (2019).
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971 (2018).
Ting, P. Y. et al. Guide Swap enables genome-scale pooled CRISPR–Cas9 screening in human primary cells. Nat. Methods 15, 941–946 (2018).
Henriksson, J. et al. Genome-wide CRISPR screens in T helper cells reveal pervasive crosstalk between activation and differentiation. Cell 176, 882–896 (2019).
Dong, M. B. et al. Systematic immunotherapy target discovery using genome-scale in vivo CRISPR screens in CD8 T cells. Cell 178, 1189–1204 (2019).
Ye, L. et al. In vivo CRISPR screening in CD8 T cells with AAV–Sleeping Beauty hybrid vectors identifies membrane targets for improving immunotherapy for glioblastoma. Nat. Biotechnol. 37, 1302–1313 (2019).
Long, L. et al. CRISPR screens unveil signal hubs for nutrient licensing of T cell immunity. Nature 600, 308–313 (2021).
Chen, Z. et al. In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262–1280 (2021).
Eyquem, J. et al. Targeting a CAR to the TRAC locus with CRISPR/Cas9 enhances tumour rejection. Nature 543, 113–117 (2017).
Roth, T. L. et al. Reprogramming human T cell function and specificity with non-viral genome targeting. Nature 559, 405–409 (2018).
Roth, T. L. et al. Pooled knockin targeting for genome engineering of cellular immunotherapies. Cell. 181, 728–744 (2020).
Torikai, H. et al. A foundation for universal T-cell based immunotherapy: T cells engineered to express a CD19-specific chimeric-antigen-receptor and eliminate expression of endogenous TCR. Blood 119, 5697–5705 (2012).
Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).
Arrowsmith, C. H., Bountra, C., Fish, P. V., Lee, K. & Schapira, M. Epigenetic protein families: a new frontier for drug discovery. Nat. Rev. Drug Discov. 11, 384–400 (2012).
Fraietta, J. A. et al. Disruption of TET2 promotes the therapeutic efficacy of CD19-targeted T cells. Nature 558, 307–312 (2018).
Kim, H. K. et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239 (2018).
Shin, H. & Wherry, E. J. CD8 T cell dysfunction during chronic viral infection. Curr. Opin. Immunol. 19, 408–415 (2007).
Good, C. R. et al. An NK-like CAR T cell transition in CAR T cell dysfunction. Cell 184, 6081–6100 (2021).
Bodapati, S., Daley, T. P., Lin, X., Zou, J. & Qi, L. S. A benchmark of algorithms for the analysis of pooled CRISPR screens. Genome Biol 21, 62 (2020).
Renauer, P. SAMBA: CRISPR Screen analysis with moderated Bayesian statistics and adaptive gene aggregation scoring. R package version 1.1.0. https://github.com/Prenauer/SAMBA (2022).
Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 171, 1221–1223 (2017).
Nelms, K., Keegan, A. D., Zamorano, J., Ryan, J. J. & Paul, W. E. The IL-4 receptor: signaling mechanisms and biologic functions. Annu. Rev. Immunol. 17, 701–738 (1999).
Gray-Owen, S. D. & Blumberg, R. S. CEACAM1: contact-dependent control of immunity. Nat. Rev. Immunol. 6, 433–446 (2006).
Bandala-Sanchez, E. et al. T cell regulation mediated by interaction of soluble CD52 with the inhibitory receptor Siglec-10. Nat. Immunol. 14, 741–748 (2013).
Tosa, N. et al. Critical function of T cell death-associated gene 8 in glucocorticoid-induced thymocyte apoptosis. Int. Immunol. 15, 741–749 (2003).
Liu, J. et al. Enhanced CD4+ T cell proliferation and Th2 cytokine production in DR6-deficient mice. Immunity 15, 23–34 (2001).
Rutishauser, R. L. et al. Transcriptional repressor Blimp-1 promotes CD8+ T cell terminal differentiation and represses the acquisition of central memory T cell properties. Immunity 31, 296–308 (2009).
Chang, M. et al. The ubiquitin ligase Peli1 negatively regulates T cell activation and prevents autoimmunity. Nat. Immunol. 12, 1002–1009 (2011).
Vacca, M. et al. NLRP10 enhances CD4+ T-cell-mediated IFNγ response via regulation of dendritic cell-derived IL-12 release. Front. Immunol. 8, 1462 (2017).
Joshi, N. S. & Kaech, S. M. Effector CD8 T cell development: a balancing act between memory cell potential and terminal differentiation. J. Immunol. 180, 1309–1315 (2008).
Louis, C. U. et al. Antitumor activity and long-term fate of chimeric antigen receptor-positive T cells in patients with neuroblastoma. Blood 118, 6050–6056 (2011).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Gyory, I., Fejer, G., Ghosh, N., Seto, E. & Wright, K. L. Identification of a functionally impaired positive regulatory domain I binding factor 1 transcription repressor in myeloma cell lines. J. Immunol. 170, 3125–3133 (2003).
Yoshikawa, T. et al. Genetic ablation of PRDM1 in antitumor T cells enhances therapeutic efficacy of adoptive immunotherapy. Blood 139, 2156–2172 (2022).
Morgan, M. A., Mould, A. W., Li, L., Robertson, E. J. & Bikoff, E. K. Alternative splicing regulates Prdm1/Blimp-1 DNA binding activities and corepressor interactions. Mol. Cell. Biol. 32, 3403–3413 (2012).
Martins, G. & Calame, K. Regulation and functions of Blimp-1 in T and B lymphocytes. Annu. Rev. Immunol. 26, 133–169 (2008).
Shin, H. et al. A role for the transcriptional repressor Blimp-1 in CD8+ T cell exhaustion during chronic viral infection. Immunity 31, 309–320 (2009).
Crotty, S., Johnston, R. J. & Schoenberger, S. P. Effectors and memories: Bcl-6 and Blimp-1 in T and B lymphocyte differentiation. Nat. Immunol. 11, 114–120 (2010).
Shin, H. M. et al. Epigenetic modifications induced by Blimp-1 regulate CD8+ T cell memory progression during acute virus infection. Immunity 39, 661–675 (2013).
Xin, A. et al. A molecular threshold for effector CD8+ T cell differentiation controlled by transcription factors Blimp-1 and T-bet. Nat. Immunol. 17, 422 (2016).
Fu, S.-H., Yeh, L.-T., Chu, C.-C., Yen, B. L.-J. & Sytwu, H.-K. New insights into Blimp-1 in T lymphocytes: a divergent regulator of cell destiny and effector function. J. Biomed. Sci. 24, 49 (2017).
Zhang, Z. et al. Hypermethylation of PRDM1/Blimp‐1 promoter in extranodal NK/T‐cell lymphoma, nasal type: an evidence of predominant role in its downregulation. Hematol. Oncol. 35, 645–654 (2017).
Zhu, L. et al. Blimp-1 impairs T cell function via upregulation of TIGIT and PD-1 in patients with acute myeloid leukemia. J. Hematol. Oncol. 10, 124 (2017).
Huang, S. Histone methyltransferases, diet nutrients and tumour suppressors. Nat. Rev. Cancer 2, 469–476 (2002).
Győry, I., Wu, J., Fejér, G., Seto, E. & Wright, K. L. PRDI-BF1 recruits the histone H3 methyltransferase G9a in transcriptional silencing. Nat. Immunol. 5, 299–308 (2004).
Ancelin, K. et al. Blimp1 associates with Prmt5 and directs histone arginine methylation in mouse germ cells. Nat. Cell Biol. 8, 623–630 (2006).
Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).
Magnúsdóttir, E. et al. Epidermal terminal differentiation depends on B lymphocyte-induced maturation protein-1. Proc. Natl Acad. Sci. USA 104, 14988–14993 (2007).
Mascanfroni, I. D. et al. IL-27 acts on DCs to suppress the T cell response and autoimmunity by inducing expression of the immunoregulatory molecule CD39. Nat. Immunol. 14, 1054–1063 (2013).
Agresta, L., Hoebe, K. H. & Janssen, E. M. The emerging role of CD244 signaling in immune cells of the tumor microenvironment. Front. Immunol. 9, 2809 (2018).
Kleinstiver, B. P. et al. Genome-wide specificities of CRISPR–Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 34, 869–874 (2016).
Kim, D. et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 34, 863–868 (2016).
Yan, W. X. et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat. Commun. 8, 1–9 (2017).
Canaj, H. et al. Deep profiling reveals substantial heterogeneity of integration outcomes in CRISPR knock-in experiments. Preprint at https://www.biorxiv.org/content/10.1101/841098v1 (2019).
Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR–Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).
Fry, T. J. et al. CD22-targeted CAR T cells induce remission in B-ALL that is naive or resistant to CD19-targeted CAR immunotherapy. Nat. Med. 24, 20 (2018).
Mullard, A. FDA approves second BCMA-targeted CAR-T cell therapy. Nat. Rev. Drug Discov. 21, 249 (2022).
Adusumilli, P. S. et al. Regional delivery of mesothelin-targeted CAR T cell therapy generates potent and long-lasting CD4-dependent tumor immunity. Sci. Transl. Med. 6, 261ra151 (2014).
O’Rourke, D. M. et al. A single dose of peripherally infused EGFRvIII-directed CAR T cells mediates antigen loss and induces adaptive resistance in patients with recurrent glioblastoma. Sci. Transl. Med. 9, eaaa0984 (2017).
Ahmed, N. et al. HER2-specific chimeric antigen receptor-modified virus-specific T cells for progressive glioblastoma: a phase 1 dose-escalation trial. JAMA Oncol. 3, 1094–1101 (2017).
Posey, A. D. Jr et al. Engineered CAR T cells targeting the cancer-associated Tn-glycoform of the membrane mucin MUC1 control adenocarcinoma. Immunity 44, 1444–1454 (2016).
Brudno, J. N. & Kochenderfer, J. N. Chimeric antigen receptor T-cell therapies for lymphoma. Nat. Rev. Clin. Oncol. 15, 31 (2018).
Kim, M. Y. et al. Genetic inactivation of CD33 in hematopoietic stem cells to enable CAR T cell immunotherapy for acute myeloid leukemia. Cell 173, 1439–1453 (2018).
Wang, Y. et al. CD133-directed CAR T cells for advanced metastasis malignancies: a phase I trial. Oncoimmunology 7, e1440169 (2018).
Weiss, T., Weller, M., Guckenberger, M., Sentman, C. L. & Roth, P. NKG2D-based CAR T cells and radiotherapy exert synergistic efficacy in glioblastoma. Cancer Res. 78, 1031–1043 (2018).
Wei, J., Han, X., Bo, J. & Han, W. Target selection for CAR-T therapy. J. Hematol. Oncol. 12, 62 (2019).
Reinhard, K. et al. An RNA vaccine drives expansion and efficacy of claudin-CAR-T cells against solid tumors. Science 367, 446–453 (2020).
Maude, S. L. et al. Chimeric antigen receptor T cells for sustained remissions in leukemia. N. Engl. J. Med. 371, 1507–1517 (2014).
Dai, X. et al. One-step generation of modular CAR-T cells with AAV-Cpf1. Nat. Methods 16, 247–254 (2019).
Concordet, J. P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).
Francois, A. et al. Accurate titration of infectious AAV particles requires measurement of biologically active vector genomes and suitable controls. Mol. Ther. Methods Clin. Dev. 10, 223–236 (2018).
O’Roak, B. J. et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338, 1619–1622 (2012).
Wang, G. et al. Mapping a functional cancer genome atlas of tumor suppressors in mouse liver using AAV-CRISPR-mediated direct in vivo screening. Sci. Adv. 4, eaao5508 (2018).
Boyle, E. A., O’Roak, B. J., Martin, B. K., Kumar, A. & Shendure, J. MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing. Bioinformatics 30, 2670–2672 (2014).
Kong, N. R., Chai, L., Tenen, D. G. & Bassal, M. A. A modified CUT&RUN protocol and analysis pipeline to identify transcription factor binding sites in human cell lines. STAR Protoc. 2, 100750 (2021).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Zhu, Q., Liu, N., Orkin, S. H. & Yuan, G. C. CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis. Genome Biol 20, 192 (2019).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Hahne, F. & Ivanek, R. Visualizing genomic data using Gviz and Bioconductor. Methods Mol. Biol. 1418, 335–351 (2016).
Wang, G. G. et al. Haematopoietic malignancies caused by dysregulation of a chromatin-binding PHD finger. Nature 459, 847–851 (2009).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687 (2017).
Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44 (2009).
Dai, X. et al. Massively parallel knock-in engineering of persistent CAR-Ts [CLASH]. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207143 (2022).
Dai, X., Park, J., Du, Y. & Chen, S. Massively parallel knock-in engineering of persistent CAR-Ts. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE219061 (2022).
Dai, X., Park, J. & Chen, S. Massively parallel knock-in engineering of persistent CAR-Ts [XDRNA]. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207404 (2022).
Dai, X., Chow, R., Gu, J., Chen, S. Massively parallel knock-in engineering of persistent CAR-Ts. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201997 (2022).
We thank M. Sznol, A. Bersenev, S. Seropian, I. Isufi, M. Müschen and D. Krause for discussions. We thank all members of the Chen laboratory as well as various colleagues in Yale Genetics, SBI, CSBC, MCGD, Immunobiology, BBS, YCC, YSCC and CBDS for assistance and/or discussions. We thank various Yale Core Facilities, such as YCGA, HPC, WCAC and KBRL, for technical support. S.C. is supported by NIH/NCI/NIDA (DP2CA238295, R01CA231112, U54CA209992-8697, R33CA225498 and 1RF1DA048811); DoD (W81XWH-20-1-0072 and W81XWH-21-1-0514); the Alliance for Cancer Gene Therapy; the Sontag Foundation (Distinguished Scientist Award); Pershing Square Sohn Cancer Research Alliance; Dexter Lu; the Ludwig Family Foundation; the Blavatnik Family Foundation; and the Chenevert Family Foundation. X.D. is supported by the Charles H. Revson Senior Postdoctoral Fellowship. J.J.P. is supported by an NIH Medical Scientist Training Program grant (T32GM136651). R.C. is supported by an NIH Medical Scientist Training Program grant (T32GM136651) and the National Research Service Award fellowship (F30CA250249). P.A.R. is supported by an NIH training grant (T32GM007499), the Lo Fellowship and NIH/NCI Diversity Supplement. S.S. is supported by a Mark Foundation for Cancer Research Emerging Leader Award, a Paul G. Allen Frontiers Group Distinguished Investigator Award and NIH/NIGMS R01GM122984.
A patent application has been filed by Yale University on CLASH (S.C., X.D., J.J.P. and Y.D. as inventors). S.C. is a founder of Cellinfinity Bio, which licensed the CLASH patent. S.C. is also a founder of EvolveImmune Tx, Chen Consulting and Chen Tech, unrelated to this study.
Peer review information
Nature Biotechnology thanks Li Tang, Hyongbum Henry Kim and Joseph Fraietta for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–13 of supplementary datasets and tables
This zip file contains six datasets. (1) CLASH library, in vitro and in vivo screening experiments and analyses. (2) All CLASH-MIPS processed data and correlation analyses, with metadata. (3) All Nextera amplicon sequencing indel variant frequencies, with metadata. (4) PRDM1 Δexon3 CD22 CAR-T timecourse mRNA-seq. (5) Genome-wide chromatin binding of PRDM1 WT and exon3 skip mutant via CUT&RUN in human CD22 CAR-T cells. (6) All genome-wide AAV on-target and off-target integration processed data, with metadata.
Oligo sequences used in this study are listed in an Excel file.
Statistical source data for Figs. 1 and 3–6 and Extended Data Figs. 1, 5–9 and 12.
Source Data Fig. 4
Unprocessed western blots
Source Data Fig. 6
Unprocessed western blots
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dai, X., Park, J.J., Du, Y. et al. Massively parallel knock-in engineering of human T cells. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-022-01639-x