Data availability
All processed screening data are provided as Supplementary Tables. Source data are provided with this paper.
Code availability
Code for generating in silico predicted structures is deposited here: https://github.com/gnikolenyi/izar_vis (ref. 73).
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Acknowledgements
N.K. and S.B.S. are equally contributing second authors. B.I. is supported by National Institute of Health grants (R37CA258829, R01CA280414, R01CA266446, U54CA274506); and additionally by the Pershing Square Sohn Cancer Research Alliance Award; the Burroughs Wellcome Fund Career Award for Medical Scientists; a Tara Miller Melanoma Research Alliance Young Investigator Award; the Louis V. Gerstner, Jr. Scholars Program; and the V Foundation Scholars Award. This work was supported by a Herbert Irving Comprehensive Cancer Center (HICCC) Velocity Grant (to B.I.), the HICCC Human Tissue Immunology and Immunotherapy Initiative and NIH Grant P30CA013696. Medical illustrations were prepared by U. Mackensen. The illustration in Extended Data Fig. 9a was created with https://www.biorender.com.
Author information
Author notes
These authors contributed equally: Zachary H. Walsh, Parin Shah.
These authors jointly supervised this work: Johannes C. Melms, Benjamin Izar.
Authors and Affiliations
Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
Zachary H. Walsh, Parin Shah, Neeharika Kothapalli, Shivem B. Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Johannes C. Melms & Benjamin Izar
Department of Medicine, Division of Hematology and Oncology, Columbia University Irving Medical Center, New York, NY, USA
Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Neil Vasan, Johannes C. Melms & Benjamin Izar
Columbia Center for Translational Immunology, New York, NY, USA
Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Johannes C. Melms & Benjamin Izar
Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
Zachary H. Walsh, Parin Shah, D. Zack Brodtman, Giuseppe Leuzzi, Meri Rogava, Michael Mu, Patricia Ho, Sinan Abuzaid, Neil Vasan, Alberto Ciccia, Johannes C. Melms & Benjamin Izar
Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
Gergo Nikolenyi, Mohammed AlQuraishi & Benjamin Izar
Department of Genetics and Development, Columbia University Medical Center, New York, NY, USA
Giuseppe Leuzzi & Alberto Ciccia
Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
Joshua D. Milner
Contributions
B.I. and Z.H.W. conceived the study. B.I. provided overall supervision with support from J.C.M. Z.H.W., P.S. and J.C.M. planned, designed and executed all key experiments. S.B.S., M.M., P.H., M.R. and S.A. performed experiments. N.K. performed computational analyses of screens with support from Z.H.W. and D.Z.B. G.N. performed structural modeling and visualizations. N.V., M.A., J.D.M., A.C. and G.L. provided additional guidance for the design, execution and interpretation of screens. Z.H.W., P.S., J.C.M. and B.I. wrote the manuscript with input and approval from all authors.
Corresponding author
Ethics declarations
Competing interests
B.I. is a consultant for or received honoraria from Volastra Therapeutics, Johnson & Johnson (Janssen), Novartis, Eisai, AstraZeneca and Merck and has received research funding to Columbia University from Agenus, Alkermes, Arcus Biosciences, Checkmate Pharmaceuticals, Compugen, Immunocore, Regeneron and Synthekine. Z.H.W. and B.I. filed a patent application based on this work. The other authors do not have competing interests.
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Nature Biotechnology thanks Dimitrios Wagner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Optimization of workflows for base editing in primary human T cells.
a, Overview of approach for targeted base editing in primary human T cells. b-d, Target sites of sgRNAs against CD2, B2M, and TRBC1/2 sites predicted to generate gene knockout through several mechanisms (SPLd = splice donor site mutation, SPLa=splice acceptor site mutation, SM=start codon mutation, ES=conversion to early stop codon). e, Representative flow cytometry histograms from one human donor showing ABE-mediated knockout of CD2 and B2M using sgRNAs indicated in (b-c), and f, CBE-mediated knockout of CD2, TRBC1/2, and B2M using sgRNAs indicated in (b-d). g, Quantification of base editing efficiency in (e) (n = 3 independent human donors). h, Quantification of base editing efficiency in (f), (n = independent human 4 donors for B2M_ES and TRBC1/2_ES; n = 2 independent human donors for B2M_SPLd and CD2_SM). i, Representative flow cytometry dotplots and histograms demonstrating CBE-mediated knockout of TCRab. For histograms, red indicates gated mTurquoise-negative cells, and blue indicates gated mTurquoise-positive cells. j, Quantification of ABE-mediated knockout of B2M with lentiviral integration of B2M_SM_1 sgRNA and electroporation of ABE mRNA in CD4 and CD8 T cell subsets (n = 2 independent human donors). k, Editing efficiency (measured by % B2M loss on flow cytometry) and viability of T cells transduced with B2M_SM_1 sgRNA and electroporated with varying doses of ABE. Vertical dotted line represents ABE dose selected (per 1e6 T cells) for screens. Error bars represent mean +/− SD (panels g, h, j).
Source data
Extended Data Fig. 2 Tiling screen targets, library transduction, and pooled base editing of T cells.
a, Classification of sgRNAs in the ClinVar library based on mutation subtype. b, Schematic of gene targets for the 12-Gene tiling screen and their function in T cells. c, Classification of sgRNAs in the 12-Gene tiling library based on mutation subtype. d, Schematic for generation of library base-edited T cells. e, Transduction efficiency of ClinVar base editor library in n = 2 independent human donors.
Extended Data Fig. 3 Metrics for rigor and reproducibility of large-scale base editing screens.
a, Density plots showing LFC values of different categories of guides from the ClinVar library at Day 35 post-electroporation of the long-term expansion screen arm. Dashed line represents the bottom 5% of the distribution of combined empty window and silent mutation controls. Indicated are the percentages of guides in each category falling below this threshold. sgRNAs generating variants in CD3D, CD3E, CD3G, or CD3Z were binned into the ‘CD3 complex’ category. The second donor from the screen is shown (in companion to Fig. 2a). b, Scatter plot showing LFC values of negative control sgRNAs (including both empty window and silent mutations) in both donors from the ClinVar Library at Day 28 post-electroporation in the long-term expansion screen arm. c-d, Distribution of robust rank aggregation (RRA) scores for gene-wise dropout analysis in the c, CD25 hi vs lo (activation) sort and d, CFSE lo vs hi (short-term proliferation) sort arms of the ClinVar library across both donors. The top 5 negatively selected genes in CD25 hi vs lo and in CFSE lo vs hi are listed. e, Shared positive control sgRNA (n = 600) were identified between the ClinVar and 12-gene tiling screens and sgRNA LFCs from matched long-term proliferation arm timepoints (Day 28 of ClinVar Screen, Day 26 of 12-gene Tiling Screen) are plotted. For each screen, the average LFC of each sgRNA across both donors is plotted. Simple linear regression with two-sided Pearson test (panel e).
Extended Data Fig. 4 Analysis of ClinVar screen across readouts.
a, Scatterplot showing LFC of selected sgRNAs generating mutations in LCK, SOS1, and PTPRC. Timepoint shown is Day 28 post-electroporation in the ClinVar long-term expansion screen arm. b, Volcano plot showing enriched and depleted guides in the CFSE lo vs hi proliferation sort. For visualization purposes, one mutation for each labeled sgRNA is shown. One representative donor is shown. False discovery rate (FDR) cutoff
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