Abstract
CRISPR-enabled screening is a powerful tool for the discovery of genes that control T cell function and has nominated candidate targets for immunotherapies1,2,3,4,5,6. However, new approaches are required to probe specific nucleotide sequences within key genes. Systematic mutagenesis in primary human T cells could reveal alleles that tune specific phenotypes. DNA base editors are powerful tools for introducing targeted mutations with high efficiency7,8. Here we develop a large-scale base-editing mutagenesis platform with the goal of pinpointing nucleotides that encode amino acid residues that tune primary human T cell activation responses. We generated a library of around 117,000 single guide RNA molecules targeting base editors to protein-coding sites across 385 genes implicated in T cell function and systematically identified protein domains and specific amino acid residues that regulate T cell activation and cytokine production. We found a broad spectrum of alleles with variants encoding critical residues in proteins including PIK3CD, VAV1, LCP2, PLCG1 and DGKZ, including both gain-of-function and loss-of-function mutations. We validated the functional effects of many alleles and further demonstrated that base-editing hits could positively and negatively tune T cell cytotoxic function. Finally, higher-resolution screening using a base editor with relaxed protospacer-adjacent motif requirements9 (NG versus NGG) revealed specific structural domains and protein–protein interaction sites that can be targeted to tune T cell functions. Base-editing screens in primary immune cells thus provide biochemical insights with the potential to accelerate immunotherapy design.
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Data availability
All guide library information from this manuscript is provided in Supplementary Tables 1 and 2 (for the NGG screen) and Supplementary Tables 4 and 5 (for the NG screen). MAGeCK test output for all screens is provided in Supplementary Table 3. Raw sequencing data for all base editing screens have been deposited on GEO (GSE244774). Source data are provided with this paper.
Code availability
Analysis and visualization were done with matplotlib and seaborn in Python or with ggplot2 in R, with editing for clarity before publication. Code and data used to create key figures have been deposited at Zenodo (https://doi.org/10.5281/zenodo.8415038).
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Acknowledgements
We thank Z. Steinhart, F. Blaeschke and all other members of the Eyquem, Ye and Marson laboratories for helpful discussions and feedback on the manuscript; J. Woo, R. Manlapaz and J. Sawin for organization and management; T. Tolpa and S. Pyle for help with illustrations and figure design; J. Srivastava and the Gladstone Institute’s flow cytometry core for assistance with cell sorting; and R. Innerhofer, M. Hollenstein, T. Koller and K. Schmetterer for covering clinical shifts and for scientific input during the revision process. Some elements in the schematics of Figs. 1 and 3 were sourced from BioRender.com. This work was supported by the NIAID (P01AI138962, 1P01AI55393), NIDDK (R01DK129364), the Larry L. Hillblom Foundation (grant no. 2020-D-002-NET), Northern California JDRF Center of Excellence, a gift from the Byers Family and the James B. Pendleton Charitable Trust. The Marson lab has received funds from the Parker Institute for Cancer Immunotherapy (PICI), the Lloyd J. Old STAR award from the Cancer Research Institute (CRI), the Simons Foundation, the CRISPR Cures for Cancer Initiative and K. Jordan. R.S. was supported by the Max Kade Foundation and the Austrian Society for Laboratory Medicine. C.W. is supported by an NCI F99/K00 Fellowship (K00CA245718). R.D. is supported by an NIAID research supplement to promote diversity in health-related research (P01AI138962). M.O. is supported by Astellas Foundation for Research on Metabolic Disorder and Chugai Foundation for Innovative Drug Discovery Science (C-FINDs). L.A.G. is supported by a NIH New Innovator Award (DP2 CA239597), a Pew-Stewart Scholars for Cancer Research award, and the Goldberg-Benioff Endowed Professorship in Prostate Cancer Translational Biology and the Arc Institute. J.K.P., M.O., and A.M. are supported by the NHGRI (2R01HG008140). Sequencing performed at the UCSF CAT is supported by UCSF PBBR, RRP IMIA, and NIH 1S10OD028511-01 grants. Sorting at the Gladstone flow core is supported by NIH S10 RR028962, James B. Pendleton Charitable Trust, and DARPA. B.R.S. was supported by NIH grants K08CA273529 and L30TR002983. D.D. was supported by the UCSF Scholars At Risk programme.
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Authors and Affiliations
Contributions
R.S. and C.C.W. contributed equally to this work and their names are listed alphabetically. R.S., C.C.W. and A.M. conceptualized the study. RS., C.C.W. and R.D. designed and executed the majority of experiments. R.S., C.C.W., R.D. and Z.A.-G. analysed and visualized the data. Z.A.-G. helped design and execute NG base editor testing and screening and validation experiments. D.D. and B.R.S. helped with CRISPR knock-in experiments. M.L. helped with cloning lentiviral transfer plasmids for base editors. M.O., J.K.P., C.W. and C.J.Y advised on statistics and performed base-level analyses. V.A., R.H. and J.E. helped with cytotoxicity experiments. G.X., L.G. and L.A.G. helped with mRNA delivery of base editors. Y.Y.C. helped with cell isolation and sorting. R.S., C.C.W., S.E.D. and A.M. created figures and wrote the manuscript.
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Competing interests
A.M. is a co-founder of Function Bio, Arsenal Biosciences, Spotlight Therapeutics and Survey Genomics, serves on the boards of directors at Function Bio, Spotlight Therapeutics and Survey Genomics, was a board observer at Arsenal Biosciences, is a member of the scientific advisory boards of Function Bio, Arsenal Biosciences, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, Tenaya, and Lightcast owns stock in Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, PACT Pharma, Tenaya, and Lightcast, and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, 23andMe, PACT Pharma, Juno Therapeutics, Tenaya, Lightcast, Trizell, Vertex, Merck, Amgen, Genentech, AlphaSights, Clearview Healthcare, Rupert Case Management, Bernstein, GLG, Survey Genomics, and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. C.W., S.E.D., R.S. and A.M. are shareholders of Function Bio. The Marson laboratory has received research support from Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead, and Anthem. J.E. is a compensated co-founder at Mnemo Therapeutics. J.E. owns stocks in Mnemo Therapeutics and Cytovia Therapeutics. J.E. is a compensated scientific advisor for Enterome, Treefrog Therapeutics and Resolution Therapeutics. The Eyquem lab has received research support from Cytovia Therapeutic, Mnemo Therapeutics and Takeda. J.E. is a holder of patents pertaining to but not resulting from this work. L.A.G. has filed patents on CRISPR approaches and is a co-founder of Chroma Medicine. R.S., C.W. and A.M. are listed as inventors on patent applications related to this work. The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Optimization and assessment of base editing efficacy in primary human T cells.
a, Distribution of protein surface expression levels for CD3e, CD5, or CD7 in CD4+ or CD8+ T cells base edited with three sgRNAs targeting each gene or AAVS1 control with ABE (left) and CBE (right). Predicted mutations for each guide are annotated above each distribution in the CD4+ plots. n = 2 independent donors. b, Sanger sequencing traces for ABE and CBE editing using the three CD3e sgRNAs for both ABE and CBE. Consensus sequences are in black above and detected BE mutations are highlighted in red under traces. c, Summary boxplots of T cell editing outcomes expressed as % cells negative for CD3, CD5, or CD7 protein expression using the guides in (a), with (Blast) or without (noBlast) blasticidin selection. n = 2 independent donors. d, Protein level and genomic level editing for efficient sgRNAs targeting CD5 and CD7, shown as percent negative (flow cytometry, protein level) and percent edited (NGS, genomic level). Genomic level editing was analyzed with CRISPResso2. n = 2 independent donors for protein-level assessment and 1 matching donor for genomic-level assessments.
Extended Data Fig. 2 ABE and CBE screens are reproducible across human donors.
a, Scatter plots showing LFC (log2-fold changes) of pairwise donor-to-donor correlations (high/low bins) for each screen. Three human donors were used for all screens except the IFNγ-ABE and CD25-CBE screens, where two were used for analyses. Pearson correlation coefficient is given for each comparison. b, Comparison of sgRNA level effect sizes between CD25 and PD1 screens (left) or CD25 and TNFα screens (right), shown as LFC (log2-fold changes, high/low bins).
Extended Data Fig. 3 Base edits with strong functional effects are enriched in structured regions of proteins.
Scatter plot showing the correlation between AlphaFold predicted local Distance Difference Test (pLDDT) scores and screen LFC (log2-fold changes) for residues in proteins whose genes had |LFC | > 1.0 in the TNFα screen. Lower (<50) AlphaFold pLDDTs scores are predictive of structurally disordered regions of proteins. Predictions were obtained from pre-computed AlphaFold structures for canonical UniProt accessions for each screen gene.
Extended Data Fig. 4 Tiling base edits reveal mutations with discordant effects across screens.
a, Average effects (LFC, log2-fold changes) of non-synonymous, non-terminating base edits at each residue (ABE, top; CBE, bottom) on PD-1, CD25 expression, and IFNγ production are plotted across open reading frames encoding VAV1 (left) and NFKBIA (right). The dotted line indicates the average log2-fold change of the top 3 terminating (knockout) guides in the first half of the coding sequence, with blue for negative effect and red for positive effect on TNFα levels. Annotated domains from UniProt are shown below and are colored by the average effect of guides targeting those regions. b, Violin plots show distribution of log2-fold changes of CBE (dark purple) and ABE (light purple) guides tiling positive (top) and negative (bottom) regulator genes of PD-1, CD25 expression, and IFNγ production. Dotted lines indicate the average log2-fold change of the top 3 terminating (knockout) guides in the first half of the coding sequence. Blue and red dots indicate guides with strongly opposing effects with respect to the knockout guides for the same gene.
Extended Data Fig. 5 Comparative analysis of residue vs. base-level analysis and correlation to clinical variants.
a, Comparison between residue-level analysis derived from average LFC (log2-fold change) and base-level analysis derived from a multiple regression model for VAV1 and NFKBIA. For residue-level: p values were derived with Fisher’s method from MAGeCK results for individual guides; for base-level: p values were derived from a two-sided Wald test. b, Residue-level and base-level analysis for PIK3CD showing comparisons when guides with high off-target scores are filtered from analysis as well as when the predicted editing window for ABE is expanded to include the 9th position. c, Effect size for base-level analysis vs. LFC (log2-fold change) for residue-level analysis is plotted for the TNFα ABE (left) and CBE (right) screens. d, Variants are binned based on MAGeCK guide-level FDR. Enrichments of base edited variants in Clinvar “pathogenic,” “likely pathogenic,” and “benign” categories are plotted for each group for the PD1, CD25, TNFα, and IFNγ screens (from n = 3 donors); error bars represent 95% confidence intervals.
Extended Data Fig. 6 Supplementary validation and characterization of specific base edits in arrayed format.
a, Editing by ABE mRNA and synthetic sgRNA co-electroporation for each sgRNA chosen for validation, assessed by deep amplicon sequencing and analyzed with Crispresso254. Guide sequences are in gray, predicted editing window in green, and PAM in dark gray. b, LFC (log2-fold changes) of levels of the indicated cytokines over control (mean of 2 AAVS1 control gRNAs) measured by intracellular staining and flow cytometry are plotted. n = 6 human donors; mean ± SE; *p < 0.05, **p < 0.01, ***p < 0.001. P-values were derived using a two-tailed independent two-sample t-test. c, Cytokine secretion in culture supernatants for T cells with the indicated base edits were measured by Luminex. Heatmap represents LFC (log2-fold changes) over the mean of cells edited with two AAVS1 control sgRNAs. n = 4 human donors.
Extended Data Fig. 7 Stimulation responses of base edited T cells in arrayed validation.
Individual plots of cytokine and cell surface protein expression for CD4+ or CD8+ T cells base edited with the indicated guides (red, positive in TNFα screen; blue, negative in TNFα screen; gray, AAVS1 control), measured by flow cytometry and normalized to AAVS1 control, over a range of anti-CD3/28/2 immunocult (IC) doses ranging from 0 (IC0) to 6.25ul/ml (IC6.25). n = 6 human donors, shown as mean ± SE.
Extended Data Fig. 8 Characterization of a PIK3CD allelic series.
a, Bar graphs corresponding to Fig. 3i show LFC (log2-fold change) in expression of the indicated cytokines in arrayed validation for a series of 9 PIK3CD guides relative to AAVS1 control in CD8+ T cells; color indicates LFC (log2-fold change) value of each guide in the original ABE TNFα screen. n = 6 human donors, shown as mean ± SE. b, A-T to G-C editing by co-electroporation of ABE mRNA and synthetic PIK3CD sgRNAs, assessed by deep amplicon sequencing and analyzed with Crispresso254. Guide sequences are in gray, predicted editing window in green, and PAM in dark gray. c, Correlation between PIK3CD LFC (log2-fold change) values from the TNFα screen and validation experiment (Fig. 3i) in CD4+ T cells. d, mRNA expression of PIK3CD and IL2RA in PIK3CD edited T cells. Mean ± SD, n = 4 independent donors. e-g, Single amino acid substitution by CRISPR/Cas9 knockin. All edits include a synonymous K282K mutation deleting the PAM site. e, Flow cytometry histograms showing TNFα expression in CD4+ gated T cells. Percent positive cells in gray. f, Percent cytokine positive CD4+ and CD8+ gated T cells with the indicated knockins. n = 2 human donors (red and blue dots) shown as mean. g, Corresponding gene editing outcomes for knockin experiments as reported by CRISPResso2. n = 2 human donors shown as mean.
Extended Data Fig. 9 Cytotoxic function of base edited T cells in arrayed validation.
Cytotoxicity (measured by A375 target cell killing) of antigen-specific T cells base edited with control, positive (increased T cell activation, red), or negative (decreased T cell activation, blue) guides targeting selected genes measured with Incucyte live-cell imaging over time. n = 6 human donors, shown as mean (line) ± SE (shaded area).
Extended Data Fig. 10 Establishment and performance of NG-PAM dependent base editing with SpG Cas9.
a, Predicted distribution of fraction of editable residues, within the original 385 genes in the NGG-PAM Cas9 screen, using either WT nCas9 (NGG PAM) or SpG Cas9 (NG PAM) for ABE. Box plots show median, center quartiles, and extremes within 1.5 * IQR. b, Distribution of surface protein expression levels for CD5 or CD7 in pan T cells gated for CD4+ (right) or CD8+ (left) base edited with one (CD5) or two (CD7) ABE NGG sgRNAs targeting each gene and one ABE NG sgRNA targeting each gene plus AAVS1 control. c, LFC (log2 fold changes) in the TNFα screens using WT nCas9 (NGG, y-axis) vs. using SpG Cas9 (NG, x-axis) for 13,334 overlapping guides between the two screens. The average knockout effect of specific genes present in both screens is shown with overlaid blue/red dots and labels. Knockout effects were calculated using the top 3 predicted knockout guides. d, Scatter plots showing LFC (log2-fold changes) of pairwise donor-to-donor correlations for each NG screen. Two human donors were used for all NG screens.
Supplementary information
Supplementary Fig. 1
Gating hierarchy for FACS of base-editing screens.
Supplementary Fig. 2
Gating hierarchy for establishing lentiviral base editing in T cells.
Supplementary Fig. 3
Gating hierarchy for intracellular cytokine expression.
Supplementary Fig. 4
Gating hierarchy for surface activation marker expression.
Supplementary Table 1
Genes, corresponding protein isoforms from RefSeq chosen for guide selection, and canonical UniProt accessions used for mapping amino acid locations, for the WT (NGG PAM) nCas 9 screens.
Supplementary Table 2
Guide library used for the WT (NGG PAM) nCas9 screen: gene, guide identifier, guide target sequence, editing window (ABE and CBE), genomic locus, genomic span of editing window (ABE and CBE), target strand, predicted amino acid mutations (ABE and CBE), and predicted amino acid mutations mapped to UniProt canonical sequence (ABE and CBE), as well as count of highly scored off-target sites are provided for each guide.
Supplementary Table 3
MAGeCK test output for all screens, both NGG and NG PAM screens. P-values and FDR are derived as defined in the MAGeCK algorithm.
Supplementary Table 4
Genes, corresponding protein isoforms from RefSeq chosen for guide selection, and canonical UniProt accessions used for mapping amino acid locations, for the SpG (NG PAM) nCas 9 screens.
Supplementary Table 5
Guide library used for the SpG (NG PAM) nCas9 screen: gene, guide identifier, guide target sequence, editing window, genomic locus, genomic span of editing window, target strand, predicted amino acid mutations, and predicted amino acid mutations mapped to UniProt canonical sequence are provided for each guide.
Supplementary Table 6
Base-level statistics from a multiple linear regression model for standard screen analysis.
Supplementary Table 7
Analysis with ABE window extended to position 9.
Supplementary Table 8
Analysis with highly scored off-targets removed.
Supplementary Table 9
Antibodies used for flow cytometry.
Supplementary Table 10
Read counts from MAGeCK count for for sgRNA libraries.
Supplementary Table 11
sgRNA sequences used in arrayed experiments.
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Schmidt, R., Ward, C.C., Dajani, R. et al. Base-editing mutagenesis maps alleles to tune human T cell functions. Nature 625, 805–812 (2024). https://doi.org/10.1038/s41586-023-06835-6
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DOI: https://doi.org/10.1038/s41586-023-06835-6
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