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Activity-based selection for enhanced base editor mutational scanning

Nature Genetics volume 57, pages 2920–2929 (2025)Cite this article

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Abstract

Base editing is a CRISPR-based technology that enables high-throughput, nucleotide-level functional interrogation of the genome that is essential for understanding the genetic basis of human disease and informing therapeutic development. Base editing screens have emerged as a powerful experimental approach, yet significant cell-to-cell variability in editing efficiency introduces noise that may obscure meaningful results. Here we develop a co-selection method that enriches for cells with high base editing activity, substantially increasing editing efficiency at a target locus. We evaluate this activity-based selection method against a traditional screening approach by tiling guide RNAs across TP53, demonstrating its enhanced capacity to pinpoint specific mutations and protein regions of functional importance. We anticipate that this modular selection method will enhance the resolution of base editing screens across many applications.

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Fig. 1: Cas9-NG base editor tiling of TP53 and CBE optimization.
Fig. 2: Activity-based selection method development.
Fig. 3: Activity-based selection base editor tiling of TP53.
Fig. 4: TP53 screen validation.

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Data availability

The read counts for all pooled screens are provided as Supplementary Data and are available via Zenodo at https://doi.org/10.5281/zenodo.16642753 (ref. 81). The z scores from TP53 tiling screen with the use of activity-based selection under etoposide challenge are available in MaveDB78 under experiment set urn:mavedb:00001245-a, with the scores sets urn:mavedb:00001245-a-1 and urn:mavedb:00001245-a-2 representing the ABE and CBE screen, respectively82. For the analysis of external TP53 screens, the DMS screen z scores are obtained from the MaveDB score set urn:mavedb:00000068-a-1, and PE data are sourced from GitHub (http://github.com/samgould2/p53-prime-editing-sensor). Source data are provided with this paper.

Code availability

All custom code used for analysis is available on GitHub (http://github.com/ellkap/Base-editor-activity-based-selection) and is available via Zenodo at https://doi.org/10.5281/zenodo.16624354 (ref. 83). For data analysis, we used PoolQ (v.3.11.0, Broad Institute of MIT and Harvard), CRISPick (Broad Institute of MIT and Harvard), EditR (v.1.0.10, University of Minnesota), CRISPResso2 (v.2.3.2, Massachusetts General Hospital), MAGeCK (v.0.5.9, Dana-Farber Cancer Institute) and SnapGene (v.7.20, GSL Biotech LLC). We also used Fragmid (Broad Institute of MIT and Harvard) for vector design. To design base editing tiling libraries we used BEAGLE (Broad Institute of MIT and Harvard). To design the epegRNA we used PrimeDesign (Massachusetts General Hospital). To validate the splice-targeting sgRNA we used Cas-OFFinder (Seoul National University College of Medicine). For flow cytometry analysis, we used FlowJo (v.10; Becton Dickinson & Company). For graphing we used Python.

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Acknowledgements

We thank all members of the Genetic Perturbation Platform (GPP) production teams: D. Hernandez, M. Roberson, B. Escude Velasco, E. Josue Ibarra, N. Smith, D. McLaughlin, T. Lokyitsang and X. Yang for producing sgRNA libraries and lentivirus; A. Bowie, O. Bare, Y. Lee, Q. Celuzza, M. Felt and A. Herman for logistics support; M. Greene, D. Alan, M. Tomko, B. Wen, A. Tamayo and T. Green for software engineering support; the Broad Institute Genomics Platform Walk-up Sequencing group for Illumina sequencing; and the Functional Genomics Consortium for funding support. We especially thank N. Miller and I. Nwolah for laboratory assistance; F. Zheng and P. Roy for analysis advice; and A. Uebele, D. Gibson and S. Merzouk for a close reading of the manuscript.

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Author notes

  1. These authors contributed equally: Eleanor G. Kaplan, Ryan J. Steger.

Authors and Affiliations

  1. Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Eleanor G. Kaplan, Ryan J. Steger, Spencer T. Shah, Laura M. Drepanos, Audrey L. Griffith, Ganna Reint & John G. Doench

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Contributions

Conceptualization: E.G.K., R.J.S. and J.G.D. Data curation: E.G.K. and R.J.S. Formal analysis: E.G.K., R.J.S. and L.M.D. Funding acquisition: J.G.D. Investigation: E.G.K., R.J.S., S.T.S., A.L.G. and G.R. Methodology: E.G.K., R.J.S. and J.G.D. Software: L.M.D. Supervision: J.G.D. Visualization: E.G.K. and R.J.S. Writing—original draft: E.G.K. Writing—review and editing: E.G.K., R.J.S., S.T.S., L.M.D., G.R. and J.G.D.

Corresponding author

Correspondence to John G. Doench.

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Competing interests

J.G.D. consults for Microsoft Research, BioNTech, PhenomicAI, Servier and Pfizer. J.G.D. consults for and has equity in Tango Therapeutics. J.G.D. serves as a paid scientific advisor to the Laboratory for Genomics Research, funded in part by GSK, and the Innovative Genomics Institute, funded in part by Apple Tree Partners. J.G.D. receives funding support from the Functional Genomics Consortium: AbbVie, Bristol Myers Squibb, Janssen and Merck. J.G.D.’s interests are reviewed and managed by the Broad Institute in accordance with its conflict of interest policies. A patent application related to this work has been filed. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Cas9-NG base editor screen quality and validation.

(a) Replicate correlations of ABE (top) and CBE (bottom) arms across all drug conditions, comparing no drug arms to pDNA and drug arms to no drug arm. Pearson correlations are reported. LFC = log fold change. (b) Annotated TP53 domains. Domain start and end positions are labeled. TAD = transactivation domains, OD = oligomerization domain. (c) Validation of target allele abundance of sgTP53 1, 2, and 3. The sgRNAs were transduced into cells expressing Cas9-NG-ABE and treated with Nutlin starting on Day 7. Allelic fractions at each time point were determined by PCR of the target site and Illumina sequencing. LFC in abundance from Day 7 to Day 21 is shown and color coded. Wildtype sequence is underlined. Mutated amino acids are shown in red. Percent reads are normalized to total reads with >2% frequency.

Extended Data Fig. 2 Activity-based selection method development and extension.

(a) Replicate correlations (r) between splice-targeting guide library (SGL) and intron variant library replicates for ABEs (left) and CBEs (right). Note that the ABE SGL Rep A + Intron Rep B sample was lost to contamination. (b) Representative example of flow cytometry gating strategy shown with ABE sgRNA 1 activity-based selection condition. Populations were gated for live cells, then single cells, then transduced cells via GFP-FITC signal, and finally editing efficiency of CD274 was determined via APC signal. (c) Percent base editing by nucleotide as determined by Sanger sequencing of targeted CD274 locus. Only within-window edits are shown. (d) Percent unedited cells (CD274-APC+) remaining in presence- versus activity-based selection methods with the intron inserted within the hygromycin resistance gene. SpG-Cas9 base editors were used. (e) GFP assay to evaluate possible cryptic splicing by comparing the GFP-intron construct in cells with and without base editors. (f) Percent unedited cells (CD274-APC+) remaining in presence- versus activity-based selection methods with puromycin resistance and SpRY-Cas9 base editors. (g) SpRY-Cas9 versus SpG-Cas9 self-editing at the integrated sgRNA locus. Two splice-targeting sgRNAs were used for both ABE and CBE, and the average sgCD274 self-editing is plotted. Individual sgCD274 self-editing rates are shown as dots.

Extended Data Fig. 3 Identification of guide features that influence on-target activity in TP53 tiling screen etoposide arm.

(a) Rule Set 3 Sequence score of TP53 tiling screen positive control guides, namely those that introduce missense, nonsense, or splice-site mutations not previously identified as benign, discretized by activity. The results of this analysis are presented separately with the use of a z-score cutoff of −2, −3, or −4 to define guides as active. Significance is calculated between active and inactive guides using the Mann-Whitney Wilcoxon one-sided test. This analysis incorporates data with the use of both the ABE8e and TadCBEd editor. n = number of guides represented in each activity bin. (b) (a), but with the use of the BE-Hive model to predict base editing guide efficacy instead of Rule Set 3 Sequence score. n = number of guides which fall in each activity bin. (c) (a), but with the use of the FORECasT-BE model to predict base editing guide efficacy with ABE8e. n = number of guides represented in each activity bin. (d) (c), but estimating model performance at predicting base editing guide activity with TadCBEd. n = number of guides represented in each activity bin. (e) Sequence motifs representing nucleotide identities at each position relative to the editable nucleotide that are enriched among active positive control guides. Active guides are defined as those with a z-score below −2, whereas inactive guides represent those with a z-score between −2 and 2. Analysis is stratified by base editor.

Extended Data Fig. 4 Activity-based selection base editor tiling of TP53 (continued).

(a) Illumina sequencing read match rate of library sgRNAs after deconvolution with PoolQ. (b) Potential self-editing mechanism. Rather than identifying and editing the endogenous target, the sgRNA may bind to itself by forming a one-nucleotide bulge to recognize the NGN PAM using the first G of the tracer sequence. Alternatively, GTT may function as a PAM sequence. (c) Self-editing rate of no drug conditions by selection method and base editor. Shown in gray is baseline pDNA self-editing rate with no editors to account for sequencing error rates. (d) Screen replicate Pearson correlations of the etoposide arm before and after applying self-editing computational correction to all conditions. (e) sgRNA LFC distribution graphs for ABE (left) and CBE (right) with presence-based selection (top) and activity-based selection (bottom) conditions, comparing the no drug final time point to pDNA sequencing. Splice-site control sgRNAs target pan-lethal genes. (f) sgRNA distribution between selection conditions for ABE (left) and CBE (right) arms. Pearson correlations are displayed. Dashed line indicates y = x. (g) Prime editing rate at HEK3 locus by selection method and prime editor. The HEK3 epegRNA was used in all vectors. Three puromycin resistance-targeting epegRNAs were designed and tested for activity-based selection conditions. (h) Self-editing rate by tracrRNA of presence- and activity-based selection methods across two sgRNAs via Sanger sequencing of the sgRNA cassette. Bars depict average of two replicates, individually shown as dots. (i) A > G editing efficiency by tracrRNA of presence- versus activity-based selection methods across two sgRNAs via Illumina sequencing of the TP53 target loci. Bars depict average of two replicates, individually shown as dots. Panel b created using BioRender.com.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and references.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary tables listing guide RNA sequences, intron sequences, tracrRNA sequences, primers, reagents and software.

Source data

Source Data Fig. 1

Unprocessed western blot corresponding to Fig. 1d.

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Kaplan, E.G., Steger, R.J., Shah, S.T. et al. Activity-based selection for enhanced base editor mutational scanning. Nat Genet 57, 2920–2929 (2025). https://doi.org/10.1038/s41588-025-02366-0

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