Isolating live cell clones from barcoded populations using CRISPRa-inducible reporters


We developed a functional lineage tracing tool termed CaTCH (CRISPRa tracing of clones in heterogeneous cell populations). CaTCH combines precise clonal tracing of millions of cells with the ability to retrospectively isolate founding clones alive before and during selection, allowing functional experiments. Using CaTCH, we captured rare clones representing as little as 0.001% of a population and investigated the emergence of resistance to targeted melanoma therapy in vivo.

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Fig. 1: CaTCH: a barcode-guided CRISPRa-inducible reporter for isolation of specific cell clones.
Fig. 2: CaTCH-isolated treatment-naïve clones initially respond to RAFi/MEKi treatment and acquire resistance in vivo.

Data availability

All datasets generated during the current study are available from the corresponding authors upon reasonable request. RNAseq data have been deposited at the Gene Expression Omnibus under accession number GSE139236. Source data are provided with this paper.


  1. 1.

    Reya, T., Morrison, S. J., Clarke, M. F. & Weissman, I. L. Stem cells, cancer, and cancer stem cells. Nature 414, 105–111 (2001).

    CAS  PubMed  Google Scholar 

  2. 2.

    Mojtahedi, M. et al. Cell fate decision as high-dimensional critical state transition. PLoS Biol. 14, e2000640 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Simons, B. D. & Clevers, H. Strategies for homeostatic stem cell self-renewal in adult tissues. Cell 145, 851–862 (2011).

    CAS  PubMed  Google Scholar 

  4. 4.

    Shakiba, N. et al. Cell competition during reprogramming gives rise to dominant clones. Science 364, eaan0925 (2019).

    CAS  PubMed  Google Scholar 

  5. 5.

    Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Yamanaka, S. Elite and stochastic models for induced pluripotent stem cell generation. Nature 460, 49–52 (2009).

    CAS  PubMed  Google Scholar 

  7. 7.

    Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–483 (2015).

    CAS  PubMed  Google Scholar 

  8. 8.

    Ramos, P. & Bentires-Alj, M. Mechanism-based cancer therapy: resistance to therapy, therapy for resistance. Oncogene 34, 3617–3626 (2015).

    CAS  PubMed  Google Scholar 

  9. 9.

    McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

    CAS  PubMed  Google Scholar 

  10. 10.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Kebschull, J. M. & Zador, A. M. Cellular barcoding: lineage tracing, screening and beyond. Nat. Methods 15, 871–879 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Bhang, H. C. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

    CAS  PubMed  Google Scholar 

  13. 13.

    Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Chavez, A. et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Al’Khafaji, A. M., Deatherage, D. & Brock, A. Control of lineage-specific gene expression by functionalized gRNA barcodes. ACS Synth. Biol. 7, 2468–2474 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Meeth, K., Wang, J. X., Micevic, G., Damsky, W. & Bosenberg, M. W. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29, 590–597 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Shi, H. et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 80–93 (2014).

    CAS  PubMed  Google Scholar 

  19. 19.

    Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ravindran Menon, D. et al. A stress-induced early innate response causes multidrug tolerance in melanoma. Oncogene 34, 4448–4459 (2015).

    CAS  PubMed  Google Scholar 

  21. 21.

    Trumpp, A. & Wiestler, O. D. Mechanisms of disease: cancer stem cells—targeting the evil twin. Nat. Clin. Pract. Oncol. 5, 337–347 (2008).

    CAS  PubMed  Google Scholar 

  22. 22.

    Friedman, R. Drug resistance in cancer: molecular evolution and compensatory proliferation. Oncotarget 7, 11746–11755 (2016).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Hobbs, G. A. et al. Atypical KRAS G12R mutant is impaired in PI3K signaling and macropinocytosis in pancreatic cancer. Cancer Discov. 10, 104–123 (2020).

    CAS  PubMed  Google Scholar 

  24. 24.

    Zafra, M. P. et al. An in vivo KRAS allelic series reveals distinct phenotypes of common oncogenic variants. Preprint at (2019).

  25. 25.

    Rebbeck, C. et al. SmartCodes: functionalized barcodes that enable targeted retrieval of clonal lineages from a heterogeneous population. Preprint at (2018).

  26. 26.

    Akimov, Y., Bulanova, D., Abyzova, M., Wennerberg, K. & Aittokallio, T. DNA barcode-guided lentiviral CRISPRa tool to trace and isolate individual clonal lineages in heterogeneous cancer cell populations. Preprint at (2019).

  27. 27.

    Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855.e19 (2018).

    CAS  Google Scholar 

  28. 28.

    Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Calderwood, S. K. Tumor heterogeneity, clonal evolution, and therapy resistance: an opportunity for multitargeting therapy. Discov. Med. 15, 188–194 (2013).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Smith, M. P. et al. Inhibiting drivers of non-mutational drug tolerance is a salvage strategy for targeted melanoma therapy. Cancer Cell 29, 270–284 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Bodenhofer, U., Kothmeier, A. & Hochreiter, S. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463–2464 (2011).

    CAS  Google Scholar 

  34. 34.

    Loew, R., Heinz, N., Hampf, M., Bujard, H. & Gossen, M. Improved Tet-responsive promoters with minimized background expression. BMC Biotechnol. 10, 81 (2010).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  36. 36.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Marini, F. & Binder, H. pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components. BMC Bioinf. 20, 331 (2019).

    Google Scholar 

  40. 40.

    Krämer, A., Green, J., Pollard, J. & Tugendreich, S. Causal analysis approaches in Ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Babicki, S. et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 44, W147–W153 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164–e164 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Mayakonda, A., Lin, D.-C., Assenov, Y., Plass, C. & Koeffler, H. P. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28, 1747–1756 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, 1–16 (2017).

  50. 50.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Zuber, J. et al. Toolkit for evaluating genes required for proliferation and survival using tetracycline-regulated RNAi. Nat. Biotechnol. 29, 79–83 (2011).

    CAS  PubMed  Google Scholar 

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We thank all members of the Obenauf and Zuber laboratories for experimental support and discussions, in particular M. Roth, M. Muhar, I. Barbosa, V. Pinamonti and I. Krecioch. We thank the Stark laboratory, especially C. Neumayr and M. Pagani for sharing of reagents and cell lines. We thank M. Bosenberg for providing YUMM 1.7 mouse melanoma cells. This work was funded by the Starting Grants of the European Research Council (ERC-StG-759590 to A.C.O. and ERC-StG-336860 to J.Z.), the Vienna Science and Technology fund (LS16-063 to A.C.O. and T.W.) and the Austrian Science Fund (SFB-F4710, to J.Z.). Research at the IMP is generously supported by Boehringer Ingelheim.

Author information




C.U. and A.C.O. conceived the study, designed the experiments, interpreted the results and wrote the manuscript. A.C.O. supervised the study. C.U. developed experimental tools and the CaTCH library; performed most in vitro experiments and parts of the in vivo treatment studies; all flow cytometry analysis; data analysis and parts of the computational analysis. F.H. performed in vivo work and experimental work for QuantSeq library preparation as well as computational analysis of the data (Extended Data Fig. 8b,c,f), statistical analysis (Extended Data Figs. 5a and 7c,d) and provided conceptual and experimental input. L.F. performed all in vivo treatment studies of isolated CaTCH clones (Fig. 2f and Extended Data Figs. 7c and 8) and the in vitro experiment and western blot in Extended Data Fig. 4d. J.J. provided conceptual input on library design, library cloning strategy, plasmids and technical input. K.F. provided computational analysis of CaTCH barcoding data (Fig. 2c,e and Extended Data Figs. 5b–d and 6b–e). T.N. provided computational analysis of CaTCH barcoding and whole-exome sequencing data (Extended Data Fig. 9) and Nras mutation analysis (Extended Data Fig. 4e). S.C. performed in vivo work and library preparations. L.H. established the in vivo model systems and provided conceptual and experimental input. J.J.L. analyzed the CaTCH plasmid library NGS data and generated Extended Data Fig. 2b. T.R.B. provided the mRNA sequencing analysis pipeline and mRNA sequencing analysis as well as help with data analysis. M.F. provided the cloning-protocol, expertise and helped with CaTCH library cloning. T.W. and J.Z. provided experimental and technical input and cowrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Anna C. Obenauf.

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Extended data

Extended Data Fig. 1 Development and optimization of the CaTCH reporter.

a, Functional comparison of dCas9-VPR to dCas9-VP64 using a doxycycline-inducible reporter system with sgRNAs targeting either one (−53 bp or −203 distance to TSS), two, or seven target sites (tetO sites). Doxycycline induced activation via rtTA3 was used as a positive control. Reporter activation was measured by FACS. Left: Corresponding FACS histograms of GFP activation (normalized to mode); Right: Quantification of percent activated of parent (upper panel) and of signal strength in mean fluorescent intensity (MFI, lower panel), n = 3 biologically independent samples, for CTRL n = 2, bar graphs display the mean ± standard deviation (s.d.). b, Evaluating the optimal sgRNA positioning using two reporter constructs (D1 and D2). Left: FACS histograms of GFP activation with the individual sgRNAs in D1 or D2, ordered by sgRNA-distance to the TSS (−58 bp, −82 bp, −106 bp, −130 bp, −154 bp, −202 bp, −250 bp, −298 bp); Right: Quantification of percent activated and MFI; n = 2 biologically independent samples, bar graphs display the mean. c, Determining the effect of multiple sgRNA target sites and their spacing on GFP activation by comparing a reporter construct with spaced BCs (R1) with a reporter construct with BCs side by side (R2). Left: FACS histograms of GFP activation; Right: Quantification of percent activated and MFI, n = 2 biologically independent samples, bar graphs display the mean.

Extended Data Fig. 2 Design and Assessment of the CaTCH library.

a, Design of the CaTCH barcode cassette with 3 independent sgRNA target sites. Each barcode sequence is semi-random with defined base restrictions. b, CaTCH reporter activation by targeting sgRNA-sites individually or simultaneously. Left: FACS histograms of GFP activation; Right: Quantification of percent activated and MFI, n = 3 biologically independent samples, n = 1 for CTRL. All bar graphs display the mean ± s.d. c, Complexity of the CaTCH library plasmid pool determined by NGS (~237 million reads). Left Y-axis: Barcode distribution in the library, shown in bars. Right Y-axis: Relative, cumulative barcode representation, shown as a red line. X-axis: Frequency of unique BCs. The bar graph on the right shows the sum of all identified unique barcodes resulting in ~130 million unique barcodes detected in the library.

Extended Data Fig. 3 Order of construct delivery and delivery method influence the resolution of reporter activation.

a, Sensitivity of CaTCH reporter activation (full dataset of spike-in experiment from Fig. 1c–e). FACS plots on the top show the GFP signal only, while plots at the bottom additionally visualize the spiked in iRFP+ cell populations. The experiment was repeated independently 3 times with similar results and 6 times for the 0.001% spike in. b, Experimental outline of two different reporter activation strategies. CaTCH approach: After the (1) dCas9-VPR construct, the (2) BC-controlled GFP-reporter is stably introduced into the cells. Reporter activation is achieved by lentiviral (3) sgRNA transduction. Alternative approach: (1) First, a sgRNA is stably expressed and functions as an identifier in the cells. (2) Subsequently, an sgRNA-specific GFP-reporter plasmid and a separate dCas9-VPR construct are simultaneously, transiently co-transfected for reporter activation. c, FACS data of GFP-reporter activation of both approaches in a spike-in experiment similar to Fig. 1c. iRFP+ spiked-in cells are visualized in red. Gates were set to obtain 0 unspecific events in the 0% spike-in control after delivery of sgRNA/reporter plasmid and dCas9-VPR. Reporter activation was measured by GFP signal. Every experimental condition was performed in triplicates, except 0.001% spike-ins, which were performed in six replicates. d, Quantification of the GFP-reporter activation. Correctly activated = percent of iRFP+(spike-in) of GFP+(reporter-positive) events. Activation efficiency = percent of GFP+ within iRFP+ events. Data displayed as mean ± standard error of mean (s.e.m.). n = 3 biologically independent samples, n = 6 for 0.001% spike-in. e, Delivery rates of a plasmid constitutively expressing GFP in several cell lines with lipofection or lentiviral delivery. Data measured by FACS, n = 2 biologically independent samples. Bar graph displays the mean.

Extended Data Fig. 4 Tracing and isolation of pre-existing therapy resistant cell clones in vitro.

a, FACS plots corresponding to the quantification of NrasG12D spike in experiment of Fig. 1g. b, Experimental outline of CaTCH reporter activation and isolation displayed in Fig. 1h. c, FACS analysis of CaTCH isolated cells from Fig. 1h after expansion. Most GFP+cells are also iRFP+, indicating isolation of the correct clone. The experiment was performed once. d, Immunoblot of MAPK-pathway (pErk) after short term RAFi-treatment in vitro of the bulk cell line, RAFi-selected NrasG12D cells, and treatment-naïve CaTCH-isolated NrasG12D cells. Quantification of pErk normalized to total protein levels is indicated by the numbers. The experiment was performed independently twice with similar results. e, NGS indicating the proportion of reads for NrasWT and NrasG12D, n = 3 biologically independent samples for vehicle- and RAFi-treated samples, n = 1 for others. Source data

Extended Data Fig. 5 CaTCH reveals a strong clonal selection of RAFi/MEKi-treated melanoma cells in vivo.

a, Respective survival curves of resistance generation experiment in vivo from Fig. 2b. Statistical analysis was performed by two-sided Log-rank (Mantel-Cox) test, comparing RAFi-MEKi-treated to untreated. **P < 0.01, ns = non-significant. (Untreated = 4 tumors isolated from 4 mice; RAFi/MEKi = 5 tumors isolated from 5 mice). b, Violin plot showing the distribution of identified BCs within samples. REF = starting cell line, T1-T9 = individual tumors, one tumor per mouse; median:line, middle 50% of data: box, 1.5*IQR: whiskers; individually depicted points indicate outliers; the exact number of barcodes per individual tumor can be derived from Fig. 2c. c, Pearson correlation matrix of all identified BCs, n = 58269 unique BCs. d, Heatmap showing normalized read-counts of selected BCs in the indicated samples.

Extended Data Fig. 6 CaTCH reveals a modest clonal selection of RAFi/MEKi-treated melanoma cells in vitro.

a, Experimental outline. The same population of CaTCH barcoded melanoma cells used in the in vivo experiment from Fig. 2 was seeded for RAFi/MEKi-treatment in vitro. 6 replicates were used per condition. RAFi/MEKi treated cells were sampled after 1 week and 5 weeks on treatment. DMSO treated samples were sampled after passaging them once. b, Total number of unique barcodes identified by NGS in the respective samples. REF1-2, two replicates of the starting cell line; DMSO R1-6, 6 DMSO treated replicates; RAFi/MEKi R1-6, 6 RAFi/MEKi treated replicates sampled after 1 or 5 weeks on treatment. c, Violin plot showing the distribution of identified BCs within samples from Supplementary Fig. 6b; median:line, middle 50% of data: box, 1.5*IQR: whiskers; the exact number of barcodes per individual sample can be derived from Supplementary Fig. 6b. d, Pearson correlation matrix of identified BCs, n = 45928 unique BCs. e, BC composition identified by NGS. BCs comprising more than 1% of the sample are highlighted individually in color, BCs accounting for < 1% barcode proportion were totaled (white bars). Same colors indicate same BCs. Gray sections are different BCs.

Extended Data Fig. 7 CaTCH isolation of specific clones allows their phenotypic characterization.

a, NGS of CaTCH isolated clones identified in the in vivo experiment (see Fig. 2d). Shown is the BC composition after CaTCH isolation of the indicated BC from the starting population (treatment-naïve and depleted, Fig. 2e) or from tumor-derived RAFi/MEKi-resistant cell lines. BCs > 1% of the sample are highlighted individually in color, BCs < 1% were totaled in white bars. The correct, targeted BC within each sample is shown at the bottom section of every bar, in its respective color code from Fig. 2d, e. b, BC composition of different isolations of BC14559 or BC43158. Both BCs were always identified together, indicated in the respective BC color. The BC which was targeted for isolation is indicated below each bar. c, Tumor growth curves and survival curves of in vivo RAFi/MEKi treatment response of three randomly single cell sorted CaTCH-barcoded clones (C1, C2, C3). Sorted clones were transduced with a barcode-unspecific CaTCH reporter activating sgRNA and sorted for activated GFP signal before injection. Growth curves displayed as mean ± s.e.m. Statistical analysis on survival curves was performed by two-sided Log-rank (Mantel-Cox) test, comparing individual clones on treatment to untreated. *P < 0.05, ns=non-significant. n = tumors, two tumors per mouse, C1: UT n = 6, RAFi/MEKi n = 8; C2: UT n = 6, RAFi/MEKi n = 8; C3: UT n = 4, RAFi/MEKi n = 8). d, Survival curves corresponding to the tumor growth curves from Fig. 2f. Statistical analysis was performed by two-sided Log-rank (Mantel-Cox) test, comparing individual clones on treatment to untreated. *P < 0.05, **P < 0.01, ns=non-significant. n=mice. BULK: TNUT n = 3, TNRAFi/MEKi n = 5; BC121: TNUT n = 3, RUT n = 3, TNRAFi/MEKi n = 5, RRAFi/MEKi n = 5; BC952: TNUT n = 3, RUT n = 3, TNRAFi/MEKi n = 4, RRAFi/MEKi n = 5; BC2487: TNUT n = 3, RUT n = 3, TNRAFi/MEKi n = 4, RRAFi/MEKi n = 5; BC13: TNUT n = 3, TNRAFi/MEKi n = 4; BC2721: TNUT n = 3, TNRAFi/MEKi n = 5; BC2646: TNUT n = 3, RUT n = 2, TNRAFi/MEKi n = 5, RRAFi/MEKi n = 3; BC14559: TNUT n = 3, RUT n = 3, TNRAFi/MEKi n = 3, RRAFi/MEKi n = 5.

Extended Data Fig. 8 CaTCH-isolated clone with reduced response shows pre-existing transcriptional features of RAFi/MEKi-resistance in vivo.

a, Experimental outline and individual tumor growth curves of injected cell lines in vivo, aligned to treatment start. Tumors were harvested at day 0 (untreated) or at day 3 of RAFi/MEKi treatment (on RAFi/MEKi therapy). b, PCA plots of RNAseq analysis. Three tumors were sequenced per cell line and condition. This experiment was performed once. c, Heatmap of top 500 high-variance genes with three main gene clusters indicated on the right: Top GO term per cluster: Cluster 1 – Cellular response to interferon beta, Cluster 2 – negative regulation of cell proliferation, Cluster 3 – Cell division; see Supplementary Table 1 for extended lists. n = 3 biologically independent samples (tumors) per cell line and condition; GO-terms and statistics (Fisher’s exact test) were derived in R using topGOtable (pcaExplorer). d, Heatmap showing differentially expressed genes (deviation from the mean > 1 or < −1) within GO term ‘cell division’ (GO:0035458); see Supplementary Table 2 for gene list. e, Venn diagram showing overlap of differentially regulated genes (on RAFi/MEKi therapy vs. untreated) within treatment-naïve clones, DESeq2, threshold of Log2FC±1; see Supplementary Table 3 for full gene lists. f, Top 50 mediators identified in Ingenuity upstream mediator analysis of RAFi/MEKi-treated tumors, compared to BC13, only selected mediators are displayed; see Supplementary Table 4 for full list. g, Heatmap showing normalized read counts of indicated genes across samples on RAFi/MEKi therapy; values for each tumor shown individually; see Supplementary Table 5 for full list.

Extended Data Fig. 9 Whole exome sequencing identifies de novo mutations in RAFi/MEKi-resistant clones.

a, Total number of detected variants per sample. b, Summary of variant classification visualized as box plots; n = 5 (5 individual cell clones); box displays median with 25th and 75th quartile, whiskers correspond to minimum and maximum values; values correspond to the data in Supplementary Fig. 9a. c, Overview of SNV classes. d, Read tracks of the Kras locus at codon 12 for clones BC14559TN and BC14559R. The KrasG12R mutation was exclusively identified in BC14559R.

Extended Data Fig. 10 Gating strategy for FACS sorting of CaTCH library transduced cells.

Representative gating strategy for sorting of CaTCH library infected cells. The cells were previously infected with and sorted for dCas9-VPR (mCherry) and then infected with the CaTCH library (BFP). BFP-positive cells were sorted for low GFP (CaTCH reporter) expression, which enhances reporter functionality. Each plot shows the subpopulation gated for in the preceding plot to the left.

Supplementary information

Reporting Summary

Supplementary Tables 1–5

1. Top 500 high-variance genes—Top 10 GO terms within three main clusters from Extended Data Fig. 8c. 2. Differentially expressed genes (deviation from the mean > 1 or < −1) within GO term ‘cell division’ (GO:0035458) from Extended Data Fig. 8d. 3. Gene lists from Venn diagrams from Extended Data Fig. 8e. 4. List of top 50 identified upstream mediators from Ingenuity Pathway Analysis (IPA) from Extended Data Fig. 8f. 5. List of normalized read counts of selected genes from Extended Data Fig. 8g

Source data

Source Data Extended Data Fig. 4

Unprocessed western blots.

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Umkehrer, C., Holstein, F., Formenti, L. et al. Isolating live cell clones from barcoded populations using CRISPRa-inducible reporters. Nat Biotechnol (2020).

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