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Functional CRISPR dissection of gene networks controlling human regulatory T cell identity

Nature Immunology volume 21, pages 1456–1466 (2020)Cite this article

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Abstract

Human regulatory T (Treg) cells are essential for immune homeostasis. The transcription factor FOXP3 maintains Treg cell identity, yet the complete set of key transcription factors that control Treg cell gene expression remains unknown. Here, we used pooled and arrayed Cas9 ribonucleoprotein screens to identify transcription factors that regulate critical proteins in primary human Treg cells under basal and proinflammatory conditions. We then generated 54,424 single-cell transcriptomes from Treg cells subjected to genetic perturbations and cytokine stimulation, which revealed distinct gene networks individually regulated by FOXP3 and PRDM1, in addition to a network coregulated by FOXO1 and IRF4. We also discovered that HIVEP2, to our knowledge not previously implicated in Treg cell function, coregulates another gene network with SATB1 and is important for Treg cell–mediated immunosuppression. By integrating CRISPR screens and single-cell RNA-sequencing profiling, we have uncovered transcriptional regulators and downstream gene networks in human Treg cells that could be targeted for immunotherapies.

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Fig. 1: Pooled Cas9 RNP screens identify regulators of FOXP3, CTLA-4 and IFN-γ levels in Treg cells.
Fig. 2: Arrayed flow cytometry characterization of TF KO Treg cells.
Fig. 3: Deep phenotypic analysis of altered protein expression resulting from individual TF ablations.
Fig. 4: Multidimensional characterization of selected hits from arrayed Cas9 RNP screen.
Fig. 5: Distinct phenotypic landscapes in scRNA-seq of TF KO human Treg cells.
Fig. 6: Functional dissection of gene networks downstream of key TFs in human Treg cells using scRNA-seq data.

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Data availability

The scRNA-seq data generated in this project can be found at the link https://drive.google.com/drive/u/0/folders/1pXuKlCwdxsK69cUU-aMg3-embxES9uaO in the ‘scRNA-seq_files’ folder, which contains the filtered gene-barcode expression matrix and associated metadata. The ‘treg_rnaseq_bams’ folder contains the bam files used to determine Teff and Treg cell–specific TFs. The external datasets used in this project are from the Roadmap Epigenomic Project (http://www.roadmapepigenomics.org/; ChIP-seq and RNA-seq data of different human T cell subsets), Schmidl et al. 2014 (ref. 28) (FOXP3 ChIP-seq data of primary human Treg cells) and Ohkura et al. 2020 (ref. 17) (Treg and Teff DNA methylation data). Please contact the corresponding authors for any further requests.

Code availability

The scRNA-seq data-processing scripts developed in this project can be found at the link https://drive.google.com/drive/u/0/folders/1pXuKlCwdxsK69cUU-aMg3-embxES9uaO in the ‘scripts’ subfolder of the ‘scRNA-seq_files’ folder. The processing scripts for the pooled and arrayed data can be found in the ‘pooled_arrayed_files’ folder at the above link.

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Acknowledgements

We thank members of the Marson, Ye, Spitzer and Bluestone laboratories for helpful suggestions and technical assistance; S. Sakaguchi for sharing Treg and Teff DNA methylation data; A. Levine for suggestions on the manuscript; and E. Wan for technical assistance with single-cell RNA-seq. The UCSF Flow Cytometry Core was supported by the Diabetes Research Center (NIH grant no. P30 DK063720). We also thank the CyTUM-MIH Flow Cytometry core for assistance. We thank V. Tobin for assistance in coordinating blood donations at UCSF and the German Heart Center Munich for the provision of buffy coats. This research was supported by Juno Therapeutics; NIH grant nos. DP3DK111914-01 (A.M.), P50GM082250 (A.M.) and DP5OD023056 (M.H.S.); grants from the Keck Foundation (A.M.); the National Multiple Sclerosis Society (A.M.; grant no. CA 1074-A-21); and gifts from J. Aronov, G. Hoskin, K. Jordan and B. Bakar. A.M. holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund and received the Lloyd Old STAR career award from the Cancer Research Institute. The Marson laboratory has received funding from the Innovative Genomics Institute and the Parker Institute for Cancer Immunotherapy. A.M. and M.H.S. are Chan Zuckerberg Biohub investigators. K.S. was supported by the German Research Foundation (DFG). M.L. was supported by the Hanns-Seidel-Stiftung.

Author information

Author notes

  1. Siddharth S. Raju

    Present address: Broad Institute, Cambridge, MA, USA

  2. These authors contributed equally: Kathrin Schumann, Siddharth S. Raju.

Authors and Affiliations

  1. Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München (TUM), Munich, Germany

    Kathrin Schumann, Michael Lauber & Saskia Kolb

  2. Institute for Advanced Study, TUM, Munich, Germany

    Kathrin Schumann, Michael Lauber & Saskia Kolb

  3. Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, CA, USA

    Siddharth S. Raju, Sasha Targ, Rachel E. Gate & Chun Jimmie Ye

  4. Department of Microbiology and Immunology, University of California, San Francisco, CA, USA

    Siddharth S. Raju, Eric Shifrut, Jessica T. Cortez, Jonathan M. Woo, Theodore L. Roth, Ruby Yu, Michelle L. T. Nguyen, Dimitre R. Simeonov, David N. Nguyen, Jeffrey A. Bluestone, Matthew H. Spitzer & Alexander Marson

  5. Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, CA, USA

    Siddharth S. Raju & Matthew H. Spitzer

  6. Diabetes Center, University of California, San Francisco, CA, USA

    Eric Shifrut, Jessica T. Cortez, Jonathan M. Woo, Theodore L. Roth, Ruby Yu, Michelle L. T. Nguyen, Dimitre R. Simeonov, David N. Nguyen, Qizhi Tang, Jeffrey A. Bluestone & Alexander Marson

  7. Innovative Genomics Institute, University of California, Berkeley, CA, USA

    Eric Shifrut, Jessica T. Cortez, Jonathan M. Woo, Theodore L. Roth, Ruby Yu, Michelle L. T. Nguyen, Dimitre R. Simeonov, David N. Nguyen & Alexander Marson

  8. Department of Medicine, University of California, San Francisco, CA, USA

    Nikolaos Skartsis, David N. Nguyen, Chun Jimmie Ye & Alexander Marson

  9. Department of Surgery, University of California, San Francisco, CA, USA

    Vinh Q. Nguyen & Qizhi Tang

  10. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

    Jeffrey A. Bluestone, Matthew H. Spitzer, Chun Jimmie Ye & Alexander Marson

  11. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Matthew H. Spitzer, Chun Jimmie Ye & Alexander Marson

  12. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA

    Matthew H. Spitzer & Alexander Marson

  13. Gladstone Institutes, San Francisco, CA, USA

    Alexander Marson

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  1. Kathrin Schumann
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Contributions

K.S. and A.M. designed the study. K.S., S.S.R. and A.M. interpreted data and wrote the manuscript. K.S. designed all experiments and performed all electroporation experiments for arrayed Cas9 RNP screens and scRNA-seq. S.S.R. performed all computational analyses for arrayed Cas9 RNP screens and scRNA-seq data. M.L. performed pooled CRISPR screen experiments. E.S. and M.L. analyzed data generated in pooled Cas9 RNP screens. S.K. performed experiments comparing Treg and Teff TF KO cells. J.T.C., N.S., V.Q.N. and D.N.N. planned and performed GvHD experiments. S.K., T.L.R., J.M.W, R.Y., J.S., M.L.T.N. and D.R.S. assisted with experiments. S.T. assisted with computational analysis of data generated in arrayed Cas9 RNP screens and scRNA-seq. R.E.G. contributed to computational analysis of RNA-seq data to select candidate genes. M.H.S. performed Scaffold analysis. Q.T., J.A.B., M.H.S. and C.J.Y provided helpful comments and discussion.

Corresponding authors

Correspondence to Kathrin Schumann or Alexander Marson.

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Competing interests

A.M. is a cofounder, member of the boards of directors and a member of the scientific advisory boards of Spotlight Therapeutics and Arsenal Biosciences. A.M. served as an advisor to Juno Therapeutics, was a member of the scientific advisory board of PACT Pharma and was an advisor to Trizell. A.M. owns stock in Arsenal Biosciences, Spotlight Therapeutics and PACT Pharma. The Marson laboratory has received research funding from Epinomics, Sanofi, GlaxoSmithKline, Anthem and Gilead. J.A.B. is a member of the scientific advisory boards of Arcus, Celsius and VIR and is a member of the boards of directors of Rheos and Provention. J.A.B has recently joined Sonoma Biotherapeutics as president and CEO. Q.T. is a cofounder of Sonoma Biotherapeutics. R.E.G is CEO and cofounder of Dropprint Genomics. C.J.Y. is a cofounder of Dropprint Genomics. C.J.Y. is a member of the scientific advisory board at Related Sciences and is an advisor to TReX Bio. C.J.Y owns stock in Dropprint Genomics and Related Sciences. M.H.S. receives research funding from Roche/Genentech, Bristol-Myers Squibb and Valitor, and has been a paid consultant for Five Prime Therapeutics, Ono Pharmaceutical and January Inc. D.R.S. is a cofounder of Beeline Therapeutics. This research project was supported by Juno Therapeutics. A provisional patent has been filed based on the results described here (A.M., K.S. and J.A.B.).

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Peer review information Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Limited effects of control site indel mutations on target protein levels in pooled RNP screens.

a, FACS-gating strategy for pooled Cas9 RNP screens. Treg cells were gated on singlet live cells. Representative example from 1 of 4 human blood donors. b, FACS sorting strategy to isolate FOXP3-high (hi) and FOXP3-low (lo) (left) and CTLA-4-hi and CTLA-4-lo Treg cells (right) electroporated with nontargeting control RNP (ctrl; top) and with a pool of RNPs targeting 40 individual TFs (bottom). Representative examples from experiments in one of 4 human blood donors. c–e, log2 fold enrichment of indels in FOXP3-hi vs. FOXP3-lo (c), IFNg-hi vs. IFNg-lo (d) and CTLA-4-hi vs. CTLA-4-lo cell populations (e) at ctrl regions within 1 kb up- or downstream of the predicted gRNA cut site. Note: indel mutations in one control site near HIVEP2 were associated with altered CTLA-4 levels, which could be an artefact or could be due to effects on a regulatory element. Right graph (e) shows ctrl amplicons in the CTLA-4-hi and CTLA-4-lo sorted Treg cells without HIVEP2 KO control amplicon after IL-6 stimulation. (c–e), Mean of log2 fold enrichment of experiments performed in cells from 4 human blood donors with the exception of the control amplicons for FOXO1, ELF1 and ZFY, which could not be sequenced successfully in all conditions (see Supplementary Table 1).

Extended Data Fig. 2 Flow cytometry gating strategy to define changes in Treg cell phenotype in arrayed Cas9 RNP screen.

a, Initial gating strategy to identify live cells. b, Gating strategy to assess Treg cell stability for personality and Scaffold plots. Control nontargeting Cas9 RNP-treated Treg cells (ctrl) after IL-12 conditioning are shown in light blue and FOXP3 KO Treg cells after IL-12 stimulation are shown in orange. a, b, Representative results for one nontargeting control Cas9 RNP and one FOXP3-targeting Cas9 RNP in one out of two human blood donors. c, % of live cells based on flow cytometry staining for all conditions tested in the arrayed Cas9 RNP screen.

Extended Data Fig. 3 Extended analysis of arrayed Cas9 RNP screen data.

Comparison of results generated in pooled (log2(#indels in ‘marker high’ population/#indels in ‘marker low’ population) versus arrayed Cas9 RNP screens (log2(% ‘marker high’ in KO/% ‘marker high’ in ctrl)) for FOXP3 (a) and CTLA-4 expression (b) with and w/o IL-12 stimulation for 10 selected TFs with notable effects on protein levels (see Methods and Supplementary Table 4). For the pooled screen, the calculated values are based on 4 human blood donors. For the arrayed screen, the calculated values are based mean fold-change values determined from 3 independent gRNAs and 2 human blood donors. The R value is based on these data points (highlighted in the graphs). Grey shading is provided by the loess algorithm in R, which uses polynomial regression to locally fit a surface to each point. c, Schematic workflow of Scaffold generating landmark nodes and unsupervised clustering for visualization of TF KO cell subpopulations. d, e, Phenotypic characterization of ctrl, IKZF2 and FOXP3 KO Treg cells with two-dimensional flow cytometry and personality plots w/o (d) and with IL-12 treatment (e) in Treg cells from two human blood donors (D1 and D2) with two of three different Cas9 RNPs targeting each gene (extended version of Fig. 3). Note: IL-4 was excluded from these plots due to low absolute levels that skewed fold-change analyses.

Extended Data Fig. 4 Phenotypic comparison of Treg and Teff TF KO cells.

We selected a subset of candidate Treg TFs. In addition, here we selected additional candidate TFs that are preferentially expressed in conventional CD4+T cell subsets (referred to here as Teff cells) based on RNA-seq data (Roadmap Epigenomics Project15). Corresponding H3K27ac and RNA-seq data (Treg, Tnaive, TH, TH stim and TH17 stim) are shown on the left for each tested TF. On the right: Representative personality plots of TF KO Treg cells and TF KO Teff cells after IL-12 stimulation of one of two donors tested. Note: Treg cells from this blood donor had distinct cytokine responses compared to those included in the larger arrayed TF KO screen experiments. IRF8 and MYBL1 were targeted with 3 independent gRNAs, while the other TFs were targeted with a previously validated gRNA (includes data of 2 human donors and 1 technical replicate for each condition). crRNA sequences and editing efficiencies are shown in Supplementary Table 3. Note: changes in IL-4 regulation helped to distinguish effects of TF KO in Treg versus Teff cells in these experiments and IL-4 is therefore included in these personality plots.

Extended Data Fig. 5 Distribution of cells states altered by TF ablation in Treg cells.

Cell density maps highlight the altered distribution of cell states assessed by scRNA-seq (visualized with t-SNE dimensionality reduction, Fig. 5a) in individual TF KO Treg cell conditions with and w/o IL-12 treatment. The colour scale represents the population densities of particular cell states across regions of the t-SNE map. Data were generated in ex vivo expanded Treg cells from 2 human blood donors, the same data referenced in Fig. 5. Detailed list of all conditions: Supplementary Table 3.

Extended Data Fig. 6 Extended characterization of TF KO Treg cell scRNA-seq data.

a, Comparison of results generated by flow cytometry for TF KO Treg cells in arrayed Cas9 RNP screens and scRNA-seq data after IL-12 treatment. Each of the 10 data points on the scatter plot represents arrayed Cas9 RNP screens (log2(% ‘marker high’ in KO/% ‘marker high’ in ctrl)) vs the scRNA-seq data (log2(mean expression KO/mean expression in ctrl)) for a given targeted TF. For the arrayed screen, the calculated values are based on 3 independent gRNAs and 2 human blood donors. For the scRNA-seq data, the calculated values are based on mean expression fold-change in a given KO and stimulation condition across 2 human blood donors. The R value is based on these data points (highlighted in the graphs). Grey shading is provided by the loess algorithm in R, which uses polynomial regression to locally fit a surface to each point. b, Force-directed network graphs highlight gene modules in TF KO Treg cells without IL-12 treatment. Genes that depended (directly or indirectly) on each TF (yellow) are indicated by green arrows and genes repressed by each TF are marked with red arrows. c, Heatmap summarizing the results of the network graph in a. Green indicates that TF KO reduces expression of a gene, while red indicates that TF KO increases expression of a gene. Scale bar: log2(TF KO value of gene/ctrl value of gene). Data were generated in ex vivo expanded Treg cells from 2 human blood donors. Detailed list of all conditions: Supplementary Table 3.

Extended Data Fig. 7 Functional assessment of HIVEP2 KO Treg cells in a humanized mouse model of GVHD.

PBMC and Treg cells from 2 human blood donors were injected in a 2:1 ratio into NSG mice. Survival rate (a) and weight changes (b) were monitored over 40 days. PBMC alone (n = 5), PBMC+AAVS1 KO Treg cells (targeted a control safe harbour locus; n = 3), PBMC+FOXP3 KO Treg cells (n = 2), PBMC+HIVEP2 KO Treg cells (n = 3). The mice were all male age matched from three litters and randomized to different experimental groups. (c) Detailed information about the number of mice used and their survival time. Experiment 1 and 2 refers to the two different human blood donors. crRNA sequences and editing efficiencies are shown in Supplementary Table 3.

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Schumann, K., Raju, S.S., Lauber, M. et al. Functional CRISPR dissection of gene networks controlling human regulatory T cell identity. Nat Immunol 21, 1456–1466 (2020). https://doi.org/10.1038/s41590-020-0784-4

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