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Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens

Nature Genetics volume 53, pages 322–331 (2021)Cite this article

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Abstract

The expression of inhibitory immune checkpoint molecules, such as programmed death-ligand (PD-L)1, is frequently observed in human cancers and can lead to the suppression of T cell–mediated immune responses. Here, we apply expanded CRISPR-compatible (EC)CITE-seq, a technology that combines pooled CRISPR screens with single-cell mRNA and surface protein measurements, to explore the molecular networks that regulate PD-L1 expression. We also develop a computational framework, mixscape, that substantially improves the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation. Applying these tools, we identify and validate regulators of PD-L1 and leverage our multimodal data to identify both transcriptional and post-transcriptional modes of regulation. Specifically, we discover that the Kelch-like protein KEAP1 and the transcriptional activator NRF2 mediate the upregulation of PD-L1 after interferon (IFN)-γ stimulation. Our results identify a new mechanism for the regulation of immune checkpoints and present a powerful analytical framework for the analysis of multimodal single-cell perturbation screens.

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Fig. 1: CITE-seq and ECCITE-seq identify regulators of PD-L1 protein expression.
Fig. 2: Calculating the perturbation signature removes confounding variation.
Fig. 3: Mixscape removes cells that escape perturbation.
Fig. 4: BRD4 and CUL3 are negative regulators of PD-L1 expression.
Fig. 5: The CUL3–KEAP1 complex indirectly regulates PD-L1 through NRF2.

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Data availability

Raw and processed sequencing data are available at the Gene Expression Omnibus (GEO accession number, GSE153056). Processed data are also available at SeuratData (https://github.com/satijalab/seurat-data) to facilitate access with a single command (InstallData(ds=’thp1.eccite’)).

Code availability

The code for mixscape is freely available as open-source software as part of the Seurat package for single-cell analysis (https://github.com/satijalab/seurat). A vignette demonstrating the application of mixscape to this dataset is available in the Supplementary Data and also as an online resource (https://satijalab.org/seurat/mixscape_vignette.html).

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Acknowledgements

We acknowledge R. Levine, T. Papagiannakopoulos and members of the Satija and Technology Innovation Labs at NYGC for general discussion, P. Roelli for assistance with preprocessing and N. Bapodra and N. Sanjana for advice on vector and library design. This work was supported by the Chan Zuckerberg Initiative (EOSS-0000000082 to R.S., HCA-A-1704-01895 to P.S. and R.S.) and the National Institutes of Health (DP2HG009623-01 to R.S., RM1HG011014-01 to P.S. and R.S., R21HG009748-03 to P.S.).

Author information

Authors and Affiliations

  1. Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Efthymia Papalexi, Andrew W. Butler, Bernadette Bracken, Yuhan Hao & Rahul Satija

  2. New York Genome Center, New York, NY, USA

    Efthymia Papalexi, Andrew W. Butler, Samantha Foster, Bernadette Bracken, William M. Mauck III, Hans-Hermann Wessels, Yuhan Hao & Rahul Satija

  3. Technology Innovation Lab, New York Genome Center, New York, NY, USA

    Eleni P. Mimitou & Peter Smibert

  4. BioLegend Inc., San Diego, CA, USA

    Bertrand Z. Yeung

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  1. Efthymia Papalexi
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Contributions

E.P., E.P.M., P.S. and R.S. conceived the research. E.P., E.P.M., S.F., B.B., W.M.M., H.-H.W., Y.H., B.Z.Y. and P.S. performed experimental work. E.P., A.W.B. and R.S. performed computational analyses. All authors participated in interpretation and in writing the manuscript.

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Correspondence to Rahul Satija.

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Competing interests

In the past 3 years, R.S. worked as a consultant for Bristol-Myers Squibb, Regeneron and Kallyope and served as an SAB member for ImmunAI and Apollo Life Sciences. P.S. is a co-inventor on a patent related to this work. B.Z.Y. is an employee at BioLegend, which is the exclusive licensee of the New York Genome Center patent application related to this work.

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Peer review information Nature Genetics thanks Samantha Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Unwanted sources of variation drive mRNA-based clustering (related to Fig. 2).

a, UMAP visualization of the ECCITE-seq dataset based on cellular transcriptomes. Clusters are driven by different sources of variation shown in different colors (cell cycle state, CRISPR perturbation, stress). Figure is similar to Fig. 2a, but with labels for the ER-stress cluster. b, Single-cell heatmap showing the up-regulation of a specific gene module in the ER-stress cluster. EnrichR analysis demonstrates that this gene set is enriched (adjusted p-value < 5*10−20) for ‘response to endoplasmic reticulum stress’. c, Similar to (A) but computed using only NT cells. This demonstrates that confounding sources of heterogeneity are present even in the absence of perturbation.

Extended Data Fig. 2 Identifying optimal parameters for calculating perturbation signature.

a, Scatterplots showing the per cell correlation of mixscape classification posterior probabilities between k = 20 and k = 3, k = 10, k = 30 and k = 200. b, Mixscape classification agreement k = 20 and all other k. c, Same as in (a) only this time comparing finding neighbors before and after integration. In both cases k was set to 20. d, Same as in (b) only this time showing classification agreement between before and after integration.

Extended Data Fig. 3 Calculating local perturbation signatures controls for unwanted sources of variation.

Similar to Fig. 2d, but the cells from each individual perturbation are specifically highlighted. In addition to some perturbations which form specific clusters (for example IRF1), other perturbations (for example BRD4 and SMAD4) exhibit weaker evidence of sub-clustering, suggesting that improved analysis strategies would help to reveal their perturbation state.

Extended Data Fig. 4 Mixscape models targeted cells as a heterogeneous mixture.

For each cell, we calculated a perturbation score (Supplementary Methods) representing its strength of perturbation compared to the average of NT controls. We calculated this not only for targeted cells, but also for cells expressing NT gRNA in order to estimate the variance in the control population. Here, we show the distribution of perturbation scores as a function of mixscape classification (similar to Fig. 3a). Dots on the x-axis represent single-cell perturbation scores and are colored to match the mixscape classifications. Non-perturbed cell densities (NP, light grey) overlap with the non-targeting control cell densities (NT, dark grey).

Extended Data Fig. 5 Benchmarking mixscape against MIMOSCA.

a, Left: Barplots showing the % of KO (red) and NP (light grey) cells within each gRNA class as classified by mixscape, and MIMOSCA (Right). To assess the potential for overfitting, prior to running the dataset, we randomly sampled 1,000 cells expressing NT gRNA and re-labeled them as a new targeted gene class, representing a negative control (NEG CTRL, marked with a black box). Only mixscape correctly classifies all of these cells as NP. b, Single-cell mRNA expression heatmap with IFNGR2g2 cells being grouped by mixscape and MIMOSCA classification. Cells classified by both methods as KO (Class ‘D') exhibit downregulation of IFNγ pathway genes, while cells classified by both methods as NP (Class ‘A') resemble NT controls. When mixscape classifies cells as NP and MIMOSCA classifies as KO (Class ‘C'), cells resemble NT controls, suggesting that the mixscape classification is correct. Class ‘B' (2 cells total) was removed for visualization due to low cell number. c, Violin plots showing PD-L1 protein expression in IFNGR2g2 cells grouped by their MIMOSCA and mixscape classification (see legend in (B)). Class ‘C' cells resemble NT controls, suggesting that the mixscape classification is correct. d, Barplot showing the % of reads with no INDELS (grey), inframe (orange) and frameshift (red) mutations across all MIMOSCA and mixscape IFNGR2g2 cell classifications. Class ‘C' cells resemble NT controls, suggesting that the mixscape classification is correct (n= =20,729 cells over 3 viral transduction replicates).

Extended Data Fig. 6 Benchmarking mixscape against MUSIC.

a, Left: Barplots showing the % of KO (red) and NP (light grey) cells within each gRNA class as classified by mixscape, and MUSIC (Right). To assess the potential for overfitting, prior to running the dataset, we randomly sampled 1,000 cells expressing NT gRNA and re-labeled them as a new targeted gene class, representing a negative control (NEG CTRL, marked with a black box). Only mixscape correctly classifies all of these cells as NP. b, Single-cell mRNA expression heatmap with IFNGR2g2 cells being grouped by mixscape and MUSIC classification. Cells classified by both methods as KO (Class ‘D') exhibit downregulation of IFNγ pathway genes, while cells classified by both methods as NP (Class ‘A') resemble NT controls. When mixscape classifies cells as NP and MUSIC classifies as KO (Class ‘C'), cells resemble NT controls. When mixscape classifies cells as KO and MUSIC classifies as NP, cells exhibit evidence of perturbation. Therefore, classes ‘B' and ‘C' suggest that when the methods disagree, the mixscape classification is correct. c, Violin plots showing PD-L1 protein expression in IFNGR2g2 cells grouped by their MUSIC and mixscape classification. Classes ‘B' and ‘C' suggest that when the methods disagree, the mixscape classification is correct. d, Barplot showing the % of reads with no INDELS (grey), inframe (orange) and frameshift (red) mutations across all MUSIC and mixscape IFNGR2g2 cell classifications. Classes ‘B' and ‘C' suggest that when the methods disagree, the mixscape classification is correct (n = =20,729 cells over 3 viral transduction replicates).

Extended Data Fig. 7 Number of detected cells in ECCITE-seq correlates with gene essentiality scores.

a, Barplot showing the CERES scores for each target gene class generated from AVANA CRISPR screens on THP-1 cells. Low CERES scores for MYC, SPI1, BRD4 and CUL3 suggest these genes are essential for cell survival. b, Barplot showing the number of cells recovered from each target gene class in the ECCITE-seq experiment. For target genes with low CERES scores we only recover a small number of cells most likely due to decreased survival of KO cells (n= =20,729 cells over 3 viral transduction replicates).

Extended Data Fig. 8 Bulk RNA-seq on single gRNA KO samples validates ECCITE-seq findings.

a, Heatmap showing expression of CUL3 and BRD4 KO signature genes as identified by ECCITE-seq DE on bulk RNA-seq samples. b, Same as in (A) only this time showing the CUL3 and BRD4 KO cells from the ECCITE-seq experiment. Cells are split into groups based on their gRNA ID.

Extended Data Fig. 9 CUL3 KO cells have a unique transcriptomic signature.

a, Single-cell mRNA expression heatmap showing that CUL3 KO cells upregulate a module of genes in comparison to NT and CUL3 NP cells (including the PD-L1 transcript (CD274), highlighted on the heatmap). b, Single-cell mRNA expression heatmap showing that the CUL3 transcriptomic signature is not IFNγ-related, suggesting CUL3 is acting through an alternative pathway to regulate PD-L1 at the transcriptional level. For both (b) and (c) heatmaps, lists of genes were obtained using FindMarkers() function in Seurat (Wilcoxon Rank sum test). mRNA counts are log-normalized and scaled (z-score).

Extended Data Fig. 10 Mixscape increases the signal to noise ratio by removing ‘escaping’ cells.

a, Volcano plots showing DE genes before and after mixscape classification for BRD4 and CUL3 KO cells. b, UpSet plot showing the intersection between DE genes from before and after mixscape classification.

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Supplementary Methods and Figs. 1–7.

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Mixcape vignette

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Papalexi, E., Mimitou, E.P., Butler, A.W. et al. Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens. Nat Genet 53, 322–331 (2021). https://doi.org/10.1038/s41588-021-00778-2

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