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Innate-like functions of natural killer T cell subsets result from highly divergent gene programs

Nature Immunology volume 17, pages 728739 (2016) | Download Citation

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Abstract

Natural killer T cells (NKT cells) have stimulatory or inhibitory effects on the immune response that can be attributed in part to the existence of functional subsets of NKT cells. These subsets have been characterized only on the basis of the differential expression of a few transcription factors and cell-surface molecules. Here we have analyzed purified populations of thymic NKT cell subsets at both the transcriptomic level and epigenomic level and by single-cell RNA sequencing. Our data indicated that despite their similar antigen specificity, the functional NKT cell subsets were highly divergent populations with many gene-expression and epigenetic differences. Therefore, the thymus 'imprints' distinct gene programs on subsets of innate-like NKT cells that probably impart differences in proliferative capacity, homing, and effector functions.

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  • 26 April 2016

    In the version of this supplementary file originally posted online, Supplementary Figures 7, 9, and 21 were rotated and cropped incorrectly. These errors have been corrected in this file as of 26 April 2016.

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Acknowledgements

We thank C. Kim, L. Nosworthy and K. Van Gundt for assisting with single cell sorting; J. Day for assistance with next generation sequencing; Z. Fu, A.M. Gholami and J. Greenbaum for help with processing and analysis of sequencing data; and K.M. Ansel (University of California, San Francisco) and A. Rao (La Jolla Institute for Allergy & Immunology ) for HS V–deficient mice. Supported by the Alexander von Humboldt Foundation (L.C.), The William K. Bowes Jr Foundation (P.V.) and the US National Institutes of Health (R01 HL114093 to P.V.; U19 AI100275 to P.V.; and R37 AI71922 and R01 AI105215 to M.K.).

Author information

Author notes

    • Lukas Chavez

    Present address: Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Isaac Engel
    • , Grégory Seumois
    •  & Lukas Chavez

    These authors contributed equally to this work.

    • Pandurangan Vijayanand
    •  & Mitchell Kronenberg

    These authors jointly directed this work.

Affiliations

  1. La Jolla Institute for Allergy & Immunology, La Jolla, California, USA.

    • Isaac Engel
    • , Grégory Seumois
    • , Lukas Chavez
    • , Daniela Samaniego-Castruita
    • , Brandie White
    • , Ashu Chawla
    • , Pandurangan Vijayanand
    •  & Mitchell Kronenberg
  2. David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.

    • Dennis Mock
  3. Clinical and Experimental Sciences, Southampton NIHR Respiratory Biomedical Research Unit, Faculty of Medicine, University of Southampton, Southampton, UK.

    • Pandurangan Vijayanand
  4. Division of Biological Sciences, University of California San Diego, La Jolla, California, USA.

    • Mitchell Kronenberg

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Contributions

I.E., G.S., P.V. and M.K. conceived of the work, designed, performed and analyzed experiments, and wrote the paper; I.E. performed all cell isolation, iNKT subset phenotyping and functional experiments with wild-type and HS V–deficient mice; G.S. performed 'micro-scaled' RNA-Seq, ChIP-Seq and single-cell RNA-Seq experiments; D.S.-C. and A.C. assisted in the analysis of bulk and single-cell RNA-Seq data under the supervision of L.C.; B.W. assisted in single-cell sequencing experiments under the supervision of G.S. and P.V.; and D.M. provided statistical input and support and direction for the single-cell RNA-Seq analysis.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Pandurangan Vijayanand or Mitchell Kronenberg.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–21

Excel files

  1. 1.

    Detailed description of 11 bulk RNA-Seq, 12 H3K27ac ChIP-Seq and 203 single-cell RNA-Seq assays that passed quality control checks.

    For each assay, the table lists sample IDs, cell type of the sample, and experimental set. The last column contains total number of mapped reads per each assay, excluding mitochondrial reads.

  2. 2.

    List of differentially enriched genes (DEGs) in iNKT cell subsets based on bulk RNA-Seq.

    For each DEG the table lists the iNKT cell enriched subset, adjusted P values together with fold change for each pairwise comparison, and cell type and average RPKM value (Online Methods and Supplementary Table 3).

  3. 3.

    Classification of the differentially expressed genes (DEGs) into categories.

    Each DEG from the Supplementary Table 2 was assigned to one of eighteen groups based on the combined results in the three iNKT cell subset comparisons. DEGs with biologically similar patterns of outcomes were grouped into three categories: ‘NKT1-, NKT2- and NKT17-enriched’ genes (Online Methods).

  4. 4.

    Biological process-enrichment analysis of NKT1-, NKT2- and NKT17- enriched genes.

    The enrichment analysis was performed using software from the DAVID bioinformatic database (Online Methods).

  5. 5.

    List of differentially enriched enhancer regions (DERs) obtained from any of the three pairwise comparisons of NKT1, NKT2 and NKT17 subsets.

    List of differentially enriched enhancer regions (DERs) obtained from any of the three pairwise comparisons of NKT1, NKT2 and NKT17 subsets. For each DER the table lists the chromosomal location, raw counts, normalized counts (RPKM), raw P values, adjusted P value and fold change for each cell-type pairwise comparison (Online Methods).

  6. 6.

    List of the top 520 most differentially expressed genes (DEGs) from single- cell RNA-Seq assays comparing the four iNKT subsets (Online Methods).

    For each DEG the table lists the Trimmed Mean of M-values (TMM) normalized log2 read counts for each of the 203 single cells.

  7. 7.

    List of the 50 most differentially expressed genes (DEGs) from single-cell RNA-Seq assays in NKT2 cells compared to other iNKT cell subsets.

    For each DEG the table lists the chromosomal location, Gene ID, P values together with fold change for each cell-type pairwise comparison, Q value (false discovery rate), TMM normalized log2 read counts for each of the 203 single cells (Online Methods).

  8. 8.

    List of the 50 most differentially expressed genes (DEGs) from single-cell RNA-Seq assays in NKT17 cells compared to other iNKT subsets.

    For each DEG the table lists the chromosomal location, Gene ID, P values together with fold change for each cell-type pairwise comparison, Q value (false discovery rate), TMM normalized log2 read counts for each of the 203 single cells (Online Methods).

  9. 9.

    Overlap of genes upregulated in analogous ILC and iNKT subsets.

    A recently published study1 compared ILC1, ILC2, and ILC3 or LTi-like ILC (ILC3) subsets to identify ILC subset-specific transcripts. The list of ILC subset enriched transcripts were compared to the lists of genes with increased expression in the NKT1, NKT2, or NKT17 subsets, as compared to both of the other iNKT subsets. Table 12 indicates the numbers of genes found to be upregulated in both an ILC and an iNKT subset, with all possible ILC and iNKT cell subset comparisons listed. Numbers in parentheses indicate the total number of genes uniquely upregulated in each ILC and iNKT cell subset. Numbers in bold indicate significantly increased numbers of genes in comparison to the mean chance expectations, as determined by a hypergeometric probability test (P value for the ILC1 and NKT1 group: 8.0 * 10−40, ILC2 and NKT2: 1.9 * 10−4, ILC3 and NKT17: 3.1 * 10−28. P values for all of the other subset comparisons were .05). The genes in common between ILC1 and NKT1, ILC2 and NKT2, and ILC3 and NKT17 are shown to the right of the table.

  10. 10.

    List of the 50 most differentially expressed genes (DEGs) from single-cell RNA-Seq assays in NKT1 cells compared to other iNKT subsets.

    For each DEG the table lists the chromosomal location, Gene ID, P values together with fold change for each cell-type pairwise comparison, Q value (false discovery rate), TMM normalized log2 read counts for each of the 203 single cells (Online Methods).

  11. 11.

    List of the 50 most differentially expressed genes (DEGs) from single-cell RNA-Seq assays in NKT0 cells compared to other iNKT subsets.

    For each DEG the table lists the chromosomal location, Gene ID, P values together with fold change for each cell-type pairwise comparison, Q value (false discovery rate), TMM normalized log2 read counts for each of the 203 single cells (Online Methods).

  12. 12.

    Transcription factor motif enrichment analysis. The enrichment analysis was performed using the findMotifsGenome.pl function of the HOMER software package (Online Methods).

    Table shows known motifs that are significantly enriched in at least one out of six groups of differentially enriched enhancer regions (DERs) identified by pairwise comparison of the three iNKT cell subsets.

Videos

  1. 1.

    Single-cell RNA-Seq analysis

    Principal component analysis of 203 single-cell transcriptomes distinguished iNKT cell subsets. Data generated from two experimental replicates performed on single cells isolated from thymuses pooled from three mice in each experiment. Each dot shown in yellow (NKT0), green (NKT1), blue (NKT2) and magenta color (NKT17) is from a single cell. Percentage of variance in each PC, principal component is shown in parenthesis next to the PC.

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DOI

https://doi.org/10.1038/ni.3437

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