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Eomes expression identifies the early bone marrow precursor to classical NK cells

Nature Immunology volume 25, pages 1172–1182 (2024)Cite this article

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Abstract

Natural killer (NK) cells traffic through the blood and mount cytolytic and interferon-γ (IFNγ)-focused responses to intracellular pathogens and tumors. Type 1 innate lymphoid cells (ILC1s) also produce type 1 cytokines but reside in tissues and are not cytotoxic. Whether these differences reflect discrete lineages or distinct states of a common cell type is not understood. Using single-cell RNA sequencing and flow cytometry, we focused on populations of TCF7+ cells that contained precursors for NK cells and ILC1s and identified a subset of bone marrow lineage-negative NK receptor-negative cells that expressed the transcription factor Eomes, termed EomeshiNKneg cells. Transfer of EomeshiNKneg cells into Rag2−/−Il2rg−/− recipients generated functional NK cells capable of preventing metastatic disease. By contrast, transfer of PLZF+ ILC precursors generated a mixture of ILC1s, ILC2s and ILC3s that lacked cytotoxic potential. These findings identified EomeshiNKneg cells as the bone marrow precursor to classical NK cells and demonstrated that the NK and ILC1 lineages diverged early during development.

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Fig. 1: Identification of Eomes+ cells in the mouse BM.
Fig. 2: EomeshiNKneg cells are distinct from NK cells and ILC1s.
Fig. 3: EomeshiNKneg cells are distinct from other progenitors.
Fig. 4: EomeshiNKneg cells emerge postnatally.
Fig. 5: EomeshiNKneg cells generate functional NK cells in vitro.
Fig. 6: EomeshiNKneg cells are precursors committed to the NK cell lineage.
Fig. 7: PLZF+Eomes+ cells have both ILC1 and NK potential.
Fig. 8: EomeshiNKneg cells give rise to functional NK cells in vivo.

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Data availability

Transcriptomic datasets generated during the current study have been uploaded to the Gene Expression Omnibus repository under accession number GSE256395. Materials generated during the current study will be freely available, and requests should be addressed to B.D.M.

Code availability

The R and Python code used to generate the scRNA-seq analyses and figures in this study have been deposited at Zenodo at https://doi.org/10.5281/zenodo.10892070 (ref. 63).

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Acknowledgements

We thank M. Olson and C. Ciszewski for cell sorting and the University of Chicago Functional Genomics Facility for RNA-seq support. B.D.M. was supported by NIH grant T32 DK007074-47, and H.D.A. was supported by the National Science Foundation Graduate Research Fellowship Program under grant number 2140001. This study was supported by NIH grants 5R37-AI038339-27 and 5R01-AI144094 to A.B. and the Digestive Diseases Research Core Center C-IID P30 DK42086 at the University of Chicago to B.J. and A.B. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work is dedicated to Albert Bendelac who passed away during manuscript preparation.

Author information

Author notes

  1. These authors contributed equally: Zhitao Liang, Hope D. Anderson.

  2. These authors jointly supervised this work: Benjamin D. McDonald, Albert Bendelac.

Authors and Affiliations

  1. Committee on Immunology, University of Chicago, Chicago, IL, USA

    Zhitao Liang, Veronica Locher, Samantha J. Riesenfeld, Bana Jabri & Albert Bendelac

  2. Department of Pathology, University of Chicago, Chicago, IL, USA

    Zhitao Liang, Crystal O’Leary & Albert Bendelac

  3. Biophysical Sciences Graduate Program, University of Chicago, Chicago, IL, USA

    Hope D. Anderson

  4. Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA

    Samantha J. Riesenfeld

  5. Department of Medicine, University of Chicago, Chicago, IL, USA

    Samantha J. Riesenfeld, Bana Jabri & Benjamin D. McDonald

  6. Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA

    Samantha J. Riesenfeld

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  1. Zhitao Liang
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Contributions

Z.L. designed research, performed experiments and analyzed data. V.L. and C.O. helped perform experiments. H.D.A. and S.J.R. analyzed the scRNA-seq data. B.J., B.D.M. and A.B. designed the experiments and analyzed the data. A.B. conceived the study. Z.L., H.D.A., S.J.R., B.J. and B.D.M. wrote the paper. All authors reviewed and approved the final manuscript except A.B. who passed away during manuscript preparation.

Corresponding author

Correspondence to Benjamin D. McDonald.

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The authors declare no competing interests.

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Nature Immunology thanks Niklas Björkström and Marco Colonna for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Ioana Staicu, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Gating strategies for BM precursors.

a, Gating strategy used for identifying NK1.1+Eomes-GFP+IL-7Rα− NK cells, NK1.1+Eomes-GFP−IL-7Rα+ ILC1s, NK1.1−Eomes-GFP+Tcf7-mCherry+ EomeshiNKneg cells, Tcf7-mCherry+α4β7+CD244+IL-7Rα−CD90− EILPs, and Tcf7-mCherry+α4β7+CD244+IL-7Rα+CD90+PD1+ ILCPs among CD4−CD8−CD3ε−TCRβ−TCRγδ−CD19−B220−Gr1−CD11c−CD25−Ter119−(Lin−) cells isolated from the BM of Tcf7mCherry/+EomesGFP/+Zbtb16hCD4/+RorcThy1.1/+ mouse by flow cytometry. b, Gating strategy used for identifying CD244+CD27+IL-7Rα+Flt3– preNKP and CD244+CD27+IL-7Rα+Flt3–CD122+ rNKP among CD4−CD8−CD3ε−TCRβ−TCRγδ−CD19−B220−Gr1−CD11c−CD25−Ter119−(Lin−) cells isolated from the BM of Tcf7mCherry/+EomesGFP/+Zbtb16hCD4/+RorcThy1.1/+ mouse by flow cytometry as in a.

Extended Data Fig. 2 scRNA-seq annotation and data filtering.

a, UMAP embedding of scRNA-seq data shows a combined total of 31,774 cells from the BM of 35 mice, with cells annotated by cluster (color, index number) if the cluster was removed prior to downstream analysis, and colored gray otherwise, that is, if the cells were maintained for downstream analysis (for example, Figs. 1–3 and 7). b, Dot plot shows expression (dot color, size, as in Fig. 1b) of genes differentially expressed in annotated clusters (x axis) compared to all other clusters (‘Other’) in the dataset. FDR-adjusted P < 0.05; abs. log2FC > 0.5. c, d, UMAP embedding as in a colored by the log10 of the total number of unique molecular identifiers (UMIs), indicating unique transcript molecules (c), and by the log10 of the total number of unique genes encoded by the transcripts detected (d). e, f, UMAP embedding of filtered scRNA-seq data (as in Fig. 1a) colored by the log10 of the total number of unique molecular identifiers (UMIs), indicating unique transcript molecules (e), and by the log10 of the total number of unique genes encoded by the transcripts detected (f). Points in c-f, corresponding to cells, are plotted in ascending order of their deviation from the median color value, such that the extreme values are displayed at the forefront. g, Violin plots show log of size-normalized expression (Methods) of curated genes in the Rorc+ APC cluster (Fig. 1a). Horizontal lines in violins denote the 25th percentile, median, and 75th percentile of normalized expression, while white diamonds denote the mean.

Extended Data Fig. 3 preNKP, rNKP, and aceNKP markers do not identify a transcriptionally distinct population in scRNA-seq data.

a,b, UMAP embeddings (as in Fig. 1a), with cells colored by normalized expression of pre-NKP and rNKP markers (a), and aceNKP markers (b).

Extended Data Fig. 4 EomeshiNKneg develop independently of PLZF.

a, Quantification of EomeshiNKneg cells in the BM of WT (n=3) and Zbtb16 +18/32Δ/Δ (n=4) mice. Data are representative of two independent experiments. Data represent mean ± s.e.m. b, Bar graph showing the relative abundance of BM Eomes+NK1.1+DX5+ NK cells and Eomes−NK1.1+DX5−IL-7Rα+ ILC1s in WT (n=3) and Zbtb16 +18/32Δ/Δ (n=4) mice. Data are representative of two independent experiments. Data represent mean ± s.e.m.

Extended Data Fig. 5 NK cells emerge post-natally.

a, UMAP clustering of high dimensional flow cytometry data and corresponding heatmap displaying relative expression DX5, L-selection, Eomes, KLRG1, CD49a, TRAIL, CD69 and CD200r among CD45+CD3ε−NK1.1+NKp46+ liver lymphocytes from 1–6 week-old mice. b, bar graph showing the number of DX5+CD49a− NK cells (n=5) or DX5−CD49a+ ILC1s (n=5) among CD45+CD3ε−NK1.1+NKp46+ liver lymphocytes of 1–6 week-old mice. Data are representative of three independent experiments. Data represent mean ± s.e.m. **P<0.01, ***P<0.001, ****P < 0.0001. c, bar graph showing the frequency of DX5+CD49a− NK cells (n=4), DX5−CD49a+ ILC1s (n=4) and DX5loCD49a+ undifferentiated cells (n=4) among CD45+CD3ε−NK1.1+NKp46+ liver lymphocytes from 1–6 week-old mice. Data are representative of two independent experiments. Data represent mean. d, UMAP clustering of high dimensional flow cytometry data showing the composition of DX5+CD49a− NK cells and DX5−CD49a+ ILC1s among CD45+CD3ε−NK1.1+NKp46+ liver lymphocytes from 1–6 week-old mice.

Extended Data Fig. 6 Instability of Eomes and DX5 in vitro.

a, Representative flow cytormetry plot expression of Eomes-GFP and DX5 on Eomes-GFP−NK1.1+IL-7Rα+ ILC1s, α4β7+CD244+IL-7Rα+CD90+PD1+ ILCPs, EomeshiNKneg cells or Eomes-GFP+NK1.1+ NK cells on day 7 of co-culture with OP9 cells with IL2, IL7 and SCF. b, Bar graph showing DX5 expression on single NK cell (n=49) and single ILC1 (n=30) on day 7 of co-culture with OP9 cells with IL2, IL7 and SCF. Data represent mean ± s.e.m. c, Representative flow cytometry plot showing the reconstitution of CD3ε−CD19−NK1.1+ NK cells (top) and NK1.1−CD90+IL-7Rα+CD25+IL-33Rα+ ILC2s (bottom) in the lung of CD45.2/CD45.2 Rag2−/−IL2rg−/− mice at week 2 post-intravenous transfer of equal mixes of CD45.1/CD45.2 EomeshiNKneg cells and CD45.2/CD45.2 Tcf7-mCherry+α4β7+CD244+IL-7Rα+CD90+PD1+ ILCPs.

Extended Data Fig. 7 Comparison of scRNA-seq trajectory inference results.

a, PAGA graph shows degree of connectivity (line weight) between clusters (dots) from scRNA-seq data (Fig. 1). Thicker lines represent a stronger connection, while dot size scales with number of cells in cluster. b, UMAP embedding (colored by cluster, as in Fig. 1a) shows RNA velocity streamlines (arrows), indicating cellular state transitions inferred by scVelo from all cells (Methods). c, UMAP embedding (as in Fig. 7d), shows an overlaid grid of average RNA velocity vectors (arrows), as an alternative view of the streamline visualization of inferred cellular state transitions in Fig. 7d. d, UMAP embedding shows RNA velocity streamlines (as in b) indicating cellular state transitions inferred by TopicVelo from all cells (Methods).

Extended Data Fig. 8 Topic-specific cell weights and streamlines from TopicVelo analysis.

a, UMAP embeddings (as in Fig. 1a) of cells, colored by their weight for each of 11 ‘topics’, or gene programs, inferred (without supervision or prior knowledge) via a probabilistic topic modeling analysis. Titles indicate post hoc topic annotations, determined by literature-based associations with the genes differentially expressed in each topic (Methods, Supplementary Table 3). b, Close-ups of the UMAP embedding (colored by cluster, as in Fig. 1a) show RNA velocity streamlines (arrows) for curated topic-specific cellular state transitions (titles), inferred by TopicVelo from topic-specific cells (displayed in each close-up) and genes, and then integrated to compute the NK/ILC1-focused transition matrix (Fig. 7d, Extended Data Fig. 6c, Methods).

Extended Data Fig. 9 Eomes expression marks the loss of ILC2/3 potential.

a, Representative flow cytometry plots showing PLZFneg and PLZFlo EomeshiNKneg cell-derived NK1.1–ICOS+ ILC2/ILC3s, NK1.1+ICOS–TRAIL+KLRG1– ILC1-like cells and NK1.1+ICOS–TRAIL–KLRG1+ NK-like cells on day 7 of co-culture with OP9 cells with IL-2, IL-7 and SCF. b, Representative flow cytometry plots showing Eomesneg and Eomesint ILCP cell-derived NK1.1–ICOS+ ILC2/ILC3s, NK1.1+ICOS–TRAIL+KLRG1– ILC1-like cells and NK1.1+ICOS–TRAIL–KLRG1+ NK-like cells on day 7 of co-culture with OP9 cells with IL-2, IL-7 and SCF. c, Representative flow cytometry plot showing the reconstitution of CD3ε−CD19−NK1.1+ NK cells (top) and NK1.1−CD90+IL-7Rα+CD25+IL-33Rα+ ILC2s (bottom) in the lung of CD45.2/CD45.2 Rag2−/−γc−/− mice at week 2 post-intravenous transfer of equal mixes of CD45.1/CD45.2 PLZFloEomeshiNKneg cells and CD45.2/CD45.2 Tcf7-mCherry+α4β7+CD244+IL-7Rα+CD90+PD1+ ILCPs.

Extended Data Fig. 10 Model of innate lymphocyte development from BM precursors.

Downstream of CLP, Tcf7-expressing EILP gains the expression of an intermediate level of PLZF and develop into helper ILCs and NK cells. A fraction of EILPs upregulate Eomes expression to become NK/ILC1-restricted PLZFloEomeshiNKneg cells which can either further downregulate PLZF on the way to become NK cells or can lose both PLZF and Eomes expression to become ILC1s. Alternatively, a fraction of PLZFint EILPs can further upregulate PLZF to become ILCPs. These ILCPs can generate all ILC lineages. However, a small subset of ILCPs upregulate Eomes, lose ILC2/3 potential, and can generate NK cells or ILC1s. Created with BioRender.com.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

Results of ‘pseudo-bulk’ differential expression tests using DESeq2, including P values adjusted for multiple comparisons (‘padj’ column; Methods). Sheet names indicate the comparisons tested.

Supplementary Table 2

Gene names indicate transcripts from the KEGG pathway ‘cytokine–cytokine receptor interaction’ that are expressed in more than 10% of cells in the Cd24a+ EILP cluster from the scRNA-seq data (Fig. 1a and Methods).

Supplementary Table 3

Results of statistical tests estimating whether a gene’s topic weight was distinctive in a given topic (sheet name) relative to other topics in the model (Methods), including probability estimates that account for multiple hypotheses (‘lfsr’ column, representing local false sign rate). All tests with a local false sign rate of less than 0.2 are reported.

Supplementary Table 4

Percentile thresholds for the topic cell weights, which are used to estimate membership in a given topic for modeling topic-specific dynamics (Methods), are displayed for both the focused (Fig. 7d) and global (Extended Data Fig. 7d) TopicVelo analyses. A cell’s topic weight must be above the percentile threshold for the cell to be considered a member in the topic.

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Liang, Z., Anderson, H.D., Locher, V. et al. Eomes expression identifies the early bone marrow precursor to classical NK cells. Nat Immunol 25, 1172–1182 (2024). https://doi.org/10.1038/s41590-024-01861-6

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