Research ArticleINNATE LYMPHOID CELLS

Single-cell transcriptional analysis reveals ILC-like cells in zebrafish

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Science Immunology  16 Nov 2018:
Vol. 3, Issue 29, eaau5265
DOI: 10.1126/sciimmunol.aau5265

Fishing for ILCs in zebrafish

To date, most studies on innate lymphoid cells (ILCs) have been focused on their functions in mammals. It is well known that both lymphoid cells and adaptive immunity are not unique to mammals but are shared by all vertebrates. By studying lymphoid cells in rag1-deficient zebrafish that lack both B and T cells, Hernández et al. have examined the functions of ILC-like cells in zebrafish. Using single-cell RNA-seq to profile gene expression in gut-resident lymphocytes of rag1-deficient zebrafish after immune challenge, they have identified lymphocytes in zebrafish that correspond to mammalian ILC1, ILC2, and ILC3 lineages. Studying ILCs in zebrafish should lead to a better understanding of the evolutionary functions of ILCs.

Abstract

Innate lymphoid cells (ILCs) are important mediators of the immune response and homeostasis in barrier tissues of mammals. However, the existence and function of ILCs in other vertebrates are poorly understood. Here, we use single-cell RNA sequencing to generate a comprehensive atlas of zebrafish lymphocytes during tissue homeostasis and after immune challenge. We profiled 14,080 individual cells from the gut of wild-type zebrafish, as well as of rag1-deficient zebrafish that lack T and B cells, and discovered populations of ILC-like cells. We uncovered a rorc-positive subset of ILCs that could express cytokines associated with type 1, 2, and 3 responses upon immune challenge. Specifically, these ILC-like cells expressed il22 and tnfa after exposure to inactivated bacteria or il13 after exposure to helminth extract. Cytokine-producing ILC-like cells express a specific repertoire of novel immune-type receptors, likely involved in recognition of environmental cues. We identified additional novel markers of zebrafish ILCs and generated a cloud repository for their in-depth exploration.

INTRODUCTION

Vertebrate immune systems consist of the innate arm, which responds immediately to challenge, and the adaptive arm, which responds via acquired antigen receptors. In mammals, myeloid cells (granulocytes, mast cells, monocytes/macrophages, and dendritic cells) form the innate immune system, whereas B and T lymphocytes contribute to the adaptive immune response (1, 2). Recently discovered innate lymphoid cells (ILCs) represent a rare population of lymphocytes (35). Unlike T and B cells, ILCs do not express antigen receptors or undergo clonal expansion when stimulated. Instead, in the absence of adaptive antigen receptors, ILCs sense environmental cues mostly through cytokine receptors and promptly respond to signals by producing distinct cytokines. More recently, it has been demonstrated that both murine and human ILCs express a receptor for the neuropeptide neuromedin, secreted by cholinergic neurons that directly sense worm products and control the expression of innate type 2 cytokines (6). During homeostasis, humans and mice contain four populations of ILCs: natural killer (NK) cells and three subsets of helper ILCs (ILC1, ILC2, and ILC3). NK cells bear similarity to cytotoxic T cells (CD8+ cells), which directly kill cells infected with intracellular pathogens. Helper ILCs in human and mouse are classified as ILC1, ILC2, and ILC3 on the basis of their transcription factor (TF) and cytokine secretion profiles, as well as phenotypic cell-surface markers (35, 7). Both T helper cell 1 (TH1) and ILC1 express T-bet (encoded by tbx21), as well as so-called “TH1 cytokines” such as interferon-γ (IFN-γ) and tumor necrosis factor–α (TNFα), and act against intracellular pathogens. TH2 and ILC2 express GATA binding protein 3 (GATA-3), secrete interleukin-4 (IL-4), IL-13, and amphiregulin, and contribute to defense against helminths and venoms. TH17 and ILC3s express retinoic acid–related orphan receptor γ T (RORγt) (encoded by rorc) as well as IL-17a, IL-17f, and IL-22 and promote immunity against extracellular bacteria and fungi (3, 4, 8, 9). To date, the bulk of our knowledge of ILCs comes from studies in humans and in mice (3, 4, 10, 11).

The different immune cell types are usually distinguished on the basis of expression of specific CD (cluster of differentiation) markers. However, a homogeneous population of blood cells, as defined by surface markers, may include many distinct transcriptional states with different functional properties (1215). In addition, the surface markers used to define distinct human and murine leukocyte subsets are not the same, making it difficult to compare cell types across different species. Therefore, there is a need for unbiased methodologies that define immune cell types based on cellular state rather than cell surface markers. This is particularly relevant for species other than mouse and human, where specific antibodies for distinct blood and immune cell types are not readily available.

In zebrafish, the heterogeneity of hematopoietic cells has mostly been investigated with fluorescent transgenic reporter lines, because very few antibodies for surface markers are available (16). These approaches have confirmed the presence of erythrocytes, thrombocytes, neutrophils, macrophages, eosinophils, T cells, B cells, and NK cells in zebrafish. Comprehensive transcriptome atlases exist for many of these cell types (1719). Because these studies focused on steady-state conditions, they were limited in their ability to characterize the response mechanisms after immune challenge. Compared with mice and humans, little is known about the diversity of cytokine-producing ILCs in zebrafish, and a detailed characterization of their transcriptional profiles is still lacking.

Here, we characterized the repertoire of innate and adaptive lymphocytes in zebrafish. Using single-cell RNA sequencing (scRNA-seq), we generated a comprehensive atlas of cellular states of lymphocytes collected from various organs in steady state and after immune challenge. By studying cytokine expression of lymphocytes in rag1−/− zebrafish, we have identified cells that resemble ILC2 and ILC3 cells described in mice and in humans.

RESULTS

rag1−/− mutants lack T and B cells but have cytokine-producing cells in the gut

Rag1- and Rag2-deficient mouse strains, which lack adaptive but retain ILCs (2022), have provided substantial insight into ILCs. These mice showed expression of many cytokines previously considered to be T cell specific and therefore provided the first evidence of the existence of helper ILCs (2022). Thus, to focus on ILCs in zebrafish, we turned to rag1−/− mutants. As in mice, rag1−/− zebrafish lack T and B lymphocytes (23) but retain NK cells (24).

In line with previous reports (2325), rag1−/− zebrafish displayed a reduced population of lymphoid cells in the gut as defined by forward scatter (FSC)/side scatter (SSC), gating on fluorescence-activated cell sorting (FACS) (Fig. 1, A and B). Further, bulk quantitative polymerase chain reaction (qPCR) on FACS-sorted cells from the lymphoid population of rag1−/− zebrafish showed two- and fourfold decreases in the expression of T cell markers such as cd3z and trac, respectively, compared with the wild-type zebrafish, whereas the expression level of il7r and lck remained the same (Fig. 1C). To verify that rag1−/− zebrafish lack adaptive lymphocytes, we sequenced 171 lck:EGFP+ single cells collected from gut and kidney of the rag1−/− zebrafish and applied TraCeR (26), a novel method for reconstruction of T cell receptor (TCR) sequences from scRNA-seq data to search for V(D)J recombination events in individual cells. No TCR rearrangements were detected in cells isolated from rag1−/− zebrafish (table S1). These data confirm that the rag1−/− provides an excellent tool to examine the innate lymphocytes in zebrafish.

Fig. 1 Rag1−/− zebrafish have cytokine-producing cells in the gut.

(A) Representative FACS plots showing the percentage of cells in the lymphocytes’ gate (as defined by FSC/SSC gating) in the gut of wild-type zebrafish (left) and rag1−/−mutant (right). (B) Percentage of cells in the lymphocytes’ gate within 50,000 recorded events in the gut of wild-type and rag1−/−mutant zebrafish. Bars represent the geometric mean ± 95% confidence interval to estimate the total number of lymphocytes. Mann-Whitney test. (C) qPCR expression of T cell–associated markers (cd3z and trac) and lymphocytes’ markers (il7r and lck) in mutant and wild-type zebrafish. Bars represent the geometric mean ± 95% confidence interval to estimate fold changes. Mann-Whitney test. (D) Scheme of short-term inflammation experiment. IP, intraperitoneal. (E) qPCR expression of immune type 1 (ifng1-1 and ifng1-2), immune type 2 (il4 and il13), and immune type 3 (il17a/f3 and il22) signature cytokines in the gut of the wild-type (rag1+/+) and mutant (rag1−/−) zebrafish after 6 hours of immune challenge with V. anguillarum or A. simplex. Bars represent the geometric mean ± 95% confidence interval to estimate fold changes. One-way analysis of variance (ANOVA) test.

Mammals contain three populations of helper ILCs (ILC1, ILC2, and ILC3) that rapidly respond to different tissue signals by producing effector cytokines (2729). To study this process in zebrafish, we established short-term inflammation models that trigger cytokine expression of potential ILCs in zebrafish gut (Fig. 1D). Formalin-inactivated Vibrio anguillarum has been used as a fish vaccine and is known to induce type 3 immunity (30); whereas the nematode Anisakis simplex, a common fish parasite, is expected to induce type 2 immunity (31). We injected wild-type and rag1−/− zebrafish intraperitoneally with phosphate-buffered saline (PBS; control) or extracts of inactivated V. anguillarum or of lyophilized A. simplex. Six hours after injection, we dissected the guts and evaluated the expression of signature cytokines by quantitative reverse transcription–PCR (Fig. 1, D and E). We found that, in both wild-type and rag1−/− zebrafish, injection of V. anguillarum extract induced the expression of TH1/ILC1 cytokines, such as ifng1-1 and ifng1-2, as well as TH17/ILC3 cytokines il17a/f3 and il22 (Fig. 1E). The expression levels of the TH2/ILC2 cytokines il4 and il13 remained unchanged in V. anguillarum compared to PBS-injected zebrafish. Conversely, injection of A. simplex extract induced the expression of TH2/ILC2 cytokines il4 and il13 but not of ifng1-1, ifng1-2, tnfa, il17a/f3, and il22 (Fig. 1E).

These findings have two important implications. First, they confirm that intraperitoneal injection of V. anguillarum extract induces a type 1/type 3 immune response in zebrafish gut and that injection of A. simplex extract induces a type 2 immune response. Second, they reveal the presence of cytokine-producing cells in the gut of immune-challenged rag1−/− zebrafish, in the context of T cell deficiency. Given that mammalian ILCs have phenotypes that mirror polarized TH subsets in their expression of effector cytokines, our data suggest that the gut in zebrafish contains bona fide ILC subtypes.

scRNA-seq reveals ILC2- and ILC3-like cells in zebrafish

ILCs comprise around 0.5 to 5% of lymphocytes in barrier tissues in mammals and hence represent a rare population of cells (9, 32). As the LCK gene is expressed in all three ILC subtypes in humans (33) (fig. S1), we reasoned that its expression pattern could be conserved in zebrafish. To capture ILC subtypes in zebrafish, we used our short-term inflammation protocol on Tg(lck:EGFP) rag1−/− zebrafish. scRNA-seq of thousands of lck:EGFP+ cells isolated from a gut of immune-challenged rag1−/− mutants provided a powerful approach to study cytokine-producing ILCs in zebrafish.

10x Genomics captures single cells in droplets, such that 5000 cells can be captured and subsequently sequenced within a single run (34). As above, we injected Tg(lck:EGFP) rag1−/− mutant zebrafish intraperitoneally with PBS, inactivated V. anguillarum or lyophilized A. simplex extracts, and sorted lck:EGFP+ cells from the gut 6 hours after injection. To ensure that a sufficient number of cells were loaded on 10x, we combined an equal number of lck:EGFP+ cells for each condition (PBS, A. simplex, and V. anguillarum) from three different zebrafish (nine zebrafish in total). By using this approach, we generated a comprehensive dataset that included 3211 single lck:EGFP+ cells from the guts of PBS-injected, 3626 cells from A. simplex–injected, and 3487 cells from V. anguillarum–injected rag1−/− zebrafish (Fig. 2). On average, we detected 600 genes per cell (fig. S2). This relatively modest number of detected genes was observed in all our datasets and could be linked with the small size of lymphocytes and their low RNA content (35). Clustering, followed by unbiased identification of marker genes for each cluster (see Materials and Methods), revealed ILC-like cells in all three datasets (Fig. 2, figs. S3 to S5A, and table S2).

Fig. 2 Analysis of the lck+ cells, collected from the gut of rag1−/− zebrafish.

(A) PBS-injected zebrafish. (B) V. anguillarum–injected zebrafish. (C) A. simplex–injected zebrafish. The 2D projection of tSNE analysis of 10x RNA-seq data showing heterogeneity of ILCs. Dot plots show the level of expression of marker genes and the percentage of cells per cluster that express the gene of interest.

We first analyzed lck:EGFP+ cells from PBS-injected zebrafish and identified several clusters of lymphocytes that expressed ifng1-2 and granzyme genes (gzm3 and gzmk; Fig. 2A). This transcriptional signature resembles mammalian NK cells. In addition, we identified cells that exclusively expressed ifng1-1 but not granzymes or NK- lysins (cluster 4 in Fig. 2A and table S2). Cells in cluster 4 could be considered ILC1-like cells in zebrafish.

Our analysis revealed two rare (0.8 and 3.8%) populations of rorc+ cells (clusters 1 and 3 in Figure 2A, figs. S3 to S5A, and table S2). The TF RORγt (encoded by rorc) is expressed at low levels in circulating and tissue-resident ILC precursors (ILCPs) in human, as well as in mature ILC3 in human and mouse (36, 37). Rorc is required for the development and function of ILC3 (38, 39). We found that the rorc+ clusters did not express cytotoxicity-associated genes such as granzymes (gzm3 and gzmk) or NK-lysins (nkl.2). To investigate the potential function of these two rorc+ clusters, we performed differential expression (DE) analysis followed by gene ontology (GO) enrichment analysis (see Materials and Methods). We found that rorc+ cluster 1 was associated with GO terms like “response to stress” and “protein folding ” (fig. S5B) and showed high expression of the prosurvival gene mcl1b, TF sox13, and TNF-β (tnfb), an ortholog of human lymphotoxin α (LTA), but was negative/low for novel immune-type receptor genes (e.g., nitr2b, nitr7b, nitr9, nitr4a, etc.), as well as ifng1-2/ifng1-1 (from here on nitrrorc+ cluster) (Fig. 2 and figs. S3 and S4). In contrast, rorc+ cluster 3 was associated with terms like “immune system process”, “response to IFN-γ”, and “response to other organisms” (fig. S5B) and was positive for tnfa, as well as novel immune-type receptors nitr9 and nitr4a, but also negative for ifng1-2/ifng1-1 (from here on nitr+rorc+ cluster; Fig. 2A and figs. S3 and S4) (40, 41). Cells within this cluster also expressed gata3, which has been shown to be indispensable for the development of all helper ILCs and expressed by subsets of ILCs at different levels (42).

In V. anguillarum–injected zebrafish, nitr+rorc+ cells (cluster 1), but not nitrrorc+ cells (cluster 10), expressed il22 and tnfa (Fig. 2B, figs. S3 and S4, and table S2). In humans and mice, ILC3 cells produce the cytokines IL-22 and TNFα cells upon stimulation with bacteria, triggering antimicrobial response and repair programs in epithelial cells during infection (4346). These data strongly suggest the existence of ILC3-like cells in the zebrafish gut that respond to immune challenge by producing relevant cytokines.

In zebrafish injected with lyophilized A. simplex, nitr+rorc+ cells (cluster 9) expressed il13 and gata3 (Fig. 2C, figs. S3 and S4, and table S2). The gata3 TF is highly expressed in ILC2 cells and is required for their development (46); it plays a critical role in activating IL-13 production in ILC2 upon stimulation, thus promoting anti-helminth immunity. Therefore, these cells (Fig. 2C) resemble mammalian ILC2 cells. Again, il13-producing cells were nitr+ but lacked expression of granzymes, nkl.2, and ifng1-2.

Immune-challenged zebrafish (V. anguillarum– or A. simplex–injected) also had a population of foxp3a+ cells (Fig. 2, B and C; figs. S3 and S4; and table S2). Cells in this cluster were negative for IFN-γ genes, nitr genes, and granzymes, as well as cd4-1. It is tempting to speculate that these cells might represent the zebrafish equivalent of recently reported mammalian regulatory ILCs (47). However, it should be noted that we did not detect expression of il10 in this cluster.

To validate that the rorc+ ILCs are a genuine constituent of the zebrafish gut, and not only present in rag1−/− zebrafish, we sequenced and analyzed additional 3756 green fluorescent protein–positive (GFP+) cells from the gut of PBS-injected wild-type lck:EGFP zebrafish. As expected, most of these cells were T cells that expressed cd8a or cd4-1 (specifically regulatory T cells). However, we also identified two clusters of rorc+ cells that were negative for T cell marker genes, namely, nitrrorc+ cells (cluster 10, Fig. 3) that expressed mcl1b and tnfb as well as nitr+rorc+ cells (cluster 9, Fig. 3) that expressed tnfa (Fig. 3). These cells represented 0.8 and 1.9% of the lck:EGFP+ cell population of wild-type zebrafish (table S2). Together, our transcriptional profiling of innate lymphocytes in rag1-deficient and wild-type zebrafish identified ILC-like populations.

Fig. 3 Analysis of the lck+ cells collected from the gut of wild-type zebrafish.

(A) The 2D projection of tSNE analysis of 10x RNA-seq data showing heterogeneity of innate and adaptive lymphocytes’ pool. (B) Dot plot shows the level of expression of signature genes and the percentage of cells per cluster that express the gene of interest.

ILC-like subtypes have distinct response to immune challenge

Our dataset revealed distinct, heterogeneous populations of ILCs within the guts of PBS-, V. anguillarum–, and A. simplex–injected zebrafish. We hypothesized that these distinct populations are biologically relevant, reflecting the response of immune cells to disparate stimuli. To test this hypothesis, we evaluated whether the distinct cell types identified in control zebrafish matched those present in V. anguillarum– and A. simplex–injected zebrafish and whether the specific treatments led to transcriptional changes in these cell types. We performed integrated analysis of PBS-, V. anguillarum–, and A. simplex–injected zebrafish using a recently developed computational strategy for scRNA-seq alignment (48). This methodology was specifically designed to allow comparison of RNA-seq datasets across different conditions (Fig. 4A). By following the Seurat alignment workflow, we uncovered “shared” cell types across all three datasets and compared their gene expression profiles.

Fig. 4 Integrated analysis of PBS-, V. anguillarum–, and A. simplex–injected rag1−/− zebrafish.

(A) Dot plot with the expression level of selected marker genes in each of the clusters. The size of the dots indicates the percentage of cells within the cluster that express the gene of interest; each cluster contains cells from three different conditions. (B) Volcano plot showing the top 20 differentially expressed genes between nitr+rorc+ (cluster 7) and nitrrorc+ (cluster 13) cells originated from rag1−/− PBS-injected zebrafish using aligned dataset. FC, fold change.

Our analysis showed that the nitr+rorc+ cells found separately in the three conditions (clusters 3, 1, and 9 in Fig. 2, A to C, respectively) corresponded to a unique cell subtype (cluster 7 in Fig. 4A and fig. S6A). Cells in this cluster up-regulated il22, rorc, and tnfa after in vivo stimulation with inactivated V. anguillarum (Fig. 4A and fig. S6B). In contrast, injection of A. simplex resulted in up-regulation of il13 and gata3 (Fig. 4A), but ifng1-1, ifng1-2, tnfb, and nitr genes remained unaltered relative to the control (Fig. 4A).

Similarly, the nitrrorc+ population of cells identified separately in PBS-, V. anguillarum–, and A. simplex–injected zebrafish (clusters 1, 10, and 7 in Fig. 2, A to C, respectively) grouped as distinct cluster (cluster 13 in Fig. 4A). These cells expressed mcl1b, sox13, and tnfb (ortholog of human LTA; Fig. 4, A and B). Mcl1 is prosurvival gene relevant for maintenance of viability but not of proliferation and is often expressed in long-lived cells (4951), whereas the human tnfb ortholog LTA is expressed in lymphoid tissue inducer (LTi) cells and is involved in the regulation of cell proliferation, differentiation, and survival (52, 53). Unlike nitr+rorc+ ILCs, nitrrorc+ cells did not respond to immune stimuli by expressing cytokines (Fig. 4B and fig. S6B).

We next asked whether unstimulated nitr+rorc+ cells express unique surface receptors that enable them to respond to the immune challenge. In addition to nitr9 and nitr4a, nitr+rorc+ ILCs specifically expressed novel immune-type receptors nitr6b and nitr5 as shown by DE analysis (Fig. 4B). In contrast to human unstimulated ILC subsets, less than 10% of unstimulated nitr+rorc+ cells expressed cytokine receptors, Toll-like receptors, and other pattern recognition receptors. The developmental origins and hierarchical relationship between nitrrorc+ and nitr+ rorc+ populations, however, remain unclear.

Last, cells identified in cluster 4 in PBS-injected zebrafish, cluster 4 in V. anguillarum–injected zebrafish, and cluster 2 in A. simplex–injected zebrafish (Fig. 2, A to C) grouped as cluster 2 (Fig. 4A). These cells showed clear up-regulation of ifng1-1 after immune challenge with V. anguillarum and no expression of granzymes or NK-lysins. These data further support that these cells potentially represent ILC1-like cells in zebrafish.Together, our analyses of more than 10,000 single cells collected from the gut of rag1−/− zebrafish identified previously unappreciated diversity of ILCs in zebrafish and revealed how this heterogeneity translates to cell-specific immune responses.

Innate immune response shows high degree of heterogeneity between individuals

Because immune response can vary between individuals upon challenge (54), we tested the robustness of our findings by investigating whether individual zebrafish are particularly over- or underrepresented within each of the identified clusters. The identification of ILC-like populations required the analysis of thousands of cells from multiple zebrafish injected with PBS, V. anguillarum, or A. simplex. To assign a likely donor ID to each cell, we used somatic mutations present within transcripts to locate genomic sites which vary between cells (for details, please see Materials and Methods). Using these sites, we assigned a genotype to each cell at each genomic site and subsequently clustered cells based on shared mutational profiles. This allowed us to assign each cell to a likely donor (Fig. 5).

Fig. 5 Genotype clustering of cells from the rag1−/− zebrafish.

(A) PBS-injected zebrafish. (B) V. anguillarum–injected zebrafish. (C) A. simplex–injected zebrafish. The first three PCs were used, and the clusters were generated using the probabilistic PCA algorithm. Individual 1 (violet color), individual 2 (green color), individual 3 (pink color), and undetermined (light violet color). Bar plots showing frequency (log scaled) of different donor within the cell type clusters under different challenge conditions.

Our analysis revealed that within the ILC1-like clusters from each experiment, there was a notable contribution from all donors (Fig. 5). Given the size of the ILC1-like clusters (558, 652, and 677 cells in PBS-, V. anguillarum–, and A. simplex–injected zebrafish, respectively), the distribution of donor cells suggests that ILC1-like cells are a stable population of cells in zebrafish gut. Similar conclusions could be made about the nitr+rorc+ ILC population in PBS (123 cells) and the ILC3-like population of cells in V. anguillarum–injected zebrafish (195 cells). However, the ILC2-like cells within the A. simplex–injected zebrafish were only detectable in individuals 2 and 3, suggesting that the stimulus was not strong enough to trigger response in individual 1.

We also observed an unequal donor contribution across other clusters in PBS-, V. anguillarum–, or A. simplex–injected zebrafish. The genotype composition of clusters showed a more significant skew toward individual donors within the immune challenge experiments (Fig. 5, B and C) than for the PBS control (Fig. 5A). In particular, cluster 3 (923 cells) within the V. anguillarum experiment seemed largely dominated by individual 3 (820 cells, 88.8%), whereas individual 1 (28 cells, 3%) and individual 2 (43 cells, 4.6%) were less present. Conversely, in cluster 2 (1263 cells), individual 3 contributed 38 cells (3%), whereas individuals 1 and 2 contributed 420 cells (33.2%) and 771 (61%) cells, respectively. Examining the transcriptional properties of these clusters showed that the two clusters are actually very similar (Fig. 2). In addition, an analysis of A. simplex–injected zebrafish (clusters 1 and 3) yielded very similar results, whereby the apparent disparity of individual 1 in cluster 3 is accounted for by the relative abundance of individual 1 cells within the transcriptionally similar cluster 1. It is therefore possible that the two cell type clusters, 2 and 3 in V. anguillarum–injected zebrafish and 1 and 3 in A. simplex–injected zebrafish, actually represent the functionally similar biological cell type, and the observed differences correspond to individual immune response.

Single-cell atlas of innate and adaptive lymphocytes in zebrafish

To allow easy retrieval of sequencing data from zebrafish innate and adaptive lymphocytes, we generated a cloud repository (www.sanger.ac.uk/science/tools/lymphocytes/lymphocytes/) with transcriptional profiles of more than 14,000 single cells collected from healthy and immune-challenged zebrafish using 10x Genomics and Smart-seq2 methodology (please see Explanatory Note in the Supplementary Materials).

To capture the diversity of lymphoid cell types, we purified and sequenced the RNA from single cells collected from primary lymphoid organs (kidney and thymus), secondary lymphoid organs (spleen), and barrier tissues (gut and gills) of healthy, unstimulated adult zebrafish. We used three different transgenic lines: Tg(lck:EGFP) (55), which labels T cells and NK cells (18); Tg(cd4-1:mCherry) (56), which labels CD4 T cells and macrophages; and Tg(mhc2dab:GFP, cd45:dsRed) (57) which is expected to label B cells (when sorted as GFP+/DsRed) (fig. S7A, table S3).

We performed scRNA-seq (Smart-seq2) of reporter-positive cells; 542 of 796 cells passed quality control (QC) and were subjected to further analysis (fig. S8). On the basis of 3374 highly variable genes (HVGs) inferred from biological cell-to-cell variation (fig. S9A), we generated diffusion maps and clustered cells within the three-dimensional (3D) diffusion space. Our hierarchical clustering approach revealed three main populations.

The cells in the first cluster (C1) appeared to be T cells with high expression of cd4-1, cd8a, and lck (fig. S7B). As expected, cells in this cluster originated from cd4-1:mCherry and lck:EGFP transgenic cells collected from kidney, gills, gut, thymus, and spleen (fig. S9B). To further confirm our computational prediction that cells in C1 are T cells, we applied TraCeR (26). We were able to unambiguously detect V(D)J recombination events in 224 of 362 cells (fig. S7C and table S1), and all observed TCR rearrangements were different. The second cluster (C2) had a signature of B lymphocytes and cells in this cluster originated from kidneys of the Tg(mhc2dab:GFP, cd45:dsRed) line (fig. S9, B and C). They showed expression of immunoglobulin-heavy variable 1-4 (ighv1-4), an ortholog of human immunoglobulin heavy constant mu gene (IGHM). We detected B cell receptor (BCR) rearrangements in 36 cells from this cluster using BraCeR, therefore confirming their B cell identity (fig. S7C and table S1) (58). The cluster three (C3) was exclusively composed of cells that originated from cd4-1:mCherry transgenic cells collected from gills, gut, and spleen (fig. S9, B and C). These cells had a high expression of macrophage receptor with collagenous structure (marco) and macrophage expressed gene 1 (mpeg1.1), strongly indicative of their macrophage identity. This was not unexpected, as cd4-1:mCherry has been found to label both CD4 T cells and macrophages (56).

Thus, scRNA-seq of lck:EGFP+, mhc2dab:GFP+/cd45:dsRed, and cd4-1:mCherry+ cells identified the adaptive lymphocytes in zebrafish, namely, T and B cells, and their transcriptional signatures. Although these reporter lines might not label the entire spectrum of indicated cell types, this is the most comprehensive transcriptional atlas of blood cell types in zebrafish to date.

DISCUSSION

Our work provides a comprehensive atlas of both adaptive and innate lymphocytes across different organs in healthy and immune-challenged zebrafish. We identified populations of innate lymphocytes in rag1-deficient and wild-type zebrafish that resemble helper ILC subtypes in mammals. By analyzing 14,080 lck:EGFP+ single cells collected from gut of unstimulated and stimulated zebrafish, we discovered two previously unknown populations of rorc+ ILC-like cells in zebrafish, nitr+rorc+, and nitr-rorc+, which appear, in some ways, to recapitulate natural cytotoxicity receptor (NCR+) ILC3 and NCR ILC3 subsets, respectively, in humans and mice (5). We obtained functional insight into these two distinct populations of rorc+ cells by exposing adult zebrafish to specific stimuli that rapidly induce corresponding cytokines in the gut.

The survey of cell surface receptors also suggested substantial differences in the expression of cytokine receptors in ILC-like cells in zebrafish compared with their mammalian counterparts. Whereas both human and mouse ILCs constitutively express receptors for cytokines and are mainly activated by cytokines released by the epithelium or antigen-presenting cells, in our dataset, zebrafish nitr+rorc+ ILC-like cells express cytokine receptors in less than 10% of cells. More recently, other receptors such as aryl hydrocarbon receptor, Toll-like receptors, and other pattern recognition receptors, as well as NCRs, have been reported to enable mouse and human ILC3 cells to directly sense environmental cues and induce cytokine expression (59, 60). Again, zebrafish ILC-like cells did not express the orthologs of these receptors but instead expressed novel immune type receptors (NITRs). Teleost genomes contain multiple nitr genes that are considered to be the functional homologs of mammalian NCRs and killer cell immunoglobulin-like receptors (40). However, our data should be interpreted in light of the methodology we used. Deeper sequencing or analysis of protein expression might provide additional information on the level of expression of various receptors in ILCs in zebrafish. Given the prominent expression of nitr genes in zebrafish ILCs, it remains to be seen whether and how these receptors modulate ILC functions.

In addition to ILC3 cells, another population of Rorc-expressing ILCs has been identified in mammals— LTi cells (61). LTis contribute to the formation of lymph nodes and gut-associated lymphoid tissue, including Peyer’s patches, isolated lymphoid follicles, and cryptopatches. Zebrafish gut does not have such organized lymphoid structures (62), and consequently, it is presumed that they do not have LTis (52). In our datasets, we identified a population of nitrrorc+ cells that exclusively expressed tnfb, a zebrafish ortholog of human LTA and a marker gene for LTi cells. Although nitrrorc+ share some transcriptional features with LTis, it is hard to speculate on the function of nitr-rorc+ cells in zebrafish and whether they are evolutionarily linked with mammalian LTi cells. Zebrafish nitrrorc+ cells expressed mcl1b which is prosurvival gene relevant for maintenance of viability but not of proliferation and is often expressed in long-lived cells (4951).

We also identified ifng-producing cells. Zebrafish have two ifng genes (ifng1-1 and ifng1-2), which show mutually exclusive expression in our dataset. The products of two ifng genes bind to different receptors in zebrafish and are thus functionally specialized (63). The ifng1-2 was coexpressed with granzymes or granulysins in the population transcriptionally resembling NK-like cells. In contrast, ifng1-1 expressing cells had no expression of granzymes nor granulysins and thus represent a distinct subtype of innate lymphocytes, possibly ILC1-like cells. Similar to human ILCs from tonsils, ifng-producing cells showed no expression of the TH1 master regulator T-bet (33).

Like humans and mice, zebrafish appear to contain an established ILC1-like population that responds to immune challenge. In addition, zebrafish have NK-like cells that are more numerous than helper ILCs in the gut of rag1-deficient zebrafish and show basal level of expression of ifng1-2. However, in contrast to mammals, zebrafish appear to display a population of ILCs that does not produce detectable levels of cytokines in homeostasis. Circulating and tissue-resident ILCPs in human and mouse also express Rorc at low levels and differentiate into multiple ILC subtypes in vitro (36, 37). ILCPs cannot be stimulated to produce effector cytokines; only already differentiated ILC1, ILC2, and ILC3 respond to immune challenge by producing relevant cytokines (36, 37). scRNA-seq of ILCs isolated from the gut of unstimulated mice (64) revealed that every subset of ILCs contained cells expressing cytokine transcripts, a feature we did not observe in PBS-injected zebrafish. It remains possible, however, that because of the modest number of detected genes, we were not able to detect a low basal level of cytokine production in these cells. Although we document several distinct populations of ILCs in this study, further studies are needed to understand whether there are precursor-progenitor relationships and/or plasticity between these lineages. Furthermore, our studies have focused on rag1−/− zebrafish. Developing tools would be essential for studying how ILCs cooperate with other lymphocytes to drive immune responses.

MATERIALS AND METHODS

Study design

The aim of this study was to characterize innate and adaptive lymphocytes in zebrafish in steady state and after the immune challenge using scRNA-seq. Multiple zebrafish, either in steady state or exposed to immune challenge, were used to collect cells for sequencing. To verify that all cells were intermixed on the basis of their transcriptional similarities, principal components analysis (PCA) and diffusion maps were used. After the sequencing, zebrafish were genotyped using probabilistic PCA on single-nucleotide polymorphisms (SNPs) identified on the basis of de novo variant calling.

Zebrafish strains and maintenance

The maintenance of zebrafish wild-type line (AB), transgenic lines Tg(lck:EGFP) (55), Tg(cd4-1:mCherry) (56), Tg(mhc2dab:GFP, cd45:dsRed) (57), and rag1hu1999mutants [also known as rag1t26683/26683 (23)] was performed in accordance with European Union regulations on laboratory animals. Hu1999 mutation results in a premature stop codon in the middle of the catalytic domain of the Rag1 protein and is considered a null allele (23).

Fluorescence-activated cell sorting

Kidneys from heterozygote transgenic zebrafish, either wild-type or rag1−/− mutant, were dissected and processed as previously described (17). The guts, spleens, gills, and thymuses were dissected and placed in ice-cold PBS/5% fetal bovine serum. Single-cell suspensions were generated by first passing through a 40-μm strainer using the plunger of a 1-ml syringe as a pestle. These were then passed through a 20-μm strainer before adding 4′,6-diamidino-2-phenylindole (cat. no. B30437; Beckman Coulter) to the samples. For Smart-seq2 experiment, individual cells were index-sorted into 96-well plates using a BD Influx Index Sorter. Cells from kidney, gut, gills, spleen, and thymus from nontransgenic zebrafish line were used for gating.

For the 10x experiment, guts from either Tg(lck:EGFP) rag1−/− mutant or wild-type zebrafish were isolated, and single-cell suspensions were prepared as described above. Three zebrafish, per each condition (i.e., zebrafish intraperitoneally injected with PBS, lyophilized A. simplex, or inactivated V. anguillarum) were used to collect the total of 12,000 lck+ cells (4000 per zebrafish) for the 10x experiment.

Plate-based single-cell RNA processing

The Smart-seq2 protocol (65) was used for whole-transcriptome amplification and library preparation as previously described. Generated libraries were sequenced in paired-end mode on Hi-Seq4000 platform.

Droplet-based single-cell RNA processing

After the sorting, cells were spun down and resuspended in ice-cold PBS with 0.04% bovine serum albumin at the concentration of 500 cells/μl. Libraries were constructed using Chromium Controller and Chromium Single Cell 3′ Library & Gel Bead Kit v2 (10x Genomics) according to the manufacturer’s protocol for 5000 cells recovery. Briefly, cellular suspension was added to the master mix containing nuclease-free water, RT reagent mix, RT primer, additive A, and RT Enzyme Mix. Master mix with cells was transferred to the wells in the row labeled 1 on the Chromium Single Cell A Chip (10x Genomics). Single Cell 3′ Gel Beads were transferred into the row labeled 2, and partitioning oil was transferred into the row labeled 3. The chip was loaded on Chromium Controller to generate single-cell Gel Bead in Emulsion (GEMs). GEM–reverse transcription (RT) was performed in a C1000 Touch Thermal cycler (Bio-Rad) at the following conditions: 53°C for 45 min, 85°C for 5 min, held at 4°C. After GEM-RT, cleanup was performed with DynaBeads MyOne Silane Beads (Thermo Fisher Scientific). Complementary DNA (cDNA) was amplified using a C1000 Touch Thermal cycler at the following conditions: 98°C for 3 min, 12 cycles of (90°C for 15 s, 67°C for 20 s, and 72°C for 1 min), 72°C for 1 min, held at 4°C. Amplified cDNA was cleaned with the SPRIselect Reagent Kit (Beckman Coulter), and quality was assessed using 2100 Bioanalyzer (Agilent). Libraries were constructed following the manufacturer’s protocol and sequenced in paired-end mode on Hi-Seq4000 platform.

Short-term inflammation experiments

V. anguillarum strain 1669 was grown in tryptic soy broth medium to OD600 (optical density at 600 nm) 1.5. Bacterial pellet (9 ml of full-grown culture) was resuspended in NaCl (9 g/liter), 0.35% formaldehyde, and incubated overnight at 20°C. The suspension was washed four times in NaCl (9 g/liter) and resuspended in 800 μl of the same isotonic solution.

A. simplex larvae, extracted from wild herring (Clupea harengus), were lyophilized using a freeze dryer/lyophilizer Alpha 1-2 LD plus (Martin Christ) following manufacturer’s instructions: Samples were placed in glass vials with vented rubber caps, placed in a freeze dryer holding tray, and placed at −80°C until ready to lyophilize. The freeze dryer machine was cooled down before use, and worms were exposed to lyophilization for 18 hours at −44° to −45°C and pressure at 0.071 to 0.076 mbar. Lyophilized larvae were homogenized in 1 ml of PBS using a FastPrep-24 instrument (MP Biomedicals) with 6.4-mm ceramic sphere in 2-ml tubes for 20 s at 6g.

Five microliters of each extract was mixed with 15 μl of sterile PBS and transferred to a 1.5-ml Eppendorf tube. Micro-Fine U-100 insulin syringes were loaded with the suspension mix and injected intraperitoneally into the midline between the pelvic fins.

RNA isolation and qPCR experiment

RNA was isolated with TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. One microgram of RNA was reverse-transcribed using the M-MLV Reverse Transcriptase Kit (Invitrogen). Real-time PCR was performed using the Rox SYBR Green MasterMix dTTP Blue Kit (Takyon) and run on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems).

The following primers were used: ifng-1-1, 5′-ACCAGCTGAATTCTAAGCCAA -3′ (forward) and 5′-TTTTCGCCTTGACTGAGTGAA-3′ (reverse); ifng-1-2, 5′- CATCGAAGAGCTCAAAGCTTACTA -3′ (forward) and 5′-TGCTCACTTTCCTCAAGATTCA-3′ (reverse); tnfa, 5′-TTCACGCTCCATAAGACCCA3′ (forward) and 5′-CAGAGTTGTATCCACCTGTTA-3′ (reverse); il13, 5′-GAAGTGTGAGCATGATTATTTC3′ (forward) and 5′-CTCGTCTTGGTGGTTGTAAG-3′ (reverse); il4, 5′- CCTGACATATATGAGACAGGACACTAC-3′ (forward) and 5′-TTACCCTTCAAAGCCATTCC-3′ (reverse); il17a/f3, 5′-AAGATGTTCTGGTGTGAAGAAGTG-3′ (forward) and 5′-ACCCAAGCTGTCTTTCTTTGAC-3′ (reverse); l22, 5′TGCAGAATCACTGTAAACACGA-3′ (forward) and 5′-CTCCCCGATTGCTTTGTTAC-3′ (reverse); cd3z, 5′-CCGGTGGAGGAGTCTCATTA-3′ (forward) and 5′-CTCCAGATCTGCCCTCCTC-3′ (reverse); ef1a, 5′-ACCTACCCTCCTCTTGGTCG-3′ (forward) and 5′-GGAACGGTGTGATTGAGGGAA-3′ (reverse).

Samples were analyzed using ΔΔCt method. The mean Ct value of housekeeping gene (ef1a) was used for normalization. The 2 ΔΔCt values were graphed with the geometric mean ± 95% confidence intervals to estimate the fold change. The raw qPCR data for the short-term inflammation experiment can be found at the online repository (https://zenodo.org/record/1437804).

Alignment and quantification of scRNA-seq data

For the samples that were processed using the Smart-seq2 protocol, the reads were aligned to the zebrafish reference genome (Ensemble BioMart version 89) combined with the sequences for EGFP, mCherry, mhc2dab, and ERCC spike-ins. Salmon v0.8.2 (66) was used for both alignment and quantification of reads with the default paired-end parameters, whereas library type was set to inward (I) relative orientation (reads face each other) with unstranded (U) protocol (parameter –l IU).

For the samples that were processed using the Chromium Single Cell 3′ protocol, Cell Ranger v2.1 was used to demultiplex raw base call (BCL) files generated by Illumina sequencers into FASTQ files and perform the alignment, barcode counting, and unique molecular identifiers counting. Ensembl BioMart version 91 was used to generate the reference genome.

QC of single-cell data

For the Smart-seq2 protocol, transcript per million (TPM) values reported by Salmon were used for the QC. Wells with fewer than 900 expressed genes (TPM > 1) or having more than either 60% of ERCC or 45% of mitochondrial content were annotated as poor-quality cells. As a result, 322 cells failed QC, and 542 single cells were selected for further study.

Chromium Single Cell 3′ samples were filtered on the basis of the median absolute deviation of the distribution of the number of detected genes. In addition, the percentage of mitochondrial content was set to less than 10%. After QC, 3211 single cells from the rag1−/− PBS-injected sample, 3626 single cells from the rag1−/− A. simplex–injected samples, 3487 single cells from the rag1−/− V. anguillarum–injected samples, and 3756 from the rag1+/+ PBS-injected samples were used in downstream analysis.

Downstream analysis of Smart-seq2 data

For each of the 542 single cells, counts reported by Salmon were transformed into normalized counts per million (CPM) and used for further analysis. This was performed by dividing the number of counts for each gene with the total number of counts for each cell and by multiplying the resulting number by a factor of 1,000,000. Genes that were expressed in less than 1% of cells (e.g., five single cells with CPM > 1) were filtered out. In the final step, we ended up using 16,059 genes across the 542 single cells. The scran R package (version 1.6.7) (67) was then used to normalize the data and remove differences due to the library size or capture efficiency and sequencing depth.

To identify the HVGs, we used the Brennecke Method (68). We inferred the noise model from the ERCCs and selected genes that vary higher than 20% variation. This was performed by using the “BrenneckeGetVariableGenes” command of M3Drop v1.4.0 R package setting false discovery rate equal to 0.01 and minimum percentage of variance due to biological factors (minBiolDisp) equal to 0.2. In total, 3374 were annotated as HVGs.

To verify that all cells were intermixed (in the reconstructed 3D component space) based on their transcriptional similarities and not based on the zebrafish of origin, we used PCA and diffusion maps [destiny R package (version 2.6.1)].

The first three diffusion components were clustered using shared nearest neighbor (SNN) modularity optimization-based clustering algorithm implemented by Seurat Package. We used the “FindClusters” command. Three clusters were selected for further analysis.

BraCeR and TraCeR analysis

We have used TraCeR (26) and BraCeR (58) tools to reconstruct the sequences of rearranged TCR and BCR genes from our Smart-seq2 scRNA-seq data. To build combinatorial recombinomes (tracer/bracer build command) for the Danio rerio species, FASTA files describing all V, J, C, and D sequences were collected from the international ImMunoGeneTics information system (www.imgt.org) (69). For TCR, complete information of the α and β chain location was available, whereas for the BCR, H and L location sequences were available. Using a threshold of 50 TPMs for gene expression, we identified a total of 244 single cells as TCR positive and 36 as BCR positive.

Downstream analysis of 10x Genomics data

The downstream analysis of the 10x data was performed using the Seurat (version 2.2.0) and the cellranger (version 1.1.0) R packages. Briefly, raw counts that passed the QC were centered by a factor of 1000 and log transformed. HVGs were detected on the basis of their average expression against their dispersion, by means of the “FindVariableGenes” Seurat command with the following parameters: mean.function equal to ExpMean, dispersion.function equal to LogVMR, x.low.cutoff equal to 0.0125, x.high.cutoff equal to 3, and y.cutoff equal to 0.5. The number of HVGs across samples varied between 1500 and 2500 genes, accordingly.

HVGs were used for the calculation of the PCs using Seurat’s “RunPCA” command. For the 3D t-distributed stochastic neighbor embedding (tSNE) transformation (“RunTSNE” command), we used PCs with JackStraw statistics lower than 0.01. The later statistics were estimated using the Seurat’s “JackStraw” command with 200 replicate samplings. The proportion of the data that were randomly permuted for each replicate was set to 1%.

Clustering in the 3D tSNE space was performed (pheatmap version 1.0.8) using Euclidean distance and centroid linkage. The silhouette scores were used to estimate the optimal number of clusters. However, the final decision on the number of clusters was made on case-by-case basis. Positive marker genes that expressed in at least half of genes within the cluster were calculated with “FindAllMarkers” Seurat command, using Wilcoxon rank sum test with threshold set to 0.25. Dot plots in Figs. 2 to 4 were generated using the Seurat’s “DotPlot” and “SplitDotPlotGG” command.

Seurat alignment strategy

To perform direct comparison of clusters that belong to the same cell type across different conditions, we adopted the Seurat Alignment workflow (48). We calculated HVGs, for each of the three different conditions and selected 961 HVGs that were expressed in at least two datasets. Canonical correlation analysis (CCA) was then performed to identify shared correlation structures across the different conditions using the “RunMultiCCA” command. Twenty significant CCA components were selected by means of the shared correlation strength, using the “MetageneBicorPlot” command. Aligned CCA space was then generated with the “AlignSubspace” Seurat command. Thirteen clusters were identified using the SNN modularity optimization-based clustering algorithm (“FindClusters” command) on the 20 significant CCA-aligned components at 0.5 resolution. Dot plot of genes at different clusters on the aligned data was generated by using the SplitDotPlotGG command.

Barcode extraction and initial variant calling

For each cell barcode, transcript extraction and indexing of the generated files were done using SAMtools package (https://github.com/samtools/samtools). The cellular datasets were merged for subsequent analysis. Variant genomic sites were identified de novo within each experiment condition through the use of the mpileup and call functions from the BCF tools package (https://github.com/samtools/bcftools). The resulting variant call format (VCF) file contained all genomic sites that showed variation among the transcripts from single cells. Poor-quality variants were filtered out.

Variant calling per cell

For each experiment condition, the VCF file produced by the initial variant calling was used as a reference to genotype each cell at each quality-controlled variant site. The cellular VCFs were merged to create a single VCF file containing the genotype of each cell at each variant site identified in the experiment. The variants were filtered to exclude those variant sites present in less than 5% of cells. This resulted in 2592 variants identified within the rag1−/− PBS-injected zebrafish, 2778 variants within the rag1−/− V. anguillarum–injected zebrafish, and 2879 variants within the rag1−/− A. simplex–injected zebrafish.

Genotype clustering

To process the VCF file, the VariantAnnotation package (70) was used to filter out variants that did not originate from SNPs. Using the snpStats package (71), for each processed experiment (Ncells × NSNPs), matrix was created containing the genotype of each cell at each variant site. Genotypes were numerically encoded as: 0 = homozygous reference allele, 1 = heterozygous, 2 = homozygous alternative allele, and NA = missing genotype. For dimensionality reduction of the matrix, probabilistic PCA (72) from the pcaMethods package (73) was used, and for clustering of the cells by principal component, Mclust package (74) was used. All relevant scripts have been uploaded to an online repository at https://github.com/dhall1995/genotyping_scRNAseq.

Statistics

Statistical analyses were conducted using R, Python, or GraphPad Prism. The types of statistical tests and significance levels are described in respective figure legends. The results were considered statistically significant when P value was lower than 0.05 and were marked in the figures as ****P < 0.0001, ***P < 0.001, **P < 0.01, and *P < 0.05.

SUPPLEMENTARY MATERIALS

immunology.sciencemag.org/cgi/content/full/3/29/eaau5265/DC1

Table S1. TraCeR and BraCeR analysis.

Table S2. Number of cells in each cluster across different experimental conditions (10x RNA-seq data).

Table S3. Index sorting data.

Fig. S1. Expression of marker genes in human ILCs.

Fig. S2. QC of 10x data.

Fig. S3. Heatmaps with expression of marker genes in lck+ cells collected from rag1−/− gut.

Fig. S4. t-SNE plots with expression of marker genes in lck+ cells collected from rag1−/− gut.

Fig. S5. Identification of nitrrorc+ and nitr+rorc+ population of cells.

Fig. S6. PBS-, V. anguillarum–, and A. simplex–injected rag1−/− mutant zebrafish aligned datasets.

Fig. S7. Identification of immune cell types in zebrafish during steady-state hematopoiesis.

Fig. S8. QC of SmartSeq2 data.

Fig. S9. Clustering of Smart-seq2 data from Tg(lck:EGFP), Tg(cd4-1:mCherry), and Tg(mhc2dab:GFP, cd45:dsRed) zebrafish transgenic lines.

REFERENCES AND NOTES

Acknowledgments: We would like to thank WTSI Cytometry Core Facility for their help with index cell sorting and N. Boughton and the Core Sanger Web Team for hosting the cloud web application. We would also like to thank the CRUK Cambridge Institute Genomics Core Facility for their contribution in sequencing the data and WTSI Single Cell Genomics Core Facility for their assistance with 10x experiments. We would also like to thank I. Lindeman and M. Stubbington for their help with using BraCeR and TraCeR tools and G. Eberl for reading the manuscript and providing valuable suggestions. We gratefully acknowledge the support of P. Herbomel (Institut Pasteur), in whose laboratory zebrafish rearing and inflammation assays were carried out. We would like to thank D. McCarthy from the European Bioinformatics Institute for support in the implementation of the zebrafish genotype analysis. We would like to thank A. Hurlstone for providing Tg(cd4-1:mcherry) line and D. Traver for providing Tg(mhc2dab:GFP, cd45:DsRed) line. Funding: This study was supported by Cancer Research UK grant number C45041/A14953 (to A.C. and E.I.A.), European Research Council project 677501–ZF_Blood (to A.C. and P.M.S.), EMBO Long-Term Fellowship ALTF-807-2015 (to P.P.H.), ANR grant 17-CE15-0017-01–ZF-ILC (to P.P.H.) and ANR-16-CE20-0002-03 (to J.-P.L.), H2020-MSCA-IF-2015 grant 708128–ZF-ILC (to P.P.H.), ANR-10-LABX-73 (“revive” to P. Herbomel), and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute. Author contributions: P.P.H. conceived and proposed the original idea of identifying ILCs in zebrafish, under the supervision of J.-P.L.; C.M.C. provided lyophilized A. simplex; and P.B. generated V. anguillarum extract. P.P.H. and J.-P.L. designed inflammation experiments, and P.P.H. and A.F.R. performed them; E.I.A. carried out the complete single-cell analysis and created the cloud repository under the supervision of A.C.; P.M.S. performed single-cell experiments and designed and generated all figures with input from A.C.; D.H. performed genotyping analysis; and E.I.A., P.M.S., P.P.H., P.B., J.-P.L., and A.C. contributed to the discussion of the results. A.C. wrote the manuscript with input from P.M.S., E.I.A., J.-P.L., and P.P.H. Competing interests: The authors declare that they have no competing interests. Data and materials availability: Raw data can be found under the accession number E-MTAB-7117 and E-MTAB-7159 on ArrayExpress. The raw qPCR data for the short-term inflammation experiment can be found at the online repository, https://zenodo.org/record/1437804. Rag1hu1999 zebrafish can be obtained from the European Zebrafish Resource Center (www.ezrc.kit.edu). Tg(lck:EGFP) line can be obtained from the Zebrafish International Resource Centre (http://zebrafish.org/home/guide.php). Tg(lck:EGFP) rag1hu1999 zebrafish may be requested from P.P.H. or J.-P.L. Tg(cd4-1:mCherry) zebrafish can be requested from A.H. (adam.hurlstone{at}manchester.ac.uk). Tg(mhc2dab:GFP, cd45:DsRed) zebrafish can be requested from D.T. (dtraver{at}uscd.edu).
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