Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung
BMC Biology volume 21, Article number: 218 (2023)
Group 2 innate lymphoid cells (ILC2s) are critical sources of type 2 cytokines and represent one of the major tissue-resident lymphoid cells in the mouse lung. However, the molecular mechanisms underlying ILC2 activation under challenges are not fully understood.
Here, using single-cell transcriptomics, genetic reporters, and gene knockouts, we identify four ILC2 subsets, including two non-activation subsets and two activation subsets, in the mouse acute inflammatory lung. Of note, a distinct activation subset, marked by the transcription factor Nr4a1, paradoxically expresses both tissue-resident memory T cell (Trm), and effector/central memory T cell (Tem/Tcm) signature genes, as well as higher scores of proliferation, activation, and wound healing, all driven by its particular regulons. Furthermore, we demonstrate that the Nr4a1+ILC2s are restrained from activating by the programmed cell death protein-1 (PD-1), which negatively modulates their activation-related regulons. PD-1 deficiency places the non-activation ILC2s in a state that is prone to activation, resulting in Nr4a1+ILC2 differentiation through different activation trajectories. Loss of PD-1 also leads to the expansion of Nr4a1+ILC2s by the increase of their proliferation ability.
The findings show that activated ILC2s are a heterogenous population encompassing distinct subsets that have different propensities, and therefore provide an opportunity to explore PD-1's role in modulating the activity of ILC2s for disease prevention and therapy.
Group 2 innate lymphoid cells (ILC2s) are members of a family of innate immune cells comprising five subsets–NK cells, ILC1, ILC2, ILC3, and LTi cells–which form the first line against infections  and, especially ILC2s, are abundantly dispersed in barrier tissues including airway, skin, gastrointestinal tract and urogenital tract . Activated ILC2s produce copious amounts of type-2 cytokines (e.g. interleukin-5 (IL-5) and IL-13) which respond to non-specific alarmins such as IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) under infection or insults [3, 4]. ILC2s are generated from hematopoietic progenitors that primarily reside in the bone marrow where committed ILC2 progenitors were differentiated from common lymphoid progenitors (CLPs), common innate lymphoid progenitors (CILPs), common helper innate lymphoid progenitors (CHILPs), and innate lymphoid cell progenitors (ILCPs) in a tightly regulated stepwise approach  which are dictated by various transcription factors such as Id2 , Tox [6, 7], Nfil3 , Tcf7 , Ets1 , Gata3 [11,12,13], RORα [14, 15], Gfi1  and Bcl11b [17,18,19].
Initially, ILC2s were discovered implicating in the early immune responses to parasitic infection , but now it is becoming clear that they exert pleiotropic functions in various tissues’ homeostasis and physiology, such as mediating tissue repair through amphiregulin , regulating thermogenesis by converting white fat into beige fat [22, 23], sensing neuronal factors NMU [24, 25] and VIP , and remodeling intestine epithelium by forming Tuft-ILC2 circuit [27, 28]. Many studies have demonstrated the cross-talk between ILC2s and immune cells or non-immune cells. ILC2s can function as initiators of adaptive immunity  or as responders to signals such as cytokines, lipid mediators, and hormones produced by immune cells or structured cells .
Dysregulation of ILC2 activity is implicated in a wide variety of type 2-mediated diseases, including allergic rhinitis, atopic dermatitis, and asthma [30, 31]. Recently, several studies reported that the frequency of ILC2s was increased in the peripheral blood , sputum , and airways  of asthmatic patients. Their numbers were positively correlated with the severity of asthma as more ILC2s were detected in the blood and sputum of severe asthma compared to those with mild asthmatics . Moreover, it has been reported that the super-enhancers specific to ILC2s were much more active than that of Th2 cells in the asthmatics, supporting a pathogenic role for ILC2s in patients with allergic asthma . Challenging the mouse with protease allergens such as papain, house dust mites, or Alternaria alternate has demonstrated that ILC2s are the key players in the initiation, amplification, and modulation of type 2 immune responses in the airways [36, 37].
It has been reported that mouse lung consists of a functional heterogenous ILC2 population in the hemostatic healthy condition  or under the challenges such as influenza virus infection , helminth infection , and alarmin cytokine stimulation . However, the mechanisms and underlying regulatory networks that drive the segregation and the activation pathways of various ILC2 subsets in the lung are not clear.
PD-1 is a potent immune inhibitory receptor and is expressed on lymphocytes including ILC2s .PD-1 knockout in mice leads to development of lupus-like autoimmune diseases . Blockade or inhibition of PD-1/PD-L1 signal pathway in ILC2s results in an increase in ILC2 number, type 2 cytokine production and anti-cancer immunity [43,44,45].
Here, we revealed that four ILC2 subsets existed in mouse inflammatory lung and a specific activation subset was identified based on the expression of transcription factor Nr4a1. Nr4a1+ILC2s displayed many signatures such as memory, migration, and wound healing, all of which were not detected in other ILC2 subsets. We further demonstrated that the Nr4a1+ILC2s activation and their differentiation process were negatively regulated by molecule PD-1. Understanding the heterogeneity and activation mechanisms of ILC2s will provide a theoretical principle for the treatment of inflammation-related diseases and help to develop new interventions for therapy.
The scRNA-seq analysis identifies four lung ILC2 subsets under acute inflammation
Given the fact that ILC2s are a rare cell population with varying surface makers depending on the mouse genetic background and stimulation, the Bcl11b-tdTomato reporter mice, which label ILC2s in Lin− population of the lung [18, 19], were used for this study. Our previous study shows that PD-1 is expressed on lung ILC2s and its expression is upregulated upon ILC2 activation . We thus purified Lin−CD45+IL-7Rα+ Bcl11b- tdTomato+ ILC2s from the lungs of both Bcl11btdTomato/tdTomato mice and Pdcd1−/−;Bcl11btdTomato/+ mice to dissect the heterogeneity of ILC2s and their activation pathways (Fig. 1a and Additional file 1, Fig. S1a). The samples were collected at days 0, 7, and 14 (d0, d7, and d14) after 4 consecutive papain intranasal challenges and were subjected to plate-based smart-seq2  single-cell analysis.
After quality control (Additional file 1, Fig. S1b-d) and the removal of contaminated cells, 703 cells (including 341 WT ILC2s and 362 PD-1 KO ILC2s) were used for this study. We first focus on the analysis of WT cells. Unsupervised clustering grouped them into 4 clusters (clusters 1, 2, 3, and 4, denoted as C1-C4) (Fig. 1b and c) with different gene expression patterns (Fig. 1d and e) by Scanpy . The expressions of ILC2-associated genes such as Id2, Gata3, Il7r, and Bcl11b validate their ILC2 identity (Fig. 1d). Each cluster consisted of cells from different time points (d0, d7, and d14) at various frequencies (Fig. 1f). In contrast to C1 and C2 clusters that consisted of a majority of d0 and d14 cells, C3, and C4 clusters had the enrichment of d7 cells (Fig. 1f and g), suggesting that C3 and C4 cells were likely activated. Indeed, the C3 and C4 cells had higher expression levels of ILC2 activation-associated genes Il5, Il13, and Pdcd1 compared to the C1 and C2 cells (Fig. 1d). Therefore, two non-activation ILC2 subsets and two activation ILC2 subsets were detected in the mouse inflammatory lung.
To characterize C1-C4 subsets, we performed analyses of pairwise differential expression genes (DEGs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 20, 98, 257, and 243 signature genes in C1, C2, C3, and C4 respectively (P value < 0.05, log2FC > 1) (Fig. 1h and Additional file 2, Table S1). GO enrichment analysis revealed that several basic biological processes GO terms, including “cytoplasmic translation”, “RNA splicing”, and “mRNA processing”, were overrepresented in C1 signature genes (Fig. 1i), which was consistent with the KEGG analysis that “ribosome” and “spliceosome” were significantly overrepresented (Fig. 1j). The GO terms such as “cell cycle’’, “cell division’’, and “translation” were significantly overrepresented in C2 signature genes (Fig. 1i, Additional file 1, Fig.S1e, and S1f). In line with our previous observations that ILC2 activation-associated genes were highly expressed in C3 and C4 cells, the GO term “immune system process” was overrepresented in their signature genes (Fig. 1i). The GO terms such as “positive regulation of gene expression”, “positive regulation of MAPK cascade”, “cell differentiation” and “ribosome biogenesis” were overrepresented in C3 signature genes, whereas the GO terms including “positive regulation of inflammatory response”, “protein phosphorylation”, “cell migration”, and “apoptotic process” were overrepresented in C4 signature genes, which were consistent with the KEGG pathway analysis (Fig. 1j). Given the absence of C4 cells at d14 coupling with the overrepresentation of GO terms “cell migration’ and “apoptotic process” in their signature genes (Fig. 1f, g and i), we reason that C4 cells might migrate out of the lung or die in it by apoptosis.
Collectively, our scRNA-seq analysis reveals four distinctive ILC2 subsets including two non-activation subsets and two activation subsets in the mouse inflammatory lung.
Characterization of the transcriptomic signatures of ILC2 activation subsets
To validate the ILC2 activation subsets, three published mouse lung ILC2s datasets (GSE122762 ; GSE123994 ; GSE117567 ) [38, 51] were integrated by Scanpy  and were used as a pre-labeled single-cell reference to annotate our data by SingleR . Almost all C3 and C4 cells fell into the activation subset (Fig. 2a). Therefore, this analysis confirms cells in C3 and C4 were activated.
To interrogate the ILC2 activation subsets, the activation degrees of their cells were assessed based on the ILC2 activation genes signature from the previous report . Both C3 and C4 cells had higher activation scores compared to either C1 or C2 cells (Fig. 2b). Of note, the cells with the highest activation scores were enriched in the C4 (Fig. 2b).
To explore two ILC2 activation subsets in greater detail, we performed the DEGs analysis between these two activation subsets. 986 and 106 genes (P value < 0.01, log2FC > 1) were upregulated in C3 and C4 respectively (Fig. 2c). For example, the epigenetic modifier Dnmt3a, prion protein Prnp, and anti-apoptotic protein Bcl2 were upregulated in C3 cells (Fig. 2c and Additional file 4, Fig. S2a). The higher expression of Bcl2 in C3 cells indicated that they have better viability than C4 cells, which is in line with the observations of the existence of C3 cells, but not C4 cells, at d14 (Fig. 1g). Interestingly, the typic immune cell migration genes Klf2  and sphingosine 1-phosphate receptor 1 (S1pr1)  were specifically expressed in C4 cells (Fig. 2j).
In contrast to the enrichments of “protein transport”, “nucleocytoplasmic transport”, “translation”, “cytoplasmic translation”, and “ribosomal small subunit export from nucleus” terms in C3 cells (Additional file 4, Fig. S2b and S2c), the C4 cells overrepresented pathways “positive regulation of inflammatory response”, “phosphorylation”, “cell migration”, “TNF signaling pathway”, “MAPK signaling pathway”, and “PI3K-Akt signaling pathway” (Fig. 2d and e).
Previous studies showed that wound-healing genes were enriched in mouse lung ILC2s . We thus scored our lung ILC2s by the expressions of wound healing genes and found that C4 cells scored highest (Fig. 2f). The wound healing genes Batf and Areg were enriched in C4 compared to other subsets (Fig. 2g) and C4 cells had the highest proliferation score as well (Fig. 2h), implying the C4 cells play a more active role in restoring airway epithelial integrity and tissue homeostasis in acute lung inflammation.
It has been reported that mouse lung ILC2s had the potential to differentiate into memory-like ILC2s . Given the lack of available ILC2 memory signature genes, we scored T cell memory-related genes  on our lung ILC2s. We found only C4 cells exhibited T cell memory signature (Fig. 2i). Therefore, C4 cells were likely reported memory ILC2s which respond quicker and stronger upon future challenges with the same or unrelated stimuli .
Taken together, we have identified two distinct ILC2 activation subsets. One has better viability that can sustain to resolution phage (detected on both d7 and d14). The other one, which is detected on d7 only, is more activated, more proliferative, and has an enrichment of genes on migration, wound healing, and immune memory (Fig. 2j).
The transcription factor Nr4a1 marks a distinctive ILC2 activation subset
We projected C1-C4 cells into a 3D graph and found that C4 cells stood out from the others (Fig. 3a). We thus focused on investigating this subset. Although C4 cells had lower ILC2 tissue-resident scores compared to C1 or C2 cells (Fig. 3b), many tissue-resident memory T cells (Trm) associated genes (e.g. Nfkbid, Gadd45b, Dusp1, Zfp36, Junb, Tnfaip3, Hilpda, Nr4a1, Cd69) were significantly enriched in the C4 signature genes (Fig. 3c). Paradoxically, C4 cells also had significantly higher expressions of circulating central memory T cells (Tcm) and effector memory T cells (Tem) associated genes (e.g. Klf2, S1pr1, Klhl6, Rasgrp2, Klf3 Dock2, Itfb1, Apt10d) (Fig. 3c). We scored both Trm-associated genes and Tcm/Tem-associated genes on activated ILC2s individually. The results showed that C4 cells got both higher Trm and Tcm/Tem scores compared to other cluster cells (Fig. 3d and e), suggesting C4 gains a distinctive feature that is quite different from other subsets.
Compared to other clusters, the nuclear receptor transcription factor Nr4a1 and surface molecule CD69 were much higher expressed in the C4 (Fig. 3f). We hence utilized the Nr4a1-eGFP reporter  to validate the C4 cells by flow cytometry. The Nr4a1-eGFP reporter mice were intranasally challenged with papain and lung tissues were collected at d0, d7, and d14 for analysis (Fig. 3g). In agreement with the transcriptomic data on the dynamic changes of C4 cells in the papain-induced lung inflammation course (Fig. 1g), the CD45+Lin−IL-7Rα+IL-33R+CD25+PD-1hiCD69hiNr4a1+ cells were primarily detected on d7 (Fig. 3h and i), suggesting they were C4 cells.
We then generated Nr4a1eGFPcre/+;Il5tdTomato/+ dual reporters mice. Flow cytometry analysis of the lung ILC2s from these mice d7 after papain challenge (Fig. 3j) showed Il5-expressing activated ILC2s were demarcated into two populations by the expression of Nr4a1 (Fig. 3k), further confirming there were two activation subsets and the one, marked by the expression of Nr4a1, was C4.
Collectively, the Nr4a1 marks an ILC2 activation subset that has a distinctive dynamic pattern in mouse acute lung inflammation course and exhibits both Trm and Tcm/Tem transcriptome signatures.
The SCENIC analysis identifies the master regulators for the Nr4a1+ILC2 subset
To gain insight into the gene regulatory network driving the unique features of Nr4a1+ILC2s from a global perspective, we utilized the single-cell regulatory network inference and clustering (SCENIC) algorithm  to identify potential master regulators.
We found that Irf1(42g) and Klf2 (76 g) were the top regulons overrepresented in C4 (Fig. 4a and b). One of the direct targets of Klf2 is S1pr1, which was reported to have an essential role in ILC2 trafficking between organs . Given the absence of C4 cells in the d14 lung tissue (Fig. 1g), we speculated that the C4 cells might move out of the lung rather than die in it at the resolution phase of inflammation.
Interestingly, the C4 marker gene Nr4a1, which was a direct common target of Klf2 and Irf1 regulators (Fig. 4c), directly regulates many C4 signature genes including Trm-associated genes (e.g. Zfp36, Dusp1, Nr4a3, Hilpda, Btg2, Fos, Nfkbid, Nfkbiz, Nfkbia, Junb, Cd69, Egr1), lymphocyte activation marker genes (Cd69, Areg), and immune inhibitory gene Pdcd1 (Fig. 4d). Therefore, Nr4a1 is a master regulator of C4 cells.
PD-1 negatively regulates Nr4a1+ILC2s by restraining the cell activation
As mentioned above, Pdcd1 is a direct target of Nr4a1 and is the only immune inhibitory gene examined that is highly expressed in the Nr4a1+ILC2 subset (Fig. 5a), we then study the role of PD-1 in Nr4a1+ILC2s under acute lung inflammation by using single PD-1 KO ILC2s transcriptome data (Fig. 1a). After the integration of the above 341 WT cells, 703 cells in total were used for further analysis. Similarly, 4 clusters (denoted as Cm1, Cm2, Cm3, and Cm4) were observed in the unsupervised clustering assay (Fig. 5b). We annotated the cells with previous cluster information (C1-C4) on each cell from Cm1-Cm4 clusters and found that almost all WT cells in the Cm1-Cm4 clusters were the same ones from the C1-C4 clusters correspondingly (Additional file 5, Fig. S3a). Thus, Cm3 and Cm4 were two activation subsets with high expressions of activation-associated genes such as Il5, Il13, and Areg (Additional file 5, Fig. S3b), whereas Cm1 and Cm2 were two non-activation subsets. As expected, the C4 marker gene Nr4a1 was enriched in the Cm4 cluster (Additional file 5, Fig. S3b). The PD-1 KO Cm4 cells were validated in a flow cytometry analysis of the Pdcd1−/−; Nr4a1eGFPcre/+ mice administrated with papain intranasally (Fig. 5c and d). Compared to WT Cm4 cells in Nr4a1eGFPcre/+ mice, more PD-1 KO Cm4 cells were detected in Pdcd1−/−; Nr4a1eGFPcre/+ mice (Fig. 5e).
We then assessed the effects of PD-1 deletion on Nr4a1+ILC2s. Gene expression comparison showed PD-1 KO Nr4a1+ILC2s had similar scores of memory, Trm, Tem/Tcm, and wound healing relative to control WT Nr4a1+ILC2s (Fig. 5f), demonstrating the major signatures of those cells were not changed through PD-1 KO.
Nevertheless, PD-1 KO Nr4a1+ILC2s had higher enrichments of the immune system process of GO and KEGG categories (Fig. 5g and Additional file 5, Fig. S3c) and the frequency of Il5-expressing cells was increased in those PD-1 KO cells (Fig. 5h). Moreover, gene set enrichment analysis (GSEA) showed that PD-1 deficiency led to a metabolic shift towards glucolipid metabolism in Nr4a1+ILC2s (Additional file 5, Fig. S3d), which is consisted with previous reports showing that deletion of PD-1 switched activated ILC2 metabolism to glycolysis .
To determine the effects of PD-1 deletion on the cell composition of the clusters, we first calculated the cells in non-activation subsets of WT and PD-1 KO ILC2s. We found that non-activation WT ILC2s comprised 80% of Cm1 cells and 20% of Cm2 cells, whereas non-activation PD-1 KO ILC2s consisted of 13% of Cm1 cells and 87% of Cm2 cells (Fig. 5i). The transcriptome difference existed between WT and PD-1 KO non-activation ILC2s. GO and KEGG analyses revealed PD-1 KO non-activation ILC2s had overrepresentations of immune system process pathways such as “positive regulation of inflammatory response”, “TNF signaling pathway”, “NF kappa B signaling pathway’, and “Oxidative phosphorylation” (Additional file 5, Fig. S3e).
We next calculated the cells in activation subsets of WT and PD-1 KO ILC2s. We found that WT-activated ILC2s comprised 69% of Cm3 cells and 31% of Cm4 cells, whereas PD-1 KO-activated ILC2s consisted of 47% of Cm3 cells and 53% of Cm4 cells (Fig. 5j). The increase in the frequency of PD-1 KO Cm4 cells is compatible with their higher proliferation score relative to the control WT Cm4 cells (Fig. 5k).
Besides the increased proliferating capacity, the increase of PD-1 KO Cm4 cells’ numbers might be also caused by the enhanced Cm4 cell generation from non-activation cells. We then thought to determine whether PD-1 KO promotes Cm4 cell differentiation. We constructed the activation trajectories of ILC2s by monocle pseudotime analysis . The results showed that activation of WT ILC2 followed a Cm1-Cm2-Cm3-Cm4 order, whereas activation of PD-1 KO ILC2 followed Cm1-Cm2-Cm3 or Cm1-Cm2-Cm4 order (Fig. 5l). In the absence of PD-1, Cm2 cells can directly differentiate into Cm4 cells, which was not detected in WT ILC2s. We thus compared the gene expressions between WT and PD-1 KO Cm2 cells and found that PD-1 KO Cm2 cells had significant enrichments of immune response pathways (Additional file 5, Fig. S3f), demonstrating that PD-1 deficiency places the Cm2 cells in a state prone to activation.
In addition, we observed that fewer activated ILC2 cells were detected at d14 after the challenge in Pdcd1−/− mice (Additional file 5, Fig. S3g), suggesting that PD-1 is involved in regulating the duration of ILC2 activation subsets under acute inflammation.
Taken together, we found that PD-1 is a negative regulator of the Nr4a1+ILC2 subset, where PD-1 modulates the activation, differentiation trajectory, duration, and metabolism.
PD-1 deficiency leads to an upregulation of activation-related regulons in Nr4a1+ILC2s
To investigate the gene regulatory network driving the activation of Nr4a1+ILC2s after PD-1 deletion, we performed SCENIC analysis. The overrepresentation of inflammation response associated regulons such as Irf2 (74 g) and Irf8 (30 g) in PD-1 KO Cm4 cells (Fig. 6a, b and c) were in agreement with the above results that PD-1 restrained the Cm4 activation. The enrichments of metabolic pathways related regulons including Tfeb (24 g)  and Usf1 (20 g)  in PD-1 KO Cm4 cells were compatible with PD-1 regulating the metabolism in Cm4 cells.
Since the deletion of PD-1 had a profound effect on the non-activation Cm2 subset, which resulted in an accelerated activation of the Cm4 cells, we assessed the regulons in the PD-1 KO Cm2 cells. In consistence with the above data (Fig. 5l and Additional file 5, Fig. S3f), the SCENIC analysis identified many regulons that were enriched for the proliferation and inflammatory response in PD-1 KO Cm2 cells (Fig. 6d and e). Altogether, these data show that PD-1 negatively regulates the activation-related regulons in Nr4a1+ILC2s.
ILC2s are the major tissue-resident lymphoid cells in the lung mucosa and play essential roles in its hemostasis and inflammatory diseases. By using scRNA-seq combined with the mouse genetics approaches, we identified four lung ILC2 subsets including two activation subsets and two inactivation subsets, which display distinct transcriptomic signatures and have different dynamic patterns during acute papain intranasal challenges. We further demonstrated that PD-1 actively regulate all those subsets as the deletion of PD-1 profoundly affects both the activation subsets and the non-activation subsets in terms of changing their transcriptome and regulons, which lead to the changes in cell activation state and differentiation trajectories.
An activated cluster, marked by Nr4a1 and only detected on day 7 after the papain challenge, clearly stands out from the others and is featured by several unique signatures. One is that they specifically express high levels of migration associated-genes (e.g., Klf2, S1pr1). ILC2s were initially reported as tissue-resident cells in parabiosis experiments, where the mice were surgically joined to share their bloodstreams and the cell's migratory capacity was examined [63, 64]. However, recent studies in animal models have shown that ILC2s can circulate between organs upon activation by Nippostrongylus brasiliensis infection, protease allergen papain challenge, or systemic IL-33 administration . Particularly, one study showed that ILC2s migrate from the lung to the liver after intranasal IL-33 treatment and those lung-derived liver ILC2s produce more IL-5, IL-13, and IL-6 compared to resident lung ILC2s . In line with this data, the Nr4a1+ILC2 subset, identified in this study, has the highest activation levels compared to other subsets in the lung upon papain intranasal challenge. Considering the high expression of migration-associated genes in Nr4a1+ILC2s, it is very likely that the Nr4a1+ILC2s are the cells that migrate into the livers.
The memory T cells signature of Nr4a1+ILC2s is in keeping with the earlier findings that allergen-experienced ILC2s acquire memory-like properties in the lung and the draining mediastinal lymph node (mLN) . Given the dynamic of the Nr4a1+ILC2 subset in the inflammation course detecting on day 7 and disappearing on day 14 in the lung, we reason that stimulation-induced Nr4a1+ILC2s emigrate from the lung in the early phase and migrate into other organs such as mLN  and liver , where they remain the high expression of IL-5 and IL-13 for a much longer time and regulate the immunity of the organ as demonstrated in the previous study . Moreover, the Nr4a1+ILC2s have paradoxical expressions of Trm and Tem/Tcm signature genes, indicating that Nr4a1+ILC2s exhibit the residency transcriptomic signature imprinted by the lung but gain the migratory transcriptomic signature upon activation, and further confirming both residual residence nature and circulation nature of these cells.
In addition, the wound healing signature of Nr4a1+ILC2s is largely in agreement with their functions in modulating tissue integrity and homeostasis as reported . Given both the residency and migratory natures of the Nr4a1+ILC2s, we speculate that these cells play more active roles in various tissues’ homeostasis under systemic infection or insults.
We found that many genes associated with the signatures mentioned above are the direct targets of Nr4a1, which is the marker gene of this activated subset. Nr4a1 is an orphan nuclear receptor that comprises a central two-zinc DNA-binding domain, an N-terminal transactivation domain, and a C-terminal ligand-binding domain, and works as a transcription factor in a ligand-independent manner [67, 68]. Nr4a1 is considered as an immediate-early gene, whose expression is rapidly and transiently induced by a variety of extracellular signals. Indeed, the expression of Nr4a1 in ILC2 was induced by IL-33 administration in vivo . Two recent studies demonstrated that Nr4a1 plays an essential role in Trm cell generation [70, 71]. Given the similarity of gene expression between Nr4a1+ILC2s and Trm cells, we reason that Nr4a1 is an important regulator for this unique ILC2 activation subset. The function of Nr4a1 and its orchestrating mechanisms in ILC2 activation warrant further investigation.
Our previous study firstly reported that PD-1 is expressed on ILC precursors and mature ILC2s, where PD-1 expression is distributed from 20 to 40% in various mucosa tissues, and PD-1 expression is substantially increased in the lung ILC2s upon influenza virus infection . The following studies demonstrated that PD-1 is a negative regulator of ILC2 function in cytokine production , in Nippostrongylus brasiliensis expulsion , and anti-tumor immunity , and PD-1 also acts as a metabolic checkpoint in ILC2s . In agreement with those data, we found that PD-1 negatively regulates Nr4a1+ILC2s by modulating their activation, proliferation, differentiation trajectory, and metabolism. Of note, besides the negative roles of PD-1 in the Nr4a1+ILC2 subset itself, PD-1 also functions in its ancestors that are non-activation subsets. Loss of PD-1 sets the non-activation ILC2s in a state prone to activation and leads to a change in activation trajectory that the Nr4a1+ILC2s directly differentiated from a non-activation subset without passing another activation subset which has a lower activation score. Moreover, we observed that, in the absence of PD-1, activated ILC2 contraction occurs earlier as much fewer activated PD-1 KO ILC2s were detected at day 14 compared to the activated WT ILC2s after the papain challenge. Thus PD-1 is a negative regulator of ILC2 activation but is a positive regulator of activated ILC2 duration.
In summary, our combined scRNA-seq and genetics mouse analyses of lung ILC2s under acute inflammation enabled the identification of distinct ILC2 subsets in the mouse lung, delineation of lung ILC2 activation trajectories, and characterization of a distinct activation subset marked by Nr4a1, as well as demonstrating the critical role of PD-1 in regulating these subsets. Our study highlights the heterogeneity of lung ILC2s, and the capacity of specific ILC2 subsets to migrate between tissues and organs upon activation. Understanding the molecular mechanisms underlying ILC2 migration will provide a perspective for the development of therapies specifically targeting the migratory population of ILC2s in type 2 immune-mediated diseases.
The findings show that activated ILC2s are a heterogenous population encompassing distinct subsets that have different propensities, and therefore provide an opportunity to explore PD-1's role in modulating the activity of ILC2s for disease prevention and therapy.
The Bcl11btdTomato/tdTomato knock-in reporter mice were previously reported . Pdcd1−/− (028276) mice were purchased from Jackson Laboratory. Pdcd1−/−;Bcl11btdTomato/+ mice were generated by crossing Pdcd1−/− mice to Bcl11btdTomato/tdTomato mice. Bcl11btdTomato/tdTomato and Pdcd1−/−;Bcl11btdTomato/+ mice were used for scRNA-seq experiments that were done in parallel at Sanger Institute. These mice were 6–12 weeks of age at the time of experiments and were maintained at RSF of the Sanger Institute.
Nr4a1eGFPCre/+ mice (016617) and Il5tdTomato/+ (030926) mice were purchased from Jackson Laboratory. Pdcd1−/−;Nr4a1eGFPCre/+, Pdcd1−/−;Il5tdTomato/+ mice were generated by crossing Pdcd1−/− mice to Nr4a1eGFPCre/+ mice or Il5tdTomato/tdTomato mice respectively. Nr4a1eGFPCre/+ mice were crossed with Il5tdTomato/tdTomato to generate Nr4a1eGFPCre/+;Il5tdTomato/+ mice. All mice were 6–12 weeks of age at the time of experiments and at least 3 mice of one genotype were used in each experiment. These mice were housed in the animal facility of Tongji University.
Fluorochrome-labeled monoclonal antibodies (clones denoted in parentheses) against B220 (RA3-6B2), CD19 (6D5), CD3 (145-2C11), CD8 (53–6.7), TCRβ (B20.6), CD49b (DX5), TCRγδ (GL3), NK1.1 (PK136), Nkp46 (29A1.4), CD11b (M1/70), CD11c (N418), Gr1 (RB6-8C5), Ter119 (TER-119), IL-7Rα (SB/199), CD25 (PC61), CD45 (30-F11), IL-33R (RWST2-2), PD-1(RMP1-30), CD69(H1.2F3) were purchased from BD biosciences, BioLegend, or eBioscience.
The mice were anesthetized with 3% isoflurane and then were intranasally administered with papain (40 μg in 40 μl PBS) every day for 4 days. After the last challenge, lungs were collected and sorted or analyzed on day 0, day 7, and day14.
Preparation of cell suspensions
Cells from the lung were prepared according to the instructions of the Lung Dissociation kit (Miltenyi Biotec, 130–095-927). The tissues were digested in a shaking water bath at 37 °C for 12 min.
Flow cytometry assay and cell sorting
Red blood cells were removed using ACK Lysing Buffer (Gibico, A1049201). Cells were suspended in a solution of 1% (vol/vol) FBS in PBS. Before antibody labeling, Fc receptors were blocked with anti-CD16/32 (BD bioscience, 553,142). Cells were stained with antibodies on ice for 15–20 min before washing. Cells were analyzed on the Fortessa X20 cell analyzer (BD bioscience) or sorted on the FACS Aria Fusion cell sorter (BD bioscience) according to the manufacturers’ standard operating procedures. Data were analyzed with FlowJo software, version 10.
Generation of single-cell RNA-seq library
FACS sorted ILC2s from Pdcd1−/− Bcl11btdTomato/+ and Bcl11btdTomato/tdTomato mouse lungs were subjected to single-cell RNA-seq library preparation using smart-seq2 method [46, 73]. Briefly, single ILC2s were sorted into 96-well plates pre-filled with lysis buffer and external RNA spike-ins (Ambion) (1:500,000). First-strand synthesis and template-switching were then performed, followed by 25-cycle of pre-amplification. Complementary DNAs were purified by AMPure XP magnetic beads (Agencourt) using an automated robotic workstation (Zephyr). Quality of cDNAs was checked with the Bioanalyzer (Agilent) using high sensitivity DNA chip. Multiplex (96-plex) libraries were constructed and amplified using Nextera XT library preparation kit (Illumina). The libraries were then pooled and purified with AMPure XP magnetic beads. The quality of the library is then assessed by the Bioanalyzer (Agilent) before submission to the DNA sequencing. Pair-ended 75-bp reads were generated by HiSeq2500 sequencers.
Read alignment and gene quantification
The pre-processed BAM files with the same indexes were first merged and converted to raw FASTQ files. The FASTQ sequences were then realigned to a modified GRCm38 mouse genome. The alignment was performed using STAR  and sorted by SAMtools  for expression quantification by featureCounts .
Cell clustering and visualization
ScRNA-seq analysis was performed using Scanpy (version 1.8.1)  package in Python (version 3.8.8). The data were normalized and scaled to remove unwanted sources of variation according to Scanpy criteria. The normalized data was reduced the dimensionality by running principal component analysis (PCA) in Scanpy, and the first 10 principal components were used for further analysis. Computing neighbors and running Louvain clustering to identify the cell clusters and visualize them. A range of louvain resolutions was tested, and a resolution of 0.5 was used. Scanpy’s Finding marker genes function was used to find differentially expressed genes among subsets. Wilcoxon Ran sum tests were used to determine the significance of the differential expression. The gene expression and cell distribution are visualized using functions from the scanpy package.
Using SingleR to annotate single-cell RNA-seq data
To make sure the accuracy of our data by comparing it with other activated ILC2 datasets. We used Seurat to integrate three activated ILC2 datasets from published papers (GSE122762 ; GSE123994 ; GSE117567 ) [38, 51] and then used integrated data as pre-labeled single-cell reference to annotate our unlabelled dataset by SingleR(1.2.4)(https://www.bioconductor.org/packages/release/bioc/vignettes/SingleR/inst/doc/SingleR.html) in R.
Score for cell characteristics
We used the AUCell package in R to measure the level of activity of a particular collection of genes in each cell of the single-cell transcriptome in order to score each cell's characteristic activity. We begin by importing our processed cluster-labeled data into R. The gene expression ranking in each cell is determined using an expression matrix and the AUCell_buildRankings function with preset settings. Second, based on previously published research, we categorized the gene sets associated with ILC2 activation, wound healing, tissue residence, memory, proliferation, Trm, and Tcm/Tem, and then we scored each cell with various traits. Area under curve (AUC) values were computed for each gene set and cell using the AUCell_calcAUC function based on gene expression ranking. Finally, the ggplot function was used to visualize the score.
Trajectory analysis with Monocle 3
The single-cell trajectory was constructed using the Monocle3 (version 1.2.4) package in R. To determine the effect of PD-1 KO on ILC2 activation trajectory, we used monocle3 to analyze wild-type samples and PD-1 KO samples respectively. First used the preprocess_cds function to process the data, since the processed data were imported, the norm_method parameter was set to "none". And then dimensionality reduction and cluster cells were performed. The function learn_graph is the fourth step in the trajectory building process after preprocess_cds, reduce_dimension, and cluster_cells. After learn_graph, order_cells were called. Pseudotime analysis requires the selection of a cell as an origin for the pseudotime trajectory. Origin assignment was based on the Spearman’s rank assignments for each cell. The following cells were used as origins for their respective cell-type trajectories: C1 and Cm1 ILC2s. Then the orderCells() function was used to assign cells a pseudotime value. Finally, we visualized the findings by plot_cells function.
Gene set enrichment analysis
To identify gene signature enrichment between different samples, we used cluster profile (version 3.18.1) package in R (version4.0.2). All pathway enrichment analyses were performed using the clusterprofile (version 3.18.1) package in R (version4.0.2) to identify gene signature enrichment between different samples, and the enriched pathways were visualized.
SCENIC analysis and visualization graphics
SCENIC  analysis on the scRNA-seq data without separating individual samples was performed, following the workflow using the default parameters in SCENIC R setup. The co-expression network was generated using GENIE3, and potential direct-binding targets (regulons) were based on DNA-motif analysis. And using AUCell identifies and scores gene regulatory networks or regulons in single cell level. The motif bindings were inferred based on publicly available motif binding databases provided by the S. Aerts lab. Following the regulon calculation, the individual cells’ regulon AUC values from 3.4_regulonAUC. The AUC distributions were compared and analyzed for changes in regulon activity between WT cells and PD-1 KO cells. The gene regulatory network map was created by software Cytoscape (version 3.9.1) .
Data were presented as means ± SEM and were analyzed using GraphPad Prism 9 (GraphPad Software). Differences between means were analyzed by one-way ANOVA, Wilcoxon rank sum test, and unpaired T-test were used to determine the statistical significance. In the animal model study samples from individual mice were processed and analyzed separately. The representative data shown in this study represents the result from at least three independent experiments with similar results. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; and ****, P ≤ 0.0001 were considered significant.
Availability of data and materials
The raw scRNA-seq data of this study have been deposited in the European Nucleotide Archive database and are accessible through the accession number PRJEB65049. The trimed sequencing metrics of scRNA-seq data has been deposited on https://github.com/Yuzhang01010101/ILC2.All other relevant data are available from the corresponding author on request.
Group 2 innate lymphoid cells
Tissue-resident memory T cell
Effector/central memory T cell
Programmed cell death protein-1
T-Distributed Stochastic Neighbor Embedding
Differentially expressed genes
Log2 fold change
Vivier E, Artis D, Colonna M, Diefenbach A, Di Santo JP, Eberl G, et al. Innate Lymphoid Cells: 10 Years On. Cell. 2018;174(5):1054–66.
Klose CSN, Artis D. Innate lymphoid cells as regulators of immunity, inflammation and tissue homeostasis. Nat Immunol. 2016;17(7):765–74.
Artis D, Spits H. The biology of innate lymphoid cells. Nature. 2015;517(7534):293–301.
Eberl G, Colonna M, Di Santo JP, McKenzie AN. Innate lymphoid cells. Innate lymphoid cells: a new paradigm in immunology. Science. 2015;348(6237):aaa6566.
Klose CSN, Flach M, Mohle L, Rogell L, Hoyler T, Ebert K, et al. Differentiation of type 1 ILCs from a common progenitor to all helper-like innate lymphoid cell lineages. Cell. 2014;157(2):340–56.
Seehus CR, Aliahmad P, de la Torre B, Iliev ID, Spurka L, Funari VA, et al. The development of innate lymphoid cells requires TOX-dependent generation of a common innate lymphoid cell progenitor. Nat Immunol. 2015;16(6):599–608.
Yang Q, Monticelli LA, Saenz SA, Chi AWS, Sonnenberg GF, Tang JB, et al. T Cell Factor 1 Is Required for Group 2 Innate Lymphoid Cell Generation. Immunity. 2013;38(4):694–704.
Xu W, Domingues RG, Fonseca-Pereira D, Ferreira M, Ribeiro H, Lopez-Lastra S, et al. NFIL3 orchestrates the emergence of common helper innate lymphoid cell precursors. Cell Rep. 2015;10(12):2043–54.
Yang Q, Li F, Harly C, Xing S, Ye L, Xia X, et al. TCF-1 upregulation identifies early innate lymphoid progenitors in the bone marrow. Nat Immunol. 2015;16(10):1044–50.
Zook EC, Ramirez K, Guo XH, van der Voort G, Sigvardsson M, Svensson EC, et al. The ETS1 transcription factor is required for the development and cytokine-induced expansion of ILC2. J Exp Med. 2016;213(5):687–96.
Hoyler T, Klose CS, Souabni A, Turqueti-Neves A, Pfeifer D, Rawlins EL, et al. The transcription factor GATA-3 controls cell fate and maintenance of type 2 innate lymphoid cells. Immunity. 2012;37(4):634–48.
Klein Wolterink RG, Serafini N, van Nimwegen M, Vosshenrich CA, de Bruijn MJ, Fonseca Pereira D, et al. Essential, dose-dependent role for the transcription factor Gata3 in the development of IL-5+ and IL-13+ type 2 innate lymphoid cells. Proc Natl Acad Sci U S A. 2013;110(25):10240–5.
Yagi RJ, Zhong C, Northrup DL, Yu F, Bouladoux N, Spencer S, et al. The Transcription Factor GATA3 Is Critical for the Development of All IL-7R alpha-Expressing Innate Lymphoid Cells. Immunity. 2014;40(3):378–88.
Ferreira ACF, Szeto ACH, Heycock MWD, Clark PA, Walker JA, Crisp A, et al. ROR alpha is a critical checkpoint for T cell and ILC2 commitment in the embryonic thymus. Nat Immunol. 2021;22(2):166-U06.
Wong SH, Walker JA, Jolin HE, Drynan LF, Hams E, Camelo A, et al. Transcription factor ROR alpha is critical for nuocyte development. Nat Immunol. 2012;13(3):229-U43.
Spooner CJ, Lesch J, Yan D, Khan AA, Abbas A, Ramirez-Carrozzi V, et al. Specification of type 2 innate lymphocytes by the transcriptional determinant Gfi1. Nat Immunol. 2013;14(12):1229–36.
Califano D, Cho JJ, Uddin MN, Lorentsen KJ, Yang Q, Bhandoola A, et al. Transcription Factor Bcl11b Controls Identity and Function of Mature Type 2 Innate Lymphoid Cells. Immunity. 2015;43(2):354–68.
Walker JA, Oliphant CJ, Englezakis A, Yu Y, Clare S, Rodewald HR, et al. Bcl11b is essential for group 2 innate lymphoid cell development. J Exp Med. 2015;212(6):875–82.
Yu Y, Wang C, Clare S, Wang JX, Lee SC, Brandt C, et al. The transcription factor Bcl11b is specifically expressed in group 2 innate lymphoid cells and is essential for their development. J Exp Med. 2015;212(6):865–74.
Neill DR, Wong SH, Bellosi A, Flynn RJ, Daly M, Langford TK, et al. Nuocytes represent a new innate effector leukocyte that mediates type-2 immunity. Nature. 2010;464(7293):1367–70.
Monticelli LA, Sonnenberg GF, Abt MC, Alenghat T, Ziegler CG, Doering TA, et al. Innate lymphoid cells promote lung-tissue homeostasis after infection with influenza virus. Nat Immunol. 2011;12(11):1045–54.
Brestoff JR, Kim BS, Saenz SA, Stine RR, Monticelli LA, Sonnenberg GF, et al. Group 2 innate lymphoid cells promote beiging of white adipose tissue and limit obesity. Nature. 2015;519(7542):242–6.
Lee MW, Odegaard JI, Mukundan L, Qiu Y, Molofsky AB, Nussbaum JC, et al. Activated type 2 innate lymphoid cells regulate beige fat biogenesis. Cell. 2015;160(1–2):74–87.
Klose CSN, Mahlakoiv T, Moeller JB, Rankin LC, Flamar AL, Kabata H, et al. The neuropeptide neuromedin U stimulates innate lymphoid cells and type 2 inflammation. Nature. 2017;549(7671):282–6.
Wallrapp A, Riesenfeld SJ, Burkett PR, Abdulnour RE, Nyman J, Dionne D, et al. The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation. Nature. 2017;549(7672):351–6.
Pascal M, Kazakov A, Chevalier G, Dubrule L, Deyrat J, Dupin A, et al. The neuropeptide VIP potentiates intestinal innate type 2 and type 3 immunity in response to feeding. Mucosal Immunol. 2022;15(4):629–41.
von Moltke J, Ji M, Liang HE, Locksley RM. Tuft-cell-derived IL-25 regulates an intestinal ILC2-epithelial response circuit. Nature. 2016;529(7585):221–5.
Schneider C, O’Leary CE, von Moltke J, Liang HE, Ang QY, Turnbaugh PJ, et al. A Metabolite-Triggered Tuft Cell-ILC2 Circuit Drives Small Intestinal Remodeling. Cell. 2018;174(2):271-84 e14.
Halim TY, Hwang YY, Scanlon ST, Zaghouani H, Garbi N, Fallon PG, et al. Group 2 innate lymphoid cells license dendritic cells to potentiate memory TH2 cell responses. Nat Immunol. 2016;17(1):57–64.
Kim BS, Artis D. Group 2 innate lymphoid cells in health and disease. Cold Spring Harb Perspect Biol. 2015;7(5):a016337.
Halim TY. Group 2 innate lymphoid cells in disease. Int Immunol. 2016;28(1):13–22.
Bartemes KR, Kephart GM, Fox SJ, Kita H. Enhanced innate type 2 immune response in peripheral blood from patients with asthma. J Allergy Clin Immunol. 2014;134(3):671-8 e4.
Chen R, Smith SG, Salter B, El-Gammal A, Oliveria JP, Obminski C, et al. Allergen-induced Increases in Sputum Levels of Group 2 Innate Lymphoid Cells in Subjects with Asthma. Am J Respir Crit Care Med. 2017;196(6):700–12.
Smith SG, Chen R, Kjarsgaard M, Huang C, Oliveria JP, O’Byrne PM, et al. Increased numbers of activated group 2 innate lymphoid cells in the airways of patients with severe asthma and persistent airway eosinophilia. J Allergy Clin Immunol. 2016;137(1):75-86 e8.
Stadhouders R, Li BWS, de Bruijn MJW, Gomez A, Rao TN, Fehling HJ, et al. Epigenome analysis links gene regulatory elements in group 2 innate lymphocytes to asthma susceptibility. J Allergy Clin Immunol. 2018;142(6):1793–807.
Zheng H, Zhang Y, Pan J, Liu N, Qin Y, Qiu L, et al. The Role of Type 2 Innate Lymphoid Cells in Allergic Diseases. Front Immunol. 2021;12:586078.
Cosmi L, Liotta F, Maggi L, Annunziato F. Role of Type 2 Innate Lymphoid Cells in Allergic Diseases. Curr Allergy Asthma Rep. 2017;17(10):66.
Ghaedi M, Shen ZY, Orangi M, Martinez-Gonzalez I, Wei L, Lu X, et al. Single-cell analysis of RORalpha tracer mouse lung reveals ILC progenitors and effector ILC2 subsets. J Exp Med. 2020;217(3):e20182293.
Wu X, Kasmani MY, Zheng S, Khatun A, Chen Y, Winkler W, et al. BATF promotes group 2 innate lymphoid cell-mediated lung tissue protection during acute respiratory virus infection. Sci Immunol. 2022;7(67):eabc9934.
Zeis P, Lian M, Fan X, Herman JS, Hernandez DC, Gentek R, et al. In Situ Maturation and Tissue Adaptation of Type 2 Innate Lymphoid Cell Progenitors. Immunity. 2020;53(4):775-92 e9.
Yu Y, Tsang JC, Wang C, Clare S, Wang J, Chen X, et al. Single-cell RNA-seq identifies a PD-1(hi) ILC progenitor and defines its development pathway. Nature. 2016;539(7627):102–6.
Nishimura H, Nose M, Hiai H, Minato N, Honjo T. Development of lupus-like autoimmune diseases by disruption of the PD-1 gene encoding an ITIM motif-carrying immunoreceptor. Immunity. 1999;11(2):141–51.
Helou DG, Shafiei-Jahani P, Lo R, Howard E, Hurrell BP, Galle-Treger L, et al. PD-1 pathway regulates ILC2 metabolism and PD-1 agonist treatment ameliorates airway hyperreactivity. Nat Commun. 2020;11(1):3998.
Moral JA, Leung J, Rojas LA, Ruan J, Zhao J, Sethna Z, et al. ILC2s amplify PD-1 blockade by activating tissue-specific cancer immunity. Nature. 2020;579(7797):130–5.
Taylor S, Huang YF, Mallett G, Stathopoulou C, Felizardo TC, Sun MA, et al. PD-1 regulates KLRG1(+) group 2 innate lymphoid cells. J Exp Med. 2017;214(6):1663–78.
Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014;9(1):171–81.
Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15.
Ghaedi M, Takei F. Single cell RNA sequencing analysis of CD45low/+ lineagelow lung cells from adult and neonatal (12-day-old) RORα lineage tracer mice.GEO. 2019. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122762. Accessed 25 Oct 2019.
Ghaedi M, Takei F. Gene expression analysis of adult lung ILC2s. GEO. 2020. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123994. Accessed 27 Oct 2020.
Ricardo-Gonzalez RR, Van Dyken SJ, Locksley RM. Tissue signals imprint ILC2 identity with anticipatory function [single cell RNA-Seq]. GEO. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117567. Accessed 10 Sept 2018.
Ricardo-Gonzalez RR, Van Dyken SJ, Schneider C, Lee J, Nussbaum JC, Liang HE, et al. Tissue signals imprint ILC2 identity with anticipatory function. Nat Immunol. 2018;19(10):1093–9.
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–72.
Mackay LK, Minnich M, Kragten NA, Liao Y, Nota B, Seillet C, et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science. 2016;352(6284):459–63.
Sebzda E, Zou Z, Lee JS, Wang T, Kahn ML. Transcription factor KLF2 regulates the migration of naive T cells by restricting chemokine receptor expression patterns. Nat Immunol. 2008;9(3):292–300.
Matloubian M, Lo CG, Cinamon G, Lesneski MJ, Xu Y, Brinkmann V, et al. Lymphocyte egress from thymus and peripheral lymphoid organs is dependent on S1P receptor 1. Nature. 2004;427(6972):355–60.
Martinez-Gonzalez I, Matha L, Steer CA, Ghaedi M, Poon GF, Takei F. Allergen-Experienced Group 2 Innate Lymphoid Cells Acquire Memory-like Properties and Enhance Allergic Lung Inflammation. Immunity. 2016;45(1):198–208.
Moran AE, Holzapfel KL, Xing Y, Cunningham NR, Maltzman JS, Punt J, et al. T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse. J Exp Med. 2011;208(6):1279–89.
Aibar S, Gonzalez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083–6.
Huang Y, Mao K, Chen X, Sun MA, Kawabe T, Li W, et al. S1P-dependent interorgan trafficking of group 2 innate lymphoid cells supports host defense. Science. 2018;359(6371):114–9.
Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496–502.
Matye D, Gunewardena S, Chen J, Wang H, Wang Y, Hasan MN, et al. TFEB regulates sulfur amino acid and coenzyme A metabolism to support hepatic metabolic adaptation and redox homeostasis. Nat Commun. 2022;13(1):5696.
Laurila PP, Soronen J, Kooijman S, Forsstrom S, Boon MR, Surakka I, et al. USF1 deficiency activates brown adipose tissue and improves cardiometabolic health. Sci Transl Med. 2016;8(323):323ra13.
Gasteiger G, Fan X, Dikiy S, Lee SY, Rudensky AY. Tissue residency of innate lymphoid cells in lymphoid and nonlymphoid organs. Science. 2015;350(6263):981–5.
Moro K, Kabata H, Tanabe M, Koga S, Takeno N, Mochizuki M, et al. Interferon and IL-27 antagonize the function of group 2 innate lymphoid cells and type 2 innate immune responses. Nat Immunol. 2016;17(1):76–86.
Matha L, Takei F, Martinez-Gonzalez I. Tissue Resident and Migratory Group 2 Innate Lymphoid Cells. Front Immunol. 2022;13:877005.
Matha L, Romera-Hernandez M, Steer CA, Yin YH, Orangi M, Shim H, et al. Migration of Lung Resident Group 2 Innate Lymphoid Cells Link Allergic Lung Inflammation and Liver Immunity. Front Immunol. 2021;12:679509.
Sekiya T. Comparison Between Nr4a Transcription Factor Regulation and Function in Lymphoid and Tumor Treg Cells. Frontiers in Immunology. 2022;13:866339.
Odagiu L, May J, Boulet S, Baldwin TA, Labrecque N. Role of the Orphan Nuclear Receptor NR4A Family in T-Cell Biology. Front Endocrinol (Lausanne). 2020;11:624122.
Schneider C, Lee J, Koga S, Ricardo-Gonzalez RR, Nussbaum JC, Smith LK, et al. Tissue-Resident Group 2 Innate Lymphoid Cells Differentiate by Layered Ontogeny and In Situ Perinatal Priming. Immunity. 2019;50(6):1425-+.
Boddupalli CS, Nair S, Gray SM, Nowyhed HN, Verma R, Gibson JA, et al. ABC transporters and NR4A1 identify a quiescent subset of tissue-resident memory T cells. J Clin Invest. 2016;126(10):3905–16.
Milner JJ, Toma C, Yu BF, Zhang K, Omilusik K, Phan AT, et al. Runx3 programs CD8(+) T cell residency in non-lymphoid tissues and tumours. Nature. 2017;552(7684):253-+.
Batyrova B, Luwaert F, Maravelia P, Miyabayashi Y, Vashist N, Stark JM, et al. PD-1 expression affects cytokine production by ILC2 and is influenced by peroxisome proliferator-activated receptor-gamma. Immun Inflamm Dis. 2020;8(1):8–23.
Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30.
Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19:5.
Aibar S, Gonzalez-Blas CB, Moerman T, Van AHT, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-+.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
We are grateful to our laboratory colleagues for their assistance with experiments and advice. We would like to acknowledge the Frontier Science Center for Stem Cell Research flow cytometry core facility and Tongji Laboratory Animal Research Center.
We would like to acknowledge the Frontier Science Center for Stem Cell Research flow cytometry core facility and Tongji Laboratory Animal Research Center. This work was supported by the Natural Science Foundation of China (31,870,877), and the National Key R&D Program of China (2021YFA1100903, 2021YFA1100900). Z.M. is supported by the Natural Science Foundation of China (32,270,707), the National Key R&D Program of China (2021YFF1200900, 2021YFF1200903), R&D Program of Guangzhou Laboratory, Grant No. SRPG22-003, SRPG22-006, SRPG22-007.
Ethics approval and consent to participate
All the mice used for scRNA-seq were from colonies maintained at RSF of the Sanger Institute. The housing and breeding of mice and experimental procedures were according to the UK 1986 Animals Scientific Procedure Act and local institute ethics committee regulations.
All the mice used for FACS analysis were accommodated in the Tongji University animal care facility and were maintained under pathogen-free conditions. All the experiments were approved and conducted following the guidelines of the Animal Care Committee of Tongji University under approval number TJAB03423101.
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All authors have given consent for publication.
The authors declare that they have no competing interests.
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Quality control of scRNA-seq data and the enrichment of pathways in C2 subsets. a. FACS sorting strategies of ILC2s for scRNA-seq analysis (samples from 2 mice at each timepoint of WT or PD-1 KO mice were mixed). b-c. The violin plot showing the gene numbers, count numbers, mitochondrial ratios, and ERCC ratios of WT and PD-1 KO samples. The ERCC ratio cut-off is 25%. The mitochondrial ratio cut-off is 10%. Cells with sequencing depths greater than 2, 000 and number of genes greater than 1, 000 were used for further analysis. d. t-SNE plots showing T cell maker genes expressions. e-f. The bar chart shows the GO (e) and KEGG (f) pathways analysis of DEGs (P value <0.05 and log2FC >1) in C2 compared with C1.
The names of 20, 98, 257, and 243 signature genes in C1, C2, C3, and C4 respectively, which matched Fig. 1h.
The transcriptomic signature of C3 cells. a. The top 20 DEGs in C4 and C3 cells respectively. b-c. The bar chart shows the GO (b) and KEGG (c) pathways analysis of DEGs (P value <0.05 and log2FC >1) in C3 compared with C4.
The PD-1 deletion effects on ILC2. a. t-SNE plot showing the distributions of WT cells in the re-clustered integrated data (341 WT and 362 KO). b. Stacked violin plots showing the distributions of expression of ILC2-associated genes. The y-axis indicates the log2 (normalized count + 1) expression levels. c. The bar chart shows the KEGG pathways analysis of DEGs (P value <0.05 and log2FC >1) in Cm4 of PD-1 KO compared with WT. d. GSEA analysis of DEGs between PD-1 KO versus WT Nr4a1+ILC2s (p<0.05). e. The bar chart shows the GO (left) and KEGG (right) pathways analysis of DEGs (P value <0.05 and log2FC >1) in the non-activation cells between PD-1 KO and WT. f. The bar chart shows the GO (left) and KEGG (right) pathways analysis of DEGs (P value <0.05 and log2FC >1) in the Cm2 between PD-1 KO and WT. g. Histogram showing the composition of the clusters within different time points samples.
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Xu, S., Zhang, Y., Liu, X. et al. Nr4a1 marks a distinctive ILC2 activation subset in the mouse inflammatory lung. BMC Biol 21, 218 (2023). https://doi.org/10.1186/s12915-023-01690-3