# The transcription factor NFIL3 drives innate lymphoid cell specification from lymphoid progenitors

Joseph Léger, Eva Artano, Delphine Coulais, Naomie Belletoise, Raoudha

Fadhloun, Devin Kenney, Avinash Bhandoola, Christelle Harly

► To cite this version:

Joseph Léger, Eva Artano, Delphine Coulais, Naomie Belletoise, Raoudha Fadhloun, et al.. The tran-
scription factor NFIL3 drives innate lymphoid cell specification from lymphoid progenitors. Immunity,
2025, 10.1016/j.immuni.2025.10.005 . hal-05348242

HAL Id: hal-05348242

https://hal.science/hal-05348242v1

Submitted on 5 Nov 2025

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**Title:** The transcription factor NFIL3 drives innate lymphoid cell specification from lymphoid progenitors

**Authors:** Joseph Léger¹, Eva Artano¹, Delphine Coulais¹, Naomie Belletoise¹, Raoudha Fadhloun¹, Devin Kenney²†, Avinash Bhandoola²‡*, Christelle Harly¹,²,³‡*

**Affiliations:**

¹Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, 44007 Nantes, France

²Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

†Present address: Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA

³Lead contact: christelle.harly@cnrs.fr

‡Senior authors

*Correspondence: avinash.bhandoola@nih.gov (AB); christelle.harly@cnrs.fr (CH)

**SUMMARY**

Understanding the mechanisms that initiate innate lymphoid cell (ILC) specification may reveal how ILC functions in immunity and tissue homeostasis are programmed. Using an *Il7r*-lineage tracing mouse strain, we identified developmental intermediates between lymphoid progenitors and *Tcf7**+ ILC progenitors in the mouse bone marrow (BM). Transcriptional analysis of these intermediates identified very early expression of the transcription factor NFIL3. *Nfil3*−/− mice lacked all BM ILC-specified precursors. Forced expression of NFIL3 in lymphoid progenitors
induced Tcf7+ ILC-lineage cells capable of generating all ILC subsets and conventional natural killer cells. Transient activity of NFIL3 in lymphoid precursors recapitulated BM ILC development *in vitro*, leading to the generation of all adult ILC. Mechanistically, NFIL3 initiated ILC specification by inducing expression of key transcriptional regulators of ILC development - *Tox*, *Id2*, *Gata3*, *Tcf7*, and *Zbtb16*. Our findings indicate an apex role for NFIL3 in ILC specification and provide an *in vitro* approach to generate ILC-lineage cells with potential therapeutic utility.

## INTRODUCTION

Innate lymphoid cells (ILC) are strategically located at barrier sites and in tissues, constituting a first line of defense of the immune system. They play overlapping functions with T cells, but additionally fulfill unique immune and non-immune functions in tissue homeostasis and repair¹. Efforts to evaluate ILC potential to treat pathologies are underway². Identifying the factors that initiate ILC specification and understanding their mechanism of action may give insight into how ILC functions are programmed during development. This knowledge might be used to manipulate ILC functional programming for therapeutic purposes.

In adult mice, ILC development occurs continuously in the bone marrow (BM), and this process contributes to replenish ILC compartments in tissues throughout life³. ILC precursors, characterized by the expression of the transcription factor TCF-1 (encoded by *Tcf7*)⁴, arise from all-lymphoid progenitors (ALP)⁵ that give rise to all lymphocytes⁶. Early ILC-specified precursors (sEILP) retain the ability to generate dendritic cells (DC)⁷, whereas downstream ILC precursors (cEILP and ILCP) characterized by the expression of PLZF (encoded by *Zbtb16*) have lost this ability, and are committed to the ILC fate⁷,⁸.

Several transcription factors play important functions during ILC development, namely NFIL3⁹⁻¹⁵, TOX⁵,⁷,¹⁶, ID2⁴,¹⁷, GATA-3⁵,¹⁷, TCF-1⁴,⁷,¹⁸, and PLZF⁵,⁸. However, their precise
functions remain to be defined. TCF-1 is required at ILC-commitment, to induce ILC-genes
such as *Zbtb16* and repress DC lineage genes<sup>7,19</sup>. GATA-3 regulates *Tcf7*<sup>20</sup> and cooperates with
TCF-1 to enforce ILC-commitment<sup>19</sup>. ID2 inhibits the activity of E-proteins, thereby
contributing to the loss of T and B cell potential<sup>21,22</sup>. Requirements for TOX and NFIL3 are less
understood as very few ILC precursors are detected in deficient mice, making analysis
difficult<sup>5,7,11,12,16</sup>.

Here we studied the initiation of ILC lineage specification in mouse BM. We
characterized developmental intermediates between ALP and Tcf7+ ILC precursors, and
established that the proline and acidic amino-acid-rich (PAR)-like basic leucine zipper (bZIP)
transcription factor Nuclear Factor, Interleukin 3 Regulated (NFIL3) acts upstream of other
transcription factors associated with ILC development. When forcibly expressed in vitro in
ALP, NFIL3 triggered a developmental process that recapitulated the steps of ILC development
characterized in mouse BM and drove the generation of all canonical helper ILC lineages and
conventional Natural Killer cells (cNK). Analysis of transcriptional and epigenetic changes
following increased expression of NFIL3 during ILC development and characterization of
NFIL3 genome-wide binding in ILC precursors revealed the mechanism by which NFIL3 drives
ILC specification from ALP.

RESULTS

Identification of precursors that initiate ILC specification

ILC precursors arise from IL-7Ra⁺ lymphoid precursors ALP<sup>5,23</sup>, but expression of IL-7Ra
decreases at early stages of ILC development<sup>4</sup>. To identify precursors that initiate ILC
specification, we devised a minimally-biased gating strategy to isolate early precursors arising
from ALP, regardless of their expression of IL-7Ra. We used the *Il7r* lineage-tracer mouse
strain in which YFP expression is permanently triggered at ALP stage (*Il7r*-iCre R26-stop-
YFP<sup>5,24</sup> to define a population called Il7r<sup>LT+</sup> (Figure 1A). Il7r<sup>LT+</sup> included the YFP<sup>+</sup> fraction of ALP (in the Il7r<sup>LT+</sup>α4β7<sup>lo</sup> population) and the Flt3<sup>hi</sup> fraction of the earliest known ILC-specified precursor (Flt3<sup>hi</sup> sEILP, called Il7r<sup>LT+</sup>Tcf7<sup>+</sup>), and should include all developmental intermediates between these two populations. Consistently the Il7r<sup>LT+</sup>α4β7<sup>hi</sup> population generated NK1.1<sup>+</sup> and ICOS<sup>hi</sup> ILC in larger numbers than Il7r<sup>LT+</sup>Tcf7<sup>+</sup> upon culture (Figure 1B), indicating that it may include ILC-specified precursors upstream of sEILP. We investigated whether this Il7r<sup>LT+</sup>α4β7<sup>hi</sup> population could be isolated without the Il7r<sup>+</sup> lineage-tracer. However, the α4β7<sup>hi</sup> population (Figure 1C) was composed of only about 20% of Il7r<sup>LT+</sup> cells (Figure 1D), and we could not identify surface markers specific for either the Il7r<sup>LT+</sup> or the Il7r<sup>LT-</sup> cells within the α4β7<sup>hi</sup> population (Figure S1A,B).

We analyzed the Il7r<sup>LT+</sup> and α4β7<sup>hi</sup> populations by single-cell RNA-sequencing (scRNA-seq), and integrated them to previously characterized ILC precursor populations<sup>7</sup>. Mature B cells that had been spiked in each sample to validate the integration step were removed from further analysis, together with other contaminating populations (Figure S1C,D). Il7r<sup>LT+</sup> and α4β7<sup>hi</sup> cells spanned the gap between ALP and Tcf7<sup>+</sup> ILC precursors, and Il7r<sup>LT+</sup> cells additionally overlapped with ALP and with Tcf7<sup>+</sup> ILC precursors (Figure 1E). Pseudotemporal reconstruction identified two developmental trajectories (Figure 1F), and key genes identified the start and end points of each trajectory (Figure 1G): one trajectory was from Flt3<sup>hi</sup> ALP to Bcl11b<sup>+</sup> ILC and another one from Flt3<sup>hi</sup> ALP to Cd74<sup>+</sup> DC. Tcf7<sup>+</sup> and Zbtb16<sup>+</sup> identified the known successive ILC precursor stages: the Tcf7<sup>+</sup>Flt3<sup>hi</sup>Zbtb16<sup>lo</sup> sEILP, and the ILC-committed Tcf7<sup>+</sup>Flt3<sup>lo</sup>Zbtb16<sup>hi</sup> cEILP and ILCP (Figure 1G)<sup>7,8</sup>. Our analysis is thus consistent with characterized stages of ILC development and the developmental bifurcation toward DC fate of ILC precursors before ILC commitment<sup>7</sup>. It further confirmed the composition of the Il7r<sup>LT+</sup> and the α4β7<sup>hi</sup> populations: the Il7r<sup>LT+</sup> population included ALP, an early fraction of sEILP, and developmental intermediates between these two populations. We found that 85.6%
of the *Il7r*<sup>LT+</sup> cells were included in the ILC developmental trajectory (918 out of 1073 cells). However, the α4β7<sup>hi</sup> population included the α4β7<sup>hi</sup> *Tcf7*<sup>-</sup> fraction of the *Il7r*<sup>LT+</sup> population, together with diverse contaminants (Figures S1C,D and 1H). Only 19% of the α4β7<sup>hi</sup> cells were included in the ILC developmental trajectory (61 out of 320 cells), which was consistent with the low *Il7r* lineage tracing of this population (Figure 1D). The *Il7r* lineage-tracer mice thus enabled us to isolate developmental intermediates between ALP and *Tcf7*<sup>+</sup> ILC-specified precursors (Figure 1H).

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## Identification of candidate controllers of ILC specification

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To identify transcription factors expressed at the initiation of ILC specification, we examined transcriptional changes along the ILC developmental trajectory (Figure 1F). This analysis grouped 75 variably expressed transcription factors into 8 clusters according to their expression patterns during ILC specification (Figure 2A). Four clusters of upregulated genes included many known controllers of ILC development and maturation (Clusters 1-4; Figure 2A), including all known controllers of early ILC development, namely *Tox*, *Id2*, *Gata3*, *Tcf7*, and *Zbtb16* (Cluster 1; Figure 2A). One cluster of transiently upregulated genes included *Nfil3*, which plays important functions in BM ILC development<sup>9-15</sup>, and several genes whose functions during ILC specification remain to be investigated (Cluster 5, Figures 2A and S1E). Three clusters included downregulated genes (Clusters 6-8; Figure 2A).

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Transcription factors that drive lineage specification might confer *de novo* chromatin accessibility, enabling activation of lineage-specific programs<sup>25-27</sup>. We therefore re-analyzed DNase-seq data on ALP, EILP, and ILCP<sup>7</sup>. 3436 regions that opened from the ALP to EILP stages were split into three categories depending on their chromatin configuration in ILCP (Figure S2A,B), and DNA motif enrichment was determined for each category. Regions that were stably open or progressively opened were not enriched with motifs of interest<sup>19</sup>. Regions
that transiently opened in EILP presented motif enrichment for transcription factors that are
important during hematopoiesis and not specific for ILC development (ETS, RUNX and IRF
families; Figure 2B, S2C). Additionally, these regions showed enrichment for binding motifs
shared by transcription factors of the bZIP family, including motifs specific for the PAR bZIP
subfamily (Figure 2B). Our scRNA-seq analysis identified the PAR-like bZIP factor Nfil3 to be
upregulated at the initiation of ILC specification (Cluster 5; Figure 2A). RNA-seq analysis of
ILC precursors⁷ confirmed this finding, and identified three additional bZIP factors
significantly upregulated from ALP to sEILP: Cebpa, Creb3l2, and Batf3 (Figure 2C, S2D). A
similar analysis was performed on the 642 regions that closed from ALP to EILP (Figure
S2A,B,E). Our analysis thus identified transcription factors that may have functions in
chromatin opening during early ILC specification.

The transcription factors NFIL3<sup>9-15</sup>, TOX<sup>5,7,16</sup>, ID2<sup>4,17</sup>, GATA-3<sup>5,17</sup>, TCF-1<sup>4,7,18</sup>, and
PLZF<sup>5,8</sup> are expressed during early ILC development (Figure 2D) and play important roles
during this process. Using our scRNA-seq dataset of ILC development, we inferred regulatory
interactions between them, using the Lag-based Expression Association for Pseudotime series
(LEAP) algorithm<sup>28</sup>. We imposed a gap in correlation to consider delays between the expression
of a controller and its targets, and to exclude correlations that would be caused by gene
coregulation (Figure 2E, Table S1). Our model concurred with known regulations, namely the
regulation of *Id2*<sup>12</sup> and *Tox*<sup>11</sup> by NFIL3 during specification, and the regulation of *Id2* and
*Gata3* by TCF-1 at commitment<sup>7</sup>, and it predicted additional interactions. In particular, our
model predicted that *Nfil3* regulated expression of all other key transcription factors. *Nfil3* was
highly expressed at the initiation of ILC specification, before other key controllers (Figure 2D),
and its DNA binding motif was enriched in chromatin regions that opened at ILC specification
(Figure 2B). These observations are consistent with mechanisms used by transcription factors
that drive lineage specification through epigenetic regulation<sup>25-27</sup>.
NFIL3 is required for the initiation of ILC specification, upstream of TOX

To investigate the precise stage of requirement for NFIL3 during ILC specification, we quantified ILC precursors in BM of *Nfil3<sup>-/-</sup> Tcf7<sup>EGFP</sup>* mice. Because ALPs, as initially defined<sup>6</sup>, may include α4β7<sup>+</sup> ILC-specified precursors that express and require NFIL3<sup>29</sup>, we used a refined definition that excluded such subsets<sup>7</sup>. α4β7<sup>lo</sup> ALPs were not significantly impacted by NFIL3 deficiency, whereas EILPs and ILCPs<sup>5,7</sup> were greatly reduced or absent (Figure 3A). Because α4β7 may not be properly expressed in *Nfil3<sup>-/-</sup>* mice<sup>29</sup>, we quantified EILPs and ILCPs without using this marker<sup>5</sup> (Figure S3), and confirmed that TCF-1-expressing ILC-specified precursors failed to develop in the absence of NFIL3. To further define the initial requirement of NFIL3 during ILC specification, we isolated *Il7r<sup>LT+</sup>* BM cells from *Nfil3<sup>-/-</sup> Il7r-iCre R26-stop-YFP* mice (Figures 3B) and analyzed them by scRNA-seq together with the previous WT scRNA-seq samples (Figure 3C). By examining the fate of *Nfil3<sup>-/-</sup>* cells along the ILC developmental trajectory (Figure 3D), we found that *Nfil3<sup>-/-</sup>* lymphoid precursors failed to progress beyond the point when WT cells upregulate *Nfil3* expression (Figure 3E). NFIL3 is thus essential for the initiation of ILC specification.

TOX plays important functions during early ILC development<sup>5,7,16</sup>, but its precise stage of requirement is not known. ILC precursors, including early TCF-1<sup>+</sup> ILC precursors, are almost absent in *Tox<sup>-/-</sup>* mice<sup>5,7,16</sup>. Analysis of the *Il7r<sup>LT+</sup>* population suggested that α4β7<sup>hi</sup> ILC precursors were still present in *Tox<sup>-/-</sup> Il7r-iCre R26-stop-YFP* mice but did not properly upregulate *Tcf7* (Figure 3F). Analysis of *Tox<sup>-/-</sup> Il7r<sup>LT+</sup>* by scRNA-seq together with WT and *Nfil3<sup>-/-</sup> Il7r<sup>LT+</sup>* confirmed that small numbers of ILC-specified precursors were present in *Tox<sup>-/-</sup>* mice (Figure 3G,H)<sup>5,7</sup>. Comparison of these precursors with WT ILC-specified precursors at similar developmental stages further showed that *Nfil3* was properly induced in *Tox<sup>-/-</sup>* ILC-
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specified precursors (Figure 3I). Thus, NFIL3 is required for the generation of all BM ILC-
specified precursors, prior to TOX requirement.

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NFIL3 promotes the generation of ILC-lineage cells from ALP

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NFIL3 regulates *Id2*<sup>12</sup> and *Tox*<sup>11</sup>, and we predicted that it induces expression of *Tcf7*, *Gata3* and *Zbtb16* (Figure 2E). Because the lack of BM ILC-specified precursors precluded the analysis of NFIL3 function in *Nfil3<sup>-/-</sup>* mice, we alternatively investigated the effect of ectopically expressing NFIL3 in BM ALP using the Murine Stem Cell Virus (MSCV) retroviral system<sup>30</sup>. ALP isolated from *Tcf7<sup>YFP</sup>* mice (Figure S4A-B) were transduced with *Nfil3*-expressing or control retrovirus. After 2 days in culture on BM stromal layers in the presence of stem cell factor, Flt3-ligand and interleukin (IL)-7, ALP transduced with *Nfil3*-expressing virus generated Mac-1<sup>-</sup>*Tcf7<sup>+</sup>* cells, that reached about 30% of all cells after 3-4 days in culture (Figure 4A). Only ~1% of cells derived from ALP transduced with control virus were *Tcf7<sup>+</sup>* (Figure 4A). By considering that the frequency of transduced ALP measured at day 2 (when expression of the reporter was optimal) was similar to day 0, we estimated that each *Nfil3*-transduced ALP generated 4 Mac-1<sup>-</sup>*Tcf7<sup>+</sup>* cells by day 4 (Figure 4B).

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As *Tcf7* is induced by Notch signaling during T cell development<sup>20,31,32</sup>, we investigated
whether Notch signaling was involved in induction of *Tcf7* by NFIL3. We compared *Tcf7<sup>+</sup>* cells
induced by *Nfil3* with *Tcf7<sup>+</sup>* T cell precursors induced by retroviral expression of the
constitutively active intracellular domain of Notch1 (*Icn1*)<sup>33</sup> in ALP (Figure 4C). Expression
of the canonical Notch target *Dtx1* was undetectable in *Nfil3*-induced *Tcf7<sup>+</sup>* cells (Figure 4D)
suggesting that these cells were not receiving Notch signals. Furthermore, co-transduction of a
retrovirus encoding for dominant negative mastermind-like (*Dnmaml*) that inhibits Notch
signaling<sup>34</sup> had no effect on *Tcf7* expression by *Nfil3*-transduced ALP (Figure 4E). Therefore,
NFIL3 induces *Tcf7* expression independently of Notch signaling. Conversely, we examined

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whether NFIL3 contributed to *Tcf7* induction downstream of Notch signaling during T cell development. *Nfil3* was not induced by *Icn1* transduction (Figure 4D), and *Nfil3* deficiency did not affect *Tcf7* induction by *Icn1* transduction (Figure 4F). Furthermore, T cell development was not affected by *Nfil3* deficiency *in vivo* (Figure S5)³⁵. Therefore, *Tcf7*⁺ cells are induced by NFIL3 and Notch signaling through distinct mechanisms.

We further compared *Tcf7*⁺ cells induced by *Nfil3* with *Tcf7*⁺ T cell precursors induced by *Icn1*. Both *Nfil3*- and *Icn1*-induced *Tcf7*⁺ cells expressed transcription factors that are shared between T- and ILC-lineage cells (*Tcf7*, *Gata3*, *Hes1*; Figure 4D). However, *Nfil3*-induced *Tcf7*⁺ cells lacked expression of key T cell and adaptive lymphocyte genes (*pTa*, *Rag2*, *Rag1*; Figure 4D) and consistently, did not generate *Thy1.2*<sup>hi</sup>*CD25*<sup>+</sup> T-lineage cells<sup>31</sup> (Figure 4G). Instead, *Nfil3*-induced *Tcf7*⁺ cells highly expressed the ILC transcription factors *Tox*, *Id2*, and *Zbtb16* that are minimally expressed by T cell precursors (Figure 4D). Therefore, consistent with our predictions, NFIL3 expression in ALP promoted expression of all known key ILC genes, namely *Tox*, *Id2*, *Gata3*, *Tcf7*, and *Zbtb16*.

We investigated whether the *Nfil3*-induced *Tcf7*⁺ cells were ILC-lineage cells by examining their expression of NK1.1 and ICOS, which are expressed by cNK and helper ILC generated in culture⁸. At day 2 after transduction with *Nfil3*-expressing virus, the Mac-1⁻ progeny of ALP lacked NK1.1 or ICOS (Figure 4H). At day 4, both markers were expressed on a small fraction of *Tcf7*⁺ cells, but undetectable on *Tcf7*⁻ cells (Figure 4H). We isolated the Mac-1⁻*Tcf7*⁺ and *Tcf7*⁻ cells obtained at day 4 and cultured them for 4 additional days. *Tcf7*⁺ cells gave rise to a large fraction of NK1.1⁺ cells and ICOS<sup>hi</sup> cells, whereas Mac-1⁻*Tcf7*⁻ cells did not, similar to ALP transduced with a control virus (Figure 4I). NFIL3 expression in BM ALP thus rapidly induced the generation of *Tcf7*⁺ cells that expressed ILC transcription factors and gave rise to cells that express the ILC markers NK1.1 and ICOS.
NFIL3 promotes the generation of all adult ILC lineages from BM and FL ALP

We further characterized the cells generated from NFIL3-transduced ALP. A large fraction of the ICOS<sup>hi</sup> cells expressed higher CD25 and GATA-3 as compared to NK1.1<sup>+</sup> cells (Figure 5A) and produced TNF-α and IL-13 when activated with Phorbol-12-myristate-13-acetate (PMA) and ionomycin (Figure 5B). A subset of these cells additionally produced IL-5 when cultured in the presence of Notch ligand, IL-25 and IL-33 for 3 days before PMA and ionomycin stimulation (Figure 5C). These ICOS<sup>hi</sup> cells thus largely corresponded to ILC2<sup>36,37</sup>. Additionally, a small subset of ICOS<sup>hi</sup> cells co-expressed the transcription factor RORγt and α4β7 (Figure 5A), indicating they were ILC3. Consistently, a small fraction of ICOS<sup>hi</sup> cells produced IL-17A when activated with PMA and ionomycin (Figure 5B)<sup>36</sup>. IL-22<sup>+</sup> cells were not detected. NK1.1<sup>+</sup> cells produced TNF-α and IFN-γ when activated with PMA and ionomycin (Figure 5B). To investigate whether these NK1.1<sup>+</sup> cells corresponded to cNK or ILC1, we injected Nfil3-induced Tcf7<sup>+</sup> cells or control-transduced cells into NOD scid gamma deficient (NSG) mice, that lack ILC and support the development of exogenous ILC. After 4 weeks, Nfil3-induced Tcf7<sup>+</sup> cells, but not control-transduced cells, generated progeny in spleen and liver (Figure 5D). These almost exclusively corresponded to TCR<sup>-</sup>NK1.1<sup>+</sup>NKP46<sup>+</sup> cells (Figure 5E), and included spleen DX5<sup>+</sup> cNK and liver VLA1<sup>+</sup> ILC1 (Figure 5F)<sup>36</sup>. Altogether, our data indicate that Nfil3-induced Tcf7<sup>+</sup> cells can give rise to all adult ILC, namely cNK, ILC1, ILC2, and ILC3.

An NFIL3 requirement during early ILC development in fetal liver (FL) has not been investigated in detail. Consistent with previous observations<sup>12</sup>, we found that TCF-1<sup>+</sup>ROR-γt<sup>+</sup> LTi-lineage cells were present in normal numbers in the FL of NFIL3 deficient E18.5 embryos (Figure S6A). However, TCF-1<sup>+</sup>ROR-γt<sup>-</sup> non-LTi ILC precursors were reduced more than 10-fold (Figure S6A). NFIL3 thus plays crucial functions in ILC specification in FL but is not required for LTi development.
We examined the effect of forcing expression of NFIL3 in FL ALP *in vitro*. Nfil3-transduced FL ALP generated *Tcf7*-expressing cells by day 4 (Figure S6B), that upregulated expression of NK1.1 and ICOS at later time points (Figure S6C). ICOS<sup>hi</sup> cells produced the ILC2 cytokines IL-5, IL-13 and TNF-α and the ILC3 cytokines IL-17A and IL-22 upon activation (Figure S6D). NK1.1<sup>+</sup> cells produced the cNK and ILC1 cytokines TNF-α and IFN-γ (Figure S6D). Therefore, ectopic expression of NFIL3 in FL ALP *in vitro* drives the generation of all canonical adult ILC, namely cNK, ILC1, ILC2, and ILC3; and NFIL3 plays similar function in BM and FL.

Several transcription factors have been proposed to drive ILC generation *in vitro* from populations of hematopoietic precursors (ID2, GATA-3, BATF)<sup>12,38,39</sup>. We thus investigated whether forced expression of these factors in ALP could promote ILC generation similar to NFIL3. *Gata3* and *Id2* are upregulated from ALP to sEILP stage (Figure S7A,B) and are required during early ILC development<sup>4,5,17</sup>. TCF-1 is not properly induced in ILC precursors of GATA-3 deficient mice<sup>20</sup>, and PLZF expression was reduced in TCF-1<sup>+</sup> EILP that still developed in these mice (Figure S7C). However, retroviral expression of *Gata3* in ALP did not induce upregulation of *Tcf7* (Figure S7D). *Id2* expression in ALP induced a small subset of *Tcf7*-expressing cells (Figure S7D). However, it did not promote the generation of NK1.1<sup>+</sup> and ICOS<sup>hi</sup> ILC (Figure S7E), and had almost no effect on NFIL3 deficient ALP (Figure S7F). Ectopic expression of *Id2* induces PLZF<sup>+</sup> cells<sup>12</sup>, however, this largely depended on concomitant Notch signaling (Figure S7G), which is not required for ILC development<sup>40,41</sup>. Previous work found that *Batf* was upregulated in the heterogeneous αLP subset compared to lymphoid precursors<sup>39</sup>. However, transcriptional profiling of ILC precursors did not show upregulated *Batf* expression during early ILC development (Figure S7A,B). Instead, the related factor *Batf3* was weakly upregulated from ALP to sEILP (Figures 2C and S2D). Neither *Batf* nor *Batf3* induced ILC specification when ectopically expressed in ALP (Figure S7H).
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Therefore, of the transcription factors we assessed, only NFIL3 was sufficient to drive ILC generation when forcibly expressed in ALP *in vitro*.

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Transient NFIL3 activity in ALP drives ILC development in vitro

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We asked whether NFIL3 promotes the generation of ILC-lineage cells by expanding pre-existing ILC-specified cells that could be included within ALP. However, ALP isolated from *Nfil3*⁻/⁻ mice, which lack all ILC-lineage cells in BM, efficiently generated *Tcf7*⁺ cells when transduced with a *Nfil3*-expressing retrovirus (Figure 6A, S8A). Furthermore, *Nfil3*-induced *Tcf7*⁺ cells generated NK1.1⁺ and ICOS<sup>hi</sup> ILC-lineage cells in similar numbers, whether they were derived from *Nfil3*⁺/⁺ or *Nfil3*⁻/⁻ ALP (Figure 6B). Therefore, NFIL3 does not promote the generation of ILC solely by expanding ILC-specified cells.

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We characterized the *Tcf7*⁺ cells that arose from *Nfil3*-transduced ALP at 4 days of culture by RNA-seq, and compared them with control-transduced ALP and *ex vivo* isolated ILC precursors. Transduction with *Nfil3*-expressing retrovirus induced *Nfil3* expression to similar levels as observed during ILC development *in vivo* (Figure S8B), and *Nfil3*-induced *Tcf7*⁺ cells were transcriptionally close to ILCP (Figures 6C and S8C). Examination of transcription factors variably expressed during ILC development⁷ confirmed that *Nfil3*-induced *Tcf7*⁺ cells were transcriptionally distinct from ALP and control GFP-transduced ALP, and similar to the ILC-committed precursors cEILP and ILCP (Figure 6D).

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We characterized populations deriving from *Nfil3*-transduced ALP at different time points in culture. Two days after *Nfil3* transduction, about 30% of Mac-1⁻ cells expressed TCF-1 (Figure 6E). Within TCF-1⁺ cells, the frequency of PLZF⁺ cells was initially low, and gradually increased until reaching more than 50% at day 4 (Figure 6E). At later time points, this frequency decreased, and the cells became largely TCF-1⁺PLZF⁻ (Figure 6F), which coincided with the appearance of ILC cell surface markers NK1.1⁺ and ICOS<sup>hi</sup> (Figures 6G).

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These expression changes mirrored normal BM ILC development, that proceeds through early TCF-1<sup>+</sup>PLZF<sup>-</sup> sEILP, later ILC-committed precursors TCF-1<sup>+</sup>PLZF<sup>+</sup> cEILP and ILCP, and finally, NK1.1<sup>+</sup> or ICOS<sup>hi</sup> ILC that downregulated PLZF expression<sup>5,7,8</sup>. Our work indicates that NFIL3 upregulation in ALP triggers a developmental process that recapitulates *in vitro* the hallmarks of BM ILC development.

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NFIL3 is transiently required during BM ILC development<sup>9,12,29</sup>. We examined whether transient activity of NFIL3 is sufficient to drive ILC development from ALP *in vitro*. We generated a retroviral construct to express an NFIL3-ER-T2 fusion that enabled inducible and reversible control of NFIL3 activity<sup>42</sup>. Consistent with constitutive trapping in the cytoplasm, NFIL3-ER-T2 did not drive ILC development from ALP (Figure S8D). Addition of 4-hydroxytamoxifen (4-OHT) to the culture media, allowing NFIL3-ER-T2 to translocate to the nucleus, induced the development of ALP into Tcf7<sup>+</sup> cells within 4 days, similar to WT NFIL3 (Figure S8D). These cells further generated NK1.1<sup>+</sup> and ICOS<sup>hi</sup> ILC *in vitro* after 4-OHT withdrawal, in numbers comparable to Tcf7<sup>+</sup> cells that constitutively expressed NFIL3 (Figure 6H). Nfil3<sup>-/-</sup> ALP also generated ILC after transient activation of NFIL3-ER-T2 (Figure 6H), indicating that the effect of NFIL3-ER-T2 did not rely on endogenous NFIL3. Finally, Tcf7<sup>+</sup> cells generated by transient activation of NFIL3-ER-T2 in ALP were able to give rise to ILC *in vivo* upon transfer into NSG mice (Figure 6I, S8E), and generated similar numbers of ILC whether they were originated from Nfil3<sup>+/-</sup> or Nfil3<sup>-/-</sup> ALP (Figure 6I). Thus, transient activity of NFIL3 triggers ILC development from ALP.

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## NFIL3 initiates ILC specification independently of TOX

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We investigated NFIL3 mechanism of action during ILC development. All genes that were regulated in the same direction downstream of NFIL3 *in vitro* and *in vivo* were considered as potential NFIL3 gene targets (Figure S9A,B). They included 72 upregulated candidates strongly
enriched for genes involved in lymphocyte development and function (Figure S9C) such as *Tox*, *Id2*, *Gata3*, *Tcf7*, and *Zbtb16*; and 30 downregulated genes. We predicted that *TOX*, induced by *NFIL3*, may partly mediate *NFIL3* function by regulating *Id2*, *Gata3*, *Tcf7*, and *Zbtb16* (Figure 2E). To investigate which *NFIL3* candidate gene targets were regulated by *TOX*, we examined their expression in *Tox*-deficient ILC-specified precursors compared to WT precursors using our scRNA-seq data. Most genes whose expression was altered very early during ILC development were still regulated in the absence of *TOX*. Some of these genes, such as *Gata3* and *Id2* (Figure 7A), appeared properly regulated in the absence of *TOX*, whereas others, such as *Ccl5* or *Tnfrsf9* (Figure S9D), showed an initial upregulated expression that failed to be maintained. The induction of some later ILC genes such as *Tcf7* and *Zbtb16* was delayed (Figure 7A), suggesting that *TOX* contributed to their upregulated expression. To investigate whether *TOX* was sufficient to regulate *NFIL3* candidate gene targets, we transduced ALP with *Tox*-expressing retrovirus, cultured them for 4 days, and analyzed them by RNA-seq. Among *NFIL3* candidate gene targets, *TOX* was only sufficient to regulate expression of *Tcf7*, *Zbtb16*, *Ccl5*, and *Tnfrsf9* in WT ALP, however, it failed to do so in *Nfil3*⁻/⁻ ALP (Figure 7B). Altogether, our data indicate that *NFIL3* initiates ILC specification independently of *TOX*, including *Id2* and *Gata3* induction. Once expressed, *TOX* cooperates with *NFIL3* to provide additional regulatory input to some genes, such as *Tcf7*, and *Zbtb16*.

To investigate whether *NFIL3* might provide direct regulatory input to its proposed gene targets, we defined its chromatin binding in ILC precursors. We generated an *Nfil3*-HA retroviral construct that could be specifically targeted with anti-HA antibody (Figure S10A) and that was able to induce ILC specification similar to WT *NFIL3* (Fig. S10B). We defined *NFIL3* binding in *Nfil3*-HA-transduced ALP cultured for 2 days by CUT&RUN with an anti-HA antibody, using ALP transduced with untagged *Nfil3* as negative control. Peak calling identified 17,572 *NFIL3* binding sites (Figure S10C), most enriched for an *NFIL3* known motif,
and for a *de novo* PAR bZIP motif recognized by NFIL3 (Figure S10D)⁴³. NFIL3 binding was detected at key locations identified in other cell types, such as the *Per1*⁴⁴, *Per2*⁴⁵, and *Per3*⁴⁶ promoters and the *Zeb2* enhancer⁴⁷ (Figure S10E). However, NFIL3 binding was largely distinct between ILC precursors and other cell types (Figure S10E,F)⁴⁷,⁴⁸. In ILC precursors, NFIL3 peaks were mostly located in intergenic and intronic regions (Figure S10G). They were not associated with overall changes in chromatin accessibility during ILC development, although DNase-seq showed a slightly higher signal in EILP as compared with ALP and ILCP (Figure S10H). NFIL3 binding sites were located in the vicinity of 10,042 out of the 32,621 experimentally validated genes coding for proteins, antisense RNA, and long non-coding RNA, which did not show overall expression changes downstream of NFIL3 (Figure S10I).

NFIL3 peaks were detected in the first intron of *Tox*, in the intronic enhancer of *Zbtb16*⁴⁹, and in a distal enhancer of *Gata3*⁵⁰ (Figure 7C). Overall, we identified 118 NFIL3 binding peaks located in the vicinity or known regulatory elements of 58 of the 102 NFIL3 candidate gene targets (Figure 7D, Table S3). NFIL3 may regulate additional genes through distal regulatory elements (eg. *Id2* putative enhancer, Figure S11A) or through short-lived binding that can be challenging to document (eg. *Tcf7* enhancer²⁰, Figure S11A)(Table S3). We further investigated the mechanism by which NFIL3 may regulate its gene targets. The 118 NFIL3 binding locations associated with the 58 identified gene targets were split into regions associated with repressed genes, and regions associated with activated genes. NFIL3 binding sites associated with downregulated genes (Figure 7E) were enriched for the NFIL3 canonical PAR bZIP motif (Figure 7F), and were disproportionally located in intergenic and promoter regions (Figure 7G). Chromatin accessibility of these regions decreased overall from EILP to ILCP stages (Figure 7H, Figure S11B). NFIL3 binding sites associated with upregulated genes (Figure 7I) were enriched for a bZIP motif associated with Fos or BATF (Figure 7J), were predominantly located in intronic regions (Figure 7K), and showed overall chromatin opening
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from ALP to EILP stages (Figure 7L, Figure S11C). Thus, NFIL3 may both activate and repress gene expression using distinct mechanisms, and may regulate chromatin accessibility.

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DISCUSSION

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We characterized developmental intermediates between upstream ALP and *Tcf7*<sup>+</sup> ILC progenitors to investigate the mechanisms underlying the initiation of ILC specification.

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Transcriptional analysis of these intermediates by scRNA-seq together with epigenetic analysis of known ILC progenitors identified NFIL3 as a candidate controller initiating early ILC development. NFIL3 was required for the generation of all BM ILC-specified precursors, thus establishing NFIL3 as the earliest requirement in ILC development, before TOX<sup>5,7,16</sup>, ID2<sup>4</sup>, GATA-3<sup>5,17</sup>, TCF-1<sup>4,7,18</sup>, and PLZF<sup>5,8</sup>. By ectopically expressing NFIL3 in ALP, we demonstrated that NFIL3 triggered a developmental process that recapitulated BM ILC development *in vitro*, resulting in the generation of all adult ILC. We further showed that NFIL3 is necessary and sufficient to drive ILC specification from FL ALP. Finally, we showed that NFIL3 initiate ILC specification independently of TOX, and directly regulated expression of key gene targets. We propose that upregulation of NFIL3 in ALP triggers ILC-specification in BM and FL.

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Although several steps of ILC development have been identified, the precursors initiating ILC specification remained to be characterized. A previous study attempted to characterize such a precursor in adult mouse BM, using the assumption that it should be phenotypically similar to the upstream precursor ALP including expression of IL-7Rα but would express higher levels of α4β7<sup>29</sup>. However, this population had a similar developmental potential to ALP, and was still present in Nfil3<sup>-/-</sup> mice, although in reduced numbers. Using an Il7r<sup>+</sup> lineage-tracer mouse to characterize ALP-derived precursors regardless of their expression of IL-7Rα, we identified ILC-specified precursors prior to Tcf7<sup>+</sup> upregulation. We showed that

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this population required NFIL3 to develop, but not TOX. Our work thus improves our understanding of ILC development, by distinguishing two successive developmental checkpoints during early ILC specification.

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We analyzed this intermediate population by scRNA-seq together with previously identified precursors to provide a transcriptional characterization of early ILC development. Our analysis identified candidate controllers of ILC development, and predicted functional relationships between known controllers of ILC development. We proposed a regulatory network that underlies the initiation of ILC specification downstream of NFIL3, by incorporating previously proposed regulatory interactions (*Nfil3>Id2*¹², *Nfil3>Tox*¹¹, *Gata3>Tcf7*²⁰, *Tcf7>Zbtb16*⁷) and additional interactions for which we provided experimental support. We showed that *Id2* and *Gata3* are induced downstream of NFIL3 independently of TOX, and that *Zbtb16* and *Tcf7* required regulatory inputs from both TOX and NFIL3. We predicted that additional input could come from *Id2* or *Gata3* and provided functional data supporting regulation by both factors. Although the regulatory network we proposed focused on the initiation of ILC specification downstream of NFIL3, our predictions can be used to explore regulatory interactions at other stages of ILC development. As an example, we predicted that TOX regulates *Gata3* and *Id2* after their initial induction by NFIL3, which may enable sustained expression throughout ILC development despite *Nfil3* downregulation at ILC commitment.

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Several transcription factors had been proposed to induce ILC development *in vitro* (ID2, GATA-3, BATF)¹²,³⁸,³⁹. However early precursors are still present in BM of mice deficient for each of these factors, indicating that they are not required for early steps of ILC specification⁴,¹⁷,³⁹,⁵¹. Furthermore, the experimental settings used to establish roles for these factors may not have been adequate to assess *bona fide* ILC development in the BM. First, experiments investigating ID2 and BATF were performed under conditions of strong Notch
signaling<sup>12,39</sup>, which is dispensable for BM ILC specification<sup>40,41</sup> and drives development of T cell precursors that are phenotypically similar to ILC<sup>52</sup>. Second, ILC-lineage cells were not excluded from starting populations, and cytokines that efficiently promote ILC expansion *in vitro*<sup>53,54</sup> were used to assess roles for GATA-3 and BATF<sup>38,39</sup>. Therefore, previous studies might have assessed roles for the tested factors in ILC expansion rather than development. In the present study, we excluded the contribution of rare ILC-specified BM precursors by using Tcf7-YFP-Thy1.2<sup>-</sup> ALP, and culture conditions that do not promote expansion of ILC-lineage cells. In these stringent conditions, we found no evidence for ILC specification induced by transcription factors other than NFIL3.

Interestingly, although we showed that ILC precursors are absent from the BM of *Nfil3*<sup>-/-</sup> mice, the development of some subsets of tissue-resident NK cells seems unaffected by NFIL3 deficiency (for review, see<sup>13</sup>). Our work thus clarifies the debated origin of this lineage<sup>55,56</sup> by establishing that tissue-resident NK cells develop independently of canonical BM ILC precursors. Similarly, although we confirmed previous results showing that ILC precursors are greatly reduced or absent in *Nfil3*<sup>-/-</sup> FL<sup>12</sup>, we found that LTi were minimally affected. This result explains why lymph nodes, Peyer's patches, and crypto patches still develop in *Nfil3*<sup>-/-</sup> mice<sup>9,10</sup>. It further supports the recent working model from the Bendelac lab, in which LTi diverge from other ILC very early during ontogeny, prior to sEILP stage<sup>57</sup>. The present study supports the idea that fetal LTi and ILC3 develop by distinct cellular pathways, and further, establishes that they differ in their dependence on NFIL3 for development.

## Limitation of the study

Our analysis of NFIL3 requirement during ILC specification is limited to the well-established BM developmental pathway. Although we show that NFIL3 plays similar functions in FL, other pathways of ILC development that are yet to be characterized may be less reliant on this factor,
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as evidenced by the presence of residual ILC subsets in *Nfil3*⁻/⁻ mice (for review, see¹³).

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Furthermore, our analysis of the reconstitution potential of *Nfil3*-induced ILC precursors *in vivo* is limited to the main ILC subsets that develop in NSG mouse models, which do not reflect the diversity of ILC that develop in normal mice throughout life. It is thus possible that some ILC subsets are not reconstituted by these *in vitro*-generated ILC precursors.

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## RESOURCE AVAILABILITY

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### Lead contact

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Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Christelle Harly, christelle.harly@cnrs.fr.

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### Material availability

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All materials generated in this study are available from the lead contact upon request.

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### Data and code availability

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* scRNA-seq and RNAseq data, both newly generated or reanalyzed from⁷, as well as CUT&RUN data generated in the current study are publicly available in the Gene Expression Omnibus database with the accession number GSE291077, as of the date of publication. Previously generated RNA-seq data (GSE113767)⁷, DNase-seq data (GSE128483)⁷, ChIP-seq data (GSE59486)⁴⁸ and CUT&RUN data (GSE188579)⁴⁷ were also reanalyzed in this work.

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* All code is available on GitHub : https://github.com/JosephLeger/Paper_NFIL3_dev_ICL.

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* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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ACKNOWLEDGMENTS

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We thank our colleagues for valuable discussions. AB and CH were supported by the Intramural
Research Program of the CCR, NCI. CH was supported by INSERM, CNRS, Université de
Nantes, Fondation pour la Recherche Médicale, (DEQ20170839118), Conseil Région Pays de
la Loire (Etoiles Montantes), ANR (LabEX IGO, ANR-11-LABX-0016; HémaNext, ANR-16-
IDEX-0007; JCJC-AAPG2022, ANR-22-CE15-0040-01). We thank the CCR Flow Cytometry
Core and Sequencing Facilities, and the SFR F. Bonamy Cytocell-Flow Cytometry Core
Facility (supported by LabEX IGO, ANR-11-LABX-0016 and Biogenouest) and UTE-animal
facility for technical support. This work utilized the SFR F. Bonamy computational resources
of the BIRD-bioinformatic core.

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AUTHOR CONTRIBUTIONS

CH and AB designed the study. CH, JL, DC, NB, RF, and DK carried out the experimental
work. CH, JL, DC, NB and EA analyzed the results and produced the figures. CH wrote the
manuscript. All authors read, commented on, and revised the manuscript. All authors have
approved the current version of the manuscript and its submission to *Immunity*.

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DECLARATION OF INTERESTS

A patent on the use of NFIL3 to generate ILC precursors *in vitro* has been submitted (application number PCT/EP2025/072604). The authors declare no other financial competing interest.

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## MAIN FIGURE LEGENDS

<figure><img src="image_2.png" /><figcaption>**Figure 1. Characterization of intermediates of ILC development.**</figcaption></figure>

(A) Flow cytometric strategy for isolating *II7r*<sup>LT+</sup> BM cells from *II7r*-iCre R26-stop-YFP *Tcf7GFP/+* mice. (B) Left, flow cytometric analysis of *II7r*<sup>LT+</sup> separated as α4β7<sup>lo</sup>, α4β7<sup>hi</sup>, and
Tcf7⁺ as in (A), after 8 days of culture. Mac-1⁻ cells are shown. Right, quantification of NK1.1⁺ and ICOS⁺ᵢᵢ ILC numbers presented as means ± s.d. for n = 3 wells. A 2-tailed unpaired Student's t-test was performed to determine significance (****P < 0.001). (C) Flow cytometric strategy for isolating α4β7⁺ᵢᵢ BM cells from Tcf7⁺GFP/+ mice (blue box). (D) Left, flow cytometric analysis of α4β7⁺ᵢᵢ cells in Il7r⁻iCre R26-stop-YFP Tcf7⁺GFP/+ mice (black histogram) showing α4β7⁺ᵢᵢIl7r⁺LT⁺ cells (red gate). α4β7⁺ᵢᵢ cells in Tcf7⁺GFP/+ mice are shown (grey histogram). Right, frequency of α4β7⁺ᵢᵢIl7r⁺LT⁺ cells in α4β7⁺ᵢᵢ cells, presented as means ± s.d. for n = 5 mice from 3 independent experiments. (A-D) Arrows show successive gating and numbers indicate the percentage of cells in each gate. Data are representative of 3 independent experiments. (E-G) UMAP plots showing scRNA-seq analysis of ex vivo ILC precursors. (E) ALP (n = 792 cells) and Tcf7⁺ (n = 1707 cells) BM populations, before (left), and after (right) integration with Il7r⁺LT⁺ (n = 1056 cells) and α4β7⁺ᵢᵢ cells (n = 177 cells). (F) Pseudotemporal reconstruction showing developmental progressions toward ILC (top) and DCs (bottom). (G) Density of expression of indicated genes. (H) Scheme of ILC development. Circles indicate developmental stages defined by the markers indicated below. Arrows indicate developmental transitions. The composition of the Il7r⁺LT⁺ and α4β7⁺ᵢᵢ populations are indicated respectively by light and dark blue boxes. Their intersection defines Il7r⁺LT⁺α4β7⁺ᵢᵢ cells (in red). See also Figure S1.

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<figure><img src="image_3.png" /><figcaption>**Figure 2.** Identification of controllers of ILC development.</figcaption></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

(A,D,E) scRNA-seq analysis of ILC precursors as in Figure 1. (A) Scaled expression of the variable transcription factors (n = 75) along ILC development, according to the pseudotemporal reconstruction from Figure 1G (12 bins). Genes were grouped in 8 clusters using K-Means
clustering. Expression of individual genes is shown in grey, centroid is in black. Samples of origin are shown (bottom). (B) DNase-Seq analysis focused on regions transiently opened at EILP stage (from Figure S2A, n=192). Left, quantification of read density in ALPs, EILPs, ILCPs, centered on peaks. Right, known motif for the 5 top transcription factor subfamilies enriched in these regions. Transcription factors associated with the top motif of each subfamily is indicated. (C) Transcript expression for the indicated genes measured by RNA-seq analysis of ALP (n=7), sEILP (n=5), cEILP (n=6), and ILCP (n=3). Data are presented as means ± s.d. P-val calculated using a moderated t-test are shown for the comparison between ALP and sEILP. \* P < 0.05, \*\*P < 0.005, \*\*\*P < 0.0005. (D) Scaled expression of the indicated genes measured by scRNA-seq along ILC development, according to the pseudotemporal reconstruction from Figure 1F (12 bins). Samples of origin are shown (bottom). (E) Gene regulatory network inferred from transcriptional changes during ILC development. See also Figure S2 and Table S1.

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<figure><img src="image_8.png" /><figcaption>Figure 3. NFIL3 is required for ILC specification in the BM, prior to TOX.</figcaption></figure>

(A) Left, flow cytometric analysis of BM cells from Nfil3⁻/Tcf7GFP/+ and Nfil3⁺/+ or Nfil3⁺/− Tcf7GFP/+ littermate mice. Right, quantifications of ALP, EILP, and ILCP numbers. (B) Left,
flow cytometric analysis of BM cells from Nfil3⁻/Il7r⁻-iCre R26-stop-YFP Tcf7GFP/+ mice and Nfil3⁺/⁺Il7r⁻-iCre R26-stop-YFP Tcf7GFP/+ littermate controls, showing the gating strategy of Nfil3⁻/Il7rLYT+. Right, quantification of frequencies of α4β7lo, α4β7hi, and Tcf7⁺ in Il7rLYT+ precursors. (A,B) Data are presented as means ± s.e.m. for n =6 (A) or 4 (B) mice per group, pooled from 2 independent experiments. A 2-tailed unpaired Student's t-test was performed to determine significance (*P* < 0.05, **P* < 0.01, ****P < 0.0001). (C-E) scRNA-seq analysis of Nfil3⁻/Il7rLYT+ (n = 1018 cells) isolated as in B and integrated to WT ILC precursors from Figure 1. (C) UMAP plots showing samples. WT cells other than Il7rLYT+ are in yellow. (D) Pseudotemporal reconstruction. (E) Nfil3 expression along WT ILC development, according to the pseudotemporal reconstruction from D (11 bins). Samples of origin are shown (bottom). (F) Flow cytometric analysis of BM cells from Tox⁻/ Il7r⁻-iCre R26-stop-YFP Tcf7GFP/+ mice and Tox⁺/ Il7r⁻-iCre R26-stop-YFP Tcf7GFP/+ littermate controls, showing the gating strategy of Tox⁻/Il7rLYT+. (G-I) scRNA-seq analysis of Tox⁻/Il7rLYT+ (n = 603 cells) isolated as in F and integrated to WT ILC precursors and Nfil3⁻/Il7rLYT+ from C. (G) UMAP plots showing samples. (H) Pseudotemporal reconstruction. (I) Min-max scaled Nfil3 expression along ILC development, according to the pseudotemporal reconstruction from H (11 bins), in Il7rLYT+ cells of the indicated genotypes. (A,B,F) Arrows show successive gating and numbers indicate the percentage of cells in each gate. Data are representative of 3 independent experiments. See also Figure S3.

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<figure><img src="image_9.png" /><figcaption>Figure 4. NFIL3 expression in ALP induces the generation of ILC-lineage cells in vitro.</figcaption></figure>

(A) Left, flow cytometric analysis of ALP isolated from Tcf7<sup>YFP/+</sup> mice, transduced with GFP-
retroviruses, either control or encoding *Nfil3*, and cultured for the indicated number of days.
GFP⁺ cells are shown. Right, quantification of Mac-1⁻YFP⁺ cell frequency. (B) Estimate
quantification of Mac-1⁻*Tcf7*⁺ progeny at day 4 per GFP⁺ input cell as in A. (C) Left, flow
cytometric analysis of ALP isolated from *Tcf7*⁺YFP⁺ mice, transduced with GFP-retroviruses,
either control or encoding for *Nfil3* or *Icn1*, and cultured for 4 days. GFP⁺ cells are shown.
Right, quantification of Mac-1⁻YFP⁺ cell frequency. (D) QRT-PCR analysis of cells as in C.
Control-GFP⁺ cells were isolated by flow cytometric sorting as Mac-1⁻CD11c⁻GFP⁺, and *Nfil3*-
and *Icn1*-GFP⁺ cells as Mac-1⁻CD11c⁻GFP⁺YFP⁺. (E) Flow cytometric analysis of ALP isolated
from *Tcf7*⁺YFP⁺ mice, co-transduced with GFP-retroviruses (control, *Nfil3*, or *Icn1*) and VEX-
retroviruses (control, black line or *Dnmam1*, red line), and cultured for 4 days. Gated on Mac-
1⁻CD11c⁻GFP⁺VEX⁺ cells. The shaded grey histogram shows cells co-transduced with control-
GFP and control-VEX retroviruses. (F) Flow cytometric analysis of ALP isolated from
*Nfil3*⁺⁺*Tcf7*⁺YFP⁺ or *Nfil3*⁻⁻*Tcf7*⁺YFP⁺ mice, transduced with the indicated GFP-retroviruses, and
cultured for 4 days. Mac-1⁻GFP⁺ cells are shown. (G) Flow cytometric analysis of cells isolated
from cultures as in C, and cultured for 7 additional days. Control-GFP⁺ cells were isolated as
Mac-1⁻GFP⁺, and *Nfil3*-GFP⁺ and *Icn1*-GFP⁺ cells were sorted as Mac-1⁻GFP⁺YFP⁺. Right,
quantification of Thy1.2<sup>hi</sup>CD25<sup>+</sup> T-lineage cell numbers. (H) Left, flow cytometric analysis of
ALP isolated from *Tcf7*⁺YFP⁺ mice, transduced with *Nfil3*-GFP retroviruses and cultured for 2 or
4 days. Mac-1⁻GFP⁺ cells are shown. Right, cells are further gated as YFP⁺ or YFP⁻. Arrows
show successive gating. (I) Left, flow cytometric analysis of YFP⁺ and YFP⁻ cells, isolated from
4 days cultures as in H, and cultured for 4 additional days. Right, quantification of NK1.1⁺ and
ICOS<sup>hi</sup> ILC numbers. Control-GFP⁺ cells were isolated as Mac-1⁻GFP⁺ from GFP-transduced
ALP cultured for 4 days, and cultured for 4 additional days. (A,B,G-I) Numbers indicate the
percentage of cells in each gate. Data are presented as means ± s.d. for n = 3 independent
experiments (A,C,G) or n = 3 wells in an experiment (B,D,I). A 2-tailed unpaired Student's t-
test was performed to determine significance (*P* < 0.05, **P* < 0.01, ***P* < 0.005 for A-C,G,I;

*P* < 0.01, **P* < 0.001, ***P* < 0.0001 for D). All data are representative of 3 independent

experiments. See also Figures S4 and S5.

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<figure><img src="image_10.png" /><figcaption>Figure 5. NFIL3 promotes the generation of all adult ILC lineages.</figcaption></figure>

(A-C) Flow cytometric analysis of cells generated from ALP transduced with Nfil3-Thy1.1-retrovirus and cultured for 10 days. Gated on Mac1⁻ Thy1.1⁺ cells. (A) NK1.1⁺ cells are shown in black, ICOShi cells are shown in red. (B-C) brefeldin A (10ug/mL) was added for the last 4h of culture, with (black histogram) or without (grey shaded histogram) PMA (50ng/mL) and

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ionomycin (500ng/mL). (B) The top row shows ICOS<sup>hi</sup> cells and the bottom row shows NK1.1<sup>+</sup> cells. (C) Cells were cultured on OP9-D11 in the presence or absence of mouse IL-25 (50ng/mL) and mouse IL-33 (50ng/mL) for the last 72h. ICOS<sup>hi</sup> cells are shown. (D-F) Flow cytometric analysis of liver and spleen harvested from NSG mice inoculated with *Nfil3*-induced ILC precursors, or control cells. ALP were isolated from Tcf7<sup>YFP/+</sup>, transduced with control-GFP or *Nfil3*-GFP retroviruses and cultured for 4 days as in Figure 4A. Control-transduced cells were isolated by flow cytometric sorting as Mac-1<sup>-</sup>CD11c<sup>-</sup>GFP<sup>+</sup>, *Nfil3*-induced ILC precursors as Mac-1<sup>-</sup>CD11c<sup>-</sup>GFP<sup>+</sup>YFP<sup>+</sup>. Each NSG mouse was inoculated with 2000 cells and analyzed 4 weeks later. Quantifications are presented as means ± s.e.m. for n = 4 mice per group, pooled from 2 independent experiments. A 2-tailed unpaired Student's t-test was performed to determine significance (*P* < 0.05). (D) Gating (left) and quantification (right) of GFP<sup>+</sup>CD45.2<sup>+</sup> cell numbers. (E) Gating (left) and quantification (right) of NK1.1<sup>+</sup>NKP46<sup>+</sup> group 1 ILC frequency in GFP<sup>+</sup>CD45.2<sup>+</sup> cells from C. (F) Gating (left) and quantifications (right) of NK1.1<sup>+</sup>VLA-1<sup>hi</sup>DX5<sup>lo</sup> liver ILC1 and NK1.1<sup>+</sup>VLA-1<sup>lo</sup>DX5<sup>hi</sup> spleen cNK numbers. (A-F) Numbers indicate the percentage of cells in each gate. Arrows show successive gating. All data are representative of 3 independent experiments. See also Figures S6 and S7.

622

623
<figure><img src="image_11.png" /><figcaption>Figure 6. Transient NFIL3 expression in ALP drives ILC development.</figcaption></figure>

(A) Top, flow cytometric analysis of ALP isolated from *Nfil3*<sup>-/-</sup>*Tcf7*<sup>YFP/+</sup> and *Nfil3*<sup>+/-</sup>*Tcf7*<sup>YFP/+</sup>
littermate mice, transduced with the indicated GFP-retroviruses, and cultured for 4 days. GFP+
cells are shown. Bottom, quantification of Mac-1⁻YFP⁺ cell frequency. (B) Top, flow
cytometric analysis of cells isolated from cultures as in A, and cultured for 4 additional days.
control-GFP⁺ cells were isolated as Mac-1⁻GFP⁺, and Nfil3-GFP⁺ cells were sorted as Mac-1⁻
GFP⁺YFP⁺. Bottom, quantification of NK1.1⁺ and ICOS<sup>hi</sup> ILC numbers. (C,D) RNA-seq
analysis of Nfil3-induced Tcf7⁺ cells (NFIL3, n=3), control-transduced cells (GFP, n=3)(day 4
of culture as in Figure 3A), and ex vivo ALP (n=7), sEILP (n=5), cEILP (n=6), and ILCP (n=3).
(C) Hierarchical clustering using complete linkage calculated from Euclidian distances. (D)
Heat map of expression of chosen transcription factors. Data are averaged from the replicates
of each sample shown in C. (E-G) Flow cytometric analysis of cells generated from ALP
transduced with the indicated Thy1.1-retroviruses and cultured for the indicated number of
days. Mac1⁻Thy1.1⁺ cells are shown. (H,I) Flow cytometric analysis of progeny of the indicated
cells. ALP were isolated from Nfil3<sup>+/+</sup> Tcf7<sup>YFP/+</sup> or Nfil3<sup>-/-</sup> Tcf7<sup>YFP/+</sup> mice, transduced with the
indicated Thy1.1-retroviruses and cultured for 4 days in the presence of 4-OHT (200uM) as in
Figure S8D. Ctrl-transduced cells were then sorted as Mac1⁻Thy1.1⁺, and Nfil3-transduced cells
were sorted as Mac1⁻Thy1.1⁺YFP⁺. (H) Cells were cultured for 4 additional days in absence of
4-OHT. Top, Mac1⁻Thy1.1⁺ cells are shown. Bottom, quantification of NK1.1⁺ and ICOS<sup>hi</sup> ILC
numbers. (A,B,E-H) Numbers indicate the percentage of cells in each gate. Data are
representative of 3 independent experiments. (A,B,H) Data are presented as means ± s.d. for n
= 3 independent experiments (A) or n = 3 wells in an experiment (B,H). A 2-tailed unpaired
Student's t-test was performed to determine significance (*P < 0.05, **P < 0.01, ***P < 0.005,
****P < 0.001, *****P < 0.0001). (I) 2000 sorted cells were injected into individual NSG mice.
Liver, gut, and spleen were harvested and analyzed 3 months later. Quantification of ILC
subsets shown in Fig. S8E is presented as means ± s.e.m. for n = 5 mice per group, pooled from
2 independent experiments. A 2-tailed unpaired Student's t-test was performed to determine
significance (*P* < 0.05). See also Figure S8.

653

654
<figure><img src="image_12.png" /><figcaption>Figure 7. identification of NFIL3 gene targets during ILC development.</figcaption></figure>

(A) Expression of the indicated genes measured by scRNA-seq along ILC development,
according to the pseudotemporal reconstruction from Figure 3H (11 bins), in WT, *Nfil3*⁻/⁻, and *Tox*⁻/⁻ *Il7r*¹⁰⁺ cells. (B) RNA-seq analysis of Mac-1⁻GFP⁺ cells isolated from 4 days cultures from *Tox*-transduced (TOX, n=3) and control-transduced (GFP, n=3) *Nfil3*⁺/⁺ or *Nfil3*⁻/⁻ ALP.
Transcript expression for the indicated genes are shown. Data are presented as means ± s.d. P-val calculated using a moderated t-test are shown for the comparison between GFP- and TOX-transduced ALP. \* P < 0.05. (C) NFIL3 CUT&RUN and DNase-seq tracks displayed around selected genes. CUT&RUN is analyzed in ALP transduced *Thy1.1-Nfil3-HA* (NFIL3) or control *Thy1.1-Nfil3* (ctrl) cultured for two days. DNase-seq is analyzed in *ex vivo* EILP. A red bar indicates a called peak. \* indicates the presence of a known NFIL3 motif at the peak. (D) Heat map of expression of 58 genes controlled downstream of NFIL3 (Fig. S9B) and bound by NFIL3 (Table S3). Data are averaged from the replicates of each sample shown in Figure 6C.
(E-L) Characterization of the 118 NFIL3 peaks associated with the 58 candidate genes shown in D, split into down-regulated (top) and up-regulated (bottom). (E,I) Quantification of read density in regions centered on NFIL3 CUT&RUN peaks. (F,J) Top known bZIP motifs enriched. The transcription factors subfamilies associated is indicated, as well as the factors in bracket. P-val, and number of target regions that have the motif are indicated. (G,K) Genomic locations of peaks. (H,L) Quantification of read density of DNase-seq in regions centered on CUT&RUN peaks. See also Figures S9-11 and Tables 2 and 3.
KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or Antibodies</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>2B4 (PE-Cy7)</td><td>eBioscience</td><td>eBio244F4/Cat #25-2441-82; RRID: AB_2573432</td></tr><tr><td>B220 (Biotin)</td><td>eBioscience</td><td>RA3-6B2/Cat #13-0452-82; RRID: AB_466449</td></tr><tr><td>B220 (FITC)</td><td>eBioscience</td><td>RA3-6B2/Cat #11-0452-82; RRID: AB_465054</td></tr><tr><td>CD11c (Biotin)</td><td>eBioscience</td><td>N418/Cat #13-0114-82; RRID: AB_466363</td></tr><tr><td>CD11c (FITC)</td><td>eBioscience</td><td>N418/Cat #11-0114-82; RRID: AB_464940</td></tr><tr><td>CD122 (PE-Cy7)</td><td>BioLegend</td><td>TM-β1/Cat #123216; RRID: AB_2562895</td></tr><tr><td>CD19 (Biotin)</td><td>eBioscience</td><td>1D3/Cat #13-0193-82; RRID: AB_657656</td></tr><tr><td>CD19 (FITC)</td><td>eBioscience</td><td>1D3/Cat #11-0193-86; RRID: AB_657665</td></tr><tr><td>CD24 (eF450)</td><td>eBioscience</td><td>M1/69/Cat #48-0242-82; RRID: AB_1311169</td></tr><tr><td>CD25 (APC-eF780)</td><td>eBioscience</td><td>PC61.5/Cat #47-0251-82; RRID: AB_1272179</td></tr><tr><td>CD3ε (Biotin)</td><td>eBioscience</td><td>145-2C11/Cat #13-0031-82; RRID: AB_466319</td></tr><tr><td>CD3ε (FITC)</td><td>eBioscience</td><td>145-2C11/Cat #11-0031-86; RRID: AB_464884</td></tr><tr><td>CD3ε (PE)</td><td>eBioscience</td><td>145-2C11/Cat #12-0031-82; RRID: AB_465496</td></tr><tr><td>CD4 (Biotin)</td><td>eBioscience</td><td>GK1.5/Cat #13-0041-86; RRID: AB_466327</td></tr><tr><td>CD4 (FITC)</td><td>eBioscience</td><td>GK1.5/Cat #11-0041-86; RRID: AB_464894</td></tr><tr><td>CD4 (PE-eF610)</td><td>eBioscience</td><td>GK1.5/Cat #61-0041-82; RRID: AB_2574518</td></tr><tr><td>CD44 (PE-Cy7)</td><td>eBioscience</td><td>IM7/Cat #25-0441-82; RRID: AB_469623</td></tr><tr><td>CD45.1 (PerCP-Cy5.5)</td><td>eBioscience</td><td>A20/Cat #45-0453-82; RRID: AB_1107003</td></tr><tr><td>CD45.2 (eF506)</td><td>eBioscience</td><td>104/Cat #69-0454-82; RRID: AB_2637105</td></tr><tr><td>CD45.2 (PE-eF610)</td><td>eBioscience</td><td>104/Cat #61-0454-82; RRID: AB_2574562</td></tr><tr><td>CD8α (Biotin)</td><td>eBioscience</td><td>53-6.7/Cat #13-0081-82; RRID: AB_466348</td></tr><tr><td>CD8α (FITC)</td><td>eBioscience</td><td>53-6.7/Cat #11-0081-86; RRID: AB_464917</td></tr><tr><td>CD8α (PE-Cy7)</td><td>eBioscience</td><td>53-6.7/Cat #25-0081-82; RRID: AB_469584</td></tr><tr><td>DX5 (APC-eF780)</td><td>eBioscience</td><td>DX5/Cat #47-5971-82; RRID: AB_11218895</td></tr><tr><td>EOMES (PE-eF610)</td><td>eBioscience</td><td>WD1928/Cat #61-4877-42; RRID: AB_2574616</td></tr><tr><td>Flt3 (PE)</td><td>eBioscience</td><td>A2F10/Cat #12-1351-83; RRID: AB_465860</td></tr><tr><td>Flt3 (PE-CF594)</td><td>BD Biosciences</td><td>A2F10.1/Cat #562537; RRID: AB_2737639</td></tr><tr><td>GATA-3 (PE)</td><td>eBioscience</td><td>TWAJ/Cat #12-9966-42; RRID: AB_1963600</td></tr><tr><td>Gr-1 (Biotin)</td><td>eBioscience</td><td>RB6-8C5/Cat #13-5931-82; RRID: AB_466800</td></tr><tr><td>Gr-1 (FITC)</td><td>eBioscience</td><td>RB6-8C5/Cat #11-5931-86; RRID: AB_465316</td></tr><tr><td>HA (AF488)</td><td>BioLegend</td><td>16B12/Cat #901509; RRID: AB_2565072</td></tr><tr><td>ICOS (APC)</td><td>eBioscience</td><td>C398.4 A/Cat #17-9949-82; RRID: AB_11149880</td></tr></tbody></table>
<table>
  <tr>
    <td>ICOS (PE)</td>
    <td>eBioscience</td>
    <td>C398.4 A/Cat #12-9949-81; RRID: AB_466277</td>
  </tr>
  <tr>
    <td>ICOS (PE-Cy7)</td>
    <td>eBioscience</td>
    <td>7E.17G9/Cat #25-9942-82; RRID: AB_2573564</td>
  </tr>
  <tr>
    <td>IFN-γ (APC)</td>
    <td>eBioscience</td>
    <td>XMG1.2/Cat #17-7311-82; RRID: AB_469504</td>
  </tr>
  <tr>
    <td>IL-13 (eF660)</td>
    <td>eBioscience</td>
    <td>eBio13A/Cat #50-7133-82; RRID: AB_2574279</td>
  </tr>
  <tr>
    <td>IL-17A (PE)</td>
    <td>eBioscience</td>
    <td>eBio17B7/Cat #12-7177-81; RRID: AB_763582</td>
  </tr>
  <tr>
    <td>IL-22 (APC)</td>
    <td>eBioscience</td>
    <td>IL22JOP/Cat #17-7222-82; RRID: AB_10597583</td>
  </tr>
  <tr>
    <td>IL-5 (PE)</td>
    <td>eBioscience</td>
    <td>TRFK5/Cat #12-7052-82; RRID: AB_763587</td>
  </tr>
  <tr>
    <td>IL-7Rα (APC)</td>
    <td>eBioscience</td>
    <td>A7R34/Cat #17-1271-82; RRID: AB_469435</td>
  </tr>
  <tr>
    <td>IL-7Rα (APC-eF780)</td>
    <td>eBioscience</td>
    <td>A7R34/Cat #47-1271-82; RRID: AB_1724012</td>
  </tr>
  <tr>
    <td>Kit (AF700)</td>
    <td>eBioscience</td>
    <td>ACK2/Cat #56-1172-82; RRID: AB_657583</td>
  </tr>
  <tr>
    <td>Kit (PE)</td>
    <td>eBioscience</td>
    <td>2B8/Cat #12-1171-82; RRID: AB_465813</td>
  </tr>
  <tr>
    <td>Kit (PE-Cy7)</td>
    <td>eBioscience</td>
    <td>2B8/Cat #25-1171-82; RRID: AB_469644</td>
  </tr>
  <tr>
    <td>Ly-6D (eF450)</td>
    <td>eBioscience</td>
    <td>49-H4/Cat #49-5974-80; RRID: AB_2574089</td>
  </tr>
  <tr>
    <td>Ly-6D (FITC)</td>
    <td>BioLegend</td>
    <td>49-H4/Cat #138606; RRID: AB_11203888</td>
  </tr>
  <tr>
    <td>Mac-1 (APC)</td>
    <td>eBioscience</td>
    <td>M1/70/Cat #17-0112-82; RRID: AB_469343</td>
  </tr>
  <tr>
    <td>Mac-1 (Biotin)</td>
    <td>eBioscience</td>
    <td>M1/70/Cat #13-0112-82; RRID: AB_466359</td>
  </tr>
  <tr>
    <td>Mac-1 (FITC)</td>
    <td>eBioscience</td>
    <td>M1/70/Cat #11-0112-86; RRID: AB_464937</td>
  </tr>
  <tr>
    <td>Mac-1 (PE)</td>
    <td>eBioscience</td>
    <td>M1/70/Cat #12-0112-82; RRID: AB_2734869</td>
  </tr>
  <tr>
    <td>NFIL3 (AF647)</td>
    <td>eBioscience</td>
    <td>S2M-E19/Cat #51-5927-82; RRID: AB_2802304</td>
  </tr>
  <tr>
    <td>NK1.1 (Biotin)</td>
    <td>eBioscience</td>
    <td>PK136/Cat #13-5941-82; RRID: AB_466804</td>
  </tr>
  <tr>
    <td>NK1.1 (FITC)</td>
    <td>eBioscience</td>
    <td>PK136/Cat #11-5941-82; RRID: AB_465318</td>
  </tr>
  <tr>
    <td>NK1.1 (PE-Cy7)</td>
    <td>eBioscience</td>
    <td>PK136/Cat #25-5941-82; RRID: AB_469665</td>
  </tr>
  <tr>
    <td>NK1.1 (PE-eF610)</td>
    <td>eBioscience</td>
    <td>PK136/Cat #61-5941-82; RRID: AB_2574642</td>
  </tr>
  <tr>
    <td>NKP46 (PE)</td>
    <td>eBioscience</td>
    <td>29A1.4/Cat #12-3351-82; RRID: AB_1210743</td>
  </tr>
  <tr>
    <td>PLZF (APC)</td>
    <td>eBioscience</td>
    <td>9E12/Cat #17-9322-82; RRID: AB_2637113</td>
  </tr>
  <tr>
    <td>PLZF (PE)</td>
    <td>eBioscience</td>
    <td>Mags.21F7/Cat #12-9320-82; RRID: AB_11148934</td>
  </tr>
  <tr>
    <td>RORγt (APC)</td>
    <td>eBioscience</td>
    <td>AFKJS-9/Cat #17-6988-82; RRID: AB_10609207</td>
  </tr>
  <tr>
    <td>RORγt (PE)</td>
    <td>eBioscience</td>
    <td>AFKJS-9/Cat #12-6988-82; RRID: AB_1834470</td>
  </tr>
  <tr>
    <td>T-BET (PE-Cy7)</td>
    <td>eBioscience</td>
    <td>4B10/Cat #25-5825-82; RRID: AB_11042699</td>
  </tr>
  <tr>
    <td>TCF-1 (AF488)</td>
    <td>Cell Signaling</td>
    <td>C63D9/Cat #6444; RRID: AB_2797627</td>
  </tr>
  <tr>
    <td>TCRβ (APC-eF780)</td>
    <td>eBioscience</td>
    <td>H57-597/Cat #47-5961-82; RRID: AB_1272173</td>
  </tr>
  <tr>
    <td>TCRβ (Biotin)</td>
    <td>eBioscience</td>
    <td>H57-597/Cat #13-5961-82; RRID: AB_466819</td>
  </tr>
  <tr>
    <td>TCRβ (FITC)</td>
    <td>eBioscience</td>
    <td>H57-597/Cat #11-5961-85; RRID: AB_465324</td>
  </tr>
  <tr>
    <td>TCRβ (PerCP-Cy5.5)</td>
    <td>eBioscience</td>
    <td>H57-597/Cat #45-5961-82; RRID: AB_925763</td>
  </tr>
  <tr>
    <td>TCRγδ (APC)</td>
    <td>eBioscience</td>
    <td>GL-3/Cat #17-5711-82; RRID: AB_842756</td>
  </tr>
  <tr>
    <td>TCRγδ (Biotin)</td>
    <td>eBioscience</td>
    <td>GL-3/Cat #13-5711-82; RRID: AB_466668</td>
  </tr>
  <tr>
    <td>TCRγδ (FITC)</td>
    <td>eBioscience</td>
    <td>GL-3/Cat #11-5711-85; RRID: AB_465239</td>
  </tr>
</table>
<table><tr><td>TCRγδ (PerCP-eF710)</td><td>eBioscience</td><td>GL-3/Cat #46-5711-82; RRID: AB_2016707</td></tr><tr><td>Ter119 (Biotin)</td><td>eBioscience</td><td>TER119/Cat #13-5921-82; RRID: AB_466797</td></tr><tr><td>Ter119 (FITC)</td><td>eBioscience</td><td>TER119/Cat #11-5921-82; RRID: AB_465311</td></tr><tr><td>Thy1.1 (eF450)</td><td>eBioscience</td><td>HIS51/Cat #48-0900-82; RRID: AB_1272254</td></tr><tr><td>Thy1.2 (APC-eF780)</td><td>eBioscience</td><td>53-2.1/Cat #47-0902-82; RRID: AB_1272187</td></tr><tr><td>Thy1.2 (eF450)</td><td>eBioscience</td><td>53-2.1/Cat #48-0902-82; RRID: AB_1272200</td></tr><tr><td>Thy1.2 (FITC)</td><td>eBioscience</td><td>53-2.1/Cat #11-0902-82; RRID: AB_465153</td></tr><tr><td>TNF-α (PE)</td><td>eBioscience</td><td>MP9-XT22/Cat #12-7321-82; RRID: AB_466199</td></tr><tr><td>VLA1 (APC)</td><td>BioLegend</td><td>HMα1/Cat #142606; RRID: AB_2562253</td></tr><tr><td>α4β7 (PE)</td><td>eBioscience</td><td>DATK32/Cat #12-5887-82; RRID: AB_657803</td></tr><tr><td>α4β7 (PerCP-eF710)</td><td>eBioscience</td><td>DATK32/Cat #46-5887-82; RRID: AB_2573793</td></tr></table>

Chemicals, peptides, and recombinant proteins

<table><tr><td>α-MEM media</td><td>Gibco</td><td>Cat #15430584</td></tr><tr><td>GlutaMAX</td><td>Gibco</td><td>Cat #35050061</td></tr><tr><td>Penicillin-Streptomycin</td><td>Gibco</td><td>Cat #15140122</td></tr><tr><td>Mouse Flt3-ligand</td><td>PeproTech</td><td>Cat #250-31L</td></tr><tr><td>Mouse SCF</td><td>PeproTech</td><td>Cat #250-03</td></tr><tr><td>Mouse IL-7</td><td>PeproTech</td><td>Cat #217-17</td></tr><tr><td>Mouse IL-25</td><td>PeproTech</td><td>Cat #210-17E</td></tr><tr><td>Mouse IL-33</td><td>PeproTech</td><td>Cat #210-33</td></tr><tr><td>Polybrene</td><td>Sigma-Aldrich</td><td>Cat #H9268</td></tr><tr><td>Phorbol-12-myristate-13-acetate</td><td>Sigma-Aldrich</td><td>Cat #P8139</td></tr><tr><td>Ionomycin</td><td>Sigma-Aldrich</td><td>Cat #I0634</td></tr><tr><td>Brefeldin A</td><td>Sigma-Aldrich</td><td>Cat #B7651</td></tr><tr><td>(Z)-4-Hydroxytamoxifen</td><td>Abcam</td><td>Cat #ab141943</td></tr><tr><td>Taqman PCR Master Mix</td><td>Applied Biosystems</td><td>Cat #4304437</td></tr></table>

Critical commercial assays

<table><tr><td>Foxp3Transcription FactorStaining Bufferset</td><td>eBioscience</td><td>Cat #00-5523-00</td></tr><tr><td>RNeasy Plus Micro Kits</td><td>Qiagen</td><td>Cat #74134</td></tr><tr><td>SuperScript II Kit</td><td>Invitrogen</td><td>Cat #18064014</td></tr><tr><td>SMARTer Ultra Low Input RNA Kit version 3</td><td>Clonotech</td><td>Cat #634850</td></tr><tr><td>Nextera XT DNA Library Preparation Kit</td><td>Illumina</td><td>Cat #FC-131-1024</td></tr></table>
<table><tr><td>Chromium Single Cell 3' kits version 2 chemistry</td><td>10x Genomics</td><td>Cat #120234</td></tr><tr><td>CUTANA ChIC/CUT&amp;RUN Kit Version 3</td><td>EpiCypher</td><td>Cat #14-1048</td></tr><tr><td colspan="3">Deposited data</td></tr><tr><td>Newly generated and reanalyzed RNA-seq, scRNA-seq, DNase-seq and CUT&amp;RUN datasets</td><td>This paper</td><td>GSE291077</td></tr><tr><td>Published RNA-seq dataset</td><td>Harly et al.<sup>7</sup></td><td>GSE113767</td></tr><tr><td>Published DNase-seq dataset</td><td>Harly et al.<sup>7</sup></td><td>GSE128483</td></tr><tr><td>Published ChIP-seq dataset</td><td>Fang et al.<sup>48</sup></td><td>GSE59486</td></tr><tr><td>Published CUT&amp;RUN dataset</td><td>Liu et al.<sup>47</sup></td><td>GSE188579</td></tr><tr><td colspan="3">Experimental models: Cell lines</td></tr><tr><td>OP9-GFP</td><td>Schmitt &amp; Zúñiga-Pflücker<sup>58</sup></td><td>N/A</td></tr><tr><td>OP9-DI1</td><td>Schmitt &amp; Zúñiga-Pflücker<sup>58</sup></td><td>N/A</td></tr><tr><td>OP9-DI4</td><td>Schmitt &amp; Zúñiga-Pflücker<sup>58</sup></td><td>N/A</td></tr><tr><td colspan="3">Experimental models: Organisms/strains</td></tr><tr><td>B6-Ly5.2 (CD45.1)</td><td>The Jackson Laboratory</td><td>Strain #002014; RRID: IMSR_JAX:002014</td></tr><tr><td>NOD scid gamma deficient (NSG)</td><td>The Jackson Laboratory</td><td>Strain #005557; RRID: IMSR_JAX:005557</td></tr><tr><td>Tcf7<sup>EGFP</sup></td><td>Yang et al.<sup>4</sup></td><td>N/A</td></tr><tr><td>Tcf7<sup>YFP</sup></td><td>Harly et al.<sup>7</sup></td><td>N/A</td></tr><tr><td>Il7r-iCre R26-stop-YFP</td><td>Schlenner et al.<sup>24</sup></td><td>N/A</td></tr><tr><td>Nfil3<sup>-/-</sup></td><td>Kashiwada et al.<sup>59</sup></td><td>N/A</td></tr><tr><td>Gata3<sup>flox</sup></td><td>Pai et al.<sup>60</sup></td><td>N/A</td></tr><tr><td>Tox<sup>-/-</sup></td><td>Aliahmad et al.<sup>61</sup></td><td>N/A</td></tr><tr><td colspan="3">Recombinant DNA</td></tr><tr><td>Icn-GFP</td><td>Pui et al.<sup>33</sup></td><td>N/A</td></tr><tr><td>Gata3-GFP</td><td>Hernandez-Hoyos et al.<sup>62</sup></td><td>Addgene Plasmid #34836</td></tr><tr><td>Dnmaml1-GFP</td><td>Weng et al.<sup>34</sup></td><td>N/A</td></tr><tr><td>MigR1</td><td>Pear et al.<sup>63</sup></td><td>Addgene Plasmid #27490</td></tr><tr><td>P2A</td><td>Gabriel et al.<sup>64</sup></td><td>Addgene Plasmid #74056</td></tr><tr><td>Thy1.1</td><td>Wu et al.<sup>65</sup></td><td>Addgene Plasmid #17442</td></tr><tr><td>MSCV</td><td>Unpublished</td><td>Addgene Plasmid #52114</td></tr><tr><td>Nfil3 mouse cDNA</td><td>This paper</td><td>N/A</td></tr></table>
<table><tbody><tr><td>Batf mouse cDNA</td><td>Smith et al.<sup>66</sup></td><td>Addgene Plasmid #34575</td></tr><tr><td>Batf3 mouse cDNA</td><td>Rosa et al.<sup>67</sup></td><td>Addgene Plasmid #139837</td></tr><tr><td>MSCV-IRES-Thy1.1</td><td>Wu et al.<sup>65</sup></td><td>Addgene Plasmid #17442</td></tr><tr><td>ER-T2</td><td>Zhang et al.<sup>68</sup></td><td>Addgene plasmid #87693</td></tr><tr><td>Nfil3-ER-T2-Thy1.1</td><td>This paper</td><td>N/A</td></tr><tr><td>Nfil3-HA-Thy1.1</td><td>This paper</td><td>N/A</td></tr><tr><td colspan="3">Software and algorithms</td></tr><tr><td>FlowJo (10.7.1)</td><td>BD Biosciences</td><td>https://www.flowjo.com</td></tr><tr><td>BBMap (39.00)</td><td>Brian Bushnell<sup>69</sup></td><td>https://sourceforge.net/projects/bbmap/</td></tr><tr><td>Trimmomatic (0.39)</td><td>Bolger et al.<sup>70</sup></td><td>https://github.com/usadellab/Trimmomatic</td></tr><tr><td>STAR (2.7.10b)</td><td>Dobin et al.<sup>71</sup></td><td>https://github.com/alexdobin/STAR</td></tr><tr><td>RSEM (1.3.2)</td><td>Li & Dewey<sup>72</sup></td><td>https://github.com/deweylab/RSEM</td></tr><tr><td>Bowtie2 (2.5.1)</td><td>Langmead & Salzberg<sup>73</sup></td><td>https://github.com/BenLangmead/bowtie2</td></tr><tr><td>HOMER (4.11)</td><td>Heinz et al.<sup>74</sup></td><td>http://homer.ucsd.edu/homer/</td></tr><tr><td>MACS2 (2.2.7.1)</td><td>Zhang et al.<sup>75</sup></td><td>https://github.com/macs3-project/MACS/</td></tr><tr><td>Samtools (2.23.5)</td><td>Danecek et al.<sup>76</sup></td><td>https://github.com/samtools/samtools</td></tr><tr><td>Bedtools (2.30.0)</td><td>Quinlan & Hall<sup>77</sup></td><td>https://github.com/arq5x/bedtools2</td></tr><tr><td>Deeptools (3.5.0)</td><td>Ramírez et al.<sup>78</sup></td><td>https://github.com/deeptools/deepTools</td></tr><tr><td>Cell Ranger (7.0.1)</td><td>10x Genomics</td><td>https://www.10xgenomics.com/support/software/cell-ranger/latest</td></tr><tr><td>R (4.1.3)</td><td>{R Core Team}<sup>79</sup></td><td>https://www.r-project.org</td></tr><tr><td>edgeR (3.36.0)</td><td>Ritchie et al.<sup>80</sup></td><td>https://bioinf.wehi.edu.au/edgeR/</td></tr><tr><td>sva (3.35)</td><td>Zhang et al.<sup>81</sup></td><td>https://github.com/zhangyuqing/ComBat-seq</td></tr><tr><td>Seurat (4.3.0)</td><td>Satija et al.<sup>82</sup></td><td>https://github.com/satijalab/seurat</td></tr><tr><td>Simspec (0.0.0.9000)</td><td>He et al.<sup>83</sup></td><td>https://github.com/quadbio/simspec</td></tr><tr><td>Slingshot (2.2.1)</td><td>Street et al.<sup>84</sup></td><td>https://github.com/kstreet13/slingshot</td></tr><tr><td>LEAP</td><td>Specht et al.<sup>28</sup></td><td>https://github.com/cran/LEAP</td></tr><tr><td>iGraph (1.3.5)</td><td>Csárdi et al.<sup>85</sup></td><td>https://github.com/igraph/igraph</td></tr><tr><td>DiffBind (3.4.11)</td><td>Stark & Brown<sup>86</sup></td><td>https://bioconductor.org/packages/DiffBind/</td></tr><tr><td>Analysis Workflow</td><td>This paper</td><td>https://github.com/JosephLeger/Paper_NFIL3_dev_ILC</td></tr><tr><td>Gephi (0.10)</td><td>Bastian et al.<sup>87</sup></td><td>https://gephi.org</td></tr><tr><td>GraphPad Prism (8.4.3)</td><td>GraphPad</td><td>https://graphpad.com</td></tr></tbody></table>

## EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

### Mice

B6-Ly5.2 (CD45.1) and NOD scid gamma deficient (NSG) mice were obtained from the Jackson Laboratory. Tcf7<sup>EGFP4</sup>, Tcf7<sup>YFP7</sup>, Il7r-iCre R26-stop-YFP<sup>24</sup>, Nfil3<sup>-/-59</sup> Gata3<sup>flox60</sup>, and Tox<sup>-/-61</sup> mice have previously been described. The mice used were 8–10 weeks old and of either
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sex. Animal procedures were approved by the relevant Ethical Committee (Comité d'Ethique en matière d'expérimentation animale des Pays de la Loire, Direction Départementale de Protection des Populations, APAFIS #21254; and National Institutes of Health Animal Care and Use Committee).

## METHOD DETAILS

### Flow cytometry

BM cell suspensions were incubated with a mix of purified rat, mouse and hamster immunoglobulin G (IgG) before the addition of specific antibodies. Antibodies specific for B220 (RA3-6B2), Ly-6D (49-H4), CD19 (1D3), Mac-1 (M1/70), Gr-1 (RB6-8C5), CD11c (N418), Ter119 (TER119), NK1.1 (PK136), CD3ε (145-2C11), CD8α (53-6.72), CD8β (H35-17.2), CD4 (GK1.5), TCRβ (H57-597), TCRγδ (GL-3), Kit (ACK2 and 2B8), CD24 (M1/69), CD44 (IM7), Thy1.2 (53-2.1), α4β7 (DATK32), IL-7Rα (A7R34), Flt3 (A2F10), ICOS (C398.4 A and 7E.17G9), 2B4 (eBio244F4), CD25 (PC61.5), CD45.2 (104), Thy1.1 (HIS51), NKP46 (29A1.4), DX5 (DX5), TNF-α (MP9-XT22), IFN-γ (XMG1.2), IL-13 (eBio13A), IL-5 (TRFK5), IL-22 (IL22JOP), IL-17A (eBio17B7), RORγt (AFKJS-9), T-BET (4B10), EOMES (WD1928), TOX (TXRX10), PLZF (Mags.21F7 and 9E12), GATA-3 (TWAJ), and NFIL3 (S2M-E19) were from eBioscience; Antibodies specific for CD122 (TM-β1), Ly-6D (49-H4), VLA1 (HMα1), and HA (16B12) were from BioLegend; anti-Flt3 (A2F10) was from BD Biosciences; and anti-TCF-1 (C63D9) was from Cell Signaling. Several lineage 'cocktail' (Lin) were used, depending on the organ analyzed. The Lin for BM staining is a mix of the following antibodies: anti-Ly-6D, B220, CD19, Mac-1, Gr-1, CD11c, Ter119, NK1.1, CD3ε, CD8α, CD4, TCRβ and TCRγδ. The Lin for thymus staining is the same as BM, except it does not include anti-Ly-6D and CD4. The Lin for FL staining is the same as BM, except it does not include anti-Ly-6D, Mac-1 and CD4. TOX, PLZF, GATA-3, RORγt, T-BET, EOMES, NFIL3, HA and
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TCF-1 expressions were detected by intracellular staining using eBioscience's transcription factor staining buffer set according to the manufacturer's instructions. TNF-α, IFN-γ, IL-13, IL-5, IL-22, and IL-17A expressions were detected by intracellular staining in permeabilization buffer (eBioscience), following fixation with 2% paraformaldehyde. Live/dead discrimination was performed by staining with DAPI or LIVE/DEAD Fixable Blue (Invitrogen). Samples were acquired using an LSRFortessa, a Symphony, an AriaIII, or a CANTOII flow cytometer (BD Biosciences) and analyzed using FlowJo software (10.7.1) (BD Biosciences). All analyses are presented on singlet live cells as shown in Figure S3a. GFP/YFP separation was achieved using the filters 515/30, 495LP and 542/27, 525LP. Cells were sorted using an Aria flow cytometer (BD Biosciences).

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## BM progenitor isolation, transduction, culture and quantification

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ILC precursor populations were isolated by flow cytometric sorting from Lin-depleted BM cells as previously described⁸⁸. In particular Lin-Thy1.2-YFP-Kit⁺Flt3⁺IL-7Rα⁺ ALPs used in culture experiments were isolated as shown in Figure S4. Precursors were transduced and cultured on irradiated OP9 stromal layers in α-MEM media supplemented with 5% fetal bovine serum, glutamine, penicillin and streptomycin. All cultures were supplemented with mouse stem cell factor, mouse Flt3-ligand and mouse IL-7 at 30 ng.ml⁻¹. When indicated, OP9 transduced with Dll1 or Dl4 were used⁵⁸. For transduction, retroviral supernatant and polybrene (4 ng.ml⁻¹, Sigma-Aldrich) were added to the cells, and centrifuged for 2 hours at 32°C, 2000rpm. To ensure the reproducibility of the results, we used an infection rate of less than 37%, so that transduced cells would only express one copy of the transgene (Poisson distribution)⁸⁹. All cytokines were purchased from PeproTech. Phorbol-12-myristate-13-acetate, ionomycin, and brefeldin A were purchased from Sigma-Aldrich. (Z)-4-Hydroxytamoxifen (4-OHT) was from Abcam. CD45.2⁺ cells were considered for analysis of ALP progeny. Quantifications of cell
numbers following culture of rare precursors was done as following. The number of cells in the
starting population was calculated from the number of cells counted by the cell sorter, corrected
for the error rate experimentally determined for each sorter by hemocytometer counting. The
number of cells after culture was calculated from the number of cells analyzed by flow
cytometry, corrected for the loss during cell staining and analysis, as experimentally determined
by hemocytometer counting for each experiment. Quantification of progeny of transduced cells
4 days after transduction (Figure 4B, S8A) was estimated by considering that the frequency of
transduced ALP measured at day 2 (when expression of the reporter was optimal) was similar
to the one at day 0.

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744 Retroviral constructs

745 *Icn1*-GFP³³ and *Gata3*-GFP⁶² (Addgene plasmid #34836) have been described. *Dnmaml1*-VEX
746 was built by mutating the GFP from *Dnmaml1*-GFP³⁴ into a violet excitable GFP (VEX)⁹⁰.
747 Additional GFP- retroviral vectors were built by inserting the indicated mouse cDNA (cloned
748 in house) in the poly-linker of MigR1⁶³ (Addgene plasmid #27490). control-GFP correspond to
749 MigR1. Thy1.1- retroviral vectors were built by inserting the corresponding mouse cDNA
750 followed by P2A⁶⁴ (from Addgene plasmid #74056) and *Thy1.1*⁶⁵ (from Addgene plasmid
751 #17442), between the restriction sites EcoRI and AgeI of an MSCV backbone (from Addgene
752 plasmid #52114). *Nfil3* mouse cDNA was cloned in house, *Batf*⁶⁶ and *Batf3*⁶⁷ mouse cDNAs
753 were purchased from Addgene (plasmids #34575 and #139837 respectively). Control-Thy1.1
754 correspond to MSCV-IRES-Thy1.1⁶⁵ (from Addgene plasmid #17442). For *Nfil3-ER-T2-*
755 Thy1.1, the sequence encoding for the ER-T2⁶⁸ (from Addgene plasmid #87693), was inserted
756 in C-term of *Nfil3* after the linker
757 GGGAGCGGAGGAGGTTCGGTGGAGGTGGTTCTGGA. For *Nfil3-HA*-Thy1.1, the
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sequence TACCCATACGATGTTCCAGATTACGCT encoding for the canonical HA-tag was
inserted in C-term of *Nfil3* after the same 36 base pair linker as for ER-T2 tag.

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qPCR and bulk RNA-seq

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RNA was extracted by RNeasy Plus Micro Kits (Qiagen) according to the manufacturer's in-
structions. Quality control was performed by bioanalyzer (Agilent), and RNA samples with
RNA integrity number of >9 were subsequently used. For qPCR, RNA was reverse transcribed
to cDNA, using SuperScript II Kit (Invitrogen). Real-time PCR was performed using pre-made
Taqman PCR Master Mix and probes specific for indicated transcripts (Applied Biosystems)
and analyzed on a StepOnePlus Real-Time PCR system (Applied Biosystems). Relative tran-
script abundance was determined using the ΔΔCt method after normalization to Gapdh. For
RNA-seq experiments shown in Fig. S1A and 6C,D, previously generated RNA-seq data⁷ were
reanalyzed along with additional RNA-seq data generated using the same protocol: messenger
RNA-Seq libraries were prepared using a SMARTer Ultra Low Input RNA Kit version 3 (Clon-
tech) and Nextera XT DNA Library Preparation Kit (Illumina). Paired-end sequence reads of
126 base pairs were generated with a HiSeq 2500 sequencer (Illumina). Samples shown in Fig-
ure S1A were sequenced in one batch. Samples shown in Figure 6C,D were sequenced in two
batches. Batch 1 was newly generated, batch 2 was reanalyzed from⁷. They are named as “sam-
ple.replicateNumber.batchNumber”. For samples shown in Figure 7B, library construction and
sequencing were done by BGI. In brief, RNA-Seq library construction was done using cDNA
amplification via SMART-SeqII combined with transposase-based library construction, librar-
ies were circularized, and sequencing was executed by DNA Nanoballs (DNB™) technology.
For samples sequenced with the Illumina technology, adapter sequences and low-quality reads
were removed from raw FASTQ reads using Trimmomatic (0.39). For samples prepared with
the DNB™ technology, trimming was not needed. Estimation of levels of gene expression from

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raw FASTQ reads was performed using RSEM<sup>72</sup> (1.3.2) by alignment to the mouse genome (mm39) using STAR<sup>71</sup> (2.7.10b) with Ensembl GTF (release 108). Further analysis for visualization, expression changes, and statistics were realized using Limma-Voom<sup>80</sup> (edgeR 3.36.0), with quantile normalization and batch correction using ComBat-Seq<sup>81</sup> (sva 3.35.). Genes with P-val < 0.05 and absolute value of Log2 Fold Change > 1 were considered statistically significant. Visualization was done using R (4.1.3)<sup>79</sup>.

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## scRNAseq

scRNA-Seq data were generated as previously described<sup>7</sup>. B cells isolated from spleen were added to each of the scRNA-Seq samples as an internal control. scRNA-Seq libraries were prepared using Chromium Single Cell 3' kits, according to the manufacturer's instructions (version 2 chemistry; 10x Genomics). The obtained libraries were sequenced with a NextSeq system (version 2 chemistry; Illumina). The α4β7<sup>hi</sup> sample was prepared in the same batch as the ALP and Tcf7<sup>+</sup> samples from<sup>7</sup>. The three Il7r<sup>LT+</sup> samples were prepared together in a separated batch. Primary analysis was performed with Cell Ranger version 7.0.1 software using the default parameters. The data were processed using Seurat package (4.3.0)<sup>82</sup> and R (4.1.3)<sup>79</sup>. From the first batch, only cells expressing between 200 and 5000 genes and less than 2% of mitochondrial genes were kept. From the second batch, cells expressing between 1200 and 5000 genes and less than 4% of mitochondrial genes were kept. Read counts were Log Normalized and scaled while cell cycle difference scores and mitochondrial percentages were regressed. The two experiments were integrated using Cluster Similarity Spectrum<sup>83</sup> (simspec 0.0.0.9000). Clustering was performed using default Seurat parameters, and contaminant cell types were identified based on the top 30 cluster gene markers using ImmGen<sup>91</sup>. Pseudotime reconstruction analysis was performed using Slingshot (2.2.1)<sup>84</sup>. Variable transcription factors identified using Seurat mean.var.plot method were separated in 8 clusters based on their expression pattern

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along the pseudotime using K-means clustering. The number of clusters was defined based on total within sum of squares. Visualization of expression variation curves was realized from scaled data by separating cells through equal pseudotime bins and calculating mean expression of cells in each bin. Bins containing less than 5 cells were not plotted. For comparisons between transcripts, expression was scaled from 0 to 1 using min-max scaling. Correlation network analysis along the pseudotime was performed on normalized expressions using LEAP<sup>28</sup>, considering genes expressed in at least 50 cells along the trajectory and allowing a lag of one-third of the cells between correlated genes. Correlation of ≥0.1625 were considered for further analysis, corresponding to a false discovery rate <0.05 according to LEAP permutation step. From the remaining genes, correlations with a lag of 0 were excluded from Nfil3 candidate genes. Resulting correlation network was converted into a graph using iGraph (1.3.5)<sup>85</sup> and visualized using Gephi (0.10)<sup>87</sup>.

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## CUT&RUN data generation

NFIL3 CUT&RUN was performed with the CUTANA ChIC/CUT&RUN Kit Version 3 (EpiCypher), on nuclei from Thy1.1<sup>+</sup>Mac-1<sup>-</sup> cells isolated from 2 days cultures of Nfil3-transduced ALP. 38000 ILC precursors generated with Nfil3-HA-Thy1.1 retrovirus were used to obtain NFIL3 specific binding, and 27000 ILC precursors generated with Nfil3-Thy1.1 were used to control for background with the anti-HA. Both cell samples were split into two technical replicates, nuclei extraction was performed before proceeding to CUT&RUN. Incubation with the anti-HA antibody (Invitrogen SG77) was for 2 hours. Libraries were prepared from extracted DNA fragments using MGIEasy Universal DNA Library Prep Set. Sequence reads of 50 base pairs were sequenced by DNBSEQ-G400 (MGI).

## CUT&RUN and ChIP-seq analysis
Newly generated and publicly available datasets<sup>47,48</sup> were analyzed. Duplicates raw FATSQ reads were removed using Clumpify (BBMap 39.00)<sup>69</sup>, and adapter sequences and low-quality reads were removed using Trimmomatic (0.39)<sup>70</sup>. The remaining reads were aligned to mouse genome (mm39) using Bowtie2 (2.5.1)<sup>73</sup> using previously described parameters<sup>92</sup>. Reads mapped with a MAPQ score<10 were filtered out using Samtools (2.23.5)<sup>76</sup>. Peak calling was done using HOMER (4.11)<sup>74</sup> -style factor default parameters, with untagged NFIL3 sample as input control. For reanalyzed CUT&RUN data, available IgG samples were used as input control. Reanalyzed ChIP-seq data were processed without input, as it was not available. Motif enrichment analysis and peak annotation to the nearest transcription start site (TSS) was performed using HOMER (4.11)<sup>74</sup>. This annotation identified 94 binding sites located in close proximity to the promoters of 49 out of the 102 NFIL3 candidate gene targets. 24 additional NFIL3 peaks initially assigned to other genes whose promoter was closer or to long non-coding RNA of unknown functions were manually re-assigned to NFIL3 candidate gene targets when justified (eg. peaks located in gene body or in known regulatory elements). Locations of all NFIL3 binding of potential interests are described in Table S3. Mapped reads were converted in bigwig files for visualization using Deeptools (3.5.0)<sup>78</sup> with RPKM normalization method, and used for heatmap representation generated by Bedtools (2.30.0)<sup>77</sup>. UCSC tracks and NFIL3 known motifs genome wide were generated with HOMER (4.11)<sup>74</sup> makeUCSCfile and scanMotifGenomeWide functions using default parameters.

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853 DNase-seq analysis

Previously generated DNase-seq data⁷ were analyzed. Duplicates raw FATSQ reads were removed using Clumpify (BBMap 39.00)⁶⁹, and adapter sequences and low-quality reads were removed using Trimmomatic (0.39)⁷⁰. The remaining reads were aligned to mouse genome (mm39) using Bowtie2 (2.5.1)⁷³ using default parameters. Reads mapped with a MAPQ

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score<10 were filtered out using Samtools (2.23.5)⁷⁶. For peak calling, MACS2 (2.2.7.1)⁷⁵ with
--nodomel --shift 50 -extsize 100 parameters was used. Differential peaks and variation patterns
were identified using DiffBind (3.4.11)⁸⁶ with FDR threshold set to 0.25, and motif enrichment
analysis and peak annotation was performed using HOMER (4.11)⁷⁴. Mapped reads were
converted in bigwig files for visualization using Deeptools (3.5.0)⁷⁸ with RPKM normalization
method, and used for heatmap representation generated by Bedtools (2.30.0)⁷⁷. UCSC tracks
were generated from mapped reads using HOMER (4.11)⁷⁴ makeUCSCfile function with
default parameters.

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QUANTIFICATION AND STATISTICAL ANALYSIS

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Statistical analysis was performed on groups with limited variance using GraphPad Prism
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(8.4.3). Differences between groups of mice or wells were determined by 2-tailed unpaired
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Student's t-test calculated with Prism. Differences of gene expression between RNA-seq
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samples were determined by moderated t-test using Limma-Voom⁸⁰. P < 0.05 was considered
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significant. Sample sizes were empirically determined. No samples or animals were excluded
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from the analysis, and no randomization or blinding was used.

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SUPPLEMENTAL INFORMATION

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**Table S2. LEAP analysis of WT scRNA-seq, related to Figure 7.** LEAP results of all genes
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predicted to be regulated by *Nfil3*. Genes with Lag 0 and correlation value below FDR cutoff
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were removed.

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**Table S3. NFIL3 binding at its gene targets, related to Figure 7.** NFIL3 binding peaks
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located in the vicinity or known regulatory elements of the 102 NFIL3 candidate gene targets.
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Peaks annotated by HOMER to a target gene are presented in the first column. Manually annotated peaks added are presented in the second column together with justification or supplementary observations in the third column.

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## REFERENCES

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1. Vivier, E., Artis, D., Colonna, M., Diefenbach, A., Di Santo, J.P., Eberl, G., Koyasu, S., Locksley, R.M., McKenzie, A.N.J., Mebius, R.E., et al. (2018). Innate Lymphoid Cells: 10 Years On. Cell **174**, 1054–1066. https://doi.org/10.1016/j.cell.2018.07.017.

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2. Jacquelot, N., Seillet, C., Vivier, E., and Belz, G.T. (2022). Innate lymphoid cells and cancer. Nat Immunol **23**, 371–379. https://doi.org/10.1038/s41590-022-01127-z.

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