# Sequential Lymphotoxin Beta Receptor and Retinoic Acid Receptor signals regulate cDC2 fates

Albert A Nguyen, Logan Fisher, Jennifer S Y Ahn, Michelle Zuo, Tyler Park, Anushka Yadav, Miki S Gams, Kei Haniuda, Dragos C Dasoveanu, Conglei Li, et al.

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Albert A Nguyen, Logan Fisher, Jennifer S Y Ahn, Michelle Zuo, Tyler Park, et al. Sequential Lymphotoxin Beta Receptor and Retinoic Acid Receptor signals regulate cDC2 fates. 2026. hal-05536515

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# Sequential Lymphotoxin Beta Receptor and Retinoic Acid Receptor signals regulate cDC2 fates

**Albert Nguyen**

University of Toronto https://orcid.org/0000-0001-8724-7476

**Logan Fisher**

Weill Cornell Medical College https://orcid.org/0000-0003-4680-1527

**Jennifer Ahn**

University of Toronto https://orcid.org/0009-0005-5305-8956

**Michelle Zuo**

University of Toronto https://orcid.org/0000-0002-6704-453X

**Tyler Park**

Memorial Sloan Kettering Cancer Center https://orcid.org/0000-0003-1246-1544

**Anushka Yadav**

Memorial Sloan Kettering Cancer Center https://orcid.org/0000-0002-4333-6028

**Miki Gams**

University of Toronto

**Kei Haniuda**

University of Toronto https://orcid.org/0000-0002-9313-6542

**Dragos Dasoveanu**

University of Toronto

**Conglei Li**

Chinese University of Hong Kong, Shenzhen

**Lesley Ward**

University of Toronto

**Angela Wang**

University of Toronto https://orcid.org/0000-0001-6991-7368

**Zakieh Tayyebi**

https://orcid.org/0000-0002-7821-9905

**Daria Trofimova**

Queen's University

**Trevor McKee**

HistoWiz Inc. https://orcid.org/0000-0002-2195-6146

**Cathy Mendelsohn**

Columbia University

**John Allingham**
## Article

### Keywords:

**Posted Date:** October 16th, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-4784425/v2

**License:** ©⑈ This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License

**Additional Declarations:** There is **NO** Competing Interest.
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Sequential Lymphotoxin Beta Receptor and Retinoic Acid Receptor signals regulate cDC2 fates

Albert A. Nguyen¹,*, Logan Fisher²,*, Jennifer S.Y. Ahn¹, Michelle Zuo¹, Tyler Park³, Anushka Yadav⁴, Miki S. Gams¹,⁵, Kei Haniuda¹, Dragos C. Dasoveanu¹, Conglei Li¹,⁶, Lesley A. Ward¹, Angela A. Wang¹, Zakieh Tayyebi⁷, Daria Trofimova⁸, Trevor McKee⁹, Cathy L. Mendelsohn¹⁰, John S. Allingham⁷, Cynthia J. Guidos¹,⁵, Blandine Maître¹,¹¹, Jennifer L. Gommerman¹,† and Chrysothemis C. Brown²,⁴,¹²,‡

¹Department of Immunology, University of Toronto, Toronto, Ontario, Canada. M5R 3C5

²Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine Graduate School of Medical Sciences; New York, NY, USA

³Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

⁴Immune Oncology Program, Memorial Sloan Kettering Cancer Center; New York, NY, 10065 USA

⁵Program in Cell & Systems Biology, Hospital for Sick Children Research Institute

⁶School of Medicine, The Chinese University of Hong Kong, Shenzhen, China

⁷Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

⁸Department of Biomedical and Molecular Sciences, Queen's University

⁹Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. M5R 3C5

¹⁰Department of Pathology and Cell Biology, Columbia University

¹¹Université de Strasbourg, Etablissement Français du Sang (EFS) Grand Est, BPPS UMR_S 1255, Strasbourg, France

¹²Department of Pediatrics, Memorial Sloan Kettering Cancer Center; New York, NY, 10065, USA

¹³Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center; New York, NY, 10065, USA

*Equal contribution

†Corresponding author
37 Authors and Affiliations

38 Department of Immunology, University of Toronto, Toronto, Ontario, Canada.

39 Albert A. Nguyen, Jennifer S.Y. Ahn, Michelle Zuo, Kei Haniuda, Dragos C. Dasoveanu, Miki

40 Gams, Cynthia Guidos, Conglei Li, Lesley A. Ward, Angela A. Wang, Blandine Maître, and

41 Jennifer L. Gommerman

42 Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine Graduate

43 School of Medical Sciences, New York City, NY, USA

44 Logan Fisher and Chrysothemis C. Brown.

45 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center,

46 NY, USA

47 Tyler Park and Zakieh Tayyebi,

48 Immune Oncology Program, Memorial Sloan Kettering Cancer Center; New York, NY,

49 USA

50 Anushka Yadav and Chrysothemis C. Brown.

51 Hospital for Sick Children Research Institute, Program in Cell & Systems Biology

52 Miki S. Gams and Cynthia J. Guidos.

53 The Chinese University of Hong Kong School of Medicine, Shenzhen, China

54 Conglei Li.

55 Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario,

56 Canada

57 Daria Trofimova and John S. Allingham.

58 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto,

59 Ontario, Canada.

60 Trevor McKee.

61 Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
62 Cathy L. Mendelsohn.

63 Université de Strasbourg, Etablissement Français du Sang (EFS), Strasbourg, France

64 Blandine Maître

65 Department of Pediatrics, Memorial Sloan Kettering Cancer Center; New York, NY, USA

66 Chrysothemis C. Brown

67 Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center; New York, NY, USA

69 Chrysothemis C. Brown

70
71 Abstract

72 Type II conventional dendritic cells (cDC2) are functionally and phenotypically heterogenous.

73 Previous work in mice and humans identified two cDC2 subsets (cDC2A and cDC2B) and a
74 monocytic DC3 subset. However, the microenvironmental cues governing their distinct
75 differentiation pathways remain unclear. Here we delineate murine cDC2 lineage relationships
76 and the sequential signals required for cDC2A maintenance. We show that cDC2s, arising from
77 the CLEC9A+ cDC progenitor, encompass T-bet-expressing cDC2A and two cDC2B subsets
78 distinguished by MGL2 expression, with monocytic DC3 exhibiting transcriptional overlap with
79 Mgl2- cDC2B. Among these subsets, T-bet+ cDC2A dominate the spleen where they require
80 cell-intrinsic retinoic acid (RA) signaling to sustain their differentiation via Notch signals.

81 Lymphotoxin beta receptor signaling on splenic cDC2s limits F-actin content retaining cDC2 at
82 sites of retinol delivery. In summary, these data establish the developmental and transcriptional
83 relationships between diverse cDC2 subsets and identify signals that regulate their prevalence in
84 specific lymphoid tissues.

85
86 **Main Text**

87 Conventional dendritic cells (cDCs) encompass two developmentally and functionally distinct
88 subsets, XCR1⁺ cDC1 and cDC2, the latter distinguished by expression of either SIRPα or
89 CD11b. While cDC1s are required for cytotoxic CD8⁺ T cell priming, cDC2s have been
90 implicated in CD4⁺ T cell mediated immunity, most notably Th2 ¹,² and Th17 ³,⁴,⁵

91 differentiation. In contrast to the cDC1 lineage, cDC2s are transcriptionally and functionally
92 heterogenous. Previous studies identified two major cDC2 subsets, variably termed cDC2A and
93 cDC2B ⁶, or DC2 and DC3 ⁷. A comparative analysis of cDC2A/B and DC2/3 transcriptomes
94 demonstrated that cDC2A are analogous to DC2 whereas cDC2B correspond to DC3, with DC3
95 arising from a Ly6C⁺ monocyte-DC progenitors (MDP) ⁷. This is at odds with the finding that
96 cDC2A and cDC2B arise from Clec9a⁺ classical DC progenitors, with fate specification
97 occurring in the bone marrow (BM) ⁸,⁹. One possibility, suggested by a single cell RNA
98 sequencing (scRNA-seq) analysis of DCs, is that cDC2B and DC3 co-exist as two
99 transcriptionally and ontogenically distinct DC subsets ⁹. Adding to the complexity, a recent
100 study suggested that ESAM⁺ cDC2s, previously thought to reflect cDC2A, encompassed two
101 phenotypically and ontogenically distinct subsets of DC2s – CD11b¹⁰ESAM⁺ cDC2A and
102 CD11bʰi ESAM⁺ DC2 – with the latter representing the dominant DC2 subset within the spleen
103 ¹⁰. A better understanding of the distinguishing transcriptional programs and features of cDC2A,
104 cDC2B and DC3 is key to unravelling their unique functions.

105 The differential abundance of cDC2A vs cDC2B across tissues and lymph nodes suggests
106 that, following their specification in the BM, additional peripheral cues shape the identity of
107 these subsets and/or the recruitment of pre-specified cDC2 progenitor cells ⁸. Such cues are not
108 well described yet are critical to our understanding of how secondary lymphoid tissues can
support distinct immunological outcomes. For example, cDC2A are dependent on Notch signaling, and expression of Notch2 in CD11c⁺ cells is required for splenic Tfh and intestinal Th17 responses ³, ⁴, ¹¹. On the other hand, cDC2B are dependent on *Klf4* ², and conditional deletion of *Klf4* in CD11c⁺ cells impairs Th2 responses, resulting in poor immunity against *S. mansoni* challenge in the skin, and house dust mite challenge in the lung ². Understanding tissue-specific cues that regulate cDC2 cell fates is thus key to modulating CD4⁺ T cell immune responses.

In this study, we investigated the full spectrum of cDC2 heterogeneity and the environmental cues that shape cDC2 fates in lymphoid tissues. We show that cDC2s encompass cDC2A and two subsets of cDC2B distinguished by expression of MGL2, with *Mgl2*⁻ cDC2B displaying transcriptional overlap with previously described monocytic DC3. While cDC2A and cDC2B subsets were derived from CLEC9A⁺ common DC progenitors (CDP), their relative proportions varied across secondary lymphoid tissues, indicating a role for environmental cues in regulating the balance between these subsets. Using expression of T-bet by cDC2A to selectively disrupt candidate tissue-specific signals, we identified sequential roles for the TNF superfamily member receptor lymphotoxin beta receptor (LTβR) and retinoic acid (RA), a metabolic derivative of dietary Vitamin A, in promoting and maintaining cDC2A differentiation, respectively.

## Lineage tracing and scRNA-seq define cDC2A, cDC2B and DC3 subsets in lymphoid tissues

The aim of our study was to identify the signals that regulate cDC2 heterogeneity within secondary lymphoid tissues. Since differences in cDC2 composition could reflect homing of distinct progenitors to these sites, or distinct tissue cues driving differentiation of a common
progenitor into separate cDC2 lineages, we first rigorously validated previously proposed cDC2 subsets and their ontogeny using lineage-tracing and gene expression. To distinguish cDC2A, cDC2B and DC3 subsets, we generated *Clec9a<sup>Cre/Cre</sup> Tbx21<sup>RFP-CreERT2</sup>R26<sup>sl-YFP</sup> mice in which descendants of CLEC9A<sup>+</sup> CDP are labeled with YFP, and cDC2A can be distinguished from cDC2B/DC3 by expression of RFP<sup>11</sup>. Analysis of splenic cDC2 subsets revealed that ~95% of T-bet<sup>+</sup> cDC2A and ~85% of T-bet<sup>-</sup> cDC2 were YFP<sup>+</sup> with similar proportions of fate-mapped cells observed across different lymphoid tissues (Fig. 1a and Extended Data Fig. 1a). By contrast, CD88<sup>+</sup>Ly6C<sup>+</sup> monocytes exhibited negligible YFP labeling. To determine if the unlabeled T-bet<sup>-</sup> cDC2s represented monocytic DC3, we examined expression of CD16/CD32, a proposed marker for DC3<sup>21</sup>. Surprisingly, we observed increased proportions of CD16/32<sup>+</sup> cells among YFP<sup>+</sup> T-bet<sup>-</sup> cDC2Bs relative to their YFP<sup>-</sup> counterparts (Extended Data Fig. 1b). To better identify markers that would distinguish ontogenically distinct cDC2B and DC3 subsets, we performed scRNA-seq analysis of YFP<sup>+</sup> and YFP<sup>-</sup> Lin<sup>-</sup> (TCRβ<sup>-</sup>CD19<sup>-</sup>SiglecF<sup>-</sup>NK1.1<sup>-</sup>) MHCII<sup>+</sup> cells isolated from spleen and gut-draining mesenteric lymph nodes (mLN) of *Clec9a<sup>Cre/Cre</sup> Tbx21<sup>RFP-CreERT2</sup>R26<sup>sl-YFP</sup> mice (Extended Data Fig. 1c). Cells were labelled with oligonucleotide-tagged antibodies to allow retrospective identification according to their tissue of origin and ontogeny. Unsupervised clustering alongside analysis of canonical DC marker genes identified a cluster of *C5ar1* and *Apoe*-expressing monocyte/macrophages (cluster 4), plasmacytoid DCs (pDCs; cluster 1), CCR7<sup>-</sup> cDC1s (cluster 6), CCR7<sup>+</sup> ‘migratory’ DCs (cluster 7) and pre-DC2/transitional DCs (cluster 10). CCR7<sup>-</sup>CD11b<sup>+</sup> DC2s, the focus of this study, spanned one cluster of *Tbx21<sup>Esam</sup>+</sup> cDC2A (cluster 2) and two clusters expressing *Cd209a*, *Cx3cr1* and *Clec12a*, signature markers for both cDC2B and DC3<sup>6, 7, 9</sup> (Fig. 1b, c). *Esam<sup>+</sup>* cDC2s (cluster 2) exhibited uniformly high levels of *Itgam* (Fig. 1c), encoding CD11b, and
155 ~95% of these cells were YFP+ (Fig. 1d), confirming their cDC2A identity. This cDC2A cluster
156 represented the dominant cDC2 subset among YFP-labeled splenic cDCs (Fig. 1e), consistent
157 with our earlier flow cytometry analysis.

158 Analysis of differential gene expression alongside signature cDC2 genes demonstrated
159 that cluster 5 represented Mgl2+Clec10a+ cDC2Bs that were universally labeled by YFP (Fig.
160 1d, f). Cells in cluster 3 expressed higher levels of genes previously suggested to distinguish
161 DC3 from cDC2B, including Lilr4b and Lilrb4a (Fig. 1f) 9. However, rather than clusters 3 and 5
162 representing ontogenically distinct DC3 and cDC2B subsets, analysis of Clec9a-fate mapping
163 revealed that >95% of cluster 3 cells were also YFP+ (Fig. 1d). These findings suggested that
164 cDC2B cells comprise two transcriptionally distinct subsets or cell states, distinguished by Mgl2
165 or Clec10a expression, consistent with the original description of splenic cDC2B cells 6. Cell
166 surface protein analysis confirmed co-expression of MGL2 and CLEC10A by a subset of cDC2B
167 (Extended Data Fig. 1d). In line with our scRNA-seq analysis, close to 100% of MGL2+ and
168 ~85% MGL2- cDC2B were descended from Clec9a+ DC progenitors (Fig. 1g).

169 We wondered whether the YFP-T-bet-MGL2-cDC2s represented monocytic DC3. In
170 support of this, expression of Zbtb46, a transcription factor that distinguishes cDCs from
171 monocytes, was lower in YFP- cells compared to their YFP+ counterparts (Extended Data Fig.
172 1d, e). To definitively establish the overlapping and distinct transcriptional phenotypes between
173 cDC2B and DC3, we integrated our scRNA-seq dataset with publicly available data from Brown
174 et al., Liu et al., and Rodrigues et al 6, 7, 9. Reanalysis of the scRNA-dataset from Rodrigues et al.
175 confirmed the presence of two Cx3cr1+ cDC2 clusters (Extended Data Fig. 1f, g and
176 Supplementary Table 1) with differential expression of DC3 genes (Extended Data Fig. 1h)
177 and differential Cd300c fate-mapping (Extended Data Fig. 1i). We thus annotated these cells as
178 DC3 or cDC2B respectively, in line with their annotation in the original study 9. Visualization of
179 the integrated data (Fig. 1h) demonstrated that cells identified as cDC1, cDC2A, CCR7+ DCs
180 and pDCs in all 4 studies localized together in their respective regions of the UMAP, confirming
181 that the integration method accurately maps the same type of cells with similar transcriptional
182 programs. Consistent with previous analyses 7, DC2 cells mapped to cDC2A. Intriguingly, cells
183 annotated as DC3 by Rodrigues et al. or Liu et al. mapped to the cluster of Mgl2- cDC2B defined
184 by Brown et al. and further validated in this study, whereas cells annotated as cDC2B (Rodrigues
185 et al.) or pro-DC3 (Liu et al.) aligned with Mgl2+ cDC2B. Collectively these findings
186 demonstrate that cDC2B encompass two transcriptionally distinct cell states or subsets that can
187 be distinguished by expression of either MGL2 or CLEC10A, and that previously defined
188 monocytic DC3 are transcriptionally overlapping with Mgl2- cDC2B cells.

189 Together, these data establish the landscape of cDC2s, identifying one population of T-
190 bet+ cDC2A and two subpopulations of cDC2B (Extended Data Fig. 1j). This provides a
191 framework for interrogating the tissue-specific cues that shape cDC2 identity using robust
192 markers to distinguish cDC2A and cDC2B subsets.

193
194
**Environmental cues determine the balance of cDC2 subsets within lymphoid tissue**
195
Having established the utility of *Clec9a* lineage tracing alongside T-bet and either MGL2
196 or CLEC10A expression to delineate the full spectrum of cDC2A, cDC2B and DC3 subsets, we
197 next determined how cDC2 composition varies across different lymphoid tissues. To this end, we
198 performed flow cytometry analysis of DC subsets across spleen, skin-draining peripheral lymph
199 nodes (pLN), lung-draining mediastinal lymph nodes (medLN), mLN and Peyer's patches (PP)
200 of *Clec9a*<sup>Cre/Cre</sup> *Tbx21<sup>RFP-CreERT2</sup>R26<sup>sll-YFP</sup>* mice. Within the spleen and mLN, cDC2As
represented the majority (~65-80%) of CCR7<sup>-</sup>CD11b<sup>+</sup> DCs (Fig. 1i). By contrast within the pLN, cDC2A comprised a minor fraction (< 20%) of CCR7<sup>-</sup>CD11b<sup>+</sup> DCs with increased proportions of CLEC10A<sup>+</sup> cDC2B. The frequency of CLEC10A<sup>-</sup> cDC2B and DC3 subsets were similar across different lymphoid tissues.

Overall, these data suggest that environmental cues within the lymphoid tissue determine the relative abundance of cDC2s subsets with enrichment of cDC2A in spleen and gut lymph nodes relative to skin-draining lymph nodes.

**RAR signals are required for the maintenance of cDC2A in vivo.**

We next investigated the environmental signals that determine relative abundance of cDC2A vs cDC2B. Given the dominance of cDC2A in the spleen, we focused on this organ. Within the spleen, cDC2 are localized to the marginal zone bridging channels (MZBC), a prime location for the accumulation of blood-borne antigens and macromolecules <sup>12</sup>. A previous study demonstrated a role for Vitamin A in maintaining Notch2-dependent ESAM<sup>+</sup> splenic cDC2 <sup>13</sup>. To systemically impair RAR signaling in C57/Bl6 mice *in vivo*, we administered a Vitamin A Deficient (VAD) diet to pregnant dams from E14, and weaned mice onto a VAD diet. This regime ensures long-term liver stores of maternally-derived retinol are gradually depleted in the pups as they matured <sup>14</sup>. As previously reported <sup>13</sup>, flow cytometry analysis revealed that 8-10-week-old VAD mice had reduced splenic cDC2/cDC1 ratios compared with control diet-fed mice (**Extended Data Fig. 2a**).

Among cDC2s, the frequency of ESAM<sup>+</sup> cells and their level of ESAM expression were reduced in VAD mice compared to controls (**Extended Data Fig. 2b, c**). Imaging Mass Cytometry (IMC) analysis of spleen sections revealed a loss of DCIR2<sup>+</sup> cDC2<sup>15</sup> in the MZBC of
224 VAD mice compared to controls despite similar follicle size (Extended Data Fig. 2d-e). In
225 addition, supplementation of VAD diet with RA over a 10-day period resulted in elevated cDC2
226 frequency and numbers, and increased ESAM expression on cDC2 compared to vehicle control
227 treated mice (Extended Data Fig. 2f-h). Consistent with previous reports 13, 16, 17, manipulation
228 of RA levels did not alter cDC1 numbers in either the spleen or PPs (Extended Data Fig. 2i-k),
229 despite a reduction in cDC2 in VAD animals. Collectively these data demonstrate that RAR
230 signaling is required to maintain ESAM-expressing MZBC-resident cDC2 in the spleen and
231 CD11b+ cDC2 in the PP.

232 The vast majority of gut-associated cDC2, including those within the PPs, do not express
233 notable levels of ESAM despite the overall abundance of T-bet+ cDC2A (Extended Data Fig.
234 3a, b). We therefore turned to Tbx21<sup>RFP-Cre</sup> mice to specifically query the role of RA in the
235 maintenance of cDC2A in the spleen, PP and mLN. We first assessed cDC subsets in adult
236 Tbx21<sup>RFP-Cre</sup> mice whose RAR signaling was acutely inhibited with a pan-RAR small molecule
237 antagonist BMS-493 (gating strategy depicted in Extended Data Fig. 3c). BMS-treated animals
238 exhibited significantly reduced splenic cDC2A frequency and numbers compared to DMSO-
239 treated controls, while cDC2B numbers were unchanged across treatment groups (Fig. 2a-d).
240 Similarly, in VAD Tbx21<sup>RFP-Cre</sup> mice, 9 days of RA supplementation in adulthood resulted in a
241 significant elevation in the frequency of cDC2A compared with vehicle control supplementation
242 (Fig. 2e-j). Taken together, these findings show that RA signaling regulates the accumulation of
243 T-bet+ cDC2A in the spleen, PP and mLN.

244

245

*Maintenance of T-bet<sup>+</sup> cDC2A requires cell-intrinsic RAR signaling.*
246 RA impacts multiple cell types and could thus promote T-bet expression in cDC2 in a
247 direct or indirect manner ¹⁸. Moreover, a VAD diet impacts splenic cellular composition ¹⁹.
248 Indeed, analysis of splenic immune cells in VAD vs control mice revealed a reduction in CD4⁺
249 and CD8⁺ T cells and an increase in neutrophils (Extended Data Fig. 4a-g). To assess a cDC2A-
250 intrinsic role for RAR signaling, we utilized dnRara¹⁸S/1S mice which express a dominant-negative
251 form of the retinoic acid receptor RARα (dnRAR) downstream of a loxP-flanked STOP
252 cassette²⁰, thereby disrupting ligand dependent RAR signaling ²¹.

253 Given the recent identification of a pre-specified T-bet⁺ cDC2A (‘pre-DC2A’) in the BM
254 ⁸, we first investigated whether inhibition of RA signaling would impact cDC2A development at
255 this early stage. To test this, we generated Zbtb46Cre dnRara¹⁸S/1S mice, thereby blocking RAR
256 signaling within Zbtb46-expressing pre-cDCs. We observed comparable frequencies of SiglecH⁺
257 pre-cDC2A and SiglecH⁻ pre-cDC2B subsets in Zbtb46Cre dnRara¹⁸S/1S mice relative to littermate
258 controls (Extended Data Fig. 5a, b), suggesting that RAR signaling is not essential for early
259 specification decisions for the pre-cDC2A lineage in the BM. By contrast, Tbx21<sup>RFP-</sup>
260 Cre<sup>del</sup>Rara<sup>1S/1S</sup> mice exhibited reduced frequencies and numbers of cDC2A in the spleen with no
261 observable changes in cDC2B (Fig. 3a-e), consistent with our earlier findings in VAD mice.
262 Interestingly, although T-bet<sup>+</sup> cDC2A are relatively infrequent in the pLN, there was no further
263 reduction in pLN cDC2A frequencies driven by expression of the dnRAR construct (Extended
264 Data Fig. 5c, d), suggesting that the requirements for RAR signaling for maintaining cDC2A
265 vary across secondary lymphoid tissues.

266 Since T-bet s expressed in many cell types, we next determined the cell intrinsic role of
267 RAR signaling in cDC2As. To do this, we generated BM chimera mice with a mixture of
268 Zbtb46<sup>iDTR</sup> (80%) BM + either 20% Tbx21<sup>RFP-Cre</sup>dnRara<sup>1S/1S</sup> or Tbx21<sup>RFP-Cre</sup> littermate control
269 donor BM (so-called “zDC” chimeras). Post BM-engraftment, we treated chimeras with
270 diphtheria toxin (DT) for 10 days to ablate Zbtb46-expressing cDCs and allow for re-seeding of
271 the cDC compartment by dnRAR-expressing vs WT precursor cells (Fig. 3f). Expression of
272 dnRAR specifically in T-bet+ cDC2A resulted in a reduction in cDC2A frequencies and numbers
273 within the spleen and mLN (Fig. 3g-i). However, we did not observe any differences in PP T-
274 bet+ cDC2A frequencies (Fig. 3j). These findings were further supported by results from 50:50
275 competitive BM chimeras whereby splenic dnRAR-expressing cDC2 displayed reduced ESAM
276 expression and impaired fitness compared to control cDC2 (Extended Data Fig. 5e-h). In
277 contrast, cDC2B were unaffected by the expression of the dnRAR construct (Extended Data
278 Fig. 5i).

279 These data demonstrate a cell-intrinsic role for RAR in the regulation of splenic and
280 mLN, but not PP cDC2A, revealing tissue-specific differences in cDC2A differentiation.

281

282 *An RAR-Notch signaling circuit maintains cDC2A identity in the spleen*

283 Previous literature has shown that Notch signaling is a key regulator of cDC2A
development 3, 4, 6. Analysis of the canonical Notch-dependent target genes *Dtx1* and *Hes1*
284 revealed reduced expression in cDC2A expressing the dnRAR construct compared to controls,
285 suggesting that RAR signaling contributes to the regulation of Notch signaling in cDC2A. By
286 contrast, cDC2B exhibited a trend towards an increase in *Hes1* expression (Fig. 3k, l),
287 suggesting that under conditions of cDC2A reduction, cDC2B may abnormally access Notch
288 ligands including DLL1.
289

290
Although disrupted Notch2 signaling impairs cDC2A numbers ³,⁴, whether these signals are required for cDC2A lineage specification and/or maintenance of cDC2A identity is unclear.

To address this question, we generated competitive chimeras with a 50:50 mix of Tbx21<sup>RFP-Cre</sup>Rbpj<sup>fl/fl</sup> and CD45.1 donor BM (Fig. 3m). While we did not observe differences in the proportion of T-bet<sup>+</sup> cDC2A in this setting (Fig. 3n, o), we noted a significant reduction in ESAM expression among Rbpj-deficient cDC2A (Fig. 3p, q). Together, these results demonstrate a role for Notch signaling in T-bet<sup>+</sup> cDC2A for maintaining ESAM expression in splenic cDC2A.

### LTβR signaling precedes RAR signaling to promote splenic cDC2A development

**cDC-intrinsic expression** of the TNF superfamily receptor member LTβR is required for the maintenance of splenic Notch2-dependent ESAM<sup>+</sup> cDC2 numbers via provision of LTαβ ligand by B cells and ILC3<sup>22, 23</sup>. However, whether LTβR is required for cDCA vs cDC2B maintenance is not known. To answer this, we generated CD45.1 Tbx21<sup>RFP-Cre</sup>Ltbr<sup>-/-</sup> mice and generated 50:50 mixed bone marrow chimeras with CD45.1 Tbx21<sup>RFP-Cre</sup>Ltbr<sup>-/-</sup> and CD45.2 Tbx21<sup>RFP-Cre</sup>Ltbr<sup>+/+</sup> control mice (Fig. 4a). While cDC1 and cDC2B were derived equally from both Ltbr<sup>+/+</sup> and Ltbr<sup>-/-</sup> precursor cells (Fig. 4a), Ltbr<sup>+/+</sup> splenic cDC2A had a strong competitive advantage over Ltbr<sup>-/-</sup> cDC2A (Fig. 4b, c), confirming a critical role for LTβR signaling in regulating cDC2A abundance.

Next, to determine if LTβR signaling occurs upstream or downstream of RAR signaling, we subjected Ltbr<sup>+/+</sup>:Ltbr<sup>-/-</sup> chimeras to daily RA supplementation (Fig. 4d). We observed an increase in the frequency of WT but not Ltbr<sup>-/-</sup> ESAM<sup>+</sup> cDC2A (Fig. 4e), suggesting that DC-intrinsic LTβR signals function upstream of RAR signaling in the regulation of cDC2A. Similar
results were observed in zDC chimeras following RA supplementation with increased frequencies of ESAM⁺ cDC2A in *Ltbr⁺/+* but not *Ltbr⁻/⁻* derived cDC2A (Fig. 4f). Moreover, administration of an anti-LTβR agonist monoclonal antibody (4H8), previously shown to augment cDC2 numbers²², failed to rescue cDC2 numbers in VAD or BMS-493 treated mice (Fig. 4g-h). The inability of anti-LTβR agonism to rescue cDC2 numbers in RA-deficient settings was not due to impaired LTβR expression on cDC2 since both *Ltbr* transcript and LTβR protein levels were unaffected by manipulation of RA levels (Extended Data Fig. 6a-d). Thus, agonism of LTβR in the absence of RA is insufficient to rescue splenic cDC2.

To determine if LTβR signaling operates to maintain cDC2A prior to or after acquisition of T-bet expression, we generated *Tbx21<sup>RFP-Cre</sup>Ltbr<sup>fl/fl</sup>* mice and validated deletion of *Ltbr* in cDC2A (Extended Data Fig. 6e, f). We then generated BM chimeras using either *Tbx21<sup>RFP-Cre</sup>Ltbr<sup>fl/fl</sup>* or *Tbx21<sup>RFP-Cre</sup>* littermate control donor BM. Interestingly, we did not observe a difference in either cDC2A frequencies or numbers (Fig. 4i, j). To confirm these findings, we also generated zDC chimeras in which *Ltbr* was deleted in only T-bet⁺ cDC, and again observed no significant reduction in splenic cDC2A frequencies or numbers (Fig. 4k-m). In contrast, in *Zbtb4<sup>Cre</sup>Ltbr<sup>fl/fl</sup>* mice which delete *Ltbr* in cDC2s prior to acquisition of T-bet, a significant reduction in cDC2A frequency was observed (Extended Data Fig. 6g, h), consistent with our previous competitive BM chimeras (Fig. 4a-c).

In summary, these results demonstrate a role for LTβR signaling in cDC2A development, prior to expression of T-bet.

LTβR signaling confers preferential access to blood-proximal niches
We next explored the mechanism by which LTβR signaling promotes cDC2A differentiation within the spleen. Previous studies suggested a role for LTβR signaling in regulation of cDC2 proliferation and self-renewal ³, ²². In support of this, we observed elevated BrdU labelling of splenic cDC2A compared to cDC2B (~50% vs. 31% respectively) following 48hrs of BrdU administration in Tbx21<sup>RFP-Cre</sup> mice (Fig. 5a, b). In addition, analysis of Ki67<sup>+</sup> expression confirmed that cDC2A are more proliferative than cDC2B in the spleen (Fig. 5c, d), not unlike what was previously observed for unfractionated cDC2 ³, ⁸, ²². However, we did not observe differences in either BrdU incorporation or Ki67 staining in *Ltbr<sup>-/-</sup>* cDC2A (Fig. 5e, f), indicating that the proliferative capacity of cDC2A is not determined by LTβR signaling.

Following dietary uptake, retinoids are packaged into chylomicrons followed by subsequent transport which is facilitated through carrier lipoproteins as well as the serum amyloid A (SAA) family of proteins and retinol-binding protein 4 (RBP4) ²⁴, ²⁵. The cell surface receptor for SAA-retinol complexes is the lipoprotein receptor-related protein 1 (LRP1) and SAA-mediated delivery of retinol to LRP1-expressing intestinal CD11c<sup>+</sup> myeloid cells is required for retinaldehyde dehydrogenase expression ²⁵, ²⁶. To determine how retinol is transported to splenic cDC2s during homeostasis, we focused our attention on RBP4. Analysis of immune cells in ImmGen ²⁷ as well as our scRNAseq profiling of cDC subsets across LNs revealed that *Stra6l* is most highly enriched in cDC2A (Extended Data Fig. 7). qPCR analysis of *Stra6l* expression in FACS-isolated splenic cDC subsets confirmed selective expression of *Stra6l* by cDC2A (Fig. 5g). We did not observe expression of *Lrp1*, an alternative retinol binding protein, in any of the cDC subsets (Fig. 5g). In addition, flow cytometry analysis demonstrated increased frequency of RBPR2<sup>+</sup> cells among cDC2A relative to cDC2B and cDC1 subsets (Fig. 5h, i). Overall, these results suggest that cDC2A have enhanced capacity for retinol uptake.
Given that LTβR-deficient cDC2s fail to respond to RA, we hypothesized that LTβR expression is required for optimal access to and/or binding of Retinol/RBP4 complexes by splenic cDC2s. Notably, the frequency of RBPR2⁺ cells was reduced among *Ltbr*⁻/⁻ cDC2A, as well as their expression of *Stra6l*, which also trended lower in *Ltbr*⁻/⁻ cDC2B (Fig. 5i, j). To determine the location of WT vs *Ltbr*⁻/⁻ cDC2A within the spleen, we generated single BM chimeras using *Tbx21<sup>RFP-Cre</sup>*-*Ltbr*⁻/⁻ or *Tbx21<sup>RFP-Cre</sup>*-*Ltbr*⁺/⁺ donor BM and performed IMC on splenic sections. Mice were injected with anti-CD31 i.v. 30 minutes prior to sacrifice to identify vasculature, and analyzed as per Extended Data Fig. 2e with the addition of an anti-RFP antibody to identify *Tbx21*-expressing cells. We observed decreased numbers of *Ltbr*⁻/⁻ cDC2A in the MZBC relative to WT cDC2A (Fig. 5k, l). Moreover, *Ltbr*⁻/⁻ cDC2A were in closer proximity to vasculature (Fig. 5k, m). In summary, these experiments demonstrated that *Stra6l* and its corresponding RBPR2 protein are enriched in cDC2A and that LTβR signaling on cDC2s regulates positioning of cDC2A within the spleen as well as their expression of *Stra6l/RBPR2*.

372

373 **LTβR signaling regulates F-actin and splenic retention**

The mis-localization of *Ltbr*⁻/⁻ cDC2 in the spleen prompted us to examine whether LTβR influences cDC2 motility which may affect their splenic retention and consequent access to retinol. F-actin abundance in cDC2 has previously been used as a correlative readout of DC anchoring <sup>28</sup>. We therefore adapted a flow cytometry assay that uses fluorophore-conjugated phalloidin peptides to quantify intracellular F-actin content. In contrast to cDC1 and cDC2B which were uniformly F-actin high, ~20% of cDC2A expressed low levels of F-actin (Fig. 6a, b), at levels comparable to those observed in sessile cells such as pLN-derived fibroblasts (Extended Data Fig. 8a). Furthermore, the intensity of phalloidin staining was lower in cDC2A

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relative to cDC1 and cDC2B (Fig. 6c), suggesting that cDC2A are the least motile cDC subset.

Examination of phalloidin staining revealed increased F-actin content in both *Ltbr*⁻/⁻ cDC2 subsets, most notably within cDC2As (Fig. 6d-f). Furthermore, the proportion of F-actin low cDC2As was decreased among *Ltbr*⁻/⁻ cDC2A relative to *Ltbr*-sufficient cDC2A from the same mouse (Fig. 6g), indicating a cDC-intrinsic role for LTβR signaling in the regulation of F-actin expression. Finally, despite the much larger reservoir of *Ltbr*⁺/⁺ splenic cDC2s, we observed similar proportions of *Ltbr*⁻/⁻ and *Ltbr*⁺/⁺ cDC2 in the blood, (Fig. 6h, i), suggesting escape of *Ltbr*⁻/⁻ cDC2 from the spleen into the blood²⁸.

To determine whether observed differences in F-actin levels resulted in egress of *Ltbr*⁻/⁻ cDC2s from the spleen, we tested whether disrupting F-actin using Mycololide B (Myc. B) toxin²⁹ would reduce the propensity of *Ltbr*⁻/⁻ cDC2s to accumulate in the blood. Analysis of Phalloidin expression following in vitro Myc. B treatment confirmed reduced F-actin levels in cDC2A cells, validating this approach (Extended Data Fig. 8b, c). Next, we administered a single i.v. dose of Myc. B into *Ltbr*⁺/⁺:Ltbr*⁻/⁻ mixed BM chimeras and observed an increase in the *Ltbr*⁺/⁺:Ltbr*⁻/⁻ cDC2 ratio in the blood 2h post-treatment compared to vehicle-treated controls (Fig. 6j). This supports the hypothesis that the increased motility of splenic *Ltbr*⁻/⁻ cDC2s leads to their disproportionate representation in the blood.

Overall, these data show that *Ltbr*⁻/⁻ cDC2 cells are more motile than *Ltbr*⁺/⁺ cDC2s and are more likely to enter the circulation.

# Discussion

Although previous studies have identified heterogeneity among cDC2s, the lineage relationships between these cell types and the cues that promote cDC fates within tissues are not

relative to cDC1 and cDC2B (Fig. 6c), suggesting that cDC2A are the least motile cDC subset.

Examination of phalloidin staining revealed increased F-actin content in both *Ltbr*⁻/⁻ cDC2 subsets, most notably within cDC2As (Fig. 6d-f). Furthermore, the proportion of F-actin low cDC2As was decreased among *Ltbr*⁻/⁻ cDC2A relative to *Ltbr*-sufficient cDC2A from the same mouse (Fig. 6g), indicating a cDC-intrinsic role for LTβR signaling in the regulation of F-actin expression. Finally, despite the much larger reservoir of *Ltbr*⁺/⁺ splenic cDC2s, we observed similar proportions of *Ltbr*⁻/⁻ and *Ltbr*⁺/⁺ cDC2 in the blood, (Fig. 6h, i), suggesting escape of *Ltbr*⁻/⁻ cDC2 from the spleen into the blood²⁸.

To determine whether observed differences in F-actin levels resulted in egress of *Ltbr*⁻/⁻ cDC2s from the spleen, we tested whether disrupting F-actin using Mycololide B (Myc. B) toxin²⁹ would reduce the propensity of *Ltbr*⁻/⁻ cDC2s to accumulate in the blood. Analysis of Phalloidin expression following in vitro Myc. B treatment confirmed reduced F-actin levels in cDC2A cells, validating this approach (Extended Data Fig. 8b, c). Next, we administered a single i.v. dose of Myc. B into *Ltbr*⁺/⁺:Ltbr*⁻/⁻ mixed BM chimeras and observed an increase in the *Ltbr*⁺/⁺:Ltbr*⁻/⁻ cDC2 ratio in the blood 2h post-treatment compared to vehicle-treated controls (Fig. 6j). This supports the hypothesis that the increased motility of splenic *Ltbr*⁻/⁻ cDC2s leads to their disproportionate representation in the blood.

Overall, these data show that *Ltbr*⁻/⁻ cDC2 cells are more motile than *Ltbr*⁺/⁺ cDC2s and are more likely to enter the circulation.

# Discussion

Although previous studies have identified heterogeneity among cDC2s, the lineage relationships between these cell types and the cues that promote cDC fates within tissues are not
fully resolved. In this study, we found that nearly all cDC2s within spleen and lymphoid tissues, regardless of subset, descend from a common DC progenitor. We also showed that DC3, previously thought to be transcriptionally and ontogenically distinct from cDC2B, are transcriptionally indistinguishable from Mgl2- cDC2B cells. The relative abundance of cDC2A and cDC2B subsets varied across lymphoid tissues: cDC2Bs are the dominant cDC2 subset in pLN whereas cDC2A are the dominant cDC2 subset in spleen and mLN. Interrogating the signals that determine the balance of these subsets, we found that a series of cDC-intrinsic signals in the spleen (LTβR followed by RAR signaling followed by Notch signaling) promotes the differentiation and maintenance of cDC2A identity. We did not observe cell-intrinsic RAR regulation of cDC2A in PP or pLN, suggesting that RAR signals are tissue-specific.

The existence of transcriptionally distinct cDC2 subsets raised the question of where and when these divergent fates are specified and what cues regulate their differentiation trajectories.
We showed that, consistent with a recent study³⁰, cDC2A are synonymous with DC2 and that cDC2B span two subsets of cells distinguished by expression of *Mgl2*, with DC3 transcriptionally aligning with *Mgl2*⁻ cDC2B. Although previous studies have shown that ~40% of DC3 cells are fate mapped by *Ms4a3*-Cre⁷,⁹, indicating monocytic identity, we found that ~85% of T-bet⁻MGL2⁻ cDC2s were fate-mapped by *Clec9a*-Cre. One possibility, is that MS4A3⁺ MDPs³¹ give rise to CLEC9A⁺ CDPs. Alternatively, monocytic DC3 may represent the fraction of non-*Clec9a* fate mapped cells among T-bet⁻MGL2⁻ cDC2s, with variability in DC3 proportions across studies reflecting differences in mice or their environments. Previous studies have attributed specific functions to MGL2/CD301b⁺ cDC2s, notably Th2-mediated immunity³²,³³. Thus, it is likely that MGL2⁻ and MGL2⁺ cDC2B are functionally distinct; however, elucidating the role of MGL2⁻ cDC2B will require new genetic tools.
Our finding that cDC2B subsets are derived from the classical CLEC9A⁺ DC progenitor agrees with a recent study of cDC2 BM progenitors⁸. Following the initial specification of pre-cDCs into either pre-cDC2A or pre-cDC2B in the BM⁸, our data argues that niche factors in peripheral lymphoid tissues promote the differentiation of mature cDC2A. A striking finding was the differential representation of cDC2 subsets across secondary lymphoid tissues, notably enrichment of cDC2B in pLN vs enrichment of cDC2A in mLN, PPs and spleen. These data suggest that distinct tissue cues shape cDC2 composition to ensure appropriate representation according to the likely adaptive immune responses required within these sites.

To this end, we identified two such factors, LTβR and RAR signals, and delineated the mechanisms underlying their regulation of cDC2A abundance in the spleen. Our results suggest that LTβR signaling mediates retention of cDC2A within the spleen by directly or indirectly reducing F-actin content. How splenic retention is achieved is not clear, although it may be related to the expression of integrin or adhesion molecules such as ESAM, ITGB1, ITGB2, and the cytoskeletal adaptor TALIN³⁴, ³⁵, whose upregulation may require cDC2-intrinsic LTβR signaling³. Once “settled”, CD97-CD55 mechanosensing axis and/or other mechanisms may subsequently act to retain cDC2A in the MZBC niche²⁸. Interestingly, splenic 33D1⁺ cDC2 which localize to the MZBC, are only partially labeled following *i.v.* administration of anti-CD11c antibody, suggesting a zonality of the MZBC itself³⁶. We hypothesize that this zonality may be governed by cDC2-intrinsic LTβR signaling, whereby LTβR-competent cDC2A retrieve blood-derived macromolecules such as RBP4/retinol. Indeed, we found that cDC2A uniquely express high levels of the retinol-binding protein receptor *Stra6l/RBPR2* in a LTβR-dependent manner.
Downstream of LTβR-dependent splenic retention signals, we postulate that RAR-
dependent Notch signals regulate the terminal stages of cDC2A development. This is in contrast
to the role of Notch2 signaling in the differentiation of pre-DC2A progenitors in the BM⁸,
suggesting temporally distinct roles for Notch2 signaling in cDC2A development. Indeed,
deletion of *Notch2* using either CD11c or *Clec9a*-Cre drivers, both of which would target cDCs
from the progenitor stage, result in dramatic decreases in ESAM⁺ cDC2 (cDC2A) that exceed the
loss of T-bet⁺ cDC2A observed in our study following ablation of Notch2 signaling in mature T-
bet⁺ cDC2A⁴,⁸.

There remain questions that need answers to fill in gaps in our model. For example, we
do not know the exact location of where cDC2A receive LTβR signals nor if vasculature-
proximal *Ltbr*⁻/⁻ cDC2A are leaving or entering the spleen. Moreover, the LTβR-independent
signals that support cDC2A retention in the spleen once RAR signaling is initiated in *Tbx21*-
expressing cDC2A are not known. Lastly, cDC2 must presumably cooperate with an adjacent
accessory cell to metabolize retinol to RA in a paracrine manner for subsequent delivery to
cDC2, as splenic DCs do not possess RALDH machinery ³⁷, ³⁸. One possibility would be splenic
stromal cells: indeed, WT1-expressing mesothelial stromal cells provide this function for the
provision of RA to cavity macrophages, and likewise intestinal epithelial cells perform a similar
function to provide RA to *Lrp1*-expressing intestinal DCs ²⁶, ³⁹. These are key questions that get
to the heart of how RA rich tissue niches are generated and, warrant follow-up investigation.

Overall, our study provides new insights into cDC2 heterogeneity and tissue-specific cues
that dictate the prevalence of different cDC2 subsets. Given the importance of specific cDC2
subsets for Tfh, Th2 and Th17 immunity ¹, ², ³, ⁴, ¹¹, a better understanding of their distribution,
472 development and maintenance will enhance our understanding of how different lymphoid tissues

473 generate distinct types of immune responses.

474
475 Acknowledgments

476 We thank C. Ware for αLTβR agonist antibodies (rat IgG anti-mouse LTβR clone 4H8), Sanford
477 Burnham Prebys Institute, the veterinary staff at the Division of Comparative Medicine and the
478 staff at the Faculty of Medicine Flow Cytometry core facility (University of Toronto) for support,
479 and all members of the Gommerman Laboratory for help. We are grateful for funding support from
480 the Canadian Institutes for Health Research / Instituts de Recherche en Santé du Canada
481 (CIHR/IRSC) to J.L.G (FDN-159922; PJT-195705), FRN-165973 to C.J.G. Flow cytometry was
482 performed at the Centre for Immune Analytics Flow Cytometry Facility, University of Toronto
483 and the SickKids Flow Cytometry Facility, supported by the SickKids Foundation and a CFI John
484 Evans Fund Leaders grant to C.J.G. This work was supported by a Parker Institute for Cancer
485 Immunotherapy Senior Fellowship (C.C.B.), a NIH NIAID DP2 award (DP2AI171116) (C.C.B),
486 Pew Biomedical Scholar Award (C.C.B), Josie Robertson Investigator Award (C.C.B.). We
487 acknowledge the use of the Integrated Genomics Operation Core, MSKCC, funded by the NCI
488 Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and
489 Henry R. Kravis Center for Molecular Oncology.

490

491
492 Author Contributions Statement

493 These authors jointly supervised this work: Jennifer L. Gommerman, Chrysothemis C. Brown.

494 A.A. Nguyen, L. Fisher, J.L. Gommerman, and C.C. Brown designed, conceptualized, and

495 conducted the experiments, analyzed the data, and wrote the manuscript. A.A. Nguyen

496 conducted the experiments with assistance from J.S.A., D.D., C.L., L.A.W., A.A.W. and B.M.

497 M.Z. and T.M. carried out imaging analyses. T.P., A.Y., and Z.T. performed scRNA sequencing

498 data analyses. K.H. aided in data analysis and presentation. M.S.G., C.J.G., and C.L.M. provided

499 mutant mice and mouse bone marrow. D.T. and J.A.L. provided Mycololide B. reagent and

500 contributed to the design of the F-actin inhibition experiments in mice. J.L. Gommerman and

501 C.C. Brown supervised the project. All authors reviewed and edited the manuscript.

502 Corresponding Authors

503 Correspondence to Jennifer L. Gommerman (jen.gommerman@utoronto.ca) or Chrysothemis C.

504 Brown (brownc10@mskcc.org).

505

506
507

**Competing interests**

508

The authors declare no competing interests.

509

510
511 **Figure Legends**

512 **Fig. 1: Tissue environmental cues determine the balance of cDC2A and cDC2B subsets.**

513 **a**, Representative flow cytometry analysis of cDC2 from *Clec9a*<sup>Cre/Cre</sup>*Tbx21<sup>RFP-CreERT2</sup>*<sup>R26<sup>sll-YFP</sup></sup> mice (top), and summary bar graphs (bottom) demonstrating frequency of YFP labeled cells for each indicated cell subset and lymphoid tissue (n = 4 mice per group).

516 **b**, Uniform manifold approximation and projection (UMAP) visualization of YFP<sup>+</sup> and YFP<sup>-</sup> Lin (TCRβ, CD19, NK1.1)<sup>-</sup> MHCII<sup>+</sup> cells from mLN and spleen of *Clec9a*<sup>Cre/Cre</sup>*Tbx21<sup>RFP-CreERT2</sup>*<sup>R26<sup>sll-YFP</sup></sup> mice profiled by scRNA-seq, colored by cluster.

519 **c**, UMAP overlaid by expression of indicated genes.

520 **d**, Bar graph showing proportion of YFP<sup>+</sup> (*Clec9a*-fate mapped) and YFP<sup>-</sup> cells within each cluster.

522 **e**, Bar graph showing proportion of cDC clusters identified in (b) among splenic YFP<sup>+</sup> cDCs.

523 **f**, Heat map showing scaled, imputed expression of top 70 DEGs (one versus the rest, fold change (FC) > 1.5, adjusted P < 0.01) for indicated CCR7<sup>-</sup> cDC2 clusters.

525 **g**, Representative flow cytometry analysis of splenic and mLN T-bet(RFP)<sup>-</sup> cDC2s from *Clec9a*<sup>Cre/Cre</sup>*Tbx21<sup>RFP-CreERT2</sup>*<sup>R26<sup>sll-YFP</sup></sup> mice (left), and summary bar graphs (right) demonstrating frequency of YFP labeled cells for MGL2<sup>+</sup> vs MGL2<sup>-</sup> cDC2B (n = 3 mice per group).

528 **h**, UMAP visualization of dendritic cells from 1c, Rodrigues et al., Liu et al., and Brown et al. colored by original cell type annotation or cell type annotation from Extended Data Fig. 1f (Rodrigues et al.).

531 **i**, Representative flow cytometry demonstrating gating strategy for identification of cDC2A, cDC2B and DC3 subsets in lymph nodes and spleen of *Clec9a*<sup>Cre/Cre</sup>*Tbx21<sup>RFP-CreERT2</sup>*<sup>R26<sup>sll-YFP</sup></sup> mice (left), and summary graph showing cDC2 subset proportions in indicated lymphoid tissues (right) (n = 3 mice).

535
536 Data in **a**, **g** and **i** are representative of 2 independent experiments. Data are mean ± s.e.m., each
537 symbol represents an individual mouse. One-way ANOVA (a) or unpaired two-tailed t test (g).
538
**P** < 0.01.

539

540 **Fig. 2: RAR signals in vivo are required for the maintenance of the cDC2A subset.**
a, Representative flow cytometry plots of spleen cDC2A (Sirpa⁺T-bet⁺) frequencies in *Tbx21<sup>RFP-Cre</sup>* mice treated *i.p.* with BMS493 or a DMSO vehicle control over a 9-day regimen. DC were pre-gated for Live B220<sup>+</sup>Ly6C<sup>-</sup>CD11c<sup>hi</sup>MHCII<sup>hi</sup> cells.

b-d, Compiled data of a, showing numbers (b) and frequencies (c) of cDC2A and numbers of cDC2B (d), DMSO (n = 4) and BMS (n = 7).

e, Representative flow cytometry plots of spleen cDC2A frequencies of *Tbx21<sup>RFP-Cre</sup>* animals treated with a Vitamin A control or VAD diet and VAD diet animals treated with RA *i.p.* for 5 days and 9 days.

f, h, Compiled data of e, showing cDC2A frequencies of total DC (f), cDC2A frequencies of total splenocytes (g), and T-bet expression of Sirpa⁺ cDC2 (h), Ctrl + DMSO (n = 11), VAD + DMSO (n = 8), VAD + RA (2d) (n = 4), VAD + RA (5d) (n = 5), VAD + RA (9d) (n = 4)).

i, Compiled data showing T-bet expression in PP cDC2, Ctrl + DMSO (n = 9), VAD + DMSO (n = 7), VAD + RA (5d) (n = 4), VAD + RA (9d) (n = 4)).

j, Compiled data showing T-bet expression in mLN resident cDC2, Ctrl + DMSO (n = 6), VAD + DMSO (n = 5), VAD + RA (5d) (n = 3), VAD + RA (9d) (n = 4)).

Data in **b-d**, **i**, and **j** are representative of two independent experiments and **f-h** are representative of four independent experiments. Statistics were measured by two-sided Mann-Whitney U test in **b**, **c**, and **d** (\*\*P<0.01) and Kruskal-Wallis test with Dunn's multiple-comparisons test (\*P<0.05, \*P<0.01, \*\*\*P<0.001, and \*\*\*\*P<0.0001) in **f**, **g**, **h**, **i** and **j**. Error bars represent the mean ± s.d, each symbol represents an individual mouse.

<caption>**Fig. 3: cDC-intrinsic RAR signaling is required for the maintenance of cDC2A.**</caption>

a, Representative flow cytometry plots of spleen cDC2A frequencies in animals (right) vs. littermate controls (left).

b-e, Compiled data of a, showing frequencies (b) and numbers (c) spleen cDC2A, frequency of T-bet⁺ cells among cDC2 (d), and numbers of cDC2B (e). *Tbx21<sup>RFP-Cre</sup>* (n = 12) and *Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup>* (n = 18) for b and d. *Tbx21<sup>RFP-Cre</sup>* (n = 8) and *Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup>* (n = 13) for c and e.

f, Schematic of zDC-DTR mixed BM chimera approach to study cDC2A-intrinsic loss of RAR signaling *in vivo*.
g, h, Compiled data showing frequency showing T-bet⁺ cells among cDC2 (g) and numbers of cDC2A (h) in zDC-DTR chimeras containing Tbx21<sup>RFP-Cre</sup> (n = 10) or Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup> donor BM (n = 15).

i, j, Compiled data showing T-bet expression in (i) mLN-resident CD11c<sup>hi</sup>MHCII<sup>int</sup> cDC2 and (j) PP-resident cDC2 in zDC-DTR chimeras containing Tbx21<sup>RFP-Cre</sup> or Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup> donor BM. Tbx21<sup>RFP-Cre</sup> (n = 4) and Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup> (n = 7) for i and Tbx21<sup>RFP-Cre</sup> (n = 6) and Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup> (n = 8) for j.

k, l, Relative expression of (k) Dtx1 and (l) Hes1 measured by qPCR in spleen cDC2A vs.

cDC2B from zDC-DTR chimeras containing Tbx21<sup>RFP-Cre</sup> (n = 5) or Tbx21<sup>RFP-Cre</sup>dnRara<sup>lsl/lsl</sup> (n = 9) donor BM.

m, Schematic of mixed competitive BM chimera approach to study cDC2A-intrinsic loss of Notch signaling in vivo.

n, Representative flow cytometry plots showing ESAM and T-bet RFP expression from CD45.1⁺ B6 and CD45.2⁺ Tbx21<sup>RFP-Cre</sup> or Tbx21<sup>RFP-Cre</sup>Rbpj<sup>fl/fl</sup> donors in chimeras.

o-q, Compiled data of n, showing T-bet expression (o), ESAM MFI (p), and ESAM expression (q) in CD45.2⁺ cDC2 containing Tbx21<sup>RFP-Cre</sup> or Tbx21<sup>RFP-Cre</sup>Rbpj<sup>fl/fl</sup> donor-derived cells (n = 8 per group).

Data in b and d are representative of six independent experiments; c and e are representative of four independent experiments; i, j, k, l, and o-q are representative of two independent experiments. Statistics were measured by two-sided Mann-Whitney U test in b, c, d, e, g, h, i, j, k, l, o, p, q (ns, P>0.05, **P<0.01, ***P<0.001, and ****P<0.0001). Error bars represent the mean ± s.d, each symbol represents an individual mouse.

**Fig. 4: LTβR signaling precedes RAR signaling to promote splenic cDC2A development.**

a, Schematic of competitive (50:50) mixed BM chimeras employing CD45.1 Tbx21<sup>RFP-Cre</sup>Ltbr<sup>-/-</sup> and Tbx21<sup>RFP-Cre</sup>Ltbr<sup>+/+</sup> CD45.2 cells and representative flow cytometry plots that compare chimerism in the cDC2A and cDC2B compartments.

b, c Compiled data of a, showing T-bet expression (b) and CD45.1(KO)/CD45.2(WT) chimerism (c) of splenic cDC subsets (adjusted according to B cell chimerism) (n = 14 per group).
602 **d**, Schematic of competitive (50:50) mixed BM chimeras employing CD45.1 $Ltbr^{-/}$ and CD45.2 $Ltbr^{+/+}$ cells.
603

604 **e**, % ESAM expression on WT and $Ltbr^{-/}$ cDC2 from competitive (50:50) mixed BM chimeras.
605 $Ltbr^{+/+}$ + DMSO ($n = 9$), $Ltbr^{+/+}$ + RA ($n = 9$), $Ltbr^{-/}$ + DMSO ($n = 7$), $Ltbr^{-/}$ + RA ($n = 9$).

606 **f**, Frequency of ESAM$^+$ cDC2A among cDC2 derived from $Ltbr^{+/+}$ (left) or $Ltbr^{-/}$ (right) donor
607 BM after 10 days of DT-ablation in mixed $Zbtb46^{DTR}$ 80:20 ($Zbtb46^{DTR}:CD45.1^+$) or
608 ($Zbtb46^{DTR}:CD45.1^+Ltbr^{-/}$) BM chimeras that were either treated with RA or DMSO vehicle
609 control. $Ltbr^{+/+}$ + DMSO ($n = 9$), $Ltbr^{+/+}$ + RA ($n = 11$), $Ltbr^{-/}$ + DMSO ($n = 6$), $Ltbr^{-/}$ + RA ($n$
610 = 11).

611 **g**, **h**, Compiled frequencies of cDC2 in VAD animals (g) or BMS493-treated animals (h) that
612 were treated with anti-LTβR agonist antibodies (4H8) or isotype control. Ctrl ($n = 4$), VAD +
613 αLTβR ($n = 5$), VAD + αHEL ($n = 5$) for g; $n = 5$ per group for h.

614 **i**, **j**, Compiled frequencies (**i**) and numbers (**j**) of splenic T-bet$^+$ cDC2A in BM chimeras
615 reconstituted with either $Tbx21^{RFP-Cre}$ littermate control or $Tbx21^{RFP-Cre}$ $Ltbr^{fl/fl}$ donor cells ($n = 9$
616 per group for i, $n = 7$ mice per group for j).

617 **k**, Schematic of zDC-DTR mixed BM chimera approach to study cDC2A-intrinsic loss of LTβR
618 signaling *in vivo*.

619 **l**, **m**, Compiled frequencies (**l**) and numbers (**m**) of splenic T-bet$^+$ cDC2A after 10 days of DT-
620 ablation in mixed 80:20 BM chimeric mice whereby 80% of the BM was derived from
621 $Zbtb46^{iDTR}$ mice and 20% was derived from either $Tbx21^{RFP-Cre}$ littermate controls ($n = 8$) or
622 $Tbx21^{RFP-Cre}$ $Ltbr^{fl/fl}$ mice ($n = 9$).

623
624 Data in **b**, **c**, **e**, **f**, **j**, **l** and **m** are representative of two independent experiments; **g** and **h** are
625 representative of one experiment each; **i** is representative of three independent experiments; and **j**
626 is representative of two independent experiments. Statistics were measured by two-sided Mann-
627 Whitney U test (ns, *P* > 0.05, *P*<0.05, **P**<0.01, and ****P<0.0001) in **b**, **e**, **f**, **i**, **j**, **l**, and **m**,
628 Kruskal-Wallis Test with Dunn's multiple-comparisons test (ns, *P*>0.05 and ****P<0.0001) in **c**,
629 and two-way ANOVA with Sidak's multiple-comparisons test (ns, *P*>0.05 and ****P<0.0001) in
630 **g** and **h**. Error bars represent the mean ± s.d, each symbol represents an individual mouse.

631
632 **Fig. 5: LTβR signaling confers preferential access to blood-proximal niches.**
a, Representative flow cytometry plots demonstrating incorporation of BrdU by cDC2A and cDC2B over 48h.

b, Compiled data of a, lines connect data points within the same mouse (n = 9 per group).
c, Representative flow cytometry plot showing Ki67 expression by cDC2A and cDC2B.
d, Compiled data of c, lines connect data points within the same mouse (n = 7 per group).
e, f, BrdU incorporation over 48h (f) and Ki67 expression (g) of cDC2 subsets in Tbx21<sup>RFP-Cre</sup> Ltbr<sup>+/+</sup> (n = 4) or Tbx21<sup>RFP-Cre</sup> Ltbr<sup>-/-</sup> (n = 5) BM chimeras.
g, Relative expression of *Stra6l* and *Lrp1* measured by qPCR in spleen cDC subsets (n = 4 per group).
h. Representative histograms showing RBPR2 staining intensity in splenic cDC2A (red) compared to cDC2B and cDC1.
i, Compiled data showing RBPR2 expression in splenic cDC subsets from Tbx21<sup>RFP-Cre</sup> controls (n = 8) and Tbx21<sup>RFP-Cre</sup> Ltbr<sup>-/-</sup> (n = 6) mice.
j, Compiled data showing *Stra6l* expression in splenic cDC subsets from Tbx21<sup>RFP-Cre</sup> controls (n = 6) and Tbx21<sup>RFP-Cre</sup> Ltbr<sup>-/-</sup> (n = 5) mice.
k, Representative images of *Ltbr<sup>+/+</sup>* (top) and *Ltbr<sup>-/-</sup>* (bottom) spleen cross sections showing (left) overlap of anti-CD31 *i.v.* labeled endothelium and total CD31 staining alongside B220<sup>+</sup> follicle and CD169<sup>+</sup> MZ (scale bar represents 250 µm), (right) zoomed-in image of (left) showing T-bet<sup>RFP</sup> DCIR2<sup>+</sup> cDC2A co-localized around CD31 *i.v.* labeled endothelium. Scale bar represents 250 µm in left image and 50 µm within inset-magnified images. White triangles indicate IV<sup>+</sup>CD31<sup>+</sup> endothelium and white arrowheads indicate identified T-bet<sup>+</sup>DCIR2<sup>+</sup> cDC2A.
l, m, Compiled data showing average number of T-bet<sup>+</sup>DCIR2<sup>+</sup> cells per bridging channel (l) and average distance of T-bet<sup>+</sup>DCIR2<sup>+</sup> cells from IV<sup>+</sup>CD31<sup>+</sup> blood vessel (m), (n = 6 per group).

Data in **b** and **d** are representative of three independent experiments; **e, f, g** are representative of one experiment; **i, j, l** and **m** are representative of two independent experiments. Statistics were measured by Wilcoxon matched-pairs signed rank tests in **b** and **d** (ns, *P* > 0.05 and **P**<0.01), two-way ANOVA with Sidak's multiple-comparisons test (ns, *P*>0.05 and ****P<0.0001) in **e, f, and g**, and two-sided Mann-Whitney U tests in **i, j, l**, and **m** (ns, *P*>0.05, *P*<0.05, and **P*<0.01).

Error bars represent the mean ± s.d, each symbol represents an individual mouse.
664 **Fig. 6: LTβR signaling regulates F-actin and splenic retention.**

665 **a**, Representative histogram depicted phalloidin staining index in cDC subsets and B cells.

666 **b-c**, Compiled data of **a** showing (b) frequency of positive phalloidin-labeling in cDC subsets and (c) MFI of AF488-phalloidin within cDC subsets (n = 4 per group).

668 **d**, Representative flow cytometry plots showing phalloidin staining of Tbx21⁺ and Tbx21⁻ cDC2 from Ltbr⁺⁺(left) and Ltbr⁻⁻(right) DCs in a 50:50 competitive BM chimera.

670 **e-g**, Compiled data of **d** showing (e) MFI of phalloidin staining in Ltbr⁺⁺ and Ltbr⁻⁻ cDC subsets in the spleens, (f) normalized phalloidin MFI ratios of Ltbr⁻⁻:Ltbr⁺⁺ in cDC subsets, and (g) frequency of phalloidin-low cells in the Ltbr⁺⁺ and Ltbr⁻⁻ splenic cDC2A compartments (n = 12 per group).

674 **h**, Representative flow cytometry plots showing blood cDC from CD45.1/2⁺ Ltbr⁺⁺(bottom-left) and CD45.1⁺ Ltbr⁻⁻(bottom-right) compartments in 50:50 competitive BM chimeras.

676 **i**, Compiled data from **h**, comparing cDC2 chimerism (Ltbr⁺⁺/Ltbr⁻⁻) between the spleen and blood (n = 12 per group).

678 **j**, DC chimerism in the blood of 50:50 (Ltbr⁺⁺:Ltbr⁻⁻) BM chimeras that were treated with single dose of DMSO vehicle control (n = 7) or Myc. B i.v. (1 ug) (n = 9) 2h post-treatment.

680

681 Data in **b** and **c** are representative of one experiment; **e-g** and **i** are representative of two independent experiments; and **j** are representative of three independent experiments.

683 Statistics were measured by Kruskal-Wallis test with Dunn's multiple-comparisons test in **b**, **c**, and **f** (ns, *P*>0.05, *P*<0.05, **P**<0.01, and **P**<0.01), multiple Wilcoxon paired-T tests with 684 Holm Sidak's adjustment (ns, *P*>0.05, and ***P*<0.001) in **e**, Wilcoxon paired-T test in **g** and **i** (***, *P*<0.001), and two-sided Mann-Whitney U test in **j** (**, *P*<0.01). Error bars represent the mean ± s.d., each symbol represents an individual mouse.
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813
814
815 **Methods**

816 Mice

817 6-8-week-old B6 and B6-Ly5.2 (CD45.1) mice were purchased from Charles River. Zbtb46<sup>DTR</sup>
818 (Strain:019506) and R26<sup>lsl-YFP</sup> (Strain: 007903) mice were purchased from Jackson Laboratories.

819 *Tbx21*<sup>RFP-Cre</sup> mice were provided by A. Rudensky (Memorial Sloan Kettering Cancer Center).

820 *Clec9a*<sup>Cre</sup> mice were provided by Caetano Reis e Sousa (The Francis Crick Institute). dn*Rara*<sup>lsl/lsl</sup>
821 mice express a dominant-negative form of the retinoic acid receptor RAR-α, RAR403,
822 downstream of a *loxP*-flanked STOP cassette. These were provided by C.L. Mendelsohn
823 (Columbia University). *Ltbr*<sup>fl/fl</sup> mice were provided by J. Bromberg (University of Maryland).

824 *Ltbr*<sup>-/-</sup> CD45.1 and *Ltbr*<sup>-/-</sup> CD45.2 mice were from an internal colony. *Tbx21*<sup>RFP-CreERT2</sup> and
825 *Clec9a*<sup>Cre</sup> mice have been previously described <sup>1, 2</sup>. *Rbpj*<sup>fl/fl</sup> mice were provided by Cynthia
826 Guidos (University of Toronto) <sup>3, 4</sup>. All mice were housed under specific-pathogen-free conditions
827 at the Division of Comparative Medicine (DCM), University of Toronto or Sloan Kettering
828 Institute (SKI) animal facility. Mice were analyzed between 6-12 weeks of age unless otherwise
829 stated. Both male and female mice were included in the study, and we did not observe sex-
830 dependent effects. All mice analyzed were age and litter matched unless otherwise specified. All
831 animals used in this study had no previous history of experimentation and were naïve at the time
832 of analysis. All experiments were conducted ethically under animal use protocols reviewed by
833 the DCM and University Animal Care Committee in accordance with the Canadian Council on
834 Animal Care, the Hospital for Sick Children's Animal Care Committee, and the SKI Institutional
835 Animal Care and Use Committee.

836 BM chimeras were generated by intravenously transferring donor cells of indicated genotypes
837 into host animals that were lethally irradiated with two separate doses of 550 rad of γ-irradiation
given 4h apart. Recipient mice were given neomycin sulfate (2 g/L, Bioshop Canada) in their drinking water for the first two weeks post-irradiation and left for an additional 6-8 weeks before experimentation to ensure full reconstitution of the immune system. For ablation of Zbtb46-expressing cells in mixed BM chimeras, a first dose of diphtheria toxin (DT) (Sigma-Aldrich, D0564) was administered intraperitoneally at 20 ng/g body weight followed by subsequent doses of 4 ng/g DT every three days to maintain DC ablation. DT treatment was maintained for 10 days with the mice being euthanized 12-24h after the last DT treatment as described previously by Meredith et al⁵.

*In vivo* labeling of blood-associated endothelium in the spleen was performed by intravenously administering 10 µg of APC-conjugated anti-CD31 (390, Biolegend) for 30 min prior to animal euthanasia, as previously described by Wu et al⁶. *In vivo* inhibition of F-actin was performed by intravenously administering 1 µg of Mycalolide B⁷ (provided by John Allingham, Queen's University at Kingston, Canada).

DC isolation from lymphoid tissues and blood

Spleens were mashed into single cell suspensions prior to digestion. Cell suspensions were then digested in Hank's Buffered Saline Solution (with 10 mM HEPES (Gibco), 150 mM NaCl, 5 mM KCl, 1mM MgCl₂, 1.8 mM CaCl₂ added, final pH 7.2-4) using 1 mg/ml Collagenase D (Roche) and 0.2 mg/ml DNase I (Thermofisher) for 45 min at 37°C with continuous agitation. 1 mM EDTA was added to solution to stop digestion for 10 min at 20°C. PP and mesenteric lymph node (mLN) tissues were minced with a surgical blade and then digested using 1 mg/ml Collagenase P (Sigma-Aldrich) (2 mg/ml was used for PP tissues) and 0.1 mg/ml DNase I (Sigma-Aldrich) in complete media (10% FBS, 10 mM HEPES, 1% penicillin/streptomycin
861 (Sigma-Aldrich), 1 mM L-glutamine (Sigma-Aldrich) for 40 min at 37°C with continuous agitation. After digestion, LN and PPs were gently pipetted up and down until tissues dissolved
862 into a homogenous solution. Blood was collected from mice terminally using a cardiac puncture
863 with a 25G needle into Microvette® Lithium heparin capillary tubes. RBCs were lysed using
864 eBioscience 1X RBC Lysis Buffer for 4 min. Finally, all cell suspensions were washed using
865 PBS and filtered through a 70 µm strainer.
866

867
868 Flow Cytometry
869 Cell suspensions were isolated from tissues, as described above, and blocked with 2.4G2
870 antibody (generated in-house from B cell hybridomas and purified using protein G affinity
871 chromatography beads, used at 1 µg/10⁶ cells). For antibody staining, the following reagents
872 were used in PBS supplemented with 2% fetal bovine serum: B220 (RA3-6B2), BrdU (MoBU-
873 1), CD8a (53-6.7), CD11b (M1/70), CD31 (390), CD45.1 (A20), CD45.2 (104), CD117 (2B8),
874 CD135 (A2F10), Epcam (OX-7), Ki67 (SolA15), LTβR (3C8), Rabbit IgG (Polyclonal), Ly6C
875 (HK1.4), MHC II (I-A/I-E), and TER-119 (TER-119) from Thermofisher; CD5 (53-7.3), CD11c
876 (N418), CD43 (S11), CD170 (S17007L), Clec10A (LOM-14), ESAM (1G8), Ly6D (49-H4),
877 Podoplanin (8.1.1), SIRPa/CD172 (P84), CD301b/Mgl2 (URA-1) and XCR1 (ZET) from
878 Biolegend; RBPR2 (Polyclonal) from St. John's Laboratory; and Siglech (551) from BD
879 Biosciences. Dead cells were excluded using LIVE/DEADTM Fixable Aqua Viability Dye
880 (Thermofisher) and non-singlet events were removed using FSC-H/W characteristics.
881 Intracellular staining was enabled by using the Cytofix/Cytoperm Kit (BD Biosciences). For
882 detection of intracellular F-actin content, cells were stained with 37.5 mM AF488-conjugated
883 Phalloidin (Thermofisher) for 1h at 4°C. All data was acquired on a X20 FortessaTM (BD
884 Biosciences) or BD FACSymphony™ A3 using BD FACSDiva™ (BD Biosciences) and a
885 combination of the following lasers and filters: R639 (670/30, 780/60), YG561 (586/15, 780/60),
886 B488 (525/50, 710/50), V405 (450/50, 525/50, 610/20, 710/50, 780/60), and UV349 (379/28,
887 515/30, 610/25, 670/25, 810/40). Subsequent data analysis was conducted using FlowJo V.10
888 software (Treestar).

889
900
901 In-vivo modulation of RAR signaling
902 Chronic inhibition of RAR signaling was achieved by feeding pregnant dams (pups at
903 E14) with a Vitamin A Deficient diet (TD.10991, Envigo) or Vitamin A Control diet (TD.10992,
904 Envigo). Diets were continued post-birth and post-weaning until mice reached 8-10 weeks of age
905 for analysis. Acute inhibition of RAR signaling was achieved by intraperitoneal administration
906 of 220 µg BMS493 (Tocris) daily or DMSO vehicle control over a 9-day regimen. Exogenous
907 RA supplementation to enhance or rescue RAR signaling was performed by intraperitoneal
908 administration of 250 µg all-trans RA (Tocris) over a 10-day regimen or as otherwise indicated
909 in the experiment. Pharmacological reagents were stored in DMSO for long-term storage and
910 subsequently diluted in PBS for in vivo administration.

901 Cell sorting of DC subsets
902 For sorting of purified cDC subsets, tissues were isolated as described above. CD11c+
903 cells were enriched from cell suspensions using the EasySep™ CD11c Positive Selection Kit II
904 (Stem Cell) as per manufacturer's instructions. An APC-conjugated CD11c antibody (N418, 1
905 ug/ml) was added alongside CD11c selection cocktail to fluorescently label surface CD11c.
906 Staining for the additional surface markers and for viability was subsequently performed on
907 CD11c-enriched cells and cDC subsets were subsequently sorted on a FACS Aria™ IIIu (BD Biosciences). Sorted cells were collected into 50/50% PBS/FBS solution or directly into RLT lysis buffer provided by Qiagen™.

910
911 BrdU labeling
912 To track proliferation of immune cells over a 48h duration, 5 bromo-2'-deoxyuridine (BrdU) was injected into mice at a dose of 10 mg/kg intraperitoneally 48h, 24h, and 1h before 913 euthanization. In between doses, mice were also continuously fed drinking water containing 0.75 914 mg/ml BrdU. Cells were stained intracellularly for incorporated BrdU using the eBioscience™ 915 BrdU Staining Kit (Thermofisher) as per the manufacturer's instructions, using Alexa Fluor 647 916 anti-BrdU antibody (Thermofisher). 917

918
919 LTβR Agonism

920 αLTβR agonist antibodies (rat IgG anti-mouse LTβR clone 4H8) and anti-HEL control 921 antibodies were received as a gift from C. Ware (Sanford Burnham Prebys Institute). αLTβR 922 antibodies were administered intraperitoneally into animals at a daily dose of 100 µg thrice at 923 days 0, 3, and 6 before euthanizing mice on day 9 for analysis. Control animals were given equal 924 dose of α-Hel isotype control antibodies. 925

926 Mouse scRNA sequencing

927 Mouse DC subsets were enriched from a pool of mesenteric lymph nodes or spleen, obtained 928 from four *Clec9aCre/CreTbx21<sup>RFP-CreERT2</sup>R26<sup>sl1-YFP</sup>* mice. Lymphoid organs were digested in RPMI-929 1640 supplemented with 5% fetal calf serum, 1% L-glutamine, 1% penicillin-streptomycin,
10 mM HEPES, 1 mg ml⁻¹ collagenase A (Sigma, 11088793001) and 1 U ml⁻¹ DNase I (Sigma, 10104159001)(final pH = 7.2-4) for 45 min at 37 °C, 250 rpm. Digested samples were filtered through 100-µm strainers and centrifuged to remove collagenase solution. Cells were depleted of Lin⁺ (TCRb, TCRγδ, CD19, Ter-119, NK1.1)⁺ cells by staining with biotinylated antibodies followed by magnetic bead negative selection (M-280 dynabeads; ThermoFisher). Cells were incubated with anti-CD16/32 in sorting buffer (2% FBS in PBS) for 10 min at 4°C to block binding to Fc receptors. Extracellular antigens were stained for 30 min at 4°C in sorting buffer. mLN and spleen samples were separately “stained” with different TotalSeq™ oligo-conjugated antibodies consisting of a pool of antibodies directed against CD45 (30-F11) and H2 MHC Class I alloantigens (clone M1/42; Biolegend). Cells were washed and resuspended in sorting buffer with SYTOX blue (Invitrogen) for exclusion of dead cells. Live, Lin (CD19, NK1.1, TCRb, SiglecF)⁻MHCII⁺YFP⁺ or YFP⁻ cells were sorted into complete RPMI-20% FBS, before being pelleted and resuspended in complete RPMI-2% FBS. YFP⁺ and YFP⁻ cells were combined as follows: 1,600 mLN and 13,400 splenic YFP⁺ cells, 9,700 mLN and 5,300 splenic YFP⁻ cells. Cells were stained with Trypan blue and Countess II Automated Cell Counter (ThermoFisher) was used to assess both cell number and viability. Following QC, the single cell suspensions were loaded onto Chromium GEM-X 3' Chip (10x Genomics PN 1000690) and GEM generation, cDNA synthesis, cDNA amplification, and library preparation of 2,300-3,800 cells proceeded using the Chromium GEM-X Single Cell 3' Kit v4 (10X Genomics PN 1000691) according to the manufacturer's protocol. cDNA amplification included 11 cycles and 13-35 ng of the material was used to prepare sequencing libraries with 10 cycles of PCR. Indexed libraries were pooled equimolar and sequenced on a NovaSeq X in a PE28/88 paired end run using the NovaSeq X 10B Reagent Kit (100 cycles) (Illumina). An average of 31 thousand paired reads
953 was generated per cell. Amplification products generated using the methods described above
954 included both cDNA and feature barcodes tagged with cell barcodes and unique molecular
955 identifiers. Smaller feature barcode fragments were separated from longer amplified cDNA using
956 a 0.6X cleanup using aMPure XP beads (Beckman Coulter catalog # A63882). Libraries were
957 constructed using the 3' Feature Barcode Kit (10X Genomics PN 1000276) according to the
958 manufacturer's protocol with 10 cycles of PCR. Indexed libraries were pooled equimolar and
959 sequenced on a NovaSeq X in a PE28/88 run using the NovaSeq X 10B Reagent Kit (100 cycles)
960 (Illumina). An average of 421 million paired reads was generated per sample. FASTQ files were
961 aligned to mm10 (Cell Ranger mouse reference genome mm10-2020-A) and counted by Cell
962 Ranger v9.0.1 with default parameters. HTO sequencing data were aligned to the HTO barcodes,
963 and UMIs were counted for each cell using CITE-seq-Count. Samples were then demultiplexed
964 using the Cell Ranger Multi pipeline⁸.

965
966 Mouse single-cell RNA-seq computational analysis
967
968 Pre-processing of the 10X scRNA-seq dataset for dendritic cells: The singlet barcodes were
969 further filtered based on the number of RNA-seq transcripts (>400), the number of detected
970 genes (>250), and the fraction of mitochondrial transcripts (<8%). Finally, any genes detected in
971 <2 cells in the scRNA-seq data were discarded, leaving 19,358 genes. After clustering the
972 scRNA-seq data (described in 'Dimensionality reduction, cell clustering, and visualization'), we
973 identified 13 contaminant clusters spanning B cells, plasma cells, neutrophils and ILC3 and three
974 low quality clusters which were excluded from downstream analyses. In total, 2,198 cells
975 remained, with a median scRNA-seq library-size of 12,382. Cluster labels were manually
976 assigned and curated based on expression of canonical genes, differentially expressed genes
976 identified by MAST analysis ⁹ (described in 'differential gene expression tests') as well as cell type annotations assigned by SCimilarity ¹⁰.

978
979 **Dimensionality reduction, cell clustering, and visualization:** Following quality control filtering, raw counts were library-size normalized, log transformed ('log-normalized' expression values) and scaled using Scanpy v1.10.3. A nearest-neighbour graph was constructed using the top 40 principal components (PCs) with 25 nearest neighbours. Clustering was performed using PhenoGraph¹¹ (k=25, clustering_algo = 'louvain') generating 30 clusters. Cells annotated as pDC, cDC1, cDC2A, cDC2B, CCR7⁺ DC, monocytes/macrophages, were re-clustered with PhenoGraph (k=25, clustering_algo = 'louvain') generating 16 clusters. We identified **one** minor contaminant cluster (TC I) and five low quality clusters which were excluded from **downstream** analyses. Cell clustering was visualized using UMAP ¹², computed from the nearest neighbour graph built by PhenoGraph.

989
990 **Differential gene expression tests:** Differentially expressed genes (DEGs) between groups of cells were identified with MAST ⁹, performed using Seurat functions. MAST was run on the log-normalized expression values. In all tests, genes were only considered if they were detected in at least 1% of the cells in at least one of the two groups compared (min.pct=0.01, logfc(threshold=0). In one-vs-rest DE tests comparing multiple groups, each group was compared to all the cells from other groups. Specific DE comparisons are described in the results. DEGs were reported according to their log-fold change (>1.5) and adjusted p-value (<0.01). The top DEG markers were subsequently selected for each group, based on fold change and p-value.
999
1000 **Data imputation**: MAGIC imputation¹³ was applied to the log-normalized expression values to de-noise and recover missing values. Imputed data were used only for visualization of gene expression on heatmaps where specified.
1001
1002
1003
1004 **Analysis of publicly available dendritic cell scRNA-seq datasets**: scRNA-seq profiles of splenic dendritic cells were downloaded from publicly available datasets (GSE262474, GSE137710, and figshare https://doi.org/10.6084/m9 figshare.22232056.v1).¹⁴, ¹⁵, ¹⁶ For Brown et al. (GSE262474) and Liu et al. (figshare), cluster cell type annotations were available, and no reanalysis of the data was performed. For the scRNA-seq datasets from Rodrigues (GSE262474), each sample was first demultiplexed based on HTO counts using HTODemux function in Seurat v4.4.0. Next, RNA count matrices for the cell barcodes classified as singlets from demultiplexing were merged into a single count matrix. The RNA count matrix was further filtered based on the number of transcripts (>2,000 and <80,000), the number of detected genes (>1000 and <7,000), and the fraction of mitochondrial transcripts (<5%). The filtered count matrix was library-size normalized, log-transformed ('log-normalized' expression values) and then centered and scaled ('scaled' expression values) using Seurat v4.4.0. Any genes detected in <2 cells were discarded.
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016 Principal component analysis (PCA) was performed on the scaled data (npcs=50). A nearest-neighbor graph was constructed using the first 30 principal components (PCs) with 30 nearest neighbors. Clustering was performed using Louvain algorithm (resolution=0.8) on the shared nearest-neighbor graph. Cell clustering was visualized using UMAP computed from the same nearest neighbor graph used for clustering. Cluster labels were manually assigned and curated based on expression of canonical genes, differentially expressed genes identified by MAST
1017
1018
1019
1020
1021
1022 analysis. Clusters annotated as dendritic cells or monocyte/macrophages were selected for
1023 downstream integration.

1024
1025 **Integration of dendritic cell scRNA-seq datasets:** The four scRNA-seq datasets were integrated
1026 using Seurat's anchor-based integration method. First, 5,000 genes were first selected based on
1027 their repeated variability across datasets to identify anchors using canonical correlation analysis.
1028 The integration of the datasets was then performed using 'IntegrateData' function. Next, the
1029 expression values of genes in the integrated dataset were scaled and used for PCA. A UMAP
1030 embedding of the integrated dataset was computed based on the top 30 principal components and
1031 used for visualization of the cells.

1032

1033 RT-PCR

1034 Total RNA was isolated using a Qiagen™ RNeasy Micro Kit as per manufacturer's
1035 instructions from flow-sorted cDC subsets as described above. RNA was converted into cDNA
1036 using Superscript IV Reverse Transcriptase kit (Thermofisher). RT-PCR was performed with
1037 SYBR Green Master Mix (Thermofisher) on a CFX384 Touch™ Real-Time PCR Detection
1038 System (BioRad). Primer sets are listed below and were generated by IDT DNA Technologies:

1039 *Hprt* forward: CCC CAA AAT GGT TAA GGT TGC

1040 reverse: AAC AAA GTC TGG CCT GTA TCC

1041 *Lrp1* forward: GGA CCA CCA TCG TGG AAA

1042 reverse: TCC CAG CCA CGG TGA TAG

1043 *Rbpr2* forward: TCC TGG GGA ACC ACT TTG GA

1044 reverse: CCA TGC CTC CGA TGA AAA GC
1045 *Ltbr* forward: CCA GAT GTG AGA TCC AGG GC

1046 reverse: GAC CAG CGA CAG CAG GAT G

1047 *Dtx1* forward: TGG AAC GAG ATT CAC CAC AA

1048 reverse: CTC AGC CAG CAC GTT GTC TA

1049 *Hes1* forward: TCC TGA CGG CCA ATT TGC

1050 reverse: GGA AGG TGA CAC TGC GTT AGG

1051

1052 Imaging Mass Cytometry (IMC) Acquisition and Analysis

1053 Spleen tissues were fixed in 4% PFA for 1h and embedded in 30% sucrose overnight.

1054 Histological analysis was performed on 7 µm frozen spleen sections that were fixed in acetone
1055 for 7 min at -20°C. Sections were rehydrated in PBS and non-specific protein interactions were
1056 blocked using Super Block™ solution (Sigma). For imaging mass cytometry, slides were stained
1057 for 1h at 20°C or at 4°C overnight with assay-dependent concentrations of metal-conjugated
1058 antibodies (Supplementary Table 2). For the *Ltbr*⁺/+ and *Ltbr*⁻/⁻ cohorts, image acquisition was
1059 carried out on the Standard BioTools (formerly Fluidigm) Hyperion XTi Imaging System
1060 following manufacturer's instruction at a laser frequency of 800 Hz using CYTOF™ software
1061 version v9.2.1 and step size of 1 µm. Images were taken at a resolution of 1 µm/pixel using cell
1062 mode. Regions of interest ranging from 1000 µm × 1000 µm to 1,500 µm × 1,500 µm were
1063 selected based on bright-field images. 2-3 regions of interest were collected per spleen section to
1064 ensure accurate representation. For the VAD cohort, image acquisition was carried out on the
1065 Standard BioTools (then Fluidigm) Hyperion Imaging System following manufacturer's
1066 instruction at a laser frequency of 200-400 Hz using CYTOF™ software version v5.1.602
1067 software. Images were acquired at a resolution of 1 µm/pixel, step size of 1 µm and ablation
1068 energy of 5-6Db. Regions of interest ranging from 1500 µm × 1500 µm to 2000 µm × 2000 µm
1069 were selected based on bright-field images

1070 For IMC analysis, merged images of appropriate channels to identify follicles and
1071 bridging channels (CD3, B220, CD169), cDC2A populations (identified as CD3<sup>B</sup>B220<sup>Ly6G</sup>-
1072 CD11c<sup>+</sup>DCIR2<sup>+</sup>T-bet-RFP<sup>+</sup>), and blood-adjacent endothelium (CD31<sup>+</sup>CD31-IV<sup>+</sup>) were Created
1073 in MCD Viewer. Average DCIR2 intensity in the bridging channel, number of cDC2A in the
1074 bridging channel, and distance to closest IV<sup>+</sup> blood vessel, and follicle and PALS sizes were
1075 measured in ImageJ v1.54 using the contour selection tool or line selection tool at a global scale
1076 of 1 pixel = 1 µm.

1077 Raw 16-bit ome.tiff files were generated from MCD files or corresponding .txt files. Data
1078 were passed through the Percentile Normalization GUI Image deNoising (PENGUIN v0.1.1)
1079 pipeline ¹⁷. Cell masks were constructed for each ROI using a combination of CD45, CD11c,
1080 CD3, B220, and CD11b surface staining using the Mesmer algorithm ¹⁸, available in the
1081 steinbock IMC analysis toolkit (v.016.3) ¹⁹. Per cell intensities were measured for each channel
1082 using the steinbock toolkit and loaded into Rstudio (v2023.03.0) for single-cell analysis using
1083 SingleCellExperiment (v1.20.0), Seurat (v5.3.0), and SpatialExperiment (v.1.10.1) packages.
1084

1085 Statistics

1086 Statistical analysis and graphing were performed using Prism (GraphPad) software. Analysis of
1087 all data was done with unpaired two-tailed t test, one or two-way ANOVA with a 95%
1088 confidence interval, two-sided Mann-Whitney U non-parametric test (unpaired) or a Kruskal-
1089 Wallis test (paired), as specified in the text or legends. P < 0.05 was considered significant, after
1090 correcting for multiple comparisons: *p* < 0.05; **p** < 0.01; ***p** < 0.001; ****p** < 0.0001.
Details as to number of replicates, sample size, significance tests, and value and meaning of *n* for each experiment are included in the Methods or Figure legends. Statistical tests were performed with Prism (GraphPad Software). scRNA-seq experiments were carried out once. Mice were non-randomly allocated to experimental groups to ensure equal distribution of genotypes between treatments. Researchers were not blinded as to genotype or treatment during the experiments. No measures were taken to estimate sample size of to determine whether the data met the assumptions of the statistical approaches used.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data and material availability

The mouse sequencing data are available through the Gene Expression Omnibus under accession number GSE302089. This manuscript makes use of previously published scRNA-seq data from GSE262474, GSE137710 and figshare https://doi.org/10.6084/m9.figshare.22232056.v1.

## Code availability

This study did not report original code.
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<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 3</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4</figcaption></figure>

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

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

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>
<caption>Fig. 5</caption>

a

Pre-gated: B220<sup>+</sup>CD11c<sup>+</sup>MHCII<sup>+</sup>

<figure><img src="image_19.png" /></figure>

b

Spleen

<figure><img src="image_20.png" /></figure>

c

CD11c<sup>+</sup>SIRPα<sup>+</sup> cDC2
(Spleen)

<figure><img src="image_21.png" /></figure>

d

Spleen

<figure><img src="image_22.png" /></figure>

e

CD11c<sup>+</sup>SIRPα<sup>+</sup> cDC2
(Spleen)

f

CD11c<sup>+</sup>SIRPα<sup>+</sup> cDC2
(Spleen)

<figure><img src="image_23.png" /></figure>

g

<figure><img src="image_24.png" /></figure>

h

<figure><img src="image_25.png" /></figure>

i

CD11c<sup>+</sup> (Spleen)

<figure><img src="image_26.png" /></figure>

j

Stra6l

<figure><img src="image_27.png" /></figure>

k

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

l

RFP<sup>+</sup>
DCIR2<sup>+</sup>

<figure><img src="image_30.png" /></figure>

m

RFP<sup>+</sup>
DCIR2<sup>+</sup>

<figure><img src="image_31.png" /></figure>

CD31 CD31 i.v.

B220 CD169

RFP

DCIR2

CD31 i.v. CD169
<figure><img src="image_32.png" /><figcaption>Fig. 6</figcaption></figure>
a

Clec9a<sup>Cre/Cre</sup>Tbx21<sup>RFP-CreERT2</sup>Rosa26<sup>sl-YFP</sup>

pLN: Gated on Lin<sup>-</sup>

<figure><img src="image_33.png" /></figure>

b

Clec9a<sup>Cre/Cre</sup>Tbx21<sup>RFP-CreERT2</sup>Rosa26<sup>sl-YFP</sup>

Gated on Lin<sup>-</sup> Ly6C<sup>-</sup>CD88<sup>-</sup>CD11c<sup>+</sup>MHCII<sup>+</sup>CCR7<sup>-</sup>CD11b<sup>+</sup>T-bet(RFP)<sup>-</sup> cDC2

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

c

Clec9a<sup>Cre/Cre</sup>Tbx21<sup>RFP-CreERT2</sup>Rosa26<sup>sl-YFP</sup>: Sorting strategy for scRNA-seq analysis

<figure><img src="image_36.png" /></figure>

d

Clec9a<sup>Cre/Cre</sup>Tbx21<sup>RFP-CreERT2</sup>Rosa26<sup>sl-YFP</sup>

Spleen: Lin<sup>-</sup>Ly6C<sup>-</sup>CD88<sup>-</sup>CD11c<sup>+</sup>MHCII<sup>+</sup>CCR7<sup>-</sup>

<figure><img src="image_37.png" /></figure>

e

Zbtb46

<figure><img src="image_38.png" /></figure>

f

<figure><img src="image_39.png" /></figure>

g

<figure><img src="image_40.png" /></figure>

h

<figure><img src="image_41.png" /></figure>

i

<figure><img src="image_42.png" /></figure>

j

<figure><img src="image_43.png" /></figure>
<figure><img src="image_44.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>Spleen</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Follicle</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_53.png" /><figcaption>i</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>j</figcaption></figure>

<figure><img src="image_55.png" /><figcaption>k</figcaption></figure>

<figure><img src="image_56.png" /></figure>
<figure><img src="image_57.png" /><figcaption>a
Pre-gated: B220<sup>hi</sup>CD11c<sup>hi</sup>MHCII<sup>hi</sup>Sirpa<sup>+</sup> cDC2</figcaption></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /><figcaption>c
Pre-gated on Live Singlets</figcaption></figure>

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>
a

Live CD45+

<figure><img src="image_63.png" /></figure>

b

B cells

<figure><img src="image_64.png" /></figure>

c

T cells

<figure><img src="image_65.png" /></figure>

d

Macrophages

<figure><img src="image_66.png" /></figure>

e

Neutrophils

<figure><img src="image_67.png" /></figure>

f

Monocytes

<figure><img src="image_68.png" /></figure>

g

NK cells

<figure><img src="image_69.png" /></figure>
a

Pre-gated: Lin<sup>−</sup>CD11c<sup>+</sup>MHCII:Sirpa<sup>−</sup>CD43<sup>+</sup>CD135<sup>+</sup>

<figure><img src="image_70.png" /></figure>

b

<figure><img src="image_71.png" /></figure>

c

<figure><img src="image_72.png" /></figure>

d

<figure><img src="image_73.png" /></figure>

e

<figure><img src="image_74.png" /></figure>

f

<figure><img src="image_75.png" /></figure>

g

<figure><img src="image_76.png" /></figure>

h

<figure><img src="image_77.png" /></figure>

i

<figure><img src="image_78.png" /></figure>
<caption>a</caption>

Ltbr

<figure><img src="image_79.png" /><figcaption>b</figcaption></figure>

Ltbr

(Spleen cDC2)

<figure><img src="image_80.png" /><figcaption>c</figcaption></figure>

cDC1 (spleen)

<figure><img src="image_81.png" /><figcaption>d</figcaption></figure>

cDC2 (spleen)

<figure><img src="image_82.png" /><figcaption>e</figcaption></figure>

Tbx21<sup>RFP</sup>

<figure><img src="image_83.png" /></figure>

Tbx21<sup>RFP-CreLtbr<sup>-/-</sup></sup>

<figure><img src="image_84.png" /></figure>

Tbx21<sup>RFP-CreLtbr<sup>fl/fl</sup></sup>

<figure><img src="image_85.png" /><figcaption>f</figcaption></figure>

CD11c<sup>+</sup>SIRPα<sup>+</sup>
(spleen)

<figure><img src="image_86.png" /><figcaption>g</figcaption></figure>

Littermate Controls

<figure><img src="image_87.png" /></figure>

Zbtb46<sup>Cre</sup>Ltbr<sup>fl/fl</sup>

<figure><img src="image_88.png" /><figcaption>h</figcaption></figure>

CD11c<sup>+</sup>SIRPα<sup>+</sup>
(spleen)

<figure><img src="image_89.png" /></figure>
<figure><img src="image_90.png" /><figcaption>a</figcaption></figure>
a

<figure><img src="image_91.png" /></figure>

b

Pre-gated: CD11c+Sirpa+ cDC2

<figure><img src="image_92.png" /></figure>

c

<figure><img src="image_93.png" /></figure>
Supplementary Files

This is a list of supplementary files associated with this preprint. Click to download.

* SupplementaryTable2.csv

* SupplementaryTable1.csv