An immunobiliary single-cell atlas resolves crosstalk between type 2 conventional dendritic cells and γδ T cells in cholangitis

Domain: neuroscience
Published: 2026-04-10
Editorial outcome: minor_revision

Abstract
The liver biliary niche serves as a reservoir of tissue-resident immune cells and supports tissue fibrosis upon damage, yet the role of peribiliary immune cells during cholangitis remains poorly understood. Here, we induce cholestatic liver injury mirroring human biliary diseases with bile acid retention in mice to establish a spatial and multimodal single-cell RNA-sequencing atlas of the liver and liver-draining lymph nodes (LN). We characterized a hepatic disease state trajectory from dendritic cell precursors (preDCs) to a mature subset of pro-inflammatory Mgl2+ type 2 conventional dendritic cells (cDC2B) and observed dynamic crosstalk with γδ T cells inducing an Il17 response (γδT17). Dissection of the cDC2B-γδT cell communication node identified the Icosl-Icospair as an important cell contact-dependent interaction, which was validated in vitro. In vivo, cDC2B depletion attenuated γδT17 responses in cholestatic liver injury, and liver fibrosis was reduced in a model of inducible γδ T cell depletion and in an Il17-deficient background. Our work demonstrates dynamic turnover of cDC2 within the biliary niche during cholestasis, and a profibrogenic function of γδ T cells contingent on the induction by peribiliary cDC2B, highlighting relevant disease determinants within the immunobiliary and liver-draining LN niche.

Introduction
Cholestasis and associated biliary immune responses may contribute to liver disease development, since associated inflammation may amplify tissue injury responses independent of its main etiologic factor. For instance, primary sclerosing cholangitis (PSC) is a multifactorial autoimmune-related disease with rising occurrence in the US and EU1,2, and the disease course may lead to cirrhosis and the requirement of liver transplantation. While there are different subtypes of PSC3, and PSC is frequently linked to other chronic inflammatory diseases, the common histological features include periductal fibrosis, proliferation of reactive bile ducts known as ductular reaction (DR), bile duct stenosis, dilatation and rarefication leading to biliary cirrhosis, cholestasis and altered myeloid responses4,5. Clinical presentation of PSC and its chronic disease activity may include intermittent episodes of acute cholangitis and cholestasis6 and involve dysfunctional cDC2 and T cell responses contributing to disease severity7,8, yet the role of unconventional T cells (UTC), their potential crosstalk with cDC2 and their role in disease amplification remains to be elucidated.

In the hepatic cDC2 niche, continuous reconstitution by circulating cells and the DC life-cycle have been characterized in steady-state and disease9,10, and follow the steps of liver sinusoid attachment, transmigration to the space of Dissé and migration towards the portal field stroma11. The portal microenvironment may furthermore instruct DCs towards a more anti-inflammatory phenotype12, however, a significant fraction of the liver cDC2 pool has been characterized as proinflammatory cDC2B13 and the behavior of recently recruited DCs versus niche-adapted cells remains challenging to resolve in inflammation and resolution.

In this study, we explore the tissue niche, differentiation dynamics, and functional roles of cDC2 during cholangitis in the 0.1% 3,5-Diethoxycarbonyl-1,4-Dihydrocollidine Diet (DDC)-diet mouse model14. By administering DDC for durations of 3–25 days and following inflammatory resolution for 7 days, we established a high-resolution temporal and multimodal single-cell RNA-sequencing (scRNA-seq) atlas covering the cellular dynamics in cholestasis that shape the immunobiliary niche and the liver draining lymph nodes (LN). Combined with spatial transcriptomics, this resource provides novel insights into differentiation dynamics of cDC2 and their heterocellular interactions and complements available spatiotemporal DDC/PSC data of more abundant cell types5,15. Our data reveal crosstalk of cDC2 with γδ T cells governing the inflammatory response, which we confirm in other acute models of liver injury and functionally validate in vivo with the help of diphtheria toxin receptor (DTR)-based depletion of cDC2B using the CD301b-DTR (Mgl2-DTR) and Tcrd-GDL models16,17. Together, our work highlights new site-specific and -independent avenues of cDC2B–γδ T cell interactions in cholangitis.

Results
To determine spatial immune cell type localization in human steady-state liver, we first explored publicly available spatial transcriptomics (ST) data (“Methods”) and found enrichment of CD1C and CD3E transcripts near large vessels and biliary tracts (Fig. 1A). For cell type annotation of this high-resolution ST reference, we generated a human non-cholestatic liver disease control scRNA-seq reference dataset with the aim to enrich for immune cell types. This dataset comprised ~33k cells from 6 patients (clinical characteristics summarized in Fig. S1A) purified by combining enrichment of biliary epithelial cells (BECs) and immune cell populations with unbiasedly sampled cells (“Methods”, Figs. 1B and S1B–D). We performed spatial cell type annotation by running RCTD18, which defined DCs, continuous endothelial cells (CECs), BECs and T cells to be part of the immunobiliary niche (Fig. 1C). Topological niches were predicted by Seurat’s BuildNicheAssay and confirmed the existence of a portal immunobiliary (“1”) and lobular niche (“2”; Fig. 1C), providing evidence for the immunobiliary niche as a T cell- and DC-containing microenvironment. These findings were confirmed using non-cholestatic disease control human liver tissues derived from a characterized tissue microarray (TMA)19,20 by immunohistochemical detection and correlation analysis of CK7+ BECs, CD34+ CECs, PDPN+ lymphatics (clone D2-40), CD207+ cDC2, CD3+ T cells and TRDC+ cells among other markers enriched at the peribiliary niche (Fig. 1D, E). The immunobiliary niche was conserved in mouse, where multicolor immunofluorescence (IF) revealed co-localization of Epcam+ BECs with CD146+ CECs and CD45+ immune cells (Figs. 1F and S2A). CD11c+ cells were detected in the biliary niche of steady-state mice and co-expressed CD172a (encoded by Sirpa). Furthermore, CD301b+ (encoded by Mgl2) cells were detected, likely representing cDC2 (Fig. 1G, H).

The xenobiotic DDC model has been described to induce an inflammatory response of the biliary tree, partly mediated by BEC cholangiocyte-driven reactive changes that also affect the peribiliary niche14,15. To locally perturb the immunobiliary niche, we made use of the DDC diet, that is known to induce cholangitis and ductular reaction (Fig. 1I)14. qPCR detected DDC-induced upregulation of biliary and myeloid marker genes suggested expansion of these populations (Fig. S2B). Physical interaction of CD45+ immune cells with Epcam+ bile ducts was validated by IF image co-localization, indicating a dynamic immunobiliary crosstalk upon ductular reaction (Fig. S2C–E). To unbiasedly investigate dynamics of gene expression across cell types residing in the immunobiliary niche, we performed scRNA-seq on liver tissue isolated at five different timepoints of DDC diet (day (D)3, D5, D9, D19, and D25) and from control liver (D0) (Fig. 1J). To enrich all cell types at equal ratios, two independent rounds of cell isolation and sequencing were performed at all timepoints (Methods) and we were able to capture abundant cells of the liver immunobiliary niche, as well as rare liver immune cells such as cDCs (Figs. 1K and S2F–H, Supplementary Data S1, Methods).

Based on the mechanism of action of DDC in causing cholangitis, we first analyzed the BEC scRNA-seq subset. To dissect the BEC subset of our scRNA-seq atlas, we performed a separate clustering analysis of the two main cholangiocyte populations (clusters 9, 22; 5,137 cells, Fig. 1K) resulting in 16 more granular clusters (Figs. 2A and S3A). Our data recapitulates two major subsets: Dmbt1+ Muc4+ Sox17+ BEC (cluster 2, 6, 11) and Sox9+ BEC (remaining clusters). Among Sox9+ BEC, clusters 4, 8 and 16 showed an increased expression of chemotaxis- and adhesion-regulating genes (Ccl2, Ccl20, Vcam1, Mif, Csf1, Tnf, Il23a) (Fig. 2B, C), which are known to regulate cDC2 and T cells among other immune cell populations. In particular, Il23 is a key cytokine for the induction of an Il17-response which is a known driver of liver inflammation and fibrosis21. Increased transcript abundance of Tnf and Il23a was confirmed within DDC tissue at D10 and D19 using qPCR (Fig. S3B).

To assess the general nature of this pro-inflammatory early liver damage response, we revisited published data of a bile duct ligation (BDL) model and a chronic model of repetitive CCl4-induced liver damage22. Acute biliary injury (BDL) showed highly similar gene expression patterns within Sox9+ BECs, while chronic CCl4-induced damage did not lead to upregulation of Tnf, Il23a, Cxcl16 and Nfkb2 expression (Fig. S2C, D). Intersecting upregulated genes within our Sox9+ BEC DDC data (clusters 4, 8 vs. 3, 14) and Sox9+ BEC BDL data (clusters 3, 4, 7 vs. 1, 2, 9) revealed a common set of 128 genes (Fig. S2E), which were enriched for gene ontology annotations related to cell communication and inflammatory response (Fig. S2F). These findings highlight small duct BECs as important immunobiliary niche regulators in acute liver damage, including cDC2 and T cells, independent of the noxious background (DDC/BDL). In this niche, the Nfkb pathway inducer Tnf may trigger a proinflammatory change within the cDC containing immunobiliary niche. To functionally test Nfkb-driven alterations in biliary niche-residing cDCs triggered by BEC-secreted Tnf, we treated cDCs isolated from control livers with Tnf in vitro23 and observed induction of the maturation markers Cd80, Ccl17, Ccl22 in cDCs, indicating increased maturation dynamics (Fig. 2D).

To test the existence of an analogous Tnf-inducible Nfkb pathway activation node in the human liver immunobiliary niche within DCs, we made use of our human ST liver dataset and Niche Covariation (NiCo) analysis within a representative portal field data subset24. Cell type interaction prediction by NiCo recapitulated the immunobiliary niche and indicated a cellular localization of DCs within a defined cell type neighborhood hosting BECs and CECs (Figs. 2E, F and S3G, H). Since in the steady state we expected an inactivity of this communication node, we screened for covariation of BEC and DC gene programs represented by latent factors (Fa), and detected a negative covariation of Fa2 and DC Fa1 (Fig. 2F, G). Correlating ligand-receptor pairs contained an interaction of BEC-derived GRN with DC-derived TNFRSF1A/B, an interaction mediating either pro- or anti-inflammatory functions dependent on the proteolytic cleavage of the gene product25.

In conclusion, these results highlight small duct BEC-derived conserved and model-independent inflammatory gene expression patterns that may modify the surrounding myeloid niche.

Based on their spatial proximity to the BEC compartment and their capacity to activate lymphocytes, we explored the temporal disease dynamics of DC in our DDC disease atlas. While DC subtype frequencies were approximately equally distributed in steady-state, a frequency shift towards cDC2 predominance was observed starting from D3 (Fig. 3A). To confirm this shift by FACS analysis, we made use of a previously published gating strategy to discern cDC1 and cDC226 (Fig. S4A). Increased cDC2 proportions at D5/6 and D10 after DDC diet were observed, extending previous observations on frequency changes within the cDC1/2 compartment in short-term DDC-fed mice7 (Fig. 3B). Cell state dynamics of cDC1/2 were comparably affected in a model of acute hepatotoxic injury and ductular reaction (CDE diet; Fig. S4B).

This common dynamical switch towards cDC2 predominance prompted us to analyze cell state dynamics of cDC2 within our DDC atlas (cluster 5 and 15 of the main atlas, 7563 cells), on which we performed separate clustering to obtain a more granular annotation (Fig. 3C). Clusters 3, 12, 15 were excluded from further analysis based on remnant expression of cDC1 genes (Xcr1, Cadm1, Gcsam). Anti-inflammatory cDC2A and pro-inflammatory cDC2B can be distinguished based on their gene expression profiles13. The expression of Clec10a and Mgl2, hallmark genes for a cDC2B identity, indicated a predominant pro-inflammatory phenotype of the cDC2 subset, while maturation markers Ccr7 (cluster 11) or Ly6c2 (cluster 4) reflected temporal and differentiation-related changes along the x-axis in the UMAP representation (Fig. 3D). Prompted by these observations, we focused on trajectory analysis within the cDC2B compartment. Disease onset was associated with the expression of “maturation-on” (MAT-ON) genes (cluster 11), which have been described in the context of DC maturation27 and included, e.g., Ccl17, Ccl22, and Arpin (Fig. S4C). Cells derived from later time points (from D5 onwards) included Mgl2neg DC precursors (preDCs) expressing Cd7, Ly6c2, Tcf4, Runx2, Siglech, and Csf1r (Fig. S4C). The majority of cDC2 derived from D9 onwards were Mgl2-negative, suggesting that Mgl2 expression may be a proxy for the cDC2B tissue residency and maturation state. However, Mgl2 expression in cDC2B is downregulated as part of the MAT-OFF switch, as seen in tissue egressing Ccr7+ DCs (cluster 11).

We applied VarID228 to infer transition probabilities between clusters, which further supported the concept of a cDC2B trajectory linking disease-specific cell states, where Mgl2+ clusters were interconnected as part of the early DC maturation trajectory (clusters 1–7–9; Fig. S4D). To systematically investigate gene expression dynamics during maturation, we calculated self-organizing maps (SOMs) of pseudotemporal gene expression profiles along this trajectory (Fig. S4E)29. The genes in SOM modules 5, 13, and 20, which were upregulated along the trajectory (Fig. S4F) encoded soluble factors, transcription factors or proteins interacting with Nfkb1/2 (Relb, Nfkbia, Nfkbid, Nfkbiz; Fig. S4F). Since cDC2B have been linked to the induction of Il17-responses30, we next examined the expression of genes within the gene ontology pathway “positive regulation of T helper 17 type immune responses” in the Mgl2+ clusters. Indeed, increased expression of pathway genes, such as Jak2, Nfkbiz, Nfkbid, Nlrp3 was observed along the Mgl2+ cDC2B disease trajectory (clusters 1–7–9, Fig. S4G). Il17-response induction at the biliary niche was supported by DDC-induced Il23a production in BECs (Fig. 2B, C).

For the validation of increased recruitment of preDCs in prolonged DDC diet, Mgl1 and Mgl2 were co-detected using immunohistochemistry, which mark immature cDCs and macrophages31. As such, combined Mgl1 and Mgl2 detection does not enable quantification of mature Mgl2+ cDC2B but detects macrophages and cDC2 independent of their maturation state. Mgl1/2+ cells were specifically enriched in inflamed biliary foci and total numbers were increased at DDC D10 and D19 compared to controls (Figs. 3E and S4H). This specific cellular localization was also evident in a model of lobular liver injury and steatohepatitis (Fig. S4H, I). To assess the maturation state of hepatic cDC2, we further investigated whether the loss of Mgl2 mRNA within cDC2 in prolonged disease can be equally observed on the protein level. Indeed, the relative proportion of Mgl2+ cDC2B was significantly reduced from 34.4%±0.2% (D0) to 19.8%±4.9% at D10 (Figs. 3D, F and S4A). Finally, we tested the plasticity of Mgl2neg cDC2 precursors by comparing cDC2 derived from long-term DDC diet (D18/19) to cDC2 that underwent an episode of disease recovery (DDC D18/19 + 7 days of recovery, “STOP”; Fig. 3G). Analyzing cDC2A/B gene signatures revealed induction of the restorative cDC2A gene signature during disease resolution (cluster 2 and 6), while proinflammatory cDC2B genes remained partially expressed (Fig. 3H).

In conclusion, the cDC2 scRNA-seq data reveal DC maturation and temporal exhaustion of liver-resident Mgl2+ cDC2B at disease progression, along with recruitment of Mgl2neg preDCs that may differentiate towards a proinflammatory or restorative phenotype depending on the niche signal input.

To better delineate T cell subsets with key functions during the inflammatory response and tissue fibrosis, we determined Il17a expressing T cell populations in our scRNA-seq atlas32. The main Il17a-expressing population was γδ T cells (cluster 15) co-expressing Trdc, Sox13 and Blk33 (Fig. S5A, B), which segregated into Il17a high cells positive for Scart1 (encoded by Cd163l1), Trdv4 and Trgv6, and Il17a low cells expressing Scart2 (encoded by 5830411N06Rik) (Fig. S4C). Since γδ T cells were sparsely represented in our atlas (282 cells), we multiplexed enriched γδ T cells from a hepatic control (D0), DDC D5- and D9-fed mice for scRNA-seq (Fig. 4A, B). Gene signatures defining Vγ6+ and Vγ4+ γδ Τ cells indicated that Scart1+ cells were Vγ6+, while Scart2+ cells were Vγ4+34. Both populations expressed Nr4a1 and Cd5, indicating T cell receptor (TCR) activation, and Scart1+ γδ T17 cells (clusters 0, 3) expressed Il17a at higher levels (Figs. 4B and S5D). γδ T17 cells expressed different receptors that would enable their interaction with cDC2 and induce Il17 production, including, e.g., the Ccl2-Ccr2, Ccl20-Ccr6, Il1b-Il1r1, Il23a-Il23r, Ccl17/Ccl22-Ccr4 axes (Fig. 4C). These observations suggest a multifaceted crosstalk of γδ T cells with BEC and cDC2, expressing Ccl17, Ccl22 and Il1b (Fig. S4C). Close proximity relationships depicted by IF using γδ T cell reporter mice (Tcrd-GDL model17) further substantiated such a hypothesis (Fig. S5E, F).

To extend this observation, we conducted a ligand-receptor analysis of a merged liver cDC2 and γδ T cell dataset using CellChat35 (Fig. S6A–C). This analysis further identified contact-dependent and soluble interactions, including Icosl-Icos, Pvr-Cd226 or Cxcl16-Cxcr6, Lgasl9-Cd44 (Fig. S6B, C).

To support the hypothesis of differential signaling patterns in Scart1+/Scart2+ γδ T cells giving rise to distinct transcriptional regulation, we ran transcription factor (TF) regulon predictions using SCENIC36. TF regulons exhibited differential activities within and between Scart1+ and Scart2+ γδ T17 subsets (Fig. 4D). For example, Klf2 and Klf4 were more active within Scart2+ γδ T17 cells, in accordance with published data37,38. We then used regulon specificity scores (rss) to assess TF regulon cell type specificity. Rora, Maf, Irf4 TF regulon activity in Scart1+ γδ T17 cells (cluster 0 and 3) displayed high rss values, while Klf2 and Jund were specific for Scart2+ clusters 1 and 5 (Figs. 4E and S7A). Motif analysis for Rora, Irf4, and Xbp1 predicted target gene expression in Scart1+ γδ T cells, including Il17a (Fig. 4F). Flow cytometric analysis of Il17a on αβ T cells (βTCR+) and γδ T cells confirmed increased relative γδ T17 cell numbers in cholangitis at D5/6 and D10 with a comparable but delayed trend in the CDE model (Fig. S7B).

To interrogate whether γδ T and αβ T cells compete for signal cues within the peribiliary niche, Tcrd-knockout (KO) mice were treated with DDC. Indeed, compensation of the γδ T17 function was reflected by a higher percentage of Il17a+ αβ T cells in Tcrd-KO versus control mice at DDC D5, suggesting potential competition for γδ T17/Th17 polarization cues in liver cholestasis within a shared microanatomical niche (Fig. S7C).

In vitro treatment of γδ T cells with DDC D5 bile did not induce Il17a expression (Fig. S7D). Conditioned medium of DDC D5 derived DCs showed a trend towards inducing increased Il17a+ γδ T cell frequencies (p=0.06), however, compared to direct co-culture, the fraction of Il17a+ cells was low (Fig. S7E). Direct co-culture of DDC D5- or control liver-derived DCs with UTCs from control spleen led to increased Il17a production in γδ T cells but only weak induction in αβ T cells, indicating a DC-derived contribution to the induction of a γδ T17 cell state (Figs. 4G and S7F). Based on this observation, we used TCR stimulation in vitro to further elucidate the impact of contact-based/soluble mediators in inducing a γδ T17 cell state. Specific stimulation with recombinant Il18, Cxcl16, Lgals9 and activating Icosl-antibodies revealed a moderate γδ T17 cell state induction for Icos activation, while soluble mediators did not induce significant differences (Figs. 4H and S7G). This prompted us to test a disease-contributing role of γδ T cells in DDC-induced damage response and cholestasis using a constitutive (Tcrd-KO) and conditional depletion model of γδ T cells (Tcrd-GDL17). Conditional γδ T cell depletion was associated with reduced circulating total bilirubin and alkaline phosphatase levels indicating reduced cholestasis and disease activity (Fig. 4I). Furthermore, tissue-derived transcript levels of Pdgfrb, Col1a1 were reduced in Tcrd-KO and/or Tcrd-GDL liver tissues implying a functional contingency of liver fibrosis on γδ T cell-derived Il17a (Fig. 4J). To directly test the effect of Il17a on fibrosis, we treated wildtype (wt) and Il17a/f KO mice with a long term DDC diet (16 days) and quantified the number of Pdgfrb+ myofibroblasts and collagen (Sirius Red) in tissue sections. The number Pdgfrb+ cells and collagen area was significantly reduced in Il17a/f KO versus control mice, mirroring a profibrogenic role of Rorc-induced Il17-responses39 (Fig. 4K, L). Similarly, tissue-derived transcript levels for Acta2, Des, Col1a1 were reduced in DDC-treated Il17a/f KO vs. controls (Fig. S7H).

Together, these results suggest Il17a-producing γδ T17 cells as an early-onset fibrosis-supporting communication node in murine cholangitis.

For undeterred inspection of tissue dynamics in the cDC2B-γδ T cell niche, we generated imaging-based spatial transcriptomics data of DDC D5 liver. For high confidence cell type identification of γδ T cells and cDC2B, we used a 379 gene atlas sing panel adjunct with 100 add-on genes to further improve cell type resolution of cDC2 and γδ T cells (Methods, Table S1). QC metrics (233 median transcripts/cell and >52 million high-quality decoded transcripts) indicated a high quality ST dataset that was further analyzed using NiCo24. For NiCo analysis, we generated a merged and cleaned DDC atlas reference of the main atlas (Fig. 1K) and liver γδ T cells (Fig. 4A) comprising ~81k cells (Methods, Fig. S8A). DDC D5 ST data confirmed enrichment of cDC2 and γδ T17 cells at the periportal and biliary site (Fig. 5A, B). Label transfer of annotated scRNA-seq cell labels resulted into classification of 25 spatial cell types including small and large duct BEC, Il17a+ γδ T cells and DC subsets (Fig. 5C, D). NiCo predictions of cell-cell interactions identified an intertwined network containing specialized immune (e.g. naïve T vs. Il17a+ γδ T cells) and functional niche environments (e.g. proliferating niche). Immunobiliary cell types including small duct (sd) BEC, cDC2 and Il17a+ γδ T cells exhibited mutual colocalization within a niche (Fig. 5E, F). To demonstrate colocalization of γδ T17 and cDC2 within the immunobiliary niche, we used the add-on pool of our ST dataset, which contained multiple DC and γδ T cell-specific genes (Mgl2, Clec10a, Trdv4, Cd163l1, 5830411N06Rik, Il23r) and enabled the assessment of T17 effector state (e.g., Rorc, Irf4, Fig. 5G). Niche covariation analysis for small duct (sd) BEC and cDC2 suggested commonly regulated gene programs within identified latent factors (Fa1) that were captured by NiCo’s factor analysis (Fig. S8B)24. Spearman correlation analysis of highly correlating genes for Fa1 in sd BEC identified genes representing cellular stress responses (S100a11, Hscp90ab1) and changes in cell mobility/adhesion (Anxa2, Cd9). Highly correlating genes for Fa1 of cDC2 included genes related to DC maturation and antigen presentation (H2-Ab1, H2-Eb1, H2-Aa). These results implied coordinated cellular responses in this close proximity neighborhood. We further investigated if such covarying gene programs may be connected to cell-cell communication between respective cell types. NiCo’s inbuilt cell-cell communication analysis predicted sd BEC-derived signals that may modify cDC2 cell states (Fig. S8C). Once again, the ligand receptor pair Tnf-Tnfrsf1a was detected, similar to the human steady-state communication analysis (compare Fig. 2). Based on the identification of sd BEC, cDC2, Il17a+ γδ T cell neighborhoods in DDC D5 mouse liver, we further investigated, if similar cell-cell proximity relationships are detectable in human liver diseases such as PSC. While PSC pathophysiology is more complex and not identical with the acute injury-driven immune response in a xenobiotic model of cholangitis, we could still detect TRDC+ and CD207+ cells, which showed a localization-dependent concentration towards inflamed biliary foci compared to a lobular localization (average TRDC+ cell concentration in portobiliary and lobular regions was 66.33±54.07/mm2 and 19.87±25.38/mm2; Fig. 5H). Furthermore, close proximity niche relationships were detected by CD207/TRDC and CLEC10A/TRDC in situ hybridization (Fig. 5H).

In conclusion, DDC D5 ST data in early cholestasis recapitulated in situ distribution of rare immune cell populations such as cDC2/ γδ T cells and their niche neighborhoods at initiation of an early fibrotic response.

To uncover the role of Mgl2+ cDC2B in regulating γδ T cell recruitment and function in the context of DDC-induced biliary injury, we depleted cDC2B using diphtheria toxin (DT) in Mgl2-DTR mice16 (“DDC D5-DT”, Figs. 6A and S9A). The cDC2 subset of these data confirmed a strong abrogation of Clec10a+ Mgl2+ cDC2Bs and maturation-associated gene expression was reduced, while preDC-associated genes were upregulated and cDC1/pDC were only marginally affected (Figs. 6B and S9B, C). Pathway enrichment among differentially expressed genes between cDC2 from the depleted versus control animals reflected a dysfunctional, preDC-associated cellular state according to gene signatures that affect cell migration (GPCR, chemokines), and outside-in signaling (TNFR1 signaling, Fig. S9D). Upon cDC2B depletion, BECs exhibited reduced expression of pro-inflammatory mediators Tnf, Il23a, Ccl2 and Ccl20 (Fig. S9E). Top differentially expressed genes within the γδ T cell subset included Il17a, which was significantly reduced in cDC2-depleted animals (Fig. 6C, D, Methods).

Flow cytometric analysis of Mgl2-depleted versus control animals revealed a significant reduction of Il17a+ γδ T cell frequencies, with a similar trend in βTCR+ αβ T cells (p=0.07, Fig. 6E). To capture liver γδ T cells at higher resolution, we enriched γδ T cells from Mgl2-depleted mice for scRNA-seq and interrogated the frequency distribution of γδ T17 cells by comparing DDC D5 and DDC D5-DT derived γδ T cells (Fig. 6F). Reduced total Scart1/2+ γδ T17 cell numbers were observed in the DT condition, with a more prominent reduction in the Scart2+ population (44% vs. 85% reduction, Fig. 6G, H). Further subtyping of Scart1/2+ γδ T17 cells revealed individual clusters with circulatory and resident gene signatures40,41, respectively (Figs. 6G and S9F, G). A reduced proportion of circulatory cells in the D5-DT versus the D5 condition indicated altered γδ T cell recruitment and tissue half-life in cDC2B-depleted mouse livers (Fig. 6G, H). qPCR analysis of tissue-derived transcript levels of D5/D10 with DT-mediated cDC2-depletion resulted in decreased abundance of profibrotic transcripts (Pdgfrb, Des) indicative of reduced fibrosis initiation (Fig. S9H).

To address how cDC2B depletion and replacement of immature cDC2 may affect γδ T17 polarization across molecular layers, we performed a combined single-cell transcriptome RNA-seq and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) analysis (10x Multiome) on a control and a disease time-point, where all subclusters including preDCs can be observed (control (D0) and DDC D5, see “Methods”; Figs. 6I and S10A–C). In the cDC2 subset, label transfer of RNA to ATAC clusters revealed similar manifold architecture and transitioning cell states in both data modalities proposing concordant cDC2 cell state regulation (Fig. 6I). While the Mgl2 locus did not exhibit obvious differential chromatin accessibility across cell states, the Ccr7 locus showed an increase in accessibility along the cDC2 differentiation trajectory (Fig. S10D).

This prompted us to test the genomic accessibility of identified DC-γδ T cell ligand-receptor pairs (Fig. S6), to screen for interactions that are epigenetically regulated and may explain reduced cDC2-γδ T cell interactions after mature cDC2B depletion. Multiple predicted interactions as part of the cDC2-γδ T cell communication node showed concordant reduced genomic accessibility and RNA expression in preDCs compared to Clec10a+ cDC2 (Figs. 6J and S10E). Individual coverage plots of key cDC2-derived signal cues exemplified reduced genomic accessibility in preDC compared to Clec10a+ cDC2 (Figs. 6K and S10F).

Taken together, these data propose perturbed circulatory and residency properties of Scart1+ and Scart2+ γδ T17 cells and reduced γδ T17 effector differentiation upon cDC2B depletion, partially due to reduced genomic accessibility of γδ T cell ligands within preDCs.

After the identification of a γδ T17 instructive role of cDC2B, we asked whether mature Mgl2+ Ccr7+ cDC2B induce Il17 production in liver-draining lymph nodes (LN), thereby promoting Th17/γδ T17 responses in an organ-site independent manner42 (Fig. 7A). To enable the identification of Mgl2+ Ccr7+ cDC2 undeterred by the maturation-associated mRNA expression loss of typical DC markers27, we performed CITE-seq on liver-draining LN at steady-state (D0), DDC D5 and D19 (19,338 cells; Fig. 7A, B). This permitted a subclassification of cDC1/2 in LN on the protein level since proteins have a much longer half-life than RNA. Indeed, the Ccr7 high DC cluster in the liver-draining LN dataset exhibits a clear separation into Cd11b+/Mgl2+ and Xcr1+ cells on the protein level, while the cDC2 subset displayed the loss of RNA marker genes (Figs. 7C and S11A). Raw numbers of CD301b+ cDC2Bs in the process of DDC-associated inflammation continuously increased in the LN scRNA-seq data mirroring liver egress (Fig. 7B–D). In liver draining LN, Mgl2+ cDC2 were in physical contact with γδ T cells, as seen in Tcrd-GDL mice (Fig. 7E). Contact and ligand-receptor-mediated interaction analysis confirmed the previously observed DC-γδ T cell signaling module (Fig. 7F, G). Again, multiple liver interactions including Icosl-Icos, Cxcl16-Cxcr6, Icam1-Itgal/Spn were also predicted in LN data promoting the idea of site-independent cDC2-γδ T cell interactions in cholestatic liver disease. Next, we analyzed a scRNA-seq dataset containing γδ T17 cells derived from liver draining LN of D0 and D5 and detected Ccr7+ Sell+ Lck+ (cluster 3), Scart1+ (cluster 0) and Scart2+ (clusters 1, 2) γδ T17 cells. We performed SCENIC analysis to identify TF networks uniquely associated with these LN γδ T17 cells (Fig. 7H–J). A unique cassette of TF-regulons with predicted activity restricted to Ccr7+ Sell+ γδ T17 cells (“recruited”, cluster 3) was identified, with the highest rss for the TFs Myb, Lef1, Bptf and Ikzf2, in accordance with the RNA expression levels (Figs. 7H and S11B). Altered γδ T17 frequencies in LN prompted us to analyze γδ T in the circulation. Indeed, FACS analysis of circulating γδ T cells from ctrl (D0) and DDC D5 time points revealed altered γδ T dynamics, with a decrease of naïve Cd62l+ Cd44- and Cd62l+ Cd44+ circulatory γδ T cells, while Cd44+ Cd62l- effector γδ T cells increased (Figs. 7K and S11C). Last, we compared γδ T17 cells derived from draining LN at DDC D5 and upon loss of Mgl2+ cDC2B at DDC D5-DT. γδ T17 populations derived from the D5-DT were almost completely abrogated in our scRNA-seq data, implying a connection of LN γδ T17 effector differentiation and cDC2 homing (Fig. S11D–F).

Altogether, we here characterized a communication network between cDC2 and γδ T cells (Fig. 7L), where altered cDC2B dynamics impact on γδ T17 effector differentiation in liver and draining LN, exerting a profibrogenic function in liver cholangitis.

Methods
Mouse models
All animal experiments were authorized by the German Regional Council of Baden-Württemberg and Bavaria (Regierungspräsidium Freiburg and Regierung Unterfranken, reference numbers G20/164, 2-1554). Mice were bred in a 12-h light/dark cycle with free access to water and food and kept under defined pathogen free conditions with littermates receiving control chow being kept and housed in separate cages in the same facility room (Tables S2 and S3). Ambient temperature ranged between 20–24°C and the humidity range was 30–70%. C57BL/6J were bought from Janvier (Saint-Berthevin Cedex, France). Il17a/f-KO59 and Tcrd-KO (Tcrdtm1, JAX:002120) mice60 were obtained from Wolfgang Kastenmüller. Tcrd-GDL mice (JAX:038040)17 were obtained from Martin Väth. Mgl2-DTR (JAX:023822)16 were obtained from Kristin Hogquist with permission from Akiko Iwasaki.
Chow supplemented with 0.1% 3,5-Diethoxycarbonyl-1,4-Dihydrocollidine (DDC) or chow deficient in choline was purchased at ssniff (Soest, Germany)14. For CDE diet experiments, mice received a chow deficient in choline and 0.05% ethionine-supplemented drinking water. Prior to organ collection, mice were killed by cervical dislocation or CO2.
For scRNA-seq experiments, male mice were used. For FACS analysis, male mice were used with the exception of the cDC1/2 and cDC2A/B quantifications, where female mice were used.

Isolation of cells, FACS, and GEM generation
Primary liver cells were isolated from 6–12-week-old C57BL/6J mice by a Collagenase D digest61. Prior to FACS sorting, cells of interest were magnetically enriched using a magnetic separator and antibody- or Streptavidin-conjugated microbeads (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). Isolates of hepatic stellate cells (HSCs) were obtained as described elsewhere62. Mouse hepatoduodenal and celiac lymph nodes42 were excised, and digested in 2 mg/ml Collagenase D for 30 min at 37°C. Live/dead exclusion in FACS was performed using Zombie-NIR fixable viability dye (Biolegend, San Diego, USA) in combination with antibodies/reagents (Tables S4–6) to sort the cell populations at equal ratios using a FACSAria III cell sorter. Information regarding the composition of individual cell populations in each dataset can be found below. GEM generation and library preparation was performed according to the manufacturer’s protocol (10x Genomics, Pleasanton, USA). Quality control of libraries was performed using Agilent Bioanalyser High Sensitivity DNA Chips. Single and dual index libraries were sequenced on a NovaSeq S1 or NextSeq 2000 with a read-depth of 40,000 reads/cell. FASTQ files were mapped to a reference using kallisto63 (DDC atlas data). CellRanger was used for mapping of transcriptome, antibody-derived tags and human data. Combined scRNA- and scATAC-seq (10x Multiome) was performed as described in the demonstrated protocol for nuclei isolation for single cell Multiome ATAC+Gene Expression Sequencing (CG000365.Rev C). Digitonin treatment was performed for 210 s, nuclei suspensions washed, quantified and loaded at a concentration between 3000–8000 nuclei/µl to target a recovery of 10,000 nuclei. Before proceeding to GEM generation, a transposition reaction was performed on the isolated nuclei, where transposase enzymes fragmented accessible chromatin regions and inserted sequencing adapters. Nuclei were then loaded into the 10x Genomics Chromium Controller on a Chip J, where they were partitioned into Gel Bead-In Emulsions (GEMs), enabling the simultaneous capture of gene expression (GEX) and chromatin accessibility data from the same nuclei. Within the GEMs, reverse transcription converted mRNA into barcoded cDNA, unique to each nucleus. Following GEM breaking, the cDNA was recovered and amplified, alongside the tagmented DNA fragments from the ATAC reaction. Both the GEX and ATAC libraries were then purified, amplified, and quantified separately. Finally, these libraries were sequenced on separate flow cells, as the GEX and ATAC libraries require different sequencing parameters. Transposition and Chip loading for Multiome experiments were conducted at the Single Cell Center of the University of Würzburg.
Cell enrichment strategies for individual scRNA-seq datasets:
• Liver steady-state and DDC-dataset: to enrich all relevant cell types at equal ratios, two independent rounds of cell isolation and sequencing were performed at day 0 (ctrl), D3, D5, D9, D18 or D19, D25. For STOP and D5-DT data, only protocol B was used. D18 and D19 timepoints were summarized as D19 to facilitate timepoint-specific interpretation within the atlas. The following cell populations were sorted: (1) alive singlet, Cd45+, Lyve-1+, Pdpn+, Epcam+, Cd31+ Lyve-1−, retinoid-autofluorescent cells (405 nm excitation, 450/40 emission)62. (2) alive cells from HSC prep, Cd45+ Cd11c+ Cd64−, Epcam+, Cd3e+ Lyve-1−.
• Mouse CITE-seq LN dataset (ctrl and DDC): the following cell types were enriched from pooled hepatoduodenal and celiac LNs at D0, D6, and D19 and mixed at equal ratios: alive singlet, Cd45+ Cd11c−, Cd45+ Cd11c+, Pdpn+, Cd45− Pdpn−.
• γδ T cell datasets: cells from liver and LN were labeled using hashtag antibodies and alive Cd45+ Cd3e+ TCRβ− TCRγδ+ cells were sorted at indicated disease timepoints. The relative yield of γδ T cells at every timepoint represents the fraction of sorted cell numbers per mouse and timepoint.
• Human liver dataset: non-cholestatic disease controls and far distant human liver tissue was used for cell isolation from 6 patients (4 female, 2 male, age range 45–76 years, receiving a lobectomy or hemihepatectomy for oncologic resection) and the following cell populations were FACS enriched at equal distribution: (1) alive, (2) CD45+ CD3E−, (3) CD45+ CD3E+, (4) CD45+ CD11C+, (5) CLEC4G+, (6) CLEC4G− CD31+, (7) EPCAM+. This gating strategy was applied to enrich for immune cells of the immunobiliary niche, while the specific localization of the respective cells was not further assessed. Cell types of the human atlas were clustered at low resolution, so that LAM did not cluster separately, but were part of the FCGR3+ cluster. Lymphatic endothelial cells and portal fibroblasts, as important cell types of the portal/immunobiliary niche, were not actively enriched for in our sorting strategy and are thus not part of the dataset.

Bioinformatic analysis of scRNA-seq data
For the initial temporal DDC dataset (Fig. 1), a threshold of 1500 transcripts for mouse and 1000 transcripts for human datasets was used. VarID228 was run with the following parameters: mintotal=1500, minexpr=5, minnumber=5, ccor=0.4. FGenes was used to remove genes without a gene symbol, mitochondrial and ribosomal genes. The pruneKnn function was run with the parameters: large=TRUE, regNB=TRUE, knn=25, and Leiden clustering performed. Cluster identity subsets used for reclustering individual cell types are listed in the results section. For the quantification of DC subsets overall (Fig. 3A), a data subset of the clusters 5, 15, 17, 18, 19, 24 was reclustered to quantify DC subsets across timepoints.
Differential gene expression analysis was conducted using the RaceID3 algorithm. Based on a background model of transcript count distribution, a negative binomial distribution across clusters was inferred. Multiple testing corrected adjusted p values (Benjamini–Hochberg) were then estimated across clusters. For the analysis of Xenium data, we used publicly available human liver control data (Table S7). Xenium data were processed within Seurat and principal components, UMAP, community detection and clustering information obtained. For the inference of human suspension-based liver scRNA-seq data, we used robust cell type decomposition (RCTD) as part of the spacexr package18. For the identification of common cellular neighborhoods, we used Seurat’s “BuildNicheAssay”, which identifies similarities based on k-means clustering of neighborhood data (neighbors.k=15). Transition probabilities within cDC2 subsets were calculated using the plotTrProbs function of VarID228. Pseudotemporal ordering of self-organizing maps (SOMs) was performed using FateID29. Signac64 was used for the analysis of merged D0, DDC D5 Multiome data. RNA and ATAC data were tested for quality metrics and clustered based on information within each modality. Predicted gene activities levels were compared to RNA expression within transcriptome-based dimensionality-reduced space.
Seurat’s ability was used to integrate multimodal data, including antibody-derived tag (ADT) data65. ADT data was transformed using centered log ratio transformation (CLR) and ADT data displayed within the gene expression space. SCENIC36 was used to infer transcription factor regulon activity. Regulon activity per cell type was visualized in a heatmap and regulon specificity scores (rss) calculated using the in build calcRSS function. Pathway enrichment analysis was performed using ReactomePA66. cDC2A/B gene signatures were derived by curation from ref. 13. Residency and circulatory gene signatures40,41 were derived from48. Vγ4 and Vγ6 gene signatures were calculated by including the top 50 differentially expressed genes within a Vγ4 cluster (1) and Vγ6 cluster (9) of a TCR repertoire information containing reference48. For Niche Covariation (NiCo) analysis24 of the human liver Xenium dataset, we utilized NiCo’s annotation module with spatial guide clustering resolution 0.5. For the calculation of niche interaction maps, cell type enrichment ratios within cellular neighborhoods of a central cell were calculated, and a logistic regression to predict central cell type identities from these ratios was performed. The regression coefficients were then used to reconstruct the interaction map. Inferred latent factors from expression data were used to identify a small number of gene programs explaining cell state variability of each cell type. Covariation analysis of these latent factors across co-localized cell types may thus capture the effect of cell-cell interactions on gene program activity24.
More detailed information about the bioinformatic methods and access to the deposited scRNA-seq data can be found in the supplementary information (Tables S7 and 8).

Generation of DDC spatial transcriptomics data and analysis
For the generation of the scRNA-seq data reference for NiCo analysis, the DDC complete data (Fig. 1J) were merged with exclusion of cluster 11 (low QC) with the hepatic γδ T cell data containing both γδ T1 and γδ T17 subsets (Fig. 4A). Merged data were clustered and annotated using Seurat (resolution 0.8) and doublet clusters (cl. 22, 31, 35, 36), clusters of poor quality and <200 cells removed (cl. 39, 40). This resulted in a scRNA-seq reference containing 81,168 cells across 26 cell types and states.
For imaging-based spatial transcriptomics, the Xenium platform (10x Genomics) was used together with the Xenium Mouse Atlas sing panel and a customized 100-gene add-on panel (Table S1) to further resolve liver and immune cell subsets. The experimental protocol followed the manufacturer’s recommendations (CG000580, CG000749, and CG000584). The Cell Segmentation Staining Reagents (PN-1000661) were used to generate segmentation data based on a combination of cell boundary and intracellular markers. Step-wise image data acquisition was conducted after the DDC tissue region of interest was marked using the in-built Xenium Analyzer. Low-quality transcripts were filtered out using the framework of the Xenium analysis software, and transcript identities were annotated based on the codebook information. Q scores representing transcript identity confidence and quality were calculated, and a Q score cutoff was chosen as 0.2.
A more detailed experimental and bioinformatic description of the ST workflow can be found here67.

Histochemical and immunohistochemical analysis of human and mouse liver tissues
Three µm thick sections of a previously characterized human tissue microarray (TMA) with a core size of 1.5 mm that contained 78 non-cholestatic disease control liver tissues19,20 was immunohistochemically stained for CK7, CK19, CD34, PDPN, CD1A, CD3, CD21, CD23, TRDC and CD207 (Table S6). Non-cholestatic liver disease controls stained negative for CD1A, CD21, and CD23. Antigen retrieval was performed using Ventana CC1 solution and primary antibodies were incubated 24–40 min. DAB solution was added after linker- and multi-incubation and sections counterstained with haematoxylin. All used antibodies can be found in the Supplementary Table 6. Antibodies were tested prior to use according to the manufacturer’s recommendations. For proteogenomics, 1 µg of labeled antibodies was used per 2 million cells. TMA slides were scanned using a Pannoramic Scan II scanning device (3DHISTECH, Budapest, Hungary) with a 40x magnification. Positive cell detection of liver tissues was performed within the in-built TMA dearrayer of QuPath after setting a single intensity threshold5. Correlation analysis of TMA cores was conducted using the corrplot package in R for stainings containing at least 20 data points.
Human PSC liver tissues (n=18) were stained against TRDC and CD207. RNAscope (Bio-Techne, Minneapolis, USA) duplex in situ hybridization of human PSC tissues was performed using commercially available probes against TRDC, CLEC10A, and CD207 according to the manufacturer’s recommendations. Tissues were provided in accordance with the regulations of the Tissue Bank of the National Center for Tumor Diseases (NCT) Heidelberg and the ethics committee of Heidelberg University (S-230/20, S-206, 207/05). The use of the fresh human resection material was approved under the ethics vote 2012-293N-MA and 2021-2320-1, including written prior patient consent. For immunohistochemical detection of mouse Mgl1/2, liver tissue antigen retrieval was performed at pH 6 in a steamer for 8 min, and Mgl1/2 antibody (#AF4297, R&D Systems, Minneapolis, USA) was incubated for 1 h at room temperature (1:75 dilution). Horse anti-goat IgG coupled alkaline phosphatase-based detection was performed for 7 min. For the detection of Pdgfrb in mouse liver tissues, sections were incubated 1 h with the primary antibody after heat-induced antigen retrieval at pH 9. An anti-rabbit secondary antibody conjugated to AP was applied (Polyview Plus AP reagent, ENZO Life Sciences GmbH, Lörrach, Germany) and the signal was visualized using Permanent AP Red (Zytovision GmbH, Bremerhaven, Germany).
• Dendritic and T cell isolation, FACS, cultivation, and semi-quantitative PCR
Cells were isolated from 6–12-week-old C57Bl/6J mice. After tissue digestion and gradient centrifugation, cells were enriched using antibody-coated microbeads and LS columns (Miltenyi Biotec, Bergisch Gladbach, Germany). A published DC gating strategy was used to detect and quantify cDC1 and cDC226 For in vitro cytokine re-stimulation, T cells were stimulated for 3 hrs with 50 ng/ml PMA (Merck, Darmstadt, Germany), 1 µg/ml ionomycin (Merck) and 1 µg/ml Brefeldin A (Merck). After re-stimulation, cells were surface-stained, fixed and permeabilized using FOXP3/Transcription Factor Staining Buffer Set (eBioscience, San Diego, USA) and stained against Il17a and data acquired using a FACSCelesta (BD, New Jersey, USA). For liver DC–splenic T cell coculture, hepatic DCs were isolated and magnetically enriched using CD11c microbeads and LS columns according to the manufacturer’s recommendations. Simultaneously to hepatic DC isolation, splenic T cells were isolated in a two-step isolation procedure to enrich the fraction of γδ T cells. First, a negative selection against CD4+ and CD8+ conventional T cells was performed using CD4/CD8 (TIL) microbeads and LD columns (Miltenyi Biotec). The negative fraction was then enriched using CD3e microbeads and LS columns.
Liver DCs and splenic T cells were seeded in a 1:1 ratio in RPMI 160 (Gibco) supplemented with 10% FCS (Corning, New York, USA) and penicillin/streptomycin (Thermo Scientific, Waltham, USA) in addition with mouse recombinant Il23 (10 ng/µl, Thermo Scientific). For TCR stimulation, 24 wells were coated overnight with anti-CD3e antibody (5 µg/ml, Biolegend), washed 3 times with PBS, and enriched splenic T cells cultured for 20 h with DDC D5 bile, Lgals9, Il18, Il23a, Cxcl16, anti-mouse Icos-activating antibody or DC-conditioned medium derived from ctrl or DDC D5-derived liver DCs (mixed with RPMI containing 10% FCS and Pen/Strep in a 1:1 ratio). Mouse bile was isolated from gallbladders from DDC D5 mice, and bile was transferred into 1.5 ml tubes. A centrifugation step was performed to remove insoluble particles, and supernatants were used for stimulation assays (1:500 dilution).
Cells were harvested for FACS analysis 20 h after culture. For the analysis of circulating γδT cells, mouse blood was withdrawn by intracardiac puncture, and erythrocytes were lysed using ACK lysis buffer (Thermo Scientific) prior to FACS staining and data acquired using a Cytek Aurora (Cytek Biosciences, Fremont, USA).
For bulk RNA isolation from cells and tissues, the Arcturus PicoPure RNA isolation kit was used (Thermo Scientific, Waltham, USA). After cDNA synthesis using RevertAid First Strand cDNA Synthesis kit (Thermo Scientific), semiquantitative PCR was setup using SYBR Green (Bio-Rad, Hercules, USA) using 40 amplification cycles61. Melting curves were generated to test for product amplification specificity, and samples were measured in technical triplicates. Gene expression was normalized using the normaqPCR package and the measurement of four independent housekeepers (Actin, Gapdh, Hprt, Ppia)7. A list of intron-spanning primers used for qPCR (GC content 40–60%) can be found in Supplementary Table 9.

Sample preparation, fluorescence microscopy, and quantification
Mouse blood was withdrawn by intracardiac puncture using a heparinized 1 ml syringe and plasma obtained by centrifugation (30 min 2000×g)68. Blood plasma-derived parameters were detected using a VE tube 30 device and Liver and Kidney Profile Disks (Mindray, Shenzhen, China). Snap-frozen liver tissues were cut into 5 µm thick tissue sections using a Leica CM3050S cryostat and fixed in ice-cold methanol. To block unspecific binding, tissue sections were incubated with goat-serum prior to incubation with primary and secondary antibodies. Sections were washed and mounted into Fluoromount G (SouthernBiotech, Birmingham, USA) with or without DAPI. Six color immunofluorescence microscopy images were obtained using a Zeiss LSM780 laser scanning confocal microscope. Detectors were adjusted to detect 6 wavelength windows using 4 excitation laser wavelengths (405, 488, 561, 639 nm). The far-red laser was used for simultaneous excitation of AF639 and AF700 fluorescent dyes, which could be detected using wavelength-specific detection windows. The 405 nm laser was used for a combined excitation of BV421 and BV605. Image tiles were obtained using an oil immersion 40x objective and stitched based on a default overlap region of 10%.
Four-color IF images were obtained using an Olympus IX83 microscope containing a Yokogawa spinning disc unit.
To optimize image display, raw images were background subtracted (rolling ball, default settings), and an adjustment of the display window was performed using FIJI8. All images within one experiment were adjusted identically. Colocalization analysis of image channels and interaction areas was processed using the “math” function in FIJI. Average cell colocalization areas per group were calculated and displayed as a heatmap.

Quantification and statistical analysis
For quantification and statistical analysis, R Studio was used, and data are presented as mean ± SD. Graphs were plotted using R Studio or Excel, and panels were organized using Adobe Photoshop and Adobe Illustrator. For information regarding independent repeats of experiments, please refer to the respective figure legends. Statistical tests are specified in the figure legends. Shapiro–Wilk test was used to test for normality. The Wilcoxon rank-sum test was used for non-normally distributed data. T-test was used for normally distributed data. ANOVA was used in combination with Tukey’s test (post hoc). Boxplots display the median and the interquartile range (IQR). Whiskers extend to data points within 1.5 IQR. The number of animals per group in each experiment is indicated in the figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall: Impressive detail and breadth of experiments that convincingly demonstrate a role for cDC-GD T cell axis in murine cholestatic-induced liver fibrosis. Methodologies are cutting-edge and sound. Unclear if unsupervised analysis however would pinpoint cDC-GD T cell interactome as main/important driver of biliary inflammation. Figures are generally excellent, informative and well organized but could be simplified to focus main messages. Supplementary is extensive and substantial text is devoted to describing supplementary results that should be reduced/consolidated to improve clarity and conciseness. Clinical features of patient material needs greater detail and number of patient material analyzed to validate cDC-GD T cell translational relevance is limited (2 PSC and no disease controls).\n1. Abstract – slightly disjointed and lacking clear focus but could be dramatically improved with minor revisions. Authors state their goal is to elucidate unconventional T cells but then jump to mapping cell state trajectory of preDCs to cDC2B before returning to GD T cells and IL-17. Brief but better description/contextualization of abbreviated terms (e.g. preDCs, cDC2B, GD T cells, IL-17) would significantly improve readability and understanding for a broader readership.\n2. Public data analyzed for Fig 1 should be cited in main text of manuscript and patients/pseudonormal material used to generate scRNA-seq reference should be briefly defined in main text with general clinical features of patients detailed in a table.\n3. Relative and absolute number of GD T cells appears limited in biliary niche by scRNA-seq and IHC of human liver tissue. What is the relative proportion of GD T cells as a percentage of total T cells measured by scRNA-seq and IF staining for TRDC? Is the HE image for data in Fig 1a, Fig 1c available? Would be informative to include as small image overlaid on Fig 1a or in supplementary.\n4. How many of the TMA samples were analyzed to confirm spatial localization of indicated markers in Fig 1d? What is the disease diagnosis of these patients? Is it surprising to observe indicated level of T cell infiltration (CD3) in ‘healthy’ liver? Same comment regarding periductal fibrosis in TRDC HE staining – why is it there if these samples are representative of ‘pseudonormal’ biliary microenvironment? Were any GD T cell markers (or T cell markers besides CD45) included in IF staining of steady state mouse liver to confirm presence of T cells/GD T cells around bile ducts?\n5. Why were different protocols used to isolate cells for scRNA-seq? Assumption that FACS could not separate all subsets simultaneously but unclear as written.\n6. Transitional text between scRNA-seq DDC atlas and subsequent subsetting of BEC cluster lacks clear rationale given that authors state they performed scRNA-seq to investigate unbiased dynamics of gene expression across the biliary niche. Difficult to discern what cell types and cell states change most dramatically during disease progression – did the unsupervised analysis point toward BEC or was this the subsetting done to answer defined hypothesis? Were there clear enrichment of cDC and GD T cell signals at any timepoint(s)?\n7. Definition of latent factors and relevance of BEC and DC gene programs unclear as written and should be further explained for non-bioinformaticians. Difficult to interpret data shown in Fig 2F and Fig 2G.\n8. Umap in Fig 3C be simplified (also split) to delineate cDC2A and cDC2B clusters? Similarly, could timepoint umap overlays in Fig 3D be consolidated into one umap with trajectory analysis shown by arrows to better indicate dynamics of cDC2 states at early to late to recovery timepoints?\n9. How closely does cDC2 state at ‘STOP’ compare to cDC2 markers at steady-state (i.e. D0)? Including numbers of mice/cells analyzed by scRNA-seq (Fig 3C and 3G) would aid intra and inter comparisons of cluster sizes. Similarly, dot overlays representing number of analyzed mice should be included for Fig 3B and 3F bar graph.\n10. From data in S5, appears Il17a, Rorc and Rora transcriptomic signatures are relatively weak (expression less than 1 LF above comparator). What is comparator here, D0? As stated by the authors, abundance of GD T cells are very low - are the receptor-ligand interactions known to facilitate recruitment and activation with cDC2 exclusive to GD T cells?\n11. What are the absolute numbers of conventional Il17a+ T cells relative to Il17a+ GD T cells? Although a greater proportion of GD T cells appear Il17a, their overall scarcity (Figure S7A) questions their importance in cholestatic fibrosis.\n12. How did DT depletion of cDC2B impact other immune subsets, particularly other T cell populations irrespective of Il17a expression?\n13. Authors repeatedly attempt to translate mouse findings to PSC however few PSC samples were analyzed (n=2) and appropriate disease controls for biliary inflammation (e.g. PBC) and general end-stage liver fibrosis (e.g. ALD, MASH) are lacking.\n14. DDC and BDL models of cholestatic liver injury were used to study GD T cells but alternative models of non-cholestatic liver injury used to study cDC1/2 dynamics (e.g. CDE diet) were not included as controls, which would help determine if relevance of GD T cells are truly specific to cholestatic disease or general liver disease feature. Use of GD T cell knockouts would clearly identify their role in cholestatic fibrosis as well as education of cDC into inflammatory or resolving cell states.", "reviewer_2": "This is an interesting study by a well established group in the field presenting a multimodal single-cell atlas of the immunobiliary niche in mouse cholangitis, complemented by human data. The authors highlight dynamic cDC2B maturation, γδ T 17 induction, and functional relevance of the ICOSL–ICOS axis. The dataset is rich, and the study addresses an important and relatively unexplored area. However, in its current form, several claims appear overstated or insufficiently supported by the presented data. The study is more descriptive than mechanistic, with limited causality in validations. Human translations are preliminary, and several interpretive issues need clarification.\nPlease find in the following my points.\nSpatial analyses (Fig. 1)\nIt is difficult to appreciate whether CD3E+ cells are truly enriched around ducts/large vessels. Quantification of their localization would strengthen this point. The density map for CD207 is unclear; why was a distinct visualization chosen for this marker only? If biliary epithelial cells (BECs) and immune cells were sorted separately, why are BECs so underrepresented relative to immune cells? What input ratios were used when loading the cells? Many proteins are inconsistently formatted (e.g., some are capitalized, others not). Please standardize (e.g., ITGAX+ cells). Figure legends indicate n=3, yet there are no quantitative analyses. Including barplots would help. Annotation strategy needs clearer explanation. A dotplot of marker genes validating cluster identities would be welcome. Timepoints in legends could be reordered chronologically for clarity. The rationale for using t-SNE (rather than UMAP throughout) is not clear.\ncDC2 trajectory and cluster interpretation (Fig. 2–3)\nThe claim that cluster 8 expresses chemotaxis/adhesion genes is not entirely convincing. Cluster 16 seems to express more of these genes except Il23a. Exclusion of clusters 3, 12, 15 due to residual cDC1 signatures requires more transparency — which genes drove this decision, and are they uniquely cDC1-specific? There is some confusion regarding Mgl2 expression: overall Mgl2+ cells increase, yet the proportion of Mgl2+ cDC2B decreases. This apparent contradiction should be clarified. There seems to be a discrepancy in the interpretation of DC recruitment. The authors claim that Mgl2⁻ preDCs are recruited along disease progression, yet their own data (e.g., Fig. 3E) show an increase in Mgl2+ cells, which they themselves describe as the more immature DCs. This would rather suggest that Mgl2+ precursors are recruited into the niche. The authors should clarify this trajectory: are Mgl2+ cells the recruited precursors that then lose Mgl2 as they mature, or are truly Mgl2⁻ preDCs entering the tissue?\nHuman validation\nThe presence of IL17+ γδ T cells in human PSC is implied but not convincingly shown. Given ongoing debate about human γδ T17, this claim should be toned down or better validated. The human validation of γδ T17 identity remains unclear in their data. Do human γδ T cells in PSC express canonical Th17-defining transcription factors (e.g., RORC, MAF, IRF4) and cytokines (IL17A, IL17F)? The current data (Suppl. Fig. S7H–J) show no IL17A transcript levels, and no clear evidence for a broader Th17 signature in human γδ T cells. Since the existence of human γδ T17 cells remains controversial, stronger evidence or a more cautious interpretation is needed. At minimum, the authors should explicitly show whether RORC, MAF, IL17A/F are expressed in their human γδ T cell subset. Cross-species conservation of interactions is intriguing but may be overstated.", "reviewer_3": "In this manuscript, the authors create a scientific narrative demonstrating the importance of type 2 cDCs and gamma delta T cells. The majority of which is described in a mouse model of biliary injury using a range of high level scientific techniques. Obviously, the role of type 2 cDCs reported here is not a new one, and it was first described in a range of mouse models by Müller AL et al (J Hepatol. 2022 Dec;77(6):1532-154) (which is acknowledged in the references. Therefore, the novelty of this study is in the potential cross talk with the gamma delta cell population in the biliary niche. In doing so it provides mechanistic insight to the role of dendritic cells in biliary injury and potential ligands that could be targeted for drug discovery.\nOverall, the authors have submitted a large amount of data to provide robustness to the proposed narrative. However I would say that within the large volume of experimental data – which skips between human and murine models, there is perhaps a distraction for the reader particularly for the reasons I highlight below.\nMy major comments for this manuscript include\n1) I think the closeness of this model (ie mirroring to PSC) requires firstly some careful clarification by the authors. PSC is an autoimmune liver disease of the liver and colon. Its genetic risk is HLA driven, and the importance of CD4+/CD8+ and T regs is well described. The inflammatory response is therefore the driver of biliary injury in PSC. This is in strong contrast to the mouse model studied here, where the induced epithelial/cholangiocyte damage secondarily induces inflammation – therefore they are not one in the same. The DCC model is therefore not a mouse model of PSC. Perhaps the authors should therefore highlight that what these have in common, and the point of their study – ie how biliary injury may amplify the local immune response and perturbate epithelial resultant injury.\n2) In the opening introduction they say PSC ultimately leads to transplant – this is not true – 50% are alive and well at 20 years. I would not say that the disease is characterised by dysfunctional myeloid responses. Therefore, to compare PSC to a toxin mediated liver injury, needs careful discussion by the authors and questions why they wish to try and reproduce some of their findings in PSC.\n3) the authors show that CD1C and CD3E transcripts are present near large vessels and biliary tracts (Fig1A). I must admit – I think here spatial has been used for spatial sake. The figure presented looses the clarity needed that would be gained by a simple HE that has been stained for these markers at low and high power, which would then show a broad range of zones 1-3 of the liver, portal tracts and central veins – to really understand the normal distribution of these cells in a normal liver. Secondly, I do not really understand how you can claim that the pseudo normal liver dataset is the biliary niche. The immune sets that have been fax sorted could come from anywhere. Why do you claim they are totally representative of the biliary niche?. However, I except that the BuildNicheassay suggests a biliary and lobular niche from the generated resultant dataset.\n4) The authors then describe once again that have generated a single cell RNA seq from DDC fed mice at days 3,5,9 and 25. Once again there is a repeated claim that all these cells are from the immunobiliary niche – once again they could come from any compartment of the liver (Portal, Z1,2,3 and central) – I think this is slightly misleading for non-mouse hepatic experts.\n5) The authors then demonstrate that the small ducts in portal fields during DCC injury release a range of inflammatory genes highlighting these are chemoattractant to T and DC cells – as they are for many cells, so this in itself is not proof of a direct interaction. Therefore, the specific Importance of these observations can only be generalised. The authors then highlight similar findings in the BDL model but not the CCL4 model – which is to be expected – given the latter is a hepatocyte damage model. This is then followed by speculation that BEC TNF production activates DC2’s and is followed by an experiment where ex-vivo the DCs are treated with LPS – a TL4 agonist. The relevance of which was not clear to me. Why were they not treated with TNF in the ex-vivo analysis. However, I fully except that the co- ligand analysis was supportive of TNF interaction between DC2’s and the biliary compartment.\n6) The authors then show that there is an expansion of DC2s in their murine model and confirmed this by FaxAnalysis. The authors then focus on the DC2B lineage – and show an accumulation of mature cells and pre-cells repopulating the niche with the expression of genes that would favour local IL-17 production. Finally, they show that this population lose their ability to express Mgl2 (and propose this as a local maturity marker).\n7) The authors then provide evidence for 2 populations of Gamma delta T cells – based on Scart1 and 2 expression. These 2 populations show differences in IL-17 expression within different TCR clones. This was shown to be due to differences in key transcription factors. In addition, competition for signals between alpha beta and gamma delta is provided for this niche. However, note is made of this low frequency population within the experiment and the pooling was required to conduct the experimental design. So, the importance of this low frequency cellular niche has to be questioned. Furthermore I am not sure that the demonstration of IL-17a knock out reversing fibrosis is direct proof that gamma delta T cells drive fibrosis concretely. This is because there are many cells that produce IL-17 in these models.\n8) The authors then show that depletion of DC2B cells show a reduction in BEC inflammatory gene expression and also show IL-17 down regulation in gamma delta T cells - suggesting a functional relationship between these 2 cellular compartments\n9) Finally, the authors show a clear evidence of Lymph Node communication between DC2 and gamma delta T cells and provide interactive ligand relationships between these 2 compartments.\nSuggestive improvements/extra work could include:\n1: I personally think that the PSC data is not needed for this paper – it’s not representative of this model and therefore its relevance to the paper has to be called into question\n2: Would it not be more beneficial to develop biliary organoids from humans and induce injury/ and see if they attract/modulate DC2 migration in migration assays\n3: Use human DC2s and co-culture with gamma delta T cells to understand interactions\n4) Why has a gamma/delta T cell knock out not be studied in this model - would that not be possible to show their importance in fibrosis in this model if this cellular source was of vital importance"}, "author_response": "Response to the reviewer comments\nThe reviewer comments are shown in black and our responses are highlighted in blue.\nChanges in the manuscript are highlighted in blue.\nReviewer #1:\nOverall: Impressive detail and breadth of experiments that convincingly demonstrate a role\nfor cDC-GD T cell axis in murine cholestatic-induced liver fibrosis. Methodologies are cutting-\nedge and sound. Unclear if unsupervised analysis however would pinpoint cDC-GD T cell\ninteractome as main/important driver of biliary inflammation.\nWe thank the reviewer for this critical feedback. We conducted our cDC2-gdT cell\ncommunication analysis (e.g. Fig. S6) in a hypothesis-driven way, based on our observation\nthat the main Il17 producing cell population were gdT cells. Based on this, we decided to\nfurther explore gdT cell communication in a supervised fashion. In that regard, this analysis\nwas not conducted in the whole dataset in an explorative manner but with a clear hypothesis\nand focused scientific question. To support this claim of the importance of gdT, we quantified\nabsolute numbers of abT17 and gdT17 cells in FACS data and point out that the trend is even\nstronger in scRNA-seq data (see R1 Q11).\nFigures are generally excellent, informative and well organized but could be simplified to\nfocus main messages. Supplementary is extensive and substantial text is devoted to\ndescribing supplementary results that should be reduced/consolidated to improve clarity\nand conciseness.\nTo streamline the manuscript, we reduced some supplemental findings and changed the text\nwherever applicable. However, we also want to point out the necessity to generate additional\ndata to address all concerns raised by the reviewers, so that also novel supplementary data\nwas generated as part of this manuscript revision.\nClinical features of patient material needs greater detail and number of patient material\nanalyzed to validate cDC-GD T cell translational relevance is limited (2 PSC and no disease\ncontrols).\nWe acknowledge this important concern and agree that the scRNA-seq data of human PSC\nand its interpretability is limited (2 patients). To circumvent this point, we also analysed\npublished gdT data from Poch et al. [3] (11 patients). Additionally, we would like to emphasize\nthat the tissue cohorts for immunohistochemistry (n=18) and in situ hybridization (n=5/6)\nwere larger. PSC scRNA-seq data currently remains scarcely available, so that we see the\nimportance of making these data accessible, but we fully agree that we cannot draw robust\nconclusion from such data. As a consequence, we removed the PSC scRNA-seq data in the\nrevised version of the manuscript.\nTo increase the clinical information of the pseudonormal liver and PSC scRNA-seq data (n=6),\nwe included a supplemental table summarizing additional patient information (novel Figure\nS1A).\nsample sex age surgical indication scRNA-seq derivation process dissociation\nID technology reagent\nMA m 52 liver metastasis 10x 3' v3.1 (Dual mechanical and, Collagenase D\n(liposarcoma) Index) enzymatic dissociation, FACS\nMA1 f 52 liver metastasis 10x 3' v3.1 (Dual mechanical and, Collagenase D\n(neuroendocrine tumor) Index) enzymatic dissociation, FACS\nMA2 f 71 in situ split timepoint 10x 3' v3.1 (Dual mechanical and, Collagenase D\ncolorectal liver Index) enzymatic dissociation, FACS\nmetastasis\nMA3 f 57 hemi hepatectomy 10x 3' v3.1 (Dual mechanical and, Collagenase D\nIndex) enzymatic dissociation, FACS\nMA4 m 44 colorectal liver 10x 3' v3.1 (Dual mechanical and, Collagenase D\nmetastasis Index) enzymatic dissociation, FACS\nMA5 f 76 pancreatic cancer, 10x 3' v3.1 (Dual mechanical and, Collagenase D\ndouble duct sign Index) enzymatic dissociation, FACS\nPSC1 f 53 PSC liver explantation 10x 3' v3.1 (Dual mechanical dissociation, Collagenase D\nIndex) enzymatic dissociation, FACS\nPSC2 m 21 PSC liver explantation 10x 3' v3.1 (Dual mechanical and, Collagenase D\nIndex) enzymatic dissociation, FACS\nNovel Fig. S1A: Summary of the human pseudonormal liver tissues used for scRNA-seq.\n1. Abstract – slightly disjointed and lacking clear focus but could be dramatically improved\nwith minor revisions. Authors state their goal is to elucidate unconventional T cells but then\njump to mapping cell state trajectory of preDCs to cDC2B before returning to GD T cells and\nIL-17. Brief but better description/contextualization of abbreviated terms (e.g. preDCs,\ncDC2B, GD T cells, IL-17) would significantly improve readability and understanding for a\nbroader readership.\nThank you for this important feedback on the abstract of our manuscript. Based on the\nfeedback, we rewrote the abstract and improved contextualization of abbreviations and tried\nto re-order the findings on cDC2 and gdT cells to make them more comprehensible. Please\nfind the adjusted abstract below:\nImproved Abstract (197/200 words):\nThe liver biliary niche serves as a reservoir of 1ssue-resident immune cells and supports 1ssue fibrosis upon\ndamage, yet the role of peribiliary immune cells during cholangi1s remains poorly understood. Mirroring human\nbiliary diseases with bile acid reten1on, we induced cholesta1c liver injury in mice to establish a spa1al and\nmul1modal single-cell sequencing atlas for liver and liver-draining lymph nodes (LN). We characterized a hepa1c\ndisease state trajectory from dendri1c cell precursors (preDCs) to a mature subset of pro-inflammatory Mgl2+\ntype 2 conven1onal dendri1c cells (cDC2B) and observed dynamic crosstalk with gdT cells inducing an Il17-\nresponse. Dissec1on of the cDC2B-gdT cell communica1on node iden1fied the Icosl-Icos axis as a cri1cal cell\ncontact-dependent interac1on that was validated in vitro. In vivo, cDC2B deple1on aPenuated gdT17 responses\nin cholesta1c liver injury and liver fibrosis was reduced in a model of inducible gdT cell deple1on and in an Il17-\ndeficient background. Conserva1on of spa1al niche architecture and molecular interac1ons was confirmed in\nhuman cholangi1s. Our work demonstrates dynamic turnover of cDC2 within the biliary niche during cholestasis,\nand a profibrogenic func1on of gdT cells con1ngent on the induc1on by peribiliary cDC2B, highligh1ng relevant\ndisease determinants within the immunobiliary and liver-draining LN niche.\n2. Public data analyzed for Fig 1 should be cited in main text of manuscript and\npatients/pseudonormal material used to generate scRNA-seq reference should be briefly\ndefined in main text with general clinical features of patients detailed in a table.\nThank you for these suggestions, that we incorporated in the main manuscript in the following\nmanner:\n• We included the data source in the Methods Table file. In particular, information on\nthe public data of Fig. 1A was added (derived from 10x genomics datasets, source is\nprovided in the supplements). This data was published as part of the 10x genomics\nwebsite, without a manuscript reference. Clinical features are not applicable for this\nsample.\n• scRNA-seq data (6 patients): we generated a novel table (Fig. S1A) that we refer to in\nthe main text of the article indicating clinical information of the resected human liver\nspecimens.\n3. Relative and absolute number of GD T cells appears limited in biliary niche by scRNA-seq\nand IHC of human liver tissue. What is the relative proportion of GD T cells as a percentage of\ntotal T cells measured by scRNA-seq and IF staining for TRDC? Is the HE image for data in Fig\n1a, Fig 1c available? Would be informative to include as small image overlaid on Fig 1a or in\nsupplementary.\nThank you for these valuable suggestions that aim to point out the importance of gdT cells\ncompared to abT cells.\nWe included information on the absolute numbers of gdT and T cells using our FACS data\nderived from Fig. S7B. For this quantification, we summed up Il17+ abT and gdT cells to\nvisualize the percentage of Il17+ cells across abT and gdT cell compartments (total numbers\nper sample analysed, see stacked barplot below). In this analysis we could see that the gdT\ncells contributed the majority of Il17+ cells (D5/6: 58,23% and D10: 50,76%). This analysis\nsupports our claim regarding the importance of these cells.\nFurthermore, we plotted log-normalized Il17a expression in our T cell subset of the DDC atlas\ndata (compare Fig S5A-C; see below). This depiction visualizes the high specificity of Il17a\nexpression to gdT cells, even more specific as to what is seen in the FACS data. This may be\nexplained due to unspecific stimulation of abT cells in the process of cytokine restimulation in\nvitro using PMA/Ionomycin/Brefeldin A. The specificity of Il17a expression in the scRNA-seq\ndata makes us believe, that the true estimation of Il17a+ cells in the abT and gdT cell\ncompartments may be better represented by the scRNA-seq data instead of the FACS data\n(due to unspecific stimulation of all cells). With both data modalities showing a high specificity\nfor the gdT cell compartment, we demonstrate strong support for the relevance of gdT cells\nas Il17 producers.\n120%\nIl17a\n100% 7\n18\n6\n11\n80%\n16\n60% g a d b T so 1 l 7 ute 1\n40% a a b b T so 1 l 7 ute 15 3 20 8 1 1 0 7 14\n13 19\n20% 2 12\n9 4\n5\n0%\nctrl DDC DDC\nD5/6 D10\nFACS and scRNA-seq quantification of Il17a producing T cell subsets: Stacked barplots displaying stratification of Il17a+ cells per sample into\nabT+ and gdT+ subsets as quantified by FACS (left). Even though gdT cells are rare, they become an important cell population making up to\n58,23% (DDC D5/6) and 50,76% (DDC D10) of Il17a+ cells. UMAP depicting the T cell subset (middle) and log-normalized Il17a expression\n(right column). In this unperturbed data, the specificity of Il17a transcripts to the gdT cluster (cluster 15) is even more prominent to detect.\nFinally, we quantified TRDC IHC data in human PSC within the larger cohort of the two cohort\nbeing analyzed (quantification of n=10 samples). We included the statement of gdT\nconcentration in PSC samples (“Overall TRDC+ cell concentration was 29.28±27.61/mm2,\nwhile local concentration within peribiliary regions could reach much higher densities (Fig.\nS7B).”, see results section). This statement helps the reader to categorize the cell\nconcentration in general.\nWe were also thankful for the suggestion to include the HE image from the ROI shown in Fig.\n1A. Unfortunately, the data to load and allocate the file to a novel slot into the Seurat object\nusing “Read10X_Image()” is not available in the open repository (datafile\n“tissue_lowres_image.png”).\nThis is why we could not include the HE of the ROI into the supplemental files as suggested.\n4. How many of the TMA samples were analyzed to confirm spatial localization of indicated\nmarkers in Fig 1d? What is the disease diagnosis of these patients? (a) Is it surprising to\nobserve indicated level of T cell infiltration (CD3) in ‘healthy’ liver? Same comment regarding\nperiductal fibrosis in TRDC HE staining – why is it there if these samples are representative of\n‘pseudonormal’ biliary microenvironment? (b) Were any GD T cell markers (or T cell markers besides CD45) included in IF staining of steady state mouse liver to confirm presence of T\ncells/GD T cells around bile ducts? (c)\nThank you for these questions and the possibility to clarify the data on our tissue cohorts.\n(a) The TMA initially contained 78 pseudonormal tissue cores from 78 patients, these\ncores were derived from far distant regions of the resected hepatic hemangioma\nspecimen as being characterized in [4, 5]. Because this TMA was heavily used (approx.\nsince cores were used up during consecutive stainings. We included the n-numbers for\neach IHC into the novel correlation plot (novel Fig. 1E, see also R2 comment 1 “spatial\nanalyses”).\nIndeed, resection specimens from far distant liver tissue of hepatic hemangiomas are\noften considered a very good source for control liver tissue, yet based on the age of\nthese patients and their lifestyle – these tissues may already have been exposed to\nhepatotoxins during the lifetime. This explains the variability of the tissues we declare\nin the paper as “pseudonormal”. We agree, that these tissues thereby reflect a range\nof different tissue states, with some tissues already exhibiting some peribiliary fibrosis.\n4\n2\n0\n2-\n(a) With regard to existing T cell subsets that have a tissue residency phenotype (T cells),\nRM\nwe would also expect to see T cells in the “pseudonormal” liver tissue. TRDC+ cells are\nindeed quite rare in this dataset, however, if they are to be detected, they also have\nthis preferential localization to the peribiliary/portal niche. Regarding the TRDC\nimmunohistochemical staining in Fig. 1D, we chose this image to exemplify the location\nof the gdT cells, while we agree with the statement of peribiliary fibrosis. We see these\nchanges in accordance with our statement of “pseudonormal” liver – a term that we\ndecided to use to contextualize that liver tissues available in archives in Institutes of\nPathology often come with some inherent abnormality as being the reason of surgical\nresection and the advanced age of the patients that undergo such surgical treatment.\n(b) For steady state mouse liver tissue, we analysed T cells using Cd3e IF (previously not\nshown and now included as novel part for Fig. S5E.\nWhile the presence of these cells could be clearly imaged at DDC D5 using standard\nimaging techniques (section thickness 5 µm), these cells were difficult to detect using\nsame techniques in steady state. The same trend was true for Tcrd-GDL reporter mice\nand their localization around the peribiliary tree: we could convincingly detect areas\nwith a clear EGFP signal in DDC D5 but not control Tcrd-GDL liver tissues. This may not\nexclude their presence at low concentrations and the possibility that these cells could\nbe detected when more extensive efforts for the imaging of these cell types would be\nundertaken (thick sectioning etc.). This holds true especially for the Scart1+ gdT\nfraction, which is characterized by the expression of a tissue residency gene signature.\nBased on the fact, that we could convincingly image these cells at DDC D5 and the\nknowledge that these cells become more abundant in DDC-induced cholestasis, we did\nnot include these points in the manuscript to avoid unnecessary complication of the\nstoryline. In the human “pseudonormal” data, these cells can be detected, as also\npointed out by the reviewer. Whether these are tissue resident cells in non-inflamed\nliver or whether these cells have been actively recruited because of inflammatory\nsignal cues cannot be fully elucidated using these samples, the marker panels being\nused and the clinical information being available.\n5. Why were different protocols used to isolate cells for scRNA-seq?\nThank you for the possibility to clarify our experimental conduct. We included an explanatory\nparagraph, that explains the use of two different scRNA-seq protocols to isolate cell\npopulations of interest (see Methods section subsection “Cell enrichment strategies for\nindividual scRNA-seq datasets”).\n6. Transitional text between scRNA-seq DDC atlas and subsequent subsetting of BEC cluster lacks clear rationale given that authors state they performed scRNA-seq to investigate unbiased dynamics of gene expression across the biliary niche (a). Difficult to discern what cell types and cell states change most dramatically during disease progression – did the unsupervised analysis point toward BEC or was this the subsetting done to answer defined hypothesis? (b)\nWere there clear enrichment of cDC and GD T cell signals at any timepoint(s)? (c)\nThank you for raising these points.\n(a) After the observation of this specific immunological niche around the biliary tree, we\nchose the xenobiotic DDC model that would specifically affect this immunological\nniche. We included an explanatory sentence detailing our rationale to first analyse the\nBEC compartment in this data. Please refer to results, chapter “DDC diet induces small\nduct inflammatory disease”, first sentence.\n(b) Based on other complementary analyses such as the IF-based morphologic\ncharacterization of this model, which clearly showed the BEC to be strongly affected,\nwe decided to focus on these cells first. To clarify the rationale, we included a short\nexplanatory sentence into the manuscript (see response to (a))\nWe would like to point out here, that a general cell abundance analysis of cell types\nwithin the whole atlas may have only limited interpretability, since the aim during\nscRNA-seq atlas generation was also to balance out drastic changes in cell abundances\nusing our cell enrichment and mixing strategy prior to GEM generation and scRNA-seq.\n(c) The later focus on cDC was again based on the morphological relationship between\nBEC and cDC, which we visualized in human (Fig. 1D) and mouse (Fig. 1F-I). However,\nindeed these cDC2 and gdT cells show a temporal distribution in the atlas across\ntimepoints indicating importance in early disease dynamics (see Fig. A,B below). For\ncDC2 a strong increase can be observed at DDC D5, making it the most abundant DC\nsubset at this timepoint. gdT cells also show increased abundances, and the abundance\ncurve is shifted to later timepoints, which may indicate a temporal dependency of gdT\ninflux and cDC2 maturation.\nTo not overly complicate the already dense manuscript, we decided to not include\nthese results, but display them here as evidence of such temporal regulation and as\nsupport of our further focus on these cells. Additionally, previous literature (Müller et\nal.) strongly pointed towards relevant dynamics within the cDC2 compartment [6].\nWe believe these additional data support our rationale to investigate cDC2 and gdT\ncells.\nProtocol-specific total number of DC and gdT cells captured in scRNA-seq data per timepoint: (A) Total number of DC subsets derived from\nprotocol A, which only enriched for Cd45+ and macrophages specifically. The abundance of DC subsets from protocol A can thus be\nquantitatively interpreted, while protocol B aimed to isolate DC at similar ratios per timepoint. (B) gdT cell numbers derived from protocol\nB. T cells had to be enriched specifically, since T cell subsets were not well captured in protocol A.\n7. Definition of latent factors and relevance of BEC and DC gene programs unclear as written and should be further explained for non-bioinformaticians. Difficult to interpret data shown in Fig 2F and Fig 2G.\nThank you for this important feedback. We extended the methods section with a description\nfor improving the understanding of NiCo’s [7] use of latent factors and the calculation of niche\ninteraction maps. Please find below the excerpt of introduced explanation:\n“For the calculation of niche interaction maps, cell type enrichment ratios within cellular\nneighborhoods of a central cell were calculated and a logistic regression to predict central\ncell type identities from these ratios was performed. The regression coefficients were then\nused to reconstruct the interaction map. Inferred latent factors from expression data were\nused to identify a small number of gene programs explaining cell state variability of each\ncell type. Covariation analysis of these latent factors across co-localized cell types may thus\ncapture the effect cell-cell interactions on gene program activity [7].”\n8. Umap in Fig 3C be simplified (also split) to delineate cDC2A and cDC2B clusters? (a)\nSimilarly, could timepoint umap overlays in Fig 3D be consolidated into one umap with\ntrajectory analysis shown by arrows to better indicate dynamics of cDC2 states at early to\nlate to recovery timepoints? (b)\nThank you for this feedback.\n(a) We believe that the grouped visualization of the cDC2 timepoints captures the cDC2\ncell states in an interpretable manner. In particular, differences between steady state\nand diseased state are captured by the y-coordinates while variability within diseased\nstates is captured along the x-axis.\nTo improve the understanding of the cDC2 trajectories, we annotated the cDC2-\nspecific map of clusters (adjusted Fig. 3C) to exemplify the directions of the timepoint-\nspecific disease trajectories. The subsets of Clec10a+ cDC2B, preDC, Ccr7+ mat DC and\nnon-diseased cDC2 will give orientation within this gene expression space.\n(b) The summarized trajectory as proposed by the reviewer is already part of the\nSupplemental Figure S4D visualizing transition probabilities calculated by VarID2 [8]\nacross clusters and timepoints.\n9. How closely does cDC2 state at ‘STOP’ compare to cDC2 markers at steady-state (i.e. D0)?\n(a) Including numbers of mice/cells analyzed by scRNA-seq (Fig 3C and 3G) would aid intra\nand inter comparisons of cluster sizes (b). Similarly, dot overlays representing number of\nanalyzed mice should be included for Fig 3B and 3F bar graph. (c)\nThank you for your valuable suggestions.\n(a) We compared the expression of the cDC2A/B gene signatures using the dotplot\nrepresentation of VarID2 and included D0 into our analysis together with the\ntimepoints D19 and D19-STOP. Here, we found no trend towards the expression of\ncDC2A or cDC2B genes. Thus, we believe these data are difficult to interpret and\nexisting dotplots in Fig. 3H already show the essence of the information. We decided\nto show the DotPlots here for the reviewer’s information but not to include this\ninformation into the main manuscript.\nDotplots displaying sample-specific cDC2A/B gene expression in DDC D0, D18/19 and STOP conditions. Log-normalized gene\nexpression is shown and fraction of cells expressing the gene is encoded by dot size.\n(b) This is a helpful suggestion, and we decided to include cell numbers into the Figures\nto make the data more interpretable. For the DC comparison overall, we included the\ntotal DC numbers into the bar plot of Fig. 3A and for cDC2 into Fig. 3D and Fig. 3G.\nWe also included an explanatory sentence in the methods section, how cDC1, cDC2,\npDC subsets were quantified based on which data subsets.\nFor the statement of the mice being used per experiment, we believe, this information\nis not informative for scRNA-seq, since for many populations not the absolute number\nof cells within one sample was sorted but only sufficient numbers to be able to conduct\nGEM generation as quickly as possible. This is in accordance with scRNA-seq protocols\nto avoid long processing time. For all sequencing experiments: Two mice of the same\ntimepoint were pooled and cell isolation conducted as stated in the methods section,\nso that this information is already accessible in the manuscript.\n(c) We included absolute numbers in the dot overlays into Fig. 3A, Fig. 3F and Fig. S4B and\ndecided to switch the display to boxplots. Data points of individual samples are now\ndepicted.\n10. From data in S5, appears Il17a, Rorc and Rora transcriptomic signatures are relatively weak\n(expression less than 1 LF above comparator). What is comparator here, D0? (a) As stated by\nthe authors, abundance of GD T cells are very low - are the receptor-ligand interactions known\nto facilitate recruitment and activation with cDC2 exclusive to GD T cells? (b)\nThank you for the possibility to clarify our methodology.\n(a) With regard to the dotplot information shown in Fig. S5B, we would like to mention\nthat this plot shows log2-normalized expression values without any comparator. This\nmeans that overall expression values were compared. Expression values of selected\ngenes such as Il17a, Rora or Rorc are encoded by dot color and these genes are\nexpressed at low levels. Dot size encodes the fraction of cells expressing the gene of\ninterest.\n(b) Abundance of gdT cells is indeed low. The importance of the gdT- cDC2 interactions\nthat we identified could be justified by two reasons:\n- First, we identified gdT cell-exclusive interactions that can selectively activate gdT cells,\nas implied by the reviewer. However, this is not the case for many of the interactions\nthat we identified.\n- Second, the importance of the interactions was demonstrated by the expression of\nmarker molecules in T cell populations of interest and the fact that these cells show an\neffector cell phenotype. Indeed, only gdT cells express high levels of Il17a, indicating a\nselective T17 response in gdT but not abT cells. Although the interaction of cDC2 with\ngdT cells may not be entirely specific, cDC2 serve as an important cellular interaction\npartner to induce gdT17 responses in this model. We also confirmed this interaction\nin vitro (coculture) and in vivo (Mgl2-DTR depletion) in our manuscript.\n11. What are the absolute numbers of conventional Il17a+ T cells relative to Il17a+ GD T\ncells? Although a greater proportion of GD T cells appear Il17a, their overall scarcity (Figure\nS7A) questions their importance in cholestatic fibrosis.\nThank you for this important suggestion.\nWe conducted the proposed quantification by FACS after cytokine restimulation and point out\nthat with 58.23 ± 19.3% (DDC D5/6) and 50.76 ± 7.9% (DDC D10) a significant proportion of\nIl17a+ cells has a gdT cell identity (see also comment of R1 Q3). As pointed out in R1Q3,\nwithout cytokine restimulation and by the assessment of Il17a transcript levels in the T cell\nsubset of the scRNA-seq data, this difference is even more prominent, indicating that gdT cells\nare the main source of Il17a.\nAdditionally, even if there is a minority of non-gdT cells expressing Il17a, we want to point out\nthe different effector differentiation dynamics of unconventional (UTC, among them gdT cells)\nand conventional T cells (abT cells) [9]. UTC have a more rapid and quick effector response\ncompared to conventional T cells, thereby essentially contributing to the initiation phase to\norchestrate a profibrotic response.\nTogether, we believe these points convincingly highlight the importance of the gdT cell\ncompartment as a source of Il17a in the DDC diet model.\n12. How did DT depletion of cDC2B impact other immune subsets, particularly other T cell\npopulations irrespective of Il17a expression?\nWe thank the reviewer for raising this important question.\nTo include information on the dependence of T cell responses and cDC2 depletion, we\nincluded a dotplot displaying the expression of Th1, Th2, Th17 marker genes and other\nmarkers of T cell activation (transcription factors (TF) and general markers of activation).\nFor this, we generated a data subset containing NK and T cells (clusters 2, 3, 10, 12, 19, Fig. A).\nThis subset contained 4364 cells, with a slight predominance of T cells in the DDC D5-DT\ncondition (DDC D5: 1727 cells; DDC D5-DT: 2637 cells). The most striking difference between\nthe T cell compartments was the fact that the T cells in the non-depleted condition were\nprimarily naïve (Sell+, Ccr7+, Cd44-, Fig. B). For gdT17 related genes, this analysis confirmed an\nattenuated Il17a response in the DT condition (Fig. C).These observations could be in line with\nthe known shortcomings of DTR models, including immunogenicity of Diphtheria toxin as\ndescribed in the literature [10].\nAnother important aspect of adverse effects that reduce specificity in DTR models is off-target\ncell-killing, e.g., of myeloid cells. To assess the specificity of the depletion strategy, we\nanalysed our scRNA-seq data and analysed the cell composition across samples within mature\ntissue resident dendritic cells (cDC1, cDC2, pDC) and of LAM/KC as another major myeloid\ncompartment in the liver (Fig. D). This strategy exemplifies the specificity of the depletion,\nwhile excluding DC precursors that will ultimately repopulate the niche.\nIndeed, Mgl2-DTR based depletion was highly specific to cDC2B (cluster 1) at DDC D5-DT\n(11.47-fold reduction). cDC1 were also reduced but to a much lower extent (1.53-fold\nreduction, Fig. D). pDC and KC/LAM were increased in number after DT depletion (2.26- and\n1.29-fold increase). These changes are in line with previous descriptions about specific caveats\nof DTR models, while simultaneously showing convincingly, that the cell type of interest is\nmost heavily affected by the depletion strategy.\nWe decided to include Figure Panel D of this information into the manuscript (novel Fig. S9B).\nWe also include a statement in the discussion section to address the shortcomings of DTR\nmodels (“… and exclude potential DT- mediated off-target effects affecting our analysis.”).\nT cell activation and myeloid cell distribution across DDC D5 and DDC D5-DT conditions:\n(A) UMAP representation displaying clusters (left) and samples (right) of the T cell subset of merged DDC D5 and DDC D5-DT data.\n(B) UMAP representation of the T cell subset of the combined DDC D5/DDC D5-DT dataset displaying log-normalized gene expression of Ccr7,\nSell, and Cd44. (C) Dotplot displaying expression of T cell marker genes (left) and gdT cell marker genes (right). Log-normalized expression is\ncolor coded and fraction of cells expressing the gene is encoded by dot size (right). (D) Stacked barplot displaying sample distribution of\nmature DC subsets and Mφ within DDC D5/DDC D5-DT conditions.\nFurthermore, we conducted qPCR of tissue derived transcripts to catch global changes over\ntime, analysing two timepoints (DDC D5 and D10) with and without DT-based depletion. For\nthese experiments, the depletion was induced as previously on day DDC D-1 and DDC D2\n(compare scheme in Fig. 6A), so that a certain degree of repopulation of mature cDC2B would\nbe expected at D10.\nWe confirmed altered fibrosis initiation on D5-DT (significant reduced tissue levels of Pdgfrb,\nDes transcripts), cDC2B depletion (Mgl2 transcript levels) and aspects of gdT cell biology (Tcrd\ntranscript levels), all of which were partially attenuated at D10, reflecting the repopulation of\nthe cDC2 niche by incoming preDC. We believe that these data support our study and included\nthis qPCR analysis into the supplemental Figure files (novel Fig 9H).\nNovel Fig S9H: Barplot displaying tissue-derived transcript levels in DDC D5, DDC D5-DT, DDC D10, DDC D10-DT conditions. Asterisks\nindicate level of significance. Data normalized to control liver tissue transcript levels. One sided T test (n= 4-6 mice/group), *p<0.05.\n13. Authors repeatedly attempt to translate mouse findings to PSC however few PSC samples\nwere analyzed (n=2) and appropriate disease controls for biliary inflammation (e.g. PBC) and\ngeneral end-stage liver fibrosis (e.g. ALD, MASH) are lacking.\nThank you for this feedback. Indeed, for the PSC scRNA-seq adequate controls are lacking. This\nmakes a comparative analysis difficult. Data of cell populations such as gdT cell and cDC2 are\ncurrently still scarcely available and existing datasets in PSC do not enrich for these cell\npopulations. We thus decided to remove the scRNA-seq data of the two PSC patients.\nAdditionally, we reframed the statements of the transferability of the findings of the DDC\nmodel towards human PSC. Unfortunately, we do not have access to a high sample number of\nthe requested non-PSC liver controls (ALD/MASH/PBC), so that such a comparison cannot be\ndelivered by us. In fact, we believe that gdT cells also play a role in other disease context that\nlead to fibrosis. To make our results more comparable, we included a quantification of 10 PSC\ntissues that were digitalized and TCRD+ cells were quantified. This information (“Overall TRDC+\ncell concentration was 29.28±27.61/mm2, while local concentration within peribiliary regions\ncould reach much higher densities (Fig. S5H).”) has been now included into the main\nmanuscript.\nFinally, we also conducted another round of duplex in situ hybridizations of TRDC and IL17A\nin human PSC liver (n=5). While the majority of TRDC+ cells was IL17A negative, thereby\nreflecting scRNAseq data and the ongoing discussion of the presence of Il17A+ gdT in human\ndisease, we could also find regions with double positive cells. Yet, even detectable, the\nfraction of cells being Il17a+ is much lower in the human samples. Based on these results and\nto not further complicate the storyline of the manuscript, we decided to not include these\nresults and increase our focus on the mouse data.\n14. DDC and BDL models of cholestatic liver injury were used to study GD T cells but\nalternative models of non-cholestatic liver injury used to study cDC1/2 dynamics (e.g. CDE\ndiet) were not included as controls, which would help determine if relevance of GD T cells\nare truly specific to cholestatic disease or general liver disease feature. (a) Use of GD T cell\nknockouts would clearly identify their role in cholestatic fibrosis (b) as well as education of\ncDC into inflammatory or resolving cell states (c).\nThank you for bringing up these important points.\n(a) To address this concern directly, we measured gdT17 cells in CDE D5 and CDE D10 providing\nadditional information to clarify the specificity of gdT17 responses in the context of\ncholestasis. Indeed, gdT17 responses were reduced at CDE D5 (p<0.001) and a trend towards\nincreased gdT-mediated and delayed Il17a responses at CDE D10/11 (p=0.058) was\ndetectable.\nThese results exemplify the early onset and prominent gdT17 responses in cholestasis. The\nresults are now part of the novel Fig. S7B.\nPart of novel. Fig. S7B. Boxplot displaying frequency of Il17a+ cells within Cd3+ abTCR+ (light grey) and Cd3+ gdTCR+ (dark grey)\ncompartments in the CDE diet model after cytokine re-stimulation. Two-sided t-test (5 independent experiments, n=5-9 mice/group). ***p\n< 0.001, n.s., not significant.\n(b) Furthermore, we used two gdT cell knockout models (Tcrd-KO and Tcrd-GDL models\nenabling constitutive and inducible KO) to study gdT cells in the context of cholestasis and\nbiliary fibrosis. While constitutive gdT knockout showed a compensation by abT cells in the\nproduction of Il17a (compare Fig S7C), we conducted additional analyses to further study\nthese cells using the DT-based, conditional depletion model. These experiments are explained\nand listed in full detail in the response of reviewer 3 point 7.\nIn brief, we assessed the degree of cholestasis using blood plasma profiling, conducted global\nanalysis using qPCR of tissue derived RNAs and isolated the global DC compartment by\nmagnetic enrichment.\n(c) The question of the re-education of DCs by gdT cells is interesting, as it would demonstrate\na bi-directionality in cellular crosstalk within the cDC2-gdT cell signalling hub.\nWe conducted a DDC diet experiments using Tcrd-KO (constitutive knockout), Tcrd-GDL\n(inducible knockout) and Bl6 controls analysing two different timepoints of DDC diet feeding.\nFor both experiments and the conditional depletion in Tcrd-GDL mice, DT was given on D -1\nbefore and D2 after starting the DDC diet (equivalent design, as compared to Mgl2-DTR\nexperiments). We analysed transcript abundance by qPCR on D5 of magnetically enriched\nCd11c+ cells with the aim to resolve expression of DC maturation markers in dependency of\ngdT cell presence in cholestasis.\nAt an early timepoint of cholestasis (DDC D5), we observed an increased abundance of\ntranscripts defining mature DC states (Ccr7, Cd80, Ccl17, Ccl22, see panel A below).\nThese data indeed show differences between the constitutive and inducible gdT knockout in\ncDC2 education, however these data may further complicate the storyline of the manuscript.\nIn fact, the manuscript mainly focuses on cDC2-induced effects on gdT cells, while re-\neducation is not further analysed. Therefore, we believe these results are beyond the scope\nof the manuscript and we decided to not include these results into the main manuscript.\nBarplots depicting normalized transcript levels in CD11c+ enriched cells after DDC D5 treatment. Data normalized to transcript levels in\nC57BL/6J mice receiving DDC for 5 days (equals 1). Group size as following: Bl6 (n=6), Tcrd-KO (n=4), Tcrd-GDL (n=5). Statistics (two sided t-\ntest shown for Tcrd-KO (TCRD-tm_KO) and Tcrd-KO (ITC_GDL, both compared to DDC D5 control).\nReviewer #2:\nThis is an interesting study by a well established group in the field presenting a multimodal\nsingle-cell atlas of the immunobiliary niche in mouse cholangitis, complemented by human\ndata. The authors highlight dynamic cDC2B maturation, γδ T 17 induction, and functional\nrelevance of the ICOSL–ICOS axis. The dataset is rich, and the study addresses an important\nand relatively unexplored area. However, in its current form, several claims appear\noverstated or insufficiently supported by the presented data. The study is more descriptive\nthan mechanistic, with limited causality in validations. Human translations are preliminary,\nand several interpretive issues need clarification.\nPlease find in the following my points.\nThank you for this positive feedback.\nSpatial analyses (Fig. 1)\nIt is difficult to appreciate whether CD3E+ cells are truly enriched around ducts/large vessels.\nQuantification of their localization would strengthen this point (a). The density map for\nCD207 is unclear; why was a distinct visualization chosen for this marker only? (b) If biliary\nepithelial cells (BECs) and immune cells were sorted separately, why are BECs so\nunderrepresented relative to immune cells? (c) What input ratios were used when loading\nthe cells? (d) Many proteins are inconsistently formatted (e.g., some are capitalized, others\nnot). Please standardize (e.g., ITGAX+ cells). Figure legends indicate n=3, yet there are no\nquantitative analyses. Including barplots would help. (e) Annotation strategy needs clearer\nexplanation. A dotplot of marker genes validating cluster identities would be welcome. (f)\nTimepoints in legends could be reordered chronologically for clarity (g). The rationale for\nusing t-SNE (rather than UMAP throughout) is not clear (h).\nThank you for pointing out various inconsistencies in our manuscript. We would like to reply\nto the requests in a point-by-point fashion:\n(a) Thank you for pointing out that a quantitative metric would help to assess enrichment\nof different cell types in the immunobiliary niche. To do so, we conducted a spearman\ncorrelation analysis of marker-positive cells detected in our tissue microarray dataset.\nAs a figure output we generated a correlation map (novel Fig. 1E) for the TMA cores,\nwhich were analysed using all markers throughout several studies [4, 5]. This enabled\ncorrelation of marker positive cell numbers that should be enriched in portal fields\nonly, e.g. CK7+ BEC with various immune markers that are enriched in the\nimmunobiliary niche. While the statistical power may vary depending on the number\ncores being analysed per staining (19 stainings were quantitatively analysed), we see\na strong correlation for CD3/CD8 and CK7, while CD207 and CK7 show a correlative\ntrend, however, not reaching the level of significance. The results of this analysis can\ngive a first hint regarding the localization of the cells, while the number of cores being\nanalysed in the TMA determine the statistical power, which varied among stainings.\nMarkers such as CK19 and TRDC with a core number was below 20, were excluded\nfrom the analysis.\nWe believe the requested correlation analysis improves our manuscript and supports\nthe hypothesis of an immunobiliary niche that contains T cells and DCs, which is later\non confirmed using independent techniques.\nCorrelation Plot (novel Fig. 1E) depicting Spearman correlation coefficients among marker-positive cells (%). The n-number of\ncores being analyzed containing pseudonormal human liver tissue are listed in the updated Fig. 1E. Levels of significance displayed\nby asterisks.\n(b) We decided to remove the display of the cell concentration in the TMA core overview\nfor CD207. This was indeed only shown for this marker, and we decided to show the\noverviews of the TMA cores for all markers instead. We included several other markers\nto give an overview that multiple immune cell types show an immunobiliary enriched\nlocalization.\n(c) We would like to clarify our FACS sorting strategy: The number of FACS-sorted cells\nand the input ratio was defined, immune cells may seem overrepresented by the fact\nthat in protocol A Cd45+ cells were used as one population of interest to capture\nabundant immune cells, while for rare immune cells additional populations were\nsorted and pooled. BEC may be slightly underrepresented based on the digestion\nprotocol causing incomplete digestion of cholangiocyte fragments, which may be\ndetected as “cells” in FACS, however, do not pass QC in scRNA-seq. In comparison to\nother published disease atlas data, we think that the BEC compartment is well covered\nin our data. Expecting a low yield in the BEC compartment, we decided to enrich for\nthis population in both protocols.\n(d) The input ratios are explained in the methods section – yet we appreciate the question,\nsince the number of cells being input and being recovered may vary, as also\nexemplified for BEC in (c).\n(e) Thank you for pointing out inconsistencies with regard to the statement of the number\nof analysed samples and the naming of proteins (e.g. ITGAX+). We corrected such\nerrors throughout the Figures (Fig. 1F, 1G, 6E, S4A, S5F). The manuscript text was also\ncorrected in this regard.\n(f) A heatmap/dotplot of marker genes is already part of the manuscript for the human\nsteady state and the DDC atlas (Fig S1B, S2H).\n(g) Thank you for this suggestion. We changed the legends depicting the timepoints and\nsamples in a chronological order in all respective figures (Fig. 1K, 2A, S2G, S3A, S5A).\n(h) We would like to point out that the tSNE embedding was solely used for visualization\nof our complete atlas. We do not derive any further analysis from this complete atlas\ndata. We would also like to note that there is no clear rationale for choosing either\nUMAP and tSNE as they can capture the same amount of information. This is rather a\nfunction of the input parameters used. Since our tSNE map visualizes the data\nstructure well, we would like to keep it.\ncDC2 trajectory and cluster interpretation (Fig. 2–3)\nThe claim that cluster 8 expresses chemotaxis/adhesion genes is not entirely convincing.\nCluster 16 seems to express more of these genes except Il23a (a). Exclusion of clusters 3, 12,\n15 due to residual cDC1 signatures requires more transparency — which genes drove this\ndecision, and are they uniquely cDC1-specific? (b) There is some confusion regarding Mgl2\nexpression: overall Mgl2+ cells increase, yet the proportion of Mgl2+ cDC2B decreases. This\napparent contradiction should be clarified. (c) There seems to be a discrepancy in the\ninterpretation of DC recruitment. The authors claim that Mgl2⁻ preDCs are recruited along\ndisease progression, yet their own data (e.g., Fig. 3E) show an increase in Mgl2+ cells, which\nthey themselves describe as the more immature DCs. This would rather suggest that Mgl2+\nprecursors are recruited into the niche. The authors should clarify this trajectory: are Mgl2+\ncells the recruited precursors that then lose Mgl2 as they mature, or are truly Mgl2⁻ preDCs\nentering the tissue? (d)\nThank you for this feedback. We would like to respond to the individual questions point by\npoint:\n(a) Thank you for this feedback, we rewrote the manuscript and include information that\nclusters 4,8 and 16 express chemotaxis/adhesion genes in the BEC subset of the data.\n(b) We visualize the log-normalized expression of cDC1 genes Xcr1, Cadm1, Gcsam to\ndemonstrate residual cDC1 signatures in clusters 3, 12, 15 (see Figure panel B below).\nThese clusters are Mgl2 and Clec10a negative (see main Fig. 3D). We believe, this is\nevidence of the different cell identity of these clusters and we now list the genes that\nprompted us to remove these clusters from our analysis in the results chapter.\nUMAP representations of cDC2 subset (A) and log-normalized expression of Xcr1, Cadm1, Gcsam, which depict the cDC1 identity in\nclusters 3,12, 15 (B).\n(c) This is an important point. Since we already anticipated a potential caveat we had\nincluded an explanatory sentence into the results chapter of these data (“…Mgl1 and\nMgl2 were co-detected using immunohistochemistry, which marks immature cDCs\nand macrophages [29]”). To provide further clarification, we added an a more detailed\nstatement to this section: “As such, combined Mgl1 and Mgl2 detection does not\nenable quantification of mature Mgl2+ cDC2B but detects macrophages and cDC2\nindependent of their maturation state instead.”\nFor clarification, it is important to note that the immunohistochemistry stains Mgl1\nand Mgl2 (Fig. 3E) and these data cannot be compared with FACS and scRNA-seq data,\nwhere Mgl2 protein or transcript is detected specifically. Yet, because of the very clear\nmorphology and adjacency relationship of chromogenic Mg1/Mgl2+ cells, we would\nlike to keep these data in the main manuscript. We hope we could clarify the\npresentation and the biological meaning of these data.\n(d) This is in line with the response for (c) and the fact that Fig. 3E does not depict a specific\nstaining for Mgl2. This should clarify, that the increased cell numbers to be detected\nat the immunobiliary niche include most likely macrophages, cDC2 and DC precursors,\nwhich can be stained by combined staining against Mgl1/Mgl2. In that regard, the\nstaining itself may be more comparative to a more broad cDC2B marker such as\nClec10a (compare UMAP in Fig. 3D). I hope these explanations resolve the confusion\nand increase the clarity of the made statements.\nWith regard to the trajectory from preDC to mature Ccr7+ cDC2, we would like to make\nthe following explanations: Ly6c2+ Cd7+ Siglech+ preDCs are Mgl2-negative, these cells\nthen upregulate Mgl2 expression as part of their tissue adaptation. In the process off\nantigen-uptake and DC maturation, these cells undergo gene expression changes\n(maturation on and off switch, respectively), which then leads to the downregulation\nof Mgl2-expression, while Mgl2 protein levels remain expressed (compare LN scRNA-\nseq data, Fig. 7C). We agree, that these expression changes are complex to grasp,\nmainly because of the maturation-off switch during DC maturation. To make this more\nclear, we modified the results section and included an explanatory sentence into the\nmain manuscript:\n“Cells derived from later timepoints (from D5 onwards) included Mgl2negative DC\nprecursors (preDCs) expressing Cd7, Ly6c2, Tcf4, Runx2, Siglech, and Csf1r (Fig. S4C).\nThe majority of cDC2 derived from D9 onwards were Mgl2-negative, suggesting that\nMgl2 expression may be a proxy for the cDC2B tissue residency and maturation state.\nHowever, Mgl2 expression in cDC2B is downregulated as part of the MAT-OFF switch\nas seen in tissue egressing Ccr7+ DCs (cluster 11).”\nHuman validation\nThe presence of IL17+ γδ T cells in human PSC is implied but not convincingly shown. Given\nongoing debate about human γδ T17, this claim should be toned down or better validated (a).\nThe human validation of γδ T17 identity remains unclear in their data. Do human γδ T cells in\nPSC express canonical Th17-defining transcription factors (e.g., RORC, MAF, IRF4) and\ncytokines (IL17A, IL17F)? (b) The current data (Suppl. Fig. S7H–J) show no IL17A transcript\nlevels, and no clear evidence for a broader Th17 signature in human γδ T cells. Since the\nexistence of human γδ T17 cells remains controversial, stronger evidence or a more cautious\ninterpretation is needed (c). At minimum, the authors should explicitly show whether RORC,\nMAF, IL17A/F are expressed in their human γδ T cell subset (d). Cross-species conservation of\ninteractions is intriguing but may be overstated (e).\nThank you for this feedback regarding the human validation. Please see our responses below:\n(a) We toned down our statements (see revised manuscript) and also removed human\nPSC scRNA-seq data including the ligand-receptor interaction analysis. Additionally, we\nconducted TRDC/IL17A in situ hybdridization, where double positive cells could be\ndetected, however the majority of TRDC+ cells were IL17A negative. These results\nreflect the controversy of the existence of such cells in the human setting. As a\nconsequence for our additional analyses, we removed the cell-cell communication\nanalyses and also the gdT analyses from the published Poch et al. data (compare\nprevious S7).\n(b) We plotted the expression of RORC, MAF, IRF4 in the gdT subset of the Poch et al. data\n(previously done for the UTC subset in previous Fig. S7, see below). Here, we decided\nto not integrate the data to see patient-specific differences in the gdT compartment.\nWhile Il17a expression is low throughout the samples, we see sample-specific\ndifferences for the expression of T17 identity forming TF, with e.g. a convincing\nexpression of RORC, MAF in “sample 14” (non-IBD PSC).\nThis expression heterogeneity of RORC, MAF, IRF4 is also recapitulated in our dataset\ncomprising two patients, while again IL17A expression is only detectable in few cells.\nWe conclude, that the gdT cell subset in human PSC is heterogeneous, where gdT cells\nexpressing a T17-identity forming TF signature may exist. These cells, however, do not\nexpress IL17A. Whether the expression of IL17A may be rapidly triggered upon\ncholestatic insults needs to be further elucidated. The data may also point out, that\ngdT cells in PSC may have other functions beyond the secretion of IL17A. This data is\nalso in line with further experimental data generated in (c, see below).\n(c) To generate additional data in situ, we conducted TRDC/IL17A duplex in situ\nhybridization in 5 human PSC tissues. Indeed, the majority of TRDC+ cells were IL17A\nnegative, however throughout the tissues double positive cells could be detected at\nlow frequency (see image below). While this data cannot fully resolve the debate about\nof human γδ T17 cells in PSC, it supports the idea of gdT heterogeneity in those samples\nthroughout certain disease stages and spatially defined tissue domains.\n(d) MAF, RORC, IL17A/F were also plotted in the PSC data, see Figure panel B below.\n(e) We rewrote the manuscript and excluded these results on human gdT cells, since the\ndata is far less clear compared to our data in mouse. Additionally, the small number of\nsamples, especially in our own scRNA-seq data makes interpretation difficult. Based\non this revision, we fully agree with the suggestion to remove such data (compare also\nR3). These changes should also adress our previous potential overstatements made in\nthe initial submission.\ngdT cells in human PSC. (A) UMAP representations depicting gdT cell subset of the Poch et al. data [3]. Magenta ellipses highlight gdT cells\nfrom sample 14, which show a TF profile matching T17 polarization, while being negative for IL17A. (B) cDC2 and gdT cell subset from own\nscRNA-seq data used for cell-cell communication analysis (compare previous Fig. S7). Ellipses indicates gdT cells matching a T17 TF expression\nphenotype, while IL17A expression is absent. (C) Duplex in situ hybdrization analysis on human PSC tissues. Assessment of novel TRDC/IL17A\nISH data confirms that the majority of TRDC+ cells are IL17A negative, while few double positive cells can also be detected (see higher\nmagnification region of interest).\nReviewer #3:\nIn this manuscript, the authors create a scientific narrative demonstrating the importance of\ntype 2 cDCs and gamma delta T cells. The majority of which is described in a mouse model of\nbiliary injury using a range of high level scientific techniques.\nObviously, the role of type 2 cDCs reported here is not a new one, and it was first described in\na range of mouse models by Müller AL et al (J Hepatol. 2022 Dec;77(6):1532-154) (which is\nacknowledged in the references. Therefore, the novelty of this study is in the potential cross\ntalk with the gamma delta cell population in the biliary niche. In doing so it provides\nmechanistic insight to the role of dendritic cells in biliary injury and potential ligands that could\nbe targeted for drug discovery.\nOverall, the authors have submitted a large amount of data to provide robustness to the\nproposed narrative. However I would say that within the large volume of experimental data –\nwhich skips between human and murine models, there is perhaps a distraction for the reader\nparticularly for the reasons I highlight below.\nThank you for the constructive feedback and the possibility to clarify and improve the main\nstatements made in our manuscript.\nMy major comments for this manuscript include\n1) I think the closeness of this model (ie mirroring to PSC) requires firstly some careful\nclarification by the authors. PSC is an autoimmune liver disease of the liver and colon. Its\ngenetic risk is HLA driven, and the importance of CD4+/CD8+ and T regs is well described. The\ninflammatory response is therefore the driver of biliary injury in PSC. This is in strong contrast\nto the mouse model studied here, where the induced epithelial/cholangiocyte damage\nsecondarily induces inflammation – therefore they are not one in the same. The DCC model is\ntherefore not a mouse model of PSC. Perhaps the authors should therefore highlight that what\nthese have in common, and the point of their study – ie how biliary injury may amplify the\nlocal immune response and perturbate epithelial resultant injury.\nThank you for the feedback and the possibility to improve our manuscript, we adjusted the\nintroduction section accordingly (Introduction, first paragraph). We agree, that the\npathophysiology is not identical in DDC and PSC and discuss common features of both\nimmune-mediated (PSC pathophysiology) as well as epithelial damage induced models\n(DDC/cholestasis pathophysiology) in the discussion part (Discussion, first paragraph).\nThe implemented changes are as following (changes in bold):\nIntroduction:\nPrimary sclerosing cholangitis (PSC) is a multifactorial autoimmune-related disease with rising\noccurrence in the US and EU [1, 2], and the disease course may lead to cirrhosis and the\nrequirement of liver transplantation. While there are different subtypes of PSC [3], and PSC\nis frequently linked to other chronic inflammatory diseases, the common histological features\ninclude periductal fibrosis, proliferation of reactive bile ducts known as ductular reaction (DR),\nbile duct stenosis, dilatation and rarefication leading to biliary cirrhosis, cholestasis and\naltered myeloid responses [4, 5]. Clinical presentation of PSC and its chronic disease activity\nmay include intermittent episodes of acute cholangitis and cholestasis [6] and involve\ndysfunctional cDC2 and T cell responses contributing to disease severity [7, 8], yet the role of\nunconventional T cells (UTC), their potential crosstalk with cDC2 and their role in amplifying\nbiliary injury responses remains to be elucidated.\nDiscussion (changes in bold):\nWhile a number of immunological susceptibility genes have been identified in PSC [43], the\ndisease-aggravating and self-perpetuating biliary niche- and injury-specific immunological\ncircuits that maintain and drive disease pathology remain incompletely understood. The DDC\ndiet model used in this study, may mimic certain aspects of cholestasis and biliary injury in\nhuman PSC and how these local cues may amplify local tissue injury responses.\n2) In the opening introduction they say PSC ultimately leads to transplant – this is not true –\n50% are alive and well at 20 years. I would not say that the disease is characterised by\ndysfunctional myeloid responses. Therefore, to compare PSC to a toxin mediated liver injury,\nneeds careful discussion by the authors and questions why they wish to try and reproduce\nsome of their findings in PSC.\nThank you for this correction. We changed the introduction section accordingly with regard to\nthe necessity of receiving a transplant (see first sentence of introduction) and the existence of\ndysfunctional myeloid responses (“altered myeloid responses” instead). We also clarify the\ndifferences of HLA T-cell mediated autoimmunity and the damage-induced model in this\nstudy. We point out that we want to understand local damage-induced cell signaling circuits\nthat may lead to disease aggravation/amplification, which are mediated by e.g. local\ncholestasis (see discussion, first paragraph).\n3) the authors show that CD1C and CD3E transcripts are present near large vessels and biliary\ntracts (Fig1A). I must admit – I think here spatial has been used for spatial sake. The figure\npresented looses the clarity needed that would be gained by a simple HE that has been stained\nfor these markers at low and high power, which would then show a broad range of zones 1-3\nof the liver, portal tracts and central veins – to really understand the normal distribution of\nthese cells in a normal liver. (a) Secondly, I do not really understand how you can claim that\nthe pseudo normal liver dataset is the biliary niche. The immune sets that have been fax sorted\ncould come from anywhere. Why do you claim they are totally representative of the biliary\nniche?. (b) However, I accept that the BuildNicheassay suggests a biliary and lobular niche from the generated resultant dataset.\nThank you for this feedback, please find below our responses to the points raised:\n(a) The spatial data in Fig. 1A was used as a screening approach. Subsequent IHC-based\nanalyses show indeed the proposed tissue localization (compare Fig. 1D). As such, we\nbelieve the use of the technique is justified in this setting. To exemplify this, we\nextended the visualization of the TMA cores for additional immune cell markers, that\nshow a particular enrichment at the portal and not lobular niche (e.g. CD117).\nWe also made a corrplot analysis of respective markers supporting our claims (new Fig.\n1E, compare response to R2).\nIn the novel Fig. 1E, a spearman correlation analysis of marker-positive cells detected\nin our tissue microarray dataset is depicted. This analysis enabled the correlation of\nmarker positive cell numbers that should be enriched in portal fields only, e.g. with\nCK7+ BEC. For example, we see a strong correlation for CD3/CD8 and CK7, while CD207\nand CK7 show a correlative trend, however, not reaching the level of significance,\nwhich may be due to different statistical power based on the number of cores being\nanalysed.\nWe believe the requested correlation analysis improves our manuscript and supports\nthe hypothesis of an immunobiliary niche that contains T cells and DCs, which is later\non confirmed using independent techniques.\nCorrelation Plot (novel Fig. 1E) depicting Spearman correlation coefficients among marker-positive cells (%). The n-number of\ncores being analyzed containing pseudonormal human liver tissue are listed in the updated Fig. 1E. Levels of significance displayed\nby asterisks.\n(b) We see the importance for clarification and reframe the statements we made in the\nrespective sections. We would like to further explain the data and changes in the\nfollowing:\nThe reviewer points out correctly, that the human scRNA-seq data is derived from\nsmall and unprocessed tissue pieces derived from human surgery. Based on this, no\nmanipulation such as perfusion was conducted, so that the immune cell location of the\nsuspension scRNA-seq is not specific to its histological location. We corrected this\nstatement in the manuscript and now state, that we aimed for an enrichment of\nimmune cells residing at the immunobiliary niche (Results, first paragraph). This aim\nwas followed by first identifying cells that specifically reside at the biliary niche, and\nthen selective enrichment of such cells in our scRNA-seq data by FACS sorting of\nrespective cells (cDC2, gdT). We agree that this does not enable specific spatial\nenrichment, since contaminating sources may give rise to our populations of interest.\nFor the human scRNA-seq data and the aim to enrich for immune cells, we modified\nour statements to avoid any overstatement:\n“For cell type annotation of this high-resolution ST reference, we generated a human\npseudonormal liver scRNA-seq reference dataset with the aim to enrich for immune\ncells.”\nExplicitly, we removed the statement, that these cells may be spatially enriched from\nthe immunobiliary niche.\nWith regard to mouse, we did a lot of morphology-based analyses (colocalization, IF,\nIHC) in combination with the findings of the human TMA and the use of bioinformatic\ntools (e.g build niche assay). We then enriched respective cell populations in an\ninformed setting, so that we expect these cells to be derived from the immunobiliary\nniche – at least to a certain extent.\nTo consolidate this finding and our key message in mouse, we conducted spatial\ntranscriptomics on the DDC D5 timepoint using the Xenium Platform with a pre-build\ncell type gene panel containing 379 genes (“mouse tissue atlassing panel”) in\ncombination with a self-designed add-on panel of 100 genes that enables specific\nlocalization analysis of cDC2 (e.g. Mgl2, Clec10a) and gdT cells (Trdv4, Scart1, Scart2,\nIl23r) among other cells (novel main Figure 5).\nThis assay confirmed the respective findings in our manuscript, including cDC2-gdT\ncells in close proximity to each other within the peribiliary niche, expression of\ntranscripts of T17-polarization related transcription factors by gdT cells (e.g. Irf4) and\nrespective niche interactions as outlined by NiCo analysis. In our point of view, this\nadditional analysis strengthens our claims about the immunobiliary niche made in the\nmanuscript. To include these results, we generated a novel paragraph in the results\nchapter (“Spatial niche deconvolution resolves cDC2B and gdT cell containing\ncholangitis-associated tissue domains”). Please see the novel Fig. 5 below including\nthe Figure legends. For the main results text please refer to the revised manuscript.\nFigure 5. SpaSal niche deconvoluSon idenSfies cDC2B and gdT cell containing cholangiSs-associated Sssue domains. (A) Portal field\noverview of cell segmentalon stain (top) and decoded transcripts (bomom) of a mouse DDC D5 liver lssue. Image overviews of whole sample\nare found on the top len. Scale bar 2 mm and 200 µm. (B) Density maps displaying DDC D5 liver lssue overview with binned expression of\nEpcam, Mgl2, Cd3e and Irf4. Binsize 80 µm. (C) Spalal UMAP (top) and spalal localon map (bomom) highlighlng NiCo cell type annotalons\nof DDC D5 liver lssue. Right column indicates cluster number, annotated cell types and predicted cell numbers in spalal seclon. NM indicates\nnot matched cells [7]. (D) Spalal maps of NiCo predicted BEC, cDC2 and Il17a+ gdT cells. (E) Top: Spalal cell type interaclon map derived by\nNiCo using a radius of 20. Normalized logislc regression coefficient cutoff c = 0.05. (F) Spalal neighbourhoods (normalized logislc regression\ncoefficients) for sd BEC, cDC2, Il17a+ gdT cells. Error bars indicate s. e. of the coefficient eslmates. (G) High resolulon images containing\nsegmentalon stain informalon (len) or transcript abundance (right) of gdT cell- (Cd163l1, 5830411N06Rik, Il23r, Trdv4), T cell- (Cd3e), T17\npolarizalon (Irf4), cDC2B- (Mgl2, Clec10a) and BEC-related (Epcam) transcripts in a DDC D5 liver portal field of interest. (H) Len: TRDC and\nCD207 immunohistochemical staining on human PSC liver lssue (n=18). Peribiliary immune infiltrates are enriched for TRDC+ and CD207+\ncells. Asterisk indicates bile duct. Scale bar, 40 µm. Middle: High- and low-resolulon visualizalon of color-coded TRDC+ and CD207+ deteclon\ncounts in human PSC lssue. Analysis indicates varying cell concentralon within different lssue compartments. Scale bar, 250 µm. Right:\nDuplex TRDC/CLEC10A, TRDC/CD207 in-situ hybridizalon of human PSC liver lssue (n=5). Cellular colocalizalon and/or close proximity\nrelalonships between cells can be detected. Scale bar 10 µm.\n4) The authors then describe once again that have generated a single cell RNA seq from DDC\nfed mice at days 3,5,9 and 25. Once again there is a repeated claim that all these cells are\nfrom the immunobiliary niche – once again they could come from any compartment of the\nliver (Portal, Z1,2,3 and central) – I think this is slightly misleading for non-mouse hepatic\nexperts.\nThank you for your feedback.\nThis comment aligns with the concerns raised in 3). We included a clarifying statement to\navoid presenting any misleading information and to better clarify our results in context. Please\nalso refer to our response to point 3) and, in particular, to the supporting novel spatial\ntranscriptomics data in new Figure 5.\nAdditionally, we would like to make the following points:\nThe experimental conduct of our tissue atlas contains an in situ perfusion step via the portal\nvein. This leads to a strong reduction of intravascular circulating immune cells, so that we\nbelieve that the later enriched cells are tissue resident. Together with our new spatial\ntranscriptomics data, this explains, why we believe the cDC2 and peribiliary T cell\ncompartment may be captured very specifically. However, we fully agree that other cells\ncaptured in our atlas such as LSEC, KC etc. are indeed not part of the immunobiliary niche.\nTo clarify this, we made the following changes:\n- We included low resolution images of the Mgl1/2 staining of Fig. 3E into the\nSupplemental Figure 4H to highlight its very specified localization (novel supplemental\nFig. 4H). The specific localization of these cells at the portal/immunobiliary niche can\nbe appreciated. However, we agree that the localization of gdT cells can be both portal\nand lobular in steady state liver.\n- We included a novel spatial transcriptomics dataset of DDC D5 to demonstrate existing\nimmune cell gradients in a ST dataset. Again, we can see a close proximity of DC2, and\ngdT17 in the disease context (compare novel Fig. 5D), while other immune populations\nmay show a more balanced distribution throughout the tissue.\nAdditionally, we also generated DDC D18 ST Xenium data, however, this data is part of a\ndifferent project and can thus not be included into this study. However, to exemplify the\nreproducibility of the identified niches between DDC D5 and D18 tissues, we are including\nthese data into this response letter.\nBased on the same NiCo analysis, the gdT17/cDC2/Ccr7+ mat DC/sd BEC niche is also\ndetected at D18, supporting our claims of the relevance of a gdT cell/cDC2 communication\nnode.\nNiCo interaction analysis of DDC D18 tissue using same scRNA-seq reference and same add-on pool. (A) Niche interaction map captures\nthe Il17a+ gdT cell communication node with interactions to cDC2. (B) Spatial map and spatial UMAPs with predicted location of cDC2 and\ngdT. Cells colocalize in close proximity to large vessels (portal fields). Unpublished data.\nWe hope the novel data in Fig. 2 and 5 (ST data including DDC D5), the extended information\nabout the Mgl1/2 immunohistochemistry data and the changes within the manuscript address\nprevious concerns of the reviewer.\n5) The authors then demonstrate that the small ducts in portal fields during DCC injury release\na range of inflammatory genes highlighting these are chemoattractant to T and DC cells – as\nthey are for many cells, so this in itself is not proof of a direct interaction. Therefore, the\nspecific Importance of these observations can only be generalised. (a)\nThe authors then highlight similar findings in the BDL model but not the CCL4 model – which\nis to be expected – given the latter is a hepatocyte damage model. (b) This is then followed by\nspeculation that BEC TNF production activates DC2’s and is followed by an experiment where\nex-vivo the DCs are treated with LPS – a TL4 agonist. The relevance of which was not clear to\nme. Why were they not treated with TNF in the ex-vivo analysis. (c) However, I fully accept\nthat the co- ligand analysis was supportive of TNF interaction between DC2’s and the biliary\ncompartment.\nThis comment includes several points that we would like to address separately:\n(a) We changed the sentence about the identified chemoattractant molecules and point out\nthat these have general effects on multiple immune cell populations. We now point out that\nthese findings have to be seen in the general context (see edited sentence in the results\nchapter “…which are known to regulate cDC2 and T cells among other immune cell\npopulations.”).\n(b) This is a valid and important point, yet we also want to point out that hepatocyte damage-\ndriven models may simultaneously cause changes in the biliary compartment (e.g. ductular\nreaction). We included novel data of an acute hepatocyte damage and steatohepatitis model\n(CDE diet), that shows a similar but delayed trend towards gdT17 responses (novel Fig. S7B,\ncompare comment R1 Q14). Furthermore, cDC2B localization in the CDE model remains\ndetectable at the portal and peribiliary site (novel Fig. S4I depicting immunohistochemical\nstaining of Mgl1/2 on CDE) even though this model induces hepatocyte damage. So, indeed\ncell-specific (hepatocyte/cholangiocyte) damage responses targeting different cell types can\nbe seen. By including this immunohistochemistry, we also strengthen the aspects of the\nMgl1/2 cell recruitment observed in DDC (compare Fig. 3E). In our point of view, in the CDE\nsetting, the absent colocalization of cDC2B and the site of hepatocyte injury could even serve\nas a potential explanation of the delayed gdT17 responses compared to the DDC model.\nAnother way to differentiate between models would be the categorization into acute and\nchronic damage models. We believe the acute damage response may be a stronger inducer of\nrespective changes in the gdT cell compartment. This may explain, why the novel CDE data, as\nan acute damage model, may also result in a delayed gdT17 response, while such differences\nmay not be observed in chronic liver damage.\nNovel Figure S4H, I: (H) Mgl1/2 immunohistochemistry depicting the specific localization of Mgl1/2-positive cells in control and DDC D19\nliver. (I) Mgl1/2 immunohistochemistry depicting positive cells at portal fields in steady state and a rapid-onset experimental model of\nsteatohepatitis (CDE D8, D14)\n(c) Thank you for pointing this out. The rationale would indeed be much more clear, if we\nstimulated the cells with Tnf instead of a Nfkb inducer such as LPS.\nWe repeated the experiment using recombinant Tnf and measured multiple maturation genes\nby qPCR after Tnf treatment. Results were strongly comparable to the previously generated\nresults with LPS. We replaced the graph in Fig. 2D (see below).\nNovel Figure 2D depicting relative RNA levels of DC maturation markers in control cultivated DCs (light grey) and in Tnf treatment (dark grey).\n6) The authors then show that there is an expansion of DC2s in their murine model and\nconfirmed this by FaxAnalysis. The authors then focus on the DC2B lineage – and show an\naccumulation of mature cells and pre-cells repopulating the niche with the expression of genes\nthat would favour local IL-17 production. Finally, they show that this population lose their\nability to express Mgl2 (and propose this as a local maturity marker).\nThank you for summarising the rationale of the manuscript in this point. We agree with the\nsummary and point out that we clarified the concerns by the other reviewers in these text\nsections (e.g. clarification of the Mgl1/2 staining and the abundance of conventional and\nunconventional T cells producing Il17a).\n7) The authors then provide evidence for 2 populations of Gamma delta T cells – based on\nScart1 and 2 expression. These 2 populations show differences in IL-17 expression within\ndifferent TCR clones. This was shown to be due to differences in key transcription factors. In\naddition, competition for signals between alpha beta and gamma delta is provided for this\nniche. However, note is made of this low frequency population within the experiment and\nthe pooling was required to conduct the experimental design (a). So, the importance of this\nlow frequency cellular niche has to be questioned (b). Furthermore I am not sure that the\ndemonstration of IL-17a knock out reversing fibrosis is direct proof that gamma delta T cells\ndrive fibrosis concretely (c). This is because there are many cells that produce IL-17 in these\nmodels.\nThank you for your feedback. Please see our responses to your questions below:\n(a) We quantify the absolute number of gdT17 and abT17 using FACS to demonstrate, that\ngdT17 cells are of importance in the process of fibrosis. This crucial point has also been raised\nby Reviewer 1 and the quantification, which shows that gdT17 are the major IL17-producing\ncell type is described in our response to R1 Q3. Furthermore, the unperturbed (non-\nrestimulated) T cell subset data even hint towards a higher specificity of Il17a expression in\nour data (compare Fig. S5B). We note that the capacity of scRNA-seq versus FACS data to\nreflect in vivo T cell states have to be discerned, with scRNA-seq data likely reflecting the in\nsitu more faithfully as laid out in our response to R1 Q3.\nFurthermore, with the new ST data included, we can also visualize gdT cells in DDC disease,\nwhich further supports an important pathophysiological role of gdT17 cells in this model\n(novel Fig. 5). With regard to the pooling of the gdT cell multiplexed experiment depicted in\nFig 4A, we would like to respond, that we conducted such pooling to save costs. Indeed, in\ncontrol liver tissue, few gdT cells can be isolated, however, this drastically changes upon DDC\ntreatment.\n(b) In summary, we demonstrate that gdT cells are an exclusive source of IL17 according to\nour scRNAseq data, and a major source in restimulated cells in vitro. Together with their\nperibiliary localization detected in our novel spatial transcriptomics data (Fig. 5), these\nobservations strongly nominate gdT cells as a key cell type governing the response to DDC\ndiet. Beyond these data, the importance of gdT cells in the initiation of a profibrotic response\nis supported by the known differences between gdT and abT cells such as a faster gdT cell\nresponse [9].\n(c) Thank you for pointing out, that Il17a may also be derived from sources other than gdT\ncells. To strengthen the significance of gdT cells as a source of Il17a and a driver of a profibrotic\nresponse, we now added experimental data from the Tcrd-GDL model for the depletion of gdT\ncells using diphtheria toxin [2] while administering DDC.\nA combined DT-based gdT depletion on Day -1 and 2 and a DDC diet until D5 was conducted\nto read out the following parameters:\na. We measured blood plasma cholestasis parameters (total bilirubin and alkaline\nphosphatase), which were significantly reduced in the context of conditional\ngdT cell depletion 5 days after initiation of the diet (see novel Fig. 4I).\nb. We measured transcript abundance of typical fibrosis markers after tissue\nextraction of RNA and cDNA synthesis and found a uniform trend towards an\nincrease of abundance of fibrosis-associated transcripts reaching levels of\nstatistical significance (novel Fig.4J).\nc. We enriched DCs by magnetic purification of CD11c+ cells and analysed the\ntranscript levels of markers associated with DC maturation. These results could\ngive some additional insight if gdT cell depletion is associated with altered DC\neducation/maturation effects (as was also suggested in R1 Q14). Indeed\nmaturation-related transcripts of DCs showed increased abundance of Ccr7,\nCcl17, Ccl22, Cd80 and Arpin. This could point towards two directions: first, the\ninflux of immature preDC could be altered and potentially reduced in the\ncontext of gdT depletion, or second, homing of fully mature DC may be altered.\nAltogether, we hope that these new data address the reviewers’ comments and\nidentify gdT cells as fibrosis-promoting cells in liver cholestasis.\nNovel Figure 4I,J depicting comparative analysis at DDC D5 condition using C57BL/6J, TCRD_KO, TCRD_GDL mice. Blood plasma parameters\nof cholestasis are reduced at DDC D5 in an inducible model of gdT cell depletion (total bilirubin and alkaline phosphatase, left). Tissue-derived\ntranscript levels of fibrosis genes in C57BL/6J, Tcrd-KO, Tcrd-GDL mice reveal differences between control and gdT ko models (right). These\nfigures have been included as novel panels into Fig. 4 (novel Fig. I,J).\n8) The authors then show that depletion of DC2B cells show a reduction in BEC inflammatory\ngene expression and also show IL-17 down regulation in gamma delta T cells - suggesting a\nfunctional relationship between these 2 cellular compartments\n9) Finally, the authors show a clear evidence of Lymph Node communication between DC2\nand gamma delta T cells and provide interactive ligand relationships between these 2\ncompartments.\n8+9: we agree with the summary of the reviewer regarding our statements and do not see any\npoints raised to address in these comments.\nSuggestive improvements/extra work could include:\n1: I personally think that the PSC data is not needed for this paper – it’s not representative of\nthis model and therefore its relevance to the paper has to be called into question\nWe put the data into context and changed our statements according to the comments of R1\nand R2. Regarding disease amplification via local biliary injury, we believe that certain aspects\nof pathophysiology of PSC may be reflected by this model, while we set the PSC disease\nevolution into clear context. Newly generated data (TRDC/IL17A ISH) etc. may imply a certain\ntranslational relevance, so we decided to keep PSC data to some extent, while we agree that\nnot all the data related to PSC has to be included into this manuscript. We followed the\nsuggestion and excluded the PSC scRNA-seq data (previous S7E-H).\n2: Would it not be more beneficial to develop biliary organoids from humans and induce\ninjury/ and see if they attract/modulate DC2 migration in migration assays\nThank you for this suggestion. Unfortunately, our laboratory does not have frequent access to\nfresh human liver tissue, so that human biliary organoids cannot be grown. We believe\nestablishing the technique of cultivation of human liver organoids, which are currently not\nestablished in our lab would be beyond the scope of the current manuscript.\nHowever, we are thankful for the idea of the reviewer to test for DC migration and we\nconducted the following experiment: We isolated mouse bile from DDC D5 mouse gall bladder\nto experimentally test if bile may have chemo-attractant properties for DCs. We used\nMatrigel-coated transwell inserts (pore size of 8 µm) and cultivated isolated DC into the upper\npart of the insert to investigate if these cells would migrate towards a concentration gradient\nof bile.\nCompared to control conditions, there was no clear trend towards increased migration using\nthis (potentially too simple) setup. These data are in line with another cultivation experiment\nwe conducted, where we treated liver DCs with DDC D5 bile and measured transcript\nabundance of DC maturation markers. Here, the results showed a significant reduction of\nCd80, Ccl17, Ccl22 transcript levels (n=3 per group, one sided t-test) supporting the idea, that\nbile acid treatment may have some DC-toxicity related effects. We believe that both\nexperiments (transwell migration, in vitro cultivation) do not add much value to the current\nmanuscript and we decided to not include these results, but want to emphasize the\nexperimental efforts we made as part of our manuscript revision.\nFinally, we also decided to treat enriched gdT cells with DDC D5 bile to see if Il17a production\nmay be induced, independent of the presence of DCs. Such data could further substantiate if\nexogenous environmental factors alone suffice to induce gdT17 states in cholestasis.\nWe mimicked the cholestatic environment in gdT by administration of DDC D5 derived mouse\nbile (1:500 dilution) and cultivated these cells for 24 h. Il17a production in gdT cells among\nuntreated and bile acid treated groups was similar and not significantly different. This\nindicates that other cell-derived factors, as further exemplified in our study are likely to be the\nmain inductors of a gdT17 cell state. We believe this result is of importance and we included\nthese novel results as new part of Fig. S7D (see novel plot below).\nNovel Supplemental Figure S7D depicting gdT17 cell states in untreated and DDC D5 bile treated gdT cells. Two independent experiments\n(n=5).\n3: Use human DC2s and co-culture with gamma delta T cells to understand interactions\nThank you for this suggestion. We have thought about the potential design of such an\nexperiment and the gain of information it could yield. For example, we could isolate and\nstimulate DC and gdT cells from human blood and later co-culture these cells to investigate\ninteraction between these cells.\nBased on the major modifications conducted as part of the revision of this manuscript, where\nwe generated additional functional readouts of mouse gdT cells and the fact that we toned\ndown the transferability of the identified mechanisms to human PSC, we believe that these\nexperiments are beyond the current scope.\n4) Why has a gamma/delta T cell knock out not be studied in this model - would that not be\npossible to show their importance in fibrosis in this model if this cellular source was of vital\nimportance\nThank you for this feedback. We are thankful for this feedback, which prompted us to conduct\nadditional in vivo experiment using Tcrd-KO (constitutive KO) and Tcrd-GDL (inducible KO)\nmice to elucidate the role of the gdT cells more specifically, their niche and the expression of\nIl17a in the context of liver fibrosis. We believe these data strongly improved our revised\nmanuscript. Please refer to R1 Q14 and R3 Q7 for the additional data generated and\nimplemented into the revised manuscript.\nReferences\n1. Itohara, S., et al., T cell receptor delta gene mutant mice: independent generaFon of alpha beta T cells and programmed\nrearrangements of gamma delta TCR genes. Cell, 1993. 72(3): p. 337-48.\n2. Sandrock, I., et al., GeneFc models reveal origin, persistence and non-redundant funcFons of IL-17-producing gammadelta T cells.\nJ Exp Med, 2018. 215(12): p. 3006-3018.\n3. Poch, T., et al., Single-cell atlas of hepaFc T cells reveals expansion of liver-resident naive-like CD4(+) T cells in primary sclerosing\ncholangiFs. J Hepatol, 2021. 75(2): p. 414-423.\n4. Thomann, S., et al., Immunologic landscape of human hepaFc hemangiomas and epithelioid hemangioendotheliomas. Hepatol\nCommun, 2024. 8(1).\n5. Thomann, S., et al., Digital Staging of HepaFc Hemangiomas Reveals SpaFal Heterogeneity in Endothelial Cell ComposiFon and\nVascular Senescence. J Histochem Cytochem, 2022. 70(7): p. 531-541.\n6. Muller, A.L., et al., Inflammatory type 2 convenFonal dendriFc cells contribute to murine and human cholangiFs. J Hepatol, 2022.\n77(6): p. 1532-1544.\n7. Agrawal, A., et al., NiCo idenFfies extrinsic drivers of cell state modulaFon by niche covariaFon analysis. Nat Commun, 2024.\n15(1): p. 10628.\n8. Rosales-Alvarez, R.E., et al., VarID2 quanFfies gene expression noise dynamics and unveils funcFonal heterogeneity of ageing\nhematopoieFc stem cells. Genome Biol, 2023. 24(1): p. 148.\n9. Godfrey, D.I., et al., The burgeoning family of unconvenFonal T cells. Nat Immunol, 2015. 16(11): p. 1114-23.\n10. Ruedl, C. and S. Jung, DTR-mediated condiFonal cell ablaFon-Progress and challenges. Eur J Immunol, 2018. 48(7): p. 1114-1119.\nResponse to the reviewer comments\nThe reviewer comments are shown in black and our responses are highlighted in blue.\nChanges in the manuscript are highlighted red as in track-changes mode.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI thank the Reviewers for addressing the original cri=cisms raised with their ini=al submission.\nIt would be greatly appreciated if page numbers for all manuscript changes that are discussed\nin the PBP response are included and clearly stated in the PBP instead of wri=ng they have\nbeen changed in the manuscript without page numbers or including the change in the PBP.\nSome=mes the text changes have been included in the PBP whereas in other loca=ons, the\ntext changes are only stated and not included in PBP and referenced to sec=ons of the\nmanuscript which makes it difficult to know what has been changed and how so without a\nsignificant re-review of the en=re manuscript. PBP figures should be labeled with figure\nnumbers (e.g. PBP fig.1) to aid review.\nWe thank the reviewer for this feedback and understand the point, that it would have been\nmore precise to state the manuscript changes more specifically at all parts of the manuscript\nbeing changed including page numbers and line numbers. We would like to apologize for this\npoint and aim to make our statements more specific and beLer comprehensible for the current\nresponse leLer.\nFor this second response leLer, we now also include a clear numbering of figures within the\nPBP response leLer or the Figures within the Manuscript and make clear reference, where the\nmain manuscript was changed.\nImportant: Our page and line references refer to the track changes mode, when all changed\ntracks are displayed. Track changes and page and line references are different, when changes\nare minimized.\n1. Abstract greatly improved. Clear and concise.\n2. Table is adequate.\n3. Propor=on of GD T cells of total T cells has not been provided. Please include GD T cell\npropor=ons alongside propor=ons of IL17+ GDT cell numbers/total IL17+ T cells (stacked bar\nplots). It is important to highlight the rarity of these cells as showing the propor=on of IL17+\ncells does to convey the scarcity of IL17+ GD T cells in general. Statement regarding density of\nGD T cells is not par=cularly informa=ve (i.e. Overall TRDC+ cell concentra=on was\n29.28±27.61/mm2, while local concentra=on within peribiliary regions could reach much\nhigher densi=es (Fig. S7B).”, see results sec=on). This statement does not clearly illustrate\nrela=ve increase of GD T cells in peribiliary areas vs non-bile duct regions. Please subset the\ntwo regions and provide rela=ve fold-increase of GD T cells in peribiliary regions vs other areas.\nThank you for this construc=ve feedback. We included the comparison of the of γδ T cells vs\nall other T cells derived from our flow cytometric analysis to illustrate the rare abundance of\nthese cells in general (PBP Fig. 1A). Even though these cells are very scarce in the liver, they\nsignificantly contribute to the pool of Il17a+ cells (PBP Fig. 1B). This observa=on in the FACS\nquan=fica=ons can also be detected in the scRNA-seq data, where at later =mepoints γδ T17\ncells comprise approx. 4% of all T cells (D9: 3.9%, D19: 2.6%, D25: 3.7%).\nFurthermore, we implemented the changes regarding the γδ T cell quan=fica=on in Fig. S7B.\nFor this analysis, we quan=fied regions of interests containing bile ducts/portal regions and\nlobular regions. Quan=fica=on of the TRDC+ cells in these regions was then conducted. 4\nregion of interests (ROI) were analysed per case (n=10 PSC). While biliary/portal region TRDC+\ncell infiltra=on was heterogeneous, we quan=fied regions with high disease ac=vity, typically\nat the portolobular interface. The average TRDC+ cell concentra=on was approx. 3.34 fold\nhigher in portobiliary-inflamed regions (66.33/mm2 +/- 54.07) vs lobular concentra=on (19.87\n+/-25.38, two-sided T test, p = 8.16*10-6). We changed the respec=ve sentence containing the\noverall previous informa=on in the main manuscript and copy the changed sentence into the\nPBP.\nNew excerpt covering TRDC+ cell concentra=on in PSC (page 11 line 305-309 in main\nmanuscript, changes in RED):\nWhile PSC pathophysiology is more complex and not iden7cal with the acute injury-driven immune response in\na xenobio7c model of cholangi7s, we could s7ll detect TRDC+ and CD207+ cells, which showed a localiza7on-\ndependent concentra7on towards inflamed biliary foci compared to a lobular localiza7on (average TRDC+ cell\nconcentra7on in portobiliary and lobular regions was 66.33 ±54.07/mm2 and 19.87±25.38/mm2; Fig. 5H).\nPBP Figure 1. (A) Stacked barplot displaying flow cytometric rela7ve frequencies of γδ T and non-γδ T cells in\nmouse liver at D0, DDC D5/6 and DDC D10 7mepoints. (B) Stacked barplots displaying stra7fica7on of Il17a+ cells\nper sample into ab T and γδ T subsets as quan7fied by FACS. (C) TRDC+ cell concentra7on in human PSC 7ssues\nwithin portobiliary and lobular niches. FACS analysis (A,B): ctrl: n=9 mice, DDC D5: n=5 mice, DDC D10: n=4 mice.\nPSC 7ssues (n=10, 40 images per ROI).\n4. As the pseudonormal =ssue shows consistent evidence of liver damage and immune\ninfiltra=on (acknowledged that TRM may be ‘normal’ or may be related to liver damage), the\nterm pseudonormal should be replaced with ‘non-cholesta=c disease controls’ to clarify that\nthese samples are not from healthy livers but individuals that can be considered useful controls\ncompared to cholesta=c liver indica=ons. Quan=fica=on of TCRD+ cells at D5 of DDC treatment\nshould be provided in addi=on to representa=ve IF image.\nThank you for these valuable sugges=on to improve the manuscript.\nWe changed the terminology of previously stated “pseudonormal” liver =ssue into “non-\ncholesta=c disease control” liver =ssue throughout the manuscript as suggested.\nWe believe, the use of the ITC-GDL TRDC+ cells are not informa=ve to quan=fy for the following\nreasons:\n• TRDC+ cells were very scarce and only to be detected using high magnifica=on (100x)\nobjec=ves and in dis=nct areas of the biliary tree. Thus, the approach to record images\nwas based on a screening approach to specifically look for portal field regions with high\nmagnifica=on, while many regions were also devoid of TRDC+ cells. These observa=ons\nare in line with our NiCo analysis (Main Fig. 5C/D, 162 Il17a+ γδ T cells on one whole\nsec=on of a DDC D5 liver) and our previous PBP response for R3 Q4 displaying DDC D18\nresults of our NiCo algorithm. All the different modali=es (microscopy, flow cytometry,\nspa=al transcriptomics) indicate a rare presence of these cells, so that other means of\nquan=fica=ons, that are already delivered in the manuscript are more accurate and\ninforma=ve for the reader.\n5. Please include explanatory statement in main text of manuscript (i.e. ‘To enrich all cell types\nat equal ra=os, two independent rounds of cell isola=on and sequencing were performed at\nall =me points’).\nThank you for the sugges=on. We included such explana=on into the main body of the\nmanuscript as suggested (last sentence of first results paragraph). The respec=ve sentence can\nbe found at page 5, line 105-106 (changes in RED):\nTo enrich all cell types at equal ra7os, two independent rounds of cell isola7on and sequencing were performed\nat all 7mepoints (see methods) and we were able to capture abundant cells of the liver immunobiliary niche, as\nwell as rare liver immune cells such as cDCs (Fig. 1K, S2F-H, Supplementary Data S1, Methods).\n6. Appropriate cita=ons of prior work suppor=ng BEC-centric DDC damage are needed for\nintroductory statement.\nThank you for the feedback. We included one reference using spa=al and single cell profiling\nof the DDC model to exemplify its use to study BEC-responses (page 4, line 133- page 5 line\n147):\nWu, B., Shentu, X., Nan, H. et al. A spa=otemporal atlas of cholesta=c injury and repair in mice.\nNat Genet 56, 938–952 (2024). hLps://doi.org/10.1038/s41588-024-01687-w\n7. Addi=onal text is useful but does not appear in main manuscript. Please clearly note page\nnumber and sec=on the new text regarding latent factor analysis has been added.\nThank you for this feedback. This was likely a mistake in the final edi=ng of the revised\nmanuscript and we would like to apologize for the inconsistency between response leLer and\nmain manuscript.\nWe included the paragraph below at methods sec=on “Genera=on of DDC spa=al\ntranscriptomics data and analysis” (page 19, line 545-550):\n“For the calculation of niche interaction maps, cell type enrichment ratios within cellular\nneighborhoods of a central cell were calculated and a logistic regression to predict central\ncell type identities from these ratios was performed. The regression coefficients were then\nused to reconstruct the interaction map. Inferred latent factors from expression data were\nused to identify a small number of gene programs explaining cell state variability of each\ncell type. Covariation analysis of these latent factors across co-localized cell types may thus\ncapture the effect cell-cell interactions on gene program activity [7].”\n8.Good, thank you for clarifying.\n9. Please include number of mice analyzed in the figure panels for Fig.3C and Fig.3G (i.e. n=2).\nPlease include changes to new figures in PBP.\nThank you for this sugges=on. We included the use of mouse numbers in Fig. 3C and Fig. 3G.\nThe changed main Figure 3 can be found below:\nChanged Main Figure 3, now containing informa7on regarding the use of mice in the panels C and G.\n10.Ok thanks for clarifica=on.\n11.Agreed that this point is similar to (3) and ques=ons there apply also to original comments\nin (11).\n12. Interpreta=on of DT deple=on is inconclusive as it appears mul=ple subsets are affected,\nnot only GD T cells. Clusters are not annotated in umap(A) so difficult to determine which are\nTh1, Th2 or Th17 AB T cells or GD T cells. Dot plots in (C) are confusing – does this represent\nall cells are only GD T cell cluster as appears to be all cells but should be shown for GD T cell\ncluster vs others that appear impacted with DT treatment. Explana=on/findings from GD KO\nmice are generally unclear and need to be rephrased.\nThank you for your feedback and the possibility to clarify our results.\nIndeed, mul=ple subsets are affected by this cellular deple=on, and the DTR system also\ninduces off-target effects and the immunogenicity of Diphtheria toxin, as also men=oned in\nthe previous PBP and literature (compare R1 Q12) [10] and objectified by the extent of cells\nthat express, e.g., Ifng across conditions (PBP Fig. 2A,B). However, a compara=ve analysis of\nNK/NKT/T cell subsets is difficult to assess, since some of the cell types have almost exclusively\nbeen captured only within one of the two condi=ons (e.g. NK/NKT cell compartment). Further\nquan=ta=ve analysis, such as FACS, has not been conducted for these cell types, making a\nconclusion if certain cell types prevail awer DT injec=on impossible.\nγδ T cells were part of this dataset within cluster 15 in both condi=ons. γδ T cells were\nexpressing Trdv4, Cd1631 (Scart1) and exclusively Il17a. The increased expression of Il17a in\nthe non-depleted condi=on is already visualized in Fig. 6C,D of the main manuscript. To\naddress the shortcomings of the DTR-model, we already included a text change in the main\nmanuscript in the previously submiLed manuscript (compare previous PBP R1 Q12).\nBased on your feedback, we changed and rephrased the passage of TRDC KO to beLer\nelucidate the shared microanatomical niche aspect of γδ T and ab T cells and the disease\npromo=ng role of γδ T cells (page 9 line 272 following, changes in RED):\nTo interrogate, if γδT and abT cells compete for signal cues within the peribiliary niche, Tcrd-knockout (KO)\nmice were treated with DDC. Indeed, compensa7on of the γδT17 func7on was reflected by a higher percentage of\nIl17a+ abT cells in Tcrd-KO versus control mice at DDC D5, sugges7ng poten7al compe77on for γδT17/Th17\npolariza7on cues in liver cholestasis within a shared microanatomical niche (Fig. S7C).\nIn vitro treatment of γδT cells with DDC D5 bile did not induce Il17a expression (Fig. S7D). Condi7oned medium\nof DDC D5 derived DCs showed a trend towards inducing increased Il17a+ γδT cell frequencies (p=0.06), however,\ncompared to direct co-culture, the frac7on of Il17a+ cells was low (Fig. S7E). Direct co-culture of DDC D5- or\ncontrol liver-derived DCs with UTCs from control spleen led to increased Il17a produc7on in γδT cells but only\nweak induc7on in abT cells, indica7ng a DC-derived contribu7on to the induc7on of a γδT17 cell state (Fig. 4G).\nBased on this observa7on, we used TCR s7mula7on in vitro to further elucidate the impact of contact-\nbased/soluble mediators in inducing a γδT17 cell state. Specific s7mula7on with recombinant Il18, Cxcl16, Lgals9\nand ac7va7ng Icosl-an7bodies revealed a moderate γδT17 cell state induc7on for Icos ac7va7on, while soluble\nmediators did not induce significant differences (Fig. 4H, S7F). This prompted us to test a disease-contribu7ng\nrole of γδT cells in DDC-induced damage response and cholestasis using a cons7tu7ve (Tcrd-KO) and condi7onal\ndeple7on model of γδT cells (Tcrd-GDL [17]). Condi7onal γδT cell deple7on was associated with reduced\ncircula7ng total bilirubin and alkaline phosphatase levels indica7ng reduced cholestasis and disease ac7vity (Fig.\n4I). Furthermore, 7ssue-derived transcript levels of Pdgfrb, Col1a1 were reduced in Tcrd-KO and/or Tcrd-GDL liver\n7ssues implying a func7onal con7ngency of liver fibrosis on γδT cell-derived Il17a (Fig. 4J). To directly test the\neffect of Il17a on fibrosis, we treated wildtype (wt) and Il17a/f KO mice with a long term DDC diet (16 days) and\nquan7fied the number of Pdgfrb+ myofibroblasts and collagen (Sirius Red) in 7ssue sec7ons. The number Pdgfrb+\ncells and collagen area was significantly reduced in Il17a/f KO versus wt mice, mirroring a profibrogenic role of\nRorc-induced Il17-responses [39] (Fig. 4K,L). Similarly, 7ssue-derived transcript levels for Acta2, Des, Col1a1 were\nreduced in DDC-treated Il17a/f KO vs controls (Fig. S7G).\nTogether, these results suggest Il17a-producing γδT17 cells as an early-onset fibrosis-suppor7ng communica7on\nnode in murine cholangi7s.\nTo visualize the respec=ve T cell popula=on in the DDC D5/DDC D5-DT condi=ons, we have\nincluded PBP Fig. 2 below.\nWe hope these explana=ons and changes address the main points being raised.\nPBP Fig. 2. T cell activation and myeloid cell distribution across DDC D5 and DDC D5-DT conditions:\n(A) UMAP representation displaying clusters (left) and samples (right) of the T cell subset of merged DDC D5 and\nDDC D5-DT data.\n(B) UMAP representation of the T cell subset of the combined DDC D5/DDC D5-DT dataset displaying log-\nnormalized gene expression of marker genes. Log-normalized expression is color coded and fraction of cells\nexpressing the gene is encoded by dot size (right).\n13. Explana=on regarding removal of PSC samples is perplexing as removal eliminates major\ntransla=on to human sexng. Data of in situ staining is discussed but now shown.\nThank you for the possibility to further clarify our modifica=ons in presen=ng parts of the\nhuman PSC data. We decided to remove data, where pa=ent sample numbers were too low\nand adequate controls were missing. This was the case for the scRNA-seq of the two PSC\npa=ents that we analyzed.\nHowever, we s=ll included major results of human PSC =ssues, where we conducted\nimmunohistochemical analysis (n=18 PSC =ssues) and in-situ hybridiza=on (n=5 PSC =ssues).\nBased on these findings, we believe that major transla=on is s=ll given, and which is why we\ndo not want to include the word “murine” into our manuscript abstract (see comment on\nrevised manuscript file).\nRegarding the In Situ Hybridiza`on (ISH) on human PSC `ssues, we would like to make the\nfollowing clarifica=on:\nIn the manuscript, we highlight the colocaliza=on of TRDC+ with CLEC10A+/CD207+ cells, which\nfit the narra=ve being generated in mouse. However, we did not include the results of IL17A+\nTRDC+ ISH, which is more difficult to interpret, since only a minority of TRDC+ cells coexpressed\nIL17A+. This par=al exclusion of ISH results may have led to a misunderstanding, that all ISH\nresults have been removed, which is not the case (see Fig. 5H).\nWe hope these explana=ons clarified the ra=onale to include or exclude results, while in\ngeneral we aimed for a mouse-centric point of view, as also suggested by the reviewers and\nthe editor.\nReviewer #2 (Remarks to the Author):\nThe authors have carefully addressed my concerns.\nWe would like to thenk Reviewer #2 for this posi=ve feedback and your previous helpful\nsugges=ons.\nReviewer #3 (Remarks to the Author):\nThankyou for the resubmission of the manuscript. First of all I would like to thank the authors\nfor the resubmission of their manuscript and for their detailed rebuLal response to the all the\npoints I previously raised.\nIn doing so, the manuscript is now far more comprehensive and generalisable to the field of\nhepatology, and provides a detailed understanding of the role of cDC2B and gamma delta cells\nin cholesta=c liver disease.\nIn par=cular the correla=on analysis and extra spa=al xenium analysis (Fig 5) add to the\nstrength of the mechanis=c data provided. Also the addi=onal GD KO experiments provide\nsupport for the importance of this cellular interac=on. Addi=onal I am grateful to the authors\nfor sharing addi=onal data on the D18 model that they are currently working on to add\nstrength to their arguments.\nFinally the addi=onal bile exposure experiments and TNF data provided take the biological\nmodel provided to its natural limits. The abstract and conclusions are also now far more\nbalanced.\nIn conclusion I am therefore sa=sfied all the points I previously raised have been addressed.\nWe would like to thank Reviewer #3 for this posi=ve feedback and your previous helpful\nsugges=ons.\nFinally, we would like to state, that we changed the display of Main Fig. 1E. In the previous\nversion, the spearman correla=ons within the corrplot func=on were calculated, while\ndisplaying the sta=s=cs of a different correla=on metric, making the sta=s=cal display\nincorrect.\nWe corrected this mistake as part of the final review of all sta=s=cs and now conducted an\ntwo-sided Spearman’s rank correla=on test and display the respec=ve p-values in the corrplot.\nThis change also demonstrates the sta=s=cal significance between CD207+ and KRT7+ cells.\nAddi=onally, in the previous version of the source data, we changed the labeling of the groups\nin S7B for DDC. When double checking the source data for re-calcula=ng the sta=s=cs, we also\nfound that previous source data for S9H was showing qPCR data of a different experiment. We\ncorrected this and now include the correct source file for S9H."}]

Decision letter