New Results

Macrophage-fibroblast crosstalk drives Arg1-dependent lung fibrosis via ornithine loading

Preeti Yadav, Javier Gómez Ortega, Whitney Tamaki, Charles Chien, Kai-chun Chang, Nivedita Biswas, Sixuan Pan, Julia Nilsson, Xiaoyang Yin, Aritra Bhattacharyya, Kaveh Boostanpour, Tanay Jujaray, Jasper Wang, Tatsuya Tsukui, Dean Sheppard, Baosheng Li, Mazharul Maishan, Hiroki Taenaka, Michael A. Matthay, Rieko Muramatsu, Lenka Maliskova, Arnab Ghosh, Walter L. Eckalbar, Ari B. Molofsky, Paul J. Wolters, Stanley J. Tamaki, Trever Bivona, Adam R. Abate, Allon Wagner, Kevin M. Tharp, Mallar Bhattacharya
doi: https://doi.org/10.1101/2023.09.06.556606

Abstract

Monocyte-derived macrophages recruited to injured tissues induce a maladaptive fibrotic response characterized by excessive production of collagen by local fibroblasts. Macrophages initiate this programming via paracrine factors, but it is unknown whether reciprocal responses from fibroblasts enhance profibrotic polarization of macrophages. We identify macrophage-fibroblast crosstalk necessary for injury-associated fibrosis, in which macrophages induced interleukin 6 (IL-6) expression in fibroblasts via purinergic receptor P2rx4 signaling, and IL-6, in turn, induced arginase 1 (Arg1) expression in macrophages. Arg1 contributed to fibrotic responses by metabolizing arginine to ornithine, which fibroblasts used as a substrate to synthesize proline, a uniquely abundant constituent of collagen. Imaging of idiopathic pulmonary fibrosis (IPF) lung samples confirmed expression of ARG1 in myeloid cells, and arginase inhibition suppressed collagen expression in cultured precision-cut IPF lung slices. Taken together, we define a circuit between macrophages and fibroblasts that facilitates cross-feeding metabolism necessary for injury-associated fibrosis.

Introduction

Pathologic fibrosis is a destructive response to tissue injury that results from the deposition of excess fibrillar collagen by activated fibroblasts. Recent studies have revealed a heterogeneity of fibroblast states following injury, and inflammatory fibroblasts have been detected as a distinct subset of mesenchymal cells present in the early period after injury prior to the onset of full-blown fibrosis1, 2, 3. However, how cytokine expression by inflammatory fibroblasts regulates fibrosis is not well understood.

The early phase of injury is also marked by an influx of monocyte-derived macrophages, which localize to fibroblasts at sites of injury4. Several studies have shown that these monocyte-derived macrophages signal to fibroblasts via paracrine mediators4, 5, 6, but the resulting crosstalk between the two cell types that may orchestrate the fibrotic progression is not well characterized. Here, we identify a paracrine circuit between macrophages and fibroblasts that is established in the late inflammatory phase following tissue injury. In a tissue injury model, macrophage-derived extracellular ATP drove fibroblast IL-6 expression. IL-6, in turn, induced macrophage expression of Arg1, which was necessary for lung collagen production via ornithine metabolism by activated fibroblasts.

Results

Extracellular ATP signaling via P2rx4 regulates fibroblast IL-6 expression

To screen for macrophage-dependent secreted factors expressed by fibroblasts, we analyzed scRNAseq data from cocultures of primary lung macrophages and fibroblasts, or fibroblasts cultured alone (Figure 1a-b, Figure S1a-b). This analysis revealed the induction of multiple clusters unique to fibroblasts cocultured with macrophages (Figure 1c). Notably, these clusters had higher levels of a gene signature for collagen genes (Figure S1c, Table S1), consistent with the pro-fibrotic effects of macrophage coculture. Focusing analysis on secreted factors7, we also noted high expression of the cytokine IL-6 in macrophage coculture-specific fibroblast clusters (Figure 1d; Figure S1d). IL-6 has emerged as a target for the treatment of lung fibrosis8, 9, 10, 11. Analysis of scRNAseq time course data for multiple lung cell types in the bleomycin model confirmed that IL-6 was most highly expressed in fibroblasts in vivo (Figure 1e), with some expression also noted in other mesenchymal cells such as smooth muscle cells and pericytes (Figure S1e). We then confirmed macrophage-dependent fibroblast IL-6 induction in cocultures derived from deceased donor human lungs (Figure S1f).

Figure 1:
Figure 1: P2rx4 regulates fibroblast IL-6 expression.

(a) Schematic of scRNAseq of primary lung macrophage and fibroblast coculture.

(b) UMAP plot of scRNAseq for macrophage-fibroblast cocultures with SingleR-based cell type annotation 4 shown. Data represent n=2 separate cultures for each condition.

(c) UMAP plot for data from (b) showing sample of origin (“Fib”=fibroblast monoculture, “Fib + Mac”= fibroblast coculture with macrophages).

(d) Left: UMAP plot showing Il6 expression. Right: Violin plot of Il6 expression. ***p<0.001 by Wilcoxon Rank Sum test corrected for multiple comparisons by Bonferroni method.

(e) Quantification of Il6 expression by cell type in cells sequenced by scRNAseq directly after isolation from the lung at steady state and multiple time points after injury (reanalysis of merged data3, 18 normalized by Sctransform58).

(f) Gene set enrichment analysis (GSEA) of fibroblast single cell transcriptomes in coculture with macrophages compared to fibroblast monoculture using GO “Response to stimulus” pathways.

(g) IL-6 ELISA of conditioned media from macrophage-fibroblast cocultures with or without fibroblast-specific P2rx4 deletion. N=5 five biological replicates per condition. **p<0.01, ***p<0.001 by Student’s t-test. Bars shows +/− SEM.

(h) IL-6 ELISA from bronchoalveolar lavage from mice with or without fibroblast-specific P2rx4 deletion. N=7, 7, 6 biological replicates, left to right. *p<0.05 by Student’s t-test. Bars shows +/− SEM.

To determine the mechanism of IL-6 induction in fibroblasts, we interrogated GO datasets under the category of “Cellular Response to Stimulus” to detect dominant stimulus-based pathways that could be attributable to the presence of macrophages. This analysis revealed that the GO pathway “Cellular Response to ATP” was enriched in fibroblasts when they were cocultured with macrophages (Figure 1f). This GO pathway is relevant because extracellular ATP (eATP) levels are elevated in the bronchoalveolar lavage fluid of patients with idiopathic pulmonary fibrosis (IPF)12. Furthermore, we had previously reported that fibroblast-specific deletion of the eATP receptor P2rx4 in mice suppresses injury-induced fibrosis13. In that work, we also found that P2rx4 was the most highly expressed receptor for ATP among the 7-member P2rx family in lung fibroblasts.

To test if eATP signaling via P2rx4 regulates IL-6 expression, we assayed IL-6 levels by ELISA of conditioned media from cocultures of WT macrophages with either WT or P2rx4 knockout (KO) fibroblasts, finding that IL-6 was decreased in the case of P2rx4 KO fibroblasts (Figure 1g). Direct treatment of cultured murine lung fibroblasts with ATPψS, a nonhydrolyzable form of ATP, increased IL-6 in the conditioned media of WT but not P2rx4 KO lung fibroblasts (Figure S2a). This effect was blocked by inhibition of p38 Map Kinase, which has been shown to mediate P2rx4 signaling14, 15 (Figure S2b). Importantly, mice with fibroblast-specific P2rx4 KO had decreased bronchoalveolar lavage IL-6 compared to wild type following bleomycin injury (Figure 1h), and siRNA KD of P2RX4 in human lung fibroblasts decreased IL-6 expression (Figure S2c). Of note, we previously found that fibroblast-specific P2rx4 KO mice had decreased lung fibrosis13. Consistent with the importance of P2rx4-dependent IL6 to the fibrotic response after injury, we confirmed that IL6 KO mice were protected from lung fibrosis (Figure S2d-e). Furthermore, confirming myeloid cells as one important source of eATP, cell ablative airway liposomal clodronate treatment significantly decreased lung lavage eATP levels (Figure S2f-h).

Fibroblast IL-6 regulates macrophage Arg1, a profibrotic factor

To examine whether paracrine signals referable to P2rx4 signaling in fibroblasts, such as IL-6 expression, might reciprocally regulate macrophage profibrotic responses, we performed differential gene expression analysis from scRNAseq data for WT macrophages cultured with either WT or P2rx4 KO fibroblasts. The top differentially expressed gene was Arg1, and its expression was dependent on fibroblast P2rx4 expression (Figure 2a). We hypothesized that fibroblast-derived IL-6, which was decreased in P2rx4 KO fibroblasts, may regulate macrophage Arg1 expression as found in other systems 16. Analysis of ligand-receptor interaction via CellChat17 and Ingenuity Pathways Analysis confirmed suppression of IL-6 signaling in the P2rx4 KO condition (Figure 2b). We then confirmed an increase of Arg1 in both cellular lysates and conditioned media of cultured lung macrophages treated with IL-6 (Figure 2c). Importantly, treatment with anti-IL6 antibody or IL6 deletion in mice decreased Arg1 protein in the bronchoalveolar lavage fluid following bleomycin injury (Figure 2d).

Figure 2:
Figure 2: IL-6 signaling regulates Arg1 expression in profibrotic macrophages.

(a) Schematic of design of co-culture scRNAseq and volcano plot for macrophages from scRNAseq of macrophage-fibroblast cocultures for WT macrophages and fibroblasts, or WT macrophages cocultured with P2rx4 KO (Pdgfrb-Cre: P2rx4 f/f) fibroblasts. Significance was determined by Wilcoxon Rank Sum test corrected for multiple comparisons by Bonferroni method. Color-labeled genes had p<0.05 and absolute value of log2 fold change >0.75. Data represent n=2 separate cocultures.

(b) Left: Ingenuity Pathways Analysis of predicted upstream regulators for macrophages cultured with WT relative to P2rx4 fibroblasts. Right: CellChat plot comparing WT and P2rx4 conditions for data from Figure 2a. Significance was determined by Wilcoxon test with p<0.05.

(c) Arg1 ELISA of cell lysates and conditioned media collected from mouse lung macrophage monoculture 72 hours after IL-6 treatment. N=3 biological replicates per condition.**p<0.01, ***p<0.001 by Student’s t-test. Bars shows +/− SEM.

(d) Arg1 ELISA of bronchoalveolar lavage fluid taken at day 14 from bleomycin-injured mice treated with anti-IL-6 antibody or IgG control, or from IL-6 KO injured mice. N=4, 4, 3, and 3 biological replicates per condition. *p<0.05 by Student’s t-test, **p<0.01. Bars shows +/− SEM.

(e) Analysis of macrophages from bleomycin lung injury (data reanalyzed from18). Left: Annotation of macrophages from multiple time points, according to C1, C2, or C3 macrophage annotation (as described in 4; C1=alveolar macrophages; C2=transitional monocyte-derived macrophages; C3=monocyte derived macrophages). Center: Proportions of C1, C2, and C3 across time. Right Feature plot of Arg1 expression and violin plot of Arg1 expression according to cluster (C1, C2, and C3). ***p<0.001 by Student’s t-test.

Interestingly, analysis of a published scRNAseq time course following bleomycin lung injury18 showed maximal Arg1 in cells annotated to be similar to the C2 transitional macrophage compartment we previously demonstrated to localize to the fibrotic niche after injury4 (Figure 2e; Figure S3a). C2 cells not only expressed higher Arg1 but also expressed higher levels of the Fab5 markers that were recently found to be associated with pro-fibrotic macrophages in both mouse and human fibrotic lung and liver datasets19. Flow cytometry confirming Arg1 expression in CD11b+CD64+ macrophages at the 10-day time point, as opposed to monocytes, alveolar macrophages, or dendritic cells (Figure 3a-b, Figure S3b). Arg1 is often cited as a macrophage marker, but studies of its functional role in fibrosis are limited. Similar to our previous findings for these transitional cells4, Arg1+ cells were found in proximity to clusters of activated fibroblasts that form after injury (Figure 3c). We then treated bleomycin-injured mice with the Arg1 inhibitor CB-115820 during the fibrotic period. Importantly, we found that lung fibrosis was significantly decreased and weight recovery improved with Arg1 inhibition (Figure 3d, Figure S4a). Taking a genetic approach with macrophage-specific Arg1 homozygous KO mice, we found that lung fibrosis was likewise decreased after injury (Figure 3e). To further test the significance of Arg1+ macrophages, we first performed scRNAseq analysis of aged mice compared to young, given that aging is a major risk factor for the development of lung fibrosis in patients. Arg1+ macrophages were increased in bleomycin-injured aged mice at an earlier time point than we observed in young mice—7 days as opposed to 14 days; furthermore, Arg1+ macrophages co-expressed the Fab5 profibrotic macrophage genes19 (Figure S4b). These data suggest a potential predisposition to the fibrotic macrophage profile in aged mice.

Figure 3:
Figure 3: Arg1 is necessary for lung fibrosis.

(a) Representative flow cytometry of mouse lungs from Arg1-YFP reporter and WT mice at steady state and 10 days post-bleomycin. MoMac=monocyte-derived macrophage (CD64+CD11b+ SiglecF-). AM=alveolar macrophage (CD64+SiglecF+).

(b) Plot of percentage of cells expressing YFP in each lineage. N=3 mice per condition.

(c) Immunofluorescence of Col1a1-GFP mice labeled with Arg1 antibody at 14 days after bleomycin injury.

(d) Hydroxyproline assay for collagen content of lungs from injured and uninjured WT mice treated with or without Arg1 inhibitor CB-1158 during days 9-15 post bleomycin. N=6, 7, 8 mice, left to right. ***p<0.001 by Student’s t-test. Bars shows +/− SEM.

(e) Hydroxyproline assay for collagen content of lungs from mice 21 days after bleomycin injury. N=10 and 9 mice, left to right. *p<0.05 by Mann-Whitney test.

Arg1 regulates availability of ornithine, a pro-fibrotic substrate

To explore the mechanism of Arg1’s profibrotic effect, we considered that Arg1 enzymatically converts arginine to urea and ornithine, and that macrophages expressing Arg1 have been found to increase ornithine in the tissue microenvironment21. Ornithine can serve as a substrate for the synthesis of proline22, a major constituent of collagens. Thus, we sought to determine if Arg1+ macrophages supply ornithine as a substrate for collagen production by fibroblasts. First, consistent with a potential for ornithine to drive collagen production, we found that exogenous ornithine increased fibroblast collagen expression at the protein level (Figure S4c). Furthermore, in cocultures, Arg1 inhibition with CB-1158, or use of fibroblasts with deletion of IL-6 or P2rx4, decreased collagen expression, whereas ornithine treatment rescued it (Figure 4a). We next tested the hypothesis that Arg1-mediated ornithine production was necessary for increasing fibroblast proline content. Consistent with this hypothesis, treatment of cocultures with CB-1158 decreased fibroblast proline (Figure 4b). When we treated primary lung fibroblasts in monoculture with 13C5-ornithine, we detected an m + 5 mass shift in proline (13C5 proline), consistent with direct conversion of ornithine to proline via the pyrroline 5-carboxylate (P5C) intermediate; glutamine is another source of production of P5C, and glutamine-free conditions, as expected, increased the proportion of 13C5 proline further (Figure S4d). To test the in vivo significance, we treated mice injured with bleomycin with or without ornithine by oral gavage twice daily during the fibrotic phase after injury and found that ornithine treatment significantly increased lung fibrosis (Figure 4c). Taken together, these results suggests that macrophage Arg1 drives fibrosis by the production of ornithine, which increases proline content augmenting collagen synthesis.

Figure 4:
Figure 4: Arg1-dependent ornithine induces fibroblast collagen expression.

(a) Representative samples of Col1a1 immunofluorescence of mouse fibroblast cocultures or macrophage-fibroblast cocultures with or without CB-1158 or ornithine treatment. Quantitation is for N=3 biological replicates per condition. *p<0.05 by 1-way ANOVA with post hoc Sidak’s multiple comparisons tests. **p<0.01, ***p<0.001, ****p<0.0001. Bars shows +/− SEM.

(b) Relative quantities of proline in lysates of murine primary lung fibroblasts isolated after coculture with macrophages, with or without CB-1158 inhibitor treatment, quantified by liquid chromatography-mass spectrometry. N=3 biological replicates for macrophages and fibroblasts, respectively. **p<0.01 by Student’s t-test.

(c) Hydroxyproline assay for collagen content of lungs from injured WT mice treated with or without ornithine (2 mg/kg) twice daily by ornithine gavage during days 7 through 20. N=8, 7 mice, left to right. **p<0.01 by Student’s t-test. Bars shows +/− SEM.

Arg1 is expressed in myeloid cells in IPF lung

To address clinical significance, we interrogated ARG1 expression in human lung samples. First, analysis of a published dataset of BAL cell gene expression by microarray23 confirmed higher expression in IPF compared to healthy controls (Figure S5a). We then confirmed ARG1 expression by immunofluorescence in IPF lung explants acquired at the time of clinical transplantation, whereas less ARG1 was expressed in deceased donor lungs not known to have lung disease (Figure 5a). To determine cell type, we performed multiplexed ion beam imaging (MIBI) using an extensive panel of myeloid and lymphoid markers. We found that ARG1+ cells were CD11B+ and CD16+, consistent with myeloid lineage, and were also positive for the granulocyte marker CD66B. Interestingly, the macrophage markers CD163 and CD68 were not expressed (Figure 5b). To confirm, we performed 10x Xenium spatial transcriptomic analysis of IPF samples and found ARG1+ cells had higher expression compared to ARG1-cells for CD11B (ITGAM), CD66B (CEACAM8) but not CD163 or CD68 (Figure S5b). Taken together, these data suggest that, in IPF, ARG1 is expressed in myeloid cells of the granulocytic lineage.

Figure 5:
Figure 5: ARG1 is expressed in IPF myeloid cells and regulates collagen expression.

(a) Representative immunofluorescence of human lung IPF and healthy control sections (n=4 and n=2, respectively) for ARG1, with Masson’s fibrosis stain of an adjacent section. The region of magnification is from the approximate area of a separate section that was imaged with Masson’s trichrome histologic stain for fibrosis.

(b) Multiplexed ion beam imaging (MIBI) analysis: Top, left and middle: tSNE projections corresponding to 2 IPF samples. Top, right: Heat map of marker expression for each phenotype cluster with indicated Z-score. Bottom, left: ARG1 immunostaining of representative IPF sample. Bottom, right: Multichannel MIBI image of a representative field of view with ARG1, CD66B, CD11B, alpha-SMA, and Histone H3 expression.

(c) 10x Xenium spatial analysis: Left: Representative field of view showing cells expressing ARG1, IL6, and EPCAM. Right: Proximity analysis of Xenium data plotting the probability ratio, P(exp | Arg1+)/P(exp), defined as the average probability of encountering an IL6+CTHRC1+ fibroblast, an IL6-CTHRC1+ fibroblast, or other cells computed at multiple radial distances from ARG1+ cells, normalized by the probability of a cell being positive for the markers in question (see Methods). 50% random subsampling of the entire tissue sample was performed 5 times, and for each subsample, a probability ratio for each marker was computed for each distance. The solid lines are averages, and the shaded regions are bounded by the minimum and maximum probability ratio generated by the subsamples. **p<0.01 and *p<0.05 by paired one-way ANOVA with post hoc Sidak’s multiple comparisons tests. The plot presents data from a patient that is representative of findings from n=2 separate patients.

(d) Immunoblot for COL1A1 from lung slice lysates prepared 24 hours after treatment in culture with the indicated inhibitors. Quantitation is for n=3 patients. **p<0.01 by Student’s t-test. Bars shows +/− SEM.

To test whether ARG1+ cells localize in proximity to IL-6+ cells, we analyzed the Xenium data of the IPF samples. We defined a proximity statistic that allowed us to quantitatively compare the probabilities of detecting an IL6+ fibroblast versus an IL6-fibroblast within a given distance from an ARG1+ cell (Methods). Interestingly, we found that ARG1+ cells were more likely to be close to IL6+ fibroblasts compared to IL6-fibroblasts, up to and including a distance of 100µm, a distance that could be considered consistent with paracrine effects (Figure 5c). For functional testing, we prepared precision-cut lung slices and treated them with CB-1158 or the IL6RA-blocking antibody tocilizumab for 24 hours. Collagen expression was decreased with both treatments compared to untreated slices, suggesting inhibition of new collagen synthesis (Figure 5d). Taken together, these data indicate ARG1 is a contributor to the pathologic accumulation of collagen in IPF.

Discussion

Our findings reveal a novel paracrine communication network between macrophages and fibroblasts that regulates fibrosis. A key advance was the discovery of a profibrotic function of inflammatory fibroblasts. Specifically, these cells expressed IL-6, which was necessary for induction of Arg1 resulting in production of the pro-fibrotic amino acid ornithine. Our coculture experiments identified macrophage-derived eATP as the initiator of P2rx4-dependent IL-6 expression in fibroblasts. eATP is a damage- and inflammation-associated molecule that has been implicated in fibroblast IL-6 expression24, 25, and we previously found that fibroblast-specific P2rx4 KO mice had decreased lung fibrosis after injury13. Here we confirmed that IL-6 induced macrophage Arg126, 27, 28 and then advanced understanding of Arg1 function by demonstrating the key profibrotic role of its reaction product ornithine as a substrate for collagen production.

Mechanistically, we define a novel metabolic pathway wherein ARG1-dependent production of ornithine directly increased collagen expression. Our metabolomic studies indicate conversion of ornithine to proline, a key substrate for collagen synthesis. However, we note that ornithine conversion to polyamines with profibrotic potential remains a further possibility not yet addressed by our work. Furthermore, ARG2 is a second arginase that could also contribute to ornithine loading of fibroblasts. Future studies should focus on how both arginases are regulated across the temporal phases of fibrosis, including both initiation of fibrosis and its progression in cases leading to organ failure.

This work addresses the limited understanding of cell-to-cell signals that underlie tissue remodeling in the fibrotic niche. In previous studies, single-cell profiling of tissues in the setting of fibrosis led to the discovery of markers for both scar-associated macrophages and multiple subtypes of fibroblasts1, 2, 29. However, crosstalk mechanisms between the macrophages and fibroblasts in tissues have not been extensively studied, with current knowledge derived mostly from in vitro modeling30. Our work maps interactome analysis from cultured cells onto the relevant tissular context, enhancing understanding of cell-to-cell communication in vivo, an unmet need in the field29. Furthermore, inflammatory fibroblasts have come to attention as present in both infectious31 and fibrotic2, 3 models; their function in fibrosis is further elucidated by our discovery of macrophage eATP-induced IL-6 and its reciprocal induction of Arg1 in macrophages, for pro-fibrotic ornithine loading of fibroblasts.

Interestingly, in schistosomiasis-induced fibrosis models, Arg1 deletion in macrophages led to an increase in type 2 inflammation, and consequently increased fibrosis, because of an accumulation of arginine, which supports CD4 T cell proliferation32. However, in IPF and in murine bleomycin-induced pulmonary fibrosis, type 2 inflammation does not appear to be an important driver of fibrosis. Inhibition of type 2 inflammation with dual IL-4 and IL-13 blockade failed to show efficacy in a clinical trial of IPF33. Furthermore, in the bleomycin-induced sterile injury model that we have used, the onset of type 2 inflammation is later than the period when significant collagen deposition has already occurred and is therefore less relevant to fibrogenesis34. Although inhibition of Arg1 might be contraindicated in patients with schistosomiasis, our data provide a strong rationale for further investigation of the potential role of Arg1 inhibition for treatment of IPF.

Previous studies have reported the expression of ARG1 in myeloid cells from lung fibrosis patients35, 36. Importantly, we confirmed functional relevance in human lung fibrosis by showing that arginase inhibition decreased lung fibrosis in precision-cut lung slices from IPF. Furthermore, our proximity analysis for ARG1+ cells and IL-6+ fibroblasts suggests that induction of ARG1 in human fibrotic lung may be dependent on IL-6, similar to our studies in mice. However, our multiparameter imaging with MIBI provided a refined view of marker expression of these cells and showed that ARG1+ cells are likely similar to granulocytes previously identified in human tumors37, 38, 39. Gene expression is well known to differ across species by immune compartment40, and the case of Arg1 provides an important instance: In mice, Arg1 was expressed in profibrotic, transitional macrophages in fibrotic areas of mouse lung, whereas in human lung fibrosis, ARG1+ cells expressed CD11B and CD66B but not other macrophage markers, consistent with granulocytic lineage.

Methods

Human lung tissues

The studies described in this paper were conducted according to the principles of the Declaration of Helsinki. IPF lung samples were obtained as explants at the time of lung transplantation. Written, informed consent was obtained from all subjects, and the study was approved by the University of San Francisco, California institutional review board. Deceased donor-control lungs not known to have lung disease were made available by Donor Network West. Because these samples were acquired from deceased individuals the study is not considered human subjects research as per UCSF and NIH policy.

Mice

Pdgfrb-Cre41, Lysm-Cre 42, P2rx4 floxed43, Arg1 floxed44, Col1a1-EGFP45, Arg1-RFP-CreERT246, and Arg1-YFP47 mice were available to the investigative team, and IL-6 KO48, WT, and R26-LSLS-TdTomato mice were obtained from JAX. Col1a1-GFP mice were obtained from David Brenner45. All mice were maintained on a C57BL/6 background and were maintained in specific pathogen-free animal barrier facility at the University of California, San Francisco. All experiments were performed on 6-8 weeks old, sex-matched mice, or in the case of aged mice, 24-month-old, sex-matched mice, and were performed in accordance with approved protocols by the University of California, San Francisco Institutional Animal Care and Use Committee.

Mouse lung injury, cell isolation, and macrophage-fibroblast coculture

For lung injury, mice anesthetized with isoflurane were instilled intratracheally with bleomycin (Fresenius, 3U/Kg), and lungs were isolated after 7 days after injury. In the case of Arg1 inhibition, bleomycin-injured mice were treated daily from day 9 to day 15 post-injury with 100 mg/kg CB-1158 (Numidargistat dihydrochloride) dissolved in water, by oral gavage. In the case of ornithine treatment, mice were treated daily by gavage with ornithine 2 mg/kg dissolved in 100 μL of water. For the creation of single-cell suspensions, lungs were harvested from euthanized mice and minced with scissors, the tissue was resuspended in RPMI with 0.2% Collagenase (10103586001, Roche), 2000 U/ml DNase I (4716728001, Roche) and 0.1 mg/ml Dispase II (4942078001, Sigma). The suspension was incubated for 1 hour at 37οC and passed through a 70 μm filter (130-110-916, MACS SmartStrainers) to obtain single cells. The cells were washed twice with 1X PBS pH 7.4 (10010023, Gibco). For macrophage isolation, a total of 107 cells were resuspended in 80μl PBS buffer supplemented with 0.5% BSA (0332, VWR Life Science) and 2mM EDTA (E57040, Research Products International). 20μl CD11b microbeads were used per 107 cells. Macrophages were obtained by positive selection by binding to CD11b microbeads and passing through MACS columns (130-049-601, Miltenyi Biotech). Isolated CD11b+ macrophages were cultured in DMEM supplemented with 10% FBS, 1% penicillin-streptomycin, and 20 ng/ml M-CSF (315-02, Peprotech) for 2 days. Primary mouse lung fibroblasts were freshly isolated after magnetic bead-based negative selection of epithelial cells (118204, Biotin anti-mouse CD326 Epcam, Clone: G8.8, BioLegend), endothelial cells (102404, Biotin anti-mouse CD31, clone: 390), immune cells (103104, Biotin anti-mouse CD45, clone: 30-F11, BioLegend), pericytes and smooth muscle cells (134716, Biotin anti-mouse CD146, clone: ME-9F1, BioLegend), and red blood cells (116204, Biotin anti-mouse Ter119, clone: TER-119, BioLegend) with biotinylated antibodies and Dynabeads (MyOne Streptavidin T1, 65601, Thermo Fisher Scientific) as previously described2. Fibroblasts were cultured for 5 days prior to trypsinization and addition to macrophages for coculture at a 1:1 ratio in DMEM complete media for 5 further days.

Human lung cell isolation and macrophage-fibroblast coculture

Human lung tissue from deceased donors not used for transplant was resected and minced in Hanks’ Balanced Salt Solution (HBSS) buffer supplemented with 0.2% Collagenase (10103586001, Roche), 2000 U/ml DNase I (4716728001, Roche) and 0.1 mg/ml Dispase II (4942078001, Sigma), 1% Penicillin-streptomycin for 1.5 hours at 37οC and 5% CO2. 1X Amphotericin B (15290026, Gibco) was added to the dissociation solution for 30 min. Digested lung tissue was lysed further with a MACS tissue dissociator (gentleMACS Dissociator, Miltenyi Biotec) using gentleMACS C tubes (130-093-237, Miltenyi Biotec) at mLUNG-01 setting. The suspension was then passed through a 70 μm filter to obtain single cells. Cells were resuspended in PBS with 0.5% BSA and 2mM EDTA. Macrophages were incubated with CD11b microbeads and loaded on MACS columns. After washing the cell fraction of unlabeled cells, CD11b+ cells were eluted and cultured in DMEM supplemented 10% FBS (10082147 Gibco), 1% penicillin-streptomycin (1670249, MP Biomedicals), and 50 ng/ml human M-CSF (300-25, Peprotech) for 2 days. For the negative selection of human fibroblasts, the following antibodies were used at a 1:200 ratio: epithelial cells (324216, Biotin anti-human Epcam, clone:9C4, BioLegend), endothelial cells (13-0319-82, Biotin anti-human CD31, clone: WM-59, Invitrogen), immune cells (368534, Biotin anti-human CD45, clone: 2D1, BioLegend), pericytes and smooth muscle (361036, Biotin anti-human CD146, clone: P1H12, BioLegend). Fibroblasts were added directly to the cultured macrophages, and the cells were cocultured in DMEM supplemented with 10% serum, 1% penicillin-streptomycin, and 1% amphotericin B for a total of 5 days.

Flow cytometry

Cocultured cells

Cocultures were trypsinized with 0.25% trypsin and resuspended in 1X PBS with 0.5% BSA and 2mM EDTA. Single-cell suspensions were pre-stained with Fc blocker for 10 min in ice followed by staining at 1:300 with anti-CD64 (139304, PE anti-mouse CD64, clone:X54-5/7.1, BioLegend) and anti-Pdgfra (25-1401-82, PE-Cy7, anti-mouse CD140a, clone: APA5, Invitrogen) antibodies for 40 min. DAPI was used to distinguish dead cells.

Mouse lung cell suspensions (for clodronate macrophage depletion experiments)

Single cell suspensions from digested mice lungs were incubated with ghost dye 516 (13-0867-T100, TONBO) 1:300 for 30min in ice followed by washing with staining buffer consisting of 1% EDTA and 1% serum in PBS. Cells were stained with anti-CD64 (139304, PE anti-mouse CD64, clone:X54-5/7.1, BioLegend) and anti-CD11b (25-0112-81, PE-Cy7, clone:M1/70, eBioscience) antibodies for 40 min at 4οC protected from light. Cells were washed with staining buffer and resuspended in 100μl staining buffer and 100μl IC fixation buffer. Samples were kept in dark at 4οC overnight followed by resuspending in 300μl staining buffer. Data was acquired using Fortessa (BD Bioscience) and analyzed using FlowJo software.

Mouse lung cell suspension (for experiments with Arg1-YFP mice)

Whole lung single cell suspensions were prepared by harvesting lung lobes into 5 ml HBSS with 40µl Liberase Tm (0.1 U/ml, Roche, Cat# 5401127001) and 20µl DNAse 1 (10mg/ml, Roche, Cat# 10104159001), followed by automated tissue dissociation (GentleMacs; Miltenyi Biotec) and tissue digestion for 30 min at 37°C on a shaker. Digested samples were processed on the GentleMacs using the “lung2” program, passed through 70µm filters, and washed, followed by red blood cell lysis and final suspension in FACS buffer. Cells were counted using a NucleoCounter (Chemometic). All samples were stained in 96-well V-bottom plates. Single cell samples were first incubated with antibodies to surface antigens for 30 min at 4°C in 50µl staining volume. Flow cytometry was performed on BD LSRFortessa X-20. Fluorochrome compensation was performed with single-stained UltraComp eBeads (Invitrogen, Cat# 01-2222-42). Samples were FSC-A/SSC-A gated to exclude debris, followed by FSC-H/FSC-A gating to select single cells and Draq7 viability dye (Biolegend) to exclude dead cells. Early transitional macrophages (TMs) were identified as CD19−, Ly6G−, NK1.1−, Siglec-F−, CD11b+,CCR2+ and CD64+. Late TMs were identified as CD19−, Ly6G−, NK1.1−, Siglec-F−, CD11b+, CCR2− and CD64+. GR1 MDSC were identified as CD19−, Ly6G+, CD64− and CD11c+. Arginase1-positive cells were identified by the presence of eYFP. Data were analyzed using FlowJo software (TreeStar, USA) and compiled using Prism (GraphPad Software). Monoclonal antibodies used were: anti-CD45 (30-F11, Biolegend), anti-CD11b (M1/70, Biolegend), anti-CD11c (N418, Biolegend), anti-NK1.1 (PK136, Biolegend), anti-CD19 (6D5, Biolegend), anti-T1/ST2 (DJ8, BD BioSciences), anti-MerTK (DS5MMER, eBiosciences), anti-CD64 (X54-5/7, eBiosciences), anti-Ly6C (HK1.4), anti-Ly6G (1A8, Biolegend), anti-SiglecF (E50-2440), and anti-I-A/I-E (MHCII) (M5/114.15.2, Biolegend), anti-CCR2 (475301, BD BioSciences).

Quantitative Real-Time PCR analysis

Macrophages or fibroblasts were lysed in 300μl TRIzol reagent (10296010, Ambion life technologies) to obtained RNA. 600 ng of RNA per sample was used to prepare cDNA using iScript reverse transcriptase supermix (Bio-Rad, 1708841). qPCR was performed for the target genes using SYBR Green super mix (Applied Biosystems, 4309155). Gene expression was normalized to Gapdh for analysis of mouse transcripts or 18S rRNA for human.

  • Mouse Gapdh Forward: AGTATGACTCCACTCACGGCAA

  • Mouse Gapdh Reverse: TCTCGCTCCTGGAAGATGGT

  • Mouse IL6 Forward: CCAAGAGGTGAGTGCTTCCC

  • Mouse IL6 Reverse: CTGTTGTTCAGACTCTCTCCCT

  • Human 18S rRNA Forward: GTAACCCGTTGAACCCCATT

  • Human 18S rRNA Reverse: CCATCCAATCGGTAGTAGCG

  • Human IL6 Forward: ACTCACCTCTTCAGAACGAATTG

  • Human IL6 Reverse: CCATCTTTGGAAGGTTCAGGTTG

ATP measurement

For ATP measurement, 1 mL of mouse lung lavage was collected in 1X PBS containing 100μM ATPase inhibitor. ATP measurement was performed immediately by measuring bioluminescence using Biotek H1 plate reader as described in the manufacturer’s protocol (A22066, ATP determination Kit, ThermoFisher Scientific). Briefly, an assay solution was prepared by mixing a working concentration of 10μM dithiothreitol, 0.2mM D-luciferase, 7.5 μg/ml firefly luciferase in 1X reaction buffer diluted with distilled water. The reaction was kept in ice and protected from light. 10 μl of sample was mixed with 90 μl of assay solution, and light emission was measured at 560 nm using a Biotek plate reader.

Macrophage depletion

Mice were treated 6 days after bleomycin lung injury with 100μl of 18.4 mM liposomal-clodronate (CLD-8909, Encapsula NanoSciences) by oral aspiration. Mice were kept under observation for 24hrs. These mice were euthanized on day 7, and bronchoalveolar lavage fluid was collected in PBS containing 100μM ATPase inhibitor, and lung cell suspensions were prepared for flow cytometry.

siRNA Knockdown

Healthy human fibroblasts were treated with 5μM siRNA (Dharmacon, D-001810-01-05 ON-TARGET plus No-targeting siRNA) control or (Dharmacon, L-006285-00-0010 ON-TARGET plus Human P2RX4 SMARTpool) using DharmaFECT in serum-free media for 12 hours, followed by supplementing with DMEM with 10% serum. Cells were incubated for an additional 48 hours. Treated cells were harvested for either RNA isolated or stimulated with 100μM ATPψS for 12 hours and culture supernatant was collected for Human IL6 ELISA assay.

IL-6 ELISA

For Mouse

Bronchoalveolar lavage fluid or conditioned culture media were collected for measurement of IL-6 with a Duoset ELISA kit (R&D Systems, DY406-05).

For Human

Culture supernatant was collected and IL6 was measured using Duoset ELISA kit (R &D system, DT206-05). Briefly, the capture antibody was coated overnight in a 96-well plate at room temperature. Wells were aspirated and washed in ELISA wash buffer and blocked by adding reagent diluent consisting of 1% BSA in PBS. After 1 hour of room temperature incubation, wells were again washed with wash buffer and samples or standards were added and incubated for 2 hours at room temperature. Detection antibody was used along with streptavidin-HRP and substrate solution. The reaction was stopped with stop solution, and optical density was measured using a microplate reader at 450 nm.

Mouse Arginase 1 ELISA

Arginase 1 levels were measured from conditioned media or from bronchoalveolar lavage, using Mouse Arginase 1 ELISA Kit (ab269541, Abcam) based on the manufacturer’s protocol. Briefly, CD11b+ mouse lung cells were seeded in 12-well dishes for 2 days in DMEM supplemented with M-Csf (315-02, Peprotech). Cells were exposed to murine IL-6 for 72 hours, and culture supernatant and cell lysate were then collected. For in vivo experiments, bleomycin-injured mice received intraperitoneal treatment with a 150 μg/kg dose of anti-IL6 antibody (MP5-20F3, BioCell) or IgG control (MAB005, R & D systems) on days 6, 9, and 12, followed by bronchoalveolar lavage isolation on day 14 from the indicated groups. For the ELISA, subsequently, 50 μl of undiluted samples were added to each well, along with a 50 μl antibody cocktail comprising capture antibody and detection antibody dissolved in antibody diluent. Plates were incubated for 1 hour at room temperature. Wells were washed thrice with wash buffer and incubated with 100 μl Tetramethylbenzidine (TMB) substrate for 15 min. The reaction was stopped using 100μl stop solution, and OD was measured at 450nm.

Histology and Immunofluorescence imaging

Mouse lung

Arg1-RFP-creERT2 /Rosa26-LSL-TdTomato/Col1a1-EGFP mice were injured for 14 days. Lungs from uninjured and injured mice were fixed in 4% formaldehyde at 4οC for 4 hours, then washed and submerged in 30% sucrose solution overnight at 4οC. Next, the tissue was incubated in 1:1 solution of 30% sucrose and OCT (4585, OCT) overnight followed by changing to OCT for 2 hours, and then a tissue block was frozen after embedding in OCT. 5 μm sections were cut from OCT-embedded tissue. For immunofluorescence imaging, slides with sections were washed with PBS and mounted on antifade DAPI mounting media and imaged using a Leica inverted widefield confocal laser scanning microscope at BIDC, UCSF.

For Sirius Red imaging, sections prepared from mouse lungs frozen in OCT were stained with Sirius Red (0.1% Sirius Red in saturated picric acid, Electron Microscopy Sciences, 26357-02) for 60 min followed by a 2-min 0.01 N HCl wash, rinsed in 70% ethanol, and air-dried overnight before imaging. Images were acquired using a bright-field microscope (Zeiss Axio scan.Z1). The images were quantified using Qupath image analysis software using the artificial neural network pixel classifier function49, with software training based on user-defined fibrotic regions in a single image.

Human lung

5 μm thick sections from patient FFPE blocks were collected for IHC staining. Slides were stained using Opal Manual IHC kit (PerkinElmer). After deparaffinization, antigen retrieval was performed in AR buffer for 45 sec at 100% power followed by 15 min at 20% power. After blocking slides were incubated with Primary antibodies ARG1 (CST) overnight at 4C. Polymer HRP was introduced to slides for 10 min, followed by signal amplification using Opal570 for 10 min at room temperature. The slides were then counterstained with DAPI mounting media and scanned using with a 10x and 40x objective of Leica inverted widefield microscope.

Western blot

Precision cut lung slices were obtained from human IPF tissue sections. Briefly, IPF tissue was injected with 2% low melting agarose (50111, Lonza) and then submerged in ice-cold PBS to allow solidification of agarose. 400 μm lung slices was generated using Compresstome (VF-310-OZ, Precisionary Instruments). Slices were kept in complete DMEM media for 2 hours to allow the release of agarose followed by changing to fresh complete DMEM. Lung slices were treated with 50 μM CB1158 or 10 μg/ml Tocilizumab for 24 hours. After incubation slices were minced using a tissue homogenizer. Cells were lysed in Pierce RIPA buffer (Thermo Fisher Scientific, 89901) with protease inhibitor cocktail (Thermo Fisher Scientific, 1861278). 10 μg of protein was run on 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (Bio-rad, 4561034) and transferred to a PVDF membrane (Thermo Fisher Scientific, 88520). The membrane was incubated with 1:1000 Col1a1(#720265, Cell Signaling Technology) overnight at 4οC. Blots were washed in 1XTBST and incubated with peroxidase-conjugated goat anti-rabbit (1:20,000, Anaspec, AS28177) for 4 hr at 4οC. These blots were developed using SuperSignal West Pico Chemiluminescent substrate (Thermo Fisher Scientific, 34080) in ChemiDoc XRS+ gel imaging system (Bio-rad). Quantification of bands was done using ImageJ.

Hydroxyproline assay

Mice were euthanized and lungs were excised and snap-frozen. Isolated lung samples were homogenized and incubated with 50% trichloroacetic acid (T6399, Sigma) on ice for 20 min. Samples were then incubated in 12N HCL (A144, Fisher) overnight at 110°C. Dried pellets were reconstituted in distilled water with constant shaking for 2 hours at room temperature. Samples were then mixed with 1.4% Chloramine T (Sigma, 85739) and 0.5 M sodium acetate (Sigma, 241245) in 10% 2-propanol (Fisher, A416) and incubated with Ehrlich’s solution (Sigma, 03891) for 15 min at 65°C. Absorbance was quantified at 550 nm, and concentration was calculated using a standard curve of commercial hydroxyproline (Sigma, H5534).

scRNAseq library preparation and sequencing

PIPseq T2 3ʹ Single Cell RNA Kit (v3.0 – cultured cells, v4.0 – directly sequenced cells) was used for pre-templated instant partitioning (PIP) to capture single-cell mRNA transcripts with PIP beads according to manufacturer’s protocol (Fluent Biosciences, FB0001026). In the case of directly sequenced cells, 7 days after bleomycin injury, cells were isolated by the isolation procedure described above for scRNAseq workflow. In the case of cocultures, 7 days after bleomycin lung injury, mice were euthanized and lungs were harvested followed by cell isolation as above. After 5 days of coculture, single cell suspension of cells was obtained by trypsinization followed by washing in 1X PBS.

Cells were washed in ice-cold PIPseq Cell Suspension Buffer (Fluent Biosciences, FB0002440). Cells were counted and stained with Trypan blue to confirm >90% viability. Single-cell library preparation was performed using the manufacturer-recommended default protocol and settings. The sequencing libraries were submitted to UCSF Center for Advanced Technology (Novaseq X) or Novogene (Novaseq 6000) for sequencing. The demultiplexed FASTQ files were aligned to mouse genome (mm10) using PIPseeker 1.0.0 (Fluent Biosciences). After sequence alignment, we observed around 50% of input cells being called when PIPseeker sensitivity level was set to 3, which was near the inflection point of the barcode rank plot. The original FASTQ files, the quality reports, and the expression matrix outputs of PIPseeker have been deposited in Gene Expression Omnibus (GEO).

scRNAseq Data Analysis

The Seurat – Guided Clustering Tutorial (March 27, 2023) was followed to convert our expression matrixes into Seurat objects (Seurat version 4.3.0)50. For quality control, we removed the cells with less than 200 genes or more than 10,000 genes and larger than 5% mitochondrial content. Seurat objects were integrated following Seurat’s Introduction to scRNAseq Integration (March 27, 2023), selecting the top 2,000 variable features as integration anchors. Cell doublets were removed with the package DoubletFinder (2.0.3) 51. Following Seurat – Guided Clustering Tutorial (March 27, 2023), we selected the top 2000 highly variable genes to obtain the cell UMAP coordinates and group the cells into clusters with a sensitivity of 0.550. Cell types were annotated using the SingleR package 1.10.044 using the ImmGen database52 as reference. The Gene Set Enrichment Analysis (GSEA) was performed using the package enrichR 3.153 (FDR<0.05), with gene ontology data taken from the database “GO_Biological_Process_2021”54. Cell communication pathways analysis was performed using the package CellChat (1.6.1)17. Upstream regulator prediction was done using the Ingenuity Pathway Analysis software (Qiagen)55. Differentially expressed genes for macrophages with p < 0.05 and average log2 fold change > 0.75 were used for analysis. Analysis results with p < 0.05 were considered significant.

Multiplexed Ion Beam Imaging and Analysis (MIBI)

Slide Preparation

Serial 5 um FFPE sections were cut onto one glass and one gold slide. Both slides were baked at 70* Celsius overnight and deparaffinized in three washes of fresh Xylene and rehydrated in EtOH (2X 100% EtOH, 2X 90% EtOH, 1X 80% EtOH, 1X 70% EtOH) and distilled water (2X) washes. Washes were performed in (include wash machine specs). An antigen retrieval slide chamber was prepared by diluting 10X Tris with EDTA antigen retrieval buffer 1:10 in diH2O (Reagent Details). The prepared slide chamber was added to a PT Module (add specs) filled with PBS and preheated to 75°C. The rehydrated slides were run in the preheated PT module at 97°C for 40 minutes, then cooled to 65°C in the PT module. The prepared slide chamber was then removed from the PT module and cooled at RT for 30 minutes. Slides were washed 2X in 1X TBS-Tween (Add reagent details).

Glass Slide IHC

Tissues were encircled by PAP pen boarders and blocked with 5% donkey serum (reagent details) diluted in TBS IHC wash buffer (reagent details) for 1 hour at RT in a moisture chamber. Wash buffer was aspirated from the slide, and Arginase-1 primary antibody (Ionpath Cat#: 715001) was stained overnight at 4°C overnight in the moisture chamber. The following day, primary antibody was aspirated, and slides were washed twice with 1X TBS-T for 5 minutes, blocked with 3% peroxide buffer for 15 minutes at RT, and washed again twice with 1X TBS-T for 5 minutes each wash before incubation with anti-rabbit secondary antibody (reagent details) for 1 hour at RT. Slides were washed twice with 1X TBS-T for 5 minutes before visualization with 100uL DAB (reagent details) for 5 minutes at RT. DAB reaction was stopped by tapping waste into contained waste bin, and then washing the slide into a slide chamber filled with diH2O 3X for 30 seconds each wash. The slide was then stained with Hematoxylin for 1 minute at RT, and washed with tap water 2X for 30 seconds. The slide was then dehydrated by washing in EtOH (1X 70% EtOH, 1X 80% EtOH, 2X 95% EtOH, 2X 100% EtOH), and Xylene (2X) before cover slipping.

Gold Slide Staining

Gold slides were transferred the Sequenza Immunostaining Center Staining System (Electron Microscopy Sciences, Hatfield, PA). Endogenous biotin-binding proteins with blocked with (Avidin/biotin-blocking reagents) for 30 minutes at room temperature. 5% donkey serum was added to the top of the chamber to wash out the avidin blocking reagents and block additional non-specific antibody binding sites for 1 hour at RT. Primary and Secondary antibodies panels were assembled with appropriate volumes of each titrated antibody and a final concentration of 0.05M EDTA. The complete cocktails were filtered through a pre-wet 0.1 um Ultragree MC Spin Filter, (cat#) then the Primary Antibody cocktail (Table S2) was added to the Sequenza top chamber and incubated overnight at 4°C. The Secondary antibody cocktail was stored at 4°C. The following day, the slides were washed by adding 1X TBS-T to the Sequenza chamber 2X before adding the Secondary Antibody cocktail to the Sequenza chamber for 1 hour at RT. Gold slides were removed from the Sequenza chamber and washed 3X with 1X TBS-T for 5 minutes each wash, 1X with filtered 2% Glutaraldehyde for 5 minutes, 3X with filtered 1X Tris pH 8.5, 2X with filtered diH2O, 1X with 70% EtOH, 1X with 80% EtOH, 2X with 90% EtOH, and 2X with 100% EtOH. Slides were allowed to dry at RT for 10 minutes before being stored in a vacuum chamber (Cat#) before MIBIscope analysis.

Image Acquisition and Processing

Gold slides were loaded into the MIBIscope (Ionpath, Menlo Park, CA) and FOVs were selected by matching tissue topography to ROIs with Arginase-1+ Staining from the serial IHC glass slide. FOVs were acquired at Fine resolution, with a dwell time of 1 second at a resolution of 0.39 um per pixel. Image QC was performed by following the Angelo Lab toffy pipeline (https://github.com/angelolab/toffy). Analysis was performed by following the Angelo Lab ark pipeline (https://github.com/angelolab/ark-analysis).

Spatial Transcriptomics (10x Xenium)

FFPE preserved sections of lung tissue were prepared for spatial transcriptomics imaging on the Xenium platform by following 10x Genomics protocols CG000582 Rev E and CG000584 Rev A. Briefly, protocol CG000582 prepares the slides for the imaging run by first hybridizing the probe panel of choice. Here, the standard human lung panel available from 10x Genomics (Cat #1000601) was supplemented with a custom panel specific for lung disease states, including pulmonary fibrosis (Table S3). Following probe hybridization, annealed probes are ligated together to create circular fragments, then circularized probes are amplified with a rolling circle PCR. After rolling circle amplification, slides are DAPI stained for nuclei then placed in the Xenium Analyzer with run reagents following CG000584. The gene panel is uploaded to the Xenium Analyzer and a primary image is take of the slides. All areas with visible tissue are selected for imaging and the imaging run is started. The Xenium processes imaging data during the run, identified cell boundaries and outputting image files along with cell by transcript by location matrices for further downstream analysis by Seurat 10x Xenium protocol.

Proximity Analysis of ARG1+ cells and IL6+ CTHRC1+ cells

The output files of Xenium Analyzer were converted to an AnnData object that contained the cell centroid coordinates and raw transcript counts. A filter of 10 counts per cell and 5 cells per gene was applied to filter out low-quality cells and sparsely detected genes. Counts were normalized to 1e4 and log-transformed. Cells were then grouped into one of the following four categories: ARG1+, IL6+ CTHRC1+, IL6-CTHRC1+, or other, with positive expression of a marker defined as having non-zero counts.

Co-occurrence probability ratio

The ‘Analyze Xenium data’ tutorial in Squidpy (version 1.5.0)56 was followed to compute the co-occurrence probability ratio of ARG1+ and IL6+CTHRC1+ cells, IL6-CTHRC1+ cells, or other cells not bearing these profiles. We performed 50% random subsampling of the data 5 times to compute the statistic and generate confidence intervals bounded by the minimum and maximum probability ratio generated by the subsamples. The co-occurrence probability ratio was computed every 50μm within a range of 250μm. Using the implementation in the 1.5.0 release of Squidpy, for each radial distance, the ratio of cells belonging to category exp (exp being any of the four categories of markers above, ie ARG1+, IL6+ CTHRC1+, IL6-CTHRC1+, or other cells not bearing these profiles) out of all cells within the given distance of an ARG1+ cell was averaged across all ARG1+ cells to compute the conditional probability in the numerator P(exp | ARG1+), while the denominator P(exp) was computed similarly but averaging probabilities that were computed by centering around every cell in the sample. Previous releases of Squidpy implemented a version of this test in the function gr.co_occurrence that used discrete interval bins (only including cells within two consecutive choices of radial distances). We chose to use inclusive intervals (including all cells within a given radial distance) for a more robust calculation of the co-occurrence probability ratio. We implemented our method into the codebase of Squidpy, with the help of Squidpy’s authors, and it is now the default implementation of the function gr.co_occurrence, starting in release 1.5.0.

Analysis of published datasets

Time series of single-cell data after bleomycin lung injury were obtained from Tsukui et al3 and Strunz et al 18. These samples were processed and merged with Seurat (4.3.0) using the function SCTransform to correct for batch effects and annotated with SingleR 1.10.044 using the ImmGen database as reference. We subsetted the macrophages from GSE14125918 repository and annotated by applying SingleR using as reference the macrophage subtypes (C1, C2, C3) defined by Aran et al4. Microarray RNA data from bronchoalveolar cells of healthy individuals and IPF patients was extracted from the GPL14550 dataset within the GSE7086723 repository. The differentially expressed genes between IPF and healthy samples were obtained by following the R workflow provided by the NCBI GEO2R platform using GEOquery (2.66.0) and limma (3.54.2) packages.

LC-MS metabolomics and 13C-flux

Primary murine lung macrophages and fibroblasts were cocultured, or fibroblasts were cultured alone, as above, for 24 hours with or without 1mM 13C5-ornithine (Cambridge Isotope Laboratories, CLM-4724-H-PK) or 1 mM CB-1158. Cell extracts were used to calculate protein equivalents by resuspension in 0.2 M NaOH, heated to 95 °C for 25 min, and determined via BCA (Pierce, 23225). Dried metabolites were resuspended in 50% ACN:water and 1/10th of the volume was loaded onto a Luna 3 um NH2 100A (150 × 2.0 mm) column (Phenomenex). The chromatographic separation was performed on a Vanquish Flex (Thermo Scientific) with mobile phases A (5 mM NH4AcO pH 9.9) and B (ACN) and a flow rate of 200 µL/min. A linear gradient from 15% A to 95% A over 18 min was followed by 9 min isocratic flow at 95% A and reequilibration to 15% A. Metabolites were detected with a Thermo Scientific Q Exactive mass spectrometer run with polarity switching (+3.5 kV / −3.5 kV) in full scan mode with an m/z range of 65-975. TraceFinder 4.1 (Thermo Scientific) was used to quantify the targeted metabolites by area under the curve using expected retention time and accurate mass measurements (< 5 ppm). Values were normalized to cell number and sample protein concentration. Relative amounts of metabolites were calculated by summing up the values for all isotopologues of a given metabolite. Fractional contribution (FC) of 13C carbons to total carbon for each metabolite was calculated as previously described 57.

Data availability

Newly generated single-cell sequencing data will be made public on acceptance for publication.

Author contributions

PY performed or assisted in the analysis of all the experiments and in figure preparation. JGO performed single cell sequencing experiments and analysis and figure preparation. KC and SP performed single cell library preparation and sequencing, supervised by ARA. NB and AB assisted in functional experiments with lung tissues and cells. XY performed human cell coculture experiments under the supervision of BL. JN performed flow cytometry of mouse lung cells supervised by ABM. KB, TJ, and JW performed mouse breeding and development of crosses for experiments. TT and DS assisted in analysis of single cell sequencing data from mouse lung fibroblasts. MM and HT provided deceased donor control lung tissues supervised by MAM. LP and AG performed Xenium spatial transcriptomic analysis supervised by WE. CC and AW designed and implemented spatial proximity analysis of Xenium data. RM generated and provided P2x4 floxed mice. WT and ST performed MIBI analysis under the guidance of TB. PJW provided lung explants from IPF patients and helped to interpret associated imaging data. KMT designed and performed metabolomic assays, assisted by PY. MB conceived of the work, supervised experimental planning and execution, and wrote the manuscript with input from PJW, DS, and KMT.

Declaration of Interests

The authors have no conflicting financial interests.

Acknowledgements

This work was supported by funding from the US Department of Defense (Grant W81XWH2110417 to MB), the UCSF Bakar Aging Research Institute (Investigator Grant to MB), Longevity Impetus Grants from Norn Group (MB and JGO), the Nina Ireland Program for Lung Health Innovative Grant Program (PY), and NIH (1R01HL142701-01 to ABM). The authors would like to acknowledge the staff within the Biological Imaging Development CoLab (BIDC) at UCSF Parnassus Heights, particularly Kyle Marchuk and Austin Edwards, for their support in microscopy experiments. Sequencing performed at the UCSF CAT was supported by UCSF PBBR, RRP IMIA, and NIH 1S10OD028511-01. We are grateful to Dr. Clifford Lowell for his valuable comments and suggestions on the manuscript. We thank Giovanni Palla and Nathan Levy for help in extending the Squidpy package.

Footnotes

  • We have updated our analysis of human lung, including MIBI as well as spatial transcriptomic data. New mouse and cellular experiments were also added.

References

  1. 1.↵
    Buechler MB, et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
    OpenUrlCrossRefPubMed
  2. 2.↵
    Tsukui T, et al. Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis. Nat Commun 11, 1920 (2020).
    OpenUrlCrossRefPubMed
  3. 3.↵
    Tsukui T, Wolters PJ, Sheppard D. Alveolar fibroblast lineage orchestrates lung inflammation and fibrosis. Nature 631, 627–634 (2024).
    OpenUrl
  4. 4.↵
    Aran D, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20, 163–172 (2019).
    OpenUrlCrossRefPubMed
  5. 5.↵
    Larson-Casey JL, Deshane JS, Ryan AJ, Thannickal VJ, Carter AB. Macrophage Akt1 Kinase-Mediated Mitophagy Modulates Apoptosis Resistance and Pulmonary Fibrosis. Immunity 44, 582–596 (2016).
    OpenUrlCrossRefPubMed
  6. 6.↵
    Satoh T, et al. Identification of an atypical monocyte and committed progenitor involved in fibrosis. Nature 541, 96–101 (2017).
    OpenUrlCrossRefPubMed
  7. 7.↵
    Meinken J, Walker G, Cooper CR, Min XJ. MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase. Database (Oxford) 2015, (2015).
  8. 8.↵
    Roofeh D, et al. Tocilizumab Prevents Progression of Early Systemic Sclerosis-Associated Interstitial Lung Disease. Arthritis Rheumatol 73, 1301–1310 (2021).
    OpenUrl
  9. 9.↵
    Group RC. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet 397, 1637–1645 (2021).
    OpenUrlCrossRefPubMed
  10. 10.↵
    Khanna D, et al. Tocilizumab in systemic sclerosis: a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Respir Med 8, 963–974 (2020).
    OpenUrl
  11. 11.↵
    Narvaez J, J LL, Alegre Sancho JJ, Molina-Molina M, Nolla JM, Castellvi I. Effectiveness and safety of tocilizumab for the treatment of refractory systemic sclerosis associated interstitial lung disease: a case series. Ann Rheum Dis 78, e123 (2019).
    OpenUrlFREE Full Text
  12. 12.↵
    Riteau N, et al. Extracellular ATP is a danger signal activating P2X7 receptor in lung inflammation and fibrosis. Am J Respir Crit Care Med 182, 774–783 (2010).
    OpenUrlCrossRefPubMedWeb of Science
  13. 13.↵
    Bhattacharyya A, et al. Macrophage Cx43 Is Necessary for Fibroblast Cytosolic Calcium and Lung Fibrosis After Injury. Front Immunol 13, 880887 (2022).
    OpenUrlCrossRef
  14. 14.↵
    Trang T, Beggs S, Wan X, Salter MW. P2X4-receptor-mediated synthesis and release of brain-derived neurotrophic factor in microglia is dependent on calcium and p38-mitogen-activated protein kinase activation. J Neurosci 29, 3518–3528 (2009).
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    Le Guilcher C, et al. The P2X4 purinergic receptor regulates hepatic myofibroblast activation during liver fibrogenesis. J Hepatol 69, 644–653 (2018).
    OpenUrlCrossRef
  16. 16.↵
    Dichtl S, et al. Lactate and IL6 define separable paths of inflammatory metabolic adaptation. Sci Adv 7, (2021).
  17. 17.↵
    Jin S, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021).
    OpenUrlPubMed
  18. 18.↵
    Strunz M, et al. Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis. Nat Commun 11, 3559 (2020).
    OpenUrl
  19. 19.↵
    Fabre T, et al. Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation. Sci Immunol 8, eadd8945 (2023).
    OpenUrlCrossRef
  20. 20.↵
    Steggerda SM, et al. Inhibition of arginase by CB-1158 blocks myeloid cell-mediated immune suppression in the tumor microenvironment. J Immunother Cancer 5, 101 (2017).
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    Tharp KM, et al. Myeloid mechano-metabolic programming restricts anti-tumor immunity. bioRxiv, 2022.2007.2014.499764 (2022).
  22. 22.↵
    Tran DH, et al. Mitochondrial NADP(+) is essential for proline biosynthesis during cell growth. Nat Metab 3, 571–585 (2021).
    OpenUrl
  23. 23.↵
    Prasse A, et al. BAL Cell Gene Expression Is Indicative of Outcome and Airway Basal Cell Involvement in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 199, 622–630 (2019).
    OpenUrlCrossRef
  24. 24.↵
    Perera LMB, et al. The Regulation of Skin Fibrosis in Systemic Sclerosis by Extracellular ATP via P2Y(2) Purinergic Receptor. J Invest Dermatol 139, 890–899 (2019).
    OpenUrl
  25. 25.↵
    Solini A, Chiozzi P, Morelli A, Fellin R, Di Virgilio F. Human primary fibroblasts in vitro express a purinergic P2X7 receptor coupled to ion fluxes, microvesicle formation and IL-6 release. J Cell Sci 112 (Pt 3), 297–305 (1999).
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    Mauer J, et al. Signaling by IL-6 promotes alternative activation of macrophages to limit endotoxemia and obesity-associated resistance to insulin. Nat Immunol 15, 423–430 (2014).
    OpenUrlCrossRefPubMed
  27. 27.↵
    Wang Q, et al. Vascular niche IL-6 induces alternative macrophage activation in glioblastoma through HIF-2alpha. Nat Commun 9, 559 (2018).
    OpenUrlCrossRef
  28. 28.↵
    Fernando MR, Reyes JL, Iannuzzi J, Leung G, McKay DM. The pro-inflammatory cytokine, interleukin-6, enhances the polarization of alternatively activated macrophages. PLoS One 9, e94188 (2014).
    OpenUrlCrossRefPubMed
  29. 29.↵
    Bhattacharya M, Ramachandran P. Immunology of human fibrosis. Nat Immunol, (2023).
  30. 30.↵
    Zhou X, et al. Circuit Design Features of a Stable Two-Cell System. Cell 172, 744–757 e717 (2018).
    OpenUrlCrossRef
  31. 31.↵
    Boyd DF, et al. Exuberant fibroblast activity compromises lung function via ADAMTS4. Nature 587, 466–471 (2020).
    OpenUrl
  32. 32.↵
    Pesce JT, et al. Arginase-1-expressing macrophages suppress Th2 cytokine-driven inflammation and fibrosis. PLoS Pathog 5, e1000371 (2009).
    OpenUrlCrossRefPubMed
  33. 33.↵
    Raghu G, et al. SAR156597 in idiopathic pulmonary fibrosis: a phase 2 placebo-controlled study (DRI11772). Eur Respir J 52, (2018).
  34. 34.↵
    Wilson MS, et al. Bleomycin and IL-1beta-mediated pulmonary fibrosis is IL-17A dependent. J Exp Med 207, 535–552 (2010).
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    Monticelli LA, et al. Arginase 1 is an innate lymphoid-cell-intrinsic metabolic checkpoint controlling type 2 inflammation. Nat Immunol 17, 656–665 (2016).
    OpenUrlCrossRefPubMed
  36. 36.↵
    Murthy S, Larson-Casey JL, Ryan AJ, He C, Kobzik L, Carter AB. Alternative activation of macrophages and pulmonary fibrosis are modulated by scavenger receptor, macrophage receptor with collagenous structure. FASEB J 29, 3527–3536 (2015).
    OpenUrlCrossRefPubMed
  37. 37.↵
    Zhang H, et al. Annexin A2/TLR2/MYD88 pathway induces arginase 1 expression in tumor-associated neutrophils. J Clin Invest 132, (2022).
  38. 38.↵
    Rotondo R, et al. IL-8 induces exocytosis of arginase 1 by neutrophil polymorphonuclears in nonsmall cell lung cancer. Int J Cancer 125, 887–893 (2009).
    OpenUrlCrossRefPubMedWeb of Science
  39. 39.↵
    Cane S, et al. Neutralization of NET-associated human ARG1 enhances cancer immunotherapy. Sci Transl Med 15, eabq6221 (2023).
    OpenUrl
  40. 40.↵
    Mestas J, Hughes CC. Of mice and not men: differences between mouse and human immunology. J Immunol 172, 2731–2738 (2004).
    OpenUrlAbstract/FREE Full Text
  41. 41.↵
    Foo SS, et al. Ephrin-B2 controls cell motility and adhesion during blood-vessel-wall assembly. Cell 124, 161–173 (2006).
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.↵
    Clausen BE, Burkhardt C, Reith W, Renkawitz R, Forster I. Conditional gene targeting in macrophages and granulocytes using LysMcre mice. Transgenic Res 8, 265–277 (1999).
    OpenUrlCrossRefPubMedWeb of Science
  43. 43.↵
    Ozaki T, et al. The P2X4 receptor is required for neuroprotection via ischemic preconditioning. Sci Rep 6, 25893 (2016).
    OpenUrlCrossRef
  44. 44.↵
    El Kasmi KC, et al. Toll-like receptor-induced arginase 1 in macrophages thwarts effective immunity against intracellular pathogens. Nat Immunol 9, 1399–1406 (2008).
    OpenUrlCrossRefPubMedWeb of Science
  45. 45.↵
    Yata Y, et al. DNase I-hypersensitive sites enhance alpha1(I) collagen gene expression in hepatic stellate cells. Hepatology 37, 267–276 (2003).
    OpenUrlCrossRefPubMedWeb of Science
  46. 46.↵
    Schneider C, et al. Tissue-Resident Group 2 Innate Lymphoid Cells Differentiate by Layered Ontogeny and In Situ Perinatal Priming. Immunity 50, 1425–1438 e1425 (2019).
    OpenUrl
  47. 47.↵
    Reese TA, et al. Chitin induces accumulation in tissue of innate immune cells associated with allergy. Nature 447, 92–96 (2007).
    OpenUrlCrossRefPubMedWeb of Science
  48. 48.↵
    Kopf M, et al. Impaired immune and acute-phase responses in interleukin-6-deficient mice. Nature 368, 339–342 (1994).
    OpenUrlCrossRefPubMedWeb of Science
  49. 49.↵
    Bankhead P, et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017).
    OpenUrlCrossRefPubMed
  50. 50.↵
    Hao Y, et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 e3529 (2021).
    OpenUrlCrossRefPubMed
  51. 51.↵
    McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst 8, 329–337 e324 (2019).
    OpenUrl
  52. 52.↵
    Heng TS, Painter MW, Immunological Genome Project C. The Immunological Genome Project: networks of gene expression in immune cells. Nat Immunol 9, 1091–1094 (2008).
    OpenUrlCrossRefPubMedWeb of Science
  53. 53.↵
    Chen EY, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
    OpenUrlCrossRefPubMed
  54. 54.↵
    Gene Ontology C, et al. The Gene Ontology knowledgebase in 2023. Genetics 224, (2023).
  55. 55.↵
    Kramer A, Green J, Pollard J, Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).
    OpenUrlCrossRefPubMedWeb of Science
  56. 56.↵
    Palla G, et al. Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19, 171–178 (2022).
    OpenUrl
  57. 57.↵
    Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11, R25 (2010).
    OpenUrlCrossRefPubMed
  58. 58.↵
    Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33, 495–502 (2015).
    OpenUrlCrossRefPubMed
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Macrophage-fibroblast crosstalk drives Arg1-dependent lung fibrosis via ornithine loading
Preeti Yadav, Javier Gómez Ortega, Whitney Tamaki, Charles Chien, Kai-chun Chang, Nivedita Biswas, Sixuan Pan, Julia Nilsson, Xiaoyang Yin, Aritra Bhattacharyya, Kaveh Boostanpour, Tanay Jujaray, Jasper Wang, Tatsuya Tsukui, Dean Sheppard, Baosheng Li, Mazharul Maishan, Hiroki Taenaka, Michael A. Matthay, Rieko Muramatsu, Lenka Maliskova, Arnab Ghosh, Walter L. Eckalbar, Ari B. Molofsky, Paul J. Wolters, Stanley J. Tamaki, Trever Bivona, Adam R. Abate, Allon Wagner, Kevin M. Tharp, Mallar Bhattacharya
bioRxiv 2023.09.06.556606; doi: https://doi.org/10.1101/2023.09.06.556606
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