Abstract
Tumor-associated macrophages are composed of distinct populations arising from monocytes or tissue macrophages, with a poorly understood link to disease pathogenesis. Here, we demonstrate that mouse monocyte migration was supported by glutaminyl-peptide cyclotransferase-like (QPCTL), an intracellular enzyme that mediates N-terminal modification of several substrates, including the monocyte chemoattractants CCL2 and CCL7, protecting them from proteolytic inactivation. Knockout of Qpctl disrupted monocyte homeostasis, attenuated tumor growth and reshaped myeloid cell infiltration, with loss of monocyte-derived populations with immunosuppressive and pro-angiogenic profiles. Antibody targeting of the receptor CSF1R, which more broadly eliminates tumor-associated macrophages, reversed tumor growth inhibition in Qpctl−/− mice and prevented lymphocyte infiltration. Modulation of QPCTL synergized with anti-PD-L1 to expand CD8+ T cells and limit tumor growth. QPCTL inhibition constitutes an effective approach for myeloid cell-targeted cancer immunotherapy.
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Data availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request. Mouse reference genome GRCm38 and GENCODE is a publicly available database used for single-cell RNA-seq analysis. Material requests should be made to the corresponding authors. Source data are provided with this paper.
References
Qian, B.-Z. & Pollard, J. W. Macrophage diversity enhances tumor progression and metastasis. Cell 141, 39–51 (2010).
Steidl, C. et al. Tumor-associated macrophages and survival in classic Hodgkin’s lymphoma. N. Engl. J. Med. 362, 875–885 (2010).
Qian, B.-Z. et al. CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature 475, 222–225 (2011).
Rivera, L. B. & Bergers, G. Intertwined regulation of angiogenesis and immunity by myeloid cells. Trends Immunol. 36, 240–249 (2015).
Zheng, Y. et al. Macrophages are an abundant component of myeloma microenvironment and protect myeloma cells from chemotherapy drug-induced apoptosis. Blood 114, 3625–3628 (2009).
Lu, T. et al. Tumor-infiltrating myeloid cells induce tumor cell resistance to cytotoxic T cells in mice. J. Clin. Invest. 121, 4015–4029 (2011).
DeNardo, D. G. & Ruffell, B. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 19, 369–382 (2019).
Beatty, G. L. et al. CD40 agonists alter tumor stroma and show efficacy against pancreatic carcinoma in mice and humans. Science 331, 1612–1616 (2011).
Gordon, S. R. et al. PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature 545, 495–499 (2017).
Thoreau, M. et al. Vaccine-induced tumor regression requires a dynamic cooperation between T cells and myeloid cells at the tumor site. Oncotarget 6, 27832–27846 (2015).
Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).
Ries, C. H. et al. Targeting tumor-associated macrophages with anti-CSF-1R antibody reveals a strategy for cancer therapy. Cancer Cell 25, 846–859 (2014).
Quail, D. F. et al. The tumor microenvironment underlies acquired resistance to CSF-1R inhibition in gliomas. Science 352, aad3018 (2016).
Papadopoulos, K. P. et al. First-in-human study of AMG 820, a monoclonal anti-colony-stimulating factor 1 receptor antibody, in patients with advanced solid tumors. Clin. Cancer Res. 23, 5703–5710 (2017).
Kumar, V. et al. Cancer-associated fibroblasts neutralize the anti-tumor effect of CSF1 receptor blockade by inducing PMN-MDSC infiltration of tumors. Cancer Cell 32, 654–668 (2017).
Beffinger, M. et al. CSF1R-dependent myeloid cells are required for NK-mediated control of metastasis. JCI Insight 3, e97792 (2018).
Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459 (2020).
Lim, S. Y., Yuzhalin, A. E., Gordon-Weeks, A. N. & Muschel, R. J. Targeting the CCL2-CCR2 signaling axis in cancer metastasis. Oncotarget 7, 28697–28710 (2016).
Griffith, J. W., Sokol, C. L. & Luster, A. D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Immunology 32, 659–702 (2014).
Brana, I. et al. Carlumab, an anti-C-C chemokine ligand 2 monoclonal antibody, in combination with four chemotherapy regimens for the treatment of patients with solid tumors: an open-label, multicenter phase 1b study. Target Oncol. 10, 111–123 (2015).
Jahchan, N. S. et al. Tuning the tumor myeloid microenvironment to fight cancer. Front. Immunol. 10, 1611 (2019).
Barreira da Silva, R. et al. Dipeptidylpeptidase 4 inhibition enhances lymphocyte trafficking, improving both naturally occurring tumor immunity and immunotherapy. Nat. Immunol. 16, 850–858 (2015).
Hollande, C. et al. Inhibition of the dipeptidyl peptidase DPP4 (CD26) reveals IL-33-dependent eosinophil-mediated control of tumor growth. Nat. Immunol. 20, 257–264 (2019).
Serbina, N. V. & Pamer, E. G. Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2. Nat. Immunol. 7, 311–317 (2006).
Tsou, C.-L. et al. Critical roles for CCR2 and MCP-3 in monocyte mobilization from bone marrow and recruitment to inflammatory sites. J. Clin. Invest. 117, 902–909 (2007).
Coillie, E. V. et al. Functional comparison of two human monocyte chemotactic protein-2 isoforms, role of the amino-terminal pyroglutamic acid and processing by CD26/dipeptidyl peptidase IV †. Biochem. 37, 12672–12680 (1998).
Cynis, H. et al. The isoenzyme of glutaminyl cyclase is an important regulator of monocyte infiltration under inflammatory conditions. EMBO Mol. Med. 3, 545–558 (2011).
Bonecchi, R. et al. Differential recognition and scavenging of native and truncated macrophage-derived chemokine (macrophage-derived chemokine/CC chemokine ligand 22) by the D6 decoy receptor. J. Immunol. 172, 4972–4976 (2004).
Hoeffel, G. et al. C-Myb+ erythro-myeloid progenitor-derived fetal monocytes give rise to adult tissue-resident macrophages. Immunity 42, 665–678 (2015).
Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).
Cassetta, L. & Pollard, J. W. Targeting macrophages: therapeutic approaches in cancer. Nat. Rev. Drug Discov. 17, 887–904 (2018).
Scheltens, P. et al. Safety, tolerability and efficacy of the glutaminyl cyclase inhibitor PQ912 in Alzheimer’s disease: results of a randomized, double-blind, placebo-controlled phase 2a study. Alzheimers Res. Ther. 10, 107 (2018).
Schilling, S. et al. Glutaminyl cyclase inhibition attenuates pyroglutamate Aβ and Alzheimer’s disease-like pathology. Nat. Med. 14, 1106–1111 (2008).
Alaluf, E. et al. Heme oxygenase-1 orchestrates the immunosuppressive program of tumor-associated macrophages. JCI Insight https://doi.org/10.1172/jci.insight.133929 (2020).
Soucie, E. L. et al. Lineage-specific enhancers activate self-renewal genes in macrophages and embryonic stem cells. Science 351, aad5510 (2016).
Ginhoux, F. & Guilliams, M. Tissue-resident macrophage ontogeny and homeostasis. Immunity 44, 439–449 (2016).
Roberts, A. W. et al. Tissue-resident macrophages are locally programmed for silent clearance of apoptotic cells. Immunity 47, 913–927 (2017).
Willemsen, L. & Winther, M. P. Macrophage subsets in atherosclerosis as defined by single‐cell technologies. J. Pathol. 250, 705–714 (2020).
Tamoutounour, S. et al. Origins and functional specialization of macrophages and of conventional and monocyte-derived dendritic cells in mouse skin. Immunity 39, 925–938 (2013).
Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334 (2019).
Fischer, W. H. & Spiess, J. Identification of a mammalian glutaminyl cyclase converting glutaminyl into pyroglutamyl peptides. Proc. Natl Acad. Sci. USA 84, 3628–3632 (1987).
Kehlen, A. et al. N-terminal pyroglutamate formation in CX3CL1 is essential for its full biologic activity. Biosci. Rep. https://doi.org/10.1042/BSR20170712 (2017).
Logtenberg, M. E. W. et al. Glutaminyl cyclase is an enzymatic modifier of the CD47–SIRPα axis and a target for cancer immunotherapy. Nat. Med. 25, 612–619 (2019).
Wu, Z. et al. Identification of glutaminyl cyclase isoenzyme isoQC as a regulator of SIRPα–CD47 axis. Cell Res. 29, 502–505 (2019).
Poeta, V. M., Massara, M., Capucetti, A. & Bonecchi, R. Chemokines and chemokine receptors: new targets for cancer immunotherapy. Front. Immunol. 10, 379 (2019).
Sierra-Filardi, E. et al. CCL2 shapes macrophage polarization by GM-CSF and M-CSF: identification of CCL2/CCR2-dependent gene expression profile. J. Immunol. 192, 3858–3867 (2014).
Fridlender, Z. G. et al. Monocyte chemoattractant protein-1 blockade inhibits lung cancer tumor growth by altering macrophage phenotype and activating CD8+ cells. Am. J. Resp. Cell Mol. 44, 230–237 (2011).
Zhu, X., Fujita, M., Snyder, L. A. & Okada, H. Systemic delivery of neutralizing antibody targeting CCL2 for glioma therapy. J. Neurooncol. 104, 83–92 (2011).
Franklin, R. A. et al. The cellular and molecular origin of tumor-associated macrophages. Science 344, 921–925 (2014).
Loyher, P.-L. et al. Macrophages of distinct origins contribute to tumor development in the lung. J. Exp. Med. 215, 2536–2553 (2018).
Kalbasi, A. et al. Tumor-derived CCL2 mediates resistance to radiotherapy in pancreatic ductal adenocarcinoma. Clin. Cancer Res. 23, 137–148 (2017).
Szymczak, W. A. & Deepe, G. S. The CCL7–CCL2–CCR2 axis regulates IL-4 production in lungs and fungal immunity. J. Immunol. 183, 1964–1974 (2009).
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, gky955 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Henningsson, R., Moratorio, G., Bordería, A. V., Vignuzzi, M. & Fontes, M. DISSEQT—DIStribution-based modeling of SEQuence space time dynamics. Virus Evol. 5, vez028 (2019).
Zappia, L. & Oshlack, A. Clustering trees: a visualisation for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).
Mildner, A. et al. Genomic characterization of murine monocytes reveals C/EBPβ transcription factor dependence of Ly6C− cells. Immunity 46, 849–862 (2017).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
Acknowledgements
The authors acknowledge the following people for technical and intellectual support: Y. Lu, J. Wu, J. Payandeh, I. Lehoux and protein structure/purification teams; B. Haley, J. Lill, W. Sandoval and P. Liu; S. Warming, U. Segal, V. Arumugam, R. Asuncion, T. Scholl, M. Dempsey, K. Veliz, M. Lamoureux, W. Ortiz, M. Reich and animal care-takers and veterinarians; Y. Chestnut, M. Singh and A. Gutierrez; pathology, necropsy and antibody engineering/production teams; A. Cordrey and the cell culture team; and W. Fu for careful reading of the manuscript. This work was funded by Genentech. Illustrations were generated by the authors or adapted from Servier medical art (France) or BioRender.com.
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R.B.d.S. and M.L.A. were responsible for conceptualization. R.B.d.S., R.M.L., S.W., J.O., V.J., Y.W., W.P., C. Everett, J.N., D.A., J.Z., Z.M., J.M.S., M.M. and S.R. were responsible for investigation and methodology development. R.B.d.S., R.M.L., X.P.-J., S.W., J.O., C. Everett, J.N. and J.Z. were responsible for data analysis. C.L., Y.-C.H., J.T.K., I.H., T.H.P. and M.R.-G. were responsible for resources. R.B.d.S., J.M.S., M.M., C. Eidenschenk, I.M. and M.L.A. were responsible for supervision. R.B.d.S. was responsible for writing the original draft. R.B.d.S., C. Eidenschenk., S.R., I.M. and M.L.A. were responsible for review and editing of the manuscript, with edits from all authors.
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Extended data
Extended Data Fig. 1 Monocyte migration is not modulated by DPP4.
a, Quantification of MCPs in plasma of naive WT mice (n = 5). b, NH2-terminal amino acid sequence of MCPs highlighting the presence of a glutamine (Q) in the first position and a proline (P) in the second position. c, Monocyte quantification in blood (n = 5), spleen (n = 4 (+/+) or 5 (–/–)) and bone marrow (n = 4 (+/+) or 5 (–/–)) of naive Dpp4+/+ and Dpp4−/− littermates. NS, P > 0.05. d, Monocyte quantification in tumors extracted from WT mice treated with Ctrl chow or DPP4i (Hepa1-6, n = 11 (Ctrl) or 9 (DPP4i); CT26 (n = 11); B16F10 (n = 10); LL/2 (n = 10)). NS, P > 0.05. e, Human (h)CCL7 was incubated in the absence or presence of hDPP4. Molecular weight profiles of resulting species are shown in Daltons (Da). f, Schematic representation of antibodies that detect MCP forms: anti-pE recognizes NH2-terminal cyclization and anti-ΔQP is specific for the truncated chemokine. Anti-total antibody recognizes the chemokine independently of its N-terminal modification. g, Cross-reactivity profile of anti-mouse (m)CCL7 capture antibodies against mCCL7 forms. AF456 recognizes mCCL7 independently of its N-terminal modification (total); 1G2 binds preferentially to pE-CCL7 and 2F3 binds preferentially to ΔQP-CCL7. Detection was done with biotinylated anti-mCCL7 AF456. AEB, average enzyme per bead. h, Cross-reactivity profile of mCCL2 capture antibodies against mCCL7 and mCCL2 forms. Detection was done with biotinylated anti-mCCL2 MAB479. i, Lower limit of detection (LLOD) of antibodies used to analyze mCCL2 and mCCL7 PTMs. Bars are medians and symbols represent individual mice. Data shown is representative (a,c,g,h) or pooled from 2 experiments (d). All experiments were repeated independently (≥ twice). Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test.
Extended Data Fig. 2 Regulation of MCP function by QPCTL and DPP4.
a, Glutaminyl cyclase activity in serum from the indicated mouse genotypes (n = 18 (WT), 6 (Qpctl–/–, Qpct–/–Qpctl–/–) or 12 (Qpct–/–). NS, P > 0.05, *P < 0.0001. b, Quantification of CCL2 PTMs in plasma from Qpctl–/– and Qpctl+/+ littermates bearing Qpctl–/– and Qpctl+/+ LL/2 tumors, respectively. (n = 9 (Qpctl–/– Total) or 10 (all other groups)). *P = 0.004, **P < 0.0001. c, Quantification of total CCL7 in serum (n = 7 (Qpctl+/+) or 11 (Qpctl–/–)) and plasma (n = 5) from Qpctl+/+ and Qpctl–/– littermates. *P = 0.008, **P < 0.0001. d, Quantification of mCCL7 PTMs in plasma of naive Qpctl–/– and Dpp4–/–Qpctl–/– littermates (n = 9). *P = 0.0002, **P < 0.0001. e, Migration of THP-1 cells to hCCL7 forms placed on the right side of a u-migration chamber. Cellular trajectories moving towards to (blue) or away from (green) the chemokine are shown. f, Akt phosphorylation (pAkt) in THP-1 cells after incubation with media (lane 1); Q-CCL7 (lanes 2 to 4) or ∆QP-CCL7 (lanes 9 and 10). Antagonist activity of ∆QP-CCL7 was evaluated by measuring pAkt following incubation with 10nM of Q-CCL7 and the indicated doses of ∆QP-CCL7 (lanes 5 to 8) or following pre-incubation with ∆QP-CCL7 (lane 11, red asterisk). Bars are medians and symbols represent individual mice. Data shown are representative experiments. All experiments were performed independently (≥ twice) besides f, which was performed once. Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test.
Extended Data Fig. 3 Model of chemokine-mediated peritoneal inflammation.
a,b,c, WT mice were intraperitoneally injected with PBS (∅) or mCCL7 forms. a, Gating strategy for identification of peritoneal leukocytes. b, Frequency of monocytes (n = 9 (∅, Q and pE) or 7 (∆QP)). NS, P > 0.05, *P = 0.0008, **P = 0.0005. c, Number of neutrophils, eosinophils, B cells, T cells and NK cells (n = 9 (∅, Q and pE) or 7 (∆QP)). d, Mouse Q-CCL7 was intraperitoneally injected in Dpp4+/+ or Dpp4–/– littermates. Number of peritoneal monocytes is depicted (n = 5 (∅ Dpp4+/+, Q-CCL7 Dpp4+/+, Q-CCL7 Dpp4–/–) or 4 (∅ Dpp4–/–)). *P = 0.03, **P = 0.008. Bars are medians and, symbols represent individual mice. Data shown is representative (d) or pooled from 2 experiments (b and c). All experiments were repeated (≥ twice). Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test.
Extended Data Fig. 4 Identification of leukocytes in mouse tissues.
a,b,c, Gating strategy for identification of leukocytes in mouse a, blood, b, spleen and c, bone marrow.
Extended Data Fig. 5 Qpctl loss in both hematopoietic or stromal compartments compromises monocytes but not lymphocytes or tissue macrophages.
a, Identification of tissue macrophages in liver, lung and brain sections from naive Qpctl+/+ and Qpctl–/– littermates. Scale is 100µM. Representative images of 5 mice per genotype (experiment done once). b, Leukocyte quantification in blood from Qpctl+/+ and Qpctl–/– littermates (n = 16 (Qpctl+/+) or 19 (Qpctl–/–)). NS, P > 0.05. c, Quantification of splenic patrolling monocytes (CD11b+CD115+Ly6C–CX3CR1+) in Qpctl+/+ and Qpctl–/– littermates (n = 10 (Qpctl+/+) or 9 (Qpctl–/–)). *P = 0.03. d,e, Chimeras were generated by reconstituting lethally irradiated WT or Qpctl–/– mice with WT or Qpctl–/– hematopoietic progenitors. d, Monocyte quantification in blood (n = 17 (WT→WT), 19 (Qpctl–/–→Qpctl–/–) or 20 (WT→Qpctl–/–, Qpctl–/–→WT) and spleen (n = 19 (WT→WT, Qpctl–/–→Qpctl–/–) or 20 (WT→Qpctl–/–, Qpctl–/–→WT). *P < 0.0001. e, Quantification of CCL7 PTMs in plasma (Total CCL7, n = 18 (WT→WT, Qpctl–/–→Qpctl–/–) or 19 (WT→Qpctl–/–, Qpctl–/–→WT); pE-CCL7, n = 19 (Qpctl–/–→Qpctl–/–, WT→Qpctl–/–) or 20 (WT→WT, Qpctl–/–→WT); ∆QP-CCL7, n = 19 (Qpctl–/–→Qpctl–/–, Qpctl–/–→WT) or 20 (WT→WT, WT→Qpctl–/–). NS, P > 0.05, *P = 0.0025, **P = 0.001, ***P = 0.0008, ****P < 0.0001. f, WT (CD45.1) and Qpctl–/– (CD45.2) monocytes were co-transferred into WT (CD45.1/CD45.2) hosts. Quantification of transferred monocytes into the peritoneal cavity following pE-CCL7 injection, normalized to the ratio of WT and Qpctl–/– monocytes transferred (n = 9). g, Quantification of splenic monocytes in Qpctl+/+ and Qpctl–/– littermates treated with Ctrl chow or DPP4i (n = 10). *P = 0.0002. h, Quantification of monocytes in blood and spleen of WT, Dpp4–/–, Qpctl–/– and Dpp4–/–Qpctl–/– littermates (n = 10 (Dpp4–/–) or 9 (WT, Qpctl–/– and Dpp4–/–Qpctl–/–)). *P = 0.0056, **P < 0.0001. Bars are medians and symbols represent individual mice. Data shown is representative (f), or pooled from 2-3 experiments (b,c,d,e,g,h). All experiments were repeated (≥ twice) besides a. Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test.
Extended Data Fig. 6 Characterization of EO771 and LL/2 mouse tumor models.
a, Quantification of Ccl2, Ccl7 and Qpctl RNA expression in LL/2 and EO771 cells. (n = 2 (LL/2) or 1 (EO771) sequenced bulk samples, bars represent means). b, WT mice were inoculated with EO771 (n = 6) or with LL/2 (n = 8) cells. Frequency of tumor-associated leukocytes was quantified at day 14. Bars are medians and symbols individual mice. c, Gating strategy for the identification of leukocytes in tumors. d,e, Bulk CRISPR/Cas9-edited Qpctl–/– LL/2 and EO771 cells were generated. d, Quantification of pE-CCL7 in the cell supernatants, plotted as percentage of total CCL7, measured in the same samples. e, In vitro growth curves of tumor cell lines (n = 2 (EO771) or 1 (LL/2) technical replicates). ∅ - parental cell line; ntc - non-targeted control. Lines are means. Data shown are representative experiments. All experiments were repeated independently (≥ twice), except a, which was done once.
Extended Data Fig. 7 Loss or inhibition of QPCTL impairs monocyte migration.
a, Qpctl+/+ and Qpctl–/– littermates were inoculated with Qpctl+/+ or Qpctl–/– EO771 cells, respectively. Quantification of tumor-associated leukocytes (n = 6). *P = 0.009. b, Gating strategy to identify tumor-infiltrating WT CD45.1+ monocytes transferred into LL/2 tumor-bearing mice. c,d, WT mice received Ctrl vehicle or QPCTLi for 4 days before LL/2 or EO771 inoculation (preventative) or at day 7 after LL/2 inoculation (therapeutic). Quantification of c, splenic monocytes (n = 8 (LL/2 preventative), 8 (EO771 ctrl), 9 (EO771 QPCTLi), 10 (LL/2 Ctrl therapeutic) or 6 (LL/2 QPCTLi therapeutic)), *P = 0.02, **P = 0.004, ***P = 0.0002, and d, tumor-infiltrating macrophages (n = 8 (LL/2 preventative), 8 (EO771 Ctrl), 9 (EO771 QPCTLi), 10 (LL/2 Ctrl therapeutic) or 6 (LL/2 QPCTLi therapeutic)). NS, P > 0.05, *P = 0.01. e,f, WT mice treated with Ctrl Isotype or anti-CSF1R blocking antibody were inoculated with e, EO771 (n = 4), NS, P > 0.05, *P = 0.03 or f, LL/2 cells (n = 7 (Isotype) or 8 (anti-CSF1R)). NS, P > 0.05, *P = 0.0003. Quantification of leukocytes in tumors excised e, 14 days or f, 19 days after tumor cell inoculation. Bars are medians and symbols represent individual mice. Data shown are representative experiments. All experiments were repeated independently (≥ twice). Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test.
Extended Data Fig. 8 Single cell RNA-seq analysis of LL/2 immune infiltrates.
a,b,c,d, Qpctl–/– and Qpctl+/+ littermates were inoculated with Qpctl–/– or Qpctl+/+ LL/2 tumors, respectively. On day 14 after tumor inoculation, tumors were excised and processed for single-cell RNA-seq. a, Number of cells recovered per sample (n = 3). b, UMAP plots of tumor immune infiltrates, from merged samples (n = 6, three samples per genotype). c, Violin plots representing quality control measurements. d, Heatmap showing normalized expression of top 10 expressed genes in each cluster. Bars are medians and symbols represent individual mice. Mono/Mac/DCs: Monocyte/Macrophage/Dendritic Cells.
Extended Data Fig. 9 Loss of Qpctl remodels the tumor myeloid compartments.
a,b,c,d,e,f,g,h,i,j, Qpctl–/– and Qpctl+/+ littermates were inoculated with Qpctl–/– or Qpctl+/+ LL/2 tumors, respectively. On day 14 after tumor inoculation, tumors were excised and processed for single-cell RNA-seq. a, Heatmap showing normalized expression of top 10 expressed genes in each Monocyte/Macrophage/DC cluster. b, Dot plots of selected markers in monocyte populations. Dot size indicates proportion of cells in each cluster expressing a gene, color shading indicates the relative level of gene expression. c, Violin plot representing Ly6c2 expression across Monocyte/Macrophage/DC clusters. d, Violin plot representing the expression of a blood Ly6C+ monocyte signature across Monocyte/Macrophage/DC clusters. e, Dot plots of selected markers in dendritic cell populations. Dot size indicates proportion of cells in each cluster expressing a gene, color shading indicates the relative levels of gene expression. f, Violin plots representing the expression of indicated genes across macrophage clusters. g, Similarity score between the Mac_Ki67 cluster and the other Monocyte/Macrophage/DC clusters. h,i, Violin plots representing the expression of indicated gene signatures across macrophage clusters. j, Violin plot representing Csf1r expression across macrophage clusters.
Extended Data Fig. 10 Loss of Ccl2 and Ccl7 mimics loss of Qpctl in LL/2 tumors.
a,b, CRISPR/Cas9-edited Ccl2–/–Ccl7–/– LL/2 cells were generated. a, Quantification of MCPs in the cell supernatants (n = 1). b, WT mice were inoculated with WT or Ccl2–/–Ccl7–/– LL/2 cells. CCL7 quantification in plasma of naive and tumor-bearing mice (n = 5 (naive and day 14) and 10 (day 19)). *P = 0.008, **P < 0.0001. c,d, WT mice were inoculated with WT, Qpctl–/– or Ccl2–/–Ccl7–/– LL/2 cells. c, Tumor growth measurements (n = 10). Gray lines represent individual mice and overlay of fitting spline cubic curves for each group is shown. *P < 0.0001. d, Tumor weight at day 14 (n = 5). *P = 0.03, **P = 0.008. e,f,g, WT mice were inoculated as described in (c). e, Identification of Ly6C+ monocytic cells (red circle) and F4/80hi macrophages (blue circle) among tumor leukocytes (gated on live, singlets, CD45+CD11b+Ly6G–). f, Quantification of Ly6C+ monocytic cells and macrophages (n = 5). g, Frequency of F4/80+MHCII+ macrophages (n = 5). *P = 0.02, **P = 0.008. h, Qpctl+/+ and Qpctl–/– EO771 bearing mice were treated with Ctrl Isotype or anti-CD8 depletion antibody. Identification of CD8+ T cells among CD3+ T cells in blood, 2 days after antibody injection. i, Qpctl+/+ and Qpctl–/– littermates were inoculated with Qpctl+/+ or Qpctl–/– EO771 tumor cells, respectively, and treated with Ctrl Isotype or anti-PD-L1 blocking antibody. Frequency of tumor-associated GzmB+CD8+ T cells (n = 12 (Qpctl+/+) or 14 (Qpctl–/–)). NS, P > 0.05. Bars are medians and symbols represent individual mice. Data shown is representative (a,b,c,d,f,g,h) or pooled from 2 experiments (i). All experiments were repeated independently (≥ twice). Statistical analysis was done using unpaired, nonparametric Mann-Whitney U-test (b,d,g,i) or Two-way ANOVA with Tukey correction (c).
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Barreira da Silva, R., Leitao, R.M., Pechuan-Jorge, X. et al. Loss of the intracellular enzyme QPCTL limits chemokine function and reshapes myeloid infiltration to augment tumor immunity. Nat Immunol 23, 568–580 (2022). https://doi.org/10.1038/s41590-022-01153-x
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DOI: https://doi.org/10.1038/s41590-022-01153-x
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