IP Library › Granted Patent US 12,618,837
Granted Patent B2
US 12,618,837 · App. 17/596,950 · Granted May 5, 2026

Method of identifying pro-inflammatory dendritic cells

Inventors: Florent Ginhoux (Singapore, SG); Charles Antoine Dutertre (Singapore, SG)
Assignee: Agency for Science, Technology and Research
G01N33/56972G01N2333/70596G01N2800/7095
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Quick Facts
Patent No.
US 12,618,837
App. No.
17/596,950
Granted
May 5, 2026
Kind
B2
Abstract

There is provided a method of identifying pro-inflammatory dendritic cells, the method comprising: determining an expression of CD5, CD14 and/or CD163 in cells, wherein CD5 − , CD14 + and/or GD163 + cells are identified as pro-inflammatory dendritic cells. Also disclosed is a method of characterising inflammation and/or inflammatory disease in a subject, the method comprising: determining a proportion of CD5 − , CD14 + and/or GD163 + dendritic cells in the subject's sample, wherein the proportion positively correlates with the level of inflammation and/or the severity of inflammatory disease in the subject.

Claims (19)

1 . A method of characterising CD1c + dendritic cells, the method comprising:

determining an expression of one or more of CD5, CD14 and CD163 in the dendritic cells,

wherein where the dendritic cells are determined to be CD5−, CD14+ and CD163+, identifying the dendritic cells as pro-inflammatory dendritic cells.

2 . The method according to claim 1 , wherein where the dendritic cells are determined to be CD163+CD14+, identifying the dendritic cells as highly pro-inflammatory dendritic cells that are more pro-inflammatory than CD163− or CD14− dendritic cells.

3 . The method according to claim 1 , the method further comprising determining a ratio of CD163+CD14+ dendritic cells to a total number of dendritic cells in a sample.

4 . The method according to claim 1 , the method further comprising determining an expression of one or more of CD11b, CD36, CD64, CD87, CD107a, CD206, CD274, CD354, FcεRIα, HLA-DQ, CD2, CD59, CD81, CD166, CD229, CD271 and Integrin β7 in the dendritic cells.

5 . The method according to claim 1 , wherein the dendritic cells have one or more of the following properties:

(i) is a conventional CD1c + dendritic cell 2 (cDC2);

(ii) is dependent on IRF4 for differentiation;

(iii) is dependent on KLF4 for differentiation;

(iv) is dependent on FLT3 ligand (FLT3L) for differentiation; and

(v) is capable of activating and/or polarizing T cells.

6 . The method according to claim 1 , the method further comprising determining a ratio of CD163+CD14+ dendritic cells to a total number of dendritic cells having one or more of the properties selected from the group consisting of:

(i) a conventional dendritic cell;

(ii) dependent on IRF4 for differentiation;

(iii) dependent on KLF4 for differentiation;

(iv) dependent on FLT3 ligand (FLT3L) for differentiation; and

(v) capable of activating and/or polarizing T cells.

7 . A kit for characterising CD1c + dendritic cells, inflammation and/or inflammatory disease, the kit comprising reagents for detecting CD5, CD14 and CD163, wherein the kit further comprises methods for identifying dendritic cells to be pro-inflammatory dendritic cells where the dendritic cells are determined to be CD5−, CD14+ and CD163+.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 22, 2021
From: GINHOUX, FLORENT; DUTERTRE, CHARLES ANTOINE
To: AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH
Reel/Frame 058461/0801 →
Priority Claims (1)
SG 10201905956V · Jun 26, 2019 · national
Continuity (1)
Related Publication 20230184762A1 · Jun 15, 2023
References Cited (67)
US 20220397568A1 · Villani · 2022 [cited by examiner]
WO 2018035364A1 · 2018 [cited by applicant]
Yin et al. Human Blood CD1c+ Dendritic Cells Encompass CD5high and CD5low lo Subsets That Differ Significantly in Phenotype. J Immunol 198: 1553-1564 (Jan. 13, 2017). [cited by examiner]
Collin et al. Human dendritic cell subsets: an update. Immunology 154: 3-20 (2018). [cited by examiner]
Aliberti, J., et al., “Essential role for ICSBP in the in vivo development of murine CD8alpha + dendritic cells”, Blood 101, 305-310 (2003). [cited by applicant]
Becher, B., et al., “High-dimensional analysis of the murine myeloid cell system”, Nat Immunol 15, 1181-1189 (2014). [cited by applicant]
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I.W.H., Ng, L.G., Ginhoux, F., and Newell, E.W. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. [cited by applicant]
Becht, E., et al., “Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting”,. Bioinforma. Oxf. Engl. 35, 301-308 (2018). [cited by applicant]
Brynjolfsson, S.F., et al., “An Antibody Against Triggering Receptor Expressed on Myeloid Cells 1 (TREM-1) Dampens Proinflammatory Cytokine Secretion by Lamina Propria Cells from Patients with IBD”, 2016 Inflamm. Bowel … [cited by applicant]
Calzetti, F., et al., “Human dendritic cell subset 4 (DC4) correlates to a subset of CD14dim/−CD16++ monocytes”, 2018 J. Allergy Clin. Immunol. 141, 2276-2279.e3. [cited by applicant]
Chen, H., et al., “Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline”, PLoS Comput. Biol. 12, e1005112 (Sep. 2016). [cited by applicant]
Davis, S., et al., “GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor”, Bioinforma. Oxf. Engl. 23, 1846-1847 (2007). [cited by applicant]
DiGiuseppe, J.A., et al., “PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data”, Cytometry B Clin. Cytom. 94, 588-601 (2018. [cited by applicant]
Dress, R., et al., “Plasmacytoid dendritic cell differentiation is distinct from the myeloid lineage and occurs from Ly6D+ early lymphoid progenitors”, 2019, Nat. Immunol, vol. 20, 852-864. [cited by applicant]
Dutertre, C.-A., et al., “Pivotal role of M-DC8 [cited by applicant]
Dutertre, C.-A., et al., “Aligning bona fide dendritic cell populations across species”, 2014, Cell. Immunol. 291, 3-10. [cited by applicant]
Finck, R., et al., “Normalization of mass cytometry data with bead standards”, Cytom. Part J. Int. Soc. Anal. Cytol. 83, 483-494 (2013). [cited by applicant]
Ginhoux, F., et al., “Tissue-Resident Macrophage Ontogeny and Homeostasis”, Immunity 44, 439-449 (2016). [cited by applicant]
Guilliams, M., et al., “Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny”, Nat Rev Immunol 14, 571-578 (2014). [cited by applicant]
Guilliams, M., et al., “Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species”, Immunity 45, 669-684 (2016). [cited by applicant]
Günther, P. et al., “A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space” ioRxiv (2019). [cited by applicant]
Hamers Anouk A.J., et al., “Human Monocyte Heterogeneity as Revealed by High-Dimensional Mass Cytometry”, Arterioscler. Thromb. Vasc. Biol. 39, 25-36 (2019). [cited by applicant]
Haniffa, M., et al., “Human tissues contain CD141hi cross-presenting dendritic cells with functional homology to mouse CD103+ nonlymphoid dendritic cells”, Immunity 37, 60-73 (2012). [cited by applicant]
Harrow, J., et al., “Gencode: the reference human genome annotation for The Encode”, Project. Genome Res. 22, 1760-1774 (2012). [cited by applicant]
Hildner, K., et al., “Batf3 deficiency reveals a critical role for CD8alpha+ dendritic cells in cytotoxic T cell immunity”, Science 322, 1097-1100 (2008). [cited by applicant]
Klarquist, J., et al., “Dendritic Cells in Systemic Lupus Erythematosus: From Pathogenic Players to Therapeutic Tools”, Mediators Inflamm. 2016, 5045248 (2016). [cited by applicant]
Kuryliszyn-Moskal, A., et al., “Vascular endothelial growth factor in systemic lupus erythematosus: relationship to disease activity, systemic organ manifestation, and nailfold capillaroscopic abnormalities”, Arch. Immu… [cited by applicant]
Lamb, J., et al., “The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease”, Science 313, 1929-1935 (2006). [cited by applicant]
Levine, J.H., et al., “Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis”, Cell 162, 184-197 (2015). [cited by applicant]
Li, H.-H., et al., “Interleukin-20 targets renal mesangial cells and is associated with lupus nephritis”, 2008, Clin. Immunol. Orlando Fla 129, 277-285. [cited by applicant]
Liao, X., et al., “Chemokines and Chemokine Receptors in the Development of Lupus Nephritis”, Mediators Inflamm. 2016, 6012715 (2016). [cited by applicant]
McInnes, L., et al., “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”, ArXiv180203426 Cs Stat (2018). [cited by applicant]
Menon, M., et al., “A Regulatory Feedback between Plasmacytoid Dendritic Cells and Regulatory B Cells Is Aberrant in Systemic Lupus Erythematosus”, Immunity 44, 683-697 (2016). [cited by applicant]
Merad, M., et al., “The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting”, Annu. Rev. Immunol. 31, 563-604 (2013). [cited by applicant]
Newell, E.W., et al., “Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes”, Immunity 36, 142-152 (2012). [cited by applicant]
Newman, A.M., “Robust enumeration of cell subsets from tissue expression profiles”, Nat. Methods 12, 453-457 (2015). [cited by applicant]
Nguyen-Lefebvre, A.T., et al., “The innate immune receptor TREM-1 promotes liver injury and fibrosis”, J. Clin. Invest. 128, 4870-4883 (2018). [cited by applicant]
Nielepkowicz-Goździńska, A., et al., “Exhaled IL-8 in systemic lupus erythematosus with and without pulmonary fibrosis”, Arch. Immunol. Ther. Exp. (Warsz.) 62, 231-238 (2014). [cited by applicant]
Parks, C.G., et al., “Systemic lupus erythematosus and genetic variation in the interleukin 1 gene cluster: a population based study in the southeastern United States”, Ann. Rheum. Dis. 63, 91-94 (2004). [cited by applicant]
Parks, D.R., et al., “A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data”, Cytom. Part J. Int. Soc. Anal. Cytol. 69, 541-551 (2006). [cited by applicant]
Patro, R., et al., “Salmon provides fast and bias-aware quantification of transcript expression”, Nat. Methods 14, 417-419 (2017). [cited by applicant]
Picelli, S., et al., “Full-length RNA-seq from single cells using Smart-seq2”, Nat. Protoc. 9, 171-181 (2014). [cited by applicant]
Rodrigues, P.F., et al., “Distinct progenitor lineages contribute to the heterogeneity of plasmacytoid dendritic cells”, 2018, Nat. Immunol. 19, 711-722. [cited by applicant]
Samy, E., et al., “Targeting BAFF and APRIL in systemic lupus erythematosus and other antibody-associated diseases”, Int. Rev. Immunol. 36, 3-19 (2017). [cited by applicant]
Schlitzer, A., et al., “IRF4 transcription factor-dependent CD11b+ dendritic cells in human and mouse control mucosal IL-17 cytokine responses”, Immunity 38, 970-983 (2013). [cited by applicant]
Schlitzer, A., et al., “Dendritic cells and monocyte-derived cells: Two complementary and integrated functional systems”, Semin. Cell Dev. Biol. 41, 9-22 (2015). [cited by applicant]
Schlitzer, A., et al., “Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow”, Nat. Immunol. 16, 718-728 (2015). [cited by applicant]
Schölkopf, B., et al., “New support vector algorithms”, Neural Comput. 12, 1207-1245 (2000). [cited by applicant]
See, P., Dutertre, et al., “Mapping the human DC lineage through the integration of high-dimensional techniques”, Science 356 (2017). [cited by applicant]
Segura, E., et al., “Human inflammatory dendritic cells induce Th17 cell differentiation”, Immunity 38, 336-348 (2013). [cited by applicant]
Setty, M., et al., “Wishbone identifies bifurcating developmental trajectories from single-cell data”, Nat. Biotechnol. 34, 637-645 (2016). [cited by applicant]
Smyth, G.K. (2004). “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat. Appl. Genet. Mol. Biol. 3, Article3. [cited by applicant]
Smyth, G.K. “Limma: Linear Models for Microarray Data”, In Bioinformatics and Computational Biology Solutions Using R and Bioconductor, R. Gentleman, V.J. Carey, W. Huber, R.A. Irizarry, and S. Dudoit, eds. (New York, N… [cited by applicant]
Sun, F., et al., “Involvement of TWEAK and the NF-κB signaling pathway in lupus nephritis”, Exp. Ther. Med. 15, 2611-2619 (2018). [cited by applicant]
Tan-Garcia, A., et al., “Intrahepatic CD206+ macrophages contribute to inflammation in advanced viral-related liver disease”, 2017, J. Hepatol. 67, 490-500. [cited by applicant]
Tang-Huau, T.-L., et al., “Human in vivo-differentiated monocyte-derived dendritic cells”, Semin. Cell Dev. Biol (2018). [cited by applicant]
Tenenbaum, J.B., et al., “A global geometric framework for nonlinear dimensionality reduction”, 2000, Science 290, 2319-2323. [cited by applicant]
Tussiwand, R., et al., “Klf4 expression in conventional dendritic cells is required for T helper 2 cell responses”, Immunity 42, 916-928 (2015). [cited by applicant]
Van der Maaten, L., et al., “Visualizing data using t-SNE”, J. Mach. Learn. Res. 9, 2579-2605 (2008). [cited by applicant]
Villani, A.-C., et al., “Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors”, Science 356 (2017). [cited by applicant]
Notification of Transmittal of the International Search Report and The Written Opinion of the International Searching Authority, or the Declaration for International Application No. PCT/SG2020/050369, “Method of Identif… [cited by applicant]
Yin, Y., et al., “Human Blood CD1c+ Dendritic Cells Encompass CD5high and CD5low Subsets That Differ Significantly in Phenotype, Gene Expression, and Functions” [cited by applicant]
Dutertre C.-A. et al., “Single-Cell Analysis of Human Mononuclear Phagocytes Reveals Subset-Defining Markers and Identifies Circulating Inflammatory Dendritic Cells”, [cited by applicant]
Collin, M., et al., Human dendritic cell subsets: an update, Cancer Research, May 1, 2018, 3-20, 154/1. [cited by applicant]
Heger, L., et al., “Subsets of CD1c+ DCs: Dendritic Cell Versus Monocyte Lineage”, Frontiers in Immunology, Sep. 30, 2020, 1-11, 11. [cited by applicant]
Helft, J., et al., “Dendritic Cell Lineage Potential in Human Early Hematopoietic Progenitors” Cell Reports, Jul. 1, 2017, 529-537, 20/3. [cited by applicant]
Supplementary European Search Report for EP Application No. EP 20833631, “Method of Identifying Pro-Inflammatory Dendritic Cells” date of completion: Jun. 15, 2023. [cited by applicant]