Abstract
The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.
Original language | English |
---|---|
Article number | 112046 |
Journal | Cell Reports |
Volume | 42 |
Issue number | 2 |
DOIs | |
State | Published - Feb 28 2023 |
Keywords
- CP: Immunology
- CP: Metabolism
- ImmGen
- immunometabolism
- mononuclear phagocytes
- myeloid cells
- network analysis
- single-cell RNA-seq
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In: Cell Reports, Vol. 42, No. 2, 112046, 28.02.2023.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes
AU - ImmGen Consortium
AU - Gainullina, Anastasiia
AU - Mogilenko, Denis A.
AU - Huang, Li Hao
AU - Todorov, Helena
AU - Narang, Vipin
AU - Kim, Ki Wook
AU - Yng, Lim Sheau
AU - Kent, Andrew
AU - Jia, Baosen
AU - Seddu, Kumba
AU - Krchma, Karen
AU - Wu, Jun
AU - Crozat, Karine
AU - Tomasello, Elena
AU - Dress, Regine
AU - See, Peter
AU - Scott, Charlotte
AU - Gibbings, Sophie
AU - Bajpai, Geetika
AU - Desai, Jigar V.
AU - Maier, Barbara
AU - This, Sébastien
AU - Wang, Peter
AU - Aguilar, Stephanie Vargas
AU - Poupel, Lucie
AU - Dussaud, Sébastien
AU - Zhou, Tyng An
AU - Angeli, Veronique
AU - Blander, J. Magarian
AU - Choi, Kyunghee
AU - Dalod, Marc
AU - Dzhagalov, Ivan
AU - Gautier, Emmanuel L.
AU - Jakubzick, Claudia
AU - Lavine, Kory
AU - Lionakis, Michail S.
AU - Paidassi, Helena
AU - Sieweke, Michael H.
AU - Ginhoux, Florent
AU - Guilliams, Martin
AU - Benoist, Christophe
AU - Merad, Miriam
AU - Randolph, Gwendalyn J.
AU - Sergushichev, Alexey
AU - Artyomov, Maxim N.
N1 - Funding Information: We thank Amanda Swain, Monika Bambouskova, and Laura Arthur for constructive comments on the manuscript. This work was supported by ImmGen Consortium grant AI072073 (NIAID, NIH) and in part by the Division of Intramural Research of the NIAID, NIH. The work was also partly supported by R01-AI125618 (NIAID) to M.N.A. D.A.M. was partly supported by a Discovery Grant from the National Psoriasis Foundation (USA). A.G. and A.S. were supported by Ministry of Science and Higher Education of the Russian Federation (Priority 2030 Federal Academic Leadership Program). M.H.S. was partly supported by institutional grants from TU Dresden, Aix-MArseille Université, INSERM, and CNRS; by grants from the “Agence Nationale de la Recherche” (ANR-17-CE15-0007-01 and ANR-18-CE12-0019-03), INCa (13-10/405/AB-LC-HS), and Fondation ARC pour la Recherche sur le Cancer (PGA1 RF20170205515); by an INSERM-Helmholtz cooperation grant; by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement number 695093 MacAge); and by an Alexander von Humboldt Professorship at TU Dresden. L.-H.H. is currently affiliated with Institute of Metabolism & Integrative Biology (IMIB), Fudan University, Shanghai 200437, China. K.-W.K. is currently affiliated with Department of Pharmacology and Regenerative Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA. P.W. is currently affiliated with Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. S.T. is currently affiliated with Center de Recherche de l'Hôpital Maisonneuve-Rosemont, Département de microbiologie, immunologie et infectiologie, Université de Montréal, Montréal, QC, H1T 2M4, Canada. H.T. is currently affiliated with Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent 9052, Belgium. K.C. is currently affiliated with UMR 1236, Université Rennes, INSERM, Etablissement Français du Sang Bretagne, Rennes 35000, France. F.G. is currently affiliated with INSERM U1015, Gustave Roussy Cancer Campus, Villejuif 94800, France. A.S. M.N.A. and A.G. designed the study and interpreted results. A.G. performed all bioinformatic experiments and draw figures. L.-H.H. held the DC migration experiment. D.A.M. performed experiments with cholesterol levels in DCs and arachidonic acid metabolites in macrophages. L.S.Y. A.K. B.J. K.K. J.W. K. Crozat, E.T. R.D. P.S. C.S. S.G. G.B. J.V.D. B.M. S.T. K.-W.K. P.W. S.V.A. V.A. J.M.B. K. Choi, M.D. I.D. E.L.G. C.J. K.L. M.S.L. H.P. M.H.S. L.P. S.D. and T.-A.Z. performed or participated in fluorescence-activated cell sorting as part of ImmGen MNP OS project. C.B. and M.M. oversaw the ImmGen MNP OS logistics and overall data/sample collection. K.S. processed raw ImmGen MNP OS RNA-seq data and produced gene counts table. H.T. performed ImmGen MNP OS gene count table normalization. M.N.A. A.G. M.G. F.G. M.M. G.J.R. V.N. D.A.M. and A.S. participated in study discussion and provided critical insights to the study. M.N.A. and A.G. wrote the manuscript and all the authors contributed to editing and suggestions. The authors declare no competing interests. Funding Information: We thank Amanda Swain, Monika Bambouskova, and Laura Arthur for constructive comments on the manuscript. This work was supported by ImmGen Consortium grant AI072073 ( NIAID, NIH ) and in part by the Division of Intramural Research of the NIAID , NIH . The work was also partly supported by R01-AI125618 ( NIAID ) to M.N.A.. D.A.M. was partly supported by a Discovery Grant from the National Psoriasis Foundation (USA). A.G. and A.S. were supported by Ministry of Science and Higher Education of the Russian Federation (Priority 2030 Federal Academic Leadership Program). M.H.S. was partly supported by institutional grants from TU Dresden , Aix-MArseille Université, INSERM, and CNRS ; by grants from the “ Agence Nationale de la Recherche ” ( ANR-17-CE15-0007-01 and ANR-18-CE12-0019-03 ), INCa (13-10/405/AB-LC-HS), and Fondation ARC pour la Recherche sur le Cancer (PGA1 RF20170205515 ); by an INSERM-Helmholtz cooperation grant; by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 695093 MacAge); and by an Alexander von Humboldt Professorship at TU Dresden . L.-H.H. is currently affiliated with Institute of Metabolism & Integrative Biology (IMIB), Fudan University, Shanghai 200437, China. K.-W.K. is currently affiliated with Department of Pharmacology and Regenerative Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA. P.W. is currently affiliated with Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. S.T. is currently affiliated with Center de Recherche de l’Hôpital Maisonneuve-Rosemont, Département de microbiologie, immunologie et infectiologie, Université de Montréal, Montréal, QC, H1T 2M4, Canada. H.T. is currently affiliated with Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent 9052, Belgium. K.C. is currently affiliated with UMR 1236, Université Rennes, INSERM, Etablissement Français du Sang Bretagne, Rennes 35000, France. F.G. is currently affiliated with INSERM U1015, Gustave Roussy Cancer Campus, Villejuif 94800, France. Publisher Copyright: © 2023 The Author(s)
PY - 2023/2/28
Y1 - 2023/2/28
N2 - The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.
AB - The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.
KW - CP: Immunology
KW - CP: Metabolism
KW - ImmGen
KW - immunometabolism
KW - mononuclear phagocytes
KW - myeloid cells
KW - network analysis
KW - single-cell RNA-seq
UR - http://www.scopus.com/inward/record.url?scp=85147216145&partnerID=8YFLogxK
U2 - 10.1016/j.celrep.2023.112046
DO - 10.1016/j.celrep.2023.112046
M3 - Article
C2 - 36708514
AN - SCOPUS:85147216145
SN - 2639-1856
VL - 42
JO - Cell Reports
JF - Cell Reports
IS - 2
M1 - 112046
ER -