Gene expression profiles and transcriptional regulatory
pathways underlying mouse tissue macrophage identity
and diversity
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Citation
Gautier, Emmanuel L, Tal Shay, Jennifer Miller, Melanie Greter,
Claudia Jakubzick, Stoyan Ivanov, Julie Helft, et al. “Geneexpression profiles and transcriptional regulatory pathways that
underlie the identity and diversity of mouse tissue macrophages.”
Nature Immunology 13, no. 11 (September 30, 2012): 11181128.
As Published
http://dx.doi.org/10.1038/ni.2419
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Nature Publishing Group
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Author's final manuscript
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Thu May 26 22:46:45 EDT 2016
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http://hdl.handle.net/1721.1/80773
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Gene expression profiles and transcriptional regulatory pathways underlying mouse tissue macrophage
identity and diversity
Emmanuel L. Gautier1,4,8, Tal Shay2, Jennifer Miller3,4, Melanie Greter3,4, Claudia Jakubzick1,4, Stoyan Ivanov8,
Julie Helft3,4, Andrew Chow3,4, Kutlu G. Elpek5,6, Simon Gordonov7, Amin R. Mazloom7, Avi Ma’ayan7, WeiJen Chua8, Ted H. Hansen8, Shannon J. Turley5,6, Miriam Merad3,4, Gwendalyn J Randolph1,4,8 and the
Immunological Genome Consortium.
1
Department of Developmental and Regenerative Biology,
3
Department of Oncological Sciences and
Department of Medicine, 7Department of Pharmacology and Systems Therapeutics & Systems Biology Center
New York (SBCNY) and 4the Immunology Institute, Mount Sinai School of Medicine, New York, NY, USA.
2
Broad Institute, Cambridge, MA, USA. 5Department of Microbiology and Immunobiology, Harvard Medical
School, Boston, MA, USA. 6Department of Cancer Immunology and AIDS, Dana Farber Cancer Institute,
Boston, MA, USA. 8Department of Pathology & Immunology, Washington University School of Medicine, St.
Louis, MO, USA.
Short title: Unravelling macrophage diversity and identity.
Corresponding author:
Gwendalyn J. Randolph, PhD
Dept of Pathology & Immunology
Washington University School of Medicine.
660 South Euclid Avenue, Campus Box 8118
Saint Louis, MO 63110.
Tel: 314 286-2345
grandolph@path.wustl.edu
Conflict of interest: The authors have declared no conflict of interest related to this work
Abstract
We assessed tissue macrophage gene expression in different mouse organs. Diversity in gene expression among
different populations of macrophages was remarkable. Only a few hundred mRNA transcripts stood out as
selectively expressed by macrophages over DCs and many of these were not present in all macrophages.
Nonetheless, well-characterized surface markers, including MerTK and FcR1 (СD64), along with a cluster of
novel transcripts were distinctly and universally associated with mature tissue macrophages. TCEF3, C/EBP,
BACH1, and CREG-1 were among the top transcriptional regulators predicted to regulate these core
macrophage-associated genes. Other transcription factor mRNAs such as Gata6 were strongly associated with
single macrophage populations. We further illustrate how these transcripts and the proteins they encode
facilitate distinguishing macrophages from DCs.
2
Introduction
The team of immunologists and computational biologists that comprise the Immunological Genome
Project (ImmGen) share the goal of generating an exhaustive definition of gene expression and regulatory
networks of the mouse immune system using shared resources and rigorously controlled data generation
pipelines1. Here, we have turned attention to gene expression and regulatory networks in tissue resident
macrophages.
Macrophages are professional phagocytic cells, often long-lived, that reside in all organs to maintain
tissue integrity, clear debris, and respond rapidly to initiate repair upon injury or innate immunity following
infection2, 3. Accordingly, macrophages are specialized for degrading and detoxifying engulfed cargo and they
are potent secretagogues with the capacity to display an array of phenotypes 4. Macrophages can also present
antigens, but lack the potency in stimulating T cells observed in dendritic cells, and usually fail to mobilize to
lymphoid tissues where naïve T cells are abundant. Partially overlapping functions between macrophages and
dendritic cells, reflected by overlapping molecular profiles, have for decades fueled some debate over the
origins and overall distinction between macrophages and dendritic cells (DCs)5.
In the last several years, significant progress has been made in identifying precursors specific to DCs 6-8.
Moreover, transcription factors have been identified, such as Batf3, which are essential for the development of
some DCs but not required for macrophage specification9. Recent advances have also been made in deciphering
the development of tissue macrophages. Counter to the prevalent idea that monocytes were precursors for tissue
macrophages, some earlier work contended that tissue macrophages arise from primitive hematopoietic
progenitors present in the yolk sac during embryonic development independently of the monocyte lineage10 and
strong support for this contention has very recently emerged through fate-mapping and genetic models11, 12.
Thus, in the adult, maintenance of tissue macrophage involves local proliferation, again independently of
monocytes and definitive hematopoiesis10,
13
. In this context, MAFB/cMAF has been shown to regulate
macrophage self-renewal14. Some transcription factors that drive development of given macrophage types such
as osteoclasts15 or red pulp macrophages16 have also been reported. However, much remains to be revealed
regarding the transcriptional regulatory pathways that control other types of macrophages or global regulatory
3
pathways that govern macrophages as a group of related cells3. The database generated by the Immunological
Genome Project creates a unique resource to compare gene expression profiles and to identify regulatory
pathways that specify or unify macrophage populations from different organs. Our analysis of the macrophage
transcriptome in this context enables the analysis of networks of genes and their regulators that can be used to
better distinguish different types of macrophages and pinpoint the differences between macrophages and DCs.
4
Results
Tissue macrophage diversity
As part of ImmGen, we sorted several tissue macrophage populations from C57BL/6J mice according to
strict, standardized procedures and analyzed these populations using whole-mouse genome Affymetrix Mouse
Gene 1.0 ST Arrays. Sorting strategies for these populations can be found on the ImmGen website
(http://immgen.org), and gene expression data are deposited in the Gene Expression Omnibus (GEO, accession
# GSE15907).
We began our analysis by examining the gene expression profiles of resting macrophage populations
that have historically been highly characterized and accepted as bona fide resident tissue macrophages
12
.
Though some classical macrophages, such as Kupffer cells of the liver and metallophilic as well marginal zone
macrophages of the spleen proved elusive for definitive identification and/or isolation by flow cytometric cell
sorting, four resting macrophage populations submitted to Immgen met the criteria of bona fide macrophage
populations: peritoneal macrophages, red pulp splenic macrophages, lung macrophages, and microglia (brain
macrophages). We thus focused our initial analysis on these four key macrophage populations. Principal
component analysis (PCA) of all genes expressed by the four sorted populations and several DC populations
revealed a relatively greater distance between the different macrophages compared with DCs (Fig. 1a). Pearson
correlation values were high for replicates within a given DC or macrophage population as per the quality
control standards of Immgen; variability within replicates for a single population varied from 0.908 ± 0.048 for
microglia to 0.995 ± 0.001 for peritoneal macrophages. Pearson correlations in gene expression profiles between
different populations of DCs yielded coefficients ranging from 0.877 (liver CD11b + versus spleen CD8+ DCs) to
0.966 (spleen CD4+CD11b+ versus spleen CD8+ DCs) (mean of all DC populations 0.931), whereas the
correlation coefficients between different tissue macrophages ranged from 0.784 (peritoneal versus splenic red
pulp) to 0.863 (peritoneal versus lung) with a mean of 0.812 (Fig. 1b). Several thousand mRNA transcripts were
differentially expressed by at least 2-fold when, for example, lung macrophages were compared to red pulp
splenic macrophages (Fig. 1c). This degree of diversity was greater than that observed when DCs from different
subsets (CD103+ versus CD11b+) were compared from different organs (Fig. 1c). Finally, a dendrogram applied
5
to the various populations showed that DCs clustered more closely than did macrophages (Fig. 1d), and this was
true whether we considered all gene transcripts in the array (data not shown) or only the top 15% ranked by
cross-population max/min ratio or coefficient of variation (Fig. 1d). Overall, these comparisons indicate a
pronounced diversity among tissue macrophage populations.
Distinct molecular signatures among tissue macrophages
The diversity among these four classical macrophage populations extended to gene families previously
associated with macrophage function - chemokine receptors, Toll-like receptors, C-type lectins, and efferocytic
receptors. For example, at least one distinct chemokine receptor observed in each population was prominently
expressed above the others (Supplementary Fig. 1a). Diversity among Toll-like receptors, C-type lectin domain
members and efferocytic receptors was also remarkable (Supplementary Fig. 1b-d). Indeed, only a few of the
mRNA transcripts profiled in these categories, including mRNA encoding the Mer tyrosine kinase receptor
(MerTK) involved in phagocytosis of apoptotic cells17, and toll-like receptors Tlr4, Tlr7, Tlr8, and Tlr13,
showed relatively uniform expression across all macrophages compared. Hundreds of mRNA transcripts were
selectively increased or decreased by at least 2-fold in only one of the macrophage populations (Fig. 2a), and
microglia in particular displayed low expression of hundreds of transcripts that were expressed in other
macrophage populations (Fig. 2a). Using Ingenuity pathway analysis tools, we found each specific signature to
be enriched in groups of transcripts with predicted specific functions, including those with oxidative metabolism
in brain macrophages, lipid metabolism in lung macrophages, eicosanoid signaling in peritoneal macrophages,
and readiness for interferon responsiveness in red pulp macrophages (Supplementary Table 1). Considering
that the gene expression profiles of four macrophage populations were simultaneously compared, the number of
transcripts that were increased or decreased ≥ 5-fold in only one macrophage population relative to all three of
the others was striking (Fig. 2b). We also noted that many transcripts were especially strongly reduced in only
one population compared to the others (Supplementary Fig. 2). Several transcription factors were markedly
increased in just one of the four macrophage populations (Fig. 2c). For example, Spic was restricted to splenic
red pulp macrophages, fitting with previous work revealing that this transcription factor plays a critical role in
6
controlling the development of these cells16. Diversity at the gene expression level translated to the protein level.
For example, CD11a and EPCAM were detected on lung macrophages but not microglia, spleen or peritoneal
macrophages; VCAM-1 and CD31 were selectively displayed by spleen macrophages; CD93 and ICAM-2 were
expressed by peritoneal macrophages but not the others macrophages; and CX3CR1 and SiglecH were
selectively found in microglia (Fig. 2d). All together, these data indicate that macrophage populations in
different organs express many unique mRNA transcripts that equip them for specialized local functions.
Identification of a core macrophage signature
In the midst of the rather vast diversity among macrophages from different organs, we next wondered if
we could identify a core gene expression profile that generally unified macrophages over other types of immune
cells. Among all hematopoietic cells, the cells anticipated to be most similar to macrophages are DCs 5. To
search for mRNA transcripts that distinguished macrophages from DCs, we compared the four selected
prototypical macrophage populations to the most well-defined classical DC populations, including resting CD8+
and CD4+CD11b+ splenic DCs, CD103+ tissue DCs and various populations of lymph node MHC-IIhi CD11c+
migratory DCs18, 19. Because tissue CD11b+ DCs may be contaminated with macrophages20, tissue CD11b+ DCs
were initially excluded from the comparison. This comparison revealed only 14 transcripts that were expressed
in all 4 macrophages but not expressed in DCs (Table 1, upper left column, bolded gene names). These included
few of those anticipated to be highly expressed in macrophages, including Fcgr1 (coding for CD64) and Tlr4.
Two of these molecules, G-CSF receptor (Csf3r) and the MHC-I-related gene Mr1 involved in activation of
mucosal-associated invariant T (MAIT) cells21, function at least partly at the cell surface. In agreement with the
pattern of mRNA expression, we found MR1 protein on spleen and lung macrophages but not classical DCs
(Supplementary Fig. 3), suggesting that MR1 on macrophages rather than DCs may drive MAIT cell
activation. Other transcripts encode proteins involved in signal transduction, such as Fert2 encoding the fms/pfsrelated protein kinase, or in metabolism and lipid homeostasis such as peroxisomal trans-2-enoyl-CoA reductase
(Pecr) and alkyl glycerol monooxygenase (Tmem195), the latter being the only enzyme that cleaves the O-alkyl
7
bond of ether lipids like platelet-activating factor that has been shown to be actively catabolized in association
with macrophage differentiation in vitro 22.
To this small number of mRNA transcripts, we added probe sets that were not absent in expression by
DCs, but were at least 2-fold lower in signal intensity in all single DC populations than the lowest intensity of
that same probe set in each macrophage population. Thus, we were able to add 25 more transcripts to this
“macrophage core” list (Table 1, lower left column, non-bolded gene names; Supplementary Table 2 includes
mean expression values for these transcripts), including those known to be associated with macrophages like
Cd14, Mertk, Fcrg3 (coding for CD16) and Ctsd (coding for Cathepsin D).
F4/80, encoded by the gene Emr1, has served as the most definitive marker of macrophages to date
5, 12
.
However, in order to identify additional mRNA transcripts widely associated with macrophages with the core
list of macrophage-associated genes, including Emr1, Mafb, and Cebpb, we found it necessary to adjust the
criteria of the above approach to include transcripts expressed in only 3 of 4 macrophage populations because,
for instance, Emr1 mRNA was low in microglia. Making this adjustment expanded the list of mRNA transcripts
associated with macrophages, adding another 93 genes (Table 1). Additional macrophage-associated genes like
Mrc1 (coding for CD206, mannose receptor), Marco and Pparg were not identified until we loosened the
criteria such that only 2 out of 4 prototypic macrophages needed to express a given transcript that was otherwise
absent or low on DCs (Table 2; Supplementary Table 3 includes expression values for these transcripts). The
mRNA encoding Cd68, widely used to identify tissue macrophages, was expressed at similar levels in DCs and
macrophages and so excluded from the list. However, at the protein level, it was still several orders of
magnitude higher in macrophages than in DCs of the spleen (Supplemental Fig. 4), an organ where mRNA
levels were scarcely different. In summary, numerous transcripts, 366 altogether (Tables 1 and 2), were absent
or markedly reduced in classical DCs relative to macrophages. However, because of the great diversity among
macrophages, only 39 of these transcripts are shared by all tissue macrophages we compared.
Co-expressed genes and predicted transcriptional regulators
8
The computational biology groups of the ImmGen project analyzed the transcriptional program of the
entire large database generated in the ImmGen project (Jojic et al, in preparation; Supplementary Note 1). First,
mRNA transcripts were clustered into 334 fine modules based on patterns of co-expression. Then a novel
algorithm termed Ontogenet, developed for the ImmGen dataset, was applied to find a regulatory program for
each fine module, based on its expression pattern, the expression pattern of regulators and the position of the
cells on the hematopoietic lineage tree. ImmGen modules, including the gene lists in each module, and
regulatory program metadata are available online (http://www.immgen.org/ModsRegs/modules.html), and the
numbering of the modules reported on the website is used herein.
When the list of the 366 mRNA transcripts associated with macrophages was mapped according to their
placement into various fine modules, 14 modules were significantly enriched for the macrophage-associated
gene signature we identified (Fig. 3a). In particular, the 11 genes that comprise module 161 were significantly
induced in all 4 macrophages used to generate the list of macrophage-associated genes (Fig. 3a). Other modules,
such as module 165, contained genes significantly induced in several specific groups of macrophages, but not in
all (Fig. 3a). The 11 genes that comprise module 161 (A930039a15Rik, Akr1b10, Blvrb, Camk1, Glul, Myo7a,
Nln, Pcyox1, Pla2g15, Pon3, Slc48a) are involved in redox regulation, heme biology, lipid metabolism, and
vesicular trafficking (Supplementary Table 4). Beyond the comparison to DCs, genes in module 161,
expressed in all macrophages, were not expressed by any other hematopoietic cell types including granulocytes
(GN) or any of the blood monocyte (MO) subsets (Fig. 3b), importantly indicating that this list of genes is
selectively associated with mature macrophage differentiation in the hematopoietic system.
As a framework for future studies on the transcriptional control of macrophage development,
maintenance and function, we examined the predicted activators Ontogenet assigned to the modules associated
with the macrophage core genes. As a specific example, the activators predicted by Ontogenet algorithm to
control the expression of the 11 gene transcripts that form module 161 are listed (Fig. 3b). Overall, a highly
overlapping set of 22 regulators emerged in the 14 macrophage-associated modules (Fig. 3c). In particular,
TCFE3, C/EBP and BACH1 were predicted activators in a majority of these modules (>75%) and especially
novel regulators like CREG-1 also came up prominently. Among the 22 regulators associated with the 14
9
modules, 18 of them are predicted using Ingenuity pathway tools to interact in a regulatory network based on
known protein-protein interactions or mutual transcriptional regulation (Fig. 3d). These regulators represent 5
main families of transcriptional factors as depicted in Fig. 3d. The statistical evaluation score generated for this
network revealed a P-value ≤ 10-35.
Beyond modules of genes that unified the 4 tissue macrophage populations we studied, several modules
were selectively associated with a single macrophage population (Supplementary Table 5). In these specific
modules, predicted regulators included SPIC for red pulp macrophages, confirming a regulation that is already
known
16
and thus supporting the predictive power of the algorithm, and GATA6 as a regulator of peritoneal
macrophages (Supplementary Table 6).
Using the core signature to identify macrophages
Finally, we utilized the resting macrophage core signature defined above to assess mononuclear
phagocyte populations that we earlier excluded from our core analysis due to low levels of information on a
given population or controversial discussions in the literature about origins or functional properties, including
whether they should be classified as DCs or macrophages. In the ImmGen database (http://www.immgen.org),
each population was assigned a classification as DCs or macrophage (Mac) a priori. For clarity and consistency
with the database, the names of these populations will be used below and in Fig. 4. These populations included
resting and thioglycollate-elicited (Thio) mononuclear phagocytes that express CD11c and MHC II
(Supplementary Fig. 5), skin Langerhans cells, bone marrow macrophages23, and putative CD11b+ tissue DCs
including those in the liver and gut. All thioglycollate-elicited cells from the peritoneal cavity, even those coexpressing CD11c and MHC II, strongly expressed genes in the macrophage core, including module 161 itself,
similar to the prototypic macrophage populations used to generate the core (Fig. 4a, 4b), indicating that these
cells are indeed macrophages despite co-expression of CD11c and MHC II. On the other hand, Langerhans cells,
and CD11c+ MHC-II+ CD11b+ cells from the liver (CD11b+ liver DCs in the ImmGen database), did not
robustly express the macrophage core signature or module 161 alone, nor did bone marrow macrophages (Fig.
4a, 4b). CD11c+ MHC-II+ CD11b+CD103- cells from the intestinal lamina propria and CD11clo MHC-II+
10
CD11b+ cells from the serosa that previously were called DCs in many studies, expressed macrophage core
genes including those from module 161, suggesting a strong relationship to macrophages (Fig. 4a, 4b).
Accordingly, these cells are now called CD11b+ gut macrophages and CD11clo serosal macrophages herein and
on the Immgen website. We clustered these mononuclear phagocytes based on their expression of the 39-gene
macrophage core to model their relatedness to each other (Fig. 4c). Langerhans cells of the skin and bone
marrow macrophages were positioned at the interface between DCs and macrophages, with a distal relationship
to classical DCs but failing to cluster with macrophages (Fig. 4c).
As mentioned earlier, non-lymphoid tissue CD11b+ DCs have been argued to be heterogeneous20. Thus,
we reasoned that the use of antibodies to cell surface proteins identified as macrophage-specific from our gene
expression analysis may discriminate macrophage “contaminants” in a heterogeneous population. Furthermore,
we aimed to determine if the same cell surface markers may also prove valuable in identifying macrophages
universally, including in organs beyond those we initially analyzed and/or where F4/80 has not proved
sufficiently definitive. We homed in on CD14, FcRI (CD64), and MerTK as cell surface proteins in the group
of 39 mRNA transcripts deemed to be low or absent in DCs but present in all macrophages, and to which quality
mAbs have been generated. Indeed, all of these proteins were expressed on all of the 4 resident macrophage
populations used in our primary analysis (Fig. 5a), with lower expression of CD14 compared with CD64 and
MerTK (Fig. 5a). Two of these tissues, spleen and lung, have significant DC populations. In the spleen, MerTK,
CD64, and CD14 did not stain CD8+ or CD11b+ DCs (Fig. 5a). However, in the lung where interstitial
pulmonary macrophages are CD11b-, there may still be an underlying heterogeneity in lung CD11b+ DCs that
includes a subset of CD11b+ macrophages 20, 24, 25. Indeed, CD14, CD64 and MerTK were expressed by a portion
of lung CD11b+ DCs, but not by CD103+ DCs (Fig. 5a). Gating on MerTK+ CD64+ cells revealed the vast
majority of such cells were SiglecF+ lung macrophages, but a small proportion of MerTK+CD64+ cells in the
lung were SiglecF- cells expressing high levels of MHC class II (Fig. 5b). Fig. 5c shows the usual gating
strategy for lung DCs, with DCs defined as Siglec F- CD11c+ MHC II+ cells. However, the small population of
SiglecF- MerTK+ CD64+ cells that may instead be macrophages (Fig. 5b) partially falls into the standard DC
gate (Fig. 5c, right dot plot). Indeed, CD11b+ DCs could be divided into CD11b+ CD24+ CD64lo MerTK11
CD14int and CD11b+ CD24lo CD64+ MerTK+ CD14hi cells (Fig. 5d). Thus, the latter likely comprises a
population of macrophages that cosegregates with DCs using many markers, but are not DCs. Indeed, the
CD11b+ DCs were segregated within Immgen on the basis of CD24 expression based on the likelihood that
those expressing CD24 were true DCs, but those without CD24 were not. Our findings suggest that this
possibility is highly likely and points to the utility of using markers like MerTK and CD64 as a panel to
facilitate the identification of macrophages from DCs.
We next turned to two tissues—liver and adipose tissue--that were not analyzed by Immgen with respect
to gene expression profiling in macrophages to determine if the use of MerTK and CD64 staining would
facilitate the identification of macrophages in those organs and distinguish them from DCs. In liver, we started
with a classical approach of plotting F4/80 versus CD11c. Eosinophils are now recognized as high side-scatter,
F4/80+ cells that express Siglec F universally
macrophages in the lung
27, 28
26
. Indeed, among macrophages, Siglec F is observed only on
(as used to identify lung macrophages here; Supplementary Fig. 6 demonstrates
that eosinophils did not contaminate lung macrophages, which were separated from eosinophils due to high
CD11c and relative lack of CD11b expression in the macrophages). In the liver, the level of F4/80 on
eosinophils overlaid with that of another population of F4/80+ cells (those with low side scatter) that were
CD11clo in liver (Fig. 5e, left plot). Even after excluding eosinophils, four gates of cells expressing varying
levels of F4/80 and CD11c were found (Fig. 5e). MerTK and CD64 was highly expressed in two of these gates,
suggesting that the cells with the highest level of F4/80 (gate 2) and many that expressed lower F4/80 (in gate 3)
were two populations of F4/80hi and F4/80lo liver macrophages, corresponding to the two types of macrophages
believed to be present in many organs
12
. The liver CD45+ cells with highest CD11c were MerTK-CD64-,
suggesting they were liver DCs (Fig. 5e). Reverse gating revealed that all MerTK+CD64+ cells fell into one of
the two putative macrophage gates (Fig. 5e). Gate 1 without eosinophils likely contains blood monocytes, which
were not positive for MerTK. A relatively similar picture was seen in adipose tissue (Fig. 5f), where the cells
with highest F4/80 were MerTK+CD64+ and those with higher CD11c and lower F4/80 were MerTK-CD64-. In
both liver and adipose tissue, MHC II was high on macrophages and DCs (Fig. 5e, f). Because F4/80 and
CD11c are both expressed by many tissue macrophages and DCs, albeit at levels that are somewhat different,
12
distinguishing macrophages and DCs based on these traditional markers can be difficult. MerTK and CD64
staining offers the advantage of sharp differences in the magnitude of expression between macrophages and
DCs. Thus, we propose that MerTK and CD64 costaining provides a powerful approach to identifying
macrophages universally and selectively in mouse tissues.
13
Discussion
The large and unique database and accompanying bioinformatic analysis of the Immunological Genome
Project provide novel insight into macrophage populations isolated from various organs of mice. A striking
initial revelation was that macrophage populations from different organs are considerably diverse, and it is likely
that further profiling in macrophages will expand upon this diversity. Only a very small group of mRNA
transcripts were associated with all macrophages but not DCs. Proteins previously predicted to distinguish
macrophages from other cell types, such as F4/80, CD68 and CD115 (C-fms/Csf1r), did not emerge as the most
powerful markers of macrophages. However, many canonical genes did, including those encoding CD14, the
high-affinity Fc receptor I CD64, the Mer tyrosine kinase involved in efferocytosis MerTK, cathespin D, and a
fms/fps protein kinase FERT2 that may strongly impact CD115 signaling (but which has not yet been studied in
macrophages). The identification of these genes as selectively macrophage-associated reinforce the key role of
macrophages in innate immunity, efferocytosis, and clearance of debris, whereas genes associated with antigen
presentation and migration to lymphoid tissues were more associated with DCs 29. However, our data do suggest
that macrophages may have a greater role in activation of MAIT cells than DCs. Based upon follow-up protein
expression analysis of MerTK and CD64 in macrophages from six different tissues, we propose that analysis of
MerTK and CD64 should serve as a starting point for identifying macrophages in tissues, as staining for these
markers appears to identify F4/80hi macrophages and other macrophages that express somewhat lower amounts
of F4/8012 in all tissues. We believe staining for MerTK and CD64 has advantage over, but can also powerfully
be used in addition to, traditional staining for F4/80, CD11c, and MHC class II. F4/80 and CD11c expression are
often overlapping between macrophages and DCs in nonlymphoid tissues, but it appears that DCs do not coexpress MerTK and CD64.
Beyond these cell surface markers closely associated with macrophage identity, we uncover other
transcripts associated only with macrophages among hematopoietic cells. In particular, immGen module 161
identified a group of genes (A930039a15Rik, Akr1b10, Blvrb, Camk1, Glul, Myo7a, Nln, Pcyox1, Pla2g15,
Pon3, Slc48a) that are co-expressed across all the ImmGen dataset and whose functions with the established
14
broad roles of macrophages, but none of them have previously been considered macrophage markers. Both the
genes from this module and their predicted regulators deserve attention in the future.
The Ontogenet algorithm makes it possible to extend the macrophage-associated genes we identified to
regulatory programs that may control them. Induced expression of a single module (#330) in red pulp
macrophages over all other macrophages and the predictions generated by the algorithm that this module is
regulated by SPI-C support the reliability of the algorithm predicted regulatory programs, as SPI-C is already
known to be required selectively for red pulp macrophage development or maintenance16. Exciting new
information also emerged, such as strong association of modules unique to peritoneal macrophages that are
predicted to be regulated by GATA6.
Gene transcripts that were highly expressed in multiple macrophage populations but not highly
expressed in DCs were associated with predicted transcriptional regulatory programs that strongly differed from
those uncovered in DCs 29. The predicted regulatory programs of modules enriched for macrophage-associated
genes include several members of the MiT family of transcription factors that has been recognized to be
specifically expressed in macrophages3 as well as transcription factors not previously associated with
macrophages, such as BACH1 and CREG-1. BACH1 has been little studied in macrophages but has recently
been linked to osteoclastogenesis30 and is a regulator of heme oxygenase 1
E1A-stimulated genes) is a secreted regulator32,
33
31
. CREG1 (cellular repressor of
associated broadly with differentiation34 and cellular
senescence35 that was strongly associated with macrophage-enriched gene modules, though it has never been
studied in the context of macrophage biology. The Ontogenet algorithm predicts RXR as the most prominent
key activator of the highly specific and universal macrophage module genes, module # 161. Future analysis of
these predictions is expected to be highly fruitful in revealing how macrophage identity and function is
controlled.
To date, the Immunological Genome Project has mainly focused on cells recovered from resting,
uninfected mice, where macrophages mainly derive from the yolk sac 12. Macrophage polarization in the context
of infection and inflammation is a topic of great interest that this study has scarcely been able to address beyond
finding that monocytes recruited to the peritoneum in response to thioglycollate upregulate mRNA transcripts
15
observed in resting tissue macrophages, even though monocytes are not precursors for resting tissue
macrophages as they are for inflammatory macrophages. The foundations laid herein suggest that future
additions to the ImmGen database of macrophages recovered during disease states will add enormously to our
understanding of how to manipulate these crucial cells to favor desired outcomes in disease. Based on the great
diversity of macrophages in different organs, which we anticipate will hold up even in inflamed organs, such
studies may be expected to ultimately generate therapeutic approaches to selectively target macrophages in
diseased organs without affecting others cell types.
16
Acknowledgements
We are extremely grateful to all of our colleagues in the ImmGen Consortium and wish to extend special thanks
to Vladimir Jojic, Jeff Ericson, Scott Davis and Christophe Benoist for their critical contributions. We also thank
eBioscience and Affymetrix for material support of the ImmGen Project. We are additionally grateful to Marco
Colonna for provision of mAbs and other reagents used during this study. ImmGen is funded by R24 AI072073
from NIH/NIAID, spearheaded by Christophe Benoist. Additional support for the present body of work was
funded by NIH grants R01AI049653 and R01AI061741 to GJR and NIH grants P50GM071558-03 and
R01DK08854 to AM. EG was supported by a postdoctoral fellowship from the American Heart Association
(10POST4160140). ARM was supported by an NIH postdoctoral fellowship 5T32DA007135-27.
17
Figure legends
Figure 1. Analysis of macrophage diversity. (a) Relative distance between different types of macrophages and
DCs was assessed using principal component analysis. (b) Correlation matrix of macrophages and dendritic
cells based on all genes probes. (c) Examples of the relatively greater diversity between macrophage populations
than DCs were plotted. The number of probes increased by a minimum of 2-fold for each population is
indicated. (d) Hierarchical clustering of macrophages and dendritic cells based on the top 15% most variable
genes.
Figure 2. Unique gene expression profiles of macrophages from different organs. (a) Scatter plots depict in
distinct colors the mRNA transcripts that are ≥ 2-fold increased (left) or decreased (right) in one macrophage
population compared to the remaining three populations. (b) Heat map and gene lists reveal mRNA transcripts
uniquely expressed by single macrophage populations by ≥ 5 fold. (c) Transcription factor mRNA transcripts
increased in only one of the four macrophage populations by ≥ 2 fold. (d) Specific cell surface markers for each
macrophage populations, identified from the gene expression profiling data, were validated by flow cytometry.
Macrophages reacting with the antibodies tested matched the pattern of gene expression observed in (b). Shaded
blue line shows isotype control and red line specific antibody.
Figure 3. Identification of gene modules enriched for macrophage-related gene signatures and their predicted
regulators. (a) The overlap size of ImmGen modules of co-expressed genes with all macrophage-associated
genes signatures (Table 1 and 2) is depicted graphically as a heat map. Only modules significantly enriched for
at least one signature are shown. Stars mark significant overlap size by hypergeometric test. (b) Simplified
hematopoietic tree showing mean expression of genes in module 161 (red – high expression; blue – low
expression). Listed are genes that constitute module 161 (top) and the predicted positive regulators of the
module (bottom). (c) A bar graph listing the positive regulators (activators) predicted by the Ontogenet
algorithm to regulate two or more modules listed in a. The frequency that each factor was associated with the 14
modules is depicted. (d). Physical and regulatory interactions between the 18 most frequently represented
18
regulators across the 14 macrophage-associated modules were interrogated using Ingenuity analysis tools. The
scheme uses arrows to depict links where there are established physical interactions, or known pathways of coactivation or inhibition.
Figure 4. Expression of macrophage core genes by other populations of mononuclear phagocytes. (a) Heat map
depicts the 39 gene transcripts increased in spleen, brain, peritoneal, and lung macrophages compared to
classical and migratory DCs. Genes from module 161 that were included among these 39 genes are segregated
and labeled “161a.” Other members of module 161 that did not meet the criteria for inclusion on Table 1 are
labeled “161b.” Populations of tissue-derived mononuclear phagocytes that were not included in the generation
of this list of genes are shown in the middle of the heat map. Various subsets of blood monocytes and
plasmacytoid DCs are depicted further to the right on the heat map. (b) The frequency that the 39 genes were
expressed in these populations at a signal intensity at least 2-fold higher than the highest expressing DC in the
original comparison is depicted. (c) A dendrogram depicting the relationship between a wide variety of
mononuclear phagocytes based on their expression of the list of 39 common macrophage-enriched genes.
Figure 5. Examination of macrophage core transcripts at the protein level in multiple tissues. (a) Histograms of
brain, peritoneum, spleen, and lung stained for CD14, CD64, and MerTK to examine expression in macrophages
and DCs from these organs. Shaded blue line shows isotype control and red line specific antibody. (b)
MerTK+CD64+ cells were more than 95% Siglec F+ macrophages, but some MHCII+ cells lacking Siglec F
expression were also found in this population. (c) Lung DC gating strategy is shown, with DCs being CD45+
cells expressing CD11c and MHCII and lacking Siglec F. (d) Gating on lung DCs revealed a significant
reactivity for CD14, CD64, and MerTK in the CD11b+ CD24- putative DCs, but lack of MerTK and CD64 in
CD24+ CD11b+ DCs. (e) Liver CD45+ cells were plotted to show F4/80 and CD11c staining. Eosinophils were
gated (Siglec F+ high SSC+) to reveal that they overlay with another population in gate 1. Replotting gates
without eosinophils revealed 4 subsets of cells that differentially express F4/80 and CD11c. These 4 gates were
examined with regard to expression of MerTK, CD64, and MHC II. Finally, reverse gating on MerTK + CD64+
19
cells was carried and these gated cells were plotted based on F4/80 and CD11c. (f) A similar approach than in
“e” was carried out here in adipose tissue. Each analysis in this figure was based on studies from at least two
replicative experiments with 3 mice per group.
20
Table 1. Gene induced in tissue macrophages relative to classical and migratory DCs
All 4 M populations
All 4 M populations
Pecr
Tmem195
Ptplad2
1810011H11Rik
Fert2
Tlr4
Pon3
Mr1
Arsg
Fcgr1
Camk1
Fgd4
Sqrdl
Csf3r
Except Peritoneal M
Except Lung M
Except Microglia
Xrcc5
Gm4878
Slco2b1
Gpr77
Gpr160
P2ry13
Tanc2
Sepn1
Mafb
Itga9
Cmklr1
Fez2
Tspan4
Abcc3
Nr1d1
Ptprm
Ctsf
Tfpi
Hgf
Pilrb2
Mgst1
Klra2
Rnasel
Fcgr4
Rhoq
Fpr1
Cd302
Slc7a2
Slc16a7
Slc16a10
Slpi
Mitf
Snx24
Lyplal1
St7
Il1a
Asph
Dnase2a
Slc38a7
Siglece
Itgb5
Rhob
Mavs
Atp13a2
Slc29a1
Slc15a3
Tmem86a
Tgfbr2
Tnfrsf21
Ptgs1
C1qa
Engase
C1qb
C1qc
Timp2
Slc11a1
4632428N05Rik
Sesn1
Plxnb2
Apoe
Except Splenic
pulp M
Cd151
Lonrf3
Acy1
red
C5ar1
Pld1
Gpr177
Arsk
Plod3
Cd33
Cebpb
Atp6ap1
Pros1
Dhrs3
Rnf13
Man2b2
Ltc4s
Plod1
Tom1
Myo7a
A930039A15Rik
Tlr8
Pld3
Gbp6
Tpp1
6430548M08Rik
Ctsd
C130050O18Rik
Pla2g15
Pilra
Lamp2
Pilrb1
Pla2g4a
Lpl
MerTK
Pstpip2
Tlr7
Serpinb6a
Cd14
Slc38a6
Tbxas1
Abcc5
Fcgr3
Lrp1
Sepp1
Pcyox1
Glul
Hmox1
Cd164
Slc17a5
Tcn2
Emr1
Dok3
Hgsnat
Ctsl
Tspan14
Comt1
Tmem77
Abca1
Bolded genes depict those whose signal intensities indicated that DC populations did not express them. Nonbolded genes were expressed in DCs, but more
highly expressed in macrophages (M). See methods for full description of how gene expression comparisons were executed and cut-offs were generated.
21
Table 2. Macrophage-induced genes present in 2 out of 4 tissue macrophage populations
Peritoneal
+
Splenic red pulp
Ccl24
Gstk1
Aspa
2810405K02Rik
B430306N03Rik
Fcna
Gm5970
Aoah
Cd5l
Gm4951
Nr1d1
Mlkl
Vnn3
Igf1
Ptgis
Pitpnc1
Fam43a
Itsn1
Ifi27l1
Rasgrp2
Aldh6a1
Epb4.1l1
Cryzl1
Lrp12
Cd300ld
Pla2g7
Cfp
Sdc3
Dusp7
Tbc1d2b
Igsf6
Man2a1
Zswim6
Ifnar2
Trf
Blvrb
Cd38
Ctsb
Tmem87b
Itfg3
Ninj1
Peritoneal + Lung
Marco
P2ry2
Aifm2
Clec4e
Plcb1
Kcnn3
Arhgap24
Cd93
Fundc2
Tspan32
Lmbr1
Adarb1
Fzd4
F7
Ccr1
Hspa12a
Cav1
Nt5e
1190002a17rik
Cav2
Gda
Frrs1
Tspan5
Pdk4
Slc36a4
Fam3c
Ms4a8a
Atoh1
Alox5
Thbd
Gstm1
Cxcl2
Nhlrc3
Fry
F10
Sord
Ncf2
Hexa
Dram1
Plaur
G6pdx
Fn1
Cybb
Dennd4c
Mpp1
S100a1
Gsr
Abcd2
Dab2
Ccl6
Sepx1
Prdx5
Dusp3
Pgd
Gp49a
Capg
Cndp2
Vps13c
Adipor2
App
Atg7
Cebpb
Lung + Splenic
red pulp
Peritoneal
Microglia
Dmxl2
Dip2c
Galnt3
Niacr1
Bckdhb
Angptl4
Lrp4
Sh3bgrl2
Gm5150
Tcfec
Sh2d1b1
Galnt6
Pdgfc
Hnmt
Mtus1
C3ar1
Dagla
Wrb
Gab1
Fkbp9
6720489N17Rik
Pparg
Megf9
Adcy3
Enpp1
Il18
Siglec1
Clec4n
Lgals8
Nceh1
Lipa
4931406c07rik
Sirpa
Rasgef1b
Wdfy3
Ermp1
Asah1
Ear1
Ear10
Ano6
Mrc1
Camk2d
Gab3
Syne2
Axl
Tcf7l2
Ctsc
D730040f13rik
Slc15a3
Plk3
Hebp1
Dst
Blvra
Sort1
Slc12a7
Clec4a3
Rab11fip5
6230427j02rik
Scn1b
Scamp1
Msrb2
Abca9
Plxdc2
Adam15
Itgam
Itga6
Vkorc1
1700017b05rik
Smad3
Smpd1
Naglu
Pmp22
Man2b2
Tnfrsf1a
Lifr
Tlr13
Slc25a37
Grn
+
Lung + Microglia
Microglia
+
Splenic red pulp
Scamp5
Ppp1r9a
Tppp
Abcb4
Kcnj2
P2ry12
Lhfpl2
Osm
Mgll
Bhlhe41
Ang
D8ertd82e
Slc37a2
Adrb1
Slc16a6
Rab3il1
Mfsd11
Flcn
Tmem63a
P2rx7
Hpgds
Hpgd
Lpcat2
Slc7a8
Maf
Tmem86a
Slc36a1
Gna12
Adap2
Lgmn
Hist1h1c
Lair1
Slc40a1
Csf1r
P4ha1
Iffo1
Dusp6
X99384
Serpine1
Abhd12
Ms4a6d
Cebpa
Lpcat3
Manea
Ctss
Ccl3
Cryl1
Man1c1
Ctns
Sgk1
Pag1
Tgfbr1
Clec5a
22
Bolded genes depict those whose signal intensities indicated that they were not expressed by DC populations used in comparison to spleen, brain,
peritoneal, and lung macrophages. Nonbolded genes were expressed in DCs, but more highly expressed in macrophages .
23
Online methods
Mice
Six-week-old male C57BL/6J mice purchased from Jackson Laboratory were used for sorting and validation.
CX3CR1-GFP knock in mice were from Jackson Laboratories, and Mr1 knockout mice 21 were generated, bred,
maintained at the Washington University School of Medicine. Mice were housed in specific pathogen–free
facilities at the Mount Sinai School of Medicine or Washington University School of Medicine and
experimental procedures were performed in accordance with the animal use oversight committees at these
respective institutions. Most of the populations in the study were sorted from resting mice. However, for
thioglycollate-elicited peritoneal macrophages, macrophages were harvested from the peritoneal cavity 5 days
after instilling 1 ml of 3% thioglycollate.
Cell identification and isolation
All cells were purified using the sorting protocol and antibodies listed on http://www.immgen.org. Cells were
directly sorted from mouse tissues and were processed from tissue procurement to a second round of sorting into
Trizol within 4 h using a Beckton-Dickinson Aria II instrument. Resting red pulp macrophages from the spleen
were sorted after nonenzymatic disaggregation of the spleen and were identified as F4/80hi cells that lacked
B220 and high expression of CD11c and MHC II 36, 37; macrophages from the resting peritoneum were collected
in a peritoneal lavage and stained to identify CD115hi cells that were F4/80hi MHC II-; resting pulmonary
macrophages were isolated from Liberase III-digested lungs (15 min. digest) and macrophages were identified
as SiglecF+ CD11c+ cells with low levels of MHC II 27, 28; and resting brain microglial macrophages were sorted
from Liberase III-digested, Percoll-gradient separated cells that were CD11b+ CD45lo F4/80lo
11
. Liver and
epididymal adipose tissues were digested in collagenase D or Liberase III, respectively, for 45 min. Liver cells
were further separated on a Percoll gradient, whereas adipocytes were floated to separate them from the stromal
vascular fraction containing CD45+ cells in adipose tissue. The Data Browser in the Immgen website is a
resource for pdf files showing FACS dot plots that depict the purification strategies and purity after isolation of
24
these and all other populations. A list of abbreviations used in the Immgen database relevant to macrophages
and DCs can be found in Supplementary Note 1.
Microarray analysis, normalization, and dataset analysis
RNA was amplified and hybridized on the Affymetrix Mouse Gene 1.0 ST array by the Immgen consortium
using double-sorted cell populations sorted directly into TRIzol. These procedures followed a highly
standardized protocol for data generation and QC documentation
38
(pdf documents found under “Protocols,”
available on http://www.immgen.org; Supplementary Notes 3-5). A table listing QC data, replicate information,
and batch information for each sample is also available on the website. All datasets have been deposited at
National Center for Biotechnology Information/Gene Expression Omnibus under accession number GSE15907.
Data analysis utilized GenePattern analysis software. Raw data were normalized using the robust multi-array
algorithm, returning linear values between 10 to 20,000. A common threshold for positive expression at 95%
confidence across the dataset was determined to be 120 (Supplementary Note 5). Differential gene expression
signatures
were
identified
and
visualized
using
(http://www.broadinstitute.org/cancer/software/genepattern/).
the
“Multiplot”
Differentially
module
expressed
of
GenePattern
probesets
were
considered as those with a coefficient of variation less than 0.5 and a p value ≥ 0.05 (Student’s T-test). Signature
transcripts were clustered (mean centered) using the “Hierarchical Clustering” module of GenePattern,
employing Pearson’s correlation as a metric, and data were visualized using the “Hierarchical Clustering
Viewer” heat map module. Clustering analyses performed across ImmGen centered on the most variable gene
sets (objectively defined as the top 15% genes ranked by cross-population max/min ratio), to avoid noise from
genes at background variation. Pearson correlation plots of gene expression profiles between different cell
populations were generated using Express Matrix software. Pathway analysis as well as transcription factor
network construction were performed using Ingenuity Systems Pathway Analysis (IPA) software. This software
calculates a significance score for each network. The score is generated using a P-value indicative of the
likelihood that the assembly of a set of focus genes in a network could be explained by random chance alone.
25
Principle component analysis (PCA) analysis
Only the 4417 genes whose variance of expression across all samples from the ten cell types was at least within
the 80th percentile of variance were considered for the PCA analysis. The RMA normalized and log2
transformed expression levels were used. PCA was conducted using MATLAB.
Generation of the core macrophage signature
A macrophage core signature was generated by comparing brain, lung, peritoneal, and red pulp macrophages to
populations deemed not to be macrophages, but authentic DCs. These DCs included CD103+ DCs from lung and
liver, CD8+ DCs from spleen and thymus, CD4+CD11b+ DCs from spleen, CD4-CD8-CD11b+ DCs from spleen,
and all DC populations (resident and migratory) isolated from skin-draining lymph nodes. A first list of possible
genes defining macrophages was generated using the median value in the group of macrophages or DCs for each
probe set in order to generate a list of probe sets with a differential median expression that was ≥ 2-fold higher
in the group of macrophages with a statistical significance of P < 0.05 (Student’s T-test) and a coefficient of
variation less than 0.5. Then this list of probe sets was filtered to remove any probe sets that did not show ≥ 120
(the threshold for positive expression) normalized intensity value in at least 2 macrophage populations. From the
remaining probe sets, we compared the mean expression values of each macrophage and DC population,
filtering to identify the lowest mean value in any single macrophage population to the highest mean value in any
of the DCs. The probe sets that were at least 2.0-fold higher in expression in the lowest expressing macrophage
compared with the highest expressing DC comprised the core genes were retained (Table 1, left column). To
account for genes observed in only some macrophages, but still not expressed in DCs, we also generated lists
wherein one or two macrophages were allowed to be excluded from consideration, but the criteria for comparing
the remaining macrophages to the DCs was otherwise carried forward as described.
Generation of gene modules and prediction of module regulators.
The gene modules, Ontogenet algorithm and regulatory programs are described in (Jojic et al, in preparation;
Method found in Supplementary Note 1). Briefly, the expression data normalization was done as part of the
26
ImmGen pipeline, March 2011 release. Data was log2 transformed. For gene symbols represented on the array
with more than one probeset, only the probeset with the highest mean expression was retained. Of those, only
the 7996 probesets displaying a standard deviation higher than 0.5 across the entire dataset were used for the
clustering. Clustering was performed by Super Paramagnetic Clustering39 with default parameters, resulting in
80 stable coarse modules of co-expressed genes. Each coarse module was further clustered by hierarchical
clustering into more fine modules, resulting in 334 fine modules.
A novel algorithm termed Ontogenet was developed for the ImmGen dataset (Jojic et al, in preparation,
Supplementary Note 1). Ontogenet finds a regulatory program for each coarse and fine module, based on
regulators expression and the structure of the lineage tree. The regulatory program uses a form of regularized
linear regression, in which each cell type can have its own regulatory program, but regulatory programs of
related cells are encouraged to be similar. This allows switching in the regulatory program but still allows
robust fitting given the available data. To visualize the expression of a module on the lineage tree, the
expression of each gene was standardized by subtraction of the mean and division by its standard deviation
across all dataset. Replicates were averaged. Mean expression of each module was projected on the tree.
Expression values are color coded from minimal (blue) to maximal (red).
Association between the macrophage core signature and gene modules
Hypergeometric test for two groups was used to estimate the enrichment of all ImmGen fine modules for the 11
gene signatures listed in Tables 1 and 2. Benjamini Hochberg FDR <= 0.05 was applied to the P-value table of
all 11 signatures across all 334 fine modules.
Antibodies used for validation studies.
Anti-mouse CD11c (N418), CD11b (M1/70), CD24a (30-F1), CD45 (30-F11), CD14 (Sa2-8) MHC-II
(M5/114.15.2), F4/80 (BM8), CD8a (53-6.7), CD103 (2E7), CD11a (M17/4), EPCAM (G8.8), VCAM1 (429),
CD31 (390), CD93 (AA4.1), ICAM2 (3C4 mIC2/4), CD68 (FA-11), and isotype control mAbs were from
Ebioscience or Biolegend. Anti-mouse MERTK (BAF591) was from R&D Systems. Anti-mouse FCGR1 (X54-
27
5/7.1) and SiglecF (E50-2440) were from BD Bioscience. Anti-mouse Mr1 antibody was previously described21.
Anti-SiglecH antibody was a kind gift from Marco Colonna (Washington University School of Medicine).
Accession codes.
GEO: microarray data, GSE15907.
28
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30
a
0.5
b
DC
1
Spleen
Lung
CD11b+ DC
Peritoneal
Mφ
Liver
CD103+ DC
_
1
Lung
1
0.966
0.907
0.877
0.926
0.939
0.810
0.829
0.842
0.799
0.932
0.877
0.951
0.937
0.799
0.836
0.845
0.792
0.941
0.961
0.934
0.771
0.834
0.801
0.804
0.913
0.940
0.811
0.878
0.843
0.836
0.968
0.788
0.876
0.838
0.806
0.822
0.889
0.865
0.821
0.807
0.784
0.822
0.863
0.800
Spleen
Spleen
CD4+ DC
Liver
CD11b+ DC
0
PC3 (12%)
CD4+
Lung
CD103+ DC
CD8+
Spleen
CD8+ DC
CD103+
DC
1
Liver
Correlation
coefficient
Lung Mφ
2
Mφ
Spleen red
pulp Mφ
Liver
CD11b+
_
2
5
Microglia
CD103+
Lung
0
_
3
_
2
_
1
CD11b+
_
0
5
1
PC1 (30%)
Spleen
red pulp
c
103
102
522
101
104
404
Peritoneal
F4/80hi
103
102
Microglia
1172
102
103
104
101
1099
103
102
3173
101
101
102
103
Peritoneal Mφ
102
103
104
d
Lung CD11b+ DC
104
Spleen red pulp Mφ
Lung CD103+ DC
Microglia
0.798
101
101
104
Mφ
213
Liver CD103+ DC
Liver CD103+ DC
Lung
104
104
1230
DC
103
102
2046
101
101
102
103
Lung Mφ
104
Mφ
Spleen CD4+
Spleen CD8+
Liver CD103+
Lung CD103+
Liver CD11b+
Lung CD11b+
Lung
Peritoneum
Spleen red pulp
Microglia
a
b
A930039a15Rik, Akr1b10, Blvrb,
Camk1, Glul, Myo7a, Nln, Pcyox1,
Pla2g15, Pon3, Slc48a1
Activators: CREG1, LMO2, XBP1, CTBP2, PPARG, CEBPA, RXRA, MAFB, CEBPB
c
d
Predicted regulator of
core modules (%)
100
75
50
25
0
b
Mφ and DC used to
generate the core
Other tissue
mononuclear phagocytes
Monocytes
Predicted regulator of
core modules (%)
a
pDC
100
75
50
25
0
161a
Pon3
Camk1
Myo7a
A930039A15Rik
Pla2g15
Glul
161b
Module
Mφ core genes (except module 161)
Pecr
Tmem195
Ptplad2
1810011H11Rik
Fert2
Tlr4
Mr1
Arsg
Fcgr1
Fgd4
Sqrdl
Csf3r
Plod1
Tom1
Pld3
Tpp1
Ctsd
Lamp2
Pla2g4
Mertk
Tlr7
Cd14
Tbxas1
Fcgr3
Sepp1
Cd164
Tcn2
Dok3
Ctsl
Tspan14
Comt1
Tmem77
Abca1
Nln
Slc48a1
Akr1b10
Pcyox1
Blvrb
_
3
0
Expression
3
c
DC CD103+ CD11b+ gut
DC CD4+ spleen
DC CD103+ gut
DC CD103+ lung
DC CD8+_ thymus
DC CD8 thymus
DC CD4+ SLN
DC CD103+ liver
DC CD8+ MLN
DC CD8+ SLN
+ spleen
_ DC CD8
_
_
DC CD11b CD4_ CD8_ SLN
CD8 SLN
DC CD11b+
CD4
_
_
+
DC MHCIIhi Lang CD103 CD11b
_
_ SLN
DC CD11b+_ CD4_ CD8_ MLN
CD8 MLN
DC CD11b CD4
_
DC MHCIIhi Lang+ CD103 CD11b+ SLN
DC MHCIIhi Lang+_ CD103+_ CD11blo SLN
DC MHCIIhi Lang CD103 CD11blo SLN
+ MLN
DC
CD4
_
_
DC CD11b+_ CD4_ CD8_ spleen
DC CD11b CD4 CD8 spleen
Mφ bone marrow
LC skin
Mφ F4/80hi peritoneum
lo
lo
+
Mφ F4/80 CD11c MHCII peritoneum
Mφ lung
DC CD11b+ liver
Mφ serosal CD11clo gut
Mφ CD11b+ gut
Microglia
Mφ red pulp spleen
+
Mφ MHCII_ Thio peritoneum
Mφ MHCII Thio peritoneum
a
b
c
d
Spleen
Lung
Peritoneal
Microglia
Supplementary Figure 1. Diversity of key genes among macrophages. The gene expression profiles of
several (a) chemokine receptors, (b) Toll-like receptors, (c) C-type lectin domain members, and (d) efferocytic
receptors in red pulp, lung, brain and peritoneal macrophages is displayed as normalized signal intensity.
3
0
-3
Expression
Supplementary Figure 2. Expression profiles of
uniquely downregulated genes in macrophages from
different organs. Heat map and gene list reveals genes
uniquely downregulated by single macrophage populations
by >= 5 fold.
Spleen
Lung
Mφ
0
103
104
105
0
103
104
105
0
103
104
105
0
103
104
105
0
103
104
105
0
103
104
105
DC CD8+/ CD103+
DC CD11b+
MR1
Supplementary Figure 3. Evaluation of Mr1
staining in macrophages and DCs from the spleen
and lung. Using a new mAb to Mr1 as described
(Chua WJ, J immunol 186,4744-4750. 2011), we
compared macrophages and conventional DCs in the
spleen or lung for Mr1 expression. Expression was
absent on DCs, but present on macrophages, though
difficult to detect as expected (Chua WJ, J immunol
186,4744-4750.
2011).
Specific
staining
on
macrophages was analyzed by staining with anti-Mr1
mAb in Mr1 knock-out mice (shown in blue profiles).
Data are representative of two independent
experiments performed.
Supplementary Figure 4. Intracellular FACS staining for CD68 in red pulp
macrophages versus CD4+ and CD8+ spleen DCs. Splenocytes were
permeabilized and stained with isotype control mAb (filled histograms) or antiCD68 mAb (open histograms).
Resting
peritoneum
10
5
104
10
4
CD115
5
10
CD115
Day 5 after
thioglycollate
103
103
0
0
0
10
3
10
4
10
5
0
F4/80
5
104
10
4
CD36
CD36
10
103
0
103
10
4
10
5
0
103
105
104
105
10
F4/80
105
105
104
104
MHC-II
MHC-II
4
103
F4/80
103
0
103
0
0
10
3
104
105
0
10
3
F4/80
F4/80
105
105
4
104
CD11c
CD11c
10
10
0
0
10
5
4
F4/80
5
10
10
3
103
0
103
0
0
10
3
10
F4/80
4
10
5
0
103
104
F4/80
105
Supplementary Figure 5. Phenotype
of resting and thioglycollate‐elicited
cells five days after thioglycollate
administration
intraperitoneally.
Staining for F4/80, CD115, CD11c and
MHC-II reveals that monocytes-derived
macrophage infiltrating the peritoneal
cavity in response to thioglycollate
upregulate CD11c on their surface with
a substantial sub-population that also
expresses MHC-II. Box in top panels
shows gate that was generated before
plotting data in panels below.
Supplementary Figure 6. Lung macrophages are not contaminated with eosinophils. Eosinophils and
lung resident macrophages can be discriminated by their level of CD11c expression. A blue gate is shown
around macrophages while a red gate delineates eosinophils. Accordingly, when projected on a FACS plot
showing CD11b vs Siglec-F, eosinophils are CD11b+ Siglec-F+ (red population) while macrophages are
CD11b-/lo Siglec-F+ (blue population). Also lung macrophages, when gated as MERTK+ CD64+ cells, are
devoid of eosinophils contaminants. Eosinophils are shown in red while macrophages, gated using the
classical strategy shown above (CD11b+ Siglec-F+), are shown in blue.
Two macrophage populations
All 4 macrophage
population
Macrophage
Fine
populations module
hypergeometric
p-value
overlap
overlap genes
size
all
112
125
130
161
3.21E-05
1.80E-04
1.12E-06
4.18E-12
4
3
4
6
CD14;CTSL;SEPP1;TMEM195
COMT1;PLOD1;TCN2
TLR4;TMEM77;TOM1;TPP1
A930039A15RIK;CAMK1;GLUL;MYO7A;PLA2G15;PON3
w/o Peritoneum
165
4.26E-05
3
GPR77;IL1A;TMEM86A
w/o Lung
168
1.26E-05
3
C1QA;C1QB;C1QC
w/o Microglia
132
165
5.29E-11
1.98E-04
7
3
C130050O18RIK;FCGR4;HGF;PILRA;PILRB1;PILRB2;TLR
LPL;MITF;SNX24
w/o Spleen
122
8.73E-08
4
CEBPB;DHRS3;PLOD3;PROS1
Peritoneum and
Spleen
165
295
1.02E-05
7.60E-05
4
3
2810405K02RIK;GM4951;GM5970;IGF1
ASPA;CD5L;FCNA
Peritoneum and
Lung
122
164
166
188
3.59E-05
4.63E-06
6.05E-10
2.02E-04
4
4
6
3
CEBPB;DRAM1;DUSP3;FN1
CLEC4E;F10;GDA;PLCB1
ALOX5;ATG7;G6PDX;PGD;PRDX5;SEPX1
CAV1;FZD4;PDK4
Lung and Spleen
133
3.37E-07
5
CLEC4A3;EAR1;EAR10;GM5150;SIGLEC1
Peritoneum and
Microglia
x
x
x
Lung and
Microglia
168
4.19E-07
4
HPGDS;P2RY12;SLC40A1;SLC7A8
Microglia and
Spleen
128
5.14E-06
3
ANG;SERPINE1;X99384
x
Supplementary Table 4. Analysis of modules significantly enriched in
macrophage-associated genes
Gene/Name
Function/Other Informations
Akr1b10, aldo-keto reductase family1 member B10
ubiquitin-dependent degradation of acetyl-coA carboxylase
a; key role in regulating phospholipids composition in cells,
reactive oxygen species, and cell survival.
Blvrb, biliverdin IX beta reductase
Converts biliverdin to bilirubin
Camk1; calcium/calmodulin-dependent kinase 1
Major signaling intermediate
Glul; glutamate-cysteine ligase (also called GCL)
Catalyzes the rate-limiting step in glutathione synthesis.
Myo7a (myosin VIIA)
Intracellular trafficking of vesicles to the lysosome
(mutations cause Usher’s syndrome)
Nln, neurolysin. Also called oligopeptidase M.
metallo carboxypeptidase in the same family as angiotensinconverting enzyme
Catabolism of prenylcysteines
Pcyox1, prenyl cysteine oxidase 1.
Pla2g15, group XV phospholipase A2
lysosomal phospholipase A2; regulates phospholipid
content/distribution.
Pon3, paroxonase 3.
Paroxonases have lactonase activity and serve as antioxidants. PON3 is mainly found on HDL.
Slc48a1, solute carrier family 48 (heme transporter),
member 1
Heme transporter that regulates intracellular heme
availability/degradation through the endosomal or lysosomal
compartment.
A930039A15Rik
unknown
Supplementary Table 5. Function and other information on the 11 genes that comprise module 161.
Tissue macrophage population
Fine module #
Predicted regulators
Spleen Red
Pulp
Lung
Peritoneal
Microglia
330
296
295, 111, 112
194, 314
Gata6
MafB, ZFHX3,
ZFP715,
Bhlhe41
SpiC
PPARγ
Supplementary Table 6. Fine modules and predicted regulators of specific
macrophage populations in different organs.
Specific Microglia
Specific Spleen
Specific Lung
283
395
388
507
Spleen versus peritoneum (fold)
Adam23
Acsl3
Sned1
Slc45a3
Kcnj10
Slc40a1
Cd55
Mpzl1
Tmem26
Slc16a9
Igf1
Sash1
Tspan15
Trpm2
Spic
Kcnj16
Cyfip2
Tgtp
Gdpd1
Hap1
Pecam1
Cd300e
St6galnac2
Gpr65
Serpina3f
Crip2
Slc22a23
E130203B14Rik
Gfra2
Dnase1l3
Tgm1
Adamdec1
C6
Myo10
Cmbl
Col14a1
Skp2
Enpp2
Apol7c
I730030J21Rik
Zdhhc23
H2-Ab1
Treml4
Epb4.1l3
H2-Aa
C2
Ptprm
Iigp1
Cd74
Sema6a
Ms4a4a
Ifit3
Ms4a14
Ms4a7
Fmnl2
Sema6d
Prnp
Stk39
Nr1h3
Rasgrp1
Ptpn22
Kcna2
Gm10672
Cd1d1
Vcam1
Bank1
Raver2
Sgip1
Ppap2b
Ece1
Arap2
Abcg3
5830443L24Rik
Gbp4
Mpa2l
Adrbk2
Gtf2ird1
Usp12
Akr1b7
1700111E14Rik
Mpp6
Dysf
Clec4a1
Cd163
Clec9a
Dgki
Klrk1
Klrc1
Plbd1
Dig2
Hs3st2
Itgad
Art2a
Zdhhc2
Hmox1
Nlrc5
A730069N07Rik
Snx25
Mmp13
Cadm1
Myo9a
Paqr9
Pcolce2
Itga9
Ccr3
Gpc4
Slc9a4
Rufy4
Plscr1
Uck2
Perp
Dna2
Phlda1
Plxnc1
Vstm2a
Slc6a4
Aldoc
Car4
A430084P05Rik
Krt19
Prkch
Gm5068
Akap5
Mfsd7c
Zfp125
Prkar2b
Tc2n
Clmn
Dnahc11
Gcnt2
Net1
Mak
Pip2
Ear2
Slc39a2
Ear1
Ear10
Sftpc
Matn2
Krt79
Cd200r4
Ccdc80
Cldn1
Atp13a3
Trem1
Epcam
Cpne5
S100a11
Lipf
Gpr120
Kazald1
Gal
Scgb1a1
Tmem216
Tmem138
Mamdc2
Fam189a2
Ch25h
Il1rn
Kynu
Plp2
Bub1b
Ly75
Prr5l
Sulf2
Nceh1
Tmem154
Kcnn3
S100a11
Ctsk
AI504432
Bcar3
Mcoln3
Tlr2
Cd2
Chi3l3
Lmo4
Lepr
Atp6v0d2
Sema3e
Afap1
C530008M17Rik
Agpat9
Spp1
Ptpn12
Naaa
Card11
Flt1
A430107O13Rik
Gpnmb
Fabp1
Il12rb2
Cidec
Cd69
Clec7a
Olr1
Siglec5
Atp10a
Ucp3
Itgal
Itgax
Cdh1
Rab11fip1
Mtmr7
Cyp4f18
Mmp8
Trim29
Mcam
1600029D21Rik
Anxa2
Nrg4
Acaa1b
Htr2c
Plp2
Slc9a7
L1cam
Serpinb2
Serpinb10
Serpinb8
Sell
Selp
F5
Rp1
Mfsd6
Fn1
Pam
Rgs18
Prg4
Tgfb2
Fabp7
Prtn3
Hal
9130014G24Rik
Arg1
Lyz1
Gas7
Mgl1
Atp2a3
Slfn1
Slfn4
Gm11428
Acaca
Alox15
Efcab5
Icam2
Fam20a
Abca6
Egln3
Cmah
Edil3
F13a1
Naip1
Flnb
Dnahc12
Rarb
Ednrb
Ank
Tiaf2
Rai14
Itgb7
Hgd
Retlna
Gbe
Cyp2ab1
Ltbp1
C4b
C4a
Lrg1
Emilin2
Xdh
Gata6
Lama3
Gypc
Klf9
Thbs1
Lbp
St8sia6
Garnl3
Sestd1
Hdc
Cd93
1110032E23Rik
S100a4
S100a6
Gbp1
Ecm1
Slc44a1
Padi4
Car6
Pf4
Cxcl13
Fam20c
Hpse
Ccl24
Stard13
Clec4d
Wnt2
Clec4e
Vmn2r26
Ifitm2
Pde2a
Apoc2
Bcam
Atp1a3
Saa3
Gprc5b
Adam8
Ifitm2
Ifitm6
Fgfr1
Efnb2
Msr1
Hp
Calml4
Aldh1a2
Nedd4
Mst1r
S1pr5
Zbtb16
C230081A13Rik
Aqp9
Cgnl1
Vsig4
75
Spleen versus peritoneum (fold)
Olfml2b
Cd34
Serpine2
4933406P04Rik
Eya4
Lrrc3
Rtn4rl1
Slc46a1
Ccl12
Ccl4
Socs3
Sparc
Serpinf1
Rapgef5
Rtn1
Gm10790
Sema4d
Hexb
B930046C15Rik
D830030K20Rik
Arhgap22
Spata13
Hn1l
LOC100038847
ENSMUSG00000079376
Hn1l
1700001E04Rik
544988
Il7r
Csmd3
Gpr84
Upk1b
St3gal6
Tagap
H2-Oa
Trem2
Tmem204
A430107D22Rik
Cables1
Cxxc5
Smad7
Ecscr
Ldhb
Ak1
Slc24a3
Slco4a1
Adora3
Fcrls
Fam46c
Olfml3
Slc2a5
Zfp691
Crybb1
Fscn1
Tmem119
Gal3st4
Siglech
Tmc7
Gpr56
Gas6
Sall1
Cx3cr1
Kcnd1
Tspan7
Gpr165
Baz1a
Chd5
Creb5
Ctnnb1
Egr2
Fosl2
Hmgn2
Lima1
Lmo4
Lsr
Maff
Ncoa4
Rara
Rbl1
Runx2
Vdr
Atad2
Hat1
Lrrfip1
Trerf1
Wwtr1
Adpgk
Ahr
Btaf1
Ciita
Creg1
Crip2
Hltf
Irf1
Irf7
Maf
Mafb
Nr1h3
Rims3
Stat1
Stat2
Zfp187
Zfp281
Spic
Dnmt3a
Lung
Peritoneum
CD11a
EPCAM
VCAM1
CD31
CD93
3
0
Crip1
Fam20c
Gata6
Irf2
Klf9
Mllt4
Mxd4
Nfe2
Rarb
Rarg
Rbpj
Rnf141
Stat4
Zbtb16
Churc1
Padi4
_
3
Bach2
Erf
Etv5
Junb
Jund
Klf12
Lmo2
Mycl1
Sall1
Smad7
Sox4
Zfpm1
Zfp691
0
3
Expression
d
_
75
494
c
3
Expression
ICAM2
Events (% of max)
b
130
Microglia versus lung
(fold)
Specific Peritoneum
Microglia versus lung
(fold)
a
CX3CR1
100
80
60
40
20
0
0
3
10
4
10
SIGLEC-H
5
10
Spleen
Microglia
a
Brain
Peritoneum
Microglia
Mφ
Spleen
CD8+ DC
Lung
CD11b+ DC
CD103+ DC
Mφ
CD11b+ DC
Mφ
CD14
Events (% of max)
FCγR1 / CD64
100
80
60
40
20
0
0
103
104
105
MERTK
b
d
Gated on
CD45+ cells
Gated on DC
5
10
105
96%
105
4
4%
10
0
0
10
0 10
CD64
3
4
10
10
5
_
All Siglec F cells
_
SiglecF MerTK+ CD64+
5
10
104
104
103
2
5
10
CD11c
105
3
10
2
3
4
10
5
10
0 10
Siglec F
cells
All
eosinophils
3
4
10
10
40
20
0
0 102
103
CD24
3
5
0 102
10
MERTK
103
104
CD103+ CD24+ CD64
_
_
MERTK CD14 DC
105
_
#2
4
6
3
10
#4
102
0
4
2
103
104
0
105
CD11c
0 102
3
10
104
#1
#1
#1
#2
#2
#2
#3
#3
#3
#4
#4
#4
#3
103
6
#1
#1
#1
#2
#2
#2
3
4
10
5
10
0
#3
#3
#3
0 102
103
MERTK
104
105
0 102
CD64
103
104
105
0 102
MHCII
103
104
105
#2
#3
#1
3
10
#4
102
0
103
104
105
0 102
103
104
105
CD64
CD11c
Gated on
CD45+ cells
Gated on MERTK+
CD64+ cells
5
10
4
10
5
10
2
10
3
10
MHCII
4
102
0
5
4
0 102
CD64
8
#1
2
10
102
0
1
#2
4
10
0 10
10
4
no eosinophils
105
CD11c
5
10
105
MERTK
All CD45+ cells
eosinophils
Gated on MERTK+
CD64+ cells
10
8
#3
#1
CD11b+ CD24+ CD64lo
_
MERTK CD14int DCs
Gated on
CD45+ cells
1
5
0 102
CD14
CD11b+ CD24lo CD64+
MERTK+ CD14hi DCs
no eosinophils
10
F4/80
CD64
MHC-II
10
F4/80
105
60
10
MHC-II
CD45+
f
104
80
MERTK
e
4
10
100
10
0
2
10
10
MERTK
2
0 10
5
10
2
10
0
10
0
4
10
CD11b
10
MHC-II
Gated on
Siglec-F_ cells
CD11c
CD11c
10
DC gating strategy
Gated on
CD45+ cells
3
0
2
F4/80
10
5
4
10
F4/80
0 10
4
3
Events (% of max)
2
c
3
0
CD14
103
2
10
CD64
10
104
MERTK
3
Isotype
Siglec-F
MERTK
10
104
3
10
102
0
#2
#1
#3
3
10
102
0
2
0 10
3
10
4
10
CD64
105
0 102
103
104
CD11c
105
Ingenuity Canonical Pathways
p-value
Molecules
Microglia
0.0005
0.0078
0.0138
0.0145
0.0148
0.0148
0.0214
0.0282
0.0331
0.0398
SLC2A1, LDHB, MMP2, SLC2A5, EDN1, MMP14, VHL
PLAU, ITGB5, RHOH, MMP2
PPM1L, PRKAB1, RPTOR, RHOH, RPS6KA1, PRR5
SIPA1, PLCG1, RHOH, MMP2, JAM3, MMP14, SELPLG
CCL4, TNFRSF17, CCL3L1/CCL3L3, TLR9
PLCG1, ARF6, PDGFB, ITGB5
PVRL2, FSCN1, TLR9, TREM2
LDHB, CHST11, CHST7
CCL2, PLCG1, TLR9
HLA-DOB, FSCN1, HLA-DOA, TLR9, TREM2
0.0065
0.0078
0.0079
0.0105
0.0135
0.0182
0.0209
0.0224
0.0251
0.0275
0.0324
0.0380
0.0417
0.0447
0.0457
0.0468
KIF23, CDC25B, KIF11, PPP2R1B, PRC1, CDC25A
LPIN1, LIPF, GLA, MGLL, GK, LPL, AKR1B1, DGAT2
MMP19, ACTG1, CXCR4, SPN, MMP8, PRKCH, MMP12, BMX, CTNNB1, CLDN1, EZR, ITGAL
CPT1A, GSTM5, Gstm3, MGST3, ABCG1, IL1RL2, ALAS1, ACSL1, RARA, FABP1, HMGCS1, ACOX1
HSD17B4, EHHADH, Acaa1b
PPP2R1B, CCNE1, CCNE2, CCRN4L
CDK6, CCNE1, CCNE2, CDC25A, RBL1
SGMS2, LPIN1, SPTLC2, GLA, NAAA, SULF2
PPP2R1B, CDK6, CCNE1, CCNE2, CDC25A, CCNA2
ACTG1, NEDD9, ITGA5, ARHGAP26, CAPN2, ITGAX, GRB7, CAPN1, RHOF, BCAR3, ITGAL
RPS6KA5, MAP3K5, IL1RL2, MAP4K1, MAPKAPK3, CREB5, IL1RN
FDFT1, SQLE, IDI1
MCM4, CDK6, DBF4
ACTG1, ITGA5, ARHGAP26, CAPN2, TNS1, CAPN1
GSTM5, Gstm3, MGST3, CDK6, CCNE1, CCNE2, RARA, CCNA2
AKAP13, PTGER2, CXCR2, CAMK2G, CNR2, FPR2, CREB5, PRKAR2B, FPR1, P2RY14, AKAP5
Histidine Metabolism
Glycosphingolipid Biosynthesis
Complement System
Inhibition of Angiogenesis by TSP1
P2Y Purigenic Receptor Signaling Pathway
Coagulation System
Butanoate Metabolism
G-Protein Coupled Receptor Signaling
0.0005
0.0011
0.0012
0.0030
0.0062
0.0105
0.0117
0.0138
0.0191
0.0200
0.0214
0.0251
0.0251
0.0257
0.0257
0.0269
0.0275
0.0282
0.0295
0.0302
0.0302
0.0355
0.0380
0.0380
0.0407
0.0417
Glycosaminoglycan Degradation
0.0479
PTGIR, DPEP2, PTGER4, ALOX15, PRDX6, PTGIS, PTGES
MSR1, APOE, ACACA, MMP9, APOC2, LBP, PLTP
TGFB2, IKBKE, CD40, ALOX15, AKT3, PRKD3, MST1R, STAT4
IKBKE, SAA1, FN1, CP, RRAS, AKT3, C4A/C4B, HP, LBP, CFB
MAN1A1, FUT8, RPN2, ARSG, DAD1
KLF9, ENO1, F10, ACACA, AKT3, HP
ITGA6, DNM1, RRAS, FLNB, PRKD3, ITGB7
ALDH2, ENO1, ALDH1A2, HK1, PFKL, PDHA1
ENPP5, ACPP, RFK
TGFB2, FZD1, WNT2, S1PR1, AKT3, S1PR5, FGFR1
IKBKE, ITGA6, RRAS, AKT3, PRKD3
PDE2A, CMAH, ALOX15, HK1, UAP1
TGFBR3, IKBKE, RRAS, CCND1, AKT3, FGFR1
MAN1A1, GLB1, ENGASE
TGFB2, CYP26A1, ALDH1A2, RARB, ZBTB16, AKT3, PRKD3, RARG
TGFB2, NQO2, NFIA, ALDH1A2, RARB, CCND1, RARG
ALDH2, ALDH1A2, DPYSL3, HIBCH
TGFB2, TGFBR3, FZD1, RARB, WNT2, CCND1, AKT3, RARG
ALDH2, HAL, ALDH1A2, HDC
ST3GAL4, GLB1, ST3GAL5
CFH, C4A/C4B, CFB
THBS1, MMP9, AKT3
P2RY1, PLCB4, RRAS, AKT3, PRKD3, GNG12
F10, F5, F13A1
ALDH2, ALDH1A2, PRDX6, PDHA1
PDE2A, RGS18, PTGER4, P2RY1, RRAS, S1PR1, AKT3, CMKLR1, PTGIR, IKBKE, FZD1,
EDNRB, GPRC5B, CXCR7, PLCB4, HTR2A, S1PR5
HPSE, GLB1, ALOX15
0.0004
0.0006
0.0007
0.0013
0.0014
0.0020
0.0029
0.0030
0.0032
0.0034
0.0037
0.0048
0.0051
0.0058
0.0063
0.0107
0.0195
0.0200
0.0209
0.0229
0.0229
0.0302
0.0372
0.0457
0.0457
IRF1, STAT1, MX1, STAT2, IFIT3
Tlr11, CD86, IL15, HLA-DRB1, TLR1, CD4, IL1B
HLA-DQB1, CD86, IL15, STAT1, HLA-DRB1, MAPK8, STAT2, HLA-DQA1, CD1D, IL1B
CIITA, CD4, ICOS, DCLRE1C, ADA
HLA-DQB1, CD86, HLA-DRB1, HLA-DQA1, IL1B
IRF1, IL15, STAT1, PTK2
ABCC3, GSTA4, HS3ST2, CHST15, IL1R1, MAPK8, UST, ACSL3, NDST1, NR1H3, IL1B
HS3ST2, CHST15, UST, NDST1, DSE
CIITA, HLA-DRB1, CD74, HLA-DQA1
GOT1, HS3ST2, CHST15, UST, NDST1
HS3ST2, CHST15, WDFY3, UST, NDST1
IFIH1, STAT1, MAPK8, STAT2, IRF7
AOAH, Akr1b7, DGKI, ADHFE1, MOGAT1, PPAP2B, PPAP2A
SOCS5, STAT1, EPOR, HLTF
Tlr11, IFIH1, NOD1, TLR1, IRF7, IL1B
GDPD1, DGKI, PLCL1, HMOX1, PPAP2B, PPAP2A
HLA-DQB1, EXOC6, HLA-DRB1, MAPK8, VAV2, HLA-DQA1, H2-T24
ABCC3, GSTA4, HS3ST2, CHST15, MAPK8, UST, AHR, HMOX1, NDST1, MAF, IL1B
GDPD1, GOT1, DGKI, PLCL1, HMOX1, PPAP2B, PPAP2A
ARSI, UGCG, GALC, PPAP2B, PPAP2A
Tlr11, CD86, TLR1, IL1B
CD55, C6, C2
CYP27A1, MAPK8, ABCB4, NR1H3, IL1B
FYB, FYN, VAV2, HMOX1, PTEN
DLC1, VCAM1, PECAM1, MAPK8, MMP13, VAV2, MMP27, PTK2
Glioma Invasiveness Signaling
mTOR Signaling
Leukocyte Extravasation Signaling
Communication between Innate and Adaptive Immune Cells
Macropinocytosis Signaling
Crosstalk between Dendritic Cells and Natural Killer Cells
Cysteine Metabolism
TREM1 Signaling
Dendritic Cell Maturation
Lung Macrophages
Mitotic Roles of Polo-Like Kinase
Glycerolipid Metabolism
Leukocyte Extravasation Signaling
LPS/IL-1 Mediated Inhibition of RXR Function
Fatty Acid Elongation in Mitochondria
Cell Cycle Regulation by BTG Family Proteins
Cell Cycle: G1/S Checkpoint Regulation
Sphingolipid Metabolism
Cyclins and Cell Cycle Regulation
Integrin Signaling
p38 MAPK Signaling
Biosynthesis of Steroids
Cell Cycle Control of Chromosomal Replication
FAK Signaling
Aryl Hydrocarbon Receptor Signaling
cAMP-mediated signaling
Peritoneal Macrophages (F4/80
hi
)
Eicosanoid Signaling
LXR/RXR Activation
IL-12 Signaling and Production in Macrophages
Acute Phase Response Signaling
N-Glycan Biosynthesis
TR/RXR Activation
Virus Entry via Endocytic Pathways
Glycolysis/Gluconeogenesis
Riboflavin Metabolism
Human Embryonic Stem Cell Pluripotency
Aminosugars Metabolism
PTEN Signaling
N-Glycan Degradation
RAR Activation
Aryl Hydrocarbon Receptor Signaling
Spleen Red Pulp Macrophages
Interferon Signaling
Communication between Innate and Adaptive Immune Cells
Dendritic Cell Maturation
Primary Immunodeficiency Signaling
Graft-versus-Host Disease Signaling
IL-15 Production
LPS/IL-1 Mediated Inhibition of RXR Function
Chondroitin Sulfate Biosynthesis
Antigen Presentation Pathway
Cysteine Metabolism
Keratan Sulfate Biosynthesis
Activation of IRF by Cytosolic Pattern Recognition Receptors
Glycerolipid Metabolism
Role of JAK2 in Hormone-like Cytokine Signaling
Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses
Phospholipid Degradation
Cdc42 Signaling
Xenobiotic Metabolism Signaling
Glycerophospholipid Metabolism
Sphingolipid Metabolism
TREM1 Signaling
Complement System
FXR/RXR Activation
Leukocyte Extravasation Signaling
Supplementary Table 1. Pathway analysis of the specific gene expression profiles distinguishing different
macrophages
a
b
c
d
Spleen
Lung
Peritoneal
Microglia
Supplementary Figure 1. Diversity of key genes among macrophages. The gene expression profiles of
several (a) chemokine receptors, (b) Toll-like receptors, (c) C-type lectin domain members, and (d) efferocytic
receptors in red pulp, lung, brain and peritoneal macrophages is displayed as normalized signal intensity.
3
0
-3
Expression
Supplementary Figure 2. Expression profiles of
uniquely downregulated genes in macrophages from
different organs. Heat map and gene list reveals genes
uniquely downregulated by single macrophage populations
by >= 5 fold.
Spleen
Lung
M!
0
103
104
0
10
3
10
4
0
10
3
10
4
105
0
103
104
105
10
5
0
10
3
10
4
10
5
10
5
0
10
3
10
4
10
5
DC CD8+/ CD103+
DC CD11b+
MR1
Supplementary Figure 3. Evaluation of Mr1
staining in macrophages and DCs from the spleen
and lung. Using a new mAb to Mr1 as described
(Chua WJ, J immunol 186,4744-4750. 2011), we
compared macrophages and conventional DCs in the
spleen or lung for Mr1 expression. Expression was
absent on DCs, but present on macrophages, though
difficult to detect as expected (Chua WJ, J immunol
186,4744-4750.
2011).
Specific
staining
on
macrophages was analyzed by staining with anti-Mr1
mAb in Mr1 knock-out mice (shown in blue profiles).
Data are representative of two independent
experiments performed.
Supplementary Figure 4. Intracellular FACS staining for CD68 in red pulp
macrophages versus CD4+ and CD8+ spleen DCs. Splenocytes were
permeabilized and stained with isotype control mAb (filled histograms) or antiCD68 mAb (open histograms).
Resting
peritoneum
Day 5 after
thioglycollate
104
104
CD115
105
CD115
105
103
103
0
0
0
103
104
F4/80
105
104
104
10
CD36
105
CD36
105
3
10
0
103
104
F4/80
105
104
MHC-II
104
MHC-II
105
3
10
0
105
0
103
104
105
0
103
104
105
0
103
104
105
F4/80
F4/80
3
0
0
103
104
F4/80
105
105
F4/80
105
4
10
CD11c
CD11c
104
3
105
10
103
0
0
10
0
103
4
103
0
0
0
103
104
F4/80
105
F4/80
Supplementary Figure 5. Phenotype
of resting and thioglycollate䇲elicited
cells five days after thioglycollate
administration
intraperitoneally.
Staining for F4/80, CD115, CD11c and
MHC-II reveals that monocytes-derived
macrophage infiltrating the peritoneal
cavity in response to thioglycollate
upregulate CD11c on their surface with
a substantial sub-population that also
expresses MHC-II. Box in top panels
shows gate that was generated before
plotting data in panels below.
Supplementary Figure 6. Lung macrophages are not contaminated with eosinophils. Eosinophils and
lung resident macrophages can be discriminated by their level of CD11c expression. A blue gate is shown
around macrophages while a red gate delineates eosinophils. Accordingly, when projected on a FACS plot
showing CD11b vs Siglec-F, eosinophils are CD11b+ Siglec-F+ (red population) while macrophages are
CD11b-/lo Siglec-F+ (blue population). Also lung macrophages, when gated as MERTK+ CD64+ cells, are
devoid of eosinophils contaminants. Eosinophils are shown in red while macrophages, gated using the
classical strategy shown above (CD11b+ Siglec-F+), are shown in blue.
Ingenuity Canonical Pathways
p-value
Molecules
Microglia
0.0005
0.0078
0.0138
0.0145
0.0148
0.0148
0.0214
0.0282
0.0331
0.0398
SLC2A1, LDHB, MMP2, SLC2A5, EDN1, MMP14, VHL
PLAU, ITGB5, RHOH, MMP2
PPM1L, PRKAB1, RPTOR, RHOH, RPS6KA1, PRR5
SIPA1, PLCG1, RHOH, MMP2, JAM3, MMP14, SELPLG
CCL4, TNFRSF17, CCL3L1/CCL3L3, TLR9
PLCG1, ARF6, PDGFB, ITGB5
PVRL2, FSCN1, TLR9, TREM2
LDHB, CHST11, CHST7
CCL2, PLCG1, TLR9
HLA-DOB, FSCN1, HLA-DOA, TLR9, TREM2
0.0065
0.0078
0.0079
0.0105
0.0135
0.0182
0.0209
0.0224
0.0251
0.0275
0.0324
0.0380
0.0417
0.0447
0.0457
0.0468
KIF23, CDC25B, KIF11, PPP2R1B, PRC1, CDC25A
LPIN1, LIPF, GLA, MGLL, GK, LPL, AKR1B1, DGAT2
MMP19, ACTG1, CXCR4, SPN, MMP8, PRKCH, MMP12, BMX, CTNNB1, CLDN1, EZR, ITGAL
CPT1A, GSTM5, Gstm3, MGST3, ABCG1, IL1RL2, ALAS1, ACSL1, RARA, FABP1, HMGCS1, ACOX1
HSD17B4, EHHADH, Acaa1b
PPP2R1B, CCNE1, CCNE2, CCRN4L
CDK6, CCNE1, CCNE2, CDC25A, RBL1
SGMS2, LPIN1, SPTLC2, GLA, NAAA, SULF2
PPP2R1B, CDK6, CCNE1, CCNE2, CDC25A, CCNA2
ACTG1, NEDD9, ITGA5, ARHGAP26, CAPN2, ITGAX, GRB7, CAPN1, RHOF, BCAR3, ITGAL
RPS6KA5, MAP3K5, IL1RL2, MAP4K1, MAPKAPK3, CREB5, IL1RN
FDFT1, SQLE, IDI1
MCM4, CDK6, DBF4
ACTG1, ITGA5, ARHGAP26, CAPN2, TNS1, CAPN1
GSTM5, Gstm3, MGST3, CDK6, CCNE1, CCNE2, RARA, CCNA2
AKAP13, PTGER2, CXCR2, CAMK2G, CNR2, FPR2, CREB5, PRKAR2B, FPR1, P2RY14, AKAP5
Histidine Metabolism
Glycosphingolipid Biosynthesis
Complement System
Inhibition of Angiogenesis by TSP1
P2Y Purigenic Receptor Signaling Pathway
Coagulation System
Butanoate Metabolism
G-Protein Coupled Receptor Signaling
0.0005
0.0011
0.0012
0.0030
0.0062
0.0105
0.0117
0.0138
0.0191
0.0200
0.0214
0.0251
0.0251
0.0257
0.0257
0.0269
0.0275
0.0282
0.0295
0.0302
0.0302
0.0355
0.0380
0.0380
0.0407
0.0417
Glycosaminoglycan Degradation
0.0479
PTGIR, DPEP2, PTGER4, ALOX15, PRDX6, PTGIS, PTGES
MSR1, APOE, ACACA, MMP9, APOC2, LBP, PLTP
TGFB2, IKBKE, CD40, ALOX15, AKT3, PRKD3, MST1R, STAT4
IKBKE, SAA1, FN1, CP, RRAS, AKT3, C4A/C4B, HP, LBP, CFB
MAN1A1, FUT8, RPN2, ARSG, DAD1
KLF9, ENO1, F10, ACACA, AKT3, HP
ITGA6, DNM1, RRAS, FLNB, PRKD3, ITGB7
ALDH2, ENO1, ALDH1A2, HK1, PFKL, PDHA1
ENPP5, ACPP, RFK
TGFB2, FZD1, WNT2, S1PR1, AKT3, S1PR5, FGFR1
IKBKE, ITGA6, RRAS, AKT3, PRKD3
PDE2A, CMAH, ALOX15, HK1, UAP1
TGFBR3, IKBKE, RRAS, CCND1, AKT3, FGFR1
MAN1A1, GLB1, ENGASE
TGFB2, CYP26A1, ALDH1A2, RARB, ZBTB16, AKT3, PRKD3, RARG
TGFB2, NQO2, NFIA, ALDH1A2, RARB, CCND1, RARG
ALDH2, ALDH1A2, DPYSL3, HIBCH
TGFB2, TGFBR3, FZD1, RARB, WNT2, CCND1, AKT3, RARG
ALDH2, HAL, ALDH1A2, HDC
ST3GAL4, GLB1, ST3GAL5
CFH, C4A/C4B, CFB
THBS1, MMP9, AKT3
P2RY1, PLCB4, RRAS, AKT3, PRKD3, GNG12
F10, F5, F13A1
ALDH2, ALDH1A2, PRDX6, PDHA1
PDE2A, RGS18, PTGER4, P2RY1, RRAS, S1PR1, AKT3, CMKLR1, PTGIR, IKBKE, FZD1,
EDNRB, GPRC5B, CXCR7, PLCB4, HTR2A, S1PR5
HPSE, GLB1, ALOX15
0.0004
0.0006
0.0007
0.0013
0.0014
0.0020
0.0029
0.0030
0.0032
0.0034
0.0037
0.0048
0.0051
0.0058
0.0063
0.0107
0.0195
0.0200
0.0209
0.0229
0.0229
0.0302
0.0372
0.0457
0.0457
IRF1, STAT1, MX1, STAT2, IFIT3
Tlr11, CD86, IL15, HLA-DRB1, TLR1, CD4, IL1B
HLA-DQB1, CD86, IL15, STAT1, HLA-DRB1, MAPK8, STAT2, HLA-DQA1, CD1D, IL1B
CIITA, CD4, ICOS, DCLRE1C, ADA
HLA-DQB1, CD86, HLA-DRB1, HLA-DQA1, IL1B
IRF1, IL15, STAT1, PTK2
ABCC3, GSTA4, HS3ST2, CHST15, IL1R1, MAPK8, UST, ACSL3, NDST1, NR1H3, IL1B
HS3ST2, CHST15, UST, NDST1, DSE
CIITA, HLA-DRB1, CD74, HLA-DQA1
GOT1, HS3ST2, CHST15, UST, NDST1
HS3ST2, CHST15, WDFY3, UST, NDST1
IFIH1, STAT1, MAPK8, STAT2, IRF7
AOAH, Akr1b7, DGKI, ADHFE1, MOGAT1, PPAP2B, PPAP2A
SOCS5, STAT1, EPOR, HLTF
Tlr11, IFIH1, NOD1, TLR1, IRF7, IL1B
GDPD1, DGKI, PLCL1, HMOX1, PPAP2B, PPAP2A
HLA-DQB1, EXOC6, HLA-DRB1, MAPK8, VAV2, HLA-DQA1, H2-T24
ABCC3, GSTA4, HS3ST2, CHST15, MAPK8, UST, AHR, HMOX1, NDST1, MAF, IL1B
GDPD1, GOT1, DGKI, PLCL1, HMOX1, PPAP2B, PPAP2A
ARSI, UGCG, GALC, PPAP2B, PPAP2A
Tlr11, CD86, TLR1, IL1B
CD55, C6, C2
CYP27A1, MAPK8, ABCB4, NR1H3, IL1B
FYB, FYN, VAV2, HMOX1, PTEN
DLC1, VCAM1, PECAM1, MAPK8, MMP13, VAV2, MMP27, PTK2
Glioma Invasiveness Signaling
mTOR Signaling
Leukocyte Extravasation Signaling
Communication between Innate and Adaptive Immune Cells
Macropinocytosis Signaling
Crosstalk between Dendritic Cells and Natural Killer Cells
Cysteine Metabolism
TREM1 Signaling
Dendritic Cell Maturation
Lung Macrophages
Mitotic Roles of Polo-Like Kinase
Glycerolipid Metabolism
Leukocyte Extravasation Signaling
LPS/IL-1 Mediated Inhibition of RXR Function
Fatty Acid Elongation in Mitochondria
Cell Cycle Regulation by BTG Family Proteins
Cell Cycle: G1/S Checkpoint Regulation
Sphingolipid Metabolism
Cyclins and Cell Cycle Regulation
Integrin Signaling
p38 MAPK Signaling
Biosynthesis of Steroids
Cell Cycle Control of Chromosomal Replication
FAK Signaling
Aryl Hydrocarbon Receptor Signaling
cAMP-mediated signaling
Peritoneal Macrophages (F4/80hi)
Eicosanoid Signaling
LXR/RXR Activation
IL-12 Signaling and Production in Macrophages
Acute Phase Response Signaling
N-Glycan Biosynthesis
TR/RXR Activation
Virus Entry via Endocytic Pathways
Glycolysis/Gluconeogenesis
Riboflavin Metabolism
Human Embryonic Stem Cell Pluripotency
Aminosugars Metabolism
PTEN Signaling
N-Glycan Degradation
RAR Activation
Aryl Hydrocarbon Receptor Signaling
Spleen Red Pulp Macrophages
Interferon Signaling
Communication between Innate and Adaptive Immune Cells
Dendritic Cell Maturation
Primary Immunodeficiency Signaling
Graft-versus-Host Disease Signaling
IL-15 Production
LPS/IL-1 Mediated Inhibition of RXR Function
Chondroitin Sulfate Biosynthesis
Antigen Presentation Pathway
Cysteine Metabolism
Keratan Sulfate Biosynthesis
Activation of IRF by Cytosolic Pattern Recognition Receptors
Glycerolipid Metabolism
Role of JAK2 in Hormone-like Cytokine Signaling
Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses
Phospholipid Degradation
Cdc42 Signaling
Xenobiotic Metabolism Signaling
Glycerophospholipid Metabolism
Sphingolipid Metabolism
TREM1 Signaling
Complement System
FXR/RXR Activation
Leukocyte Extravasation Signaling
Supplementary Table 1. Pathway analysis of the specific gene expression profiles distinguishing different
macrophages
Two macrophage populations
All 4 macrophage
population
Macrophage
Fine
populations module
hypergeometric
p-value
overlap
overlap genes
size
all
112
125
130
161
3.21E-05
1.80E-04
1.12E-06
4.18E-12
4
3
4
6
CD14;CTSL;SEPP1;TMEM195
COMT1;PLOD1;TCN2
TLR4;TMEM77;TOM1;TPP1
A930039A15RIK;CAMK1;GLUL;MYO7A;PLA2G15;PON3
w/o Peritoneum
165
4.26E-05
3
GPR77;IL1A;TMEM86A
w/o Lung
168
1.26E-05
3
C1QA;C1QB;C1QC
w/o Microglia
132
165
5.29E-11
1.98E-04
7
3
C130050O18RIK;FCGR4;HGF;PILRA;PILRB1;PILRB2;TLR8
LPL;MITF;SNX24
w/o Spleen
122
8.73E-08
4
CEBPB;DHRS3;PLOD3;PROS1
Peritoneum and
Spleen
165
295
1.02E-05
7.60E-05
4
3
2810405K02RIK;GM4951;GM5970;IGF1
ASPA;CD5L;FCNA
Peritoneum and
Lung
122
164
166
188
3.59E-05
4.63E-06
6.05E-10
2.02E-04
4
4
6
3
CEBPB;DRAM1;DUSP3;FN1
CLEC4E;F10;GDA;PLCB1
ALOX5;ATG7;G6PDX;PGD;PRDX5;SEPX1
CAV1;FZD4;PDK4
Lung and Spleen
133
3.37E-07
5
CLEC4A3;EAR1;EAR10;GM5150;SIGLEC1
Peritoneum and
Microglia
x
x
x
Lung and
Microglia
168
4.19E-07
4
HPGDS;P2RY12;SLC40A1;SLC7A8
Microglia and
Spleen
128
5.14E-06
3
ANG;SERPINE1;X99384
x
Supplementary Table 4. Analysis of modules significantly enriched in
macrophage-associated genes
Gene/Name
Function/Other Informations
Akr1b10, aldo-keto reductase family1 member B10
ubiquitin-dependent degradation of acetyl-coA carboxylase
a; key role in regulating phospholipids composition in cells,
reactive oxygen species, and cell survival.
Blvrb, biliverdin IX beta reductase
Converts biliverdin to bilirubin
Camk1; calcium/calmodulin-dependent kinase 1
Major signaling intermediate
Glul; glutamate-cysteine ligase (also called GCL)
Catalyzes the rate-limiting step in glutathione synthesis.
Myo7a (myosin VIIA)
Intracellular trafficking of vesicles to the lysosome
(mutations cause Usher’s syndrome)
Nln, neurolysin. Also called oligopeptidase M.
metallo carboxypeptidase in the same family as angiotensinconverting enzyme
Catabolism of prenylcysteines
Pcyox1, prenyl cysteine oxidase 1.
Pla2g15, group XV phospholipase A2
lysosomal phospholipase A2; regulates phospholipid
content/distribution.
Pon3, paroxonase 3.
Paroxonases have lactonase activity and serve as antioxidants. PON3 is mainly found on HDL.
Slc48a1, solute carrier family 48 (heme transporter),
member 1
Heme transporter that regulates intracellular heme
availability/degradation through the endosomal or lysosomal
compartment.
A930039A15Rik
unknown
Supplementary Table 5. Function and other information on the 11 genes that comprise module 161.
Tissue macrophage population
Fine module #
Predicted regulators
Spleen Red
Pulp
Lung
Peritoneal
Microglia
330
296
295, 111, 112
194, 314
Gata6
MafB, ZFHX3,
ZFP715,
Bhlhe41
SpiC
PPAR#"
Supplementary Table 6. Fine modules and predicted regulators of specific
macrophage populations in different organs.
Supplementary Table 2
Gene Symbol
Pecr
Tmem195
Ptplad2
1810011H11Rik
Fert2
Tlr4
Pon3
Mr1
Arsg
Fcgr1
Camk1
Fgd4
Sqrdl
Csf3r
Plod1
Tom1
Myo7a
A930039A15Rik
Pld3
Tpp1
Ctsd
Pla2g15
Lamp2
Pla2g4a
Mertk
Tlr7
Cd14
Tbxas1
Fcgr3
Sepp1
Glul
Cd164
Tcn2
Dok3
Ctsl
Tspan14
Comt1
Tmem77
Abca1
Mean probe
value in DCs
used to
generate the
core signature
41.0
40.4
38.8
71.8
41.5
48.0
68.6
69.0
93.0
72.1
98.8
43.4
45.7
69.5
133.1
124.3
109.4
128.4
160.7
241.2
367.9
151.9
294.1
176.0
57.6
87.7
88.1
160.3
82.1
109.1
289.5
595.1
322.7
139.0
142.8
313.6
188.8
307.5
101.2
SEM
1.3
5.8
3.5
13.8
3.5
8.2
6.5
2.2
3.6
4.7
7.0
3.6
4.3
9.2
6.0
4.4
4.9
5.9
21.0
21.6
64.1
10.3
26.3
21.4
10.6
40.7
20.5
37.9
17.4
24.5
25.1
70.9
26.0
6.2
9.8
28.6
13.6
37.5
12.5
Mean probe
value in the 4
core
macrophage
populations
133.5
592.5
688.8
922.3
291.3
849.3
541.3
307.8
432.8
1131.8
899.8
544.5
348.5
874.3
770.5
418.0
421.0
1567.0
1812.0
1266.5
9371.0
2072.0
1551.5
792.3
3194.8
874.5
1285.5
1301.0
1668.3
1223.8
2224.0
3854.0
3766.8
862.0
1910.8
1603.8
1702.0
1881.8
906.8
SEM
11.2
55.5
288.5
289.8
71.7
251.7
157.6
68.6
156.1
377.6
198.3
143.8
86.2
319.8
175.2
43.5
65.8
434.4
532.1
116.7
1287.0
395.6
399.0
178.4
953.3
102.8
245.5
159.9
285.2
129.0
324.8
1027.2
1101.6
111.7
436.8
201.8
372.8
394.3
83.1
Supplementary Table 3
Mean probe value in each macrophage population
Gene Symbol
Dst
Xrcc5
Slc11a1
Ncf2
Rnasel
A930039A15Rik
Sh2d1b1
Fcgr4
Tmem63a
Slc40a1
Fn1
Pecr
Marco
Pla2g4a
Sord
Mr1
Fcgr3
Lyplal1
Sgk1
Vnn3
Cd164
Sesn1
Gp49a
P4ha1
4632428N05Rik
Aifm2
Igf1
Dusp6
Enpp1
Slc16a10
X99384
Adarb1
Dram1
Slc16a7
Lrp1
Osm
Glul
Slc36a1
Pmp22
Adap2
Naglu
Grn
Tanc2
Arsg
Kcnj2
Mfsd11
Engase
Tcn2
Ltc4s
Flcn
Ctns
Aspa
Ccl6
Ccl3
Abcc3
Nr1d1
Dusp3
Pitpnc1
Slc16a6
Abca9
Cd300ld
Timp2
Mean probe value in
DCs used to
generate the core
signature
143
37
211
300
47
129
67
63
261
47
117
42
71
168
244
67
83
76
207
43
604
160
349
191
459
81
67
314
83
57
79
86
152
56
277
54
482
109
136
383
181
1945
97
91
27
136
105
342
263
281
174
17
230
392
85
92
158
118
119
99
74
115
SEM
F480hi
Peritoneu
m
Lung
Microglia
CNS
Red pulp Spleen
19
2
33
27
5
6
20
8
17
12
26
1
4
16
14
2
17
4
41
3
70
14
92
25
61
4
3
74
10
3
7
5
16
4
74
4
38
13
10
30
14
269
6
4
2
14
8
26
57
17
14
0
71
104
5
5
16
8
7
38
19
26
52
26
2836
612
644
1372
19
953
524
24
16609
121
504
538
1023
187
2272
378
482
402
2291
920
2414
625
2480
381
433
1402
222
330
56
347
728
517
3241
151
4905
237
4791
212
766
7425
65
897
54
340
2373
6221
3363
631
334
474
4113
716
382
428
1068
850
306
1855
1142
3529
1114
560
106
702
577
430
2494
1358
495
93
3616
140
699
812
665
494
1110
333
2208
35
6871
103
2176
438
639
282
128
1041
1080
475
1059
440
934
386
3844
287
2890
249
112
620
412
4958
268
226
37
346
165
1554
1638
503
497
15
7208
2090
61
122
684
134
261
37
353
288
309
106
1693
527
80
2386
30
218
1540
1220
78
162
73
529
551
225
2032
153
1995
63
3298
615
130
840
5034
84
72
2696
141
126
1123
106
111
178
1285
219
4109
516
4074
1692
923
6394
1238
281
124
609
309
4990
1957
1038
604
18
826
7803
1026
226
259
229
693
1177
51
1737
1092
113
4605
403
685
2080
708
1903
862
10227
279
111
118
1290
418
325
1259
240
996
682
2956
455
477
837
1951
88
2451
1435
392
1178
225
76
494
559
3632
42
3205
560
145
2693
370
4784
305
327
175
458
550
2302
121
891
344
211
609
141
3259
323
201
720
685
651
523
1070
Adcy3
Tmem195
Slc38a6
Syne2
Ifi27l1
Rhob
Aldh6a1
Lgmn
Dip2c
Aoah
Hist1h1c
Ninj1
Slc12a7
Tppp
Lhfpl2
Lgals8
Serpinb6a
Dok3
6720489N17Rik
Ctsl
Arsk
Scamp1
Zswim6
1810011H11Rik
Ang
Ctsb
Tspan14
Ear1
Ear10
Slc7a8
Cryl1
Slc25a37
Sepp1
Dab2
Lifr
Ano6
Lrp12
Plxnb2
Abcd2
Galnt6
Fam43a
Itgb5
Pros1
Ifnar2
Itsn1
Wrb
Fgd4
Comt1
Abcc5
App
Cryzl1
Sepx1
Tnfrsf21
Pla2g7
B430306N03Rik
Emr1
Fert2
Man2a1
Rhoq
Fpr1
Itfg3
Angptl4
Slc29a1
Ptprm
Fez2
Snx24
Gm4951
Gm5970
Csf1r
86
37
127
176
136
250
94
1022
289
32
360
170
169
65
68
228
123
146
94
152
96
108
157
60
59
1256
321
26
199
125
146
189
116
170
109
288
103
441
74
52
104
142
102
528
95
94
43
199
116
1182
149
358
236
292
88
239
41
378
83
43
247
70
308
60
94
73
36
21
529
5
6
12
19
7
25
4
165
17
2
38
15
21
4
5
17
22
9
8
11
7
6
15
6
3
108
28
12
44
26
14
17
25
39
17
50
7
42
19
2
7
20
33
55
9
5
4
15
15
119
13
37
36
97
5
87
4
32
8
9
16
4
42
3
4
9
4
1
182
63
655
601
247
621
655
293
4281
258
195
721
1649
250
108
52
642
1784
1169
86
1653
328
864
713
536
73
5041
1884
10
34
473
2355
609
1048
1423
499
1061
183
1535
1048
37
784
77
2840
1629
1523
250
852
2792
507
4993
531
2385
708
3999
500
6206
374
3113
423
740
1707
89
313
341
264
252
390
153
4009
1027
618
514
893
272
1030
218
2192
246
35
391
99
656
108
399
851
1448
834
462
1780
318
292
344
903
533
3579
1390
4732
6242
484
338
150
1105
3060
27
1177
1600
439
2547
437
108
1405
2534
534
127
161
535
1495
873
5220
409
4385
1536
50
71
1887
209
936
323
4933
292
612
1770
53
96
451
24
21
1117
123
668
137
66
161
4833
173
11536
326
49
1243
506
187
202
732
470
72
634
56
3137
259
428
356
502
1897
4269
1996
10
59
2467
511
525
1135
214
357
295
248
2046
465
63
128
4633
1452
1235
81
211
162
1414
263
843
125
310
1173
81
129
1295
136
103
158
39
282
79
1126
224
357
192
39
21
8715
628
429
1127
906
441
1369
267
6448
365
468
1524
684
918
207
52
738
2238
811
596
1073
236
248
569
1748
106
7934
1145
26
1355
1077
199
323
1607
71
99
1249
343
1986
450
365
420
2051
125
1798
689
138
629
1107
1020
1832
423
434
2007
2542
742
4230
446
1688
355
1584
999
297
1263
1697
256
1103
1047
515
7728
Pstpip2
Cd14
Cndp2
Ctsf
Rasgrp2
Rab3il1
Slc15a3
Tcf7l2
Adrb1
Prdx5
Dagla
Ms4a8a
Ms4a6d
Gda
Ermp1
Lipa
Hspa12a
Mrc1
Plxdc2
Msrb2
1190002A17Rik
Ptgs1
Itga6
Lrp4
Sqrdl
Blvra
Mertk
Tmem87b
Sirpa
Mavs
Plcb1
Epb4.1l1
Cebpb
Hnmt
Fcna
Cd302
Galnt3
Tfpi
Il1a
Siglec1
Thbd
Cd93
Abhd12
Mafb
Slpi
Ptgis
Nceh1
Pld1
Gpr160
Rnf13
Pdgfc
Cd5l
Kcnn3
Ctss
Tmem77
Sort1
Frrs1
Camk2d
Tspan5
Gbp6
Gpr177
Pag1
Gm5150
Nhlrc3
P2ry13
P2ry12
Adam15
S100a1
Fcgr1
165
91
277
76
95
183
320
300
124
578
72
80
91
111
329
557
46
92
77
86
64
77
152
76
47
285
59
189
1075
241
45
93
218
20
95
61
59
54
45
82
74
56
415
94
91
119
117
73
75
323
59
28
74
2691
287
269
95
193
78
104
86
117
39
141
135
53
270
183
71
22
20
31
4
8
17
56
33
6
43
4
8
20
40
26
120
3
21
17
4
6
7
33
4
4
42
11
19
330
11
2
5
21
1
5
7
4
4
15
6
8
7
34
9
4
6
15
11
2
23
3
2
8
272
25
35
8
20
6
13
9
12
6
7
9
3
15
20
5
930
1998
1130
311
931
107
580
287
116
2907
197
2457
625
4098
435
1922
212
116
3886
328
289
3425
6420
77
535
1084
1438
1049
1413
403
296
273
2588
232
2371
467
49
215
134
147
661
1874
988
1685
3473
1601
370
537
138
993
50
4383
310
6928
1940
864
823
356
490
583
617
297
60
366
76
77
1716
861
357
708
1120
1417
163
57
535
3503
2006
240
1969
147
2001
1426
1950
1037
4591
218
4996
385
120
669
127
314
262
443
2865
3235
495
5825
887
282
223
2637
20
77
1599
779
164
658
1609
647
297
2816
64
459
102
2970
461
243
1008
814
22
2112
8363
1582
3849
566
1568
417
569
555
1143
439
348
1973
116
406
1117
780
57
1151
629
1029
74
3072
1466
236
561
1097
283
86
634
34
173
604
57
118
2342
240
56
2692
1284
98
263
456
2272
278
4686
613
51
97
1177
107
115
119
104
260
1232
101
66
68
2845
1357
106
131
95
530
231
843
60
29
69
8102
1069
940
217
356
138
299
413
995
33
294
6879
1014
1442
125
2115
557
873
930
271
1368
2246
1924
2322
863
675
71
267
364
73
1365
2971
38
5214
53
90
51
2990
57
249
153
2327
5834
626
6572
733
43
258
542
26
3031
712
2324
211
1209
636
59
46
1212
3565
699
524
526
269
249
705
563
2415
69
5716
2936
1403
284
1534
52
571
178
290
926
342
4955
250
157
156
1275
Gstm1
Tgfbr1
Tlr4
Dennd4c
Csf3r
Sdc3
Atp13a2
Dhrs3
Asph
Manea
Abca1
D730040F13Rik
Megf9
Ptplad2
Plk3
Sepn1
Man1c1
C1qb
C1qc
C1qa
Plod1
Pgd
2810405K02Rik
Abcb4
Hgf
Cd38
Cxcl2
Arhgap24
P2rx7
Plod3
C130050O18Rik
Fry
Lmbr1
Man2b2
Rasgef1b
Wdfy3
Cmklr1
Niacr1
Ccl24
Serpine1
Pilra
Pilrb1
Pilrb2
Gna12
Ppp1r9a
Cav2
Cav1
St7
Tbxas1
Gstk1
Fkbp9
Atoh1
Capg
Mgll
Mitf
Atg7
Pparg
Clec4a3
Clec4n
Lpcat3
Iffo1
Tnfrsf1a
Tom1
Mgst1
Pon3
Pdk4
Tcfec
Fam3c
Clec5a
90
336
47
156
67
723
281
410
109
156
109
204
98
38
174
98
158
165
124
157
137
385
93
50
33
160
78
38
207
158
116
78
38
309
271
207
92
68
63
98
80
90
48
379
66
52
41
65
161
51
77
113
247
80
85
302
106
76
59
178
187
433
124
21
71
35
35
160
55
11
57
8
11
8
101
18
61
6
8
12
15
6
3
27
5
12
36
21
17
6
40
1
4
2
20
12
3
28
12
7
7
1
7
49
27
7
5
6
10
15
15
3
22
5
9
3
4
37
6
5
11
59
6
8
12
8
29
12
21
7
60
4
3
6
2
3
9
14
486
359
525
1254
964
3080
670
4240
224
293
820
189
193
1519
190
219
423
5964
7852
8332
682
1439
240
64
306
1938
788
132
545
2253
420
381
161
854
99
390
2235
170
2132
109
1761
1627
646
418
147
385
287
185
1100
290
269
366
2019
50
356
959
88
394
94
656
258
1368
530
1242
439
152
118
461
320
1365
2300
1183
963
237
797
727
3263
426
471
843
637
465
470
1021
367
854
63
55
94
539
1803
64
44
201
37
741
260
293
1137
505
543
142
702
828
931
61
740
41
1956
2616
423
248
499
124
376
204
555
1011
118
111
599
3608
1114
529
902
2682
1109
4085
1325
399
1017
440
1814
997
218
935
774
546
132
2790
323
157
1724
312
699
2397
328
300
809
249
100
190
392
275
1158
7145
7719
8503
1288
864
161
183
47
73
101
72
1032
1056
169
163
49
907
244
233
977
101
100
420
197
80
45
1751
211
39
47
93
1371
65
253
59
219
251
193
254
102
321
28
1043
484
1323
370
23
458
35
36
236
695
183
886
1366
250
572
3427
2234
830
305
231
1155
743
307
576
562
232
345
6117
5544
6173
573
602
675
528
153
2327
51
36
776
301
454
46
88
542
1309
1297
540
1199
284
116
3044
3906
1669
1688
398
31
29
177
1722
170
137
170
82
102
494
637
1603
1500
3347
384
597
898
332
198
271
27
640
348
27
Hpgds
Rab11fip5
Pcyox1
Camk1
Alox5
Adipor2
C3ar1
Clec4e
Klra2
Hebp1
Bhlhe41
Gm4878
Plaur
Blvrb
Cebpa
Tmem86a
Ctsc
Fzd4
Smpd1
Itgam
Cd151
Tspan4
Tspan32
Lair1
Gpr77
C5ar1
Apoe
Axl
Pld3
Scn1b
Cd33
Siglece
Myo7a
Slco2b1
P2ry2
Tpp1
Igsf6
Vkorc1
Ctsd
F7
F10
Gsr
D8Ertd82e
Slc7a2
Hpgd
Lpl
Hmox1
Dnase2a
Lpcat2
Pla2g15
6430548M08Rik
Hgsnat
Mtus1
Asah1
Gab1
Slc38a7
Mlkl
Maf
Slc36a4
Il18
Hexa
Vps13c
Sh3bgrl2
Bckdhb
Nt5e
Dusp7
Itga9
4931406C07Rik
Slc37a2
86
123
188
102
85
444
46
33
30
85
21
67
171
160
211
211
1189
64
169
187
90
82
55
140
79
60
722
273
167
104
225
128
108
65
60
237
315
191
404
68
109
312
63
48
116
91
166
184
382
156
102
259
64
967
64
164
53
135
129
121
982
198
39
106
50
193
69
158
99
16
5
12
7
10
62
5
2
5
10
1
5
51
10
31
10
173
5
9
67
5
3
3
36
4
10
133
92
20
6
51
32
5
5
4
23
88
17
67
5
14
24
4
5
24
23
26
8
82
9
9
23
5
90
6
5
2
11
11
17
123
21
2
5
2
26
6
7
6
43
769
931
913
2397
2621
3737
2276
389
81
26
57
1616
799
782
254
2235
345
563
7495
364
564
473
197
120
1122
16783
151
736
540
1651
169
446
45
384
932
1618
1265
7707
883
3377
1880
62
679
74
2119
1186
401
1340
946
912
5790
714
2219
258
232
280
308
410
139
4148
903
46
163
1074
683
431
315
84
92
226
1478
1298
2761
4312
36
362
1027
1158
1006
1701
2050
249
1321
551
5775
439
284
41
285
91
158
512
317
2250
541
5094
2770
80
1691
705
592
726
373
1334
653
408
11190
1819
1174
1721
292
2046
129
5291
1432
526
134
2251
250
820
101
2684
213
466
128
96
1003
1840
3234
719
583
569
290
590
48
492
137
1469
330
365
1032
309
228
1909
32
21
152
736
261
228
277
1254
1638
2207
137
665
2480
391
434
101
1807
449
545
3343
249
1060
307
3130
1234
285
3292
78
1325
281
838
11919
70
108
189
369
45
1532
111
639
472
4699
2291
125
495
847
1800
321
616
41
678
149
106
3300
118
44
77
49
1215
446
198
696
2489
100
1285
356
52
303
94
34
622
614
61
594
97
900
199
621
6814
52
224
136
169
874
46
1475
303
81
7903
6459
2682
222
332
2747
361
1256
46
1475
2069
290
6668
58
92
847
50
388
4357
694
10362
958
2414
2800
796
1349
91
2627
154
444
269
1608
238
1421
2198
319
260
343
44
437
3180
601
1110
Dmxl2
1700017B05Rik
Scamp5
Smad3
Slc17a5
Tbc1d2b
Trf
Acy1
6230427J02Rik
Tgfbr2
Ccr1
Lonrf3
Atp6ap1
Fundc2
Tlr13
Cybb
Cfp
Lamp2
G6pdx
Gab3
Mpp1
Tlr8
Tlr7
60
196
83
190
117
170
685
60
100
356
29
49
793
63
275
1168
663
307
516
134
208
38
53
11
8
4
16
9
23
223
2
7
34
3
3
73
6
41
358
180
35
56
10
30
10
16
150
483
153
1180
581
733
8063
236
481
817
1044
174
2647
304
1783
9024
8267
2610
1717
200
1902
327
617
2560
329
76
103
436
266
5357
233
137
1774
550
676
2473
237
493
9626
1082
1725
1931
477
1590
840
825
73
1424
1144
1044
182
332
3447
170
559
1796
52
184
2464
38
804
228
123
919
879
107
209
30
954
498
170
531
339
433
1905
6235
80
90
1319
18
46
1745
64
853
4012
6784
952
1130
413
378
869
1102