Open Access

Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling

  • Scott H Robbins1, 2, 3, 10,
  • Thierry Walzer1, 2, 3,
  • Doulaye Dembélé4, 5, 6, 7,
  • Christelle Thibault4, 5, 6, 7,
  • Axel Defays1, 2, 3,
  • Gilles Bessou1, 2, 3,
  • Huichun Xu8,
  • Eric Vivier1, 2, 3, 9,
  • MacLean Sellars4, 5, 6, 7,
  • Philippe Pierre1, 2, 3,
  • Franck R Sharp8,
  • Susan Chan4, 5, 6, 7,
  • Philippe Kastner4, 5, 6, 7 and
  • Marc Dalod1, 2, 3Email author
Genome Biology20089:R17

DOI: 10.1186/gb-2008-9-1-r17

Received: 28 August 2007

Accepted: 24 January 2008

Published: 24 January 2008

Abstract

Background

Dendritic cells (DCs) are a complex group of cells that play a critical role in vertebrate immunity. Lymph-node resident DCs (LN-DCs) are subdivided into conventional DC (cDC) subsets (CD11b and CD8α in mouse; BDCA1 and BDCA3 in human) and plasmacytoid DCs (pDCs). It is currently unclear if these various DC populations belong to a unique hematopoietic lineage and if the subsets identified in the mouse and human systems are evolutionary homologs. To gain novel insights into these questions, we sought conserved genetic signatures for LN-DCs and in vitro derived granulocyte-macrophage colony stimulating factor (GM-CSF) DCs through the analysis of a compendium of genome-wide expression profiles of mouse or human leukocytes.

Results

We show through clustering analysis that all LN-DC subsets form a distinct branch within the leukocyte family tree, and reveal a transcriptomal signature evolutionarily conserved in all LN-DC subsets. Moreover, we identify a large gene expression program shared between mouse and human pDCs, and smaller conserved profiles shared between mouse and human LN-cDC subsets. Importantly, most of these genes have not been previously associated with DC function and many have unknown functions. Finally, we use compendium analysis to re-evaluate the classification of interferon-producing killer DCs, lin-CD16+HLA-DR+ cells and in vitro derived GM-CSF DCs, and show that these cells are more closely linked to natural killer and myeloid cells, respectively.

Conclusion

Our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing the ontogeny and functions of leukocyte subsets, especially DCs.

Background

Dendritic cells (DCs) were initially identified by their unique ability to present antigen for the priming of naïve CD4 and CD8 T lymphocytes [1]. DCs have more recently been shown to be key sentinel immune cells able to sense, and respond to, danger very early in the course of an infection due to their expression of a broad array of pattern recognition receptors [2]. Indeed, DCs have been shown to play a major role in the early production of effector antimicrobial molecules such as interferon (IFN)-α and IFN-β [3] or inducible nitric oxide synthase [4] and it has been demonstrated that DCs can also activate other innate effector cells such as natural killer (NK) cells [5]. In light of these properties, it has been clearly established that DCs are critical for defense against infections, as they are specially suited for the early detection of pathogens, the rapid development of effector functions, and the triggering of downstream responses in other innate and adaptive immune cells.

DCs can be divided into several subsets that differ in their tissue distribution, their phenotype, their functions and their ontogeny [6]. Lymph node-resident DCs (LN-DCs) encompass conventional DCs (cDCs) and plasmacytoid DCs (pDCs) in both humans and mice. LN-cDCs can be subdivided into two populations in both mouse (CD8α and CD11b cDCs) [6] and in human (BDCA1 and BDCA3 cDCs) [7]. In mouse, CD8α cDCs express many scavenger receptors and may be especially efficient for cross-presenting antigen to CD8 T cells [8] whereas CD11b cDCs have been suggested [9, 10], and recently shown [11], to be specialized in the activation of CD4 T cells. As human cDC functions are generally studied with cells derived in vitro from monocytes or from CD34+ hematopoietic progenitors, which may differ considerably from the naturally occurring DCs present in vivo, much less is known of the eventual functional specialization of human cDC subsets. Due to differences in the markers used for identifying DC subsets between human and mouse and to differences in the expression of pattern recognition receptors between DC subsets, it has been extremely difficult to address whether there are functional equivalences between mouse and human cDC subsets [6].

pDCs, a cell type discovered recently in both human and mouse, appear broadly different from the other DC subsets to the point that their place within the DC family is debated [3]. Some common characteristics between human and mouse pDCs that distinguish them from cDCs [3] include: their ability to produce very large amounts of IFN-α/β upon activation, their limited ability to prime naïve CD4 and CD8 T cells under steady state conditions, and their expression of several genes generally associated with the lymphocyte lineage and not found in cDCs [12]. Several differences have also been reported between human and mouse pDCs, which include the unique ability of mouse pDCs to produce high levels of IL-12 upon triggering of various toll-like receptors (TLRs) or stimulation with viruses [13, 14]. Adding to the complexity of accurately classifying pDCs within leukocyte subsets are recent reports describing cell types bearing mixed phenotypic and functional characteristics of NK cells and pDCs in the mouse [15, 16]. Collectively, these findings raise the question of how closely related human and mouse pDCs are to one another or to cDCs as compared to other leukocyte populations.

Global transcriptomic analysis has recently been shown to be a powerful approach to yield new insights into the biology of specific cellular subsets or tissues through their specific gene expression programs [1721]. Likewise, genome-wide comparative gene expression profiling between mouse and man has recently been demonstrated as a powerful approach to uncover conserved molecular pathways involved in the development of various cancers [2227]. However, to the best of our knowledge, this approach has not yet been applied to study normal leukocyte subsets. Moreover, DC subsets have not yet been scrutinized through the prism of gene expression patterns within the context of other leukocyte populations. In this report, we assembled compendia comprising various DC and other leukocyte subtypes, both from mouse and man. Using intra- and inter-species comparisons, we define the common and specific core genetic programs of DC subsets.

Results

Generation/assembly and validation of the datasets for the gene expression profiling of LN-DC subsets

We used pan-genomic Affymetrix Mouse Genome 430 2.0 arrays to generate gene expression profiles of murine splenic CD8α (n = 2) and CD11b (n = 2) cDCs, pDCs (n = 2), B cells (n = 3), NK cells (n = 2), and CD8 T cells (n = 2). To generate a compendium of 18 mouse leukocyte profiles, these data were complemented with published data retrieved from public databases, for conventional CD4 T cells (n = 2) [28] and splenic macrophages (n = 3) [29]. We used Affymetrix Human Genome U133 Plus 2.0 arrays to generate gene expression profiles of blood monocytes, neutrophils, B cells, NK cells, and CD4 or CD8 T cells [30]. These data were complemented with published data on human blood DC subsets (pDCs, BDCA1 cDCs, BDCA3 cDCs, and lin-CD16+HLA-DR+ cells) retrieved from public databases [31]. All of the human samples were done in independent triplicates. Information regarding the original sources and the public accessibility of the datasets analyzed in the paper are given in Table 1.
Table 1

Information on the sources and public access for the datasets analyzed in the paper

     

Figures

Dataset

Population*

Laboratory

Public repository

Accession number

1a,c; 2a

1b,d; 2b

1e

3

4a

4b

5a

5b

Affymetrix Mouse Genome 430 2.0 data

            
 

Spleen CD8 DCs (2)

MD/SCPK

GEO [95]

GSE9810

X

 

X

X

X

 

X

 
 

Spleen CD11b DCs (2)

MD/SCPK

GEO

GSE9810

X

 

X

X

X

 

X

 
 

Spleen pDCs (2)

MD/SCPK

GEO

GSE9810

X

 

X

X

X

 

X

 
 

Spleen NK cells (2)

MD/SCPK

GEO

GSE9810

X

 

X

 

X

   
 

Spleen CD8 T cells (2)

MD/SCPK

GEO

GSE9810

X

 

X

     
 

Spleen B cells (3)

MD/SCPK

GEO

GSE9810

X

 

X

 

X

   
 

Spleen CD4 T cells (2)

AYR

GEO

GSM44979; GSM44982

X

 

X

 

X

   
 

Spleen monocytes (3)

SB

NCI caArray [96]

NA

X

 

X

   

X

 
 

Spleen monocytes (2)

BP

GEO

GSM224733; GSM224735

      

X

 
 

Peritoneal MΦ (1)

SA

GEO

GSM218300

      

X

 
 

BM-MΦ (2)

RM

GEO

GSM177078; GSM177081

      

X

 
 

BM-MΦ (1)

CK

GEO

GSM232005

      

X

 
 

BM-DCs (2)

RM

GEO

GSM40053; GSM40056

      

X

 
 

BM-DCs (2)

MH

GEO

GSM101418; GSM101419

      

X

 

Affymetrix Mouse U74Av2 data

            
 

Spleen CD4 T cells (3)

CB/DM

GEO

GSM66901; GSM66902; GSM66903

    

X

   
 

Spleen B2 cells (2)

CB/DM

GEO

GSM66913; GSM66914

    

X

   
 

Spleen B1 cells (2)

CB/DM

GEO

GSM66915; GSM66916

    

X

   
 

Spleen NK cells (2)

FT

EBI ArrayExpress [97]

E-MEXP-354

    

X

   
 

Spleen CD4 DCs (2)

CRES

GEO

GSM4697; GSM4707

    

X

   
 

Spleen CD8 DCs (2)

CRES

GEO

GSM4708; GSM4709

    

X

   
 

Spleen DN DCs (2)

CRES

GEO

GSM4710; GSM4711

    

X

   
 

Spleen IKDCs (2)

FH

GEO

GSM85329; GSM85330

    

X

   
 

Spleen cDCs (2)

FH

GEO

GSM85331; GSM85332

    

X

   
 

Spleen pDCs (2)

FH

GEO

GSM85333; GSM85334

    

X

   

Affymetrix Human Genome U133 Plus 2.0 data

            
 

Blood monocytes (3)

FRS

Authors' webpage [86]

NA

 

X

X

  

X

 

X

 

Blood CD4 T cells (3)

FRS

Authors' webpage

NA

 

X

X

  

X

  
 

Blood CD8 T cells (3)

FRS

Authors' webpage

NA

 

X

X

  

X

  
 

Blood B cells (3)

FRS

Authors' webpage

NA

 

X

X

  

X

  
 

Blood NK cells (3)

FRS

Authors' webpage

NA

 

X

X

  

X

  
 

Blood neutrophils (3)

FRS

Authors' webpage

NA

 

X

X

  

X

  
 

Blood pDCs (3)

CAKB

EBI ArrayExpress

E-TABM-34

 

X

X

X

 

X

 

X

 

Blood BDCA1 DCs (3)

CAKB

EBI ArrayExpress

E-TABM-34

 

X

X

X

 

X

 

X

 

Blood BDCA3 DCs (3)

CAKB

EBI ArrayExpress

E-TABM-34

 

X

X

X

 

X

 

X

 

Blood CD16 DCs (3)

CAKB

EBI ArrayExpress

E-TABM-34

     

X

 

X

 

PBMC-derived MΦ (2)

SYH

GEO

GSM109788; GSM109789

       

X

 

Monocyte-derived MΦ

LZH

GEO

GSM213500

       

X

 

Monocyte-derived DCs (3)

MVD

GEO

GSM181931; GSM181933; GSM181971

       

X

*The number of replicates is shown in parentheses. MD/SCPK, M Dalod, S Chan, P Kastner; AYR, AY Rudensky; SB, S Bondada; BP, B Pulendran; SA, S Akira; RM, R Medzhitov; CK, C Kim; MH, M Hikida; CB/DM, C Benoist, D Mathis; FT, F Takei; CRES, C Reis e Sousa; FH, F Housseau; FRS, FR Sharp; CAKB, CAK Borrebaeck; SYH, S Yla-Herttuala; LZH, L Ziegler-Heitbrock; MVD, MV Dhodapkar. Shown in the indicated figure in this study. BM-DC, mouse bone-marrow derived GM-CSF DCs; BM-MΦ, mouse bone marrow-derived M-CSF macrophages; monocyte-derived MΦ, monocyte-derived M-CSF macrophages; NA, not applicable; PBMC-derived MΦ, human peripheral blood mononuclear cell-derived M-CSF macrophages; peritoneal MΦ, peritoneal mouse macrophages.

To verify the quality of the datasets mentioned above, we analyzed signal intensities for control genes whose expression profiles are well documented across the cell populations under consideration. Expression of signature markers were confirmed to be detected only in each corresponding population (see Table 2 for mouse data and Table 3 for human data). For example, Cd3 genes were detected primarily in T cells and often to a lower extent in NK cells; the mouse Klrb1c (nk1.1) gene or the human KIR genes in NK cells; Cd19 in B cells; the mouse Siglech and Bst2 genes or the human LILRA4 (ILT7) and IL3RA (CD123) genes in pDCs; and Cd14 in myeloid cells. As expected, many markers were expressed in more than a single cell population. For example, in the mouse, Itgax (Cd11c) was found expressed to high levels in NK cells and all DC subsets; Itgam (Cd11b) in myeloid cells, NK cells, and CD11b cDCs; Ly6c at the highest level in pDCs but also strongly in many other leukocyte populations; and Cd8a in pDCs and CD8α cDCs. However, the analysis of combinations of these markers confirmed the lack of detectable cross-contaminations between DC subsets: only pDCs expressed high levels of Klra17 (Ly49q) and Ly6c together, while Cd8a, ly75 (Dec205, Cd205), and Tlr3 were expressed together at high levels only in CD8α cDCs, and Itgam (Cd11b) with Tlr1 and high levels of Itgax (Cd11c) only in CD11b cDCs. Thus, each cell sample studied harbors the expected pattern of expression of control genes and our data will truly reflect the gene expression profile of each population analyzed, without any detectable cross-contamination.
Table 2

Expression of control genes in mouse cells

   

Dendritic cells

Lymphocytes

Probe set ID

Gene

Myeloid cells

pDC

CD8α DC

CD11b DC

NK

CD8 T

CD4 T

B

1419178_at

Cd3g

40 ± 10

<20

<20

<20

97 ± 31

2,074 ± 287

1,974 ± 478

22 ± 3

1422828_at

Cd3d

111 ± 14

<20

<20

<20

214 ± 16

2,815 ± 11

4,520 ± 1,414

21 ± 2

1422105_at

Cd3e

115 ± 30

27 ± 10

22 ± 2

23 ± 5

26 ± 9

387 ± 58

522 ± 210

26 ± 10

1426396_at

Cd3z

<20

<20

<20

<20

1,147 ± 81

1,545 ± 10

2,117 ± 482

25 ± 9

1426113_x_at

Tcra

83 ± 8

<20

23 ± 4

<20

116 ± 39

2,517 ± 42

5,601 ± 1,818

34 ± 13

1419696_at

Cd4

24 ± 2

1,233 ± 144

<20

369 ± 49

<20

<20

1,052 ± 73

<20

1450570_a_at

Cd19

190 ± 44

<20

<20

<20

<20

<20

23 ± 5

2,259 ± 292

1449570_at

Klrb1c (NK1.1)

<20

<20

<20

<20

2,328 ± 112

<20

25 ± 7

<20

1425436_x_at

Klra3 (Ly49C)

130 ± 11

24 ± 3

156 ± 0

242 ± 31

9,186 ± 479

170 ± 61

70 ± 42

<20

1450648_s_at

H2-Ab1

6,887 ± 84

7,339 ± 5

9,101 ± 100

9,056 ± 277

81 ± 6

83 ± 56

978 ± 11

7,028 ± 239

1419128_at

Itgax (CD11c)

454 ± 5

1,928 ± 169

2,827 ± 454

4,701 ± 56

3,403 ± 45

108 ± 44

22 ± 2

<20

1457786_at

Siglech

31 ± 4

3,454 ± 536

24 ± 5

<20

<20

<20

33 ± 13

<20

1425888_at

Klra17 (Ly49Q)

98 ± 4

3,413 ± 116

30 ± 14

163 ± 2

28 ± 11

24 ± 6

38 ± 10

<20

1424921_at

Bst2 (120G8)

2,364 ± 149

5,571 ± 718

237 ± 30

196 ± 44

61 ± 24

162 ± 12

90 ± 3

88 ± 32

1421571_a_at

Ly6c

4,420 ± 261

8,255 ± 151

98 ± 5

30 ± 8

2,082 ± 365

4,530 ± 229

1,789 ± 1,242

302 ± 303

1422010_at

Tlr7

439 ± 13

846 ± 40

<20

322 ± 45

<20

<20

22 ± 2

118 ± 83

1440811_x_at

Cd8a

<20

337 ± 134

825 ± 44

<20

<20

1,235 ± 227

22 ± 2

<20

1449328_at

Ly75 (Dec205)

249 ± 27

<20

159 ± 4

22 ± 3

24 ± 6

170 ± 29

79 ± 1

21 ± 1

1422782_s_at

Tlr3

27 ± 2

25 ± 3

3,376 ± 159

287 ± 14

<20

<20

<20

52 ± 45

1422046_at

Itgam (CD11b)

956 ± 57

<20

<20

162 ± 1

188 ± 38

<20

<20

21 ± 1

1449049_at

Tlr1

1,218 ± 54

31 ± 15

101 ± 4

1,601 ± 92

<20

889 ± 109

498 ± 103

1,141 ± 484

1417268_at

Cd14

7,649 ± 169

187 ± 52

107 ± 0

115 ± 34

<20

<20

31 ± 8

27 ± 12

1449498_at

Marco

174 ± 19

<20

<20

<20

<20

<20

<20

<20

1460282_at

Trem1

415 ± 19

<20

<20

<20

<20

<20

<20

<20

Table 3

Expression of control genes in human cells

  

Lymphocytes

Dendritic cells

Myeloid cells

Probe set ID

Genes

NK

CD8 T

CD4 T

B

pDC

BDCA1

BDCA3

Mono

Neu

206804_at

CD3G

858 ± 71

1,760 ± 241

1,975 ± 132

53 ± 6

<50

<50

<50

<50

52 ± 4

213539_at

CD3D

5,413 ± 238

7,134 ± 635

6,291 ± 285

276 ± 24

<50

<50

51 ± 2

112 ± 9

276 ± 4

205456_at

CD3E

247 ± 21

569 ± 67

679 ± 91

<50

<50

<50

<50

<50

<50

210031_at

CD3Z

8,688 ± 181

5,223 ± 218

4,749 ± 123

2,996 ± 217

56 ± 10

60 ± 17

54 ± 7

914 ± 96

132 ± 15

209671_x_at

TCR@

147 ± 16

3,127 ± 260

3,462 ± 170

71 ± 7

<50

<50

<50

<50

111 ± 16

205758_at

CD8A

911 ± 26

5,259 ± 217

67 ± 10

79 ± 16

<50

<50

<50

<50

99 ± 7

207979_s_at

CD8B

77 ± 9

3,596 ± 299

<50

<50

<50

<50

<50

<50

53 ± 5

203547_at

CD4

<50

<50

391 ± 20

83 ± 20

1,301 ± 119

1,004 ± 74

278 ± 61

205 ± 34

<50

206398_s_at

CD19

<50

51 ± 1

<50

1,726 ± 115

<50

<50

<50

57 ± 12

<50

212843_at

NCAM1 (CD56)

2,074 ± 96

144 ± 14

65 ± 2

135 ± 9

<50

<50

82 ± 17

52 ± 3

<50

207314_x_at

KIR3DL2

3,131 ± 172

454 ± 14

227 ± 18

265 ± 16

<50

<50

<50

59 ± 8

<50

208203_x_at

KIR2DS5

3,472 ± 140

444 ± 7

236 ± 10

284 ± 14

<50

<50

<50

<50

<50

239975_at

HLA-DPB2

<50

<50

<50

63 ± 22

777 ± 701

1,565 ± 519

2,056 ± 577

<50

<50

210184_at

ITGAX (CD11c)

1,017 ± 50

112 ± 37

166 ± 17

752 ± 45

74 ± 21

2,151 ± 430

729 ± 98

1,284 ± 115

2,133 ± 196

210313_at

LILRA4 (ILT7)

226 ± 10

117 ± 13

346 ± 42

1,109 ± 76

7,916 ± 612

230 ± 16

1,659 ± 1,183

524 ± 41

<50

206148_at

IL3RA (CD123)

84 ± 3

59 ± 8

91 ± 2

324 ± 9

4,728 ± 365

61 ± 10

116 ± 110

120 ± 3

74 ± 12

1552552_s_at

CLEC4C (BDCA2)

93 ± 6

61 ± 5

99 ± 4

408 ± 9

6,789 ± 737

76 ± 39

859 ± 434

217 ± 8

175 ± 25

205987_at

CD1C (BDCA1)

76 ± 8

61 ± 12

159 ± 8

1,715 ± 85

64 ± 23

8,313 ± 272

722 ± 845

560 ± 59

<50

204007_at

FCGR3B (CD16)

459 ± 54

115 ± 24

65 ± 5

322 ± 46

63 ± 23

<50

51 ± 1

160 ± 11

5,554 ± 57

201743_at

CD14

94 ± 3

139 ± 5

343 ± 5

1,274 ± 113

<50

202 ± 183

<50

7,638 ± 446

4,621 ± 374

205786_s_at

ITGAM (CD11b)

5,688 ± 116

1,980 ± 147

1,161 ± 71

2,513 ± 117

360 ± 184

703 ± 28

86 ± 63

5,541 ± 193

5,232 ± 576

208982_at

PECAM1 (CD31)

2,232 ± 48

2,144 ± 91

1,487 ± 58

4,644 ± 102

3,834 ± 601

2,825 ± 290

2,680 ± 363

5,479 ± 219

7,699 ± 853

205898_at

CX3CR1

10,056 ± 53

6,633 ± 232

4,351 ± 170

6,055 ± 263

262 ± 45

1,296 ± 84

362 ± 419

5,717 ± 451

616 ± 21

39402_at

IL1B

69 ± 6

72 ± 7

52 ± 3

209 ± 27

<50

195 ± 131

69 ± 27

198 ± 9

2,920 ± 183

202859_x_at

IL8

95 ± 7

77 ± 6

72 ± 5

385 ± 26

218 ± 185

90 ± 9

680 ± 561

310 ± 17

8,685 ± 776

207094_at

IL8RA

199 ± 30

74 ± 8

81 ± 12

82 ± 2

<50

61 ± 9

67 ± 1

90 ± 1

4,784 ± 521

Mono, monocyte; neu, neutrophil.

LN-DCs constitute a specific leukocyte family that includes pDCs in both the human and the mouse

To determine whether LN-DCs may constitute a specific leukocyte family, we first evaluated the overall proximity between LN-DC subsets as compared to lymphoid or myeloid cell types, based on the analysis of their global gene expression program. For this, we used hierarchical clustering with complete linkage [32], principal component analysis (PCA) [33], as well as fuzzy c-means (FCM) partitional clustering approaches [34]. Hierarchical clustering clearly showed that the three LN-DC subsets studied clustered together, both in mouse (7,298 genes analyzed; Figure 1a) and human (11,507 genes analyzed; Figure 1b), apart from lymphocytes and myeloid cells. The close relationship between all the DC subsets in each species was also revealed by PCA for mouse (Figure 1c) and human (Figure 1d). Finally, FCM clustering also allowed clear visualization of a large group of genes with high and specific expression levels in all DC subtypes (Figure 2, 'pan DC' clusters). These analyses, which are based on very different mathematical methods, thus highlight the unity of the LN-DC family. To investigate the existence of a core genetic program common to the LN-DC subsets and conserved in mammals, clustering of mouse and human data together was next performed. We identified 2,227 orthologous genes that showed significant variation of expression in both the mouse and human datasets. After normalization (as described in Materials and methods), the two datasets were pooled and a complete linkage clustering was performed. As shown in Figure 1e, the three major cell clusters, lymphocytes, LN-DCs, and myeloid cells, were obtained as observed above when clustering the mouse or human data alone. Thus, this analysis shows that DC subsets constitute a specific cell family distinct from the classic lymphoid and myeloid cells and that pDCs belong to this family in both mice and humans. All the LN-DC subsets studied therefore share a common and conserved genetic signature, which must determine their ontogenic and functional specificities as compared to other leukocytes, including other antigen-presenting cells.
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2008-9-1-r17/MediaObjects/13059_2007_Article_1879_Fig1_HTML.jpg
Figure 1

Clustering of mouse and human leukocyte subsets. Hierarchical clustering with complete linkage was performed on the indicated cell populations isolated from: (a) mouse, (b) human, and (e) mouse and human. PCA was performed on the indicated cell populations isolated from: (c) mouse and (d) human. Mono, monocytes; neu, neutrophils.

https://static-content.springer.com/image/art%3A10.1186%2Fgb-2008-9-1-r17/MediaObjects/13059_2007_Article_1879_Fig2_HTML.jpg
Figure 2

FCM partitional clustering. FCM partitional clustering was performed on the mouse and human gene chip datasets. (a) FCM partitional clustering for mouse data. (b) FCM partitional clustering for human data. The color scale for relative expression values as obtained after log10 transformation and median centering of the values across cell samples for each gene is given below the heat map.

Identification and functional annotation of the conserved transcriptional signatures of mouse and human leukocyte subsets

Genes that are selectively expressed in a given subset of leukocytes in a conserved manner between mouse and human were identified and are presented in Table 4. Our data analysis is validated by the recovery of all the genes already known to contribute to the characteristic pathways of development or to the specific functions for the leukocyte subsets studied, as indicated in bold in Table 4. These include, for example, Cd19 and Pax5 for B cells [35], Cd3e-g and Lat for T cells [36], as well as Ncr1 [37] and Tbx21 (T-bet) [38] for NK cells. Similarly, all the main molecules involved in major histocompatibility (MHC) class II antigen processing and presentation are found selectively expressed in antigen-presenting cells (APCs). Indeed, a relatively high proportion of the genes selectively expressed in lymphocytes or in APCs has been known for a long time to be involved in the biology of these cells. However, we also found genes identified only recently as important in these cells, such as March1 [39] or Unc93b1 [40, 41] for APCs, and Edg8 for NK cells [42]. Interestingly, we also identified genes that were not yet known to be involved in the biology of these cells, to the best of our knowledge, such as the E430004N04Rik expressed sequence tag in T cells, the Klhl14 gene in B cells, or the Osbpl5 gene in NK cells.
Table 4

Specific transcriptomic signatures identified in the leukocyte populations studied

 

Expression ratio (log2) of specific genes*

Cell type

3-4

2-3

1-2

0,4-1

Myeloid cells

-

Steap4; Clec4d; Clec4e; Fpr1

Nfe2; Mpp1; Snca; Ccr1; Slc40a1; S100a9; Cd14; Tlr4 ; F5; Fcgr3; Fpr-rs2; Tlr2 ; Abhd5; Gca; Atp6v1b2; Ier3; Sod2; Pilra; Slc11a1

Sepx1; Ninj1; Hp; Sdcbp; Bst1; Ifit1; S100a8; Adipor1; Bach1; Marcks; Pira2; Wdfy3; Ifrd1; Fcho2; Csf3r ; C5ar1; Cd93; Snap23; Cebpb; Clec7a; Yipf4; Hmgcr; Slc31a2; Fbxl5

Pan-DC

 

Flt3

Sh3tc1

Trit1; Bri3bp; Prkra; Etv6; Tmed3; Bahcc1; Scarb1

cDC

-

-

Arhgap22; Btbd4; Slamf8; 9130211I03Rik; Nav1

C2ta ; Avpi1; Spint1; Cs

pDC

 

Epha2 ; Pacsin1; Zfp521; Sh3bgr

Tex2; Runx2; Atp13a2; Maged1; Tm7sf2; Tcf4; Gpm6b; Cybasc3

Nucb2; Alg2; Pcyox1; LOC637870; Scarb2; Dnajc7; Trp53i13; Plac8 ; Pls3; Tlr7 ; Ptprs; Bcl11a

B cells

Ebf1 ; Cd19 ; Klhl14

Bank1 ; Pax5

Blr1 ; Ralgps2; Cd79b ; Pou2af1 ; Fcer2a; Cr2; Cd79a ; Fcrla

Ms4a1; Blk ; Cd72 ; Syvn1; BC065085; Fcrl1; Phtf2; Tmed8; Grap; Pip5k3; Pou2f2

NK cells

-

Ncr1

Tbx21 ; Osbpl5

Rgs3; 1700025G04Rik; Plekhf1; Fasl ; Zfpm1; Edg8 ; Cd160 ; Klrd1 ; Il2rb ; Il18rap ; Ctsw; Ifng ; Prf1 ; Sh2d2a; Llgl2; Gpr178; Prkx; Gab3; Nkg7 ; Cst7; Sntb2; Runx3; Myo6; F2r; Vps37b; Dnajc1; Gfi1

Pan-T cells

-

Camk4 ; E430004N04Rik; Trat1

Cxcr6 ; Tnfrsf25; Ccdc64; Plcg1

Cd3e ; Cd5 ; Lrig1; Cd3g ; Ubash3a; Cd6 ; Lat ; Bcl11b; Tcf7 ; Icos

CD8 T cells

-

-

-

Gzmk

CD4 T cells

-

Ctla4

-

Icos ; Tnfrsf25; Cd5 ; Cd28 ; Trat1

Lymphocytes

-

-

Ablim1; Lax1; D230007K08Rik; Rasgrp1; Bcl2

Spnb2; Cdc25b; Ets1 ; Sh2d2a; Ppp3cc; Cnot6l

Myeloid, B, DC

-

H2-DMb2 ; H2-DMb1

C2ta ; March1 ; Aldh2; Bcl11a; Btk

Ctsh ; H2-Eb1 ; Cd74 ; Ctsz ; Clic4; Kynu ; 5031439G07Rik; Nfkbie; Unc93b1

Non-DC

Gimap4

-

Vps37b

Lck ; Pde3b

*Ratio expressed as Minimum expression among the cell types selected/Maximum expression among all other cell types. Genes already known to be preferentially expressed in the cell types selected are shown in boldface.

In contrast to the high proportion of documented genes selectively expressed in the cell types mentioned above, most of the genes specifically expressed in LN-DCs have not been previously associated with these cells and many have unknown functions. Noticeable exceptions are Flt3, which has been recently shown to drive the differentiation of all mouse [4345] and human [46] LN-DC subsets [47], and Ciita (C2ta), which is known to specifically regulate the transcription of MHC class II molecules in cDCs [48]. Interestingly, mouse or human LN-DCs were found to lack expression of several transcripts present in all the other leukocytes studied here, including members of the gimap family, especially gimap4, which have been very recently shown to be expressed to high levels in T cells and to regulate their development and survival [4951].

Thus, the identity of the gene signatures specific for the various leukocyte subsets studied highlights the sharp contrast between our advanced understanding of the molecular bases that govern the biology of lymphocytes or the function of antigen presentation and our overall ignorance of the genetic programs that specifically regulate DC biology. This contrast is enforced upon annotation of each of the gene signatures found with Gene Ontology terms for biological processes, molecular functions, or cellular components, and with pathways, or with interprotein domain names, using DAVID bioinformatics tools [52, 53] (Table 5). Indeed, many significant annotations pertaining directly to the specific function of myeloid cells, lymphocyte subsets or APCs are recovered, as indicated in bold in Table 5. In contrast, only very few significant annotations are found for LN-DCs, most of which may not appear to yield informative knowledge regarding the specific functions of these cells.
Table 5

Selected annotations for the conserved transcriptomic signatures identified for the cell types studied

Cell type*

Annotation

Genes

Myeloid cells

Defense response/response to pest, pathogen or parasite/inflammatory response

C5ar1, Sod2, Fcgr3, Tlr2, Ccr1, Ifrd1, Csf3r, Clec7a, Bst1, Ifit1, Clec4e, Tlr4, Clec4d, Cd14, Cebpb, Hp

 

Response to bacteria or fungi/pattern recognition receptor activity/C-type lectin

SLC11A1, TLR2, TLR4, CLEC7A, Clec4e, Clec4d

 

H_tollpathway: Toll-like receptor pathway

CD14, TLR2, TLR4

 

Regulation of cytokine biosynthesis/positive regulation of TNF-α or IL-6 biosynthesis

Fcgr3, Tlr2, Tlr4, Cebpb, Clec7a

 

Macrophage activation/mast cell activation/neutrophil chemotaxis

CD93, TLR4, Fcgr3, Csf3r

Pan-DC

Binding

ETV6, PRKRA, FLT3, SCARB1, TRIT1, BAHCC1, SH3TC1

cDC

Nucleobase, nucleoside, nucleotide and nucleic acid metabolism

NAV1, BTBD4, CIITA, SNFT

 

Molecular function unknown

Btbd4, Avpi1, Arhgap22

pDC

Transcription cofactor activity

Maged1, Bcl11a, Tcf4

 

Integral to membrane

TLR7, EPHA2, TMEPAI, SCARB2, ATP13A2, ALG2, CYBASC3, TM7SF2, GPM6B, PTPRS

 

Cellular component unknown

Maged1, Sh3bgr, Cybasc3, Alg2, Plac8

B cells

MMU04662: B cell receptor signaling pathway/B cell activation

Cr2, Cd79a, Cd79b, Cd72, Cd19, Blr1, Ms4a1

 

MMU04640: hematopoietic cell lineage

Cr2, Fcer2a, Ms4a1, Cd19

 

Defense response/response to pest, pathogen or parasite/humoral immune response

PAX5, POU2F2, CR2, MS4A1, CD72, CD19, POU2AF1, BLR1, CD79A, CD79B, FCER2

NK cells

MMU04650: natural killer cell mediated cytotoxicity/apotosis

Klrd1, Ifng, Ncr1, Fasl, Prf1, Prf1, Plekhf1

 

Defense response

IL18RAP, CTSW, IFNG, FASLG, CD160, NCR1, PRF1, KLRD1, CST7

Pan-T cells

HSA04660: T cell receptor signaling pathway/immunological synapse

CD3E, ICOS, PLCG1, LAT, CD3G, Trat1

 

Defense response/immune response

Cd5, Icos, Cd3e, Ubash3a, Lat, Trat1, Cd3g

 

HSA04640: hematopoietic cell lineage

CD3E, CD3G, CD5

CD8 T cells

No annotations

-

CD4 T cells

Defense response/immune response

Cd28, Icos, Cd5, Ctla4, Trat1

 

M_ctla4pathway: the co-stimulatory signal during T-cell activation

Cd28, Icos, Ctla4

Lymphocytes

Immune response

BCL2, LAX1, ETS1

Myeloid, B, DC

Antigen presentation, exogenous antigen via MHC class II

H2-Eb1, H2-DMb2, H2-DMb1, Cd74

 

HSA04612: antigen processing and presentation

HLA-DRB1, CIITA, CD74, HLA-DMB

 

Defense response/immune response

H2-Eb1, H2-DMb2, H2-DMb1, Bcl11a, Cd74

Non-DC

Phosphoric ester hydrolase activity

LCK, PDE3B

*The annotations recovered are written in boldface when they correspond to known specificities of the cell subset studied and are thus confirmatory of the type of analysis performed.

Thus, when taken together, our data show that LN-DC subsets constitute a specific family of leukocytes, sharing selective expression of several genes, most of which are still of unknown function. We believe that the identification of these genes selectively expressed in LN-DC subsets in a conserved manner between mouse and human will be very helpful for future investigation of the mechanisms regulating LN-DC biology by the generation and study of novel genetically manipulated animal models.

Search for a genetic equivalence between mouse and human LN-DC subsets

To search for equivalence between mouse and human LN-DC subsets, we examined their genetic relationships in the hierarchical clustering depicted in Figure 1e. Two observations can be made. First and remarkably, mouse and human pDCs clustered together. This result indicates a high conservation in their genetic program and establishes these two cell types as homologs. Indeed, human and mouse pDCs share a large and specific transcriptional signature (Table 4), with a number of genes comparable to those of the transcriptional signature of NK or T cells. To the best of our knowledge, most of these genes had not been reported to be selectively expressed in pDCs, with the exception of Tlr7 [31, 54] and Plac8 (C15) [55]. Second, although mouse and human cDCs clustered together, the two cDC subsets of each species appeared closer to one another than to the subsets of the other species. Thus, no clear homology could be drawn between human and mouse cDC subsets in this analysis. However, it should be noted that known homologous human and mouse lymphoid cell types also failed to cluster together in this analysis and were closer to the other cell populations from the same species within the same leukocyte family. This is clearly illustrated for the T cell populations as mouse CD4 and CD8 T cells cluster together and not with their human CD4 or CD8 T cell counterparts (Figure 1e). Therefore, to further address the issue of the relationships between human and mouse cDC subsets, we used a second approach. We performed hierarchical clustering with complete linkage on the mouse and human LN-DC datasets alone (1,295 orthologous LN-DC genes), without taking into account the pattern of expression of each gene in the other leukocyte subsets as it may have hidden some degree of similarity between subsets clustering in the same branch. The results of the analysis of gene expression focused on DCs confirmed that mouse and human pDCs cluster together and apart from cDCs (Figure 3). Importantly, when analyzing the DC datasets alone, mouse CD8α and human BDCA3 cDCs on the one hand, and mouse CD11b and human BDCA1 cDCs on the other hand, clustered together and shared a conserved genetic signature (Figure 3 and Table 6). Thus, although a higher genetic distance is observed between mouse and human conventional DC subsets as opposed to pDCs, a partial functional equivalence is suggested between these cell types. The majority of the genes conserved between mouse CD8α and human BDCA3 cDCs versus mouse CD11b and human BDCA1 cDCs have unknown functions and have not been previously described to exhibit a conserved pattern of expression between these mouse and human cell types. Notable exceptions are Tlr3 [31, 56] and the adhesion molecule Nectin-like protein 2 (Cadm1, also called Igsf4) [57], which have been previously described to be conserved between mouse CD8α and human BDCA3 cDCs. When comparing cDC to pDCs, a few genes already known to reflect certain functional specificities of these cells when compared to one another are identified. Tlr7 and Irf7 are found preferentially expressed in pDCs over cDCs, consistent with previous reports that have documented their implication in the exquisite ability of these cells to produce high levels of IFN-α/β in response to viruses [5860]. Ciita, H2-Ob, Cd83 and Cd86 are found preferentially expressed in cDCs over pDCs, which is consistent with their higher efficiency for MHC class II antigen presentation and T cell priming [61].
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2008-9-1-r17/MediaObjects/13059_2007_Article_1879_Fig3_HTML.jpg
Figure 3

Conserved genetic signatures between mouse and human DC subsets. Hierarchical with complete linkage clustering was performed on the indicated DC populations isolated from mouse and human.

Table 6

Conserved specific transcriptomic signatures of DC subsets compared to one another

 

Expression ratio (log2) of specific genes*

Cell type

>4

3-4

2-3

1-2

0,4-1

pDC

Pacsin1; Sla2; 2210020M01Rik

-

Epha2; Sh3bgr; Ets1; Cobll1; Blnk; Myb; Sit1; Zfp521; Nucb2; Igj; Stambpl1; Ptprcap; Spib; Glcci1; Syne2; Ahi1; Atp13a2; Tcf4; Lair1

Runx2; LOC637870; Hs3st1; Asph; L3mbtl3; Tex2; Nrp1; Npc1; Maged1; Tm7sf2; Igh-6 ; Csf2rb2; Ccr2; Cdk5r1; Fcrla; Rnasel; Arid3a; Rassf8; Tgfbr3; Tlr7 ; Trp53i11; Ltb4dh; Arhgap24; Creb3l2; Itpr2; Bcl11a; Usp11; Gpm6b; Snx9; Hivep1; Irf7 ; Cnp1; Cybasc3; Pcyox1; Aacs

Ifnar2; Ugcg; Kmo; Tspan31; Xbp1; Alg2; Txndc5; Abca5; Carhsp1; Ptp4a3; Lypla3; Cxxc5; Sema4c; Vamp1; Klhl9; BC031353; Cybb; Scarb2; Card11; Cdkn2d; 4931406C07Rik; Gimap8; Plxdc1; Lman1; 4631426J05Rik; Tcta; Mgat5; Ern1; Atp8b2; Lrrc16; Cln5; Rexo2; Atp2a3; Tspyl4; Anks3; Slc23a2; Gata2; Trp53i13; Slc44a2; Tmem63a; Dnajc7; Rhoh; Daam1; Lancl1; Aff3; Chst12; Unc5cl; Rwdd2; Armcx3; Vps13a; Mcoln2; Tm7sf3; Stch; Glt8d1; Pscd4; Ormdl3; 1110028C15Rik; Snag1; Prkcbp1; Klhl6; Cbx4; Pcmtd1; Bet1; Ccs; Tceal8; Dpy19l3; Pcnx; LOC672274; Sec11l3; Ctsb; Slc38a1; Ostm1; Acad11; Zbtb20; 1110032A03Rik; Ralgps2; Dtx3; Pls3; Ptprs; Zdhhc8; Rdh11; Bcl7a; Tbc1d2b

cDC

-

9130211I03Rik; Hnrpll; Fgl2; Id2; Slamf8

Chn2; Ddef1; Havcr2; A530088I07Rik; Rab32; Adam8; 2610034B18Rik; Dusp2; Btbd4; Pak1; Bzrap1; Anpep; Apob48r; Aif1

Arrb1; H2-Ob ; Arhgap22; Aytl1; 2810417H13Rik; Pik3cb; Nav1; Acp2; Tnfaip2; Tspan33; Ralb; Marcks; Epb4.1l2; Rab31; Aim1; Cias1; Cd86 ; Cdca7; Rin3; Hk2; Actn1; Snx8; Cd1d1; Cxcl9; Sestd1; Anxa1; Il15; Ahr; Myo1f; Avpi1; Pde8a; Stom; Spint1; Kit; 1100001H23Rik; Specc1; Bcl6; Tpi1; Kcnk6; Efhd2; Cxcl16; Ddb2; C2ta ; Tgif; Pfkfb3; Ptpn12; Pitpnm1; Rtn1; Maff; Sgk; BB220380; Tes; Elmo1; Tm6sf1; Mast2; Stx11; Dhrs3; Tlr2

Il18; Vasp; Ppfibp2; Itfg3; Wdfy3; Atad2; Hck; Cnn2; BC039210; Lima1; Fhod1; Klhl5; Flna; Egr1; Mrps27; Gas2l3; Atp2b1; Gypc; Lst1; 8430427H17Rik; Lmnb1; Junb; Irf2; Soat1; Cd83 ; Spg21; Nab2; Rbpsuh; Tiam1; Spfh1; Gemin6; Entpd1; Lzp-s; Lyzs; Slc8a1; Dusp16; Plscr1; Ptcd2; Slc19a2; Mthfd1l; Copg2; Dym; Limd2; Bag3; Csrp1; Ppa1; Nr4a2; Snx10; Hmgb3; Plekhq1; Oat; Rgs12; Numb; Hars2; Pacs1; Gtdc1; Ezh2; Swap70; Rasgrp4; Asahl; Susd3; Lrrk2; Sec14l1; Asb2; Txnrd2; E330036I19Rik; Sla; Fscn1; Nr4a1; Inpp1; Tdrd7; 4933406E20Rik; Usp6nl

mCD8 and hBDCA3

-

Clnk

Gcet2; BC028528; Igsf4a

sept3; Sema4f; Fkbp1b; Tlr3 ; Lima1; Dbn1; Plekha5; Fuca1; Fgd6; Snx22; Gfod1

Rasgrp3; Btla; Asahl; 4930506M07Rik; Lrrc1; 1700025G04Rik; Tspan33; Fnbp1; Itga6; Zbed3; 9030625A04Rik; Rab32; Ptcd2; Gas2l3; Rab11a; Ptplb; Cbr3; Pqlc2; Slamf8; St3gal5; 4930431B09Rik; Dock7; Stx3; Csrp1; Nbeal2; Gnpnat1; Slc9a9; Ncoa7

mCD11b and hBDCA1

-

-

Il1rn; Papss2; Pram1

Il1r2; Oas3; Rin2; Ptgs2; Csf1r; Tlr5; Centa1; Pygl; Igsf6; Csf3r; Tesc; Ncf2; S100a4; Rtn1; Cst7; Car2; Ifitm1; 1810033B17Rik; Lrp1; Dennd3; Ifitm3

Gbp2; Oas2; Ccl5; Pilra; Sirpa; Pla2g7; Ifitm2; Ms4a7; Cdcp1; Nfam1; BC013672; Slc7a7; Ripk2; Map3k3; Ripk5; Lactb; Rsad2; Parp14; D930015E06Rik; Gyk; Ank; Atp8b4; Emilin2; Arrdc2; Slc16a3; Fcgr3; Clec4a2; Ksr1; Itgax; Sqrdl; Hdac4; Rel; Pou2f2; Chka; Lyst; Ubxd5; Jak2; Cd300a; Lst1; Ssh1; Casp1; D12Ertd553e; Ogfrl1; Rin3; Cd302; Pira2

*Ratio expressed as Minimum expression among the cell types selected/Maximum expression among all other cell types. Genes already known to be preferentially expressed in the cell types selected are shown in boldface.

The functional annotations associated with the genes selectively expressed in specific DC subsets when compared to the others are listed in Table 7. The most significant clusters of functional annotations in pDCs point to the specific expression in these cells of many genes expressed at the cell surface or in intracellular compartments, including the endoplasmic reticulum, the Golgi stack, and the lysosome. A cluster of genes involved in endocytosis/vesicle-mediated transport is also observed. This suggests that pDCs have developed an exquisitely complex set of molecules to sense, and interact with, their environment and to regulate the intracellular trafficking of endocytosed molecules, which may be consistent with the recent reports describing different intracellular localization and retention time of endocytosed CpG oligonucleotides in pDCs compared to cDCs [62, 63]. The most significant clusters of functional annotations in cDCs concerns the response to pest, pathogens or parasites and the activation of lymphocytes, which include genes encoding TLR2, costimulatory molecules (CD83, CD86), proinflammatory cytokines (IL15, IL18), and chemokines (CXCL9, CXCL16), consistent with the specialization of cDCs in T cell priming and recruitment. Clusters of genes involved in inflammatory responses are found in both pDCs and cDCs. However, their precise analysis highlights the differences in the class of pathogens recognized, and in the nature of the cytokines produced, by these two cell types: IFN-α/β production in response to viruses by pDCs through mechanisms involving IRF7 and eventually TLR7; and recognition and killing of bacteria and production of IL15 or IL18 by cDCs through mechanisms eventually involving TLR2 or lysozymes. Many genes selectively expressed in cDCs are involved in cell organization and biogenesis, cell motility, or cytoskeleton/actin binding, consistent with the particular morphology of DCs linked to the development of a high membrane surface for sampling of their antigenic environment and for the establishment of interactions with lymphocytes. pDCs and cDCs also appear to express different arrays of genes involved in signal transduction/cell communication, transcription regulation and apotosis. A statistically significant association with lupus erythematosus highlights the proposed harmful role of pDCs in this autoimmune disease [64].
Table 7

Selected annotations for the conserved transcriptomic signatures identified for DC subsets when compared to one another

Cell type

Annotation

Genes

pDC

Endoplasmic reticulum

Ern1, Lman1, Txndc5, Rdh11, Tm7sf2, Asph, Ormdl3, Stch, Nucb2, Ugcg, Itpr2, Bet1, Sec11l3, Atp2a3

 

Golgi stack

BET1, HS3ST1, CHST12, SNAG1, LMAN1, MGAT5, GLCCI1, Pacsin1

 

Lysosome

Lypla3, Npc1, Scarb2, Ctsb, Pcyox1, Cln5

 

Endocytosis/vesicle-mediated transport

Bet1; Gata2; Igh-6; Lman1; Npc1; Pacsin1; Vamp1

 

Integral to plasma membrane

EPHA2, SCARB2, CSF2RB, SIT1, ATP2A3, IFNAR2, VAMP1, PTPRS, SLC23A2, PTPRCAP, LANCL1, TM7SF2, CCR2, TSPAN31

 

Inflammatory response

TLR7, CYBB, IRF7, CCR2, BLNK

 

Intracellular signaling cascade/I-κB kinase/NF-κB cascade

SNAG1, SLC44A2, TMEPAI, CARD11, ERN1, SLA2, IFNAR2, CARHSP1, SNX9, RALGPS2, CXXC5, CCR2, BLNK, RHOH

 

Regulation of transcription, DNA-dependent/DNA binding/transcription regulator activity/RNA polymerase II transcription factor activity/IPR004827: Basic-leucine zipper (bzip) transcription factor

1110028C15Rik; Aff3; Anks3; Arid3a; Bcl11a; Carhsp1; Cbx4; Cdkn2d; Creb3l2; Cxxc5; Ern1; Ets1; Gata2; Hivep1; Ifnar2; Irf7; Maged1; Myb; Nucb2; Prkcbp1; Runx2; Sla2; Spib; Tcf4; Tspyl4; Xbp1; Zbtb20

 

Systemic lupus erythematosus

LMAN1, CCR2, ETS1

 

Regulation of apoptosis

CDK5R1, CARD11, ERN1, CBX4, TXNDC5, CTSB

cDC

Response to pest, pathogen or parasite/defense response/immune response/response to stress/inflammatory response/cytokine biosynthesis/response to bacteria/lymphocyte activation

ANXA1; NR4A2; CIAS1; TLR2; CD83; CD86; IL18; CXCL16; MAST2; AIF1; CIITA; SNFT; Lzp-s, Lyzs; ENTPD1; CXCL9; PLSCR1; BCL6; SGK; TXNRD2; DDB2; AHR; IRF2; LST1; SOAT1; HLA-DOB; CD1D; IL15; Rbpsuh; Swap70; Hmgb3; Egr1

 

Cytoskeleton/actin binding/filopodium/cell motility

FLNA; FHOD1; CNN2; MYO1F; ACTN1; VASP; EPB41L2; FSCN1; KLHL5; MARCKS; Epb4,1l2; Mast2; Aif1; Csrp1; Elmo1; LIMA1; LMNB1; STOM; Nav1, CXCL16, ANXA1

 

Morphogenesis/cell organization and biogenesis/neurogenesis

Rasgrp4; Myo1f; Aif1; Pak1; Pacs1; Vasp; Tiam1; Lst1; Cnn2; Numb; Csrp1; Fhod1; Nav1; Rab32; Stx11; Ezh2; Epb4,1l2; Flna; Acp2; Elmo1; Ralb; Rab31; Id2; Tnfaip2; Txnrd2; Anpep; Il18; Rbpsuh, Nr4a2; Spint1

 

Signal transduction/cell communication/MMU04010:MAPK signaling pathway/regulation of MAPK activity/GTPase regulator activity/small GTPase mediated signal transduction/IPR003579:Ras small GTPase, Rab type

ADAM8; AHR; ANXA1; ARRB1; Asb2; Avpi1; CD83; CD86; Chn2; CIAS1; CXCL9; Dusp16; DUSP2; Elmo1; ENTPD1; FLNA; Hck; IL15; IL18; INPP1; Kit; Lrrk2; Mast2; NR4A1; NR4A2; PAK1; PDE8A; PIK3CB; PPFIBP2; Rab31; Rab32; Ralb; Rasgrp4; RBPSUH; RGS12; Rin3; RTN1; Sla; SLC8A1; Snx10; Snx8; Tiam1; TLR2; Arhgap22; Ddef1; Rgs12; Usp6nl

 

Transcription regulator activity

Junb, Id2, Asb2, Ddef1, Irf2, Nr4a2, C2ta, Nab2, Egr1, Nr4a1, Ahr, 9130211I03Rik, Tgif, Rbpsuh, Bcl6

 

Apoptosis

Ahr, Nr4a1, Il18, Bag3, Cias1, Elmo1, Cd1d1, Sgk, Bcl6

mCD8 and hBDCA3

Cell organization and biogenesis

DBN1, RAB32, ITGA6, FGD6, RAB11A, SEMA4F

 

Intracellular signaling cascade/small GTPase mediated signal

MIST, TLR3, SNX22; DOCK7; FGD6; RAB11A; RAB32; RASGRP3; sep3

mCD11b and hBDCA1

Immune response/defense response/inflammatory response/positive regulation of cytokine production/response to pest, pathogen or parasite/antimicrobial humoral response/IPR006117:2-5-oligoadenylate synthetase

IFITM3, PTGS2, POU2F2, LST1, GBP2, CCL5, OAS2, FCGR2A, NCF2, CSF1R, TLR5, CSF3R, IL1R2, CST7, IL1RN, NFAM1, IFITM2, IFITM1, LILRB2, OAS3, LYST, CLEC4A, IGSF6, HDAC4, PLA2G7, RIPK2, OAS2, OAS3; Rel; Fcgr3

 

Signal transduction/cell communication/signal transducer activity/positive regulation of I-κB kinase/NF-κB cascade/protein-tyrosine kinase activity/IPR003123:Vacuolar sorting protein 9; vesicle-mediated transport; endocytosis

CASP1; CCL5; CD300A; CD302; CENTA1; CHKA; CLEC4A; CSF1R; CSF3R; FCGR2A; IFITM1; IGSF6; IL1R2; IL1RN; ITGAX; JAK2; KSR1; LILRB2; LRP1; LYST; MAP3K3; MS4A7; NFAM1; OGFRL1; REL; RIN2; RIN3; RIPK2; RIPK5; RTN1; TLR5; Fcgr3

 

Chemotaxis/cell adhesion

ITGAX, CD300A, CSF3R, EMILIN2, CLEC4A, CCL5, Fcgr3

 

HSA04640:hematopoietic cell lineage

CSF1R, CSF3R, IL1R2

 

Asthma. Atopy

PLA2G7, CCL5,

The mCD11b/hBDCA1 cDC cluster of genes comprises many genes involved in inflammatory responses and the positive regulation of the I-kappaB kinase/NF-kappaB cascade. A statistically significant association with asthma also highlights the proinflammatory potential of this cell type. Recently, it has been reported that the mouse CD11b cDC subset is specialized in MHC class II mediated antigen presentation in vivo [11]. In support of our findings here that mouse CD11b cDCs are equivalent to human BDCA1 cDCs, we found that many of the genes involved in the MHC class II antigen presentation pathway that were reported to be expressed to higher levels in mouse CD11b cDCs over CD8α cDCs [11] are also preferentially expressed in the human BDCA1 cDC subset over the BDCA3 one. These genes include five members of the cathepsin family (Ctsb, Ctsd, Ctsh, Ctss, and Ctsw) as well as Ifi30 and Lamp1 and Lamp2 (see Additional data file 2 for expression values). Thus, it is possible that, like the mouse CD11b cDC subset, human BDCA1 cDCs serve as a subset of DCs that are specialized in presenting antigen via MHC class II molecules. It is also noteworthy that mCD11b and hBDCA1 cDCs express high constitutive levels of genes that are known to be induced by IFN-α/β and that can contribute to cellular antiviral defense (Oas2, Oas3, Ifitm1, Ifitm2, Ifitm3).

No significant informative functional annotations are found for the mCD8α/hBDCA3 cDC gene cluster. However, groups of genes involved in cell organization and biogenesis or in small GTPase regulator activity are found and the study of these genes may increase our understanding of the specific functions of these cells. Mouse CD8α cDCs have been proposed to be specialized for a default tolerogenic function but to be endowed with the unique ability to cross-present antigen for the activation of naïve CD8 T cells within the context of viral infection [65]. It will be important to determine whether this is also the case for hBDCA3 cDCs. From this point of view, it is noteworthy that hBDCA3 cDCs selectively express TLR3, lack TLR7 and TLR9, and exhibit the highest ratio of IRF8 (ICSBP)/TYROBP (DAP12) expression, all of which have been shown to participate in the regulation of the balance between tolerance and cross-presentation by mouse CD8α cDCs [65, 66].

Use of leukocyte gene expression compendia to classify cell types of ambiguous phenotype or function

Interferon-producing killer dendritic cells

A novel cell type has been recently reported in the mouse that presents mixed phenotypic and functional characteristics of pDCs and NK cells, IKDCs [15, 16]. A strong genetic relationship between IKDCs and other DC populations was suggested. However, this analysis was based solely on comparison of the transcriptional profile of IKDCs to DCs and not to other cell populations [15]. As IKDCs were also reported to be endowed with antigen presentation capabilities [15] and to be present in mice deficient for the expression of RAG2 and the common γ chain of the cytokine receptors [16], they have been proposed to belong to the DC family rather than to be a subset of NK cells in a particular state of differentiation or activation. However, IKDCs have been reported to express many mRNA specific for NK cells and many of their phenotypic characteristics that were claimed to discriminate IKDCs from NK cells [16] are in fact consistent with classical NK cell features as recently reviewed [67], including the expression of B220 [68] and CD11c [69, 70] (BD/Pharmingen technical datasheet of the CD11c antibody) [71]. To clarify the genetic nature of IKDCs, we reanalyzed the published gene chip data on the comparison of these cells with other DC subsets [15], together with available datasets on other leukocyte populations. We thus assembled published data generated on the same type of microarrays (Affymetrix U74Av2 chips) to build a second mouse compendium, allowing us to compare the transcriptomic profile published for the IKDCs (n = 2) with that of pDCs (n = 2), cDCs (n = 2) [15], CD8α+ (n = 2), CD4+ (n = 2) or double-negative (n = 2) cDC subsets [56], NK cells [72], CD4 T cells (n = 2), and B1 (n = 2) and B2 (n = 2) cells [18]. Information regarding the original sources and the public accessibility of the corresponding datasets are given in Table 1. As depicted in Figure 4a, the hierarchical clustering with complete linkage results of these data sets, together with our novel 430 2.0 data, clearly show that IKDCs cluster with NK cells, close to other lymphocytes, and not with DCs. Indeed, IKDCs express the conserved genetic signature of NK cells but not of DCs (Table 8 and Additional data file 4). Thus, these results strongly support the hypothesis that the cells described as IKDCs feature a specific subset of mouse NK cells that are in a particular differentiation or activation status, rather than a new DC subset.
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2008-9-1-r17/MediaObjects/13059_2007_Article_1879_Fig4_HTML.jpg
Figure 4

Clustering of mouse IKDCs and human CD16 cells. Hierarchical clustering with complete linkage was performed on the indicated cell populations isolated from: (a) mouse and (b) human. Mono, monocytes; neu, neutrophils.

Table 8

Expression of APC, DC and NK signature genes in IKDCs

         

Ratio

Probe set ID

Gene

CD8 DC

DN DC

CD4 DC

pDC

cDC

IKDC

NK

IKDC/DC

NK/DC

IKDC/NK

APC signature genes

           

98035_g_at

H2-DMb1

2,701*

3,416

4,281

1,105

2,722

179

36

0.2

<0.1

5

92668_at

Btk

454

259

331

252

277

91

20

0.4

<0.1

5

94834_at

Ctsh

1,606

2,650

2,862

2,993

1,653

129

20

0.1

<0.1

6

94285_at

H2-Eb1

8,183

7,761

7,201

5,285

14,120

1,018

74

0.2

<0.1

14

101054_at

Cd74

9,094

7,810

7,313

5,158

12,258

1,031

55

0.2

<0.1

19

92633_at

Ctsz

520

1,246

1,171

887

750

117

44

0.2

<0.1

3

94256_at

Clic4

1,668

1,067

1,234

739

717

440

295

0.6

0.4

1

160781_r_at

Unc93b1

683

710

789

301

138

36

22

0.3

0.2

2

Pan-DC signature genes

           

95295_s_at

Flt3

2,769

2,004

2,231

2,069

2,547

270

45

0.1

<0.1

6

100095_at

Scarb1

716

405

333

297

398

125

73

0.4

0.2

2

Non-DC signature genes

           

96172_at

Gimap4

29

62

20

314

319

5,274

982

263

49

5

92398_at

Vps37b

111

139

44

76

56

462

159

11

4

3

161265_f_at

Lck

99

80

105

235

199

1,991

366

25

5

5

NK signature genes

           

97781_at

Ncr1

20

20

20

73

39

1,483

120

20

2

12

97113_at

Fasl

20

28

20

22

30

440

263

15

9

2

102272_at

Cd160

75

107

62

82

58

780

246

7

2

3

100764_at

Il2rb

26

45

40

50

65

84

501

1

8

0.2

99334_at

Ifng

20

20

20

29

38

203

109

5

3

2

93931_at

Prf1

33

21

35

94

86

839

1,287

9

14

1

92398_at

Vps37b

111

139

44

76

56

462

159

11

4

3

Lineage-CD16+HLA-DR+cells

A subset of leukocytes characterized as lineage-CD16+HLA-DR+ (hereafter referred to as CD16 cells) has been reported in human blood, and claimed to be a subpopulation of DCs based on their antigen-presentation capabilities. This subset segregates apart from BDCA1 and BDCA3 DCs and pDCs upon gene expression profiling [31]. It is not found in significant amounts in secondary lymphoid organs of healthy donors, contrary to pDCs and BDCA1 or BDCA3 cDCs. It expresses specific pattern recognition receptors, such as TLR4 and TLR8, and chemokine receptors, such as CX3CR1 and CMKOR1 [31], which were initially described to be preferentially expressed by monocytes in humans [73]. As the transcriptional relationship of CD16 cells with other known DC populations was originally established based solely on the transcriptional profile of DCs, we sought to better understand the nature of these cells. For this, we reanalyzed the global gene expression profile of CD16 cells in comparison to not only DC subsets but also to monocytes, neutrophils, and lymphocytes. The results depicted in Figure 4b clearly show that the CD16 cells cluster with neutrophils and monocytes and not with LN-DCs. Indeed, we find many genes that are expressed to much higher levels in monocytes or neutrophils and CD16 cells than in LN-DC subsets (Table 9 and Additional data file 2). Interestingly, MAFB, which has been described to inhibit the differentiation of DCs but to promote that of macrophages from hematopoeitic precursors [74], is expressed to much higher levels in CD16 cells and monocytes compared to DCs (average signal intensity of 6,263 in CD16 cells compared to 3,479 in monocytes, 65 in pDCs, 309 in BDCA1 DCs and <50 in BDCA3 DCs). CD16 cells also express to high levels many genes that are absent or only expressed to very low levels in LN-DCs compared to both lymphoid and myeloid cells, in particular many members of the gimap family. Reciprocally, many of the genes characterized above as specifically expressed in human and mouse LN-DCs are absent or expressed only to low levels in CD16 cells, in particular FLT3 and SCARB1. Thus, CD16 cells likely differentiate along the canonical myeloid lineage rather than belong to the LN-DC family. However, many genes are also specifically expressed to much higher levels in LN-DC subsets and CD16 cells than in monocytes, neutrophils and lymphocytes, attesting to the existence of biological functions common, and specific, to DC subsets and CD16 cells. Thus, these results strongly suggest that CD16 cells represent a particular subset of monocytes endowed with DC-like properties. One possibility is that CD16 cells are the naturally occurring equivalents of the 'monocyte-derived DCs' generated in vitro.
Table 9

Expression of APC, DC and myeloid signature genes in CD16 cells

  

Dendritic cells

Myeloid cells

 

Ratio to DC

Probe set ID

Gene

BDCA1

BDCA3

pDC

Mono

Neu

CD16 cells

CD16

Mono

Neu

APC signature genes

          

203932_at

HLA-DMB

8,636*

7,929

5,894

5,194

173

2,581

0.3

0.6

<0.1

205101_at

CIITA

2,803

2,354

724

531

50

226

<0.1

0.2

<0.1

219574_at

MARCH1

587

777

544

1,214

58

810

1

2

<0.1

201425_at

ALDH2

9,279

7,841

6,034

8,504

706

1,760

0.2

0.9

<0.1

222891_s_at

BCL11A

569

747

4,502

310

50

213

<0.1

<0.1

<0.1

205504_at

BTK

1,120

822

1,132

1,409

281

1,786

2

1

0.3

202295_s_at

CTSH

6,197

2,528

1,211

3,949

75

2,440

0.39

0.6

<0.1

213831_at

DQA1

11,535

7,503

5,919

4,701

50

252

<0.1

0.4

<0.1

215536_at

DQB2

432

391

157

180

81

52

0.1

0.4

0.2

209312_x_at

DRB1

14,608

14,477

13,250

11,915

228

14,007

1

0.8

<0.1

209619_at

CD74

12,533

12,210

10,498

9,020

867

7,383

0.6

0.7

<0.1

210042_s_at

CTSZ

906

848

692

370

153

673

0.7

0.4

0.2

201560_at

CLIC4

920

305

663

3,023

165

354

0.4

3

0.2

217388_s_at

KYNU

2,414

1,059

2,204

3,516

50

3,738

2

1

<0.1

203927_at

NFKBIE

529

272

232

197

63

290

0.5

0.4

0.1

220998_s_at

UNC93B1

966

850

1,938

862

449

1,235

0.6

0.4

0.2

Pan-DC signature genes

          

206674_at

FLT3

3,032

5,883

2,169

208

<50

<50

<0.1

<0.1

<0.1

219256_s_at

SH3TC1

1,263

899

1,128

392

166

858

0.7

0.3

0.1

218617_at

TRIT1

1,159

1,246

1,851

509

<50

339

0.2

0.3

<0.1

231810_at

BRI3BP

691

735

836

298

146

279

0.3

0.4

0.2

209139_s_at

PRKRA

846

1,067

1,440

316

74

497

0.3

0.2

<0.1

225764_at

ETV6

2,172

2,432

1,726

1,143

938

941

0.4

0.5

0.4

208837_at

TMED3

1,317

1,852

1,859

665

<50

1,022

0.6

0.4

<0.1

219218_at

BAHCC1

87

86

250

<50

<50

<50

0.2

0.2

0.2

1552256_a_at

SCARB1

325

425

942

165

128

59

<0.1

0.2

0.1

Non-DC signature genes

          

219243_at

GIMAP4

68

<50

<50

4,404

3,504

1,334

20

65

52

221704_s_at

VPS37B

54

<50

<50

593

962

487

9

11

18

204891_s_at

LCK

<50

<50

<50

92

181

65

-

-

-

214582_at

PDE3B

78

<50

<50

129

625

114

1

2

8

Myeloid signature genes

          

225987_at

STEAP4

<50

<50

<50

877

6,090

<50

-

-

-

1552773_at

CLEC4D

<50

<50

<50

452

520

<50

-

-

-

222934_s_at

CLEC4E

214

124

133

2,837

5,885

229

1

13

28

202974_at

MPP1

591

281

377

3,721

2,408

1,341

2

6

4

205098_at

CCR1

93

<50

115

3,712

3,627

106

1

32

31

223044_at

SLC40A1

769

276

321

5,018

3,444

<50

-

6

4

224341_x_at

TLR4

94

<50

<50

1,411

2,869

540

6

15

31

204714_s_at

F5

<50

<50

<50

1,392

2,313

<50

-

-

-

203561_at

FCGR2A

1,010

44

51

2,985

7,151

2,857

3

3

7

210772_at

FPRL1

<50

<50

<50

389

3,454

70

3

-

-

204924_at

TLR2

904

211

57

2,870

5,548

1,606

2

3

6

215223_s_at

SOD2

1,474

946

528

3,528

7,599

4,236

3

2

5

222218_s_at

PILRA

1,168

150

136

2,899

4,035

3,982

3

2

3

210423_s_at

SLC11A1

81

60

38

1,767

2,930

3,334

41

22

36

203045_at

NINJ1

357

66

71

1,104

3,129

1,934

5

3

9

201669_s_at

MARCKS

521

389

<50

2,449

3,224

1,730

3

5

6

207697_x_at

LILRB2

1,271

78

774

3,353

3,711

4,903

4

3

3

1553297_a_at

CSF3R

1,902

409

156

3,433

6,687

282

0.2

2

4

220088_at

C5AR1

56

34

93

2,316

5,099

3,824

41

25

55

221698_s_at

CLEC7A

3,229

4,295

79

6,642

7,061

5,680

1

2

2

204204_at

SLC31A2

442

187

<50

1,579

2,047

1,671

4

4

5

*Average expression across replicates. Mono, monocyte; neu, neutrophil.

In vitroGM-CSF derived DCs

In vitro derived GM-CSF DCs are the most commonly used model to analyze DC biology. They are often used to investigate the interaction between DCs and other cell types or with pathogens, both in mouse (bone marrow (BM)-derived GM-CSF DCs) and human (monocyte-derived GM-CSF DCs). However, the relationship between these in vitro GM-CSF-derived DCs and the LN-DC subsets present in vivo in the steady state is not clear. A very recent publication suggests that in vitro derived GM-CSF mouse DCs may correspond to the DCs that differentiate from Ly6C+ monocytes in vivo only under inflammatory conditions and appear specialized in the production of high levels of tumor necrosis factor-α and inducible nitric oxide synthase in response to intracellular bacteria, therefore differing from LN-DCs according to both ontogenic and functional criteria [75]. To gain further insights into the relationship between monocytes, macrophages, LN-DCs, and in vitro derived GM-CSF DCs, we thus compared their global gene expression profiling in both human and mouse, using publicly available gene chip data. Information regarding the original sources and the public accessibility of the corresponding datasets are given in Table 1. The results depicted in Figure 5 clearly show that the in vitro derived GM-CSF DCs cluster with monocytes and macrophages and not with the LN-DCs. This result was further confirmed by PCA, which also showed that both mouse and human GM-CSF DCs are close to macrophages, and distant from LN-DCs (Additional data file 6). Indeed, we found many genes that are expressed to much higher levels in monocytes, macrophages and in vitro derived GM-CSF DCs than in LN-DC subsets (Tables 10 and 11). As for human CD16 cells, these genes include the transcription factor Mafb. Reciprocally, some of the genes identified in this study as specific to LN-cDCs are expressed only to much lower levels in GM-CSF DCs. However and interestingly, compared to monocytes, in vitro derived GM-CSF DCs harbor stronger levels of other lymph node resident cDC-specific genes, including scarb1, snft/9130211l03Rik, spint1, ctsh, C22ORF9/5031439G07Rik, and bri3bp. Thus, in vitro derived GM-CSF DCs seem to harbor a strong myeloid gene signature but also express some of the LN-DC-specific genes, consistent with their myeloid ontogeny and their ability to exert myeloid-type functions but also with their acquisition of DC functional properties. In conclusion, our gene chip data analysis is consistent with a very recent report suggesting that in vitro derived GM-CSF mouse DCs correspond to inflammatory DCs and differ greatly from LN-DCs [75]. Indeed, several papers have recently established that in vitro derived FLT3-L DCs constitute the true equivalent of LN-DCs and constitute the only proper surrogate model currently available for their study [7577].
Table 10

Comparison of the transcriptome of human GM-CSF monocyte-derived DCs to that of blood DCs

   

Ratio to monocytes

Probe set ID

Name

Mono

PBMC-MΦ

mo-MΦ

mo-DC

CD16

BDCA3

BDCA1

pDCs

Myeloid signature genes

         

222934_s_at

CLEC4E

2,358

0.20

0.19

0.04

-

-

0.05

-

209930_s_at

NFE2

823

0.06

0.06

0.89

0.10

-

0.06

-

202974_at

MPP1

3,622

0.40

1.25

0.68

0.33

0.08

0.15

0.11

205098_at

CCR1

3,528

0.76

1.63

1.83

0.03

-

0.03

0.03

203535_at

S100A9

11,192

0.05

0.37

0.01

0.12

0.02

0.17

0.01

201743_at

CD14

8,096

0.44

1.13

0.34

0.01

-

0.02

0.01

224341_x_at

TLR4

1,417

0.13

1.10

0.35

0.34

-

0.06

-

203561_at

FCGR2A

2,946

0.18

0.80

1.36

0.85

-

0.33

0.02

204924_at

TLR2

3,220

0.14

0.80

0.32

0.54

0.08

0.31

0.02

218739_at

ABHD5

285

0.35

0.99

0.67

0.33

-

-

-

201089_at

ATP6V1B2

3,178

2.05

2.46

1.70

0.66

0.12

0.34

0.21

201631_s_at

IER3

2,042

0.42

1.74

0.82

0.10

0.06

0.14

0.12

222218_s_at

PILRA

2,709

0.73

1.24

1.23

1.25

0.05

0.39

0.05

210423_s_at

SLC11A1

1,713

0.47

0.82

0.25

1.75

0.04

0.05

-

203045_at

NINJ1

1,190

1.69

3.59

3.41

1.59

0.27

0.44

0.26

200958_s_at

SDCBP

11,323

0.87

1.16

0.90

0.61

0.33

0.40

0.26

202917_s_at

S100A8

15,661

0.02

0.41

0.01

0.11

0.01

0.27

0.03

217748_at

ADIPOR1

2,229

0.57

0.48

1.16

0.30

0.30

0.36

0.28

201669_s_at

MARCKS

2,340

0.84

2.57

1.57

0.65

0.16

0.20

-

207697_x_at

LILRB2

3,260

0.29

0.64

0.76

1.36

0.02

0.39

0.24

228220_at

FCHO2

619

4.50

4.04

3.62

0.76

0.35

0.26

0.23

1553297_a_at

CSF3R

3,121

0.42

0.69

0.37

0.08

0.11

0.52

0.04

220088_at

C5AR1

2,059

2.56

3.63

1.30

1.60

-

0.03

0.04

212501_at

CEBPB

3,490

3.26

3.23

3.30

1.26

0.06

0.49

0.06

221698_s_at

CLEC7A

6,596

0.24

0.55

0.63

0.74

0.62

0.46

0.01

209551_at

YIPF4

526

0.85

1.65

1.91

0.41

0.37

0.44

0.37

204204_at

SLC31A2

1,933

0.94

1.14

0.69

0.76

0.10

0.22

0.03

Pan-DC signature genes

         

206674_at

FLT3

221

-

-

-

-

24.01

12.76

9.26

219256_s_at

SH3TC1

395

1.02

2.73

1.12

2.01

2.22

3.01

2.86

218617_at

TRIT1

498

0.49

0.58

0.86

0.71

2.46

2.15

3.61

231810_at

BRI3BP

301

0.98

1.42

1.99

0.98

2.35

2.10

2.70

209139_s_at

PRKRA

325

1.12

1.77

1.47

1.57

3.17

2.42

4.37

225764_at

ETV6

1,097

0.43

1.13

2.00

0.75

2.04

1.78

1.48

208837_at

TMED3

595

1.50

2.81

1.64

1.46

2.91

1.98

2.94

219218_at

BAHCC1

-

-

-

-

-

>1.7

>1.5

>4.7

1552256_a_at

SCARB1

151

8.98

6.58

7.21

-

2.33

1.70

5.30

cDC signature genes

         

206298_at

ARHGAP22

-

>5.8

>6.5

>3.1

-

>6.2

>4.6

-

227329_at

BTBD4

-

>1.6

>2.8

>5.8

-

>9.3

>8.7

-

219386_s_at

SLAMF8

98

24.75

38.66

23.99

0.51

15.48

5.30

0.51

220358_at

SNFT

148

0.62

0.34

8.62

5.66

16.01

4.82

0.34

224772_at

NAV1

64

2.01

3.25

1.40

2.00

23.87

10.50

1.62

205101_at

CIITA

481

0.29

0.12

1.09

0.48

4.51

5.28

1.43

218631_at

AVPI1

-

>18.7

>31.3

>64.8

>1.6

>3.2

>7.0

-

202826_at

SPINT1

84

4.65

7.15

8.79

0.90

2.59

2.92

0.68

208660_at

CS

1,848

1.24

0.99

1.04

0.84

1.70

1.63

0.89

APC signature genes

         

203932_at

HLA-DMB

5,137

1.28

0.64

1.37

0.44

1.45

1.62

1.14

219574_at

MARCH1

1,133

0.42

0.89

0.73

0.62

0.64

0.44

0.46

201425_at

ALDH2

8,782

0.51

0.54

0.34

0.18

0.84

1.01

0.69

222891_s_at

BCL11A

310

0.98

0.34

0.50

0.74

2.40

1.73

14.23

205504_at

BTK

1,372

0.29

0.47

0.64

1.13

0.58

0.75

0.81

202295_s_at

CTSH

3,755

1.76

2.37

2.09

0.56

0.63

1.57

0.31

209312_x_at

HLA-DRB1

12,737

1.02

0.57

1.34

1.11

1.12

1.11

1.00

209619_at

CD74

8,540

1.49

0.86

2.12

0.73

1.33

1.34

1.11

210042_s_at

CTSZ

369

0.76

1.13

17.00

1.66

2.13

2.17

1.83

201560_at

CLIC4

2,828

0.87

0.88

1.00

0.12

0.10

0.28

0.22

217388_s_at

KYNU

3,429

1.50

1.95

0.90

0.94

0.30

0.65

0.63

217118_s_at

C22orf9

1,617

3.33

3.46

2.77

1.43

1.85

1.79

1.04

203927_at

NFKBIE

173

3.30

9.96

3.13

1.45

1.39

2.60

1.25

220998_s_at

UNC93B1

847

0.60

1.31

0.97

1.31

0.99

1.06

2.27

Non-DC signature genes

         

219243_at

GIMAP4

4,384

0.15

0.11

0.19

0.27

-

-

-

221704_s_at

VPS37B

559

0.26

0.90

0.47

0.80

-

-

-

204891_s_at

LCK

96

1.48

0.52

0.52

0.59

-

-

-

214582_at

PDE3B

144

2.82

2.99

2.43

0.76

-

0.51

-

*Average expression across replicates. Genes for which expression between monocyte-derived DCs and blood DCs or blood cDCs varies more than two-fold are shown in bold. mo-DC, monocyte-derived GM-CSF DC; mo-MΦ, monocyte-derived M-CSF macrophages; mono, monocyte; PBMC-MΦ, human peripheral blood mononuclear cell-derived M-CSF macrophages.

Table 11

Comparison of the transcriptome of mouse GM-CSF BM-derived DCs to that of spleen DCs

   

Ratio to monocytes

Probe set ID

Name

Mono

Mono(2)

BM-MΦ

BM-DC

pDC

CD8 DC

CD11b DC

Myeloid signature genes

         

1420804_s_at

Clec4d

4,934

0.65

0.49

0.75

0.41

-

-

-

1420330_at

Clec4e

5,511

0.11

0.22

0.23

0.11

-

-

-

1450808_at

Fpr1

119

1.91

-

5.55

2.41

-

-

-

1452001_at

Nfe2

139

1.44

-

-

3.31

-

-

-

1450919_at

Mpp1

1,888

0.15

2.05

1.75

0.52

0.23

0.09

0.07

1419609_at

Ccr1

403

1.27

4.04

0.53

3.98

0.2

-

-

1417061_at

Slc40a1

2,588

0.68

-

0.56

0.07

0.01

0.01

0.02

1448756_at

S100a9

8,664

1.2

-

0.01

0.99

0

0

-

1417268_at

Cd14

6,745

0.1

0.3

0.6

0.19

0.02

0.01

0.01

1418163_at

Tlr4

464

0.1

0.36

0.93

0.66

-

0.07

0.06

1448620_at

Fcgr3

1,471

2.02

3.56

2.15

2.46

-

0.02

0.07

1422953_at

Fpr-rs2

839

2.04

0.12

0.85

1.58

-

-

0.05

1419132_at

Tlr2

1,763

0.11

0.42

0.24

0.48

0.04

0.1

0.14

1417566_at

Abhd5

170

0.19

0.72

0.86

2.2

0.18

0.45

0.25

1415814_at

Atp6v1b2

1,556

0.22

2.75

1.57

1.43

0.18

0.27

0.24

1427327_at

Pilra

434

1.53

0.16

0.47

2.29

0.1

-

0.21

1418888_a_at

Sepx1

4,416

0.48

0.34

0.31

0.56

0.03

0.04

0.05

1438928_x_at

Ninj1

5,574

0.03

1.3

0.46

0.36

0.03

0.02

0.02

1448881_at

Hp

400

3.19

0.14

0.06

3.09

-

-

-

1449453_at

Bst1

340

1.08

4.97

0.58

1.61

0.21

0.51

-

1419394_s_at

S100a8

10,190

1.37

0.01

0.01

0.66

-

0

-

1437200_at

Fcho2

311

1.09

1.32

1.06

0.76

0.28

0.2

0.33

1418806_at

Csf3r

2,598

0.2

0.14

0.19

0.11

-

-

0.03

1439902_at

C5ar1

317

8.21

0.19

1.63

0.37

-

-

-

1456046_at

Cd93

1,559

0.1

0.49

1.18

0.33

0.02

-

-

1418901_at

Cebpb

3,797

0.14

0.7

0.22

0.42

0.02

0.01

0.02

1420699_at

Clec7a

2,748

0.83

2.62

0.44

1.71

0.08

0.06

0.54

Pan-DC signature genes

         

1419538_at

Flt3

51

0.74

-

-

0.7

16.2

25.32

17.78

1427619_a_at

Sh3tc1

-

>1.1

>6.8

>2.8

>4.9

>5.2

>6.5

>4.6

1424489_a_at

Trit1

54

7.28

0.44

0.76

1.23

9.03

11.53

8.63

1428744_s_at

Bri3bp

161

0.84

0.6

1.44

3.28

6.09

7.24

5.98

1448923_at

Prkra

72

1.28

0.77

2.89

2.57

4.45

7.88

3.63

1434880_at

Etv6

140

5.39

1.52

0.74

1.75

5.79

6.02

7.78

1416108_a_at

Tmed3

154

0.81

3.74

2.63

4.65

10.17

4.48

3

1436633_at

Bahcc1

41

1.77

-

0.83

-

1.8

3.88

2.35

1437378_x_at

Scarb1

97

5.02

1.25

2.61

3.17

7.41

8.27

4.05

cDC signature genes

         

1435108_at

Arhgap22

63

-

-

2.37

0.57

0.59

10.65

4.43

1429168_at

Btbd4

129

0.19

0.27

-

0.47

0.81

3.89

3.8

1425294_at

Slamf8

146

1.06

39.89

1.83

1.77

0.39

8.48

5.27

1453076_at

9130211I03Rik

36

1.61

2.85

1.03

13.11

0.62

30.94

25.64

1436907_at

Nav1

102

1.59

0.74

2.63

1.96

1.21

6.08

13.14

1421210_at

C2ta

125

0.17

1.79

0.19

0.93

1.46

5.94

5.43

1423122_at

Avpi1

150

0.32

-

0.2

0.86

0.61

2.47

7.62

1416627_at

Spint1

-

>1.5

>1.1

-

>22.9

>1.6

>25.7

>30.6

1450667_a_at

Cs

396

2.47

0.9

1.19

3.54

2.83

4.64

4.5

APC signature genes

         

1419744_at

H2-DMb2

451

0.12

0.1

0.08

1.47

0.45

0.48

1.69

1443687_x_at

H2-DMb1

547

0.56

0.13

0.11

1.56

1.06

0.82

3.13

1434955_at

March1

80

32.64

0.83

1.51

3.48

3.73

13.4

8.57

1448143_at

Aldh2

867

0.47

2.14

2.07

1.32

0.95

0.65

0.45

1419406_a_at

Bcl11a

60

1.47

0.34

-

0.71

20.41

7.63

9.19

1422755_at

Btk

416

0.56

0.76

1.3

1.15

0.88

1.45

1.17

1418365_at

Ctsh

1,393

0.81

3.9

2.19

2.15

3.69

1.24

2.16

1417025_at

H2-Eb1

6,385

0.13

0.39

0.04

0.8

0.9

1.31

1.33

1425519_a_at

Cd74

8,377

0.36

0.95

0.2

0.9

0.83

0.97

0.98

1417868_a_at

Ctsz

7,061

0.05

1.16

0.95

0.85

0.5

0.3

0.49

1423393_at

Clic4

2,807

0.07

2.04

0.84

0.57

0.69

0.72

0.67

1430570_at

Kynu

31

1.23

-

-

3.21

12.87

5.16

11.56

1435745_at

5031439G07Rik

356

0.95

0.73

2.76

2.51

3.23

3.14

4.28

1458299_s_at

Nfkbie

767

0.4

0.62

0.1

0.44

1.25

0.65

1.27

1423768_at

Unc93b1

663

0.1

2.27

2.69

1.46

1.2

0.93

0.91

Non-DC signature genes

         

1424375_s_at

Gimap4

362

0.14

0.29

-

0.1

0.11

-

0.11

1424380_at

Vps37b

313

0.44

0.46

0.45

0.26

0.28

0.28

0.27

1425396_a_at

Lck

118

-

0.57

0.2

0.32

0.21

-

0.17

1433694_at

Pde3b

352

0.69

0.15

0.16

0.42

-

0.65

0.35

*Average expression across replicates. Genes for which expression between mouse bone-marrow derived GM-CSF DCs (BM-DCs) and spleen DCs or spleen cDCs varies more than two-fold are shown in bold. BM-MΦ, mouse bone marrow-derived M-CSF macrophages; MΦ, peritoneal mouse macrophages; mono, mouse spleen monocytes from the SB laboratory; mono(2), mouse spleen monocytes from the BP laboratory, as listed in Table 1.

https://static-content.springer.com/image/art%3A10.1186%2Fgb-2008-9-1-r17/MediaObjects/13059_2007_Article_1879_Fig5_HTML.jpg
Figure 5

Clustering of in vitro GM-CSF derived DCs with monocytes, macrophages and LN-resident DCs. Hierarchical clustering with complete linkage was performed on the indicated cell populations isolated from: (a) mouse, (b) human, and (c) both. The heat maps used for illustration were selected as the two clusters of genes encompassing either Flt3 or Mafb, with a correlation cut-off for similarity of gene expression within each cluster at 0.8.

Discussion

By performing meta-analyses of various datasets describing global gene expression of mouse spleen and human blood leukocyte subsets, we have been able to identify for the first time conserved genetic programs common to human and mouse LN-DC subsets. All the LN-DC subsets examined here are shown to share selective expression of several genes, while harboring only low levels of other transcripts present in all other leukocytes. These analyses indicate that LN-DCs, including pDCs, constitute a specific family of leukocytes, distinct from those of classic lymphoid or myeloid cells. Furthermore, we demonstrate a striking genetic proximity between mouse and human pDCs, which are shown for the first time to harbor a very distinct transcriptional signature as large and specific as that observed for NK cells or T cells. In contrast, a higher genetic distance is observed between mouse and human conventional DC subsets, although a partial functional equivalence is suggested between mCD8α and hBDCA3 cDCs on the one hand versus mCD11b and hBDCA1 cDCs on the other hand.

Our finding that LN-DCs constitute a distinct entity within immune cells raises the question of whether these cells form a distinct lineage in terms of ontogeny, or whether their shared gene expression profile (notably that between cDCs and pDCs) reflects a functional rather than a developmental similarity. To date, the place of both cDCs and pDCs in the hematopoietic tree is not clear [78, 79]. A BM progenitor, named macrophage and dendritic cell progenitor (MDP), has been recently identified that specifically gives rise to monocytes/macrophages and to cDCs, but not to polymorphonuclear cells or to lymphoïd cells [80, 81]. Under the experimental conditions used in the corresponding report, pDCs were not detected in the progeny of MDPs. Here, we show that the transcriptome programs of mouse spleen and human blood cDCs exhibit only a very limited overlap with that of monocytes/macrophages (Figure 2). This is consistent with the recent observation that monocytes can give rise to mucosal, but not splenic, cDCs, suggesting that splenic cDCs develop from MDPs without a monocytic intermediate [81]. While mouse pDCs have been argued to arise from both lymphoid or myeloid progenitors, their gene expression overlaps with lymphoid or myeloid cells are limited. Interestingly, a murine progenitor cell line that exhibits both cDC and pDC differentiation potential has been described recently [82], suggesting that putative pan-DC progenitors might also exist in vivo, which would be consistent with the gene profiling analyses presented here.

Our study identifies transcriptional signatures conserved between mouse and human, common to all LN-DC subsets examined, or specific to pDCs, cDCs, or individual cDC subsets. A genetic equivalence is suggested between mouse CD8α cDCs and human BDCA3 cDCs, and between mouse CD11b cDCs and human BDCA1 cDCs. In contrast to the genes selectively expressed in subsets of myeloid or lymphoid cells in a conserved manner between mouse and human, most of the genes specifically increased in all LN-DC subsets or in individual LN-DC subsets are currently uncharacterized. As a consequence, the functional annotations of the LN-DC transcriptional signatures appear much less informative than those for myeloid cells, lymphocytes or APCs. This highlights how much has already been deciphered regarding the molecular regulation of antigen presentation or lymphocyte biology, as opposed to how little we know about the genetic programs that determine the specific features of LN-DCs. We believe that our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing specific aspects of the ontogeny and functions of leukocyte subsets, especially DCs.

It should be noted that many genes are found to be expressed to very high levels in specific subsets of either mouse or man while no orthologous gene has been identified in the other species. This could be due to a true absence of orthologous genes between these two vertebrate species, or to a lack of identification of an existing orthology relationship. It is also possible that some of the genes expressed only in mouse DCs or only in human DCs, and not conserved between the two species, might represent functional homologs, similar to what is observed for human KIR and mouse Ly49 NK cell receptors. This may be the case for the human LILRA4 (ILT7) and the mouse SIGLECH molecules, as both of them signal through immunoreceptor tyrosine-based activation motif (ITAM)-bearing adaptors to downmodulate IFN-α/β production by human and mouse pDCs, respectively, upon triggering of TLRs [83, 84]. Thus, understanding the role in LN-DCs of genes identified here only in mouse or human might be important. The transcriptional signatures identified for mouse LN-DC subsets in this study have been confirmed by analyses of independent data recently published by others on mouse cDC subsets, B cells and T cells [11] or on cDCs and pDCs [15]. Most of the data for the mouse 430 2.0 compendium were generated in-house, with the exceptions being CD4 T cells and myeloid cells. In humans, we generated the data for non-DC populations, whereas data for DC subsets and CD16 cells were all generated by another group and retrieved from a public database. It is well known that datasets for the same cell type can vary considerably between laboratories. However, many of the genes identified as specific for each mouse LN-DC subset using our own data were confirmed by the analysis of other data independently generated by the groups of M Nussenzweig and R Steinman [11]. These data are given in Additional data file 5.

Our clustering analyses and PCA also showed relatively little dataset-dependent biases, and generally grouped related cell populations together, even if they were from different origins (see, for instance, the PCA clustering of in vitro derived GM-CSF DC samples, which originated from two independent datasets in Additional data file 6). In addition, we analyzed by real-time PCR the expression profile of 27 genes across mouse leukocyte subsets from biological samples independent of those used in the gene chips analysis. All the results were consistent with the gene chip data (Additional data file 7). We also confirmed specific expression of PACSIN1 in human pDCs at both the mRNA and protein levels (Additional data file 8). Finally, we believe that our approach validates the gene expression profile identified for leukocyte subsets in the strongest way possible, by demonstrating the evolutionary conservation between mouse and human. Indeed, the gene signatures that we describe here are based on genes found specifically expressed in putatively homologous subsets of mouse and human leukocytes compared to several other types of leukocytes. This approach does not rely solely on the use of independent biological samples of similar origin and on different techniques for measurement of the expression of mRNA. It actually shows that orthologous genes share the same specific expression pattern in putatively homologous immune cell subsets from two different species, under conditions where the markers used to purify the human and mouse cell populations, and the probes used to check the expression of the orthologous genes, differ considerably. Thus, we believe that the analyses presented here are extremely robust even though they were, in part, performed by creating compendia regrouping data generated by different laboratories for different cell types.

In addition to our discovery of transcriptional signatures specific to all LN-DCs or to LN-DC subsets, we demonstrate that, once identified, the transcriptional signatures of multiple cell types can be effectively used to help determine the nature of newly identified cell types of ambiguous phenotype or functions. In our attempt to appropriately place IKDCs and CD16 cells within the leukocyte family, we used the microarray data from the original reports aimed at characterizing these cells and compared them to the data from several other leukocyte populations. The conclusions of this analysis are in sharp contrast to those originally reported [15, 31]. We believe that these opposing conclusions arise from the difference in the contextual framework within which our data and that of the previously mentioned studies were analyzed. Thus, the results of our analysis of the transcriptional signature of both IKDCs and CD16 cells emphasize the need to study the transcriptional signatures of individual cell populations in the context of multiple cell types of various phenotypes and functions. Finally, this approach also allowed us to confirm a very recent report that demonstrated that in vitro derived GM-CSF mouse DCs likely correspond to inflammatory DCs and greatly differ from LN-DCs, based on ontogenic and functional studies [75]. Thus, extrapolation to LN-DCs of the results of the cell biology and functional studies performed with in vitro derived GM-CSF DCs should only be made with extreme caution.

Conclusion

This study comparing whole genome expression profiling of human and mouse leukocytes has identified for the first time conserved genetic programs common to all LN-DCs or specific to the plasmacytoid versus conventional subsets. In depth studies of these genetic signatures should provide novel insights on the developmental program and the specific functions of LN-DC subsets. The study in the mouse of the novel, cDC-specific genes identified here should accelerate the understanding of the mysteries of the biology of these cells in both mouse and human. This should help to more effectively translate fundamental immunological discoveries in the mouse to applied immunology research aimed at improving human health in multiple disease settings.

Materials and methods

Sorting of cell subsets

Duplicates of pDCs (Lin-CD11c+120G8high), CD8α cDCs (Lin-CD11chighCD8α+120G8-/low), CD11b cDCs (Lin-CD11chighCD11b+120G8-/low) and NK cells (NK1.1+TCRβ-) were sorted during two independent experiments from pooled spleens of untreated C57BL/6 mice. Splenic CD19+ B lymphocytes, CD4 T cells and CD8 T cells were sorted in other independent experiments. Purity of sorted cell populations was over 98% as checked by flow cytometry (not shown).

Processing of cell samples for the Affymetrix GeneChip assays

RNA was extracted from between 7.5 × 105 and 1.5 × 106 cells for each leukocyte subset with the Qiagen (Courtaboeuf, France) micro RNAeasy kit, yielding between 200 and 700 ng of total RNA for each sample. Quality and absence of genomic DNA contamination were assessed with a Bioanalyser (Agilent, Massy, France). RNA (100 ng) from each sample was used to synthesize probes, using two successive rounds of cRNA amplification with appropriate quality control to ensure full length synthesis according to standard Affymetrix protocols, and hybridized to mouse 430 2.0 chips (Affymetrix, Santa Clara, CA, USA). Raw data were transformed with the Mas5 algorithm, which yields a normalized expression value, and 'absent' and 'present' calls. Target intensity was set to 100 for all chips.

Individual analysis of the mouse 430 2.0 or human U133 Plus 2.0 compendia

For each compendium, all datasets were normalized with the invariant rank method and only one representative dataset was kept for redundant ProbeSets targeting the same gene. The datasets were further filtered to eliminate genes with similar expression in all samples, by selecting only the genes expressed above 50 (respectively 100) in all the replicates of at least one population for the mouse (respectively human) datasets and whose expression across all samples harbored a coefficient of variation above the median of the coefficient of variation of all ProbeSets. The final dataset consisted of 7,298 (respectively 11,507) ProbeSets for the mouse 430 2.0 compendium (respectively human U133 Plus 2.0), representing individual genes with differential expression between ex vivo isolated cell subsets. The final dataset consisted of 12,857 (respectively 6,724) ProbeSets for the mouse 430 2.0 compendium (respectively human U133 Plus 2.0), representing individual genes with differential expression between LN-DCs, monocytes/macrophages and in vitro derived GM-CSF DCs. These datasets for ex vivo isolated cells are accessible as Excel workbooks in Additional data files 1 and 2. The software Cluster and Treeview were used to classify cell subsets according to the proximity of their gene expression pattern as assessed by hierarchical clustering with complete linkage.

We implemented a function in the Matlab software to perform PCA. This function computes the eigenvalues and eigenvectors of the dataset using the correlation matrix. The eigenvalues were then ordered from highest to lowest, indicating their relative contribution to the structure of the data. For both mouse and human datasets, the first principal component accounted for most of the information (54% and 68% for mouse and human, respectively) and was associated with a similar coordinate for all samples. This component thus reflected the common gene expression among the samples. Second and third components together represented 24% and 21%, respectively, of the information for mouse and human datasets, and thus accounted for a large part of the variability. The projection of each sample on the planes defined by these components was represented as a dot plot to generate the PCA figures.

Partitional clustering was performed using the FCM algorithm, which links each gene to all clusters via a vector of membership indexes, each comprised between 0 and 1 [34]. For both mouse and human datasets, we heuristically set the number of clusters to 30, and the fuzziness parameter m was taken as 1.2 (see [34] for the determination of m). Ten independent runs of the algorithm were performed, and the one minimizing the inertia criterion was selected [34]. A threshold value of 0.9 was taken to select probe sets most closely associated with a given cluster. This selection retained 4,062 and 4,751 probe sets from mouse and human datasets, respectively. Probe set clusters were then manually ordered to provide coherent pictures, which were visualized with Treeview.

Meta-analysis of aggregated mouse and human datasets

We identified 2,227 orthologous genes that showed significant variation of expression in both the mouse 430 2.0 and U133 Plus 2.0 human datasets. This dataset is accessible as an Excel workbook in Additional data file 3. In order to compare the expression patterns of these genes between human and mouse, the log signal values for each of these genes were first normalized to a mean equal to zero and a variance equal to 1, independently in the mouse and human datasets, as previously described for comparing the gene expression program of human and mouse tumors [22, 27]. The two normalized datasets were then pooled and a hierarchical clustering with complete linkage was performed. A similar analysis was performed for the comparison of human and mouse LN-DCs, monocytes, macrophages and in vitro derived GM-CSF DCs.

Meta-analysis of mouse 430 2.0 and U74Av2 datasets

In order to classify the IKDCs based on the optimal gene signatures of the different cell subsets examined, with only minimal impact of differences in the experimental protocols used to prepare the cells and to perform the gene chips assays, the clustering of the cell populations was performed as a meta-analysis of our own mouse 430 2.0 dataset together with the published U74Av2 datasets. The Array Comparison support information of the NetAffyx™ analysis center (Affymetrix) was used to identify matched ProbSets between the two types of microarrays. Only one representative dataset was kept for redundant ProbeSets targeting the same gene. This yielded a set of 2,251 genes whose expression could be compared between the two datasets, using the same normalization method as described above. This dataset is accessible as Excel workbooks in Additional data file 4. As expected, this meta-analysis led to co-clustering of all the samples derived from identical cell types whether their gene expression had been measured by us on 430 2.0 microarrays or by others on U74Av2 microarrays, with the exception of the cDC population from [15], which segregated with pDCs rather than with the cDC subsets from the other datasets.

Data mining

Gene lists were analyzed using the DAVID 'functional annotation chart' tool accessible on the NIAID website [52, 53]. Different databases were used for these annotations: gene ontology (Amigo), knowledge pathways (KEGG), interactions (BIND), interprotein domains (INTERPRO), and disease (OMIM/OMIA). The annotations shown in Tables 5 and 7 were selected as the most highly significant terms retrieved by performing an over-representation study. To this end, a modified Fisher exact P value called the 'EASE score' was calculated to measure the enrichment in gene-annotation terms between the gene signature specific to the leukocyte subpopulation examined ('List') and the complete set of all the genes selected for the compendium analyzed ('Background'). The significance threshold was set at an EASE score below 0.05 in most instances, or below 0.1 for DC signatures that did not yield many highly significant terms as discussed in Results. Individual significant annotations encompassing many common genes or similar biological processes were regrouped using the 'Functional annotation clustering' tool of the DAVID software. More information on this type of analysis is available on the DAVID website [85].

Public access to the raw data for the datasets analyzed in the paper

Our datasets for mouse DC subsets, NK cells, CD8 T cells, and B lymphocytes have been deposited in the Gene Expression Omnibus (GEO) database under reference number GSE9810. The references for download of the public data used from the original websites where they were first made available are given in Table 1. In addition, all raw transcriptomic data analyzed here have been regrouped on our website [86] and are available for public download.

Note added in proof

During the review process of this paper, two reports were published in Nature Immunology that identified a common progenitor characterized as FLT3+M-CSF+ for mouse LN-DCs (pDCs, CD8α cDCs and CD11b cDCs), devoid of any capability to generate lymphoid cells or monocytes/macrophages, and named common dendritic progenitor (CDP) [87, 88]. This observation is thus consistent with our gene profiling analysis of human and mouse leukocytes. The question whether this pathway for LN-DCs is the major one, or just one possibility among others, including differentiation from monocytes, has been raised [89]. Our gene profiling data would suggest that most mouse LN-DCs derive from the recently identified CDP or MDP in vivo, without a monocytic intermediate, consistent with a recent report [81]. It also implies that a similar pathway must exist in humans. The relationship between the CDP and the MDP still remains to be established. Three reports have been published very recently in the Journal of Experimental Medicine that showed that IKDCs are a specific subset of NK cells, based on functional and ontogenic approaches comparing these cells to DCs and NK cells [9092]. This is consistent with the results of our clustering analysis of IKDCs with other leukocyte subsets. Finally, two recent reports have identified a new transduction pathway in human pDCs involving a B cell receptor-like ITAM-signaling pathway [93, 94]. This pathway involves the BLNK transduction molecule, which we have identified here as expressed to very high levels in mouse and human pDCs compared to the other LN-DCs (Table 6) and many other leukocytes. We believe that the conserved transcriptional signatures identified here for mouse and human LN-DC subsets will lead to many more discoveries for the understanding of the specialized functions of these cells.

Additional data files

The following additional data are available. Additional data file 1 is a Microsoft Excel workbook with raw data for the mouse gene chip compendium. Additional data file 2 is a Microsoft Excel workbook with raw data for the human gene chip compendium. Additional data file 3 is a Microsoft Excel workbook with raw data for the human/mouse gene chip compendium. Additional data file 4 is a Microsoft Excel workbook with raw data for the IKDC gene chip compendium. Additional data file 5 is a Microsoft Excel workbook giving the mouse DC subset gene signatures according to our datasets with confirmation from two other independent datasets (one for pDCs and one for cDC subsets). Additional data file 6 is a figure showing the results of PCA for investigation of the relationships between in vitro derived GM-CSF DCs and LN-DCs in mouse and human. Additional data file 7 is a table giving real-time PCR data for the pattern of expression of 27 genes across mouse leukocyte subsets. Additional data file 8 is a figure illustrating PACSIN1 expression in human pDCs versus PBMCs by RT-PCR and western blotting.

Abbreviations

APC: 

antigen-presenting cell

BM: 

bone marrow

cDC: 

conventional dendritic cell

CDP: 

common dendritic progenitor

DC: 

dendritic cell

FCM: 

fuzzy c-means

GEO: 

Gene Expression Omnibus

GM-CSF: 

granulocyte-macrophage colony stimulating factor

IFN: 

interferon

IKDC: 

interferon-producing killer dendritic cell

ITAM: 

immunoreceptor tyrosine-based activation motif

LN-DC: 

lymph node-resident DC

M-CSF: 

macrophage colony-stimulating factor

MDP: 

macrophage and dendritic cell progenitor

MHC: 

major histocompatibility

NK: 

natural killer

PCA: 

principal component analysis

pDC: 

plasmacytoid dendritic cell

TLR: 

toll-like receptor.

Declarations

Acknowledgements

The authors are indebted to Bertrand Nadel and Jean-Marc Navarro for help with the real-time PCR experiments and to Markus Plomann for the generous gift of the anti-PACSIN1 antibody. The authors also thank the staff of the animal care facilities and the flow cytometry core facility of the CIML for excellent assistance. This work was supported by an ATIP grant from the CNRS, a grant from the Association pour la Recherche sur le Cancer (ARC) and a grant from the Réseau National des Génopoles (RNG) to MD. SHR was supported by the CNRS, the Fondation pour la Recherche Médicale, and the Philippe Foundation. The CIML is supported by institutional grants from the INSERM, the CNRS, and the Université de la Méditerranée. We thank the IPSOGEN company for their advice on the analysis of the data. The authors declare no conflict of interest.

Authors’ Affiliations

(1)
CIML (Centre d'Immunologie de Marseille-Luminy), Université de la Méditerranée
(2)
U631, INSERM (Institut National de la Santé et de la Recherche Médicale)
(3)
UMR6102, CNRS (Centre National de la Recherche Scientifique)
(4)
Hematopoiesis and leukemogenesis in the mouse, IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire)
(5)
U596, INSERM
(6)
UMR7104, CNRS
(7)
UM41, Université Louis Pasteur
(8)
The Medical Investigation of Neurodevelopmental Disorders Institute, University of California at Davis Medical Center
(9)
Hôpital de la Conception, Assistance Publique-Hôpitaux de Marseille
(10)
Current address: Genomics Institute of the Novartis Research Foundation

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