Relationships Among Murine CD11chigh Dendritic Cell Subsets as Revealed by Baseline Gene Expression Patterns

Alexander D. Edwards, Damien Chaussabel, Simon Tomlinson, Oliver Schulz, Alan Sher and Caetano Reis e Sousa
J Immunol July 1, 2003, 171 (1) 47-60; DOI: https://doi.org/10.4049/jimmunol.171.1.47

Abstract

The functional relationships and properties of different subtypes of dendritic cells (DC) remain largely undefined. To better characterize these cells, we used global gene analysis to determine gene expression patterns among murine CD11chigh DC subsets. CD4+, CD8α+, and CD8α− CD4− (double negative (DN)) DC were purified from spleens of normal C57/BL6 mice and analyzed using Affymetrix microarrays. The CD4+ and CD8α+ DC subsets showed distinct basal expression profiles differing by >200 individual genes. These included known DC subset markers as well as previously unrecognized, differentially expressed CD Ags such as CD1d, CD5, CD22, and CD72. Flow cytometric analysis confirmed differential expression in nine of nine cases, thereby validating the microarray analysis. Interestingly, the microarray expression profiles for DN cells strongly resembled those of CD4+ DC, differing from them by <25 genes. This suggests that CD4+ and DN DC are closely related phylogenetically, whereas CD8α+ DC represent a more distant lineage, supporting the historical distinction between CD8α+ and CD8α− DC. However, staining patterns revealed that in contrast to CD4+ DC, the DN subset is heterogeneous and comprises at least two subpopulations. Gene Ontology and literature mining analyses of genes expressed differentially among DC subsets indicated strong associations with immune response parameters as well as cell differentiation and signaling. Such associations offer clues to possible unique functions of the CD11chigh DC subsets that to date have been difficult to define as rigid distinctions.

Dendritic cells (DC)4 are central to the initiation of immune responses and the generation and maintenance of self-tolerance (1). However, a complete understanding of the role of DC in the immune system has been complicated by the existence of multiple subtypes, which may perform different functions (2). For example, at least six DC subsets have been identified in mouse lymph nodes (3). Among these, two are found in lymph nodes draining the skin and appear to be derived from dermal DC and epidermal Langerhans cells, respectively (3). Cells similar to the dermal DC-derived population can also be found in lymph nodes that do not drain the skin, and they may represent the progeny of interstitial DC found in most nonlymphoid tissues (3). The four remaining populations appear to be present in both lymph nodes and spleen and include the recently identified plasmacytoid CD11clow DC (4, 5, 6, 7) as well as the more conventional CD11chigh DC. Mouse CD11chigh DC have been best studied in spleen and correspond to the original Steinman DC identified 30 years ago (8). Heterogeneity among spleen DC became apparent as early as 1989 (9), and on the basis of CD8α and CD11b expression, these cells were later subdivided into CD11blow CD8α+ and CD11bhigh CD8α− DC (10). CD11chigh DC are now known to comprise at least three populations following the further subdivision of CD8α− DC into the CD8α− CD4+ and CD8α− CD4− (double negative (DN)) subsets (11). Precursor-product analysis suggests that these subsets constitute stable and discrete populations and that they do not interconvert (12, 13, 14). Some reports have argued that there can be acquisition of the CD8α marker by CD8α− DC (15, 16, 17, 18), but, with rare exceptions (17), this results in cells that stain only weakly for the marker, do not express CD8α mRNA, and are clearly distinguishable from bona fide CD8α+ DC (15, 16, 18). More recently, it has been shown that plasmacytoid DC can also increase CD8α expression after activation by microbial stimuli to become CD8α+ CD11chigh DC (14). However, the resulting cells also express CD4 and fail to express DEC-205 and thus, again, are very distinct from conventional CD8α+ DC (14).

Virtually every aspect of the mouse DC subset biology remains unsettled, including their ontogeny, function, cytokine production potential, and Ag presentation capacity. Based on cell transfer experiments in which thymic lymphoid progenitors gave rise to CD8α+ DC, it was concluded that the CD8α marker could be used to differentiate between DC of lymphoid and myeloid origin (19). This has been challenged by more recent reports showing that both lymphoid and myeloid progenitors give rise to CD8α− and CD8α+ DC and that most CD11chigh DC are likely to be of myeloid origin (20, 21, 22). Functional comparisons of DC subsets have been similarly controversial. Initial observations that CD8α+ DC are intrinsically tolerogenic (23, 24) have been hard to reproduce (25, 26). More prevalent has been the idea that CD8α+ DC produce IL-12 and prime Th1 responses in vivo, whereas CD8α− DC do not make the cytokine and preferentially induce Th2 development (27, 28, 29, 30). However, we and others have found only minor differences in the ability of CD11chigh DC subsets to drive Th1/Th2 development (26, 31). In addition, all CD11chigh DC subsets can make IL-12 p40 in response to appropriate microbial stimuli, and CD8α+ and DN DC can also make IL-12 p70, demonstrating that IL-12 production by DC is not restricted to the CD8α+ subset (32, 33, 34, 35). With regard to Ag presentation, CD8α+ DC have been proposed to possess a unique pathway for MHC class I presentation of exogenous Ag (36, 37), but this has been put in question by more recent reports showing MHC class I presentation by CD8α− DC (34, 38, 39, 40). Among the various putative unique properties of murine DC subsets, those that to date remain unchallenged include the following: 1) CD4+ DC do not make bioactive IL-12 p70 (35, 41); 2) CD8α+ DC are uniquely able to take up dying cells (38, 40); and 3) murine CD8α+ DC express a different Toll-like receptor repertoire from all other murine spleen DC subsets (42). The extraordinary ability of plasmacytoid DC to produce type I IFN may be thought of as another unique DC subset function to add to this list. However, we have found that this is stimulus dependent and that, when appropriately stimulated, nonplasmacytoid mouse DC also produce high amounts of IFNα.5 Similarly, nonplasmacytoid human monocyte-derived DC produce type I IFN in response to infection with influenza (43, 44).

Independently of the controversy surrounding the functional properties of mouse DC subsets, there is also relatively little information on their phenotypes. A limited number of markers in addition to CD4 and CD8α appear to be subset-restricted. For example, CD8α+ DC express higher levels of DEC-205, heat-stable Ag (CD24a), CD36, IFN consensus sequence binding protein (ICSBP; IRF8), langerin, and integrin αE (CD103) than CD8α− DC (9, 45, 46, 47, 48, 49, 50), whereas the latter preferentially express CD11b, F4/80, RelB, and CCR6 (45, 50, 51, 52). However, most studies have grouped CD8α− DC together and failed to discriminate between DN and CD4+ DC. All these considerations suggested that it would be useful to define DC subsets at the molecular level in an attempt to understand more about their relationship to each other, provide better insights into their biology, and find alternative markers for their isolation. Toward this end, we conducted microarray analysis of mRNA purified from CD11chigh splenic DC subsets immediately after cell isolation. Our data suggest that all three subsets constitute unique populations and that the CD4+ and DN DC subsets are more similar to each other than to the CD8α+ DC subset. Furthermore, we confirm selective expression of some previously reported genes and identify CD5, CD72, and CD22 as novel markers for the CD8α− DC subsets. These data offer some new insights into the phenotype and phylogeny of DC subsets and should constitute a useful resource for future work into the biology of these cells.

Materials and Methods

Cells

C57BL/6 mice were obtained from Charles River (Margate, U.K.) or from the breeding unit of Cancer Research U.K. (South Mimms, U.K.). Spleen cell suspensions were prepared by Liberase CI (Roche, Lewes, U.K.) and DNase I digestion (32). DC-enriched fractions were prepared by labeling splenocytes with anti-CD11c MACS beads (Miltenyi Biotec, Bisley, U.K.), followed by positive selection using LS magnetic columns (Miltenyi Biotec) as previously described (35). CD11c-enriched preparations were further stained with PE-anti-CD11c, FITC-anti-CD4, and TriColor-anti-CD8α (Caltag, Burlingame, CA) and sorted on a MoFlo cytometer (Cytomation, Fort Collins, CO). Sorted DC subsets were lysed and stored at −80°C until mRNA isolation.

RNA and cRNA preparation

Total RNA was prepared using the RNeasy mini kit (Qiagen, Valencia, CA) and was used to generate cRNA probes. Preparation of cRNA, hybridization, and scanning of the microarrays were performed according to the manufacturer’s protocol (Affymetrix, Santa Clara, CA). Briefly, 12–15 μg of RNA was converted into double-stranded cDNA by RT using a cDNA synthesis kit (SuperScript Choice, Life Technologies, Gaithersburg, MD) with an oligo(dT)24 primer containing a T7 RNA polymerase promoter site-added 3′ of poly(T) (Genset, La Jolla, CA). After second-strand synthesis, labeled cRNA was generated from the cDNA sample by an in vitro transcription reaction supplemented with the Bioarray HighYield RNA transcription labeling kit (Enzo, Farmingdale, NY). The labeled cRNA was purified using RNeasy spin columns (Qiagen) and denatured at 94°C before hybridization.

Microarray hybridization

Labeled cRNA was hybridized to the Affymetrix U74A version 2 microarray while rotating at 60 rpm for ∼16 h at 45°C. After hybridization, the microarray was washed using the Affymetrix Fluidics Station in buffer containing biotinylated anti-streptavidin Ab (Vector Laboratories, Burlingame, CA; 10 min, 25°C) and stained with streptavidin-PE (final concentration, 10 μg/ml; Molecular Probes, Eugene, OR) for 10 min at 25°C. Subsequently, the microarray was washed, restained with streptavidin-PE (10 min, 25°C), and washed again before measuring fluorescence at 570 nm in an Affymetrix scanner. Data were normalized by global scaling using the Affymetrix software.

Data analysis

Data analysis was conducted using Genespring (Silicon Genetics, Redwood City, CA) and Excel (Microsoft, Redmond, WA) software packages using the criteria described in Results. Gene Ontology analysis was performed using FatiGO. 6 Analysis of patterns of term occurrences in literature abstracts was performed as described previously (53). Briefly, relevant articles were identified and indexed for each gene, and term occurrences were computed from the resulting 7000 abstracts. Terms were filtered using the following criteria: low global occurrence in the biomedical literature and high specific occurrence for at least two genes from the list. Genes and remaining terms were rearranged by two-way hierarchical clustering of term occurrence values.

Results

Microarray data generation and validation

Murine CD11chigh subsets were purified from the pooled spleens of 15–25 C57BL/6 mice, and RNA was extracted (Fig. 1⇓A). RNA obtained from three independent preparations was pooled and used to prepare targets for hybridization to Affymetrix GeneChip U74A version 2 microarrays containing 12,488 probe sets, which cover ∼9,700 distinct expressed sequence tag (EST) clusters or approximately one-third of the mouse genome.7 The entire experiment was repeated, and the average difference (AvDiff) value for each probe set, reflecting expression level (54), was compared between the two independent experiments (five individual DC subset purifications, two pools, total of 76 animals). These experiments were remarkably reproducible, as can be seen by the slope of 1 when plotting all values from the first experiment against all values from the second (Fig. 1⇓B). Most variation was seen in probe sets with AvDiff values <50 (Fig. 1⇓B), which is to be expected as this value is around the borderline of detectable signal strength. To obtain an actual measure of reproducibility, we determined the percentage of data points that varied by >2.5-fold between the two experiments (Fig. 1⇓B). Less than 1% variation was seen for the two CD8α− DC subsets, suggesting that their transcriptomes in the two experiments were remarkably consistent. In contrast, almost 4% of the data points for the CD8α+ subset varied in expression by >2.5-fold between the two experiments (Fig. 1⇓B). The increased variation in this subset may reflect increased biological variation, as this subset is very rapidly activated during isolation (A. D. Edwards and C. Reis e Sousa, unpublished observations).

FIGURE 1.
FIGURE 1.

Global gene expression was measured in sorted spleen DC subsets by GeneChip analysis. A, Purity of sorted DC subsets analyzed by FACS. B, Reproducibility of gene expression data was assessed by plotting expression levels from two replicate GeneChip experiments for every gene analyzed. Light gray points had undetectable/low signal strength (AvDiff, <50) in one or both repeats. Points falling outside of the lines had expression levels >2.5-fold higher in one repeat compared with the other repeat; numbers indicate the percentage of genes falling into this category. C, Expression levels for selected genes. Data are mean AvDiff values between the two experiments.

For each dataset, we compiled the AvDiff values of selected marker genes expected to be present or absent in the DC subset in question (Fig. 1⇑C). All subsets expressed mRNA for CD11c and I-Ab after isolation, but expressed only low levels of message for B cell (CD19) or T cell (CD90 or components of the TCR signaling machinery) markers (Fig. 1⇑C). CD4, CD8α, and DEC-205 mRNAs were all expressed by the appropriate populations (Fig. 1⇑C). Overall, the concordance between the AvDiff values of the various indicator genes and known gene expression patterns in DC subsets confirms that the data have been generated from pure DC populations with no detectable contamination by other cell types.

Comparison of gene expression patterns between splenic DC subsets

We first compiled lists of differentially expressed genes between subsets based on a simple 2.5-fold difference, a threshold that identifies many significant differences with minimal false positives (as estimated from an analysis of marker genes that were included or excluded using different thresholds; data not shown). A probe set was considered selectively expressed in subset A compared with subset B when AvDiff for A was >50 and >2.5-fold greater than AvDiff for B. The use of the >50 criterion is required to eliminate random noise from probe sets at the borderline of expression, which show high variation (see Fig. 1⇑B). In this and all subsequent analyses, a further set of three criteria was applied. 1) The datasets from the two experiments were analyzed independently, and the two analyses were subsequently interpolated, i.e., each probe set had to meet the analysis criteria in both datasets independently to be included in the final list. 2) Genes represented on the array by more than one probe set (∼2,500 EST clusters) were included in the final list if at least one probe set met the analysis criteria. 3) Finally, the list was inspected, and probe sets that might correspond to the same EST cluster were grouped so that only unique genes were included in the final version.

Pairwise comparison of each of the CD11chigh DC subsets to each other revealed >125 genes preferentially expressed in CD8α+ DC over CD4+ DC and vice versa (Table I⇓A). Similarly, >100 genes were preferentially expressed in CD8α+ DC over DN DC and vice versa (Table I⇓A). This was true independently of which of the two replicate chips for each subset was used in the comparison and, therefore, could not be attributed to chip variation (data not shown). Manual inspection of the lists generated by this analysis revealed the expected distribution of marker genes (Table II⇓⇓⇓⇓). Thus, CD8α (Mm.1858), DEC-205 (Mm.2074), ICSBP (Mm.3182), CD24a (Mm6417), and CD103 (Mm.96) were all found within the list of genes expressed at >2.5-fold greater levels in CD8α+ DC than in CD4+ or DN DC (Table I⇓). Conversely, CD4 (Mm.2209), F4/80 (Mm.2254), RelB (Mm.1741), and CCR6 (Mm.8007) all appeared among the genes expressed at >2.5-fold greater levels in CD4+ than in CD8α+ DC; the latter three genes were also found on the list of genes expressed preferentially in DN DC over CD8α+ DC (Table II⇓⇓⇓⇓). Note that F4/80 appears as a differentially expressed transcript in both the CD4+ vs DN and DN vs CD8α+ comparisons (Table II⇓⇓⇓⇓). This is because the AvDiff value is 5-fold higher in DN DC than in CD8α+ DC as well as being 3-fold higher in CD4+ DC than in DN DC. For similar reasons, ICSBP appears as differentially expressed in both CD8α+ and DN DC (Table II⇓⇓⇓⇓). Thus, differentially expressed genes do not necessarily represent unique markers for a given population.

View this table:
Table I.

Identification of genes differentially expressed between DC subsetsa

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Table IIA.

Genes expressed at >2.5-fold AvDiff in one subset vs. another, as indicated

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Table IIB.
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Table IIC.
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Table IID.

The identification of the correct set of marker genes in each subset validated the analysis strategy and suggested that other genes in the lists were similarly likely to be differentially expressed. We were surprised to find CD5, CD7, CD22, and CD72, among the genes expressed preferentially in CD8α− DC, while CD8α+ DC preferentially expressed the CD1d molecule (Table II⇑⇑⇑⇑). In addition, the lists indicated differential expression of the CD81 and CD86 costimulatory molecules between CD8α+ and CD8α− DC, in contrast to published reports (50). To validate these patterns of gene expression we measured their protein products in different DC subsets by flow cytometry, concentrating on CD Ags and others for which mAbs were easily available (Fig. 2⇓). The staining patterns confirmed that CD5, CD22, and CD72 are all preferentially expressed in CD8α− DC, whereas CD1d, CD81, and CD86 are expressed at higher levels by CD8α+ DC (Fig. 2⇓). Other markers (CD24a, CD103, F4/80) also showed the expected subset distribution pattern (Fig. 2⇓). Thus, there was a remarkable correlation between the microarray analysis and protein expression results, demonstrating that our data can be used to identify markers that distinguish DC subsets. Interestingly, the staining patterns obtained with CD8α+ and CD4+ DC were mostly unimodal (Fig. 2⇓), suggesting that these subsets are relatively homogeneous. In contrast, the DN subset was heterogeneous with respect to staining for CD5, CD22, F4/80, CD81, and CD86, suggesting that DN DC comprise subpopulations.

FIGURE 2.
FIGURE 2.

Confirmation of expression of differentially expressed genes at the protein level. CD11c-enriched splenocytes were stained with mAbs against the protein products of selected genes identified as differentially expressed by subsets in pairwise comparisons (Table II⇑⇑⇑⇑). Cells were counterstained for CD11c, CD8α, and CD4. Histograms in bold show staining levels for the indicated genes after electronic gating on each of the three subsets. Dotted histograms show staining with isotype-matched controls. Similar staining was seen in two to five independent experiments. Bar graphs above histograms show the mean AvDiff values of a probe set specific for the same gene.

Pairwise comparison of the CD4+ and DN DC subsets revealed only ∼20 differentially expressed genes (Table I⇑A), suggesting that the two CD8α− DC subsets are more similar to each other than to CD8α+ DC. To confirm this conclusion, a three-way comparison between all subsets was conducted (Table I⇑B). We selected genes that were expressed in one subset at >50- and >2.5-fold AvDiff than in both other subsets (i.e., A > B and A > C). We further refined the data by applying the three criteria listed above. This form of analysis showed that ∼80 transcripts were expressed at >2.5-fold higher levels in CD8α+ DC compared with either CD8α− DC subset and vice versa, whereas <10 genes were shared between CD8α+ DC and either CD8α− population. Surprisingly, by these criteria there was only one gene in DN DC and only five in CD4+ DC (including CD4) that were preferentially expressed compared with other DC subsets (Table I⇑B). Manual inspection of the lists revealed a number of familiar markers that validated the analysis strategy, such as DEC-205, ICSBP, CD103, and F4/80 (Table III⇓⇓).

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Table IIIA.

Genes expressed at >2.5 fold AvDiff between any two DC subsets and the third, as indicated

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Table IIIB.

Analysis of differentially expressed genes by literature mining and clustering

The set of genes identified as differentially expressed between these cell types might indicate differences in the functions of these cells. To obtain functional information about these genes, a computational analysis of published information was used. Because of the similarity between CD4+ and DN DC (see above), we concentrated on a comparison between CD8α+ and the two other CD8α− DC subsets grouped together (CD4+ and DN). Functional relationships among genes differentially expressed in CD8α+ vs CD8α− DC were investigated using a literature-based mining technique through the analysis of patterns of term occurrences in literature abstracts (53). Interestingly this analysis revealed among the most commonly shared terms in abstracts for this group of genes to be related to immunity, inflammatory, dendritic, DC, subsets, spleen, lymphoid, or myeloid. These terms were found among a vocabulary related to immunology (e.g., chemotactic, Ig, lymph, nodes, hemopoietic, bone, marrow, and lymphocyte), indicating that several of the genes we found have previously been associated with DC subsets or at least linked to immunologic phenomena. This vocabulary has not been found in other datasets similarly subjected to literature profiling (53, 55), hence validating the functional relevance of this approach. However, as shown in Fig. 3⇓, the abstracts of several other genes differentially expressed in DC subsets lacked such vocabulary. These genes have been linked to various aspect of cell biology (e.g., apoptosis, transport, metabolism, and phosphorylation) and could underlie previously unrecognized functional properties of the murine DC subsets in question.

FIGURE 3.
FIGURE 3.

Assessment of functional relationships through the analysis of literature profiles. Functional relationships among genes differentially expressed in CD8α+ (red) and CD4+/DN (blue) DC subsets were mapped through the analysis of patterns of term occurrences in literature abstracts. Genes are arranged by hierarchical clustering of term occurrence values. Shades of yellow indicate levels of term occurrence in abstracts. A subset of representative terms used in the analysis was chosen to annotate this list.

The Gene Ontology database groups genes on the basis of functional processes in which they have been implicated. Of the genes expressed in all DC subsets (AvDiff > 50; 4099 genes), ∼35% were annotated in Gene Ontology at level 2, of which most were associated with cell growth and/or maintenance (>75%), cell communication (>25%), and developmental processes (7%; Table IV⇓). In comparison, the lists of genes selectively expressed in CD4+ over CD8α+ DC or CD8α+ over CD4+ DC were enriched for entries associated with cell communication and developmental processes (Table IV⇓, highlighted). At higher detail (levels 3 and 4), both subset-restricted lists were selectively enriched for genes involved in responses to external stimuli, in signal transduction from cell surface receptors, and in embryogenesis and morphogenesis, specifically at the level of histogenesis and organogenesis (Table IV⇓, highlighted). These results demonstrate that the genes selectively expressed in DC subsets are not simply a random sample of the total repertoire of DC-expressed genes, but, rather, reflect specific biological processes.

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Table IV.

Comparison of differentially expressed genes by GO classificationa

Discussion

The phenotype and function of murine DC subsets remain controversial. We have conducted an analysis of the transcriptome of conventional murine spleen DC in an effort to better characterize these cells and to identify differential gene expression patterns that might offer insights into DC subset function. Here we confirm the differential expression of many known DC subset markers and describe for the first time differential expression of CD1d, CD5, CD22, and CD72 among CD11chigh DC. CD1d, a nonclassical MHC class I-like molecule involved in Ag presentation to NKT cells (56), is expressed by all DC, but shows highest expression in the CD8α+ subset (Fig. 2⇑). DC have recently been shown to activate NKT cells in vivo (57), but possible differences in the role of the CD8α− and CD8α+ DC subsets in this process remain to be determined. CD5, CD22, and CD72 show a much more restricted subset distribution (Fig. 2⇑) and can effectively be used as novel markers for the CD8α− subsets. CD22 has been described as playing a role in controlling B cell Ag receptor signaling in B cells by recruiting phosphatases to the signaling complex (58). Similarly, CD5, expressed in a subset of B cells, has been shown to have inhibitory functions on B cell Ag receptor signaling (59). Whether they could also be involved in negative regulation of DC activation is an area for further study. Interestingly, CD8α− DC also express CD72, a ligand for CD5 (60), raising the possibility of autocrine or paracrine functional regulation in these subsets.

We conducted the analysis on each dataset independently, an approach that was deemed to be more stringent than averaging the values from the two datasets before applying the analysis criteria. In addition, in cases where more than one probe set existed for a given gene, we included that gene if at least one probe set matched the search criteria. This approach was less stringent than selecting only genes for which all probe sets matched the analysis criteria. However, we found that the latter approach excluded many genes that were genuinely differentially expressed. For example, CD36 is represented on the array by one probe set, which shows a higher signal for the CD8α+ DC subset, but also by two additional probe sets for which there is no signal in any of the subsets (see Footnote 6). Similar results are seen with some other genes known to be differentially expressed among subsets (see Footnote 66). Probe sets for which there is no signal in any subset are effectively uncontrolled sections of the array, and we believe we are justified in excluding them. Nevertheless, even on the basis of a single probe set, CD36 did not make it into the final table of genes expressed preferentially in CD8α+ (Tables II⇑⇑⇑⇑ and III⇑⇑) despite being a known CD8α+ DC marker (46, 47). Inspection of the raw data (see Footnote 66) revealed that this was because the ratio of AvDiff values for the main CD36 probe set did not meet the criterion that it be >2.5 in both datasets: the 8/4 ratio was 3.21 in the first experiment, but only 2.48 in the second; similarly, the 8/DN ratio was 3.04 in the first dataset, but 2.15 in the second. This demonstrates that our analysis criteria can be over stringent and exclude truly differentially expressed genes. Conversely, it is possible that some of the genes that appear as differentially expressed in our lists may turn out not to be so. All analysis criteria tread a fine line between inclusion of truly differentially expressed genes and exclusion of false positives. The availability of the raw data6 will allow individual researchers to carry out their own analysis according to personal preference. Note that those raw data also include AvDiff values determined after 2 h of DC culture ex vivo, which are not discussed in this paper. Comparison of the two time points will allow analysis of genes up- or down-regulated during spontaneous DC maturation (61).

Murine CD11chigh DC have traditionally been divided simply on the basis of CD8α expression, without further subdivision on the basis of CD4. Interestingly, our findings support this historic distinction. Thus, we find that DN and CD4+ DC share many more genes with each other than with CD8α+ DC (Table I⇑B). However, whereas the CD4+ DC subset is fairly homogeneous by flow cytometric analysis, DN DC are heterogeneous with respect to the expression of CD5, CD22, F4/80, CD81, and CD86 (Fig. 2⇑). Such heterogeneity cannot be explained by cross-contamination of DN DC with either of the two other subsets or other cell types (Fig. 1⇑C and data not shown), and it is therefore likely that DN DC comprise at least two subpopulations. Despite their similarity at the gene expression level, DN and CD4+ DC differ significantly in one functional aspect: whereas DN DC, like CD8α+ DC, can make IL-12 p70 when appropriately stimulated, CD4+ DC appear unable to do so (35, 41). Similarly, CD4+ DC make less IL-12 p40 than DN DC (A. D. Edwards and C. Reis e Sousa, unpublished observations). Interestingly, both DN DC and CD8α+ DC express ICSBP/IRF8 (Tables II⇑⇑⇑⇑ and III⇑⇑), a transcription factor implicated in IL-12 p40 gene expression (62) and CD8α+ DC development (48, 49). In contrast, ICSBP is expressed at much lower levels in CD4+ DC (Tables II⇑⇑⇑⇑ and III⇑⇑), suggesting that it could account for subset differences in IL-12 production. However, we have failed to increase the ability of CD4+ DC to produce IL-12 after ICSBP overexpression (C. Reis e Sousa, unpublished observations).

The lists of differentially expressed genes we have generated (Tables II⇑⇑⇑⇑ and III⇑⇑), include many immunologically relevant molecules not previously described as differentially expressed in DC, such as some chemokines and their receptors (e.g., CXCR1, CXCL9, and CCL22), signaling components (e.g., RyR3, IFN regulatory factor 4, and STAT-4), MHC molecules (e.g., H-2DMb2), proteins involved in resistance to CTL lysis (e.g., SPI6), and many others. Broad categorization of differentially expressed genes into biological processes using Gene Ontology showed a significant association with responses to biotic stimuli, signal transduction from cell surface receptors, and development (Table IV⇑). Such an association was not seen when total DC-expressed genes were similarly categorized (Table IV⇑). Therefore, these processes identify areas of cell biology in which CD4+ and CD8α+ DC are likely to show significant differences (Table IV⇑). Interestingly, the analysis of literature abstract contents for some of the genes differentially expressed among DC subsets also highlighted genes involved in development and signal transduction. For example, Hlx and Hoxc4 belong to the homeobox family of genes and are preferentially expressed by the CD4+/DN subsets. Both genes share terms such as bone, marrow, lineage, hemopoietic, and CD34, suggesting their involvement in hemopoiesis. Indeed, both Hlx and Hoxc4 are expressed in myeloid and lymphoid cells at various stages of development (63, 64). Hlx is also involved in the signaling pathway that leads to type 1 cytokine production in T cells (65) and plays an important role in T cell development (66), suggesting that it could similarly regulate DC lineage determination. In addition, Notch4, a member of the Notch family of receptors that play an important role during T cell development (67), was identified as preferentially expressed in the CD8α+ subset. In contrast, Dtx1, found primarily in the CD8α− subsets, is a positive regulator of the Notch pathway. Signaling through Notch may be of importance for DC subset development, and Notch ligands were recently shown to induce maturation of human monocyte-derived DC (68). Nevertheless, it is premature to speculate on the role of these or any other molecules without further studies. Indeed, only a few genes that are expressed differentially between DC subsets, including RelB (51) and ICSBP (48, 49), have yet been shown to be critical for DC development and/or function. The genes highlighted here constitute important candidates for future analysis.

Acknowledgments

We thank Derek Davies, Gary Warnes, Cathy Simpson, and Ayad Eddaoudi for cell sorting, and Richard Lempicki and Jun Yang for GeneChip hybridization.

Footnotes

  • ↵1 This work was supported by Cancer Research U.K. and the U.S. National Institutes of Health.

  • ↵2 A.D.E. and D.C. contributed equally to this paper.

  • ↵3 Address correspondence and reprint requests to Dr. Caetano Reis e Sousa, Immunobiology Laboratory, Cancer Research U.K., London Research Institute, Lincoln’s Inn Fields Laboratories, 44 Lincoln’s Inn Fields, London, U.K. WC2A 3PX. E-mail address: caetano{at}cancer.org.uk

  • ↵4 Abbreviations used in this paper: DC, dendritic cells; AvDiff, average difference; DN, double negative; EST, expressed sequence tag; ICSBP, IFN consensus sequence binding protein.

  • ↵5 S. S. Diebold, M. Montoya, H. Unger, L. Alexopoulou, P. Roy, L. E. Haswell, A. Al-Shamkhani, R. Flavell, P. Borrow, and C. Reis e Sousa. Cytosolic recognition of viral infection switches non-plasmacytoid dendritic cells into high interferon producers. Submitted for publication.

  • ↵6 The raw data used for the analyses described in this manuscript have been deposited in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE339 and are freely available. The deposited dataset also includes expression data for DC subsets after 2-h culture ex vivo. www links: Gene Ontology: www.geneontology.org; FatiGO: http://bioinfo.cnio.es/cgi-bin/tools/FatiGO/FatiGO.cgi

  • ↵7 Here, we use genes and EST clusters synonymously, although the exact equivalence between the two awaits full annotation of the mouse genome.

  • 8 Note that genes can belong to more than one Gene Ontology category.

  • Received January 27, 2003.
  • Accepted April 15, 2003.

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