Research Article: Dendritic Cell Development: A Choose-Your-Own-Adventure Story

Date Published: February 18, 2013

Publisher: Hindawi Publishing Corporation

Author(s): Amanda J. Moore, Michele K. Anderson.

http://doi.org/10.1155/2013/949513

Abstract

Dendritic cells (DCs) are essential components of the immune system and contribute to immune responses by activating or tolerizing T cells. DCs comprise a heterogeneous mixture of subsets that are located throughout the body and possess distinct and specialized functions. Although numerous defined precursors from the bone marrow and spleen have been identified, emerging data in the field suggests many alternative routes of DC differentiation from precursors with multilineage potential. Here, we discuss how the combinatorial expression of transcription factors can promote one DC lineage over another as well as the integration of cytokine signaling in this process.

Partial Text

Dendritic cells (DCs) are professional antigen-presenting cells that bridge the gap between the innate and adaptive immune systems by acting as sentinels throughout the body to capture, process, and present antigen to T cells. Their ability to distinguish between self and nonself molecules allows them to deliver tolerizing or activating signals to T cells accordingly. Scientific exploration of DCs has become increasingly complex with the recognition that DCs exist as a heterogenous mixture of populations. Named for their cellular size and morphology [1], DCs all share the ability to activate naïve T cells but exhibit unique functions within each subset. These DC populations have primarily been defined by their combinatorial cell surface marker expression, but they also differ in their developmental origins, transcriptional regulation, patterns of migration or residence, and anatomical and microenvironmental localization. DCs can be broadly classified as two major subsets: the inflammatory or infection-derived DCs, which develop from monocytes in response to stimulation, and the steady-state DCs, which are present at all times. The DCs present under steady state conditions include CD8+ and CD8− conventional DCs (cDCs), plasmacytoid DCs (pDCs), and migratory CD103+ CD11b− DCs, CD103− CD11b+ DCs, and Langerhans cells (LCs) (Table 1). The CD8− cDCs can be further classified as CD4+ or CD4− DCs, which both express high levels of CD11b [2]. However, the majority of gene perturbation analyses that have examined CD8+ cDCs, CD8− cDC, and pDCs as well as global gene analysis have shown mostly congruent gene expression between the CD4+ and CD4− subsets [3]; thus, we will classify CD4+ and CD4− DCs as CD8− DCs for simplicity.

Despite the differences in the location of DC development, specific subsets share transcriptional regulatory programs, which indicates an intrinsic requirement for certain transcription factors for the DC lineage [67]. Interestingly, to date there is no known single transcription factor that is universally required for the development of all DCs, analogous to the requirement of Pax-5 for the development of all B cells [68], highlighting the versatility and plasticity of DC development and homeostasis.

Once organized into lineage-specific gene regulatory maps, the similarities and differences between cDCs and pDCs become more apparent (Figure 1). The networks are separated based on the stage of development in which each factor is proposed to function. PU.1 is a master regulator of both cDCs and pDCs, and, based on experimental evidence, it likely functions early in DC development at or immediately prior to the CDP stage. The main function of PU.1 is to turn on regulatory genes that are responsible for proper DC development, such as Id2, Flt3L, and GM-CSFR. Since signaling through GM-CSFR can activate STAT5, which inhibits IRF-8 transcription, GM-CSF might be an environmental cue to favour CD8− cDC development. Indeed, GM-CSF promotes the development of CD8− CD11b+ DCs in vitro [29]. The partial restoration of a wildtype phenotype by transducing E4BP4−/−cells with Batf3 suggests that either E4BP4 and Batf3 have similar transcriptional targets or Batf3 is upregulated by E4BP4. Conversely, the elevated levels of IRF-4 mRNA in E4BP4−/− cells indicate that E4BP4 inhibits IRF-4, directly or indirectly (Figure 1(a)).

Although the properties varying between distinct DC subsets are vast, there is emerging evidence linking DC populations by common gene expression profiles [67]. These comparisons show that lymphoid tissue-resident CD8+ cDC and nonlymphoid tissue-resident CD103+ DCs are more closely related to each other than they are to CD8− cDCs and pDCs. Similarly, migratory DCs differ from all other DC subsets and uniquely upregulate genes expressing immunomodulatory molecules, which could regulate immune response to self-antigen [67]. It is probable that the transcriptional regulators expressed earlier in DC development, such as PU.1, Ikaros, and Gfi1, primarily function to modulate precursor responsiveness to cytokine signals, growth factors, and inflammatory signals. These events allow for the production of steady state DC subsets and prompt alternative pathways of DC development during infection [31, 165]. By contrast, while the transcription factors expressed during the terminal stages of DC differentiation might be required for DC subset development, they are often also essential for specialized functions. In particular, RelB−/− and IRF-8−/− DCs express lower levels of MHC class II and costimulatory molecules, such as CD40, CD80, and CD86, following microbial or CD40L stimulation [122, 125]. The tolerogenic cytokines TGF-β and IL-10 were secreted at higher concentrations from IRF-1−/− DCs [132]. Furthermore, the transcriptional marker of cDCs, Zbtb46, has been shown to play important functions by promoting tolerogenic phenotypes of steady state cDCs until stimulated by antigen [102]. Certainly, the duality of these transcription factors for developmental and functional inputs makes targeted experiments more challenging to design. Despite the availability of many high throughput methods, such as RNA-seq or ChIP-seq, flaws in data interpretation can still arise from purifying DCs according to their surface cellular phenotypes. If a method for typing single cells by transcriptome signatures was available, it would be interesting to see how DC subsets that emerged from this analysis would compare with established DC subsets grouped by combinatorial cell surface receptor expression.

 

Source:

http://doi.org/10.1155/2013/949513

 

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