Dendritic cells (DCs) constitute a critical nexus between innate and adaptive immunity, functioning not only as antigen-presenting cells but also as orchestrators of immune responses against a wide range of pathogens across diverse tissues 1,2. In the lung, a frontline organ continuously exposed to airborne agents, DCs play an especially crucial role in maintaining immune surveillance and balancing defense with tolerance 3. Human conventional DCs (cDCs) are broadly classified into two major subsets: BDCA1⁺ (CD1c⁺, also known as cDC2), which are proficient T-helper-cell priming, and BDCA3⁺ (CD141⁺, or cDC1), recognized for their cross-presentation capacity and contribution to antiviral immunity 2,4,5.

Recent evidence indicates that these DC subsets are transcriptionally and functionally specialized, with implications for infection, tissue homeostasis, and immune-mediated disease 3,5. Systematic transcriptional profiling by microarray and single-cell RNA sequencing (scRNA-seq) has revealed distinct gene-expression patterns that differentiate BDCA1⁺ and BDCA3⁺ subsets within the lung microenvironment. Single-cell atlases such as the Human Lung Cell Atlas (HLCA) and PanglaoDB consistently show that BDCA1⁺ DCs express higher levels of pro-inflammatory cytokines and chemokines (IL1B, CXCL8, CCL5), whereas BDCA3⁺ DCs display elevated interferon-stimulated genes (ISG15, IFIT1), suggesting complementary and non-redundant functional programs that enhance immune readiness 3,6.

Both in vitro and in vivo studies support this dichotomy. During RSV infection, BDCA1⁺ DCs secrete high concentrations of pro-inflammatory cytokines (IL1B, IL6, TNFα), whereas BDCA3⁺ DCs exhibit attenuated inflammatory responses but increased expression of co-inhibitory markers (PD-L1, IL-10) 7,8. Conversely, BDCA3⁺ DCs excel in type I interferon signalling and antigen cross-presentation following TLR3 stimulation (e.g., poly I:C) 5,7, underscoring their differential predisposition towards antiviral defence mechanisms.

Despite these insights, comprehensive comparative transcriptomic analyses that integrate steady-state and stimulus-specific profiles of BDCA1⁺ and BDCA3⁺ lung DCs remain scarce. Specifically, studies systematically analysing baseline immune-gene expression, integrating pathway-level assessments, and validating cell-type specificity with single-cell data and functional inference are lacking. A more granular understanding of these transcriptional landscapes is essential for devising targeted immunotherapies and for interpreting DC dynamics in respiratory disease.

In this validation-oriented, synthesis-driven study, we analysed a publicly available microarray dataset (GSE43184) of human lung-derived BDCA1⁺ and BDCA3⁺ DCs to consolidate and refine existing transcriptomic observations. Our aims were threefold: (1) to characterise baseline immune-gene expression profiles across selected cytokines, chemokines, and interferon-stimulated genes; (2) to delineate global transcriptional architecture through principal-component analysis (PCA), hierarchical clustering, and Reactome-based functional enrichment; and (3) to validate these observations by comparative analysis with single-cell RNA-seq atlases and prior studies employing canonical viral stimuli (RSV and poly I:C).

Rather than proposing new mechanistic discoveries, this work integrates multiple data sources to validate and contextualise known differences between BDCA1⁺ and BDCA3⁺ DCs. Key findings include elevated resting expression of inflammatory cytokines in BDCA1⁺ DCs, distinct clustering patterns separating the two subsets, and enrichment of inflammatory and interferon pathways consistent with the existing literature. By emphasising synthesis and validation, our analysis underscores the reproducible transcriptomic polarisation of human lung DC subsets and highlights their relevance for understanding DC-driven immunity in infection, vaccination, and immunotherapeutic settings.

In this study, we sought to validate and integrate previously reported functional distinctions between BDCA1⁺ and BDCA3⁺ dendritic cells (DCs) in the human lung by re-analyzing publicly available transcriptomic datasets and placing these findings within the context of recent single-cell and functional studies. Using microarray data from GSE43184, we observed distinct immune-gene expression profiles: BDCA1⁺ DCs exhibited elevated basal levels of pro-inflammatory cytokines and chemokines, whereas BDCA3⁺ DCs displayed comparatively higher expression of interferon-stimulated genes (ISGs). These observations corroborate and extend prior reports, positioning our analysis as a cross-validation of existing single-cell and bulk transcriptomic findings rather than a de novo discovery effort. The observed patterns closely mirror those reported by the Human Lung Cell Atlas, which identified similar subset-specific expression signatures using single-cell RNA-seq 3,6.

Our baseline analysis revealed that BDCA1⁺ DCs express IL1B, CXCL8, CXCL10, and related inflammatory mediators at higher levels than BDCA3⁺ DCs. This pattern indicates that BDCA1⁺ DCs are transcriptionally primed for innate immune activation and amplification of inflammation, in agreement with their documented roles in T-helper-cell priming and recruitment of effector leukocytes 4,7. In contrast, BDCA3⁺ DCs were enriched for ISGs such as ISG15 and IFIT1, supporting their established specialization in antiviral signaling and antigen cross-presentation 4.

Multivariate principal-component and hierarchical clustering analyses further reinforced the transcriptional separation of the two DC subsets. Reactome-based pathway enrichment revealed that BDCA1⁺ DCs engage inflammatory, cytokine, and GPCR-related pathways—functional attributes characteristic of innate sentinels primed to respond to environmental threats.

Comparative validation using external resources strengthened these observations. Single-cell analyses from HLCA and PanglaoDB confirmed that the pro-inflammatory and chemotactic genes we identified as elevated in BDCA1⁺ DCs are also differentially expressed in vivo 3,6. This cross-platform concordance indicates that even a small-sample microarray dataset can reliably reflect the intrinsic transcriptional polarization of human lung DC subsets. Likewise, studies employing poly I:C and RSV stimulation have shown that BDCA1⁺ DCs mount robust inflammatory responses, whereas BDCA3⁺ DCs preferentially activate antiviral and regulatory programs 7,8. Together, these lines of evidence support a coherent model in which baseline gene-expression patterns reflect pre-configured immune specializations that manifest during pathogen encounter.

The distinct molecular predispositions of BDCA1⁺ and BDCA3⁺ DCs have practical implications for respiratory immunity and translational applications. BDCA1⁺ DCs, with their inflammatory bias, may respond rapidly to bacterial or fungal stimuli by producing cytokines that recruit neutrophils and T cells 7. In contrast, BDCA3⁺ DCs, enriched in interferon signaling and cross-presentation pathways, appear optimised for antiviral defense and cytotoxic T-cell priming 4. These complementary programs likely contribute to the lung’s ability to balance inflammation with antiviral protection.

Our integrative analysis further highlights IRF7 and BATF3 as candidate transcriptional regulators that may govern the balance between inflammatory and antiviral programming. Consistent with previous studies demonstrating IRF7-driven interferon amplification and BATF3-dependent cDC1 differentiation 21,22, these factors align with the expression patterns observed here, where BDCA3⁺ DCs exhibit ISG-enriched antiviral profiles and BDCA1⁺ DCs display inflammatory-cytokine dominance. Although our study does not directly test transcription-factor activity, this literature-anchored inference provides a mechanistic framework for future experimental validation and underscores the value of cross-dataset synthesis in refining existing models of DC-subset specialization.

From a therapeutic perspective, selective modulation of DC subsets could inform vaccine and immunotherapy strategies. For instance, engaging BDCA3⁺ DCs may enhance antiviral immunity through adjuvants that promote cross-presentation 10, while dampening BDCA1⁺ DC-driven cytokine cascades could mitigate hyperinflammatory conditions such as acute respiratory distress syndrome (ARDS) or severe viral pneumonia (e.g., COVID-19) 11.

While our analysis offers integrative validation, several limitations warrant consideration. The small sample size (n = 2 per subset) in GSE43184 limits statistical power, and results should thus be viewed as supportive, confirmatory evidence rather than definitive proof. The microarray platform’s restricted gene coverage further limits discovery of novel transcripts and isoforms. Additionally, our inferences regarding responses to viral stimuli are based on secondary data rather than experimental validation. Future studies employing RNA-seq or functional assays could directly test these hypotheses. Despite these constraints, the reproducibility of expression trends across independent datasets and analytical platforms underscores the robustness of the observed subset specialization.

In summary, this validation-driven transcriptomic meta-analysis demonstrates that BDCA1⁺ and BDCA3⁺ dendritic cells (DCs) in the human lung are transcriptionally and functionally distinct, even at rest. BDCA1⁺ DCs harbor a gene-expression signature enriched for inflammatory mediators, suggesting a central role in initiating and amplifying local immune responses. Conversely, BDCA3⁺ DCs exhibit higher basal expression of interferon-stimulated genes, in line with their specialized antiviral and cross-presenting functions. Rather than revealing novel mechanisms, these data consolidate and extend previously reported differences via rigorous cross-dataset validation. The transcriptional divergence observed under steady-state conditions parallels the functional dichotomy seen after activation, underscoring the biological relevance of these baseline programs. Collectively, the study confirms that human lung DC subsets maintain stable, subset-specific transcriptional landscapes congruent with their established immunological roles, providing a framework for vaccine development, immunotherapeutic design, and targeted modulation of pulmonary inflammation.

ARDS: Acute Respiratory Distress Syndrome; BATF3: Basic Leucine Zipper ATF-Like Transcription Factor 3; BDCA1: Blood Dendritic Cell Antigen 1; BDCA3: Blood Dendritic Cell Antigen 3; cDC1: Conventional Dendritic Cell Type 1; cDC2: Conventional Dendritic Cell Type 2; CD: Cluster of Differentiation; DCs: Dendritic Cells; FDR: False Discovery Rate; GEO: Gene Expression Omnibus; GPCR: G-Protein-Coupled Receptor; HLCA: Human Lung Cell Atlas; IFNB1: Interferon Beta 1; IL: Interleukin; IRF7: Interferon Regulatory Factor 7; ISGs: Interferon-Stimulated Genes; KEGG: Kyoto Encyclopedia of Genes and Genomes; PCA: Principal Component Analysis; PD-L1: Programmed Death-Ligand 1; poly I:C: Polyinosinic:Polycytidylic Acid; RMA: Robust Multi-array Average; RSV: Respiratory Syncytial Virus; scRNA-seq: Single-Cell RNA Sequencing; TLR: Toll-Like Receptor; TNF: Tumor Necrosis Factor

The author thanks the creators of the GSE43184 dataset for making their data publicly accessible through the Gene Expression Omnibus (GEO). All analyses were performed using open-source R and Python packages in a reproducible computational environment. The author also acknowledges the assistance of ChatGPT (OpenAI) for language editing and grammar refinement. The author read and approved the final manuscript and is solely responsible for the content and integrity of this work.

V.Y.: Conceptualization, Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft, Writing – Review & Editing. All authors read and approved the final manuscript.

This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The author declares that there are no financial conflicts of interest relevant to this article.

All transcriptomic data used in this study are publicly available in the NCBI Gene Expression Omnibus (GEO) database. The primary microarray dataset analyzed (GSE43184) can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43184. External validation was performed using publicly available datasets from the Human Lung Cell Atlas and curated literature sources. All data processing, analysis scripts, and figure generation workflows are available from the corresponding author upon reasonable request.

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The authors declare that they have not used generative AI (a type of artificial intelligence technology that can produce various types of content including text, imagery, audio and synthetic data. Examples include ChatGPT, NovelAI, Jasper AI, Rytr AI, DALL-E, etc) and AI-assisted technologies in the writing process before submission.

The authors declare that they have no competing interests.