In a previous study, we characterized mononuclear phagocyte (MP) subsets in porcine blood by flow cytometry, identifying cDC1 as CD14^-CD172a^lowCADM1⁺, cDC2 as CD14^-CD172a⁺CADM1⁺, pDC as CD14^-CD172a⁺CADM1^-CD4⁺ and monocytes as CD14⁺ (4). Here, applying the same staining protocol and gating strategy ( Supplementary Figure S1 ), we focused our analysis on the previously undescribed MP subset of CD14^-CD172a⁺ cells lacking both CADM1 and CD4 expression and being as frequent as pDC in blood of pigs ( Figure 1A ). Expression of the conserved DC marker Flt3 (CD135), together with the almost complete lack of monocyte markers (CD115/CSF1R and CD163), supported its identification as a bona fide DC subset ( Figure 1B ). Notably, it shared phenotypic markers with both cDC (CD11b/wCD11R1 (27) and CD205, absence of CD303) and pDC (IL3-RA/CD123, absence of CD1). Moreover, this subset expressed CD80/86 and a high level of MHC-II, thus suggesting its involvement in antigen presentation and T-cell co-stimulation. Taken together, this new DC subset displayed a phenotypic profile overlapping with both cDC and pDC phenotypes, and it shared prominent expression of IL3-RA with pDC, leading us to further address this subset as putative porcine tDC (pptDC).

Following phenotypic characterization, we investigated the transcriptional profile of the newly identified pptDC. To this end, this subset was MACS/FACS-sorted from the blood of four pigs and processed for bulk RNA-seq. Resulting data were analyzed alongside previously generated RNA-seq datasets of the four other blood MP subsets (cDC1, cDC2, pDC and monocytes) (4). Principal component analysis (PCA) of the 500 most variable genes between the five MP subsets (PC1 = 60%, PC2 = 30%) showed that all samples of the newly described cell subset (pptDC) were clustering together and away from the four other MP subsets, supporting the discovery of pptDC as a new and distinct DC subset ( Figure 2A ). As further illustrated by a subset-to-subset correlation analysis, pptDC appeared to be more closely related to cDC than to pDC, sharing the highest correlation score with cDC2 ( Supplementary Figure S2A ).

Overall, gene expression was in line with surface protein expression detected in flow cytometry ( Figure 2B ). Gene expression for additional phenotypic markers is shown in Supplementary Figure S2B . Both pptDC and pDC expressed IL3RA (not available in Ensembl pig genome annotation), as shown by read mapping to the corresponding genomic region ( Supplementary Figure S2C ).

To characterize pptDC, we next studied the expression of conserved key genes known to define the main MP subsets across species (28, 29), including those we previously reported in pig blood (4). The updated analysis of the cDC1, cDC2, pDC and monocyte data (new reference genome) was in accordance with our formerly published transcriptomic analysis (4), supported also by the subset-specific expression pattern of these markers ( Figure 2C ). For pptDC, the expression of the pan-DC markers FLT3 and BCL11A was in common with cDC1, cDC2 and pDC. Considerable levels of monocyte-specific gene expression (e.g. CSF1R, CD14, CD163) were detected in two out of four pptDC samples (#4, #5 in Figure 2C ). Notably, pptDC expressed transcripts for both IRF4 and IRF8, two transcription factors (TF) involved in the development of cDC2 and cDC1/pDC, respectively (3, 28). In addition to markers shared across DC populations, pptDC showed expression of DC subset-restricted features, such as XCR1 and ANPEP (cDC1), FCER1A, CD207 and NOTCH4 (cDC2), and RUNX2, TCF4, BLNK and SPIB (pDC).

Dendritic cells with a phenotype and transcriptome overlapping with both cDC2 and pDC are characteristic of the recently identified tDC in humans (19) and mice (22). These tDC were reported to originate from progenitors shared with pDC and to differentiate into cDC2 (24). To further explore if our new DC subset represents the porcine equivalent of tDC, we analyzed differentially expressed genes (DEGs) between pptDC and cDC2 or pDC based on pairwise comparisons (DESeq2). Complete lists of DEGs are provided as Data S1 . Putative porcine tDC showed expression of SPI1, TCF4, NOTCH2, CEPBA, KLF3, KLF8, KLF12, ZBTB46, IRF4, IRF8, STAT5A, RUNX2 and SPIB ( Figures 2C, D ), which are genes also found in tDC of humans and mice (22, 24, 30, 31), coding for cDC- or pDC-specific transcription factors (TF). Notably, Leylek et al. demonstrated by chromatin accessibility analysis that KLF3, KLF8 and KLF12 were part of the unique TF profile of tDC (30). Among the gene regulatory network governing DC development, TCF4 and ID2 are reported as mutual functional antagonists promoting pDC versus cDC differentiation, respectively (32). We found that both were expressed in pptDC ( Figure 2D ), reinforcing their intermediate nature between cDC2 and pDC. Additionally, this new subset exhibited a high transcription level of ZBTB16, the gene encoding PLZF, a TF known to induce ID2 expression (33). Interestingly, pptDC shared the expression of KLF4 with cDC2, reported to be required for the differentiation of murine circulating pDC-like cells (identified as pre-DC2) into a subset of cDC2 (26). Unlike pDC, pptDC showed low IRF7 gene expression, suggesting its limited capacity to produce type I IFN, also distinguishing tDC from pDC in human and mouse (22). Moreover, its high transcription level of CD200R1, STAT5A and RAB3IL1 in comparison to cDC2 and pDC is consistent with the spleen tDC signature described by Sulczewski et al. in mice (24).

Porcine cDC2 and pptDC could be further distinguished from pDC by their expression of AXL, a human tDC marker ( Figure 2D ). Notably, pptDC expressed high levels of both CD2 and CD5, in contrast to porcine pDC (low levels of CD2 and CD5 transcripts) and cDC2 (transcription of CD2 but very low levels of CD5 transcripts). These expression patterns were also found for human tDC, cDC2 and pDC, both transcriptionally and phenotypically (22). Thus, staining of CD2 and CD5 may be useful for distinction of porcine DC subsets in flow cytometry.

Finally, we observed a progressive increase of ITGAM, CD83, LY75, and SLA-DRA expression from pDC via pptDC toward cDC2 ( Figure 2D ), suggesting gradually increasing antigen presentation capabilities across those subsets. Taken together, phenotype and bulk transcriptomic signatures support the idea that the new porcine DC subset (CD14^-CADM1^-CD172a⁺CD4^-) represents the equivalent of tDC described in human and mouse.

Further exploration of pptDC-derived transcriptomic data revealed a unique gene signature related to pathogen recognition, antigen presentation, T-cell co-stimulation, immunoregulatory activities and cell adhesion ( Figure 2 and Supplementary Figure S3 ).

Porcine putative tDC expressed relatively low levels of pattern recognition receptors (PPR) for bacterial components (e.g. TLR4, TLR5) ( Supplementary Figure S3A ), however one pptDC sample (#5) contained high transcript levels for bacterial PRR ( Supplementary Figure S3A ), and as shown in Figure 2C , also appeared enriched for monocyte-related transcripts such as CD14. Notably, all four pptDC samples stood out by high TLR3 and IFIH1 (MDA-5) expression ( Figure 2E ), suggesting a specialization in sensing double stranded RNA. Transcripts for TLR7, TLR8 and TLR9 could also be detected in pptDC, even though higher levels were detected in other DC subsets.

Similar to cDC2, pptDC expressed a relatively high level of C-type lectin receptor (CLR)-associated genes, such as MRC1 (CD206), PLA2R1 (CLEC13C), CD207 and CLEC4F ( Supplementary Figure S3A ), suggesting their involvement in mannose-ligand recognition and phagocytic activities (34). Notably, expression of MRC1 and CLEC4F was found to be very heterogeneous across pptDC samples. Similar to cDC1 and pDC, pptDC also contained transcripts for CLEC12A (MICL) and CLEC12B, two CLR that mostly recognize endogenous ligands such as damage-associated molecular patterns (35, 36), thus suggesting a role in clearing dying cells.

Looking at gene expression related to antigen presentation and T-cell modulation ( Supplementary Figure S3B ), pptDC stood out by expressing the highest levels of certain genes that may promote T-cell activation by enhancing antigen (cross-) presentation (ATG5, UBE2D1, RAB27A) (37–39), may promote the differentiation of regulatory T cells (IL4I1) (40, 41) or Th1 cells (DPP4) (42), or may otherwise be involved in regulating T-cell responses (VSIG10, CD200) (43, 44). For other genes involved in antigen presentation, we observed a gradual increase from pDC via pptDC towards cDC. Indeed, pptDC displayed intermediate transcription levels of genes encoding MHC-II molecules (e.g. SLA-DRA, SLA-DOA, SLA-DMB, SLA-DMA), molecules involved in MHC-II trafficking and antigen processing (PIKFYVE, IFI30, CIITA, CD74, LY75, TAP2) and co-stimulatory molecules (CD83, CD40) ( Figure 2F , Supplementary Figure S2B , Supplementary Figure S3B ).

Genes encoding cytokines and cytokine receptors predominantly expressed in pptDC included IL18 and IL17RA ( Supplementary Figure S3C ). Notably, alongside cDC1, pptDC prominently expressed the beta chain of the IL-6 receptor (IL6ST), reported to function in signal transduction for various cytokines (45). When compared to pDC, pptDC contained fewer transcripts for type I interferons (IFN-OMEGA-6) and related receptors (IFNAR1 and IFNAR2), reinforcing the hypothesis of their limited involvement in type I IFN responses.

Looking at chemokines and chemokine receptors, pptDC were found to express considerable levels of XCR1 ( Supplementary Figure S3D ). Notably, this key marker for cDC1 is involved in antigen cross-presentation and CD8⁺ T-cell priming (46, 47). Moreover, pptDC contained the highest number of CCR7 and CXCR5 transcripts among cDC subsets, however at low levels (mean reads of 200 and 300, respectively). While CCR7 is a well-known marker for DC activation (48), expression of CXCR5 is expected to cause migration to the CXCL13-rich parafollicular areas of the lymph node to possibly stimulate follicular Th cells (49).

Finally, several genes encoding integrin chains showed highest expression in pptDC such as ITGB3, ITGAV and ITGA6 ( Supplementary Figure S3E ), as well as genes encoding Fc receptors (e.g. FCRL4) ( Supplementary Figure S3F ), metalloproteinases (e.g. MME and MMP9) ( Supplementary Figure S3G ) and semaphorins (e.g. SEMA4F, SEMA4C, PLXNA4) ( Supplementary Figure S3H ).

To get a more unbiased view on the heterogeneity of porcine blood DC subsets, we performed scRNA-seq (10x Genomics) on Flt3⁺ DC enriched from blood of three pigs ( Figure 3A ). Clustering of cells with a resolution of 0.6 (Leiden algorithm) resulted in the identification of thirteen distinct clusters ( Figure 3B ). Complete lists of cluster-defining marker genes, as determined by Seurat’s FindAllMarkers() function, are listed in Data S3 . Three clusters (c7, c8, c9) were excluded due to quality issues ( Supplementary Figure S4A ), as were clusters containing B cells (c13) and NK cells (c11) ( Supplementary Figures S4B, S4C ).

Eight FLT3-expressing clusters (c1, c2, c3, c4, c5, c6, c10, and c12) were analyzed in further detail. Notably, cluster 5 appeared to contain monocytic cells alongside DC (DEG including CSF1R, C5AR1, CD14, CD163, SIRPA and CD68), and cluster 10 appeared to be comprised of DC progenitors (DEG including CD34, MEIS1, DACH1, ERG, KIT, IKZF2 and MECOM) (15) ( Figures 3C, D , Data S3 ). As shown in Figures 4A–C , expression of subset-specific key genes clearly identified cluster 6 as cDC1 (BATF3, XCR1, RAB7B, ANPEP, IRF8), cluster 1 as cDC2 (FCER1A, FCGR2B, CD207, SIRPA, IRF4) and clusters 2 and 12 as pDC (CD4, SPIB, BLNK, TCF4, RUNX2, IRF8). Cells in clusters 3 and 4 expressed IRF4, IRF8, XCR1, ANPEP, FCER1A, RUNX2, SPIB, TCF4 and BLNK ( Figure 4B ), thus sharing subset-specific markers with cDC1, cDC2 and pDC, as observed with bulk RNA-seq of pptDC described above ( Figure 2C ).

A heatmap of the top 15 (adjusted p-value) DEG between the DC subsets is shown in Figure 4C and the complete gene lists are given in Data S3 . Apart from the genes mentioned above, DEG included CADM1, CLNK, ID2, SNX22, WDFY4 and DNASEIL3, for cDC1 (c6), CD1D, ITGAM (CD11b), S100A4, TLR2 and TLR4 for cDC2 (c1), and IRF7, IFNAR1, NRP1, LRP8 and SYK for pDC (c2, c12). In line with the bulk RNA-seq analysis ( Figure 2 , Data S1 ), clusters 3 and 4 were enriched for ZBTB7C, ZBTB16, KLF4, NOTCH2, DPP4 (CD26), TGFBR3 and SEMA4F, further supporting the classification of c3 and c4 as pptDC.

When estimating the proportions of the DC clusters within total blood DC ( Figure 4D ), we found that c1 (cDC2) and c2 (pDC) each represented approximately 30%, c6 (cDC1) represented 7%, and the pptDC clusters 3 and 4 represented 16% and 14%, respectively. These results are in accordance with the cell proportions previously observed by flow cytometry for the RNA-seq analysis of sorted cells ( Figure 1A ). Representative flow cytometry data for animals included in the scRNA-seq analysis is shown in Supplementary Figure S5 . Proportions of DC subsets from scRNA-seq and flow cytometry showed a moderate correlation, with an R² value of 0.65 ( Figure 4E ).

To investigate if flow-cytometry defined DC subsets are well represented in the clustering of the scRNA-seq dataset, we tested for relative enrichment of the gene signatures from sorted bulk-sequenced subsets in the scRNA-seq clusters by gene-set enrichment analysis (GSEA). Different levels of enrichment were tested (5, 10, 15, 25, 50 and 100%) (see Material and Methods ), allowing us to select 25% for optimal resolution of sc identities ( Supplementary Figure S6 ). As shown in UMAP plots ( Figure 4F ) and a heatmap ( Figure 4G ), cDC1-derived gene sets had the highest enrichment in cluster 6, cDC2-derived gene sets in cluster 1, pDC-derived gene sets in clusters 2 and 12, and pptDC-derived gene sets in clusters 3 and 4. This clear allocation of bulk signatures supports the suitability of the 4-marker sorting strategy for porcine DC subsets and confirms the cluster identification in the scRNA-seq dataset.

For further verification of pptDC identity, we used the GSEA approach described above to compare the porcine DC signatures to DC signatures derived from published bulk- and scRNA-seq studies of human blood (19, 50) and murine spleen (24) ( Figures 4H–J ). Two distinct human studies were selected, each utilizing a different scRNA-seq technology (SMARTSeq2 for Villani et al. (19), Figure 4H , and 10x Genomics for Lubin et al. (50), Figure 4I and Supplementary Figure S7 ). As expected, both human and murine cDC1, cDC2 and pDC gene signatures showed the highest relative enrichment in cluster 6 (cDC1), cluster 1 (cDC2) and in clusters 2 and 12 (both pDC), respectively. Notably, human ASDC and murine tDC (“bulk”) gene sets showed the highest enrichment in clusters 3 and 4, representing pptDC ( Figures 4H–J ), thus supporting the identification of pptDC as porcine equivalents of tDC.

As expected, cluster 4 (pDC-like pptDC) was more enriched for human and murine pDC signatures than cluster 3 (cDC2-like pptDC), whereas cluster 3 displayed greater enrichment for human and murine cDC2 signatures than cluster 4. However, sc signatures of murine tDC-subclusters (“tDC^low” and “tDC^hi”) did not show discriminating enrichment in pptDC clusters 3 or 4 ( Figure 4J ) but were rather enriched in c3 (cDC2-like pptDC) and c1 (cDC2).

In accordance with the reported origin of tDC from pro-pDC and their differentiation into cDC2-like cells (14, 15, 24, 26, 50), we found that transcripts for several TF involved in DC fate decisions (28) were sequentially increased or decreased from pDC via pptDC (c4 and c3) towards cDC2 ( Figure 5A ). Among TF, the most evident gradual decrease was observed for pDC-associated genes TCF4, IRF8, IKZF1 and BCL11A, whereas transcription of cDC-associated genes SPI1 and ID2 increased via c4 and c3. A more abrupt decrease from c4 to c3 was observed for SPIB, the gene coding for Spi-B, a transcription factor promoting development of pDC (51). Notably, RUNX2, NOTCH2, KLF4, KLF12 and JAK2 showed highest expression in c4, before decreasing again in c3, whereas ZBTB46 and ZBTB16 expression appeared to peak in c3. Lastly, several TF showed increased expression in both c3 and c4 when compared to pDC and cDC2 (ZBTB7C, STAT5A, KLF3 and CBFA2T3).

The transcriptomic signature of pptDC clusters (c3 & c4) was further characterized by comparing them against cDC2 (c1) and pDC (c2) and against each other. Heatmaps of the top 20 (adjusted p-value) DEGs are shown in Figures 5B–D . The complete gene lists are given in Data S3 . Notably tDC clusters differed in the expression of integrin transcripts ( Figure 5E ), generally reflecting their similarity to either pDC (c4: ITGAL, ITGB5) or cDC2 (c3: ITGB2, ITGAM, ITGAX). In accordance with the adoption of an increasingly cDC-like phenotype, cDC2-like tDC (c3) showed higher expression of transcripts related to antigen presentation (e.g. SLA-DRA, SLA-DQA1, CD74) and T-cell stimulation (e.g. CD40, CD83, ALCAM) than pDC-like tDC (c4) ( Figure 5F ). In line with bulk RNA-seq, pptDC clusters showed the highest expression of DPP4 and PECAM1 (two genes involved in T-cell modulation), when compared to pDC and cDC2.

To further characterize the correlation between pptDC and cDC2 or pDC, we trained an ElasticNet model computing a classification score based on the transcriptomic signatures of the different cell types ( Figures 5G, H ). According to this model, pptDC in cluster 4 showed low-to-intermediate scores for both cDC2 and pDC signatures, while pptDC in cluster 3 were characterized by a progressive increase in the cDC2 score while exhibiting a low pDC score ( Figure 5G ). In line with the differential gene expression analysis, cDC2 displayed a higher score for cluster 3 than for cluster 4, and pDC showed the reverse pattern ( Figure 5H ).

Overall, the transcriptome of pptDC spans a continuum between pDC and cDC profiles, as described for humans and mice (19, 22, 24, 52). This intermediate transcriptome, together with the core gene signature resembling human and murine tDC, justifies identifying pptDC as the porcine equivalents of ASDC/tDC. Furthermore, these results suggest that porcine tDC may differentiate into cDC2-like cells, as proposed for their murine (24, 26) and human (50) counterparts.

To further explore the cellular dynamics between porcine tDC and cDC2, we performed a trajectory inference (TI) analysis on cells in clusters 1, 3 and 4 ( Supplementary Figure S8 ). Genes that varied along the inferred trajectory corresponded to the DC subset-specific signatures previously identified by differential expression analysis. Notably, the continuum from tDC to cDC2 was marked by a gradual increase in the expression of genes associated with antigen presentation via MHC-II molecules (module 2 in Supplementary Figure S8C ), suggesting a progressive acquisition of cDC features by tDC differentiating towards cDC2-like cells.

In both mouse and human, DC3 have been described as a novel DC subset sharing dendritic and monocytic markers (12, 14, 17–19). Interestingly, cluster 5, adjacent to cDC2 (c1), appeared to contain both monocytic and dendritic cells ( Figure 3 ) and showed the highest enrichment score when performing GSEA with human DC3 signatures ( Figures 6A, B ).

Upon re-clustering of c5, two major clusters were separated from two smaller clusters ( Figure 6C ). The small clusters 3 and 4 were annotated as cDC2 (FLT3, FCER1A, CD1.1) and monocytes (high levels of CD14, LYZ), respectively, while the two major clusters 1 and 2 were annotated as putative DC3 as they contained low levels of CD14 and FCER1A transcripts alongside FLT3 and CSF1R transcripts ( Figures 6C, D ). Indeed, a significant proportion of cells in sub-clusters 1 and 2 (putative DC3) were revealed to co-express FLT3 transcripts alongside transcripts typically associated with monocytes, such as CSF1R, CD163, CD14 and C5AR1 ( Figure 6E ).

Among putative DC3, c1 stood out by higher transcription of CD163, C5AR1 and VCAN, while c2 was clearly enriched in S100A4, S100A6 and CST3 transcripts ( Figure 6F ). Compared to c1, c2 also expressed slightly higher levels of MHC-II-related genes.

The current gating strategy for identifying porcine DC subsets is based on exclusion of CD14⁺ monocytes ( Figure 1 ) and therefore likely excludes CD14-expressing DC3. This is illustrated by gradual gating based on CD14 expression prior to DC gates, as shown in Figure 6G . Indeed, CD14^low (P3) and CD14^int (P2) populations contained approximately 66% and 6% Flt3⁺ DC, respectively, mainly falling within the cDC2 gate (CADM1⁺CD172a⁺). The gated Flt3⁺CADM1⁺CD172a⁺ population thus represents a more heterogenous population, likely containing CD14-expressing DC3. This hypothesis is further supported by the shared expression of CADM1 and CD172a (SIRPA) by cDC2 (c1) and putative DC3 (c5) at the transcriptomic level ( Figure 6H ).

Blood was obtained from Swiss Large White pigs ( Table 1 ), kept under specific-pathogen-free (SPF) conditions (63) at the animal facility of the IVI (Mittelhäusern, Switzerland), by puncturing the jugular vein. As anti-coagulant, citrate-based Alsever’s solution (1.55 mM C₆H₁₂O₆; 408 mM Na₃C₆H₅O₇·2H₂O; 1.078 mM NaCl; 43 mM C₆H₈O₇, pH 6.2) was used.

For peripheral blood mononuclear cell (PBMC) isolation, blood was centrifuged at 1,000 x g for 20 min (room temperature; RT), the buffy coat was collected, diluted in PBS/EDTA (PBS; 1 mM EDTA) to a 1:1 ratio (RT) and layered onto Ficoll-paque (1.077 g/L, GE Healthcare) in Leucosep tubes (Greiner BioOne) for centrifugation at 800 x g for 25 min (RT). PBMC were collected and washed first once with cold PBS/EDTA at 350 x g for 10 min (4 °C, Ficoll-paque removal) and then once reducing the speed to 250 x g (platelet removal). Remaining, red blood cells were removed from the PBMC by incubation with cold lysis buffer (10 mM NaHCO₃; 1 mM EDTA; 0.15 M NH₄Cl, pH 7.25) for 10 min on ice, followed by two washes with cold PBS/EDTA at 250 x g for 10 min (4 °C).

The flow cytometry gating strategy used to identify mononuclear phagocyte (MP) subsets in pig blood was previously described by our laboratory (4), defining cDC1 as CD14^-CD172a^lowCADM1⁺ cells, cDC2 as CD14^-CD172a⁺CADM1⁺ cells, pDC as CD14^- CD172a⁺CADM1^-CD4⁺ cells and monocytes as CD14⁺ cells. The same staining panel was used to gate on the newly identified DC subset as CD14^-CD172a⁺CADM1^-CD4^- cells in the present study. Briefly, a four-step four-color staining of PBMC was performed. Antibodies and porcine recombinant proteins used are listed in Table 2 . Briefly, cells were first stained with the primary antibodies anti-CD172a (clone 74-22-15A) and anti-SynCAM (TSLC1/CADM1, clone 3E1), followed by a second incubation with the corresponding secondary anti-mouse-IgG2b-AF647 and anti-chicken IgY biotin. A blocking step was then performed with ChromPure mouse IgG (Jackson Immunoresearch), and cells were finally incubated with the directly conjugated antibodies anti-CD14-FITC (clone MIL2) and anti-CD4-PerCP-Cy5.5 (clone 74-12-4), and with V500-conjugated streptavidin. Based on this staining, the phenotype of the new DC subset of interest was further characterized by analyzing the expression of additional cell surface markers, alongside corresponding FMO (Fluorescence minus one) controls. Flow cytometry acquisitions were performed on a FACS Canto II (BD Biosciences) equipped with three lasers (405, 488, and 633 nm) and a Cytek Aurora (Cytek Biosciences) equipped with four lasers (405 , 488 , 561, and 640 nm), software and were further analyzed with the Flowjo software (TreeStar, version 10.10.0).

The newly identified DC subset was sorted from the blood of four pigs (12- to 24-month-old) for bulk RNA-seq analysis. First, a T-cell depletion of PBMC was performed using magnetic activated cell sorting with an anti-CD3 antibody (clone PPT3), anti-mouse IgG MicroBeads and LD columns (MACS MicroBead Technology, Miltenyi Biotec). The same four-step four-color staining as described above was performed with the CD3-negative fraction, but V500-conjugated streptavidin was replaced by APC-AF750 streptavidin, and the DC subset of interest was sorted using fluorescence-activated cell sorting (FACS; FACSAria III; BD Biosciences). Finally, cells were resuspended in TRIzol (Life Technologies) and stored at -80 °C until later RNA extraction with the Nucleospin RNA kit (Macherey Nagel) as previously described (4). RNA quantification and quality assessment was performed with an Agilent 2100 Bioanalyzer (Agilent Technologies) and a Qubit 2.0 Fluorometer (Life Technologies). High-quality RNA (approximately 500 ng; RNA integrity number (RIN) > 8) was used to prepare non-directional paired-end mRNA libraries with the TruSeq Sample Preparation Kit (v2, Illumina). The libraries were sequenced on the Illumina HiSeq2500 platform using 2 x 100 bp paired-end sequencing cycles, yielding between 26.5 and 30.1 million read pairs per sample. The Illumina BCL output files with base calls and qualities were converted into FASTQ file format and demultiplexed with the CASAVA software (v1.8.2). Raw bulk RNA-seq data for the cDC1, cDC2, pDC and monocytes (n = 3 pigs) were available from our previous work (4).

For analysis of bulk RNA-seq data, the following bioinformatics tools were used with their default parameters, unless specified otherwise. The quality of reads was assessed with fastQC v0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and both low quality bases (Phred score < 30) and Illumina TruSeq2 adapters were trimmed with Trimmomatic v0.39 (64). The reads were then mapped to the pig reference genome (assembly Sscrofa 11.1) using STAR v2.7.10a (65). The duplicate reads were identified and removed using MarkDuplicates from the Picard command-line tools v2.25.1 (https://broadinstitute.github.io/picard/). The featureCounts program included in the SourceForge Subread package v2.0.3 (66) was used to count the number of reads overlapping with each gene identified in the Ensembl pig annotation release 11.1.111. In summary, (i) a minimum of 93.7% of reads were mapped to the genome among all samples, yielding between 29.6 and 57.2 million reads aligned per sample, (ii) between 14.2 and 35.4% of reads were identified as duplicates and (iii) 58.5-68.9% of reads were assigned to a gene, corresponding to a range of 16.3-33.0 million mapped reads.

The differential gene expression analyses were performed using the Bioconductor package DESeq2 v1.42.1 (67) in R v4.3.3 (68). Only genes with |log2FC| > 1 and adjusted p-value < 0.05 were selected as differentially expressed genes (DEGs). We performed pairwise comparisons of cell subsets as well as the comparison of each subset versus all others to define the MP subset-specific transcriptomic signatures (results are available as Data S1 ). Principal component analysis (PCA) was performed with normalized and vst-transformed counts of the 500 most variable genes across samples. Sample-sample correlation analysis was based on normalized gene expression data for each sample (counts()) using the Spearman correlation coefficient with hierarchical clustering based on Spearman distances.

To enrich DC for scRNA-seq analysis, a four-step protocol combining cell staining and T-cell depletion was performed on freshly isolated PBMC from three pigs in parallel. Antibodies and porcine recombinant proteins used are listed in Table 2 . Briefly, 5 x 10⁸ PBMC were first stained with a His-tagged porcine recombinant protein Flt3L and the primary antibodies anti-CD172a (clone 74-22-15A) and anti-CD3 (clone 8E6-8C8), followed by a second incubation step with the corresponding secondary antibodies anti-His-PE, anti-mouse IgG2b AF647 and anti-mouse IgG2a biotin. Next, following incubation with Streptavidin MicroBeads (Miltenyi Biotec), CD3⁺ cells were depleted using LD columns (Miltenyi Biotec). Finally, total DC from the three pigs identified as Flt3⁺CD172a^-/+ cells were sorted in parallel using one FACS Aria II and two FACS Aria III (all BD Bioscience) at the flow cytometry and cell sorting core facility at the University of Bern.

For scRNA-seq, DC isolated from the blood of three pigs (16.5-month-old) were analyzed with 10x Genomics. Gel beads-in-emulsion (GEM) generation and barcoding, reverse transcription, cDNA amplification and 3’ gene expression library generation steps were all performed according to the Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (Dual Index) User Guide (10x Genomics CG000315, Rev E) with all stipulated 10x Genomics reagents. Generally, 9-11 µL of each cell suspension (1 500-1–900 cells/µL) and 32-35 µL of nuclease-free water were used for a targeted cell recovery of 10,000 cells. GEM generation was followed by a GEM-reverse transcription incubation, a clean-up step and 11 cycles of cDNA amplification. The resulting cDNA was evaluated for quantity and quality using a Thermo Fisher Scientific Qubit 4.0 fluorometer with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Q32851) and an Advanced Analytical Fragment Analyzer System using a Fragment Analyzer NGS Fragment Kit (Agilent, DNF-473), respectively. Thereafter, 3′ sc gene expression libraries were constructed using a sample index PCR step of 14 cycles. The generated cDNA libraries were tested for quantity and quality using fluorometry and capillary electrophoresis as described above. The cDNA libraries were pooled and sequenced with a loading concentration of 300 pM, asymmetric paired-end and dual indexed, on two shared Illumina NovaSeq 6000 sequencer using a NovaSeq 6000 S4 Reagent Kits v1.5 (200 cycles; Illumina, 20028313). The read set-up was as follows: read 1: 29 cycles, i7 index: 10 cycles, i5: 10 cycles and read 2: 91 cycles. The quality of the sequencing runs was assessed using Illumina Sequencing Analysis Viewer (v2.4.7, Illumina) and all base call files were demultiplexed and converted into FASTQ files using bcl2fastq conversion software (v2.20, Illumina). The mean reads per cell and number of cells obtained per sample ranged from 33–896 to 51–718 reads, and from 11–5311 to 14–221 cells, respectively All steps were performed at the Next Generation Sequencing Platform, University of Bern.

We analyzed the scRNA-seq dataset of human blood DC recently generated by Lubin et al. (50) (approximately 3,000 cells). DC were sorted by flow cytometry and subjected to 10x Genomics scRNA-seq. Processed data from the cellranger pipeline (barcode, feature and matrix files), available under the sample number GSM8499782 in the National Center for Biotechnology Information Gene Expression Omnibus database, were analyzed with the Seurat pipeline as described above for the porcine data (quality-based data filtering, sctransform-based normalization and linear dimensional reduction using PCA). The PCA reduction was selected for identifying nearest neighbors, clustering the cells with the Leiden algorithm (method = “igraph”, clustering resolution = 0.8), and performing non-linear dimensional reduction using UMAP for cluster visualization. Next, the differential gene expression analysis was performed as for the porcine data, using FindAllMarkers() to identify the DEGs in each cluster.

Pig gene Ensembl stable identifiers (IDs) without available gene name/symbol in the pig genome annotation file were replaced in text and figures by NCBI gene (formerly Entrezgene) accession or UniProtKB Gene Name symbol if available in the corresponding databases using the BioMart data mining tool from Ensembl (https://www.ensembl.org/biomart/martview). A list of replaced Ensembl IDs is included in Data S1 . The human gene names HLA-DRA and HLA-DOB found in the pig genome annotation were replaced by SLA-DRA and SLA-DOB respectively, the gene names of their porcine orthologs. While IL3RA is not currently annotated in the Ensembl pig genome, it is available in the NCBI reference. Thus, the porcine genomic sequence for the gene encoding IL3RA (ENSSSCG00000055271) was identified by aligning the IL3RA gene sequence from NCBI (gene identifier: 102166116) to the Ensembl pig genome (Sscrofa release 11.1.111) using the Ensembl BLAT (100% sequence identity). The sequence of the IL3RA transcript ENSSSCT00000092699, product of the ENSSSCG00000055271 gene (IL3RA), was utilized to visualize the read distribution across its corresponding genomic location (AEMK02000569.1: 775,837-784,610) for each sorted MP subset, using Integrative Genomics Viewer (IGV) software.

Figures were prepared using FlowJo™ v10.10.0 (BD Life Sciences) (80), R v4.3.3 (68), Rstudio v2024.04.1 (81), Inkscape v1.3.2 (https://www.inkscape.org), Integrative Genomics Viewer (IGV) v2.17.4 (82) softwares. FACS scheme was created using Servier Medical Art, by Servier (http://smart.servier.com).

Bulk RNA-seq data was represented as PCA and heatmaps using the ggplot2 v3.5.1 (83) and ComplexHeatmap v2.18.0 (84) R packages, respectively. Heatmaps were prepared following log10 transformation of normalized counts. Prior to log10 transformation, a pseudocount of 1 was added to the values to avoid zeros.

Visualization of scRNA-seq data was based on feature plots, dot plots, violin plots, bar plots, scatter plots and heatmaps using the Seurat v5.1.0 (70), scCustomize v2.1.2 (85), ggplot2 v3.5.1 (83) and ComplexHeatmap v2.18.0 (84) R packages. Heatmaps were generated with scaled and centered data (Seurat ScaleData() function). For improved contrast in feature plots, feature-specific contrast levels were calculated based on quantiles (q10, q90) of non-zero expression.

The cell classification scoring based on a machine learning model was visualized by scatter plots using the scikit-learn python module (75).

The trajectory inference analysis was represented as UMAP plot and heatmap using the Scorpius R package v1.0.9 (79).