GTs are important because of their unique role in the biosynthesis of a wide range of specialised metabolites that have biological, ecological and commercial significance. Here, we describe a transcriptomics atlas focused on GTs of three crop species to improve our understanding of their distinctive biology. We leverage organ‐specific gene expression data across several tissue types to identify which expression patterns are characteristic of GTs. From this, we provide the most comprehensive, current characterisation of shared and specific patterns of gene expression in GTs across the three species via the principles of orthology. We also reconstruct weighted gene co‐expression networks and the GRNs governing these specific and shared transcriptional programmes. Overall, our results provide fundamental insights into GT biology, including how the key processes operating in GTs are regulated.

Understanding GT‐specific gene expression is valuable to identify genes that are involved in GT‐associated developmental processes and specialised biosynthetic pathways. We have shown here that different aspects of GT gene expression are either shared between species or specific to individual species (Figure 2). Genes that were expressed in common by GTs of all three studied species were mainly associated with processes that are common to all GTs. These shared processes included primary (fatty acid) and specialised (flavonoid) metabolism, trichome and cell differentiation, cell wall reorganisation, as well as substrate or metabolite transport. Fatty acids can act as the building blocks for many specialised metabolites, including the cannabinoids in cannabis and bitter acids in hop that are derived from fatty acids or contain fatty acid moieties (Balcke et al., 2017; Clark et al., 2013; Stout et al., 2012; Xu et al., 2013). So, the enrichment of genes involved in lipid metabolic processes is consistent with the prolific production of these compounds in GTs. Flavonoids are also common in GTs, contributing to terpene biosynthesis and acting as ROS scavenging antioxidants (Sugimoto et al., 2022). Expression of genes associated with metabolite transport and GT differentiation was also conserved among species, processes that are likely involved in the storage of specialised metabolites within subcellular structures (Livingston et al., 2021; Schuurink & Tissier, 2020). The GT‐genes expressed species‐specifically were largely associated with the major specialised metabolites produced by individual species. These included cannabinoid biosynthesis in cannabis and amino acid metabolism/catabolism in hops. Interestingly, many of the species‐specific GT genes have no known functions, so represent appealing putative candidates for focused investigation to increase our knowledge of GT metabolism.

GRNs reconstruct and visualise putative regulatory relationships between TFs and their downstream target genes, enabling the association of these with traits of interest (Chang et al., 2013; Van den Broeck et al., 2020; Zander et al., 2020). While we focused on GT‐specific gene regulation in the present study, the transcriptomics atlases of the remaining organs (flower, leaf, stem and root) also allowed us to reconstruct GRNs for these organs in cannabis (Tables S45–48). These provide a valuable resource for investigating broader organ specificity of transcriptional regulation. Construction of the GT GRNs allowed for shared and unique regulatory features among the three species to be identified. The cannabis‐specific GT subnetwork contained target genes that were mainly involved in the cannabinoid biosynthetic pathway and many others with unknown functions. Similarly, the target genes included in the hop‐specific GT subnetwork were primarily associated with terpene and amino/organic acid metabolism (Figure S20a,b). These findings are consistent with the known specialised metabolites present in the GTs in both species. In contrast, TFs shared between species were predicted to regulate processes that are common across GT biology, including flavonoid biosynthesis and those related to GT modification. The most influential components of the GT GRNs were identified by network motif analysis, using gene expression data only. These components were all TFs, according to independent gene functional annotations. It is expected that the most influential components in a GRN are TFs, and the consistency between our network structure and gene annotations supports the validity of our GRNs. Moreover, the cognate binding motifs of these predicted regulatory TFs were enriched among their downstream target genes.

DAP‐seq is a powerful in vitro approach that determines TF–target interactions genome‐wide. However, a known technical limitation of DAP‐seq is its varying success rates between TFs (Jiao et al., 2024; O'Malley et al., 2016; Zhang et al., 2022). We experienced this issue, with only three out of eight TFs producing high‐confidence DAP‐seq datasets. This arises partly because the DNA‐binding ability of many TFs depends on specific co‐factors or interacting proteins. Refining assay conditions and co‐expressing TFs with their binding partners could help enhance the effectiveness of DAP‐seq (Jiao et al., 2024; O'Malley et al., 2016). However, the statistically significant overlaps between DAP‐seq and histone modification data indicate that our assays identified biologically relevant TFBSs. The modest magnitude of overlap is expected given that the ChIP‐seq data from cannabis trichomes and tomato seedlings represent limited developmental stages and tissue contexts, while DAP‐seq comprehensively maps potential binding sites genome‐wide, including those in chromatin regions inaccessible or organs/tissues absent from the ChIP‐seq experiments.

DAP‐seq is valuable for validating GRN predictions, but a modest overlap between the results of these complementary approaches is expected due to their fundamental technical differences (Zander et al., 2020). DAP‐seq identifies physical binding sites in vitro using purified naked DNA, while GRN analysis infers regulatory relationships from gene expression correlations (Bartlett et al., 2017; O'Malley et al., 2016). However, physical binding does not always indicate a regulatory effect because DAP‐seq can generate false‐positives by detecting binding sites that are inaccessible or non‐functional in vivo due to closed chromatin, as shown by the limited overlap between DAP‐seq binding and DNase I hypersensitivity sites (O'Malley et al., 2016). Conversely, DAP‐seq can generate false‐negatives by missing indirect targets (e.g., A TF activating a second TF, which then activates target genes) that are captured by GRN analysis. Some functional targets that require the binding of a TF complex would also be missed by the single‐protein DAP‐seq experiments, resulting in a false‐positive compared with the functional GRN. Additionally, GRNs constructed from single time‐point transcriptomics data may miss temporal regulatory dynamics or generate false‐positives/negatives due to delayed TF effects or complex feedback loops (Varala et al., 2018; Walley et al., 2016). This was evident in our results where many DAP‐seq‐identified targets of CsHD‐ZIP879, CsSRS116 and SlWO were not present in the GT‐specific GRNs (Figure 7a). In future, inclusion of other data modalities such as chromatin accessibility, protein–DNA interaction data and time‐series expression, would substantially improve the accuracy of GRN inferences (Clark et al., 2021; Jiao et al., 2024; Walley et al., 2016).

Despite modest GRN validation rates, DAP‐seq targets for all three TFs showed significant enrichment within their respective GT‐enriched WGCNA co‐expression modules (Figure 7). WGCNA identifies gene co‐expression modules based on the principle that TFs often co‐express with their target genes, providing an alternative framework for predicting regulatory relationships (Allocco et al., 2004). This approach revealed substantially stronger overlap between DAP‐seq targets and co‐expressed genes within GT‐enriched modules compared with GRN predictions. This improved concordance reflects key differences between the two analytical approaches. While GT‐specific GRNs were restricted to GT‐specific TFs and targets, the GT‐enriched WGCNA modules contained both GT‐specific genes and substantial proportions of genes highly expressed in other organs such as flowers and leaves. This suggests that the candidate GT TFs, despite being most highly expressed in GTs, also exhibit lower expression in other organs and likely regulate both GT‐specific pathways and shared developmental or metabolic processes across multiple tissues. Our strict GRN analysis, which excluded non‐GT‐specific targets, therefore likely omitted genuine regulatory interactions occurring in multiple tissue contexts. The stronger WGCNA‐DAP‐seq concordance thus reflects the broader regulatory scope of these TFs beyond GT‐exclusive functions. Further, WGCNA modules capture both direct targets and genes regulated through downstream TFs or shared regulatory mechanisms, while DAP‐seq identifies all potential binding sites in vitro without distinguishing between tissue‐specific and multi‐tissue regulatory interactions (Yin et al., 2021). In contrast, GRN predictions attempt to infer specific regulatory edges within a single tissue context, which are more stringent and prone to false‐negatives due to the complexity of transcriptional regulation involving combinatorial TF binding, chromatin context and tissue‐specific co‐factors (Bartlett et al., 2017; Marbach et al., 2012; O'Malley et al., 2016; Varala et al., 2018). Together, these complementary approaches provide orthogonal validation of DAP‐seq results while revealing that GT‐associated TFs participate in regulatory networks spanning multiple tissues, not solely GT‐specific pathways.

Transcriptome analysis, WGCNA, GRN inference and network motif analysis successfully identified known and novel GT regulators, demonstrating the power of this combined approach. Our finding aligns with studies in plants, such as A. annua, tomato and tobacco, which show that transcriptional regulation of GT development and specialised metabolite biosynthesis is primally controlled by MYB, HD‐ZIP IV and bHLH TF families (Chalvin et al., 2020). We identified the same TF families as major candidate regulators of GTs in all three species (Figure 3). MYB TFs (e.g., SlMXTA1, AaMIXTA1 and AaMYB17) are the main positive regulators of GT initiation/morphogenesis (Ewas et al., 2016; Qin et al., 2021; Shi et al., 2018). Additionally, HD‐ZIP IV TFs (e.g., SlWO, SlCD2, SlHD3, SlHD9, NtHD9 and NtHD12) are crucial for establishing GT cell identity and morphogenesis, with SlWO notably acting as a master regulator by activating SlMXTA1 to control the transition of cell fate from GTs to non‐GTs (Li et al., 2025; Wu et al., 2023; Xu, Teng, et al., 2024; Zhao et al., 2024). More recently, SlHDZ38 (a HD‐ZIP subfamily I TF) was shown to regulate GT development and specialised metabolism in tomato (Zocca et al., 2025). The bHLH TFs are critical promoters of GT initiation, typically functioning as part of the MBW complex alongside MYB and WD40 TFs. Specific bHLH members, such as AaMYC3 in A. annua exhibit a dual regulatory role: they both initiate GT development by activating AaHD1 and simultaneously boost artemisinin biosynthesis by activating pathway genes like CYP71AV1 and ALDH1, and co‐activating ADS and DBR2 (Yuan et al., 2025; Zhao et al., 2024). Similarly, the tomato bHLH SlMYC1 regulates the initiation and development of type VI GTs and associated terpene biosynthesis (Hua, Chang, Wu, et al., 2021; Xu et al., 2018). Recent research continues to expand the known regulatory network, implicating additional families, such as the Scarecrow‐like (SCL) family, with members like SlSCL3 regulating GT development and terpene metabolism in tomato (Yang et al., 2021). This suggests that a broader range of TFs may play regulatory roles in trichome biology. Consequently, the other TF families identified across the transcriptomes of all three studied species represent interesting targets for advancing our understanding of trichome biology.

A crucial aspect of GT biology that is yet to be studied in detail is understanding how GT initiation and development is regulated (Huchelmann et al., 2017). Such data are essential for crop improvement efforts in species where the contents of GTs are the key product and might be employed for targeted modification of GT size and density, leading to improvement of metabolite composition and yield. This could be further improved by identifying more TFs using a similar approach but resolved across a time‐course that spans both the pre‐ and post‐GT initiation stages. Moreover, the use of state‐of‐the‐art single‐cell RNA sequencing and high‐resolution spatial transcriptomics analyses may resolve the metabolic networks and regulatory mechanisms that facilitate metabolite production in highly specialised trichome cell‐types, revealing the functional diversity among the various GT types (Hurgobin & Lewsey, 2022).

In summary, we have shown that fundamental aspects of GT biology are shared widely across species. We have also provided the predicted architecture of how species‐specific GT gene regulation is exercised. These resources are of broad applicability to the GT research community, as well as providing useful candidates for crop improvement and synthetic biology or biotechnology applications where GTs might be reprogrammed.