Leaf shape is an important component of our relationship with the living world, representing a defining feature of how we recognise and classify plant species. There is extensive variation in the form and function of leaves within and between species. In the current study we utilised variation in leaf shape represented among individuals of a collection of Eurasian aspen (Populus tremula L.) sampled across Sweden and the remarkable extent of heterophylly present to establish morphological, cellular and transcriptional developmental time lines. We performed gene expression network and phenotypical regression analyses to identify genes of central importance or that were highly predictive of shape and size phenotypes during leaf development using a systems biology approach. We complemented this developmental study with a genome wide association study of leaf shape variation to identify single nucleotide polymorphisms associated with leaf shape and size, their genomic context and the biological role of associated genes. We then compared these association candidate genes to differentially expressed genes between groups of genotypes with highly contrasting leaf shapes, also considering whether there were expression quantitative trait loci associated with the genes. We demonstrate that our developmental gene expression series captured known biology for homologs of functionally characterised Arabidopsis thaliana genes and biological processes of importance during leaf development. We identified genes of high importance from the developmental series and natural variation analyses. These included genes with characterised functions in leaf development in addition to many novel candidates. Our systems genetics approach identified numerous genes supported by the developmental time series, phenotypic and expression association mapping and differential expression between phenotypic extremes. As such, we describe a rich resource for directing future functional characterisation studies and a comprehensive data resource characterising the role of gene expression during leaf development in aspen.

While several studies have investigated general properties of the genetic architecture of natural variation in gene expression, few of these have considered natural, outbreeding populations. In parallel, systems biology has established that a general feature of biological networks is that they are scale-free, rendering them buffered against random mutations. To date, few studies have attempted to examine the relationship between the selective processes acting to maintain natural variation of gene expression and the associated co-expression network structure. Here we utilised RNA-Sequencing to assay gene expression in winter buds undergoing bud flush in a natural population of Populus tremula, an outbreeding forest tree species. We performed expression Quantitative Trait Locus (eQTL) mapping and identified 164,290 significant eQTLs associating 6,241 unique genes (eGenes) with 147,419 unique SNPs (eSNPs). We found approximately four times as many local as distant eQTLs, with local eQTLs having significantly higher effect sizes. eQTLs were primarily located in regulatory regions of genes (UTRs or flanking regions), regardless of whether they were local or distant. We used the gene expression data to infer a co-expression network and investigated the relationship between network topology, the genetic architecture of gene expression and signatures of selection. Within the co-expression network, eGenes were underrepresented in network module cores (hubs) and overrepresented in the periphery of the network, with a negative correlation between eQTL effect size and network connectivity. We additionally found that module core genes have experienced stronger selective constraint on coding and non-coding sequence, with connectivity associated with signatures of selection. Our integrated genetics and genomics results suggest that purifying selection is the primary mechanism underlying the genetic architecture of natural variation in gene expression assayed in flushing leaf buds of P. tremula and that connectivity within the co-expression network is linked to the strength of purifying selection.

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study highresolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes highthroughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.