RT Journal Article SR Electronic T1 AspWood: High-spatial-resolution transcriptome profiles reveal uncharacterized modularity of wood formation in Populus tremula JF bioRxiv FD Cold Spring Harbor Laboratory SP 094060 DO 10.1101/094060 A1 David Sundell A1 Nathaniel R. Street A1 Manoj Kumar A1 Ewa J. Mellerowicz A1 Melis Kucukoglu A1 Christoffer Johnsson A1 Vikash Kumar A1 Chanaka Mannapperuma A1 Nicolas Delhomme A1 Ove Nilsson A1 Hannele Tuominen A1 Edouard Pesquet A1 Urs Fischer A1 Totte Niittylä A1 Bjöern Sundberg A1 Torgeir R. Hvidsten YR 2017 UL http://biorxiv.org/content/early/2017/02/23/094060.abstract AB Trees represent the largest terrestrial carbon sink and a renewable source of ligno-cellulose. There is significant scope for yield and quality improvement in these largely undomesticated species, and efforts to engineer new, elite varieties will benefit from an improved understanding of the transcriptional network underlying cambial growth and wood formation. We generated high-spatial-resolution RNA Sequencing data spanning the secondary phloem, vascular cambium and wood forming tissues. The transcriptome comprised 28,294 expressed, previously annotated genes, 78 novel protein-coding genes and 567 long intergenic non-coding RNAs. Most paralogs originating from the Salicaceae whole genome duplication were found to have diverged expression, with the notable exception of those with high expression during secondary cell wall deposition. Co-expression network analysis revealed that the regulation of the transcriptome underlying cambial growth and wood formation comprises numerous modules forming a continuum of active processes across the tissues. The high spatial resolution enabled identification of novel roles for characterised genes involved in xylan and cellulose biosynthesis, regulators of xylem vessel and fiber differentiation and lignification. The associated web resource (AspWood, http://aspwood.popgenie.org) integrates the data within a set of interactive tools for exploring the expression profiles and co-expression network.