Background: Despite their ecological and economical importance, conifers still have limited genomic resources, mainly due to the large size and complexity of their genomes. In addition, several of the available genomic resources lack complete structural and functional annotation. Transcriptomic resources have been commonly used to compensate for these deficiencies, though for most conifer species the currently available transcriptomes are limited to a small number of tissues, or capture only a fraction of the genes present in the genome. Results: Here we provide an atlas of gene expression patterns for conifer Pinus sylvestris grown under natural conditions across five tissues: embryo, megagametophyte, needle, phloem, and vegetative bud. Compared to previous studies, we used a wider range of tissues and focused our analyses on the expression profiles of genes at tissue level. We provide comprehensive information of the per-tissue normalized expression level, and indication of tissue preferential upregulation or tissue preferential expression. We identified a total of 48,001 tissue preferentially upregulated and tissue specifically expressed genes, of which 28% have annotation in the Swiss-Prot database. The annotated genes were associated with a total of 84,498 GO terms, of which 1,834 had significant enrichment in different processes and functions, for example glyoxylate cycle in megagametophyte and defense response in needle. Even though most of the genes originating from the transcriptome do not have functional information in current biological databases, the tissue-specific patterns identified here provide valuable information about their potential functions for further studies. Conclusions: The genes identified in this study will contribute to improve the annotation of the already available and forthcoming conifer genomes. This atlas of gene expression also provides ground to further the research in the areas of plant physiology, population genetics, and genomics in general. As we provide information on tissue specificity at both diploid and haploid life stages, our data will also contribute to the understanding of evolutionary rates of different tissue types and ploidy levels.