Tissue-specific gene expression is a fundamental aspect of multicellular biology, underlying the development, function, and maintenance of diverse cell types within an organism. Accounting for tissue-specific expression is a precursor to any systems-level understanding of metazoan organismal development and function and large-scale studies of spatio-temporal gene expression both at the single-gene and whole-genome level have been performed in several organisms 
. Additionally, tissue specificity is an important aspect of many complex diseases; notable examples of tissue interactions associated with disease include stroma-tumor interactions in cancer 
and tissue-specific effects of insulin signaling in diabetes 
. Although several experimental techniques have been developed to identify tissue-specific gene expression signatures, both at the single-gene and whole-genome level, our current knowledge of tissue-specific expression is incomplete.
The model organism Caenorhabditis elegans
provides a good framework for the study of tissue-specific expression. Its invariant cell lineage allows single-cell resolution of tissue-specific expression patterns through a variety of experimental techniques 
. In situ
hybridizations of the entire transcriptome are in progress 
, and GFP-promoter tagging has been applied on a large scale 
; as a result, the expression of approximately 3500 genes has been studied at the single-gene level 
, providing a “gold standard” for gene expression. Additionally, several methods have been developed to isolate mRNA samples enriched for a specific tissue or cell type, allowing global analysis using microarrays or SAGE 
Despite the variety of techniques available and the number of studies performed thus far, our understanding of tissue-specific expression in C. elegans
is not yet complete; most genes have not been analyzed at the single-gene level, nor under diverse conditions and developmental stages. Additionally, each of the individual techniques for measuring tissue-specific expression suffers from drawbacks. GFP-promoter constructs, though they present the most accurate method amenable to high-throughput analysis, may incompletely capture endogenous expression or may fail to express well, a problem that is particularly severe in the germ line due to silencing 
. Directed microarray studies, while powerful, depend on the ability to isolate mRNA from a particular tissue, since dissection is not possible in most cases, and methods to achieve this each have disadvantages: studies using mutants may report non-endogenous expression; embryonic cell sorting misses expression that only occurs in later stages of development, as post-embryonic cell sorting is not yet feasible; and poly-A binding studies depend on the ability to introduce the binding protein construct into and extract the protein out of the tissue of interest 
. Thus, the ability to directly study the expression specificity of each gene across tissues, especially small tissues, and ideally to also account for the effects of development and environmental conditions, remains challenging.
Here we present a computational method that leverages existing experimental information to expand and improve our knowledge of tissue-specific expression. Using data from whole-animal microarrays, we accurately predict tissue-specific expression in all major tissues and even for several tissues that comprise only a few cells. Our approach not only outperforms directed high-throughput studies in all but one case, but also captures information that complements existing experiments, for example, by uncovering tissue-specific expression that is only seen under specific conditions. To confirm our predictions, we experimentally verified the expression of several genes. We have made our predictions available through a dynamic web-based interface at http://function.princeton.edu/worm_tissue
to enable hypothesis generation and further experimental follow up by the community.
Using this accurate large-scale, tissue-specific information, we perform further computational analyses, such as prediction of transcriptional regulatory motifs specific to understudied tissues as well as tissue-specific miRNA target regulation. In addition, we extended our algorithm to produce tissue-specific functional interaction networks that provide a framework for discovering protein function specific to particular tissues. Our ability to uncover tissue-specific information should allow higher-detail analysis of expression and further hypothesis testing to identify expression changes that are important for biological function.