Annotating gene expression patterns − Rakyat Biologi

It was clear at the onset of this project that many genes would be expressed in every cell of the organism. What came as a surprise is that even such genes exhibit an expression pattern that reflects a quantitative difference in gene expression levels rather then qualitative difference in gene expression specificity. Identifying and correctly categorizing such ubiquitous patterns is important because their description with the standard vocabulary approach makes it difficult to separate them from genes with true restricted expression patterns. We identified two classes of ubiquitous patterns; a midgut CNS pattern associated with the cell cycle state of the cells and an “endoderm-mesoderm” pattern correlated with physiological state of cells tilted towards energy metabolism. It is interesting that late in embryogenesis the differentially stained structures that distinguish the ubiquitous patterns follow the early subdivision of the embryo into germ layers, whereas, immediately after gastrulation there are no apparent differences among the ubiquitous patterns. It will be interesting to investigate the mechanisms that cause the quantitative differences in gene expression levels among germ layer derivatives.

Our results suggest that parallel microarray analysis should be an integral part of any in situ survey of developmental processes. Microarrays not only provide independent measurements that help control the artifacts of in situ hybridization methods, but also provide a more quantitative measure of gene expression for broadly expressed genes. The combined analysis of these two datasets is synergistic. In situ hybridization reveals the spatial diversity in tight temporal clusters and microarray clustering reduces the artificial diversity introduced by assigning annotations based on the semi-quantitative in situ assay.

In the context of an anatomically well-described system such as Drosophila embryogenesis, it is possible to achieve great precision in expression pattern description. However, making distinctions based on the fine details of patterns such as different subsets of the central nervous system can be problematic when examining genes one by one. We found that it was useful to reduce the granularity of the controlled vocabulary to the level where the annotation assignments are most reliable. This approach necessarily underestimates the true diversity of expression patterns; for example, the expression of GstS1 in a distinct subset of cells in the midgut was annotated simply as midgut. On the other hand, this approach enables description of undefined subsets of cells and their grouping with the correct higher order structures. The fine details of differences among expression patterns on a cellular level can be addressed by comparing images of the expression patterns of the individual members of the broader groups defined by controlled vocabulary annotation, or by targeted experimental pair-wise comparison of genes in double labeling in situ experiments. A complementary approach to study the blastoderm uses high resolution 3D confocal image capture followed by computational segmentation analysis (2 refs Biggin 2006).