Transcriptome-based identification of candidate membrane proteins.

A full understanding of leukocyte responses to external stimuli requires knowledge of the full complement of proteins found on their surfaces. Systematic examination of the mammalian cell surfaces at the protein level is hampered by technical difficulties associated with proteomic analysis of so many membrane proteins and the large amounts of starting material required. The use of transcriptomic analyses avoids challenges associated with protein stability and separation and enables the inclusion of an amplification step; thus allowing the use of cell numbers applicable to the study of sub populations of, for example, primary lymphocytes. Here we present a transcriptomic methodology based on Serial Analysis of Gene Expression (SAGE) to recover an essentially complete and quantitative profile of mRNA species in a particular cell. We discuss how, using bioinformatic tools accessible to standard desktop computers, plasma membrane proteins can be identified in silico, from this list. While we describe the use of this approach to characterise the cell surface protein complement of a resting CD8(+) T-cell clone, it is theoretically applicable to any cell surface, where a suitable pure population of cells is available.

Deep analysis of cellular transcriptomes - LongSAGE versus classic MPSS.

BACKGROUND: Deep transcriptome analysis will underpin a large fraction of post-genomic biology. 'Closed' technologies, such as microarray analysis, only detect the set of transcripts chosen for analysis, whereas 'open' e.g. tag-based technologies are capable of identifying all possible transcripts, including those that were previously uncharacterized. Although new technologies are now emerging, at present the major resources for open-type analysis are the many publicly available SAGE (serial analysis of gene expression) and MPSS (massively parallel signature sequencing) libraries. These technologies have never been compared for their utility in the context of deep transcriptome mining. RESULTS: We used a single LongSAGE library of 503,431 tags and a "classic" MPSS library of 1,744,173 tags, both prepared from the same T cell-derived RNA sample, to compare the ability of each method to probe, at considerable depth, a human cellular transcriptome. We show that even though LongSAGE is more error-prone than MPSS, our LongSAGE library nevertheless generated 6.3-fold more genome-matching (and therefore likely error-free) tags than the MPSS library. An analysis of a set of 8,132 known genes detectable by both methods, and for which there is no ambiguity about tag matching, shows that MPSS detects only half (54%) the number of transcripts identified by SAGE (3,617 versus 1,955). Analysis of two additional MPSS libraries shows that each library samples a different subset of transcripts, and that in combination the three MPSS libraries (4,274,992 tags in total) still only detect 73% of the genes identified in our test set using SAGE. The fraction of transcripts detected by MPSS is likely to be even lower for uncharacterized transcripts, which tend to be more weakly expressed. The source of the loss of complexity in MPSS libraries compared to SAGE is unclear, but its effects become more severe with each sequencing cycle (i.e. as MPSS tag length increases). CONCLUSION: We show that MPSS libraries are significantly less complex than much smaller SAGE libraries, revealing a serious bias in the generation of MPSS data unlikely to have been circumvented by later technological improvements. Our results emphasize the need for the rigorous testing of new expression profiling technologies.

The T cell surface--how well do we know it?

The overall degree of complexity of the T cell surface has been unclear, constraining our understanding of its biology. Using global gene expression analysis, we show that 111 of 374 genes encoding well-characterized leukocyte surface antigens are expressed by a resting cytotoxic T cell. Unexpectedly, of 97 stringently defined, T cell-specific transcripts with unknown functions that we identify, none encode proteins with the modular architecture characteristic of 80% of leukocyte surface antigens. Only two encode proteins with membrane topologies found exclusively in cell surface molecules. Our analysis indicates that the cell type-specific composition of the resting CD8+ T cell surface is now largely defined, providing an insight into the overall compositional complexity of the mammalian cell surface and a framework for formulating systematic models of T cell surface-dependent processes, such as T cell receptor triggering.