Cellular life is highly complex. In order to expand our understanding of the workings of human cells, in particular in the context of health and disease, detailed knowledge about the underlying molecular systems is needed. The unifying theme of this thesis concerns the use of data derived from sequencing of RNA, both within the field of transcriptomics itself and as a guide for further studies at the level of protein expression. In paper I, we showed that publicly available RNA-seq datasets are consistent across different studies, requiring only light processing for the data to cluster according to biological, rather than technical characteristics. This suggests that RNA-seq has developed into a reliable and highly reproducible technology, and that the increasing amount of publicly available RNA-seq data constitutes a valuable resource for meta-analyses. In paper II, we explored the ability to extrapolate protein concentrations by the use of RNA expression levels. We showed that mRNA and corresponding steady-state protein concentrations correlate well by introducing a gene-specific RNA-to-protein conversion factor that is stable across various cell types and tissues. The results from this study indicate the utility of RNA-seq also within the field of proteomics.
The second part of the thesis starts with a paper in which we used transcriptomics to guide subsequent protein studies of the molecular mechanisms underlying malignant transformation. In paper III, we applied a transcriptomics approach to a cell model for defined steps of malignant transformation, and identified several genes with interesting expression patterns whose corresponding proteins were further analyzed with subcellular spatial resolution. Several of these proteins were further studied in clinical tumor samples, confirming that this cell model provides a relevant system for studying cancer mechanisms. In paper IV, we continued to explore the transcriptional landscape in the same cell model under moderate hypoxic conditions.
To conclude, this thesis demonstrates the usefulness of RNA-seq data, from a transcriptomics perspective and beyond; to guide in analyses of protein expression, with the ultimate goal to unravel the complexity of the human cell, from a holistic point of view.