Technological and methodological advances in the fields of medical and life-sciences have, over the last 25 years, revolutionised the way in which cellular activity is measured at the molecular level. Three such advances have provided a means of accurately and rapidly quantifying mRNA, from the development of quantitative Polymerase Chain Reaction (qPCR), to DNA microarrays, and second-generation RNA-sequencing (RNA-seq). Despite consistent improvements in measurement precision and sample throughput, the data generated continue to be a ffected by high levels of variability due to the use of biologically distinct experimental subjects, practical restrictions necessitating the use of small sample sizes, and technical noise introduced during frequently complex sample preparation and analysis procedures. A series of experiments were performed during this project to pro le sources of technical noise in each of these three techniques, with the aim of using the information to produce more accurate and more reliable results. The mechanisms for the introduction of confounding noise in these experiments are highly unpredictable. The variance structure of a qPCR experiment, for example, depends on the particular tissue-type and gene under assessment while expression data obtained by microarray can be greatly influenced by the day on which each array was processed and scanned. RNA-seq, on the other hand, produces data that appear very consistent in terms of differences between technical replicates, however there exist large differences when results are compared against those reported by microarray, which require careful interpretation. It is demonstrated in this thesis that by quantifying some of the major sources of noise in an experiment and utilising compensation mechanisms, either pre- or post-hoc, researchers are better equipped to perform experiments that are more robust, more accurate, and more consistent.

Recent advances in sequencing technology have given access to information extracted on a single cell level. Single cell RNA sequencing enables for transcriptomes to be sequenced, allowing for studies within and between cell types. A recently developed protocol, based on Smart-seq2, and the Proximity ligation essay, allows for the detection of protein data from single cells, in parallel with RNA. The combination of the transcriptomic and proteomic data will enhance researchers’ ability to explore cell states. In this study, we are comparing a new pulldown protocol with the widely-used Smart-seq2, as well as against FACS sorted cells. Our results show differences in the RNA sequenced between the two protocols, as well the prediction of cell cycle state based on their data. Using RNA extracted from the pulldown protocol in different time points, we also calculate the direction of development for the cells. We expect that the incorporation of proteomic data will shed light to relevant biological questions related to the cell function.