Charlotte Hutchings
I am a third-year PhD student working between AstraZeneca and Prof. Kathryn Lilley's laboratory at the University of Cambridge. My project uses novel proteomics methods to map changes in protein abundance and location within HEK293 cells when these cells are being used as factories to produce recombinant adeno-associated viruses (rAAVs). Such viruses are used as DNA delivery vehicles in both research and gene therapies. By increasing our understanding of the viral production process, I aim to identify routes to enhance the manufacturing process.
Throughout my PhD I have generated large and complex proteomics datasets and, as such, have come to enjoy the challenge of large-scale data exploration and analysis. My excitement for data has led me to publish multiple data processing workflows and write/teach workshops on using the use of R for these workflows. Moving forwards, I intend to continue into a career that involves active research (both wet lab and bioinformatic), teaching and promoting open, reproducible science.
Sessions
The potential of recombinant adeno-associated viruses (rAAVs) as therapeutic DNA delivery agents has been established, and four such gene therapies are now commercially available. The most common way in which rAAVs are manufactured is through transient triple transfection of HEK293 cells, essentially using the cells as virus production factories. Unfortunately, low rAAV yield and recovery during this process currently limits the wider use of these vehicles. Improvements in the manufacturing of rAAVs to ensure the long-term success of rAAV gene therapies will require a better understanding of the molecular mechanism(s) by which rAAVs are produced inside HEK293 cells. Using high-throughput mass spectrometry-based spatial proteomics we have generated sub-cellular protein maps of control (non-producing) and rAAV-producing HEK293 cells. Global spatial maps were acquired using the well-established Localisation of Organellar Proteins by Isotope Tagging after Differential Centrifugation (LOPIT-DC) method. Data was analysed using dedicated Bioconductor proteomics packages, namely QFeatures, MSnbase, pRoloc and handle. By exploiting the semi-supervised Bayesian methods available in pRoloc and bandle, we were able to systematically localise thousands of proteins per experiment to distinct subcellular organelles and protein complexes. Comparison of cellular protein localisation in control and rAAV-producing cells has helped to elucidate whether differential protein localisation of cellular or viral proteins could contribute to limitations in the rAAV production process.