@article{Crook2018, abstract = {Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.}, author = {Crook, Oliver M. and Mulvey, Claire M. and Kirk, Paul D. W. and Lilley, Kathryn S. and Gatto, Laurent}, doi = {10.17863/cam.32294}, journal = {PLoS Computational Biology}, month = {mar}, pages = {e1006516}, title = {A Bayesian Mixture Modelling Approach For Spatial Proteomics}, url = {https://doi.org/10.1371/journal.pcbi.1006516}, volume = {14}, year = {2018} } @article{Jarsch2020, abstract = {Filopodia are finger-like actin-rich protrusions that extend from the cell surface and are important for cell–cell communication and pathogen internalization. The small size and transient nature of filopodia combined with shared usage of actin regulators within cells confounds attempts to identify filopodial proteins. Here, we used phage display phenotypic screening to isolate antibodies that alter the actin morphology of filopodia-like structures (FLS) in vitro. We found that all of the antibodies that cause shorter FLS interact with SNX9, an actin regulator that binds phosphoinositides during endocytosis and at invadopodia. In cells, we discover SNX9 at specialized filopodia in Xenopus development and that SNX9 is an endogenous component of filopodia that are hijacked by Chlamydia entry. We show the use of antibody technology to identify proteins used in filopodia-like structures, and a role for SNX9 in filopodia.}, author = {Jarsch, Iris K. and Gadsby, Jonathan R. and Nuccitelli, Annalisa and Mason, Julia and Shimo, Hanae and Pilloux, Ludovic and Marzook, Bishara and Mulvey, Claire M. and Dobramysl, Ulrich and Bradshaw, Charles R. and Lilley, Kathryn S. and Hayward, Richard D. and Vaughan, Tristan J. and Dobson, Claire L. and Gallop, Jennifer L.}, doi = {10.1083/jcb.201909178}, journal = {Journal of Cell Biology}, month = {mar}, title = {A direct role for SNX9 in the biogenesis of filopodia}, url = {https://oadoi.org/10.1083/jcb.201909178}, volume = {219}, year = {2020} }