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Development of a sensitive, scalable method for spatial, cell-type-resolved proteomics of the human brain. Simon Davis, Connor Scott, Olaf Ansorge, and Roman Fischer J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00981 • Publication Date (Web): 15 Feb 2019 Downloaded from http://pubs.acs.org on February 16, 2019
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Journal of Proteome Research
Development of a sensitive, scalable method for spatial, cell-type-resolved proteomics of the human brain. Simon Davis1#, Connor Scott2#, Olaf Ansorge2ⱡ* and Roman Fischer1ⱡ* 1Target
Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive,
Oxford, OX3 7FZ, UK 2Academic
Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of
Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK #, ⱡequal
contributions
*correspondence:
[email protected] or
[email protected] Phone: +44 1865 743639
Abstract While nearly comprehensive proteome coverage can be achieved from bulk tissue or cultured cells, the data usually lacks spatial resolution. As a result, tissue based proteomics averages protein abundance across multiple cell types and/or localisations. With proteomics platforms lacking sensitivity and throughput to undertake deep single-cell proteome studies in order to resolve spatial or cell type dependent protein expression gradients within tissue, proteome analysis has been combined with sorting techniques to enrich for certain cell populations. However, the spatial resolution and context is lost after cell sorting. Here, we report an optimised method for the proteomic analysis of neurons isolated from post-mortem human brain by Laser Capture Microdissection (LCM). We tested combinations of sample collection methods, lysis buffers and digestion methods to maximize the number of identifications and quantitative performance, identifying 1500 proteins from 60,000 µm2 of 10 µm thick cerebellar molecular layer with excellent reproducibility. To demonstrate the ability of our workflow to resolve cell type specific proteomes within human brain tissue, we isolated sets of individual Betz and Purkinje cells. Both neuronal cell types are involved in motor coordination and were found to express highly specific proteomes to a depth of 2800 to 3600 proteins. Keywords: LC-MS/MS, Laser Capture Microdissection, Tissue Proteomics, Spatially Resolved Proteomics, Brain
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Introduction Mass spectrometry-based proteomics can now generate data of a similar depth to that of RNAseq1,2. However, most approaches require relatively large amounts of starting material in the range of 10’s to 100’s of micrograms, therefore requiring the use of bulk tissue when investigating ex vivo samples. The bulk analysis of tissue consisting of multiple cellular phenotypes will result in the analyte profiles generated being an average of those phenotypes, likely concealing important features of cellular sub-populations present within the bulk sample3. Analyses on, or approaching, the single cell level allow for the detection of these changes either through the analysis of individual cells or through analysis of cells displaying distinct phenotypes (“Pheno-proteome”). Single cell proteomics of muscle fibres4 and oocytes5 have been described previously, the study of these large or multinucleated cells is aided by the large amounts of protein present in these cells. Advances have been made in all steps of the proteomic workflow to facilitate the analysis of trace sample amounts. For example, Hughes et al developed a workflow for the processing of trace samples in a single reaction vessel which utilises protein/peptide binding to paramagnetic beads to remove detergent, digest protein and clean-up peptides6. Recently, Zhu et al described a semiautomatic nanodroplet-based platform capable of performing protein digestion in
