Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

High-throughput proteomic analysis of spatially distinct features of human brain tissue (#490)

Simon Davis 1 2 , Connor Scott 2 , Benedikt Kessler 1 , Olaf Ansorge 2 , Roman Fischer 1
  1. Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
  2. Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom


Near comprehensive proteomes of bulk tissue and cultured cells can now be readily generated. In the case of tissue proteomics, these approaches often lack spatial resolution, resulting in little to no knowledge about the underlying heterogeneity of the tissue. Isolating cells of a single phenotype or clusters of cells alleviates the signal averaging caused by measuring a diverse cell population. We developed a sensitive workflow for the proteomic analysis of neurons isolated from post-mortem human brain using laser-capture microdissection. We will use this methodology to investigate the spatial distribution of proteins throughout a heterogeneous brain tumour on a high-throughput proteomics platform.  


To test the performance of high-throughput proteomic analysis of laser-capture microdissected samples, we isolated Purkinje cells of the Cerebellum and Betz cells of the motor cortex from a post-mortem human brain. Proteins were cleaned up and digested using a modified SP3 protocol and analysed either on an Orbitrap Fusion Lumos coupled to a nano-LC using 60 minute gradients or on a Bruker TimsTOF pro coupled to an Evosep One using 11.5 minute gradients.


We detected over 3700 proteins from both cell types using the Fusion Lumos (& long gradient LC) and over 1900 proteins from both cell types using the TimsTOF pro (& short gradient LC). The long-gradient low throughput workflow gave more reproducible results in terms of protein identification overlap, >90 % between replicates, and quantitative reproducibility with a mean Pearson Correlation Coefficient (PCC) of 0.97, when compared to the short-gradient high-throughput workflow, >80 % identification overlap and mean PCC of 0.90.

Concluding Statement:

The 10x increase in throughput increases possibilities for investigation into the spatial distribution of proteins throughout a tissue. We will next apply this methodology in order to determine spatial proteomic profiles of distinct histological features within a brain tumour.

  1. 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 Journal of Proteome Research 2019 18 (4), 1787-1795 DOI: 10.1021/acs.jproteome.8b00981
  2. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics Nicolai Bache, Philipp Emanuel Geyer, Dorte B. Bekker-Jensen, Ole Hoerning, Lasse Falkenby, Peter V. Treit, Sophia Doll, Igor Paron, Johannes Bruno Müller, Florian Meier, Jesper V. Olsen, Ole Vorm, Matthias Mann Molecular & Cellular Proteomics August 13, 2018, mcp.TIR118.000853; DOI: 10.1074/mcp.TIR118.000853
  3. Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer Florian Meier, Andreas-David Brunner, Scarlet Koch, Heiner Koch, Markus Lubeck, Michael Krause, Niels Goedecke, Jens Decker, Thomas Kosinski, Melvin A. Park, Nicolai Bache, Ole Hoerning, Jürgen Cox, Oliver Räther, Matthias Mann Molecular & Cellular Proteomics December 1, 2018, First published on November 1, 2018, 17 (12) 2534-2545; DOI: 10.1074/mcp.TIR118.000900