High‐Throughput Automated Subcellular Localisation

Abstract

Defining the subcellular localisation of the proteome for an organism of interest is a critical next step following genome sequencing. Knowledge of protein subcellular localisation provides insight into the functionality of the normal cell, as well during disease states. However, the presence of gene isoforms, alternative splicing and posttranslational modifications significantly increase the number of protein variants encoded by a single gene, making this a complex task. In the last 20 years, parallel approaches using fractionation and mass spectrometry, synthesis of large libraries of open reading frames fused to genes encoding fluorescent proteins, as well as production of thousands of antibodies have all contributed to the systematic analysis of protein localisation. Alongside these methods, improved bioinformatic predictors, machine learning and deep learning algorithms have also evolved as essential tools. A combinatorial approach of these methods now brings us close to systematically defining the subcellular proteome for many organisms.

Key Concepts

  • Subcellular localisation is a critical determinant in understanding protein function.
  • Data from genome sequencing projects provide the fundamental information from which approaches to understand protein localisation can be initiated.
  • Parallel approaches using fluorescence microscopy are being applied in a high‐throughput manner to systematically reveal the subcellular localisation of large numbers of proteins in different cells.
  • The primary techniques to determine protein localisation are mass spectrometry‐based proteomics, production of antibodies and expression of fluorescently tagged proteins.
  • There is increasing use of computational biology tools to aid the automated classification of subcellular localisation.
  • Large image datasets can be interrogated by machine learning software algorithms to automatically classify proteins to specific localisations.
  • Deep learning methods, which can work independently of training datasets, have become the newest tool to automatically assign protein localisation from image sets.
  • Automated approaches combining both experimental and computational methods are likely to become the primary means by which subcellular localisation is determined from new cell systems.

Keywords: subcellular localisation; proteome; GFP‐tagging; immunofluorescence; high‐throughput automated imaging; high‐content analysis; machine learning; deep learning

Figure 1. Primary methods used to determine protein subcellular localisation. Diagram shows the relationship between the most commonly used wet‐lab and computational methods, for determining the subcellular localisation of proteins in a cell. Created with BioRender.com.
Figure 2. Emerging and future directions for protein subcellular localisation applications. Diagram shows four examples of where protein localisation projects could be applied. From upper left, localisation could be systematically determined from tissue samples (rather than cultured cells); localisation could be determined from cells from a wider range of organisms (such as plants); other imaging modalities such as electron microscopy and super‐resolution microscopy could be used to refine our knowledge of localisation within substructures of compartments; patient samples could be used to assess mislocalisation of proteins, allowing this information to be used to guide personalised medicine regimes. Created with RioRender.com.
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Further Reading

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Chalkley, Alannah S, Kelly, Suainibhe, Mysior, Margaritha M, and Simpson, Jeremy C(Aug 2020) High‐Throughput Automated Subcellular Localisation. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0020868]