IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003085.html
   My bibliography  Save this article

Unsupervised Clustering of Subcellular Protein Expression Patterns in High-Throughput Microscopy Images Reveals Protein Complexes and Functional Relationships between Proteins

Author

Listed:
  • Louis-François Handfield
  • Yolanda T Chong
  • Jibril Simmons
  • Brenda J Andrews
  • Alan M Moses

Abstract

Protein subcellular localization has been systematically characterized in budding yeast using fluorescently tagged proteins. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches that have been trained to recognize predefined image classes based on statistical features. Here, we present an unsupervised analysis of protein expression patterns in a set of high-resolution, high-throughput microscope images. Our analysis is based on 7 biologically interpretable features which are evaluated on automatically identified cells, and whose cell-stage dependency is captured by a continuous model for cell growth. We show that it is possible to identify most previously identified localization patterns in a cluster analysis based on these features and that similarities between the inferred expression patterns contain more information about protein function than can be explained by a previous manual categorization of subcellular localization. Furthermore, the inferred cell-stage associated to each fluorescence measurement allows us to visualize large groups of proteins entering the bud at specific stages of bud growth. These correspond to proteins localized to organelles, revealing that the organelles must be entering the bud in a stereotypical order. We also identify and organize a smaller group of proteins that show subtle differences in the way they move around the bud during growth. Our results suggest that biologically interpretable features based on explicit models of cell morphology will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images.Author Summary: The location of a particular protein in the cell is one of the most important pieces of information that cell biologists use to understand its function. Fluorescent tags are a powerful way to determine the location of a protein in living cells. Nearly a decade ago, a collection of yeast strains was introduced, where in each strain a single protein was tagged with green fluorescent protein (GFP). Here, we show that by training a computer to accurately identify the buds of growing yeast cells, and then making simple fluorescence measurements in context of cell shape and cell stage, the computer could automatically discover most of the localization patterns (nucleus, cytoplasm, mitochondria, etc.) without any prior knowledge of what the patterns might be. Because we made the same, simple measurements for each yeast cell, we could compare and visualize the patterns of fluorescence for the entire collection of strains. This allowed us to identify large groups of proteins moving around the cell in a coordinated fashion, and to identify new, complex patterns that had previously been difficult to describe.

Suggested Citation

  • Louis-François Handfield & Yolanda T Chong & Jibril Simmons & Brenda J Andrews & Alan M Moses, 2013. "Unsupervised Clustering of Subcellular Protein Expression Patterns in High-Throughput Microscopy Images Reveals Protein Complexes and Functional Relationships between Proteins," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-19, June.
  • Handle: RePEc:plo:pcbi00:1003085
    DOI: 10.1371/journal.pcbi.1003085
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003085
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003085&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003085?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lior Shamir & John D Delaney & Nikita Orlov & D Mark Eckley & Ilya G Goldberg, 2010. "Pattern Recognition Software and Techniques for Biological Image Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-10, November.
    2. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
    3. Won-Ki Huh & James V. Falvo & Luke C. Gerke & Adam S. Carroll & Russell W. Howson & Jonathan S. Weissman & Erin K. O'Shea, 2003. "Global analysis of protein localization in budding yeast," Nature, Nature, vol. 425(6959), pages 686-691, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lit-Hsin Loo & Danai Laksameethanasan & Yi-Ling Tung, 2014. "Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-17, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lit-Hsin Loo & Danai Laksameethanasan & Yi-Ling Tung, 2014. "Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-17, March.
    2. Oliver M Crook & Aikaterini Geladaki & Daniel J H Nightingale & Owen L Vennard & Kathryn S Lilley & Laurent Gatto & Paul D W Kirk, 2020. "A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-21, November.
    3. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    4. Julia P. Schessner & Vincent Albrecht & Alexandra K. Davies & Pavel Sinitcyn & Georg H. H. Borner, 2023. "Deep and fast label-free Dynamic Organellar Mapping," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    5. Arthur Fischbach & Angela Johns & Kara L. Schneider & Xinxin Hao & Peter Tessarz & Thomas Nyström, 2023. "Artificial Hsp104-mediated systems for re-localizing protein aggregates," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Lee, Julian, 2023. "Poisson distributions in stochastic dynamics of gene expression: What events do they count?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    8. Maya Dinur-Mills & Merav Tal & Ophry Pines, 2008. "Dual Targeted Mitochondrial Proteins Are Characterized by Lower MTS Parameters and Total Net Charge," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-8, May.
    9. Ming Ni & Antoine L Decrulle & Fanette Fontaine & Alice Demarez & Francois Taddei & Ariel B Lindner, 2012. "Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress," PLOS Genetics, Public Library of Science, vol. 8(12), pages 1-11, December.
    10. Najme Khorasani & Mehdi Sadeghi & Abbas Nowzari-Dalini, 2020. "A computational model of stem cell molecular mechanism to maintain tissue homeostasis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    11. Md. Abdulla Al Mamun & Wei Cao & Shugo Nakamura & Jun-ichi Maruyama, 2023. "Large-scale identification of genes involved in septal pore plugging in multicellular fungi," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    12. Verena Kohler & Andreas Kohler & Lisa Larsson Berglund & Xinxin Hao & Sarah Gersing & Axel Imhof & Thomas Nyström & Johanna L. Höög & Martin Ott & Claes Andréasson & Sabrina Büttner, 2024. "Nuclear Hsp104 safeguards the dormant translation machinery during quiescence," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    13. Nebojsa Jukic & Alma P. Perrino & Frédéric Humbert & Aurélien Roux & Simon Scheuring, 2022. "Snf7 spirals sense and alter membrane curvature," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    14. Jian Cui & Jinghua Liu & Yuhua Li & Tieliu Shi, 2011. "Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-16, January.
    15. Liang Qiao & Robert B Nachbar & Ioannis G Kevrekidis & Stanislav Y Shvartsman, 2007. "Bistability and Oscillations in the Huang-Ferrell Model of MAPK Signaling," PLOS Computational Biology, Public Library of Science, vol. 3(9), pages 1-8, September.
    16. Xiaomei Wu & Erli Pang & Kui Lin & Zhen-Ming Pei, 2013. "Improving the Measurement of Semantic Similarity between Gene Ontology Terms and Gene Products: Insights from an Edge- and IC-Based Hybrid Method," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    17. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.
    18. Alexey A Gritsenko & Marc Hulsman & Marcel J T Reinders & Dick de Ridder, 2015. "Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.
    19. Kiyan Shabestary & Cinzia Klemm & Benedict Carling & James Marshall & Juline Savigny & Marko Storch & Rodrigo Ledesma-Amaro, 2024. "Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    20. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003085. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.