1. Cell Biology
  2. Computational and Systems Biology
Download icon

Deciphering anomalous heterogeneous intracellular transport with neural networks

Research Article
  • Cited 0
  • Views 771
  • Annotations
Cite this article as: eLife 2020;9:e52224 doi: 10.7554/eLife.52224

Abstract

Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.

Article and author information

Author details

  1. Daniel Han

    Department of Mathematics, School of Biological Sciences, Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom
    For correspondence
    daniel.han@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9088-1651
  2. Nickolay Korabel

    Mathematics, University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Runze Chen

    Department of Computer Science, University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Mark Johnston

    School of Biological Sciences, University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Anna Gavrilova

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Victoria J Allan

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    For correspondence
    viki.allan@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4583-0836
  7. Sergei Fedotov

    Department of Mathematics, University of Manchester, Manchester, United Kingdom
    For correspondence
    sergei.fedotov@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  8. Thomas A Waigh

    Physics and Astronomy, University of Manchester, Manchester, United Kingdom
    For correspondence
    t.a.waigh@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7084-559X

Funding

Wellcome Trust (215189/Z/19/Z)

  • Daniel Han

EPSRC (EP/J019526/1)

  • Nickolay Korabel
  • Victoria J Allan
  • Sergei Fedotov
  • Thomas A Waigh

BBSRC (BB/H017828/1)

  • Victoria J Allan

Wellcome Trust (108867/Z/15/Z)

  • Anna Gavrilova

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Robert H Singer, Albert Einstein College of Medicine, United States

Publication history

  1. Received: September 26, 2019
  2. Accepted: March 22, 2020
  3. Accepted Manuscript published: March 24, 2020 (version 1)
  4. Version of Record published: April 8, 2020 (version 2)
  5. Version of Record updated: May 26, 2020 (version 3)

Copyright

© 2020, Han et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 771
    Page views
  • 111
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Biochemistry and Chemical Biology
    2. Cell Biology
    Niladri K Sinha et al.
    Research Article

    Translation of aberrant mRNAs induces ribosomal collisions, thereby triggering pathways for mRNA and nascent peptide degradation and ribosomal rescue. Here we use sucrose gradient fractionation combined with quantitative proteomics to systematically identify proteins associated with collided ribosomes. This approach identified Endothelial differentiation-related factor 1 (EDF1) as a novel protein recruited to collided ribosomes during translational distress. Cryo-electron microscopic analyses of EDF1 and its yeast homolog Mbf1 revealed a conserved 40S ribosomal subunit binding site at the mRNA entry channel near the collision interface. EDF1 recruits the translational repressors GIGYF2 and EIF4E2 to collided ribosomes to initiate a negative-feedback loop that prevents new ribosomes from translating defective mRNAs. Further, EDF1 regulates an immediate-early transcriptional response to ribosomal collisions. Our results uncover mechanisms through which EDF1 coordinates multiple responses of the ribosome-mediated quality control pathway and provide novel insights into the intersection of ribosome-mediated quality control with global transcriptional regulation.

    1. Cell Biology
    2. Microbiology and Infectious Disease
    Mark S Ladinsky et al.
    Research Article Updated

    Fusion of HIV-1 with the membrane of its target cell, an obligate first step in virus infectivity, is mediated by binding of the viral envelope (Env) spike protein to its receptors, CD4 and CCR5/CXCR4, on the cell surface. The process of viral fusion appears to be fast compared with viral egress and has not been visualized by EM. To capture fusion events, the process must be curtailed by trapping Env-receptor binding at an intermediate stage. We have used fusion inhibitors to trap HIV-1 virions attached to target cells by Envs in an extended pre-hairpin intermediate state. Electron tomography revealed HIV-1 virions bound to TZM-bl cells by 2–4 narrow spokes, with slightly more spokes present when evaluated with mutant virions that lacked the Env cytoplasmic tail. These results represent the first direct visualization of the hypothesized pre-hairpin intermediate of HIV-1 Env and improve our understanding of Env-mediated HIV-1 fusion and infection of host cells.