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.

Data availability

Supporting files are on GitHub and Zenodo.

The following data sets were generated
    1. Daniel Han
    (2019) hurst-exp
    Github, dadanhan/hurst-exp.git.

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

Version 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

  • 1,759
    Page views
  • 284
    Downloads
  • 34
    Citations

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

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)

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

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

  1. Daniel Han
  2. Nickolay Korabel
  3. Runze Chen
  4. Mark Johnston
  5. Anna Gavrilova
  6. Victoria J Allan
  7. Sergei Fedotov
  8. Thomas A Waigh
(2020)
Deciphering anomalous heterogeneous intracellular transport with neural networks
eLife 9:e52224.
https://doi.org/10.7554/eLife.52224

Share this article

https://doi.org/10.7554/eLife.52224

Further reading

    1. Cell Biology
    2. Plant Biology
    Maciek Adamowski, Ivana Matijević, Jiří Friml
    Research Article

    The GNOM (GN) Guanine nucleotide Exchange Factor for ARF small GTPases (ARF-GEF) is among the best studied trafficking regulators in plants, playing crucial and unique developmental roles in patterning and polarity. The current models place GN at the Golgi apparatus (GA), where it mediates secretion/recycling, and at the plasma membrane (PM) presumably contributing to clathrin-mediated endocytosis (CME). The mechanistic basis of the developmental function of GN, distinct from the other ARF-GEFs including its closest homologue GNOM-LIKE1 (GNL1), remains elusive. Insights from this study largely extend the current notions of GN function. We show that GN, but not GNL1, localizes to the cell periphery at long-lived structures distinct from clathrin-coated pits, while CME and secretion proceed normally in gn knockouts. The functional GN mutant variant GNfewerroots, absent from the GA, suggests that the cell periphery is the major site of GN action responsible for its developmental function. Following inhibition by Brefeldin A, GN, but not GNL1, relocates to the PM likely on exocytic vesicles, suggesting selective molecular associations en route to the cell periphery. A study of GN-GNL1 chimeric ARF-GEFs indicates that all GN domains contribute to the specific GN function in a partially redundant manner. Together, this study offers significant steps toward the elucidation of the mechanism underlying unique cellular and development functions of GNOM.

    1. Biochemistry and Chemical Biology
    2. Cell Biology
    Chenjie Xia, Huihui Xu ... Hongting Jin
    Research Article

    Glucocorticoid-induced osteonecrosis of the femoral head (GONFH) is a common refractory joint disease characterized by bone damage and the collapse of femoral head structure. However, the exact pathological mechanisms of GONFH remain unknown. Here, we observed abnormal osteogenesis and adipogenesis associated with decreased β-catenin in the necrotic femoral head of GONFH patients. In vivo and in vitro studies further revealed that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation of bone marrow mesenchymal cells (BMSCs) by inhibiting β-catenin signaling in glucocorticoid-induced GONFH rats. Col2+ lineage largely contributes to BMSCs and was found an osteogenic commitment in the femoral head through 9 mo of lineage trace. Specific deletion of β-catenin gene (Ctnnb1) in Col2+ cells shifted their commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice. Overall, we uncover that β-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of GONFH and identify an ideal genetic-modified mouse model of GONFH.