Revealing architectural order with quantitative label-free imaging and deep learning

Abstract

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of UNet architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Data availability

Our experiments generated imaging data from mouse kidney tissue and human brain tissue slices that are useful for machine learning and other analyses. We are in the process of uploading them to a public image archive.

Article and author information

Author details

  1. Syuan-Ming Guo

    Computational Microscopy, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Li-Hao Yeh

    Computational Microscopy, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2803-5996
  3. Jenny Folkesson

    Data Science, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4673-0522
  4. Ivan E Ivanov

    Computational Microscopy, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Anitha P Krishnan

    Data Science, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Matthew G Keefe

    Anatomy, UCSF School of Medicine, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Ezzat Hashemi

    Department of Neurology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. David Shin

    Anatomy, UCSF School of Medicine, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Bryant B Chhun

    Computational Microscopy, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Nathan H Cho

    Cell Atlas, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Manuel D Leonetti

    Cell Atlas, Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. May H Han

    Department of Neurology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Tomasz Nowakowski

    Anatomy, UCSF School of Medicine, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Shalin B Mehta

    Computational Microscopy, Chan Zuckerberg Biohub, San Francisco, United States
    For correspondence
    shalin.mehta@czbiohub.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2542-3582

Funding

Chan Zuckerberg Biohub

  • Syuan-Ming Guo
  • Li-Hao Yeh
  • Jenny Folkesson
  • Ivan E Ivanov
  • Matthew G Keefe
  • David Shin
  • Bryant B Chhun
  • Nathan H Cho
  • Tomasz Nowakowski
  • Shalin B Mehta

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

Reviewing Editor

  1. Birte Forstmann, University of Amsterdam, Netherlands

Ethics

Human subjects: De-identified brain tissue samples were received with patient consent in accordance with a protocol approved by the Human Gamete, Embryo, and Stem Cell Research Committee (institutional review board) at the University of California, San Francisco.

Version history

  1. Received: January 27, 2020
  2. Accepted: July 24, 2020
  3. Accepted Manuscript published: July 27, 2020 (version 1)
  4. Version of Record published: August 17, 2020 (version 2)

Copyright

© 2020, Guo 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

  • 6,592
    Page views
  • 792
    Downloads
  • 43
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, 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. Syuan-Ming Guo
  2. Li-Hao Yeh
  3. Jenny Folkesson
  4. Ivan E Ivanov
  5. Anitha P Krishnan
  6. Matthew G Keefe
  7. Ezzat Hashemi
  8. David Shin
  9. Bryant B Chhun
  10. Nathan H Cho
  11. Manuel D Leonetti
  12. May H Han
  13. Tomasz Nowakowski
  14. Shalin B Mehta
(2020)
Revealing architectural order with quantitative label-free imaging and deep learning
eLife 9:e55502.
https://doi.org/10.7554/eLife.55502

Share this article

https://doi.org/10.7554/eLife.55502

Further reading

    1. Developmental Biology
    2. Neuroscience
    Kristine B Walhovd, Stine K Krogsrud ... Didac Vidal-Pineiro
    Research Article

    Human fetal development has been associated with brain health at later stages. It is unknown whether growth in utero, as indexed by birth weight (BW), relates consistently to lifespan brain characteristics and changes, and to what extent these influences are of a genetic or environmental nature. Here we show remarkably stable and lifelong positive associations between BW and cortical surface area and volume across and within developmental, aging and lifespan longitudinal samples (N = 5794, 4–82 y of age, w/386 monozygotic twins, followed for up to 8.3 y w/12,088 brain MRIs). In contrast, no consistent effect of BW on brain changes was observed. Partly environmental effects were indicated by analysis of twin BW discordance. In conclusion, the influence of prenatal growth on cortical topography is stable and reliable through the lifespan. This early-life factor appears to influence the brain by association of brain reserve, rather than brain maintenance. Thus, fetal influences appear omnipresent in the spacetime of the human brain throughout the human lifespan. Optimizing fetal growth may increase brain reserve for life, also in aging.

    1. Cell Biology
    2. Neuroscience
    Marcos Moreno-Aguilera, Alba M Neher ... Carme Gallego
    Research Article

    Alternative RNA splicing is an essential and dynamic process in neuronal differentiation and synapse maturation, and dysregulation of this process has been associated with neurodegenerative diseases. Recent studies have revealed the importance of RNA-binding proteins in the regulation of neuronal splicing programs. However, the molecular mechanisms involved in the control of these splicing regulators are still unclear. Here we show that KIS, a kinase upregulated in the developmental brain, imposes a genome-wide alteration in exon usage during neuronal differentiation in mice. KIS contains a protein-recognition domain common to spliceosomal components and phosphorylates PTBP2, counteracting the role of this splicing factor in exon exclusion. At the molecular level, phosphorylation of unstructured domains within PTBP2 causes its dissociation from two co-regulators, Matrin3 and hnRNPM, and hinders the RNA-binding capability of the complex. Furthermore, KIS and PTBP2 display strong and opposing functional interactions in synaptic spine emergence and maturation. Taken together, our data uncover a post-translational control of splicing regulators that link transcriptional and alternative exon usage programs in neuronal development.