Graphical-model framework for automated annotation of cell identities in dense cellular images

  1. Shivesh Chaudhary
  2. Sol Ah Lee
  3. Yueyi Li
  4. Dhaval S Patel
  5. Hang Lu  Is a corresponding author
  1. Georgia Institute of Technology, United States

Abstract

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files are provided at https://github.com/shiveshc/CRF_Cell_ID.git.

Article and author information

Author details

  1. Shivesh Chaudhary

    Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Sol Ah Lee

    Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Yueyi Li

    Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Dhaval S Patel

    Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Hang Lu

    Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States
    For correspondence
    hang.lu@gatech.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6881-660X

Funding

National Institutes of Health (R21DC015652)

  • Hang Lu

National Institutes of Health (R01NS096581)

  • Hang Lu

National Institutes of Health (R01GM088333)

  • Hang Lu

National Science Foundation (1764406)

  • Hang Lu

National Science Foundation (1707401)

  • Hang Lu

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

Copyright

© 2021, Chaudhary 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

  • 2,357
    views
  • 284
    downloads
  • 32
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Shivesh Chaudhary
  2. Sol Ah Lee
  3. Yueyi Li
  4. Dhaval S Patel
  5. Hang Lu
(2021)
Graphical-model framework for automated annotation of cell identities in dense cellular images
eLife 10:e60321.
https://doi.org/10.7554/eLife.60321

Share this article

https://doi.org/10.7554/eLife.60321

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Trine Line Hauge Okholm, Andreas Bjerregaard Kamstrup ... Christian Kroun Damgaard
    Research Article

    Circular RNAs represent a class of endogenous RNAs that regulate gene expression and influence cell biological decisions with implications for the pathogenesis of several diseases. Here, we disclose a novel gene-regulatory role of circHIPK3 by combining analyses of large genomics datasets and mechanistic cell biological follow-up experiments. Using time-course depletion of circHIPK3 and specific candidate RNA-binding proteins, we identify several perturbed genes by RNA sequencing analyses. Expression-coupled motif analyses identify an 11-mer motif within circHIPK3, which also becomes enriched in genes that are downregulated upon circHIPK3 depletion. By mining eCLIP datasets and combined with RNA immunoprecipitation assays, we demonstrate that the 11-mer motif constitutes a strong binding site for IGF2BP2 in bladder cancer cell lines. Our results suggest that circHIPK3 can sequester IGF2BP2 as a competing endogenous RNA (ceRNA), leading to target mRNA stabilization. As an example of a circHIPK3-regulated gene, we focus on the STAT3 mRNA as a specific substrate of IGF2BP2 and validate that manipulation of circHIPK3 regulates IGF2BP2-STAT3 mRNA binding and, thereby, STAT3 mRNA levels. Surprisingly, absolute copy number quantifications demonstrate that IGF2BP2 outnumbers circHIPK3 by orders of magnitude, which is inconsistent with a simple 1:1 ceRNA hypothesis. Instead, we show that circHIPK3 can nucleate multiple copies of IGF2BP2, potentially via phase separation, to produce IGF2BP2 condensates. Our results support a model where a few cellular circHIPK3 molecules can induce IGF2BP2 condensation, thereby regulating key factors for cell proliferation.

    1. Cell Biology
    2. Computational and Systems Biology
    N Suhas Jagannathan, Javier Yu Peng Koh ... Lisa Tucker-Kellogg
    Research Article

    Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.