Efficient differentiation of human primordial germ cells through geometric control reveals a key role for Nodal signaling

Abstract

Human primordial germ cells (hPGCs) form around the time of implantation and are the precursors of eggs and sperm. Many aspects of hPGC specification remain poorly understood because of the inaccessibility of the early postimplantation human embryo for study. Here we show that micropatterned human pluripotent stem cells (hPSCs) treated with BMP4 give rise to hPGC-like cells (hPGCLC) and use these as a quantitatively reproducible and simple in vitro model to interrogate this important developmental event. We characterize micropatterned hPSCs up to 96h and show that hPGCLC populations are stable and continue to mature. By perturbing signaling during hPGCLC differentiation, we identify a previously unappreciated role for Nodal signaling and find that the relative timing and duration of BMP and Nodal signaling are critical parameters controlling the number of hPGCLCs. We formulate a mathematical model for a network of cross-repressive fates driven by Nodal and BMP signaling which predicts the measured fate patterns after signaling perturbations. Finally, we show that hPSC colony size dictates the efficiency of hPGCLC specification, which led us to dramatically improve the efficiency of hPGCLC differentiation.

Data availability

All code for data analysis and model simulations is available on github.com/idse/PGCsscRNA-seq data have been deposited in GEO under accession number GSE182057.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Kyoung Jo

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Seth Teague

    Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Bohan Chen

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9781-2982
  4. Hina Aftab Khan

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Emily Freeburne

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0344-577X
  6. Hunter Li

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Bolin Li

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Ran Ran

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jason R Spence

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7869-3992
  10. Idse Heemskerk

    Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, United States
    For correspondence
    iheemske@umich.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8861-7712

Funding

Medical School, University of Michigan (startup)

  • Kyoung Jo
  • Seth Teague
  • Bohan Chen
  • Hina Aftab Khan
  • Emily Freeburne
  • Hunter Li
  • Bolin Li
  • Ran Ran
  • Idse Heemskerk

ETH Zürich Foundation (Branco Weiss Fellowship)

  • Hina Aftab Khan
  • Idse Heemskerk

National Institute of General Medical Sciences (R35 GM138346)

  • Seth Teague
  • Bohan Chen

Medical School, University of Michigan (Pioneer Fellowship)

  • Kyoung Jo

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

Reviewing Editor

  1. Martin Pera, The Jackson Laboratory, United States

Version history

  1. Preprint posted: August 5, 2021 (view preprint)
  2. Received: August 5, 2021
  3. Accepted: April 7, 2022
  4. Accepted Manuscript published: April 8, 2022 (version 1)
  5. Version of Record published: May 13, 2022 (version 2)

Copyright

© 2022, Jo 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

  • 3,464
    views
  • 433
    downloads
  • 26
    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. Kyoung Jo
  2. Seth Teague
  3. Bohan Chen
  4. Hina Aftab Khan
  5. Emily Freeburne
  6. Hunter Li
  7. Bolin Li
  8. Ran Ran
  9. Jason R Spence
  10. Idse Heemskerk
(2022)
Efficient differentiation of human primordial germ cells through geometric control reveals a key role for Nodal signaling
eLife 11:e72811.
https://doi.org/10.7554/eLife.72811

Share this article

https://doi.org/10.7554/eLife.72811

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan Ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.