15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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 96 hr 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.

          eLife digest

          In humans and other animals, eggs and sperm are unique cells that pass on genetic material to the next generation. They originate from a small group of cells called primordial germ cells that form early in life in the developing embryo. Several different signal molecules including ones known as BMP4, Wnt, and Nodal, instruct certain cells in the embryo to become primordial germ cells.

          The process by which primordial germ cells are made in humans is very different to how primordial germ cells are made in mice and other so-called model animals that are commonly used in research. This has made it more challenging to uncover the details of the process in humans. Fortunately, new methods have recently been created that mimic aspects of how human embryos develop using human stem cells in a laboratory dish, providing an opportunity to gain a deeper understanding of how human germ cells form.

          Jo et al. used a technique called micropatterning to control the shape and size of groups of human stem cells growing in a laboratory dish. Treating these cells with a signal known as BMP4 gave rise to cells that resembled primordial germ cells. The team then used this system as a model to study how primordial germ cells form in humans. The experiments found that reducing Wnt signals in stem cells stopped primordial germ cells from forming in response to BMP4, confirming that Wnt signals made by the cells in response to BMP4 are essential. However, this block was overcome by providing the stem cells with another signal called Nodal. This suggests that the role of Wnt signaling in primordial germ cell formation is in part indirect by switching on Nodal in stem cells.

          Defects in eggs and sperm may lead to infertility, therefore, the findings of Jo et al. have the potential to help researchers develop new fertility treatments that use eggs or sperm grown in a laboratory from the patients’ own stem cells. Such research would benefit from first developing a better understanding of how to make primordial germ cells.

          Related collections

          Most cited references53

          • Record: found
          • Abstract: found
          • Article: not found

          Optimization by simulated annealing.

          There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cellpose: a generalist algorithm for cellular segmentation

            Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

              Single-cell RNA-sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed ‘dropout’, which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures and uncovers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations. One Sentence Summary: Graph diffusion-based imputation method recovers missing transcripts in scRNA-seq data, yielding insight into the epithelial-to-mesenchymal transition. Abstract highlights: 1. MAGIC restores noisy and sparse single-cell data using diffusion geometry. 2. Corrected data is amenable to myriad downstream analyses. 3. MAGIC enables archetypal analysis and inference of gene interactions. 4. Transcription factor targets can be predicted without perturbation after MAGIC. In brief - A new algorithm overcomes limitations of data loss in single cell sequencing experiments
                Bookmark

                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                08 April 2022
                2022
                : 11
                : e72811
                Affiliations
                [1 ] Department of Cell and Developmental Biology, University of Michigan Medical School ( https://ror.org/00jmfr291) Ann Arbor United States
                [2 ] Department of Biomedical Engineering, University of Michigan ( https://ror.org/00jmfr291) Ann Arbor United States
                [3 ] Center for Organogenesis, University of Michigan Medical School ( https://ror.org/00jmfr291) Ann Arbor United States
                [4 ] Department of Internal Medicine, Gastroenterology, University of Michigan Medical School ( https://ror.org/00jmfr291) Ann Arbor United States
                [5 ] Department of Physics, University of Michigan ( https://ror.org/00jmfr291) Ann Arbor United States
                The Jackson Laboratory ( https://ror.org/021sy4w91) United States
                California Institute of Technology ( https://ror.org/05dxps055) United States
                The Jackson Laboratory ( https://ror.org/021sy4w91) United States
                The Jackson Laboratory ( https://ror.org/021sy4w91) United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-9781-2982
                https://orcid.org/0000-0003-0344-577X
                https://orcid.org/0000-0001-7869-3992
                https://orcid.org/0000-0002-8861-7712
                Article
                72811
                10.7554/eLife.72811
                9106331
                35394424
                12338954-0843-4b9f-bfb8-1028ee56af91
                © 2022, Jo et al

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

                History
                : 05 August 2021
                : 07 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008455, University of Michigan Medical School;
                Award ID: startup
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100012652, ETH Zürich Foundation;
                Award ID: Branco Weiss Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R35 GM138346
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008455, University of Michigan Medical School;
                Award ID: Pioneer Fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Developmental Biology
                Custom metadata
                Highly reproducible and efficient human primordial germ cell differentiation was achieved through geometric confinement, which enabled quantitative dissection of the cell signaling driving this differentiation, revealing a major role of Wnt signaling is indirect by inducing Nodal signaling.

                Life sciences
                human pluripotent stem cells,cell fate patterning,primordial germ cells,cell signaling,micropatterning,human

                Comments

                Comment on this article