The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A stepwise model of Reaction-Diffusion and Positional-Information governs self-organized human peri-gastrulation-like patterning
Open accessAbstract
How position-dependent cell fate acquisition occurs during embryogenesis is a central question in developmental biology. To study this process, we developed a defined, high-throughput assay to induce peri-gastrulation-associated patterning in geometrically-confined human pluripotent stem cell (hPSC) colonies. We observed that, upon BMP4 treatment, phosphorylated SMAD1 (pSMAD1) activity in the colonies organized into a radial gradient. We developed a Reaction-Diffusion (RD) based computational model and observed that the self-organization of pSMAD1 signaling was consistent with the RD principle. Consequent fate acquisition occurred as a function of both pSMAD1 signaling strength and duration of induction – consistent with the Positional-Information (PI) paradigm. We propose that the self-organized peri-gastrulation-like fate patterning in BMP4 treated geometrically-confined hPSC colonies arises via a stepwise model of RD, and PI. This two-step model predicted experimental responses to perturbations of key parameters such as colony size, and BMP4 dose. Furthermore, it also predicted experimental conditions that resulted in RD-like periodic patterning in large hPSC colonies, and rescued peri-gastrulation-like patterning in colony sizes previously thought to be reticent to this behaviour.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The chemical basis of morphogenesis
Turing, A. M. (1952) · Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences · doi:10.1098/rstb.1952.0012 · Cited by 11,733
Mathematical Biology: II: Spatial Models and Biomedical Applications
(2003) · Interdisciplinary Applied Mathematics · doi:10.1007/b98869 · Cited by 2,293
Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation
Kondo, S., & Miura, T. (2010) · Science · doi:10.1126/science.1179047 · Cited by 1,651
A method to recapitulate early embryonic spatial patterning in human embryonic stem cells
Warmflash, A., Sorre, B., Etoc, F., Siggia, E. D., & Brivanlou, A. H. (2014) · Nature Methods · doi:10.1038/nmeth.3016 · Cited by 885
The chemical basis of morphogenesis
Turing, A. M. (1990) · Bulletin of Mathematical Biology · doi:10.1007/bf02459572 · Cited by 434
Conserved Patterns of Cell Movements during Vertebrate Gastrulation
Solnica-Krezel, L. (2005) · Current Biology · doi:10.1016/j.cub.2005.03.016 · Cited by 391
Self-organizing human cardiac microchambers mediated by geometric confinement
Ma, Z., Wang, J., Loskill, P., Huebsch, N., Koo, S., Svedlund, F. L., Marks, N. C., Hua, E. W., Grigoropoulos, C. P., Conklin, B. R., et al. (2015) · Nature Communications · doi:10.1038/ncomms8413 · Cited by 214
Cited by
Progress and potential in organoid research
Rossi, G., Manfrin, A., & Lutolf, M. P. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0051-9 · Cited by 1,182
Engineering Stem Cell Self-organization to Build Better Organoids
Brassard, J. A., & Lutolf, M. P. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.05.005 · Cited by 373
Kim, Y., Jang, H., Seo, K., Kim, J. H., Lee, B., Cho, H. M., Kim, H. J., Yang, E., Kim, H., Gim, J.-A., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.11.005 · Cited by 33
Metadata
- Authors
- Tewary, M., Ostblom, J., Prochazka, L., Zulueta-Coarasa, T., Shakiba, N., Fernandez-Gonzalez, R., & Zandstra, P. W.
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2017-01-01
- Volume
- 144
- Issue
- 23
- Pages
- 4298-4312
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- OpenAlex
- W2751928832
- Cited by
- 158
- References
- 7
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A stepwise model of Reaction-Diffusion and Positional-Information governs self-organized human peri-gastrulation-like patterning. https://app.cochat.ai/papers/10.1242/dev.149658
- The paper
- Tewary, M., Ostblom, J., Prochazka, L., Zulueta-Coarasa, T., Shakiba, N., Fernandez-Gonzalez, R., & Zandstra, P. W. (2017). A stepwise model of Reaction-Diffusion and Positional-Information governs self-organized human peri-gastrulation-like patterning. Development. https://doi.org/10.1242/dev.149658
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.