ArticleCell systems2019
Automated Design of Pluripotent Stem Cell Self-Organization.
Article in Cell systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 56 citations in OpenAlex.
- Organoids in drug development: from predictive models to regulatory integration.Drug discovery today · 2026Review
- Acoustic Bioprinting: A Glimpse Into an Emerging Field.Small methods · 2026Review
- Topological data analysis of pattern formation of human induced pluripotent stem cell colonies.Scientific reports · 2025Article
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Article
- TBXT dose sensitivity and the decoupling of nascent mesoderm specification from EMT progression in 2D human gastruloids.Development (Cambridge, England) · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- TBXT dose sensitivity and the decoupling of nascent mesoderm specification from EMT progression in 2D human gastruloids.bioRxiv : the preprint server for biology · 2023Article
- Recognition and reconstruction of cell differentiation patterns with deep learning.PLoS computational biology · 2023Article
- In-silico and in-vitro morphometric analysis of intestinal organoids.PLoS computational biology · 2023Article
- Deep learning models will shape the future of stem cell research.Stem cell reports · 2023Review
- Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling.Current stem cell reports · 2023Review
- Systems bioengineering approaches for developmental toxicology.Computational and structural biotechnology journal · 2023Review
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
- Stem cells for organoids.Smart medicine · 2022Review
- Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell-Cell Adhesion.ACS synthetic biology · 2022Article
- Diseased, differentiated and difficult: Strategies for improved engineering ofFrontiers in cellular neuroscience · 2022Review
- Bioengineering Approaches for the Advanced Organoid Research.Advanced materials (Deerfield Beach, Fla.) · 2021Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.