ArticleNature methods2025
Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Elevated zygotic let-7f-5p alters developmental trajectories and sex-specific somatic growth.iScience · 2026Article
- Synthetic embryos: current progress, application and challenges.Reproductive biology and endocrinology : RB&E · 2026Review
- Serum coating enables feeder-free culture of naive human pluripotent stem cells preserving developmental potential.The EMBO journal · 2026Article
- Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.PLoS biology · 2026Article
- Progress in stem cell-based embryo models and their applications in developmental biology and biomedicine.Nature reviews. Molecular cell biology · 2026Review
- A Deep Learning Approach to Assessing Cell Identity in Stem Cell-Based Embryo Models.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Single-Cell and Single-Embryo RNA Sequencing.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Decoding heart failure subtypes with neural networks via differential explanation analysis.Briefings in bioinformatics · 2025Article
- The Transgenerational Impact of High-Fat Diet and Diabetic Pregnancy on Embryonic Transcriptomics and Mitochondrial Health.Biomedicines · 2025Article
- Suppression of ERK signalling promotes pluripotent epiblast in the human blastocyst.Nature communications · 2025Article
- Altering metabolism programs cell identity via NADThe EMBO journal · 2025Article
- A look back at embryo models.Nature methods · 2025Article
- Article
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on material that is difficult to obtain and process, spanning early mouse and human development. Using publicly available data for these stages, we test different deep learning approaches and develop a model to classify cell types in an unbiased fashion at the same time as defining the set of genes used by the model to identify lineages, cell types and states. We used our models trained on in vivo development to classify pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.
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Registered trials
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