Related Experiment Video
Updated: May 9, 2026
05:45
Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids
Published on: March 29, 2024
TranscriptFormer: A generative cell atlas across 1.5 billion years of evolution.
James D Pearce1, Sara E Simmonds1, Gita Mahmoudabadi1
1Biohub, Redwood City, CA, USA.
Science (New York, N.Y.)
|May 7, 2026
Summary
We created TranscriptFormer, a powerful AI model that analyzes single-cell data across species and evolutionary time. It reveals universal cellular principles and accurately classifies cell types, even across vast evolutionary distances.
Area of Science:
- Computational Biology
- Evolutionary Biology
- Genomics
Background:
- Single-cell transcriptomics offers insights into cellular diversity.
- Comparing transcriptional data across species and evolutionary history is complex.
- Existing methods struggle with deep evolutionary comparisons.
Purpose of the Study:
- To develop a scalable computational framework for cross-species single-cell transcriptomic analysis.
- To leverage foundation models for understanding evolutionary principles of cellular organization.
- To enable accurate cell type classification and disease state identification across diverse species.
Main Methods:
- Development of TranscriptFormer, a family of generative foundation models.
- Training on a large dataset of 112 million cells from 12 species, covering 1.53 billion years of evolution.
- Evaluating performance on cell type classification, zero-shot disease state identification, and emergent biological insights.
Main Results:
- TranscriptFormer achieves state-of-the-art cell type classification accuracy, even for species diverged over 685 million years.
- The model demonstrates zero-shot disease state identification in human cells.
- Developmental trajectories, phylogenetic relationships, and cellular hierarchies are learned implicitly by the model.
- Universal principles of cellular organization are identified and predictable across the tree of life.
Conclusions:
- TranscriptFormer provides a powerful framework for quantitative single-cell analysis and comparative cellular biology.
- Foundation models can learn and predict universal biological principles from large-scale transcriptomic data.
- This approach opens new avenues for understanding evolution and cellular function across diverse life forms.
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