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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
The Tree of Life - Bacteria, Archaea, and Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, and Eukaryotes

The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both extant and...

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Related Experiment Video

Updated: May 9, 2026

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids
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
PubMed
Summary
This summary is machine-generated.

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.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: May 9, 2026

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids
05:45

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Published on: March 29, 2024

Hand Dissection of Caenorhabditis elegans Intestines
05:41

Hand Dissection of Caenorhabditis elegans Intestines

Published on: September 13, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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.