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Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

Keita Ito1, Tsubasa Hirakawa2, Shuji Shigenobu3,4

  • 1Graduate School of Engineering, Chubu University, Kasugai, Aichi, Japan.

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|March 19, 2025
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A new mouse-specific Geneformer model, trained on 21 million single-cell RNA sequencing profiles, accurately analyzes mouse transcriptomes and aids in disease gene discovery. This model also shows cross-species potential for human data analysis.

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Area of Science:

  • Computational biology and bioinformatics
  • Genomics and transcriptomics
  • Machine learning in biological research

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates complex transcriptome data, necessitating advanced analytical tools.
  • Geneformer, a Transformer-based deep learning model, has shown success in human transcriptome analysis.
  • A mouse-specific Geneformer is crucial due to the mouse's prominence as a model organism in research.

Purpose of the Study:

  • To develop and evaluate a mouse-specific Geneformer (mouse-Geneformer) for analyzing mouse scRNA-seq data.
  • To assess the performance of mouse-Geneformer in cell type classification and disease gene identification.
  • To investigate the cross-species applicability of mouse-Geneformer to human transcriptome data.

Main Methods:

  • Construction of a large-scale mouse transcriptome dataset comprising 21 million scRNA-seq profiles.
  • Pre-training the Geneformer architecture on the mouse dataset to create mouse-Geneformer.
  • Fine-tuning mouse-Geneformer for downstream tasks like cell type classification and in silico perturbation experiments.
  • Cross-species analysis involving ortholog mapping and fine-tuning with human scRNA-seq data.

Main Results:

  • Mouse-Geneformer effectively models the mouse transcriptome and improves cell type classification accuracy.
  • In silico perturbation experiments identified disease-causing genes, validated in vivo.
  • Mouse-Geneformer demonstrated cross-species analysis capabilities, achieving comparable accuracy to human Geneformer on human data after fine-tuning.
  • Performance varied in cross-species disease modeling, highlighting the need for species-specific nuances.

Conclusions:

  • Mouse-Geneformer is a robust tool for analyzing mouse scRNA-seq data, enhancing biological insights and disease gene discovery.
  • The Geneformer architecture is adaptable to different species with sufficient transcriptome data.
  • Cross-species analysis shows promise but underscores the importance of species-specific models for complex biological processes.
  • Mouse-Geneformer offers potential benefits for human research, especially with data types inaccessible in humans, and for non-model organisms.