Ito Keita, Hirakawa Tsubasa, Shigenobu Shuji, Fujiyoshi Hironobu, Yamashita Takayoshi
Graduate School of Engineering, Chubu University, Kasugai, Aichi, Japan.
Department of Artificial Intelligence and Robotics, Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Aichi, Japan.
PLoS Genet. 2025 Mar 19;21(3):e1011420. doi: 10.1371/journal.pgen.1011420. eCollection 2025 Mar.
Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder architecture and human scRNA-seq datasets, has demonstrated remarkable success in human transcriptome analysis. However, given the prominence of the mouse, Mus musculus, as a primary mammalian model in biological and medical research, there is an acute need for a mouse-specific version of Geneformer. In this study, we developed a mouse-specific Geneformer (mouse-Geneformer) by constructing a large transcriptome dataset consisting of 21 million mouse scRNA-seq profiles and pre-training Geneformer on this dataset. The mouse-Geneformer effectively models the mouse transcriptome and, upon fine-tuning for downstream tasks, enhances the accuracy of cell type classification. In silico perturbation experiments using mouse-Geneformer successfully identified disease-causing genes that have been validated in in vivo experiments. These results demonstrate the feasibility of analyzing mouse data with mouse-Geneformer and highlight the robustness of the Geneformer architecture, applicable to any species with large-scale transcriptome data available. Furthermore, we found that mouse-Geneformer can analyze human transcriptome data in a cross-species manner. After the ortholog-based gene name conversion, the analysis of human scRNA-seq data using mouse-Geneformer, followed by fine-tuning with human data, achieved cell type classification accuracy comparable to that obtained using the original human Geneformer. In in silico simulation experiments using human disease models, we obtained results similar to human-Geneformer for the myocardial infarction model but only partially consistent results for the COVID-19 model, a trait unique to humans (laboratory mice are not susceptible to SARS-CoV-2). These findings suggest the potential for cross-species application of the Geneformer model while emphasizing the importance of species-specific models for capturing the full complexity of disease mechanisms. Despite the existence of the original Geneformer tailored for humans, human research could benefit from mouse-Geneformer due to its inclusion of samples that are ethically or technically inaccessible for humans, such as embryonic tissues and certain disease models. Additionally, this cross-species approach indicates potential use for non-model organisms, where obtaining large-scale single-cell transcriptome data is challenging.
深度学习技术越来越多地被用于分析大规模单细胞RNA测序（scRNA-seq）数据，从复杂的转录组数据集中提供有价值的见解。Geneformer是一种使用Transformer编码器架构和人类scRNA-seq数据集进行预训练的模型，在人类转录组分析中取得了显著成功。然而，鉴于小家鼠作为生物学和医学研究中的主要哺乳动物模型的突出地位，迫切需要一个小鼠特异性版本的Geneformer。在本研究中，我们通过构建一个由2100万个小鼠scRNA-seq图谱组成的大型转录组数据集，并在此数据集上对Geneformer进行预训练，开发了一种小鼠特异性的Geneformer（小鼠-Geneformer）。小鼠-Geneformer有效地对小鼠转录组进行建模，并在针对下游任务进行微调后，提高了细胞类型分类的准确性。使用小鼠-Geneformer进行的计算机模拟扰动实验成功地鉴定出了在体内实验中得到验证的致病基因。这些结果证明了使用小鼠-Geneformer分析小鼠数据的可行性，并突出了Geneformer架构的稳健性，适用于任何有大规模转录组数据的物种。此外，我们发现小鼠-Geneformer可以跨物种方式分析人类转录组数据。在基于直系同源基因的基因名称转换后，使用小鼠-Geneformer分析人类scRNA-seq数据，然后用人数据进行微调，获得的细胞类型分类准确性与使用原始人类Geneformer相当。在使用人类疾病模型的计算机模拟实验中，我们在心肌梗死模型中获得了与人类-Geneformer相似的结果，但在COVID-19模型中仅获得了部分一致的结果，这是人类特有的特征（实验室小鼠对SARS-CoV-2不敏感）。这些发现表明了Geneformer模型跨物种应用的潜力，同时强调了物种特异性模型对于捕捉疾病机制的全部复杂性的重要性。尽管存在为人类量身定制的原始Geneformer，但人类研究仍可从小鼠-Geneformer中受益，因为它包含了人类在伦理或技术上无法获取的样本，如胚胎组织和某些疾病模型。此外，这种跨物种方法表明了对非模式生物的潜在用途，在这些生物中获取大规模单细胞转录组数据具有挑战性。
