美国哥伦比亚大学Raul Rabadan等研究人员合作提出跨人类细胞类型的转录基础模型。2025年1月8日，《自然》杂志在线发表了这项成果。

研究人员介绍了GET（通用表达变换器），这是一种可解释的基础模型，旨在揭示213个人类胎儿和成人细胞类型的调控语法。GET仅依赖于染色质可及性数据和序列信息，能够在预测基因表达时达到实验级别的准确性，即使在之前未见过的细胞类型中也能如此。GET还表现出在新型测序平台和检测中的显著适应性，能够在广泛的细胞类型和条件下进行调控推断，并揭示了普遍性和细胞类型特异性的转录因子相互作用网络。

研究人员在预测调控活性、推断调控元件和调控因子、以及识别转录因子之间的物理相互作用方面评估了GET的表现，并发现它在预测基于慢病毒的大规模并行报告基因测定的结果方面优于现有模型。在胎儿红细胞中，研究人员识别了以前的模型遗漏的远程（超过1Mbp）调控区域；在B细胞中，研究人员发现了一个淋巴细胞特异性的转录因子–转录因子相互作用，并解释了一个导致白血病风险的基因组突变的功能意义。

总之，研究人员提供了一个可广泛应用且准确的转录模型，并列出了基因调控和转录因子相互作用的目录，所有这些都具有细胞类型特异性。

据悉，转录调控涉及调控序列和蛋白质之间复杂的相互作用，指导着所有生物过程。现有的转录计算模型缺乏广泛的适用性，无法准确推断未知细胞类型和条件下的转录活动。

附：英文原文

Title: A foundation model of transcription across human cell types

Author: Fu, Xi, Mo, Shentong, Buendia, Alejandro, Laurent, Anouchka P., Shao, Anqi, Alvarez-Torres, Maria del Mar, Yu, Tianji, Tan, Jimin, Su, Jiayu, Sagatelian, Romella, Ferrando, Adolfo A., Ciccia, Alberto, Lan, Yanyan, Owens, David M., Palomero, Teresa, Xing, Eric P., Rabadan, Raul

Issue&Volume: 2025-01-08

Abstract: Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types3. GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovers universal and cell-type-specific transcription factor interaction networks. We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors and found that it outperforms current models4 in predicting lentivirus-based massively parallel reporter assay readout5,6. In fetal erythroblasts7, we identified distal (greater than 1Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific transcription factor–transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation8,9,10. In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.

DOI: 10.1038/s41586-024-08391-z

Source: https://www.nature.com/articles/s41586-024-08391-z