通过单细胞转录组和抗体谱的测序来评估抗原特异性B细胞的预测模式
Lena Erlach1, Raphael Kuhn1, Andreas Agrafiotis2
1Department of Biosystems Science and Engineering, ETH Zurich, 4057 Basel, Switzerland.
Cell systems
|December 11, 2024
概括
预测抗原特异性B细胞的机器学习模型加速了抗体的发现. 对于这些预测,B细胞中的基因表达模式比抗体序列更有效.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 传统的抗体发现依赖于对B细胞的广泛实验查.
- 机器学习 (ML) 可以通过预测抗原特异性B细胞来加速这一过程,但需要标记训练数据.
研究的目的:
- 开发和评估使用基因表达和抗体库数据预测抗原特异性B细胞的ML模型.
- 确定最能预测抗原特异性的关键特征和模型类型.
主要方法:
- 从免疫小鼠B细胞生成单细胞转录组和抗体列表测序的数据集.
- 通过实验选择标记B细胞为抗原特异性或非特异性.
- 进行了差异性基因表达分析,以找到与特异性相关的模式.
- 使用基因表达和抗体序列特征对各种ML模型 (线性和非线性) 进行基准测试.
- 用蛋白质语言模型 (PLM) 评估转移学习.
主要成果:
- 确定了与抗原特异性相关的独特基因表达模式.
- 基于基因表达的ML模型在预测抗原特异性方面表现出优异的性能,而不是基于抗体序列的模型.
- 转移学习方法显示出潜力,但没有超过基因表达特征.
结论:
- 基因表达特征是B细胞抗原特异性的强有力的预测因素.
- 利用基因表达数据的ML模型提供了一个有前途的计算方法来加速抗体发现.
- 这项工作为未来的ML驱动抗体发现工作提供了有价值的数据集和基准.
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