在高通量筛选中预测细胞对复杂扰动的反应。

IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, Jay Shendure, Jose L McFaline-Figueroa, Pierre Boyeau, F Alexander Wolf, Nafissa Yakubova, Stephan Günnemann, Cole Trapnell, David Lopez-Paz, Fabian J Theis
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引用次数: 16

摘要

多重单细胞转录组学实验的最新进展促进了药物和遗传扰动的高通量研究。然而,对组合摄动空间的详尽探索在实验上是不可行的。因此,需要计算方法来预测、解释和优先考虑扰动。在这里,我们提出了组合摄动自编码器(CPA),它结合了线性模型的可解释性和单细胞响应建模的深度学习方法的灵活性。CPA学习在单细胞水平上预测未知剂量、细胞类型、时间点和物种的转录扰动响应。使用新生成的单细胞药物组合数据,我们验证了CPA可以预测未见过的药物组合,同时优于基线模型。此外,该体系结构的模块化能够整合药物的化学表征,从而预测细胞对完全看不见的药物的反应。此外,CPA也适用于基因组合筛选。我们通过在具有多种遗传相互作用的单细胞Perturb-seq实验中在计算机上输入5329个缺失组合(占所有可能性的97.6%)来证明这一点。我们设想CPA将通过在单细胞水平上实现硅反应预测来促进有效的实验设计和假设生成,从而加速单细胞技术的治疗应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predicting cellular responses to complex perturbations in high-throughput screens.

Predicting cellular responses to complex perturbations in high-throughput screens.

Predicting cellular responses to complex perturbations in high-throughput screens.

Predicting cellular responses to complex perturbations in high-throughput screens.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

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来源期刊
Molecular Systems Biology
Molecular Systems Biology 生物-生化与分子生物学
CiteScore
18.50
自引率
1.00%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Systems biology is a field that aims to understand complex biological systems by studying their components and how they interact. It is an integrative discipline that seeks to explain the properties and behavior of these systems. Molecular Systems Biology is a scholarly journal that publishes top-notch research in the areas of systems biology, synthetic biology, and systems medicine. It is an open access journal, meaning that its content is freely available to readers, and it is peer-reviewed to ensure the quality of the published work.
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