Zero-shot reconstruction of mutant spatial transcriptomes
Zero-shot reconstruction of mutant spatial transcriptomes
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DOI：
10.1101/2022.12.16.520397
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Yasushi Okochi;Takaaki Matsui;Shuntaro Sakaguchi;Takefumi Kondo;H. Naoki
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作者：
Yasushi Okochi;Takaaki Matsui;Shuntaro Sakaguchi;Takefumi Kondo;H. Naoki
突变分析是生物学/病理学研究的核心，测量空间基因表达可以促进对紊乱组织表型的理解1-5。大量的突变体值得研究；然而，测量空间转录组的实验的高成本和技术挑战性可能成为瓶颈6。根据某些基因的空间基因表达的教学数据，可以通过单细胞 RNA 测序数据计算预测空间转录组7；尽管如此，这个过程仍然具有挑战性，因为大多数突变体的教学数据都无法获得。在各种机器学习任务中，零样本学习提供了在不使用教学数据的情况下解决一般预测问题的潜力8。在这里，我们提供了第一个零样本框架，用于从突变单细胞 RNA 测序数据预测突变空间转录组，而不使用教学数据，例如突变空间参考图集。我们通过准确预测阿尔茨海默病模型小鼠3和Nodal信号丢失的突变斑马鱼胚胎的空间转录组来验证零样本框架。我们提出了一种基于零样本框架预测的空间信息筛选方法，该方法识别了斑马鱼中新的节点下调基因。我们预计零样本框架将通过利用收集的大量突变/疾病单细胞 RNA 测序数据提供新颖的表型见解。
Mutant analysis is the core of biological/pathological research, and measuring spatial gene expression can facilitate the understanding of the disorganised tissue phenotype1–5. The large numbers of mutants are worth investigating; however, the high cost and technically challenging nature of experiments to measure spatial transcriptomes may act as bottlenecks6. Spatial transcriptomes have been computationally predicted from single-cell RNA sequencing data based on teaching data of spatial gene expression of certain genes7; nonetheless, this process remains challenging because teaching data for most mutants are unavailable. In various machine-learning tasks, zero-shot learning offers the potential to tackle general prediction problems without using teaching data8. Here, we provide the first zero-shot framework for predicting mutant spatial transcriptomes from mutant single-cell RNA sequencing data without using teaching data, such as a mutant spatial reference atlas. We validated the zero-shot framework by accurately predicting the spatial transcriptomes of Alzheimer’s model mice3 and mutant zebrafish embryos with lost Nodal signaling9. We propose a spatially informed screening approach based on zero-shot framework prediction that identified novel Nodal-downregulated genes in zebrafish. We expect that the zero-shot framework will provide novel phenotypic insights by leveraging the enormous mutant/disease single-cell RNA sequencing data collected.