Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])—widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell’s scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL’s training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

单细胞内多组学模式的同时分析是单细胞生物学的一个巨大挑战。虽然已经有令人印象深刻的技术创新证明了可行性，例如，生成单细胞转录组（单细胞 RNA 测序 [scRNA-seq]）和染色质可及性（使用测序进行转座酶可及染色质的单细胞测定 [scATAC] 的配对测量） -seq]）——由于实验的复杂性、噪声和成本，联合分析的广泛应用具有挑战性。在这里，我们介绍 BABEL，一种在单个细胞的转录组和染色质图谱之间进行转换的深度学习方法。利用可互操作的神经网络模型，BABEL 可以在相关数据训练后直接从细胞的 scATAC-seq 预测单细胞表达，反之亦然。当只有一种模态在实验上可用时，这使得可以通过计算合成配对的多组学测量。在人类和小鼠的几个配对单细胞 ATAC 和基因表达数据集中，我们验证了 BABEL 能够准确地在单个细胞的这些模式之间进行转换。BABEL 还可以很好地概括训练期间未见过的新生物环境中的细胞类型。从患者源性基底细胞癌 (BCC) 的 scATAC-seq 开始，BABEL 生成了单细胞表达，尽管从未见过 BCC 数据，但可以对复杂细胞状态进行细粒度分类。这些预测与 BABEL 训练数据相关的不同细胞类型的实验 BCC scRNA-seq 数据分析相当。我们进一步表明，BABEL 可以整合额外的单细胞数据模式，例如蛋白质表位分析，从而实现跨染色质、RNA 和蛋白质的翻译。BABEL 提供了一种强大的数据探索和假设生成方法。