Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [AML Bulk]. Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [AML Bulk]

RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets. Overall design: We profiled diagnosis and relapse pair samples from 6 pediatric AML patients by bulk RNA-seq (n=12). All patients were treated at Texas Children's Cancer Center and consented to banking of tissue for research.

单细胞分辨率的RNA谱分析技术，包括单细胞RNA测序（single-cell RNA sequencing，scRNA-Seq）与单细胞核RNA测序（single-nuclei RNA sequencing，snRNA-Seq，二者合称scnRNA-Seq），可用于解析组织的细胞组成，并揭示影响关键健康与疾病功能的细胞类群。然而，此类技术的应用存在诸多限制，其成本相对高昂，且样本采集要求严苛。借助经scnRNA-Seq表征的细胞类型来推断批量RNA测序（bulk RNA-seq）样本组成的计算反卷积方法，能够拓展此类技术的应用价值，但其实际效果仍存在争议。我们首次针对两类数据集开展了反卷积方法的系统性评估：一类为已知细胞组成的数据集，另一类为同时具备bulk RNA-seq与scnRNA-Seq谱数据的数据集。本研究的分析结果揭示了scnRNA-Seq的10X Genomics实验中普遍存在的偏倚，并阐明了精准且管控得当的数据预处理、方法选择与优化的重要性。此外，我们的研究表明，同步获取的bulk RNA-seq与scnRNA-Seq谱数据，可提升scnRNA-Seq预处理流程以及基于该流程的反卷积方法的准确性。我们提出的单细胞RNA数量感知反卷积方法（Single-cell RNA Quantity Informed Deconvolution，SQUID），结合了bulk RNA-seq数据转换与带阻尼的加权最小二乘反卷积策略，在预测细胞混合物与组织样本的细胞组成时，始终优于其他同类方法。进一步分析显示，唯有SQUID能够在儿童急性髓系白血病（acute myeloid leukemia，AML）与神经母细胞瘤数据集当中，识别出可预测预后的癌细胞亚型。整体实验设计：本研究对6名儿童AML患者的诊断与复发配对样本开展了bulk RNA-seq分析（共12例样本，n=12）。所有患者均在德克萨斯儿童医院癌症中心接受治疗，并同意将其组织样本用于科研用途。