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. 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RNA测序技术，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，二者简称scnRNA-Seq），可用于解析组织的细胞组成，揭示影响机体核心生理与疾病功能的细胞类群。然而，此类技术因成本相对高昂、样本采集要求严苛，其应用仍存在诸多限制。以scnRNA-Seq鉴定的细胞类型为参照，推断批量RNA测序样本细胞组成的计算反卷积方法，可拓展此类技术的应用价值，但其实际有效性仍存在争议。本研究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，另一类为同时匹配批量RNA测序与scnRNA-Seq谱的数据集。本研究分析揭示了10X Genomics平台scnRNA-Seq实验普遍存在的系统偏差，并阐明了精准且规范受控的数据预处理、方法选择与优化的重要性。此外，本研究结果表明，同时获取的批量RNA测序与scnRNA-Seq谱数据，可同时提升scnRNA-Seq预处理流程以及基于该流程的反卷积方法的准确性。本研究提出的单细胞RNA量指导反卷积方法（Single-cell RNA Quantity Informed Deconvolution, SQUID），结合了RNA测序数据变换与阻尼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。此外，本研究分析显示，仅SQUID可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。本研究通过批量RNA测序（bulk RNA-seq，n=12）分析了6名儿童AML患者的诊断-复发配对样本。所有患者均在德克萨斯儿童癌症中心接受治疗，并签署了组织样本用于科研的知情同意书。