Respiratory disease, characterized by changes in the cells of the lung, can affect molecular phenotype of cells and the intercellular interactions, resulting in a disbalance in the relative proportions of individual cell types. Understanding these changes is essential to understand the pathophysiology of lung disease. Conventional 'bulk' RNA-sequencing (RNA-seq), analyzing the entire transcriptome of the tissue sample, provides information about average expression levels of each gene in the mixed cell population; whereas it does not consider the cellular heterogeneity in samples composed of more than one cell type 1 . Single-cell RNA-seq (scRNA-seq) assesses the transcriptome of a complex biological sample with single-cell resolution, allowing identification of the relative frequency of discrete cell-types and analysis of their transcriptomes 1 . Nevertheless, analyzing the transcriptomic signature in large numbers of patients by scRNA-Seq is currently limited by its high costs. Mast cells are key regulatory cells driving the inflammatory process in asthma2 . Since they can be quantified by immunohistochemical staining for validation purposes, we used mast cells as an example of a rare cell population to assess the validity of our deconvolution approach. Recently, a number of bulk RNA-seq deconvolution methods have become available 3 , for instance of two deconvolution methods, namely support vector regression (SVR) 4 , the machine-learning method implemented in CYBERSORT, and Non-Negative Least Square (NNLS) 5 , using a matrix of cell-type selective genes identified with AutoGeneSc 6 . Both approaches are designed to estimate relative proportion of the main, common cell types present in the sample. When we used these methods to estimate the number of mast cells, we found a poor correlation with the number of mast cells stained by immunohistochemistry in the biopsies, suggesting the CIBERSORT and NNLS are less reliable in the case of rare cell types. We explored the possibility to use scRNA-Seq data from small numbers of subjects to specifically interrogate the relative cell type frequency of a rare cell population in a bulk RNA-Seq dataset obtained from a large asthma cohort.

中文翻译：

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scRNA-seq 的基因特征可准确量化哮喘活检中的肥大细胞


以肺细胞变化为特征的呼吸系统疾病会影响细胞的分子表型和细胞间相互作用，导致单个细胞类型的相对比例失衡。了解这些变化对于了解肺部疾病的病理生理学至关重要。传统的“批量”RNA 测序 (RNA-seq) 可分析组织样本的整个转录组，提供有关混合细胞群中每个基因的平均表达水平的信息；然而它没有考虑由超过一种细胞类型组成的样品中的细胞异质性 1 。单细胞 RNA-seq (scRNA-seq) 以单细胞分辨率评估复杂生物样品的转录组，从而可以识别离散细胞类型的相对频率并分析其转录组 1 。然而，通过 scRNA-Seq 分析大量患者的转录组特征目前因其高昂的成本而受到限制。肥大细胞是驱动哮喘炎症过程的关键调节细胞2。由于可以通过免疫组织化学染色对其进行量化以进行验证，因此我们使用肥大细胞作为稀有细胞群的示例来评估我们的反卷积方法的有效性。最近，已经出现了许多批量 RNA-seq 反卷积方法 3 ，例如两种反卷积方法，即支持向量回归 (SVR) 4 、CYBERSORT 中实现的机器学习方法和非负最小二乘法 (NNLS) 5，使用用AutoGeneSc 6 鉴定的细胞类型选择性基因矩阵。这两种方法都旨在估计样本中存在的主要常见细胞类型的相对比例。 当我们使用这些方法估计肥大细胞的数量时，我们发现与活检中免疫组织化学染色的肥大细胞数量的相关性较差，这表明 CIBERSORT 和 NNLS 在稀有细胞类型的情况下不太可靠。我们探索了使用来自少数受试者的 scRNA-Seq 数据来专门询问从大型哮喘队列获得的大量 RNA-Seq 数据集中稀有细胞群的相对细胞类型频率的可能性。