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Abstract：
Deconvolution of cell mixtures in \"bulk\" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a \"gold standard\" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.
摘要：
来自匀浆人体组织的“大量”转录组样品中的细胞混合物的去卷积对于理解疾病的病理很重要。然而,在开发和实施基于转录组学的反卷积方法方面仍然存在一些实验和计算挑战，尤其是那些使用单细胞/细胞核RNA-seq参考图谱的人，它们在许多组织中变得迅速可用。值得注意的是,反卷积算法经常使用来自具有相似细胞大小的组织的样本开发。然而,脑组织或免疫细胞群的细胞类型具有明显不同的细胞大小，总mRNA表达，和转录活性。当现有的反卷积方法应用于这些组织时，细胞大小和转录组活性的这些系统差异混淆了准确的细胞比例估计，反而可能量化总mRNA含量。此外，缺乏标准的参考图册和计算方法来促进综合分析，不仅包括大量和单细胞/细胞核RNA-seq数据，还有来自空间组学或成像方法的新数据模式。需要使用从相同的组织块和相同的个体生成的正交数据类型来收集新的多测定数据集，作为评估新的和现有的反卷积方法的“黄金标准”。下面,我们讨论了这些关键挑战，以及如何通过获取新的数据集和分析方法来解决这些挑战。
