Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.
Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.
复制标题
评估样本细胞反卷积方法在人脑转录组数据上的性能和应用。
DOI：
10.1101/2023.03.13.532468
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Zhang,Ch
中科院分区：
文献类型：
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作者：
Dai,Rujia;Chu,Tianyao;Zhang,Ming;Wang,Xuan;Jourdon,Alexandre;Wu,Feinan;Mariani,Jessica;Vaccarino,FloraM;Lee,Donghoon;Fullard,JohnF;Hoffman,GabrielE;Roussos,Panos;Wang,Yue;Wang,Xusheng;Pinto,Dalila;Wang,SidneyH;Zhang,Ch
逐样本去卷积方法估计了大量组织样本中的细胞类型比例和基因表达，但其性能和生物学应用仍然未被探索，特别是在人脑转录组数据中。在这里，使用来自大量组织RNA测序（RNA-seq）、单细胞/细胞核（sc/sn）RNA-seq和免疫组织化学的样本匹配数据评估了9种去卷积方法。共1，130，767个核/细胞从149个成年人死后的大脑和72个类器官样本。结果显示，dtangle用于估计细胞比例和bMIND用于估计样品细胞类型基因表达的性能最佳。对于八种脑细胞类型，25，273个细胞类型eQTL被鉴定为具有解卷积表达（decon-eQTL）。结果表明，deco-eQTLs比单独的组织或单细胞eQTLs更能解释精神分裂症GWAS的遗传力。与阿尔茨海默病，精神分裂症和大脑发育相关的差异基因表达也使用去卷积数据进行了检查。我们的研究结果在大量组织和单细胞数据中得到了复制，为解卷积数据在多种脑部疾病中的生物学应用提供了见解。
Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer’s disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.