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CLEAR:基于覆盖度的RNA测序有限细胞实验分析

CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq.

作者信息

Walker Logan A, Sovic Michael G, Chiang Chi-Ling, Hu Eileen, Denninger Jiyeon K, Chen Xi, Kirby Elizabeth D, Byrd John C, Muthusamy Natarajan, Bundschuh Ralf, Yan Pearlly

机构信息

Department of Physics, College of Arts and Sciences, The Ohio State University, Columbus, OH, USA.

The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.

出版信息

J Transl Med. 2020 Feb 10;18(1):63. doi: 10.1186/s12967-020-02247-6.

Abstract

BACKGROUND

Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons.

METHODS

Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5- cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG).

RESULTS

When using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies.

CONCLUSIONS

lcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

摘要

背景

为单细胞RNA测序开发的直接cDNA预扩增方案能够对珍贵的临床样本和稀有细胞群体进行转录组分析,而无需样本合并或RNA提取。我们将使用单细胞化学方法对少量细胞进行测序称为有限细胞RNA测序(lcRNA-seq)。目前,尚无定制算法可从lcRNA-seq数据中选择稳健/低噪声转录本用于组间比较。

方法

在此,我们提出了CLEAR,这是一种在lcRNA-seq数据中识别可靠定量转录本以进行差异表达基因(DEG)分析的工作流程。从原发性慢性淋巴细胞白血病(CLL)的CD5+和CD5-细胞中获得的总RNA用于开发CLEAR算法。一旦建立,便使用从小鼠齿状回(DG)富集的流式细胞术分选细胞评估CLEAR的性能。

结果

在CLL样本中,与使用所有转录本相比,使用CLEAR转录本进行下游分析时,在三种输入量下共享转录本的比例更高,并且两种细胞类型的主成分分析(PCA)分离得到改善。在小鼠DG样本中,CLEAR可识别有噪声的转录本,去除这些转录本可改善预期细胞群体的PCA分离。此外,将CLEAR应用于两个公开可用的数据集,以证明其在来自其他机构的lcRNA-seq数据中的效用。如果应用插补来限制缺失数据点的影响,CLEAR也可用于大型临床试验和单细胞研究。

结论

lcRNA-seq与CLEAR相结合,因其转录本保存特性而在我们机构中广泛用于分析免疫细胞(循环或组织浸润)。CLEAR在预处理lcRNA-seq数据以促进转录组分析和DEG分析方面填补了重要的空白。我们证明了CLEAR在分析临床样本和小鼠神经DG区域中的稀有细胞群体而无需样本合并方面的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f582/7008572/a3c3dee9966d/12967_2020_2247_Fig1_HTML.jpg

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