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使用DecontPro对基于微滴的单细胞蛋白质表达数据中的背景噪声进行表征和净化。

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro.

作者信息

Yin Yuan, Yajima Masanao, Campbell Joshua D

机构信息

Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.

Department of Mathematics and Statistics, Boston University, Boston, MA, USA.

出版信息

bioRxiv. 2023 Feb 24:2023.01.27.525964. doi: 10.1101/2023.01.27.525964.

DOI:10.1101/2023.01.27.525964
PMID:36865227
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9979990/
Abstract

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. Using an exploratory analysis of PBMC datasets, we find that some droplets that were originally called "empty" due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a "spongelet" which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

摘要

诸如CITE-seq之类的检测方法可以使用抗体衍生标签(ADT)来测量单个细胞上细胞表面蛋白的丰度。然而,许多ADT具有高水平的背景噪声,这可能会模糊下游分析。通过对PBMC数据集进行探索性分析,我们发现一些最初因RNA水平低而被称为“空”的液滴含有高水平的ADT,并且可能对应于中性粒细胞。我们在空液滴中鉴定出一种新型伪像,称为“小海绵体”,其ADT表达水平中等,且与环境噪声不同。在几个数据集中,小海绵体中的ADT表达水平与真实细胞背景峰中的ADT表达水平相关,这表明它们会与环境ADT一起导致背景噪声。然后,我们开发了DecontPro,这是一种新型贝叶斯分层模型,它可以通过估计和去除这些来源的污染来净化ADT数据。在去除异常表达的ADT同时保留天然ADT以及提高聚类特异性方面,DecontPro优于其他净化工具。总体而言,这些结果表明,应分别对RNA和ADT数据进行空液滴鉴定,并且可以将DecontPro纳入CITE-seq工作流程以提高下游分析的质量。

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