Abstract
Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.
Highlights
Single-nucleus RNA sequencing measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues
We evaluated the extent of contamination and its effect on clustering in three distinct snRNA-seq data sets: 1. in vitro differentiating human preadipocytes (DiffPA) (n = 1), 2. freshly dissected mouse brain tissue (n = 1), and 3. frozen human subcutaneous adipose tissue (AT) (n = 6)
The snRNA-seq approach is an adaptation of scRNA-seq that allows for cell-type identification when isolation of a cell suspension is not possible, such as in frozen tissues
Summary
Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. To apply snRNA-seq to tissues, homogenization of the tissue is usually required to break apart the extracellular matrix and release nuclei from c ells[4] This can release higher amounts of debris and lead to more background RNA contamination[7]. A common practice to distinguish cell/nuclei- vs background-containing barcodes relies on removing droplets below a hard cutoff of the number of reads, unique molecular indexes (UMI), or genes detected in a droplet[3,8,9,10,11]. The recent method EmptyDrops[12] addresses this filtering issue for scRNA-seq by estimating a Dirichlet-Multinomial distribution of the ambient RNA It removes droplets by testing if their expression profile deviates significantly from the ambient profile using a Monte Carlo approach[12]. While this works for single-cell, we show that these methods underperform when applied to snRNA-seq
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