Multiscale Systems Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.
NIH-Oxford-Cambridge Scholars Program, Department of Medicine, University of Cambridge, Cambridge, UK.
Nat Commun. 2022 Apr 19;13(1):2099. doi: 10.1038/s41467-022-29356-8.
Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".
多模态单细胞分析方法通过寡核苷酸偶联抗体测量蛋白质表达，有望全面解析细胞异质性，但由此产生的蛋白质计数存在大量技术噪声，可能掩盖生物学变化。在这里，我们整合实验和计算分析来揭示两个主要的噪声源，并开发了一种称为“dsb”（通过背景进行去噪和缩放）的方法，对基于液滴的蛋白质表达数据进行归一化和去噪。我们发现，蛋白质特异性噪声源于液滴生成过程中包裹的未结合抗体；因此，可以通过利用空液滴中的蛋白质水平来准确估计和纠正这种噪声。我们还发现同种型对照抗体和每个细胞中平均背景蛋白群体在单细胞中表现出显著相关性，因此我们使用它们的共同方差来纠正每个细胞中的细胞间技术噪声。我们通过分析 dsb 在跨越多个技术的八个独立数据集（包括 CITE-seq、ASAP-seq 和 TEA-seq）中的性能来验证这些发现。与现有归一化方法相比，我们的方法通过更好地揭示有生物学意义的细胞群体，从而改善下游分析。我们的方法作为一个开源 R 包提供，与 Seurat、Bioconductor 和 Scanpy 等现有的单细胞软件平台接口轻松，并可通过“dsb [ https://cran.r-project.org/package=dsb ]”访问。
