Kersey Holly N, Acri Dominic J, Dabin Luke C, Hartigan Kelly, Mustaklem Richard, Park Jung Hyun, Kim Jungsu
Medical Neurosciences Graduate Program, Indiana University School of Medicine, Indianapolis, IN, USA.
Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
bioRxiv. 2025 Mar 26:2025.03.25.645306. doi: 10.1101/2025.03.25.645306.
Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues. snRNA-seq overcomes limitations of traditional single-cell RNA-seq by using nuclei instead of cells, allowing to utilize frozen tissues and difficult-to-isolate cell types. Although various nuclei isolation methods have been developed, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for the rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissues: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods successfully captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-type-specific and cell-state-specific markers. Moreover, isolation workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.
Single-nucleus RNA sequencing (snRNA-seq) has become an essential tool for transcriptomic analysis of complex tissues. However, the quality and efficiency of data generation depend heavily on the method used for nuclear isolation. The existing isolation techniques vary in their ability to preserve nuclear integrity, minimize ambient RNA contamination, and optimize recovery rates. Despite these differences in quality, a systematic comparison of these methods, specifically for brain tissue, is lacking. This gap poses a challenge for researchers in choosing the most suitable approach for their particular experimental requirements. To address this critical issue, our study directly compared three nuclei isolation methods and evaluated their performance in terms of yield, purity, and downstream sequencing quality. By providing a comprehensive assessment, we aim to guide researchers in selecting the most appropriate isolation protocol for their snRNA-seq experiments, ensuring optimal results and advancing the study of complex brain tissues at the single-nucleus level.
单核RNA测序(snRNA-seq)能够解析复杂组织中的细胞异质性。snRNA-seq通过使用细胞核而非细胞克服了传统单细胞RNA测序的局限性,从而能够利用冷冻组织和难以分离的细胞类型。尽管已经开发出了各种细胞核分离方法,但对于它们对核完整性和后续数据质量的影响仍缺乏系统评估,这一关键差距对严谨性和可重复性具有深远影响。为了解决这一问题,我们将三种机制不同的细胞核分离策略与脑组织进行了比较:一种基于蔗糖梯度离心的方法、一种基于旋转柱的方法和一种机器辅助平台。所有方法都成功捕获了多种细胞类型,但在细胞类型比例、转录同质性以及细胞类型特异性和细胞状态特异性标记物的保留方面显示出相当大的方案依赖性差异。此外,分离工作流程对来自环境、线粒体和核糖体RNA的污染水平有不同影响。我们的研究结果表明,细胞核分离方法是影响snRNA-seq数据质量和生物学解释的关键实验变量。
单核RNA测序(snRNA-seq)已成为复杂组织转录组分析的重要工具。然而,数据生成的质量和效率在很大程度上取决于用于细胞核分离的方法。现有的分离技术在保留核完整性、最小化环境RNA污染以及优化回收率的能力方面各不相同。尽管在质量上存在这些差异,但缺乏对这些方法的系统比较,特别是针对脑组织的比较。这一差距给研究人员在选择最适合其特定实验要求的方法时带来了挑战。为了解决这一关键问题,我们的研究直接比较了三种细胞核分离方法,并从产量、纯度和下游测序质量方面评估了它们的性能。通过提供全面评估,我们旨在指导研究人员为其snRNA-seq实验选择最合适的分离方案,确保获得最佳结果,并推动在单核水平上对复杂脑组织的研究。