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Protocol for optimized nasal mucosa sample processing to obtain high-quality scRNA-seq and scATAC-seq data

Year: 2024
Type: article
Abstract: Examining nasal mucosa samples is crucial for nasal cavity disease research and diagnosis. Simultaneously obtaining high-quality data for single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq]) and epigenomics (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq]) of nasal mucosa tissues is challenging. Here, we present a protocol for processing human nasal mucosa samples to obtain data for both scRNA-seq and scATAC-seq. We describe steps for extracting human nasal mucosa tissue, mechanical and enzymatic dissociation, lysis of red blood cells, and a viability assay. We then detail procedures for library preparation and quality control. • Guidance on single-cell dissociation and nuclei extraction from human nasal mucosa samples • Details for obtaining high-viability cell suspension and high-quality nuclei suspension • High-throughput library preparation for both scRNA-seq and scATAC-seq Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Examining nasal mucosa samples is crucial for nasal cavity disease research and diagnosis. Simultaneously obtaining high-quality data for single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq]) and epigenomics (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq]) of nasal mucosa tissues is challenging. Here, we present a protocol for processing human nasal mucosa samples to obtain data for both scRNA-seq and scATAC-seq. We describe steps for extracting human nasal mucosa tissue, mechanical and enzymatic dissociation, lysis of red blood cells, and a viability assay. We then detail procedures for library preparation and quality control.
Authors: Yaling Huang, Yisha Wu, Shikai Han, Qiaoling Wang, Guomingxiu Cong, Zhongzhen Liu, Shuyan Guan, Xiaojuan Huang, Ying Liu, Jianhua Yin, Jinmei Xue, Chuanyu Liu
Language: English
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W4402331487
FWCI: 0.579
Cites: 5
Cited by: 5
Related to: 10