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Comparison of Live Cell Enrichment Methods for Single-Cell Rnaseq

单元格排序 细胞 活力测定 流式细胞术 生物 基因表达 细胞生物学 分子生物学 化学 基因 遗传学
作者
Brigita Meškauskaitė Urben,Tobias Krause,Meril Takizawa,Catherine Snopkowski,Ignas Masilionis,Ján Remšík,Yollanda Parisotto,Chrysothemis C. Brown,Adrienne Boire,Dana Pe’er,Roshan Sharma,Ronan Chaligné
出处
期刊:Blood [Elsevier BV]
卷期号:142 (Supplement 1): 7179-7179 被引量:2
标识
DOI:10.1182/blood-2023-190109
摘要

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular diversity and gene expression profiles at the individual cell level. However, to ensure accurate analysis of scRNA-seq data, it is crucial to generate high-quality single-cell suspensions by effectively removing dead cells, debris, and ambient RNA. This study aims to assess and compare the performance of five commercially available methods for dead cell removal: Fluorescence activated cell sorting (FACS), Akadeum Microbubble kit, StemCell EasySep kit, Miltenyi Biotec Dead Cell Removal Kit, and LevitasBio Levicell Live Cell Enrichment. Among the 5 methods we evaluated, FACS is considered the gold standard technique due to its remarkable efficiency and versatility. However, the high-pressure nature of FACS can exert mechanical stress on cells during the sorting process, potentially affecting cellular viability and gene expression profiles. On the other hand, the remaining four methods, while less versatile, utilize milder conditions, which may better preserve fragile cell populations. To evaluate those 5 methods, we used two tissues requiring only no to mild dissociation process: lymph node and cerebrospinal fluid (CSF). Viability of cells from each tissue was assessed using trypan blue staining after dissociation. The cell suspensions were then subjected to the five dead cell removal methods following the manufacturer's instructions. For each cell suspension, 10,000 cells were targeted using the 10x Genomics 3' v3.1 kit. The evaluation criteria encompassed hands-on time, required equipment, cost, improvement in live cell percentage, cell type recovery, gene expression profile, and cellular stress response. We hope these findings will provide valuable guidance for researchers in selecting the most suitable dead cell removal method based on their experimental requirements, thereby facilitating efficient, cost-effective, and high-quality scRNA-seq analysis.

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