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Automation enables high-throughput and reproducible single-cell transcriptomics library preparation

Authors :
David Kind
Praveen Baskaran
Fidel Ramirez
Martin Giner
Michael Hayes
Diana Santacruz
Carolin K. Koss
Karim C. el Kasmi
Bhagya Wijayawardena
Coralie Viollet
Source :
SLAS Technology, Vol 27, Iss 2, Pp 135-142 (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Next-generation sequencing (NGS) has revolutionized genomics, decreasing sequencing costs and allowing researchers to draw correlations between diseases and DNA or RNA changes. Technical advances have enabled the analysis of RNA expression changes between single cells within a heterogeneous population, known as single-cell RNA-seq (scRNA-seq). Despite resolving transcriptomes of cellular subpopulations, scRNA-seq has not replaced RNA-seq, due to higher costs and longer hands-on time. Here, we developed an automated workflow to increase throughput (up to 48 reactions) and to reduce by 75% the hands-on time of scRNA-seq library preparation, using the 10X Genomics Single Cell 3’ kit. After gel bead-in-emulsion (GEM) generation on the 10X Genomics Chromium Controller, cDNA amplification was performed, and the product was normalized and subjected to either the manual, standard library preparation method or a fully automated, walk-away method using a Biomek i7 Hybrid liquid handler. Control metrics showed that both quantity and quality of the single-cell gene expression libraries generated were equivalent in size and yield. Key scRNA-seq downstream quality metrics, such as unique molecular identifiers count, mitochondrial RNA content, and cell and gene counts, further showed high correlations between automated and manual workflows. Using the UMAP dimensionality reduction technique to visualize all cells, we were able to further correlate the results observed between the manual and automated methods (R=0.971). The method developed here allows for the fast, error-free, and reproducible multiplex generation of high-quality single-cell gene expression libraries.

Details

Language :
English
ISSN :
24726303
Volume :
27
Issue :
2
Database :
Directory of Open Access Journals
Journal :
SLAS Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.446dbb9790314867b3b4a9a1f0e0d35c
Document Type :
article
Full Text :
https://doi.org/10.1016/j.slast.2021.10.018