Reprinted from: Publication of this reprint collection is supported by paid advertising SLAS Technology 27 (2022) 135–142 Contents lists available at ScienceDirect SLAS Technology journal homepage: www.elsevier.com/locate/slast Full Length Article Automation enables high-throughput and reproducible single-cell transcriptomics library preparation David Kind a , Praveen Baskaran a , Fidel Ramirez a , Martin Giner b , Michael Hayes c , Diana Santacruz a , Carolin K. Koss a , Karim C. el Kasmi a , Bhagya Wijayawardena c , Coralie Viollet a,∗ a Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany bBeckman Coulter GmbH, Europark Fichtenhain B 13, 47807 Krefeld, Germany c Beckman Coulter Life Sciences, Indianapolis, IN, United States a r t i c l e i n f o Keywords: Single-cell scRNA-seq Automation Transcriptome Genomics a b s t r a c t 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. Introduction Since its inception in the mid-2000s, next-generation sequencing (NGS) has become a critically important technology within the scientific community, allowing researchers to draw correlations between diseases and changes in nucleic acid sequences or expression. This has advanced the study of RNA, as analyzing changes in the transcriptome of samples is cheaper and easier than ever before. Whole transcriptome analysis, or RNA sequencing (RNA-seq), is an unbiased tool used to detect and quantify changes in the transcriptome of cells. Specifically, it allows researchers to observe and measure alterations in messenger RNA (mRNA) expression levels, mRNA splicing and quality control mechanisms, and to detect mRNA mutations that may affect protein function. Even with low sample input, RNA-seq provides quantitative results with singlebase resolution [1]. ∗ Corresponding author. E-mail address: coralie.viollet@boehringer-ingelheim.com (C. Viollet). Advances in microfluidics and molecular biology have led to RNAseq methods being applied at single cell resolution. Single-cell transcriptomics (scRNA-seq) can simultaneously provide the transcriptomes of thousands of individual cells within a sample. This can be especially useful for profiling cellular samples with high degrees of heterogeneity [2]. The scRNA-seq methods available can be largely divided into two categories: 1) plate-based full-length sequencing approaches, which generate whole-transcript cDNA sequences (e.g., Smart-seq2), and 2) droplet-based, unique molecular identifier (UMI) labeling methods, where the 3’ end of the mRNA transcripts arising from different cells are uniquely tagged. UMI approaches have several distinct advantages, namely higher throughput due to the ability to multiplex cells and lower costs per cell sequenced [2]. The 10X Genomics Single Cell 3’ kit provides UMI-barcoded cDNA from single-cell suspensions using proprietary gel bead-in-emulsion (GEM) technology. In this system, uniquely barcoded gel beads containing reverse transcription reagents are mixed with a limiting dilution of cells. Following cell lysis, reverse transcription generates uniquely labeled cDNA from each cell’s polyA tailed mRNA, which is then amplified and carried forward for subsehttps://doi.org/10.1016/j.slast.2021.10.018 2472-6303/© 2021 The Authors. Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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