Automating full-length single-cell RNA-seq libraries
Interest in understanding the human genome at the single-cell level, in order to further understand cellular dynamics, has grown worldwide, as indicated by the increase in consortia devoted to this field. To aid in this research, both core facilities and automation play key roles. Core facilities provide researchers cost-effective and efficient access to new methods, while automation provides reliable, efficient, and cost-effective processing of high-throughput samples.
In this on-demand webinar, Dr. Jürgen Zimmermann, a senior engineer at the EMBL Genomics Core Facility, highlights important considerations for automation and discusses how he has optimized the Beckman Coulter Biomek for automating the SMART-Seq HT Kit for up to 192 samples, and counting. The SMART-Seq HT Kit uses oligo(dT)-priming to generate high-quality, full-length cDNA directly from 1–100 cells with a streamlined protocol.
Presented in partnership with Beckman Coulter (a Danaher company).
About the presenter
Jürgen Zimmermann, PhD
Dr. Jürgen Zimmermann is a senior engineer in automation at the European Molecular Biology Laboratory (EMBL; Heidelberg, Germany) in the Genomic Core Facility headed by Vladimir Benes. He studied Business Biology and Administration in Bonn and Kaiserslautern and holds his PhD in Biology. He has been active in the areas of lab automation, LIMS, and biochemical process development optimization in the area of nucleic acid sequencing since 1989. One of his current interests is the automation of high-throughput single-cell sequencing.
Plate-based single-cell RNA-seq technical notes
View data on the SMART-Seq Single Cell Kit's superior sensitivity and reproducibility for single-cell and nuclei applications.
High-quality, full-length RNA-seq libraries from single-cells with strand-of-origin information.
Single-cell transcriptome studies: a powerful way to highlight subtle differences between cells that may be hidden in a population
The SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing provides industry-leading performance for single-cell RNA-seq.
RNA-seq data that focuses on transcript 3' ends for differential expression analysis.
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