Automation enables high-throughput and reproducible single-cell transcriptomics library preparation
SLAS Technology, ISSN: 2472-6303, Vol: 27, Issue: 2, Page: 135-142
2022
- 5Citations
- 12Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations5
- Citation Indexes5
- CrossRef1
- Captures12
- Readers12
- 12
- Mentions1
- Blog Mentions1
- Blog1
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2472630321000182; http://dx.doi.org/10.1016/j.slast.2021.10.018; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128489159&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/35058211; https://linkinghub.elsevier.com/retrieve/pii/S2472630321000182; https://dx.doi.org/10.1016/j.slast.2021.10.018
Elsevier BV
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