Article ; Online: A contamination focused approach for optimizing the single-cell RNA-seq experiment
iScience, Vol 26, Iss 7, Pp 107242- (2023)
2023
Abstract: Summary: Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a ... ...
Abstract | Summary: Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality. |
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Keywords | Computational bioinformatics ; Transcriptomics ; Biology experimental methods ; Science ; Q |
Subject code | 310 |
Language | English |
Publishing date | 2023-07-01T00:00:00Z |
Publisher | Elsevier |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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