ArticleiScience2023
A contamination focused approach for optimizing the single-cell RNA-seq experiment.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Leveraging Single-Cell RNA Sequencing to Characterize Murine Ewing Sarcoma Xenografts.Methods in molecular biology (Clifton, N.J.) · 2027Article
- High-quality acinar cell isolation enables single-cell analysis of healthy and injured pancreas.Cell reports methods · 2026Article
- PalmaClust: A graph-fusion framework leveraging the Palma ratio for robust ultra-rare cell type detection in scRNA-seq data.bioRxiv : the preprint server for biology · 2026Article
- Dying cells as architects of the stem cell niche: a conserved mechanism driving tissue regeneration and tumor therapy resistance.Cell communication and signaling : CCS · 2026Review
- Benchmarking computational decontamination of ambient RNA.Frontiers in bioinformatics · 2026Article
- Optimized protocol for processing murine tumor-bearing lung tissue for flow cytometry and single cell RNA-sequencing.bioRxiv : the preprint server for biology · 2025Article
- Idiopathic Pulmonary Fibrosis: Cellular Heterogeneity, Mechanisms, and Therapeutic Implications.MedComm · 2025Review
- Distinct endothelial gene responses to acute exercise in skeletal muscle.American journal of physiology. Endocrinology and metabolism · 2025Article
- The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments.Research square · 2025Article
- The impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments.bioRxiv : the preprint server for biology · 2025Article
- Mining single-cell data for cell type-disease associations.NAR genomics and bioinformatics · 2024Article
- High content of nuclei-free low-quality cells in reference single-cell atlases: a call for more stringent quality control using nuclear fraction.BMC genomics · 2024Article
- MTGR1 is required to maintain small intestinal stem cell populations.Cell death and differentiation · 2024Article
- FixNCut: single-cell genomics through reversible tissue fixation and dissociation.Genome biology · 2024Article
- The leptin receptor has no role in delta-cell control of beta-cell function in the mouse.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.