ArticleLife science alliance2023
Evaluation of genetic demultiplexing of single-cell sequencing data from model species.
Article in Life science alliance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well SNature protocols · 2026Review
- Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Plate-based 10X Genomics-compatible single-cell RNA-sequencing based on Smart-seq3xpress.BMC genomics · 2025Article
- Probability of stealth multiplets in sample-multiplexing for droplet-based single-cell analysis.BMC genomics · 2025Article
- Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling.Genome biology · 2025Article
- Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data.Briefings in bioinformatics · 2025Article
- Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution.Scientific data · 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
- hadge: a comprehensive pipeline for donor deconvolution in single-cell studies.Genome biology · 2024Article
- demuxSNP: supervised demultiplexing single-cell RNA sequencing using cell hashing and SNPs.GigaScience · 2024Article
- Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression.Bioinformatics advances · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address these problems is pooling cells from multiple individuals into one sc-seq library. In humans, genotype-based computational separation (i.e., demultiplexing) of pooled sc-seq samples is common. This approach would be instrumental for studying non-isogenic model organisms. We set out to determine whether genotype-based demultiplexing could be more broadly applied among species ranging from zebrafish to non-human primates. Using such non-isogenic species, we benchmark genotype-based demultiplexing of pooled sc-seq datasets against various ground truths. We demonstrate that genotype-based demultiplexing of pooled sc-seq samples can be used with confidence in several non-isogenic model organisms and uncover limitations of this method. Importantly, the only genomic resource required for this approach is sc-seq data and a de novo transcriptome. The incorporation of pooling into sc-seq study designs will decrease cost while simultaneously increasing the reproducibility and experimental options in non-isogenic model organisms.
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