ArticleInternational journal of molecular sciences2024
Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 23 citations in OpenAlex.
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Single-cell mapping of homocysteine-induced perturbations in avian embryogenesis using LMO and BD rhapsody transcriptomics.iScience · 2026Article
- The challenges of single cell transcriptomics on difficult human tissue: the placenta.NAR genomics and bioinformatics · 2026Article
- Immune profiling of T cells: A comparative analysis of single-cell TCR sequencing using 10X Genomics and Parse Biosciences platforms.Biochemistry and biophysics reports · 2026Article
- Machine learning predictions surpass individual mRNAs as a proxy of single-cell protein expression.Genome biology · 2026Article
- Spatial Chromatin Accessibility Analysis of Intratumor Heterogeneity in Breast Cancer.Genomics, proteomics & bioinformatics · 2026Article
- Article
- Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologies.NAR genomics and bioinformatics · 2026Article
- Article
- Decoding the archipelago: single-cell biomarkers rechart the molecular geography of acute myeloid leukemia.Cell communication and signaling : CCS · 2026Review
- Multisite Assessment of Methods for Cell Preservation Upstream of Single-Cell RNA Sequencing.Journal of biomolecular techniques : JBT · 2026Article
- CapMux: a Snakemake pipeline for early demultiplexing of split-pool scRNA-seq data into sample-resolved outputs.Frontiers in bioinformatics · 2026Article
- Potential role of theFrontiers in genetics · 2026Article
- Generating cerebellar organoids from pluripotent stem cells.Disease models & mechanisms · 2026Review
- Leveraging two novel droplet-based single-cell RNA Sequencing platforms: a comparative study with 10x genomics.BMC genomics · 2025Article
- Multi-site Assessment of Methods for Cell Preservation Upstream of Single Cell RNA Sequencing.bioRxiv : the preprint server for biology · 2025Article
- Modeling neurodegeneration in the retina and strategies for developing pan-neurodegenerative therapies.Molecular neurodegeneration · 2025Review
- Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens.Nature cell biology · 2025Article
- A comprehensive analysis framework for evaluating commercial single-cell RNA sequencing technologies.Nucleic acids research · 2025Article
- Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies.BMC genomics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.
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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.