Dataset Information

Benchmarking Single-Cell mRNA-Sequencing Technologies Uncovers Differences in Sensitivity and Reproducibility in Cell Types With Low RNA Content.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has the ability to classify each cell and determine the transcriptomic profile of specific cell types and cells of a given disease state; however, sensitivity of the gene count for each cell can be a critical component to the success of a single-cell study. The recently introduced SMART-Seq Single Cell PLUS Kit (SSsc PLUS) claims to provide higher sensitivity and reproducibility versus popular methods for the sequencing analysis of single cells. Here, the cDNA-generation component of the kit, SMART-Seq Single Cell Kit (SSsc), was compared with the popular homebrew protocol, Smart-seq2, and its update, Smart-seq3. The SMART-Seq Library Prep Kit from SSsc PLUS was benchmarked against a commonly used scRNA-seq library preparation method, Illumina Nextera XT. Finally, the SSsc chemistry was tested in both full and fractional volumes on 2 popular liquid-handler devices to investigate whether the high sensitivity was maintained in miniaturization. We demonstrate that SSsc PLUS outperforms these other full-length methods in convenience, sensitivity, gene identification, and reproducibility while also offering full compatibility with automation platforms.

SUBMITTER: Holcomb I

PROVIDER: S-EPMC9258451 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Benchmarking Single-Cell mRNA-Sequencing Technologies Uncovers Differences in Sensitivity and Reproducibility in Cell Types With Low RNA Content.

Holcomb Ilona I Bansal Nidhanjali N Duong Tommy T Babb Paul P Laliberte Julie J Swaminathan Karthikeyan K Xu Hui Helen HH Melloy Leigh Ann LA Hildebrand Jerry J Farmer Andrew A

Journal of biomolecular techniques : JBT 20211201 4

Single-cell RNA sequencing (scRNA-seq) has the ability to classify each cell and determine the transcriptomic profile of specific cell types and cells of a given disease state; however, sensitivity of the gene count for each cell can be a critical component to the success of a single-cell study. The recently introduced SMART-Seq Single Cell PLUS Kit (SSsc PLUS) claims to provide higher sensitivity and reproducibility versus popular methods for the sequencing analysis of single cells. Here, the c ...[more]

PMID: 35837268

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Project description:Technological advances in transcriptome sequencing of single cells provided an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cellular subpopulations, none of these comparative studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These systems and library preparation methods are representative for the wide variety of platforms, ranging from microfluids chips to droplet-based systems and from full transcript sequencing to 3’ end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identification of differentially expressed genes as robustly assessed by two statistical models, and the second is enrichment analysis of biological signals, which is independent of sample size or number of cells evaluated. Where relevant, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples to make the data more comparable. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP cells upon nutlin-3 treatment, pinpointing putative late-responders or resistant cells, hidden in bulk RNA-seq experiments.

Dataset Information

Benchmarking Single-Cell mRNA-Sequencing Technologies Uncovers Differences in Sensitivity and Reproducibility in Cell Types With Low RNA Content.

Publications

Benchmarking Single-Cell mRNA-Sequencing Technologies Uncovers Differences in Sensitivity and Reproducibility in Cell Types With Low RNA Content.

Similar Datasets

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