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Sample multiplexing for retinal single-cell RNA-sequencing

ABSTRACT: Sample multiplexing for retinal single-cell RNA-sequencing

PROVIDER: PRJNA1071096 | ENA |

REPOSITORIES: ENA

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Sample multiplexing for retinal single-cell RNA-sequencing

Project description:Single cell RNA sequencing (ScRNA-seq) often requires sample pooling, but sample variance is rarely addressed. We perform scRNA-seq on retinal ganglion cells with retina-multiplexing to assess the whether this enables comparisons between retinas of an experiment.

2024-06-12 | GSE254587 | GEO

Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing

Project description: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 are not multiplexed. Despite a relatively lower library and cell capture efficiencies, 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, 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.

2024-03-24 | GSE246624 | GEO

CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics

Project description:High-throughput single-cell assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, which uses predefined genetic barcodes that are also heritable, enabling cell populations to be tagged, pooled, and tracked over time in the same experimental replicate. We demonstrate the utility of CellTag Indexing by sequencing transcriptomes using a variety of cell types, including long-term tracking of cell engraftment and differentiation in vivo. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

2019-04-20 | GSE130065 | GEO

Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes

Project description:Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

2017-06-13 | GSE96583 | GEO

CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [mixed Sample of cell lines and organisms]

Project description:We reported a Concanavalin A-based Barcoding Strategy (CASB) for single-cell and single-nucleus sample multiplexing, which could be followed by different single-cell sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. Besides sample multiplexing, the CASB could further improve data quality through doublet identification.

2021-03-09 | GSE153113 | GEO

CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [snATAC]

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CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [HAP1]

2021-03-09 | GSE153108 | GEO

CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [snATAC-seq]

2021-03-09 | GSE165048 | GEO

CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing [scRNA-seq]

2021-03-09 | GSE165047 | GEO

Cell hashing enable sample multiplexing, multiplet identification and super-loading on droplet-based single cell RNA-sequencing platforms

Project description:We reasoned that by using a distinct set of oligo-tagged antibodies against ubiquitously expressed proteins, we could uniquely label multiple populations of cells, multiplex them together, and use the barcoded antibody signal as a fingerprint. We refer to this approach as cellular "hashing", as our set of oligos defines a "look up table" to assign each multiplexed cell to its original sample. We demonstrate application of the technique to combine eight samples and run them simultaneously in a single droplet based scRNA-seq run. We show that cell hashtags allow sample multiplexing, confident multiplet identification and super-loading in the context of a commonly used droplet-based scRNA-seq method to drive down the per-cell cost of large-scale scRNA-seq experiments

2018-01-02 | GSE108313 | GEO

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