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A hybrid single cell demultiplexing strategy that increases both cell recovery rate and calling accuracy

ABSTRACT: Recent advances in single cell RNA sequencing allow users to pool multiple samples into one run and demultiplex in downstream analysis, greatly increasing the experimental efficiency and cost-effectiveness. However, the expensive reagents for cell labeling, limited pooling capacity, non-ideal cell recovery rate and calling accuracy remain great challenges for this approach. To date, there are two major demultiplexing methods, antibody-based cell hashing and Single Nucleotide Polymorphism (SNP)-based genomic signature profiling, and each method has advantages and limitations. Here, we propose a hybrid demultiplexing strategy that increases calling accuracy and cell recovery at the same time. We first develop a computational algorithm that significantly increases calling accuracy of cell hashing. Next, we cluster all single cells based on their SNP profiles. Finally, we integrate results from both methods to make corrections and retrieve cells that are only identifiable in one method but not the other. By testing on several real-world datasets, we demonstrate that this hybrid strategy combines advantages of both methods, resulting in increased cell recovery and calling accuracy at lower cost.

ORGANISM(S): Homo sapiens

PROVIDER: GSE230810 | GEO | 2024/05/29

REPOSITORIES: GEO

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Project description:Single cell RNA sequencing (scRNAseq) has emerged as an essential technique in biology. Given the complexity and cost of experiments it is critical that they are well planned and executed. An essential component to scRNAseq is the ability to have biological replicates. This adds to the potential cost and complexity of the experiment, but is essential as it has been shown that false discoveries are possible when lacking information pertaining to cell origin. One option to overcome the financial and experimental constraints of biological replicates in scRNAseq data is to pool samples. Upon pooling it is essential to then understand the sample origin of each cell. Experiments in humans have shown that when pooling multiple genetically distinct individuals into one sample the genetic diversity (i.e., single nucleotide polymorphisms (SNP)) can be used to assign cells to their sample origin. This approach is called SNP-based demultiplexing. The question of whether lab species, like Pleurodeles waltl, and various other non-traditional model species harbor enough genetic diversity to enable such approaches is unclear. To formally test the whether SNP-based demultiplexing is possible across various species we designed this experiment in which three spleens from three different Pleurodeles waltl from animals expressing unique fluorescent genes (one female tgTol2(CAG:Nucbow CAG:Cytbow)Simon (5.67g weight, 10.8cm snout-to-tail length), one male tgSceI(CAG:loxP-GFP-loxP-Cherry)Simon (5.36g, 11.1cm), and one female tgTol2(CAG:loxP-Cherry-loxP-H2B::YFP)Simon (6.25g, 10.8cm)) were collected. Spleens were filtered through 70um nylon filters followed by FACS, gating on fluorescent positive cells and excluding erythrocytes. After FACS cells were pooled into one tube and subjected to 10x Genomics 3' v3.1 scRNAseq aiming for 10,000 cells. Spleens were filtered through 70um nylon filters followed by FACS gating on fluorescent positive cells and excluding erythrocytes. Samples were prepared and sequenced according to 10x Genomics recommendations. After sequencing, using reads mapping to fluorescent genes to identify animal origin we then compared the ability of SNP-based demultiplexing to properly assign cells to the correct animal.

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A hybrid single cell demultiplexing strategy that increases both cell recovery rate and calling accuracy

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