Abstract 762: Practical guidelines for the design of single cell sequencing studies
Authors: Bayegan Amir , Tessier Julien , Wang Emma , Maisonet Adalis , Yan Shu , McGrath Shannon , Jackson Donald G. , Pollard Jack 
Published: June 2022

Performance of Single Cell RNA sequencing (scRNA-seq) experiments depends on multiple factors including the number of cells loaded, cell recovery rates, and sequencing coverage. We optimized such factors in 10x Genomics gene expression profiling and compared targeted panels to whole transcriptome sequencing using Human donor PBMC samples. We expect that our findings will apply to scRNA-seq studies of other sample types as well.

Optimizing cell recovery in scRNA-seq experiments is essential in order to ensure that changes in low-abundance cell populations (e.g. Treg) can be accurately quantified. Statistical analysis indicated that at least 100 cells are needed to assess cell-type specific state changes. We expect to recover 40-65% of cells loaded into the 10x chip. We found that loading 20k-30k cells/lane combined with cell hashtags for improved doublet detection improved cell recovery over the recommended loading of 10K-16K cells. We also surveyed internal single cell experiments and observed variable cell recovery across different sample types. The amount of ambient RNA in the library was correlated with lower performance in single cell studies, suggesting that cell death or damage was causing lower recovery. We found that using PBS for cell resuspension (which is less likely to cause cell rupture) performs as well as the nuclease-free water suggested by 10x.

We also assessed the impact of sequencing depth and protocol on scRNA-seq data quality. Sequencing depth impacts both experiment cost and data quality due to dropouts. We performed computational simulations of lower depth sequencing by subsampling various number of reads from PBMC experiments to obtain coverages of 10K, 20K, 40K and 80K reads per cell. We observed that 40K reads per cell, yielding about 70% sequencing saturation, provided a good balance between cost and sequencing depth.

Finally, we compared dropout rates and cell type annotation for two targeted panels, the Human Gene Signature and Human Immunology panels to whole transcriptome scRNAseq. On average we detected only 200 genes out of over 1000 represented in each panel. Dropout rates for targeted panels were only reduced at low coverage; at read depths higher than 40K reads per cell the whole transcriptome and targeted panels had similar dropout rates. Both methods detected major immune cell types, but targeted sequencing could not accurately identify some subtypes due to low number of detected genes.

We conclude that for immune cell profiling whole-transcriptome analysis at coverage of 40K reads per cell or higher with inputs of 20k-30k cells and use of sample hashtag antibodies provides the best balance of experiment cost, cell recovery and transcriptome coverage. Although these guidelines were established for Human PBMCs we expect similar outcomes with other complex cell mixtures such as dissociated tissues.

Citation Format: Amir Bayegan, Julien Tessier, Emma Wang, Adalis Maisonet, Shu Yan, Shannon McGrath, Donald G. Jackson, Jack Pollard. Practical guidelines for the design of single cell sequencing studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 762.