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

ABSTRACT: 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.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE130065 | GEO | 2019/04/20

REPOSITORIES: GEO

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Project description:ChIP-seq experiments are standard experimental procedure for interrogating epigenetic states and protein-DNA interactions. Sequencing experiments are often designed according to the trade-off between the need to obtain maximum sequencing coverage limited funds. Multiplexing samples is a common approach to minimize cost and maximize information yield. We therefore performed an extensive ChiP-seq multiplexing study to gain a better understanding of the effect of multiplexing on the resulting peak detection and genomic annotation and to provide solid guidelines for multiplexing ChIP-seq studies. For a well characterized antibody, our results indicate that multiplexing to ~20M reads (roughly 8 samples per sequencing lane) is sufficient to capture most of the biological signal. Multiplexing samples in sequencing experiments is a common approach to maximize information yield while minimizing cost. In most cases the number of samples that are multiplexed is determined by financial consideration or experimental convenience with limited understanding on the effects on the experimental results. Here we set to examine the impact of multiplexing ChIP-seq experiments on the ability to identify a specific epigenetic modification. We performed an analysis of peak detection to determine the effects of multiplexing. These include false discovery rates, size, position and statistical significance of peak detection and changes in gene annotation. We found that, for histone marker H3K4me3, one can multiplex up to 8 samples (7 IP + 1 input) at ~21 million reads each and still detect over 90% of all peaks found when using a full lane for sample. Furthermore, there are no variations introduced by indexing or lane batch effects and importantly there is no significant reduction in the number of genes with neighboring H3K4me3 peaks. We conclude that, for a well characterized antibody and therefore, model IP condition, multiplexing 8 samples per lane is sufficient to capture most of the biological signal.