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

Abstract

Single-cell technologies have seen rapid advancements in recent years, presenting new analytical challenges and opportunities. These high-throughput 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', where we transduce and label samples that can then be pooled together for downstream experimentation and analysis. By introducing predefined genetic barcodes that are transcribed and readily detected, we can reliably read out sample identity and transcriptional state via single-cell profiling. We validate and demonstrate the utility of CellTag Indexing by sequencing transcriptomes at single-cell resolution using a variety of cell types including mouse pre-B cells, primary mouse embryonic fibroblasts, and human HEK293T cells. A unique feature of CellTag Indexing is that the barcodes are heritable. This enables cell populations to be tagged, pooled and tracked over time within the same experimental replicate, then processed together to minimize unwanted biological and technical variation. We demonstrate this feature of CellTagging in long-term tracking of cell engraftment and differentiation, in vivo, in a mouse model of competitive transplant into the large intestine. Together, this presents CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell technologies.

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

“…However, additional studies will be required to fully understand the mechanisms behind how these genetic loci might impact iPSC reprogramming in lines that are derived from different individuals. Specifically, CellTag [35,36] or other barcoding methods will enable cells to be both collected and tracked throughout the entire reprogramming event or differentiation lineage.…”

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

“…d Limited to samples with high genetic diversity d Only possible if samples from different individuals can be pooled and assayed simultaneously Genetic labeling (Adamson et al, 2016;Datlinger et al, 2017;Guo et al, 2019) Unique, expressed, genetic labels are introduced into the cell sample prior to collection. Multiplets can then be detected as cell barcodes with multiple distinct genetic labels.…”

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

“…Recently developed sample multiplexing approaches can overcome this limitation in some circumstances. For example, genomic (Kang et al, 2018; Guo et al, 2018; Shin et al, 2018) and cellular sample multiplexing techniques (Stoeckius et al, 2018; Gehring et al, 2018; McGinnis et al, 2018; Gaublomme et al, 2018) directly detect most doublets in scRNA-seq data by identifying cells associated with orthogonal sample barcodes or single nucleotide polymorphisms (SNPs). By identifying and removing doublets, these techniques minimize technical artifacts while enabling users to “super-load” droplet microfluidics devices for increased scRNA-seq cell throughput.…”