Project description:Single cell RNA sequencing has enabled unprecedented insights into the molecular cues and cellular heterogeneity underlying human disease. However, the high costs and complexity of single cell methods remain a major obstacle for generating large scale human cohorts. Here we compare current state-of-the-art single cell multiplexing technologies, and provide a new widely applicable demultiplexing method, SNP-Fishing, that enables simple, robust high-throughput multiplexing leveraging genetic variability of patients.
Project description:Single cell RNA sequencing has enabled unprecedented insights into the molecular cues and cellular heterogeneity underlying human disease. However, the high costs and complexity of single cell methods remain a major obstacle for generating large scale human cohorts. Here we compare current state-of-the-art single cell multiplexing technologies, and provide a new widely applicable demultiplexing method, SNP-Fishing, that enables simple, robust high-throughput multiplexing leveraging genetic variability of patients.
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.
Project description:We developed a humanized mouse model of graft-versus-host disease in which Treg injected two days after PBMC persist in the blood of infused mice and can be characterized. This model provides in vivo assessment of manipulated Treg. We used that model to evaluate the impact of ex vivo priming of Treg with TNF-alpha on their ability to prevent xenogeneic GVHD. Indeed, it had been demonstrated that Treg function can be enhanced through stimulation of TNFR2. scRNAseq analysis was performed to assess the impact of TNF priming on Treg transcriptomics. Peripheral blood mononuclear cells were isolated from the blood of three healthy donors. Treg were isolated through immuno-magnetic selection then cultured for 24 hours with IL-2 at 10 ng/mL with or without TNF at 100 ng/mL. After 24 hours, cells were collected and washed. We performed cell multiplexing to label samples individually, then pooled them for single cell RNA sequencing. For each donor (named donors 3, 4 and 7), half cells were in the control group (no TNF) and half cells were in the experimental group (with TNF). 10x Genomics 3’ Single Cell Gene Expression with CellPlex multiplexing (Set A). Cells were labeled with Cell Multiplexing Oligos prior to pooling and library preparation.
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.
