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: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.
Project description:Microdroplet-based co-culturing assays dissect a complex multi-cellular interactions into individual cell-cell interaction events in a highly parallelized manner. To integrate single-cell sequencing approaches into such in-droplet multicellular co-culturing assays, we demonstrate a chemistry approach for encoding the identity of droplets to their belonging cells. K562 cells and THP-1 cells were encapsulated in water-in-oil droplets, where they were labeled with DNA barcodes encoding the identity of individual droplets. Then cells were released from droplets for pooled single-cell RNA sequencing. cDNA and DNA barcodes were sequenced in separate sequencing libraries. Experiments were carried out in duplicate.
Project description:Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed “single-cell combinatorial fluidic indexing” (scifi). The scifi-RNA-seq assay combines one-step combinatorial pre-indexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Pre-indexing allows us to load multiple cells per droplet and bioinformatically demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and it provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared to multi-round combinatorial indexing, scifi-RNA-seq provides an easier, faster, and more efficient workflow. In contrast to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets.
Project description:Barcode-based multiplexing methods can be used to increase throughput and reduce batch effects in large single-cell genomics studies. To evaluate methods for demultiplexing barcode-multiplexed data, we generated a dataset by labeling samples separately with barcode-tagged antibodies, mixing those samples, and progressively overloading a droplet-based scRNA-seq system.
Project description:DNA barcodes can be used to identify single cells in a sequencing data space while optical codes can be used to track single live cells in an image data space. We have developed dual image and DNA (ID)-coding, which identifies individual single cells in both live image and sequencing data spaces. Samples provided here are relevant to proof-of-concept studies of ID-coding presented in the associated publication. DNA barcoded micro-particles were encapsulated in hydrogel droplets with or without single cells. The hydrogel droplets were then subjected to “single-droplet sequencing” where whole polyA-bearing nucleic acid components within a hydrogel droplet (i.e. mRNA from cells and synthetic DNA on beads) were concatenated by the same cell barcodes.
Project description:We reasoned that by using a distinct set of oligo-tagged antibodies against ubiquitously expressed proteins, we could uniquely label multiple populations of cells, multiplex them together, and use the barcoded antibody signal as a fingerprint. We refer to this approach as cellular "hashing", as our set of oligos defines a "look up table" to assign each multiplexed cell to its original sample. We demonstrate application of the technique to combine eight samples and run them simultaneously in a single droplet based scRNA-seq run. We show that cell hashtags allow sample multiplexing, confident multiplet identification and super-loading in the context of a commonly used droplet-based scRNA-seq method to drive down the per-cell cost of large-scale scRNA-seq experiments
Project description:We describe a novel workflow named Barcode Assembly foR Targeted Sequencing, which is a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts (rBART-Seq) or genomic regions (gBART-Seq) from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, for generating amplicons with dual indices. Here, we used the gBART-Seq for genetic screening of breast cancer patients and identified BRCA mutations with very high precision.
