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Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing.

ABSTRACT: 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 preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared 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. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation.

SUBMITTER: Datlinger P

PROVIDER: S-EPMC7612019 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

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.

2021-03-11 | GSE168620 | GEO

Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

Project description:Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing

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scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing.

Project description:Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.

| S-EPMC11003106 | biostudies-literature

Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing.

Project description:Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems1-13, the application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance14-16, lack of mRNA polyadenylation and thick cell walls17. Here, we present prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)-a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing11,12,18 to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single-cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates of more than 200 mRNAs per cell for exponentially growing Escherichia coli. These characteristics enable robust discrimination of cell states corresponding to different phases of growth. When applied to wild-type Staphylococcus aureus, PETRI-seq revealed a rare subpopulation of cells undergoing prophage induction. We anticipate that PETRI-seq will have broad utility in defining single-cell states and their dynamics in complex microbial communities.

| S-EPMC8330242 | biostudies-literature

Mouse kidney nuclear isolation and library preparation for single-cell combinatorial indexing RNA sequencing.

Project description:Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq3) enables high-throughput single-nucleus transcriptomic profiling of multiple samples in one experiment. Here, we describe an optimized protocol of mouse kidney nuclei isolation and sci-RNA-seq3 library preparation. The use of a dounce tissue homogenizer enables nuclei extraction with high yield. Fixed nuclei are processed for sci-RNA-seq3, and self-loaded transposome Tn5 is used for tagmentation in library generation. The step-by-step protocol allows researchers to generate scalable single-cell transcriptomic data with common laboratory supplies at low cost. For complete details on the use and execution of this protocol, please refer to Li et al. (2022).1.

| S-EPMC9732400 | biostudies-literature

Sequencing thousands of single-cell genomes with combinatorial indexing.

Project description:Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs, which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present single-cell combinatorial indexed sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single-cell libraries for detection of somatic copy-number variants. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue and two human adenocarcinomas, and obtained a detailed assessment of subclonal variation within a pancreatic tumor.

| S-EPMC5908213 | biostudies-literature

FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5'-end single-cell RNA sequencing.

Project description:Single-cell RNA sequencing methods focusing on the 5'-end of transcripts can reveal promoter and enhancer activity and efficiently profile immune receptor repertoire. However, ultra-high-throughput 5'-end single-cell RNA sequencing methods have not been described. We introduce FIPRESCI, 5'-end single-cell combinatorial indexing RNA-Seq, enabling massive sample multiplexing and increasing the throughput of the droplet microfluidics system by over tenfold. We demonstrate FIPRESCI enables the generation of approximately 100,000 single-cell transcriptomes from E10.5 whole mouse embryos in a single-channel experiment, and simultaneous identification of subpopulation differences and T cell receptor signatures of peripheral blood T cells from 12 cancer patients.

| S-EPMC10078054 | biostudies-literature

High-content single-cell combinatorial indexing.

Project description:Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci ('s3'), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-assay for transposase-accessible chromatin, s3-ATAC) in human cortical and mouse whole-brain tissues, with mouse datasets demonstrating a six- to 13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole-genome sequencing (s3-WGS) and to whole-genome plus chromatin conformation (s3-GCC) yields 148- and 14.8-fold improvements, respectively, in usable reads per cell compared with sci-DNA-sequencing and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.

| S-EPMC8678206 | biostudies-literature

Single-cell DNA methylation sequencing by combinatorial indexing and enzymatic DNA methylation conversion

Project description:Background DNA methylation is a critical molecular mark involved in cellular differentiation and cell-specific processes. Single-cell whole genome DNA methylation profiling methods hold great potential to resolve the DNA methylation profiles of individual cell-types. Here we present a method that couples single-cell combinatorial indexing (sci) with enzymatic conversion (sciEM) of unmethylated cytosines. Results The sciEM method facilitates DNA methylation profiling of single-cells that is highly correlated with single-cell bisulfite-based workflows (r2 > 0.99) whilst improving sequencing alignment rates, reducing adapter contamination and over-estimation of DNA methylation levels (CpG and non-CpG). As proof-of-concept we perform sciEM analysis of the temporal lobe, motor cortex, hippocampus and cerebellum of the human brain to resolve single-cell DNA methylation of all major cell-types. Conclusion To our knowledge sciEM represents the first non-bisulfite single-cell DNA methylation sequencing approach with single-base resolution. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-022-00938-9.

| S-EPMC9811750 | biostudies-literature

Protocol for single-cell ATAC sequencing using combinatorial indexing in mouse lung adenocarcinoma.

Project description:Single-cell ATAC sequencing using combinatorial indexing (sciATAC-seq) enables the identification of chromatin accessibility profiles at single-cell resolution with a dual-barcoding approach during transposition and library construction. Unlike commercial droplet-based approaches, sciATAC-seq is a cost-effective, extensible strategy that permits flexibility in the experimental scale via multiplexed barcoding across samples or perturbations. In contrast, droplet-based approaches have higher cell recovery and may be advantageous when cell input is limited. The step-by-step sciATAC-seq protocol described here is amenable to diverse cell types and fixed samples. For complete details on the use and execution of this protocol, please refer to LaFave et al. (2020).

| S-EPMC8185305 | biostudies-literature

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