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A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform

ABSTRACT: A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform

PROVIDER: PRJNA1106889 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28867337_1.fastq.gz	Fastqsanger.gz
	SRR28867337_2.fastq.gz	Fastqsanger.gz
	SRR28867338_1.fastq.gz	Fastqsanger.gz
	SRR28867338_2.fastq.gz	Fastqsanger.gz
	SRR28867339_1.fastq.gz	Fastqsanger.gz

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A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform

Project description:Clinical samples promise unparalleled insights into the cellular mechanisms that underlie pathological conditions and therapeutic responses. However, they often can be precious, with few cells available for single-cell analysis, necessitating effort to maximize the amount of information that can be garnered from each. Here, we introduce a low material input, cost-effective protocol for conducting multi-omic analyses and sample hashing on single-cell suspensions using the Seq-Well S3 platform. Our protocol, designed to be both accessible and affordable, leverages readily available reagents and standard laboratory equipment, significantly lowering barriers to entry for researchers in low- and middle-income countries. The method detailed herein offers a streamlined and efficient workflow for: (1) precise staining of single-cell suspensions with antibody-oligo conjugates for accurate cell surface protein identification and effective sample multiplexing; (2) reliable generation of Seq-Well S3 sequencing libraries; (3) optional generation of bulk-RNA sequencing libraries through an optimized SMART-seq2 protocol; and, (4) robust computational pipelines for in-depth multi-omic data analysis. This protocol works with fragile and limited cell inputs (here, outlined for 15,000 cells per 200 µL - a fraction of the input required by most commercial methods). It also offers significant cost and time savings, with the entire process from cell isolation to sequencing taking only 3-6 days, plus an additional 1-2 days for data processing. In sum, this generally applicable pipeline empowers researchers around the globe to apply single-cell multiomics to advance their own research agendas.

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Project description:Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing based methods for cell lineage analysis depend on low resolution bulk analysis or rely on extensive single cell sequencing which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way towards large-scale human cell lineage discovery.

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A generic, cost-effective and scalable cell lineage analysis platform

Project description:A generic, cost-effective and scalable cell lineage analysis platform

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3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling (Glitazone)

Project description:The advent of Next Generation Sequencing has allowed transcriptomes to be profiled with unprecedented accuracy, but the steep costs associated with full-length library sequencing have posed a limit on the accessibility and scalability of the technology. To address these limitations, we developed 3’Pool-seq, a simple, cost-effective, and scalable RNA-seq method that focuses sequencing to the 3’ end of mRNA transcripts.

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3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling (Run0064)

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Comparison of different hashing methods on human PBMCs and primary mice tissues

Project description:the aim 1 was to evaluate hashing (sample multiplexing) accuracy of TotalSeq-B antibodies and CellPlex (a multiplexing solution from 10x Genomics) on 2 pools of PBMCs from 3 different donors. Thus the genetic difference between donors was used as a ground truth for sample demultiplexing based on antibody or lipid hashing hashing. the aim 2 was to evaluate TotalSeq antibody and lipid hashing on different mice primary tissues using different hashing protocols.

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