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.
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.
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.
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.
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.
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.
Project description:A scalable, cost-effective method that combines CRISPR perturbations with a single-cell indexing assay for transposase-accessible chromatin (CRISPR-sciATAC). This method links genome-wide chromatin accessibility to genetic perturbations through simultaneous capture of ATAC-seq fragments and CRISPR guide RNAs from single cells using a 96-well plate combinatorial indexing approach.
Project description:Here, we developed spatially cellular-level RNA-capture probe arrays using miniaturized microfluidic and microarray technologies. By leveraging the predetermined and cost-effective probe fixation characteristics of this methodology, we significantly reduced the consumable cost of the probe array to $0.31/mm2 and fabrication time to approximately 2 hours. Furthermore, the fabrication process does not rely on large precision instruments. Notably, the efficiency of the transcript captured by the probe array is even comparable to conventional single-cell RNA sequencing. Based on this technology, we successfully mapped the transcriptome atlas and gained insights into spatial cell heterogeneity of the mouse hippocampus.