scifi-RNA-seq

scifi-RNA-seq enables high-throughput single-cell RNA sequencing by applying single-cell combinatorial fluidic indexing to pre-index transcriptomes prior to droplet-based sequencing for large-scale studies such as cell atlas projects and single-cell CRISPR screens.


Key Features:

  • Combinatorial Pre-Indexing: One-step combinatorial pre-indexing tags individual cell transcriptomes before sequencing, allowing multiple cells to be loaded into each droplet and increasing droplet-based throughput by up to 15-fold.
  • Multiplexing Capability: The pre-indexing strategy enables multiplexing of hundreds of samples within a single experiment and reduces the need for multi-round combinatorial indexing.
  • Integration with Droplet Microfluidics: Leverages droplet microfluidic technologies for sequencing and is compatible with droplet-based platforms.
  • Efficient Workflow: A streamlined experimental workflow reduces handling steps while maintaining data quality and high throughput for large-scale experiments.

Scientific Applications:

  • Population Genomics: High-throughput profiling supports genomics studies that require analysis of vast numbers of cells.
  • Drug Screening: scRNA-seq readouts enabled by scifi-RNA-seq can be used to profile cellular responses to perturbations in drug screening applications.
  • Immunology Research: Demonstrated ability to profile TCR activation in human primary T cells, supporting studies of immune cell activation and signaling.

Methodology:

One-step combinatorial pre-indexing of single-cell transcriptomes is performed, multiple cells may be loaded per droplet, and libraries are sequenced using droplet microfluidics.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Linux, Mac, Windows
Programming Languages:
Python
Added:
1/14/2020
Last Updated:
10/17/2021

Operations

Publications

Datlinger P, Rendeiro AF, Boenke T, Krausgruber T, Barreca D, Bock C. Ultra-high throughput single-cell RNA sequencing by combinatorial fluidic indexing. Unknown Journal. 2019. doi:10.1101/2019.12.17.879304.

Documentation