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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq).

Umut Yildiz1,2,3, Sara Lobato-Moreno4,5,6, Annique Claringbould4,6,7

  • 1European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. umut.yildiz@bsse.ethz.ch.

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Summary

We developed SUM-seq, a scalable single-cell assay for simultaneous epigenome and transcriptome profiling. This cost-effective method increases throughput and multiplexing for large-scale biological studies.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Single-cell analysis

Background:

  • Single-cell epigenome and transcriptome profiling is crucial for understanding gene regulatory networks and cellular heterogeneity.
  • Existing multimodal single-cell assays face limitations in throughput and multiplexing capabilities.

Purpose of the Study:

  • To introduce a scalable and cost-effective assay, SUM-seq, for simultaneous profiling of chromatin accessibility and gene expression in single nuclei.
  • To enable high-throughput, multiplexed multiomic single-cell library preparation.

Main Methods:

  • SUM-seq combines in situ barcoding of accessible DNA and mRNA with droplet-based microfluidics.
  • The assay allows for sample multiplexing and resolution of multinucleated droplets.
  • Simultaneous profiling of chromatin accessibility and gene expression from the same cell.

Main Results:

  • SUM-seq offers significantly increased throughput and multiplexing capacity compared to existing methods.
  • The assay enables substantial scaling of samples and nuclei for large-scale projects, time-course experiments, and perturbation screens.
  • Reduced costs associated with large-scale multiomic single-cell analyses.

Conclusions:

  • SUM-seq provides a powerful and scalable solution for multiomic single-cell analysis.
  • The assay facilitates cost-effective, high-throughput characterization of cellular heterogeneity and regulatory landscapes.
  • Guidelines for experimental design, sample handling, and data processing are provided to support its implementation.