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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. 
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Updated: Sep 2, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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Sample-multiplexing approaches for single-cell sequencing.

Yulong Zhang1,2,3, Siwen Xu1,2,4, Zebin Wen1,2

  • 1Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China.

Cellular and Molecular Life Sciences : CMLS
|August 4, 2022
PubMed
Summary
This summary is machine-generated.

Sample multiplexing techniques enhance single-cell sequencing by enabling multiple samples to be processed together. These innovative methods improve efficiency and reduce costs in transcriptomics, epigenomics, and genomics studies.

Keywords:
Cell HashingMulti-omicsSpatial transcriptomicsscATAC-seqscRNA-seq

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Area of Science:

  • Biological sciences
  • Biotechnology
  • Genomics

Background:

  • Single-cell sequencing is crucial for biological and medical research.
  • Current limitations include inefficient processing, high costs, cell identification ambiguity, and batch effects when analyzing multiple samples.

Purpose of the Study:

  • To introduce sample-multiplexing approaches for single-cell sequencing across various omics fields.
  • To address the challenges hindering the application of single-cell sequencing with multiple samples.

Main Methods:

  • Sample multiplexing utilizes natural genetic variation or artificial nucleotide barcodes as sample markers.
  • Barcode strategies include anchoring, internalization, vector-based expression, and library incorporation.
  • Methods are applicable to transcriptomics, epigenomics, genomics, and multiomics, including single-cell combinatorial indexing.

Main Results:

  • Sample multiplexing allows for efficient pooling and decoding of multiple samples in single-cell experiments.
  • These approaches overcome existing technical hurdles in single-cell analysis.
  • The methods enable super-loading of single cells, increasing throughput.

Conclusions:

  • Sample multiplexing significantly advances single-cell sequencing capabilities for multi-sample studies.
  • Guidelines are provided to facilitate the adoption of these technologies.
  • The techniques promise to accelerate discoveries in various omics fields.