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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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Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.

Yufei Gao1,2, Weiwei Yin3,4, Wei Hu5

  • 1Department of Cardiology and Department of Cell Biology of the Second Affiliated Hospital, Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310012, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

Sample multiplexing enhances single-cell sequencing by tagging cells for pooled sequencing, reducing costs and batch effects. This review guides researchers in selecting methods for accelerated biological and disease mechanism discoveries.

Keywords:
computational deconvolutionsample multiplexingsingle‐cell sequencing

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Area of Science:

  • Genomics and Molecular Biology
  • Bioinformatics

Background:

  • Single-cell sequencing offers high cellular resolution but faces cost and batch effect limitations in multi-sample studies.
  • Sample multiplexing addresses these limitations by uniquely tagging cells for pooled sequencing.

Purpose of the Study:

  • To provide a comprehensive review of single-cell multiplexing techniques.
  • To highlight the interplay between experimental design and computational accuracy in sample deconvolution.
  • To summarize diverse applications of single-cell multiplexing.

Main Methods:

  • Survey of major experimental strategies for sample multiplexing.
  • Review of critical computational algorithms for accurate sample deconvolution.
  • Analysis of the link between experimental design and computational outcomes.

Main Results:

  • Sample multiplexing significantly boosts throughput and data reliability by minimizing technical variability.
  • Accurate sample deconvolution is crucial for interpreting multiplexed single-cell data.
  • Successful applications span large clinical cohorts, multi-omics, developmental biology, and drug screening.

Conclusions:

  • Single-cell multiplexing is a key technology for advancing biological research.
  • This review empowers researchers to choose optimal methods for discovery.
  • Methods accelerate understanding of disease mechanisms, therapeutic responses, and development.