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Related Concept Videos

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Related Experiment Video

Updated: Jun 23, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression.

Isar Nassiri1,2,3,4, Andrew J Kwok2,5, Aneesha Bhandari2

  • 1Nuffield Department of Medicine, Centre for Human Genetics, Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), University of Oxford, Oxford, OX3 7BN, United Kingdom.

Bioinformatics Advances
|June 24, 2024
PubMed
Summary

Expression-Aware Demultiplexing (EAD) is a new computational method for analyzing pooled single-cell RNA sequencing data. EAD uses gene co-expression patterns to accurately identify cells from different individuals without additional experimental steps.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Pooled designs in single-cell RNA sequencing (scRNA-seq) increase throughput and reduce batch effects.
  • Demultiplexing pooled samples is crucial for distinguishing cell origins in complex experiments.
  • Existing methods may require additional experimental procedures or lack accuracy.

Purpose of the Study:

  • To introduce Expression-Aware Demultiplexing (EAD), a computational method for demultiplexing pooled scRNA-seq samples.
  • To validate EAD's effectiveness using synthetic and real biological data.
  • To demonstrate EAD's applicability across different cell types and conditions.

Main Methods:

  • Developed EAD, a computational approach leveraging differential co-expression patterns between individuals.
  • Utilized synthetic sample pools to identify key interindividual differentially co-expressed genes.
  • Applied EAD to pooled samples from isogenic mice, sepsis/healthy individuals, and brain single-nuclei transcriptomes.

Main Results:

  • Top interindividual differentially co-expressed genes formed distinct cell clusters per individual, highlighting metabolic regulation.
  • EAD combined with genetic information achieved high assignment accuracy (mean 0.98) in sepsis/healthy samples.
  • Combining EAD with barcoding techniques improved assignment accuracy by an average of 1.4%.
  • EAD successfully identified cells from the same donor in different activation states and was applicable to non-immune brain cells.

Conclusions:

  • EAD provides an effective, computationally driven solution for demultiplexing pooled scRNA-seq data without extra experimental steps.
  • The method accurately distinguishes cell origins based on interindividual co-expression variations.
  • EAD enhances classification accuracy and offers broad applicability in various biological contexts.