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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Methods and Insights from Single-Cell Expression Quantitative Trait Loci.

Joyce B Kang1,2,3, Alessandro Raveane4, Aparna Nathan1,2,3

  • 1Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; email: joyce.b.kang@gmail.com, aparna.nathan.17@gmail.com.

Annual Review of Genomics and Human Genetics
|May 17, 2023
PubMed
Summary
This summary is machine-generated.

Single-cell eQTL (sc-eQTL) analysis reveals gene regulation in specific cell types, offering new insights into disease mechanisms. This approach overcomes limitations of bulk RNA sequencing by capturing cellular heterogeneity.

Keywords:
cell stateeQTLgene regulationnoncoding variantssc-eQTLsingle-cell sequencing

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Area of Science:

  • Genomics and Molecular Biology
  • Computational Biology and Bioinformatics
  • Single-cell Analysis

Background:

  • Traditional bulk RNA sequencing averages gene expression, masking cell-type-specific regulatory effects.
  • Single-cell technologies provide high-resolution transcriptional data, enabling analysis of individual cells.
  • Expression quantitative trait locus (eQTL) mapping links genetic variants to gene expression levels.

Purpose of the Study:

  • To provide an overview of experimental designs for single-cell eQTL (sc-eQTL) studies.
  • To discuss methodologies, challenges, and opportunities in sc-eQTL analysis.
  • To highlight the potential of sc-eQTL mapping for understanding context-dependent gene regulation and disease mechanisms.

Main Methods:

  • Review of recently deployed experimental designs in sc-eQTL studies.
  • Consideration of study design factors: cohort selection, cell states, and ex vivo perturbations.
  • Discussion of current computational methodologies and modeling approaches for sc-eQTL analysis.

Main Results:

  • Single-cell eQTL mapping identifies context-dependent regulatory variants that vary across cell states.
  • sc-eQTL analysis can uncover regulatory effects previously hidden by bulk analysis.
  • Identified eQTLs can colocalize with disease-associated variants, implicating specific cell states in disease pathogenesis.

Conclusions:

  • Single-cell eQTL analysis offers unprecedented resolution to dissect gene regulation in specific cellular contexts.
  • This approach can reveal novel insights into the molecular mechanisms underlying complex diseases.
  • Future opportunities lie in refining methodologies and expanding applications of sc-eQTL mapping.