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Updated: Oct 29, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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High-content single-cell combinatorial indexing.

Ryan M Mulqueen1, Dmitry Pokholok2, Brendan L O'Connell1

  • 1Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA.

Nature Biotechnology
|July 6, 2021
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Summary
This summary is machine-generated.

We developed symmetrical strand single-cell combinatorial indexing (s3), a novel method enhancing DNA conversion for single-cell genomics. This approach significantly boosts usable sequencing reads per cell across various assays, improving data quality and resolution.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Biotechnology

Background:

  • Single-cell combinatorial indexing (sci) enables high-throughput genomics but suffers from sparse data coverage.
  • Transposase-based library construction is common in sci methods.
  • Improving the efficiency of DNA conversion into sequencing libraries is crucial for enhancing data yield.

Purpose of the Study:

  • To develop a novel chemistry, symmetrical strand single-cell combinatorial indexing (s3), to increase the efficiency of DNA conversion in transposase-based single-cell genomics.
  • To evaluate the performance of s3 across different single-cell assays, including chromatin accessibility, whole-genome sequencing, and chromatin conformation capture.
  • To demonstrate the utility of s3 in resolving complex biological questions, such as subclonal genomic alterations.

Main Methods:

  • Developed s3, a uracil-based adapter switching method for improved DNA fragment conversion post-tagmentation.
  • Applied s3 chemistry to develop s3-ATAC for chromatin accessibility.
  • Integrated s3 with existing protocols for single-cell whole-genome sequencing (s3-WGS) and whole-genome plus chromatin conformation (s3-GCC).

Main Results:

  • Mouse s3-ATAC datasets showed a 6- to 13-fold increase in usable reads per cell compared to existing methods.
  • s3-WGS and s3-GCC achieved 148- and 14.8-fold improvements in usable reads per cell, respectively, over sci-DNA-sequencing and sci-HiC.
  • s3-WGS and s3-GCC successfully resolved subclonal genomic alterations in pancreatic cancer cell lines.

Conclusions:

  • The s3 platform significantly enhances DNA conversion efficiency in single-cell genomics assays.
  • s3 provides substantial improvements in usable reads per cell, enabling higher resolution and deeper insights.
  • The s3 platform is a versatile tool with broad applicability to various transposase-based single-cell genomics techniques.