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Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox.

Sjors Middelkamp1,2, Freek Manders1,2, Flavia Peci1,2

  • 1Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.

Cell Genomics
|September 18, 2023
PubMed
Summary

We developed the PTA Analysis Toolbox (PTATO) to accurately detect mutations in single-cell whole-genome sequencing data. PTATO distinguishes true mutations from artifacts, enabling sensitive study of somatic mutagenesis.

Keywords:
Fanconi anemiacancerhematopoietic stem cellsmutational signaturesprimary template-directed amplificationsingle-cell sequencingsomatic mutationsstructural variantswhole-genome amplificationwhole-genome sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Single-cell whole-genome sequencing (WGS) is crucial for understanding somatic mutations but limited by whole-genome amplification (WGA) artifacts.
  • Primary template-directed amplification (PTA) improves WGS accuracy but still produces significant artifacts.
  • Existing bioinformatic tools struggle to differentiate true mutations from PTA-induced errors.

Purpose of the Study:

  • To develop a robust bioinformatic workflow, the PTA Analysis Toolbox (PTATO), for accurate detection of somatic mutations from PTA-based WGS data.
  • To enhance the sensitivity and specificity of mutation detection in single-cell genomics.
  • To enable the study of somatic mutagenesis in cell types previously inaccessible to WGS analysis.

Main Methods:

  • Development of a comprehensive bioinformatic workflow (PTATO) integrating machine learning and recurrence-based filtering.
  • Application of PTATO to analyze single-cell WGS data generated by PTA.
  • Validation of PTATO's performance against existing bioinformatic approaches.

Main Results:

  • PTATO accurately detects single base substitutions, insertions-deletions (indels), and structural variants in PTA-based WGS data.
  • The workflow achieves high sensitivity (up to 90%) in distinguishing true mutations from PTA artifacts.
  • Analysis of Fanconi anemia patient hematopoietic stem cells revealed normal single base substitution burdens but increased deletions, a finding not possible with regular WGS.

Conclusions:

  • PTATO significantly improves the accuracy and sensitivity of somatic mutation detection in single-cell WGS data.
  • This novel bioinformatic tool overcomes limitations of PTA, enabling robust analysis of single-cell genomes.
  • PTATO facilitates the study of somatic mutagenesis in various biological contexts, including rare cell types and disease states.