What is Single Cell DNA Sequencing (scDNA-Seq)?

Single Cell DNA Sequencing (scDNA-Seq) is a powerful technology that enables the analysis of the genetic material of individual cells. Unlike traditional bulk DNA sequencing that provides an average signal across thousands or millions of cells, scDNA-Seq reveals cell-to-cell genetic variations, including mutations, copy number variations (CNVs), and structural variants.

This approach is particularly valuable in understanding tumor heterogeneity, mosaicism, clonal evolution, developmental biology, and rare cell populations.

Overview

• Goal: Genomic profiling of individual cells

• Analyzes: DNA from single isolated cells

• Applications: Cancer research, immunology, stem cell biology, embryogenesis, neuroscience

• Platform: Illumina, 10x Genomics, Mission Bio, BGI, Fluidigm, and others

• Read Type: Typically short reads; paired-end; high-depth

How Single Cell DNA Sequencing Works

1. Cell Isolation
   Individual cells are isolated using techniques like FACS, microfluidics (e.g. 10x Genomics), or laser capture microdissection.

2. Whole Genome Amplification (WGA)
   Since a single cell contains only ~6 pg of DNA, the genome must be amplified using methods like MDA, MALBAC, or DOP-PCR.

3. Library Preparation & Sequencing
   Amplified DNA is fragmented, barcoded, and prepared for high-throughput sequencing.

4. Bioinformatics Analysis
   Reads are aligned to a reference genome, and mutations, CNVs, or structural variants are detected for each individual cell.

Applications of Single Cell DNA Sequencing

• Cancer Research
  Track tumor evolution, clonal diversity, treatment resistance, and minimal residual disease.

• Developmental Biology
  Understand cell lineage and differentiation during embryogenesis.

• Neurology
  Detect somatic mutations in neurons to study aging or neurodegenerative disorders.

• Genetic Mosaicism
  Identify spontaneous mutations in only a subset of an individual’s cells.

• Microbial Genomics
  Sequence genomes of unculturable single microbes in environmental samples.

• Immunology
  Track B-cell or T-cell clonal expansion via V(D)J recombination studies.

Advantages of scDNA-Seq

• Cell-Level Resolution
  Unmasks cellular heterogeneity hidden in bulk sequencing.

• Rare Variant Detection
  Identifies mutations present in a few or even single cells.

• Lineage Tracing
  Follow clonal evolution of cells in development or disease.

• Tumor Heterogeneity Insights
  Distinguish between subclones and track evolution over time.

• Minimal Input Requirement
  Requires DNA from just one cell (~6 picograms).

Key Features of Single Cell DNA Sequencing

Limitations and Challenges

• Amplification Bias
  WGA introduces uneven coverage, allelic dropout, and errors.

• Low Coverage
  Trade-offs between sequencing depth and number of cells.

• Data Complexity
  High-dimensional data requires advanced bioinformatics and statistical modeling.

• Cost
  Higher per-sample cost than bulk sequencing due to complex workflows and low input.

• Error Rates
  WGA-induced errors can mimic somatic mutations if not corrected.

Popular Tools and Pipelines for scDNA-Seq

• Cell Ranger DNA – From 10x Genomics; pipeline for preprocessing and variant calling

• SCcaller / Monovar / SCITE – Tools for SNV detection in single cells

• Ginkgo / InferCNV / CopyKAT – CNV inference and visualization

• PhyloWGS / SciClone – Reconstructing phylogenetic trees of tumor clones

• Seurat + Custom Pipelines – For integrating scDNA with scRNA or ATAC data

• IGV – Visualization of genomic variations per cell