scDNAseq

scDNAseq, or single-cell DNA sequencing, refers to sequencing the genome of individual cells rather than a pooled sample. This approach enables the study of genetic heterogeneity within tissues, somatic mutations, copy number variations, and clonal evolution in cancer and development. By comparing many single-cell profiles, researchers can reconstruct lineage relationships, detect rare cellular clones, and assess mosaicism.

The typical workflow begins with isolation of single cells, using methods such as fluorescence-activated cell sorting, micromanipulation, or microfluidic platforms. Because a single cell has limited DNA, whole-genome amplification (WGA) is usually required to generate enough material for sequencing. Common WGA methods include multiple displacement amplification (MDA), MALBAC, DOP-PCR, PTA, and, more recently, linear amplification strategies like LIANTI. Each method introduces biases that affect coverage uniformity and allelic representation, leading to allelic dropout and amplification artifacts. Sequencing is usually performed with short-read platforms, though long-read scDNAseq approaches are emerging for complex structural variation.

Data analysis in scDNAseq involves read alignment and quality filtering, followed by copy number variation inference from read depth and single-nucleotide variant (SNV) calling, which is complicated by uneven coverage and amplification errors. Many studies emphasize aggregated analyses across cells to infer clonal structures or phylogenies, and specialized tools exist for lineage reconstruction and CNV profiling.

Applications include exploring intratumoral heterogeneity and tumor evolution, tracing developmental lineages, and studying mosaicism in tissues. Limitations include amplification bias, allelic dropout, limited genome coverage per cell, and higher cost per genome compared with bulk sequencing. Ongoing advances aim to improve accuracy, throughput, and multi-omics integration at the single-cell level.