The genetic code is organized into 23 pairs of chromosomes, and changes to this structure can determine health or disease. While most studied genetic differences involve single-letter changes, much larger structural variations also exist. Single-cell copy number variations (scCNVs) are a high-resolution method for detecting these large-scale differences in genetic material. Analyzing these variations at the individual cell level helps researchers understand how genetic differences arise and contribute to biological function.
Foundation: Understanding Copy Number Variations
Copy Number Variations (CNVs) are structural alterations in the genome where large segments of DNA are either deleted or duplicated. These segments can span from one kilobase up to several megabases of genetic material. Unlike single nucleotide polymorphisms (SNPs), CNVs involve significant stretches of the genome and often encompass entire genes. This size difference allows CNVs to substantially influence biological traits and disease risk.
Duplication results in a gain, meaning the cell possesses an extra copy of the segment. Deletion results in a loss of the segment. CNVs disrupt the typical balance of two gene copies per cell, leading to dosage imbalances. These variations are linked to both normal human diversity and susceptibility to disease.
Traditional methods for detecting these changes, called bulk sequencing, analyze DNA extracted from millions of cells simultaneously. These techniques average the genetic signal across the entire sample population. If a CNV is present in only a small fraction of cells, the signal is diluted by the overwhelming number of normal cells. This averaging effect means that important, low-frequency variations are often masked or missed by bulk analysis.
The Single Cell Difference: Heterogeneity and Specificity
Organisms are not composed of genetically uniform cells, even within the same tissue. Cellular heterogeneity, or genetic mosaicism, means that cells within a seemingly homogeneous population are genetically distinct. Single-cell CNV analysis addresses this complexity by isolating and sequencing the genome of one cell at a time. This approach identifies structural changes that would be hidden within a large, mixed sample.
Achieving single-cell resolution requires advanced technological steps due to the minuscule amount of DNA involved. The individual cell must first be physically separated from its neighbors, often using microfluidic devices. Once isolated, the faint DNA signal must be amplified millions of times through processes like multiple displacement amplification. This generates enough material to map the unique genomic profile of every individual cell.
The power of scCNVs lies in mapping the composition of these distinct subpopulations. In a tumor sample, single-cell analysis can reveal multiple subclones, each defined by a unique set of CNVs. For example, one subclone might have a duplication of an oncogene while another has a deletion in a tumor suppressor gene. Bulk sequencing cannot distinguish these specific profiles, only reporting a weighted average.
Understanding this heterogeneity is important because it drives biological selection and evolution. By analyzing scCNVs, researchers can construct phylogenetic trees, revealing the clonal history of a tissue or disease. This mapping provides a deeper understanding of the genetic diversity that allows some cell populations to survive environmental pressures.
Biological Significance: Where scCNVs Drive Disease
Single-cell CNV analysis provides detail in understanding the progression of complex diseases, particularly cancer. Cancer is a disease of genomic instability, and scCNVs identify the structural changes that drive tumor evolution. The analysis can reveal rare, aggressive subclones existing before treatment, which are often the source of therapeutic failure.
In the context of drug resistance, scCNVs track the selection process occurring under chemotherapy pressure. When a drug kills most cancer cells, a small subpopulation with a resistant CNV can survive and expand. Single-cell profiling pinpoints the exact CNV, such as a duplication of a drug target gene, that enabled the resistant subclone to dominate. Analyzing these shifts provides insights for designing combination therapies or anticipating resistance mechanisms.
Beyond cancer, scCNVs have illuminated the role of somatic mosaicism in the brain, linking genetic variation in individual neurons to neurological disorders. Studies show that a significant fraction of human neurons contain large de novo CNVs that arose after fertilization. These variations are not inherited but occur randomly during brain development. This creates a complex patchwork of genetically distinct neurons.
This neuronal mosaicism is implicated in conditions such as autism spectrum disorder and schizophrenia. Somatic CNVs that affect only a subset of brain cells are thought to contribute to the risk and severity of these disorders. The discovery of these single-cell variations suggests the genetic makeup of the brain is dynamic, opening new avenues for understanding these complex conditions.