What Are Single Cell Copy Number Variations?

The human genome, our complete set of genetic instructions, is subject to various forms of variation. One type is the Copy Number Variation (CNV), which involves changes in the number of copies of specific DNA segments. Scientists can now analyze these variations within individual cells, providing a high-resolution view of their genetic makeup and offering insights into biology and medicine.

Understanding Copy Number Variations

A Copy Number Variation is a structural alteration where a DNA segment, larger than one kilobase, is present in a variable number of copies between individuals. These variations manifest as deletions, where a DNA segment is missing, or as duplications, where extra copies exist. In the context of the human genome as an instruction manual, a CNV is like a chapter having missing or duplicated pages.

Alterations in DNA copy number can arise from errors during cellular processes like DNA replication or repair. Mistakes during these events can lead to the formation of CNVs. Certain regions of the genome, known as low-copy repeats (LCRs), are particularly susceptible to these changes because of their repetitive nature.

The impact of CNVs on an individual ranges from benign to disease-contributing. Many are part of normal human genetic diversity and have no effect on health, while some are even associated with adaptive traits. Other CNVs can have significant consequences by altering the dosage of genes within the affected segment. Having too few or too many copies of a gene can disrupt its function, which is linked to developmental disorders, cancer susceptibility, and other health conditions.

The Importance of Analyzing CNVs at the Single Cell Level

Analyzing CNVs at the single-cell level is necessary because many tissues are not genetically uniform, a phenomenon known as cellular heterogeneity. This is prominent in tumors and developing tissues, which are composed of a mosaic of cells with distinct genetic profiles. Traditional bulk analysis extracts DNA from many cells at once, providing only an averaged genetic profile of the population.

Bulk analysis has limitations when dealing with cellular heterogeneity, as an averaged profile can mask CNVs that exist in only a small subset of cells. For example, a rare group of tumor cells might have a CNV that confers drug resistance. Bulk analysis would likely miss this information because the signal from these rare cells is diluted by the majority.

Single-cell analysis overcomes this limitation by enabling the detection of rare cells. By isolating and analyzing individual cells, scientists can identify CNVs present in small but functionally important subpopulations. This capability is also used to study somatic mosaicism, which is the presence of genetically distinct cells within an individual that arise after fertilization. Single-cell CNV analysis is a tool for investigating this mosaicism and its role in development and disease.

Methods for Detecting Single Cell CNVs

Detecting CNVs within a single cell first requires isolating it from a larger population. Because of the minuscule amount of DNA in one cell, it must be copied many times through a process called whole-genome amplification (WGA). The amplified DNA is then analyzed to determine the copy number of various genomic segments.

Sequencing-based methods, like single-cell whole-genome sequencing (scWGS), are a primary approach for analyzing the amplified DNA. In scWGS, the cell’s genome is sequenced, and the number of reads mapping to different regions is counted. An increased number of reads in a region suggests a duplication, while a decreased number indicates a deletion.

Another approach uses microarrays, such as in single-cell array comparative genomic hybridization (sc-aCGH). The DNA from a single cell is labeled with a fluorescent dye and compared to a differently colored reference DNA sample. Both samples are applied to a microarray containing known DNA sequences, and the ratio of the fluorescent colors is measured to determine DNA gains or losses.

Applications of Single Cell CNV Analysis in Health and Research

Single-cell CNV analysis has numerous applications, particularly in oncology. It helps characterize the genetic heterogeneity within tumors, which is valuable for understanding their evolution as different subclones of cancer cells compete. Tracking these clonal dynamics provides insights into how tumors respond to therapy and develop resistance.

In developmental biology, this analysis is used to study the role of CNVs during embryonic development. Examining individual cells from an embryo helps scientists investigate how CNVs arise and contribute to cell differentiation and tissue formation. This research can also shed light on the origins of developmental disorders caused by CNVs present in only a subset of cells.

This analysis is also applied to neurobiology to study the role of somatic mosaicism in the brain. It has potential connections to conditions like autism spectrum disorder and schizophrenia. By analyzing CNVs in individual neurons, researchers explore if genetic variations arising during brain development contribute to these disorders. Another application is in preimplantation genetic diagnosis (PGD) to screen embryos for chromosomal abnormalities before implantation.