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Stanford-led Team Produces Personal Recombination Map From Individual's Sperm Cells

NEW YORK (GenomeWeb News) – A study online today in Cell reveals that the recombination and mutation patterns measured within one individual are more or less on par with those predicted from population and evolutionary data, though fine-scale differences hint at the impact that each individual has on human diversity as a whole.

"This individual has a lot of recombination happening at [recombination] hotspots, but there are also some that are not overlapping with the hotspots," co-first author Jianbin Wang, a graduate student in senior author Stephen Quake's bioengineering lab at Stanford University, told GenomeWeb Daily News. "We think that might be the contribution that every individual makes to the population average to increase the population diversity."

Wang and his colleagues did genomic analyses on around 100 individual sperm cells from the same 40-year-old Caucasian man, using genome-wide genotyping information on 91 of these cells to put together a personal recombination map for the individual. From genome sequence data on a few dozen sperm cells, they also looked at the rates of de novo mutation and large-scale genomic alterations in the individual's sex cells.

As such, the work adds a new layer to researchers' understanding of meiotic recombination, the mixing and matching of DNA from each pair of chromosomes that happens during meiosis as parents' haploid sperm and egg cells form from diploid precursors.

"The exact sites, frequency and degree of this genetic mixing process is unique for each sperm and egg cell," Quake said in a statement, "and we've never before been able to see it with this level of detail."

"It's very interesting that what happens in one person's body mirrors the population average," he added.

Whereas recombination and de novo mutation rate studies in humans so far have largely relied on data for populations and families, Wang, Quake, and colleagues came up with a recombination map for a single individual using genotyping data for 91 sperm cells that were isolated and amplified using a microfluidic device.

Following single cell amplification of DNA in the sperm cells, the researchers genotyped each with the Illumina Omni1S Bead Array.

Amplified DNA from eight more individual sperm cells was sequenced using the Illumina GAII or HiSeq 2000 to look at de novo mutation rates, Wang explained, while shallower genome sequencing around two-dozen more individual sperm cells, including some of those genotyped using the array, made it possible to track down larger scale genomic changes.

At the genome level, recombination patterns in the sperm cells matched those predicted previously from Caucasian population and pedigrees and studies using cytology-based sperm testing, researchers reported, with each sperm cell showing almost 23 recombination events, on average.

Likewise, when the team looked at recombination at the chromosome level, it saw patterns similar to those described in the past, including an over-representation of recombination sites in telomeric chromosome regions and a dearth of recombination around chromosome centromeres.

"The individual we studied has the same level of recombination genome-wide compared to the population," Wang said. "The general trend for the distribution of recombination along the chromosome is also the same as the population."

But when they zoomed in to a much finer scale, exploring the individual's use of previously defined recombination hotspots, he explained, the investigators found differences too.

While some of the recombination events detected fell within parts of the genome previously defined as hotspots of recombination for in the HapMap Caucasian population, others occurred outside of these areas. And hotspot usage varied from one sperm cell to the next.

"The individual cells from the same man tend to use different levels of hotspots," Wang said. "Some cells tend to have more hotspot usage and some cells tend to have lower hotspot usage."

Meanwhile, de novo mutation patterns gleaned from the eight sperm cells sequenced at highest coverage were comparable to those reported in past evolutionary studies, though the number of mutations — 25 to 36 single nucleotide alterations per sperm cell — was a bit higher than those described within parent-child trios.

These differences are suspected to stem from inter-individual variability, since each of the individual's sperm cells had similar mutation levels, pointing to stable de novo mutation rates within an individual at a given time or age.

On the genome stability side, the team found that around 7 percent of the sperm cells tested showed some signs of genome instability, including some sperm cells that were missing complete or partial chromosomes.

Beyond its potential as a method for understanding basic biological processes, those involved in the study say the single-cell analysis strategy may prove useful in a clinical setting — for instance, to help diagnose infertility or screen cells during in vitro fertilization treatment.

Though sperm cells assessed in this manner do not survive, Wang explained, testing representative sperm cells from an individual can offer information about his overall fertility.

"There's no way that you could preserve the same sperm for IVF," he said. "However, if you sampled a few single cells from a sperm pool from a man with infertility, you could see whether those cells have a normal recombination level … or whether the single cells have a high incidence of large-scale genome instability — these, too, can be a potential reason for the infertility."

In addition, because egg cells are accompanied by polar bodies that share their genetic sequence, Wang noted, it might eventually be possible to indirectly look at the genomic profile of female sex cells used in IVF by doing single cell analyses on polar bodies. At this point, though, that is not something the team has attempted.

Going forward, the researchers will likely do single cell studies involving more sperm cells, Wang said. He noted that they are also interested in using a similar strategy to study other cell types, such as stem cells or circulating tumor cells.

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