A technique for genome sequencing at the single-cell level has achieved the highest resolution ever in such studies, identifying gains or loss of single-copy DNA as small as 1 million base pairs. This result, obtained from studies of single E. coli cells and individual neurons from the human brain, compares well to the most complete previously published single E. coli genome dataset. Fifty percent more of the E. coli genome was recovered with 3- to 13-fold less sequencing data.
To achieve this result, researchers found a new way to reduce amplification bias, the most significant technological obstacle facing single-cell genomics in the past decade. Ordinarily, amplification bias—unevenness in the amplification of the one or two copies of each chromosome in a single cell—leads to difficulties in assembling microbial genomes de novo and inaccurate identification of copy number variants (CNV) or heterozygous single-nucleotide changes in single mammalian cells.
Such difficulties are all too familiar with multiple displacement amplification (MDA), the most commonly used method for amplifying DNA from single cells. Another method, however, has been introduced by researchers led by bioengineers at the University of California, San Diego (UCSD). These researchers call their approach the microwell displacement amplification system (MIDAS). It is a massively parallel polymerase cloning method in which single cells are randomly distributed into hundreds to thousands of nanoliter-sized wells. Then, the genetic material from these cells is simultaneously amplified for shotgun sequencing.
The details of this approach were described November 10 in Nature Biotechnology, in a paper entitled “Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells.” One of the paper’s authors, Jeff Gole, Ph.D., said that by reducing amplification reaction volumes 1,000-fold to nanoliter levels in thousands of microwells, the researchers “increased the effective concentration of the template genome, leading to improved amplification uniformity and reduced DNA contamination.” (Gole, who worked on the MIDAS project while he was a Ph.D. student at UCSD, is now a scientist at Good Start Genetics.)
The MIDAS technique should interest researchers from a variety of disciplines eager to sequence the genomes of single cells. For example, some researchers hope to identify and understand a wide range of organisms that cannot be easily grown in the lab. Such organisms include bacteria that live within our digestive tracts and on our skin, as well as microscopic organisms that live in ocean water. With MIDAS, such organisms could be studied more efficiently, with researchers better able to assemble whole bacterial genomes from single cells without culture.
Single-cell genetic studies are also being used to study cancer cells, stem cells, and the human brain, which is made up of cells that increasingly appear to have significant genomic diversity. “We now have the wonderful opportunity to take a higher-resolution look at genomes within single cells, extending our understanding of genomic mosaicism within the brain to the level of DNA sequence, which here revealed new somatic changes to the neuronal genome. This could provide new insights into the normal as well as abnormal brain, such as occurs in Alzheimer’s and Parkinson’s disease or schizophrenia,” said Jerold Chun, a co-author and professor in the Dorris Neuroscience Center at The Scripps Research Institute.
In their paper, the authors report that in addition to demonstrating substantial improvement in de novo genome assembly from single microbial cells, they also showed they could detect small somatic copy number variations in individual human adult neurons with minimal sequencing effort. Citing their results with adult neurons, the authors said that MIDAS allowed them to detect single-copy number changes at 1- to 2-Mb resolution.
“Our preliminary data suggest that individual neurons from the same brain have different genetic compositions. This is a relatively new idea, and our approach will enable researchers to look at genomic differences between single cells with much finer detail,” said Kun Zhang, a professor at UCSD and the corresponding author on the paper.