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Another area involved in single-cell genome research is the study of various human recombination patterns. The so-called recombination refers to the process in which two large chromosomal fragments inherited from the paternal and maternal lines in sperm cells and egg cells are broken, and then connected to each other to form a completely new genome. We know that the probability of recombination in the entire genome is not exactly the same, that is, there are so-called "recombination hot spots", and the probability of recombination at these locations is higher than in other regions of the genome. One of the earliest results of the single-cell genome analysis work is that these recombination hotspots will be different between different individuals. These hotspots are indeed hotspots for some people, but not for others. Recently, single cell research techniques have been used to analyze whole genome recombination patterns as well as the mutation rate of single sperm cells, etc., there is also the first genome-wide hot-spot behavior study for different individuals in the world. We hope that future single sperm cell genome research can also involve recombination mutants, such as research on individuals carrying rare PRDM9 alleles; and those related to sterility and infertility that can be used for research on clinically diagnosed meiotic dysfunction.

3.3 Somatic mutation research

More and more people are beginning to realize the significance and value of individual genome sequencing, but the current individual genome sequence refers to the "average" sequence of all cell genomes in the human body. Scientists have discovered for decades that there are genomic differences between certain (species) cells of the human body. For example, B lymphocytes belonging to our immune system are a good example. Each B cell expresses a specific antibody strictly, and the genes in these B cell genomes will never be reprogrammed. As already mentioned before, germ cells also undergo differentiation and differences in the process of meiosis and genetic recombination. In the process of continuous cell division and the transfer of mobile genetic elements, various mutations will slowly accumulate. These mutations have very important significance, but we’re still not particularly clear about this.

These accumulating mutations have a very close relationship with aging, especially tumors, so the two research fields of aging and tumors will definitely be the stage where single-cell genome analysis technology will show its talents. So far, researchers have used single-cell research techniques to study human sperm cells and immortalized cell line cells. They directly detected the de novo mutation rate of these cells. Others have used these techniques to test hematopoietic stem cells to determine the degree of mutation of these hematopoietic stem cells and determine whether the degree of mutation after normal hematopoietic stem cells are transformed into acute myelogenous leukemia tumor cells. It is used this to understand the evolution of these leukemia tumor cells and determine the clonal structure of breast cancer cells.

Mosaic mutations also exist in adult neural tissues. These mutations are related to neurodegenerative diseases such as Alzheimer's disease. Recently, some researchers have used single-cell MDA and other genome analysis techniques to find a large number (up to MB level) in nerve cells differentiated from induced pluripotent stem cells and postmortem brain cells obtained from autopsy. Others have used single-cell MDA technology and PCR-based genome-wide amplification technology to find that L1 retrotransposition is a potential factor that promotes somatic chimera mutations in brain cells. Less than one-third of the mutations in brain cells can also induce serious diseases, such as hemimegaloencephaly. Fluorescence in situ hybridization (FISH) has also been used to study the relationship between aneuploid neurons in the mouse brain and mouse aging. This is a fascinating field of research. There is various evidence that chimeric somatic mutations are related to body development and also have certain functions. These mutations can be found in normal mature neural tissue. This may be why differences between "normal" neurophenotypes can lead to neurological diseases. These differences may also be related to psychological diseases, and mutations will increase with age.

3.4 When single cell sequencing is required

When will it be cost-effective to enter the single-cell sequencing project? The tumor genome is a highly heterogeneous nucleic acid, and the mutation rate is very fast, so single-cell sequencing of tumor tissue is the most suitable. Although large-scale sequencing of tumor tissue does not allow researchers to clearly understand the subcloning composition of tumor tissue, but if we use single-cell sequencing technology, we can obtain more detailed information to determine the presence of nucleic acid sequences in the genome Genomic loci in highly heterogeneous situations. This staged technology greatly reduces the cost of sequencing, thus increasing the number of cells and sequencing times that can be used for single cell sequencing when sequencing a certain tumor tissue.

Although we are still not sure whether it is economically cost-effective to sequence a large number of single-cell whole genomes of a tumor tissue, but to analyze important parts of the genome, or use shallow sequencing performing low-resolution sequencing to understand the variation of gene copy number in the cell can also get the same result. In fact, Bridges did this when he conducted the fruit fly genome research 80 years ago. There is another way to replace this phased strategy, and only one step is needed, which is to conduct whole exome sequencing, so that on the one hand, we can understand the "overall" exome of tumor tissue. In addition, the composition of subclones within tumor tissue can also be found, and the cost is much more economical than whole-tumor sequencing.

3.5 Pre-implantation sequencing

Single-cell sequencing is sometimes the only means by which we find rare or unique cells. Preimplantation genetic diagnosis (PGD) is a technique commonly used by couples who are assisted by artificial assisted reproductive technology such as in vitro fertilization. Before the embryo is implanted into the mother, doctors will cultivate it from outside the body by extracting a cell from its embryo and perform genomic analysis. However, a meta-analysis of previous clinical trials found that PGD is not an effective means of screening for genetic diseases, because in randomized controlled experiments, many more advanced technologies found that the success rate is higher, and the chance of having a baby will almost double. Whole genome analysis methods such as array comparative genomic hybridization can be used to detect the genome of the embryo at a higher resolution before implantation. We hope that these higher-resolution genomic analysis techniques can be applied to PGD clinical practice work as soon as possible, and can detect structural abnormalities and even point mutations in embryos. The data obtained can help clinicians make more detailed judgments to understand which embryos are healthier and can give birth to a healthy and cute baby.

3.6 The future of single cell technology

The cost of sequencing will definitely continue to drop. In the past ten years, many biochemical DNA amplification technologies have also been born, and now a variety of single cell test methods have emerged. However, there is currently no nucleic acid amplification technology that is an absolute winner. If such a technology really appears, it will be a big surprise for everyone. But it is difficult to say which nucleic acid amplification technology is the best technology, because there are many parameters to consider. Especially the following points, such as sample type, reaction method, convenience (constant temperature reaction or temperature change reaction, one-step method or multi-step method), cost (commercialized or self-made), reliability (off-target situation, contamination product expansion Increase, uniformity and error during amplification, coverage of amplification technology, error rate, and artificial errors such as fitting) and final yield.

In addition, in comparing these different amplification technologies, be sure to use single-cell samples for statistically relevant samples for evaluation, and you should try to avoid the effects of reaction volume, reaction method, lysis conditions, pollution, sample specific differences and random differences between cells. Therefore, only by comparing these factors can we find the best amplification technology.

To be continued in Part VII…

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