In addition, there is a need to develop automated single cell isolation and genome amplification technologies. Existing technology can process hundreds of cells in the order of magnitude. We can use a commercial cell sorter to complete cell sorting, or use a mechanical hand to complete cell lysis and nucleic acid amplification reactions, or use microfluidic equipment. Complete the above set of operations automatically. Automation and miniaturization are the future development direction of single-cell sequencers. This is because only enough samples can be analyzed to fully understand the genetic diversity in the samples. We hope that chip technology, microfluidic technology, and microfabricated approach will have innovative development. This will greatly increase the throughput of the treatment, while also greatly reducing the test cost and simplifying the reaction steps, so that single-cell analysis of tens of thousands of cells can be performed in one experiment. We believe this is only a matter of time.
The single cell genome analysis technology is actually the result of the joint development of multiple technologies, and involves many basic fields in the field of life sciences, which will help us solve many major problems in the field of life sciences. We hope that with the continuous development and diversification of nucleic acid amplification technology and reaction types, the influence of single cell sequencing technology can be further expanded and applied to more fields to help us better understand and understand the entire life system.
4. The impact of single-cell sequencing on biology and medicine
Recent technological advances have made single-cell RNA sequencing possible. Exploratory research has given us insight into the dynamic process of differentiation, the response of cells to various stimuli, and the random nature of transcription. We are entering an era of single-cell transcriptomics, and this research direction will have a profound impact on biology and medicine.
The transcriptome we now refer to is mainly derived from population-level observations that have become mainstream in biological research in the past two decades. We have always been accustomed to such a research idea, which is to compare the gene expression multiplication (obvious or subtle) in the overall organization or under certain conditions, but the actual differences between cells may be more obvious. Some cells may produce very obvious changes, but others are “indifferent”, so that even if the part of the changed cells changes even more, it will be masked by the “silent majority” cells. In fact, as early as 60 years ago, it was discovered that stimulating a single cell will produce two completely different results, but if you study a large group of cells, you will get a progressive and quantifiable result.
Obviously, the detection and analysis of the gene expression of single cells is very helpful for us to understand the behavior of cells and to identify which cells are involved in the process of tissue development, maturation and disease. To achieve this goal, long-term transcriptomics research on individual cells is required. But the experimental technology has only recently developed to the level of RNA sequencing of single cells, and scientists have been able to use this technology to understand the meaningful differences in gene expression of single cells. There are also very detailed experimental guides to help researchers build sequencing libraries, and commercialized single-cell automated preparation systems such as FluidigmC1 have also greatly reduced the barriers for researchers to get involved in this field. The wide application of single-cell experimental operation techniques will have a profound impact on us, and will also help us to deepen our understanding of the state of cells, the nature of transcription and the regulation of gene expression, and even the pathological processes of diseases.
Single-cell transcriptome research mainly relies on reverse transcription. First, the RNA to be studied is reverse transcribed into cDNA, and then amplified by PCR reaction or in vitro transcription reaction, and finally the amplified product is deeply sequenced. However, the amplification reaction is very error-prone and easy to lose information. This is because the RNA contained in a single cell is very small, so it is necessary to amplify these trace amounts of nucleic acid, so that this amplification reaction produces a lot of deviations. Although technical noise will interfere with the high-resolution sequencing of low-abundance RNA molecules by researchers, the current improved experimental procedures have allowed us to obtain enough single-cell transcriptome information. For example, in the study of single-cell transcriptomics, there is a question that is repeatedly mentioned, that is, how to accurately and repeatably classify cells according to the type or state of the cells without classifying the cells. Gene expression patterns related to cell types or developmental stages are a relatively reliable basis for judgment, far more reliable than physiological variables or technical noise related to dynamic processes such as the cell cycle. In addition, some people have studied the expression differences of hundreds of genes in different cells, confirming that this single-cell research technology can indeed find meaningful information. More recent in-depth research work will further improve the signal-to-noise ratio of single-cell sequencing studies, because we will further increase the efficiency of reverse transcription and PCR reactions, and molecular barcoding strategies can also be used to control deviations that occur in nucleic acid amplification reactions.
4.2 Challenges in single cell transcriptomics research
Researchers have developed existing single-cell RNA sequencing technologies for several different purposes. For example, the full-length transcript sequence can be sequenced so that we can understand the sequence information of the entire gene and various transcript isoforms, which is also conducive to our discovery and monitoring of single-nucleotide polymorphisms and other mutations. Relying mainly on tags, the strategy of sequencing only the 5 ‘or 3′ end of the transcript can provide us with information related to the abundance of the transcript at the expense of full-length sequence information, which is conducive to large-scale quantitative research.
However, the entire single-cell sequencing community is all pursuing the same goal, which is to use an economical, high-throughput technology to sequence all the RNA in the cell. Among them, how to reduce the loss rate of RNA and increase the efficiency of reverse transcription of RNA into cDNA before performing nucleic acid amplification is a technical difficulty that requires key breakthroughs, and is also the key to improving the success rate of RNA detection. Another equally important technique is how to separate, and sort single cells, and to separate individual cell samples from the entire tissue without any disturbance to the gene expression of the cells. In addition, researchers also hope to simultaneously detect poly (A) + RNA and poly (A) –RNA, as well as various RNA modifications (such as m6A), regardless of the length of the transcript.
We have now found that in single-cell sequencing research work, the cell transcription process has a major feature that will bring great trouble to our research, that is, the cell gene expression rules we found in the research work on cell populations The level of single cells is not reliable at all. Any random disturbance may make the gene not expressed in some cells, or the expression level is very low, but it may also become very high. This variability may be because the gene expression in the cell is a random molecular process, so in a single cell, the transcription of the gene is a probability event of all or nothing. Scientists have conducted a lot of research on prokaryotes and single-cell eukaryotes, and have a very deep understanding and understanding of the random nature of gene transcription. Now more and more evidence shows that It’s the same. Therefore, we also need to pay attention to this point when conducting single-cell transcriptomics research. For example, the standard differential expression test may not be suitable for single-cell research, because among the cells studied, there may be some cells that do not have corresponding gene expression. Now there are experimental strategies suitable for this kind of research work, which can be combined with differences in transcript abundance and cell gene expression ratio to observe.
To be continued in Part VIII…