Until now, single cell suspensions (such as tissue dissociation solutions or cell culture suspensions) have been required as test samples when conducting single-cell transcriptome studies, but such samples cannot reflect the spatial organization information of cells in the tissue unless we know where in the tissue these cells come from. RNA in situ hybridization technology can partially reflect this spatial organization information and can understand the expression of specific genes in specific cells in the tissue. However, some people are now developing single-cell research technologies that can simultaneously understand spatial structure information and transcriptome information, such as array-based multiplexing strategy or in-situ sequencing. The emergence of such technologies will help us understand the situation of single-cell transcriptomes in developing, mature, or diseased tissues, allowing us to have a deeper understanding of life and disease, and discovering the interaction between transcriptomes and cells, The relationship between tissue polarity formation and local differences.
4.3 The relationship between single cell sequencing and biology
Studying gene expression in a single cell will completely subvert our understanding and understanding of gene expression regulation, and will also solve many biological problems that have plagued us for a long time. For example, whether the cells gather together is determined by the type of cell, or because it is determined by the expression profile of the cell during development. If it is determined based on the expression of cell genes, then after sequencing enough single cells, we can accurately reconstruct (this is also called reverse engineering technology) any cell. If the number of cells under study is sufficient, and the problem of experimental error has been completely resolved, then this study can discover all cell types in the tissue, including new cells that have not yet been discovered. Cells of the same population can also be used to discover gene expression profiles of specific cell types. At this time, it is also based on sequencing results, and also without knowing in advance which tissues or cells will express which marker gene. Therefore, single-cell RNA sequencing is a research method based on test results, which can quantitatively analyze cell types.
Single-cell transcriptomics research can also provide high-quality cellular transcriptome maps, not only for stable cell states, but also for complex and variable cell states, such as the state of cell differentiation or reprogramming. However, to achieve this kind of research purpose, it is necessary to perform sequencing studies on a sufficient number of single cells covering the entire stage, so as to focus on one of them in the post-mortem data analysis work (such as the beginning of different differentiation directions), get valuable research results. The sample size also reflects how many cell types we expect to get, or how many biological events will occur. Of course, this also depends on the extent of gene transcription in the human genome, because many studies have found that many genes in the human genome only have very little transcription, and only one copy of transcripts is found in an average of 10,000 cells. This transcript may be highly expressed in a small number of cells (such as an average of more than 10 copies in one cell in 100,000 cells), or it may maintain very low expression in a large number of cells level, the so-called leakage expression. Research on a large number of cells (thousands) may solve this problem, and also help us understand the overall transcription level in the cell and the gene expression regulatory network of the entire cell.
RNA sequencing analysis of human tissues and cells has proved that RNA research methods can be used for various transcriptomics and proteomics studies. During the tissue comparison, it was found that a large number of differences were very subtle, but it was found that the conditions of alternative splicing, polyadenylation, and the selection of transcription start sites were all at the single cell level. It is a full (on) or no (off) mode, which is also consistent with the previous single-cell research results. The study on the regulation mechanism of variable polyadenylation found that in the cells with relatively active proliferation and the transformed cells cultured in vitro, the 3 'non-coding region of the transcript is relatively short. Single-cell RNA sequencing technology is particularly suitable for the analysis of tumor cells in the body, because the analysis of a batch of transformed cells, mesenchymal cells and other infiltrating cells to extract transcripts separately can understand the abundance and subtype information of various transcripts Single-cell transcriptome analysis of discrete tumor and healthy tissues can also accurately determine different mRNA subtypes related to transformation status.
4.4 The significance of single cell sequencing for medicine
The significance of transcriptomics technology to medicine mainly focuses on the comparison of diseased tissues and corresponding healthy tissues, or a large number of diseased tissues can be analyzed to find the differences between them, that is, subtype identification. We mainly determine the tumor tissue by cell composition (such as infiltrating immune cells), and gene expression in transformed cells and surrounding mesenchymal cells. Therefore, several observations of different gene expression profiles are required for observation at the organizational level. High-throughput single-cell analysis of diseased tissue can simultaneously detect changes in cell composition (by means of cell clustering analysis) and corresponding changes in gene expression. We can compare specific cells in healthy tissues and diseased tissues and find specific gene expression changes related to disease. However, to understand the local changes in cell composition, it is necessary to conduct multi-point sampling studies in different parts of the same tumor tissue.
It is difficult to obtain precious clinical cell samples by tradition technique, but single cell transcriptomicsa can. For example, a very small number of circulating tumor cells (CTC) is a very good single cell research direction. There are often only a few CTC cells in a milliliter of blood, so the whole genome sequencing method for these cells is almost impossible. Two groundbreaking studies have confirmed that single-cell RNA sequencing technology can be used to determine whether CTC cells are melanoma cells or prostate cancer cells. Transcriptional profiling has also confirmed that there are no problems with cell separation steps, and specific gene expression profiling variations have also been discovered. Single-cell RNA sequencing of the full-length transcripts of CTC cells can detect mutations while successfully detecting gene expression. Transcriptomics analysis of individual CTC cells is also a non-invasive detection method, which can help clinicians to select appropriate anti-cancer drugs and treatment options, and can also monitor the progress and efficacy of the disease at any time. It is now time to judge the guiding significance of CTC transcriptomics research methods for cancer diagnosis and treatment. It is also possible to determine the future targeted treatment plan based on molecular markers on CTC cells.
4.5 Future development prospects of single cell sequencing technology
Since we have just entered the era of single-cell sequencing research, there will be many new discoveries in the near future. Whether there is a correspondence between RNA abundance and cell phenotype (such as cell size, nuclear size, etc.) is also an interesting research topic. For example, in order to maintain the protein concentration on the cell membrane of different size cells or the plasma membrane of organelles, do we need different abundance of RNA? The type of gene expression may be related to a specific region of the cell membrane or nuclear membrane, or may be related to the size of the cytoplasmic volume or the size of the nucleus. Only after understanding this information can we begin to study the problem of cell heterogeneity and the composition of cells at the tissue level. For example, comparing two tissues composed of cells of different sizes may reveal gene expression characteristics related to cell size. A more in-depth study of single-cell expression profiling may also lay a more scientific foundation for future experimental design, such as whether the next step should be carried out from the organizational level, the same cell, the single-cell level, or a combination of these three levels.
To be continued in Part IX…