From early scientific research, we have known that every cell in the body has exactly the same genetic information. Therefore, the diversity of cells in the body comes from gene expression, and each cell must express one set of genes and inhibit another set of genes to make it work properly. But it is difficult for us to pinpoint which gene (or set of genes) is essential for each cell. This is not only because of technical difficulties, but also because cells are constantly changing and adapting. Therefore, gene expression is like Schrodinger’s cat, which is difficult to grasp, but very interesting. This is the source of single-cell RNA sequencing.
In this era of transcriptome and genome sequencing, we have some good techniques to really observe what is going on. RNA sequencing is one of these great technologies. By converting RNA into cDNA, we can quantify, discover and configure RNA. Although RNA sequencing brings us deep insights, it is not without prejudice. Most RNA sequencing is performed on tissue samples or cell populations. Biological differences between cells may be mistaken for technical noise, or they may be obscured by average data.
However, single-cell RNA sequencing (scRNA-seq) goes a step further. It means taking a static picture of all gene expressions occurring in a cell at the same time. In theory, it allows us to distinguish the expression of different cells in the same tissue, which is amazing.
How does single-cell RNA sequencing work?
Isolation of Single Cells Single-cell RNA sequencing starts with isolated cells. To obtain the transcriptome of a single cell, the key step is to separate the single cell from the cell population. Cells can be separated from separated cell suspensions or tissue samples. There are many methods that can be used to separate cells, such as flow-activated cell sorting, micromanipulation, optical tweezers, microfluidics, or other emerging separation techniques. However, attention should be paid to whether the separation method is compatible with downstream applications.
If the target cells are already in suspension (such as circulating tumor cells) and the content is relatively abundant, then flow sorting will be the ideal choice. If the sample is a solid tissue, enzymes can be used to break down collagen and other extracellular proteins. However, enzymatic digestion has a greater impact on cells and may even change gene transcription. After preparing the tissue cells into a suspension, you can separate the cells you want through specific fluorescent labels. Further obtaining single cells is a tricky step, perhaps with the help of microfluidic equipment.
After scRNA-seq gets the cells, we must isolate the RNA. This will let us know which genes are expressed at a particular moment in that cell. This technology uses reverse transcriptase to reverse transcribe RNA into cDNA, then PCR amplifies the cDNA, and next-generation sequencing technology is used to sequence the amplified cDNA. Therefore, we can obtain a large amount of data. Then, the original data must be processed and analyzed through a workflow designed specifically for scRNA-seq data. A large amount of data without proper processing is meaningless. Data processing is currently a more difficult step in single-cell sequencing.
About Us
CD Genomics aims at providing the research community with high quality Next Generation Sequencing, PacBio SMRT sequencing, and microarray services. Due to the demand for our services has been increased; CD Genomics has already updated its technology platform to mainstream NGS and microarray instruments. Here are some of our solutions: human genome sequence, genotyping by sequencing, genomic data analysis, fungal genome sequencing, etc.