MedGenome Inc, Bioinformatics, Project Manager, Dr. Jing Wang
Single Cell Analysis – The Ultimate Solution to Reveal Significant Fluctuations
In 2013, single-cell sequencing was selected as the method of the year to emphasize the ability of individual cells to sequence DNA and RNA. The advantage of such high-resolution sequencing is that it reveals previously unknown cell population heterogeneity and enables more accurate analysis. The high degree of heterogeneity of tumor tissue is widely believed to be associated with the mechanism of tumor formation and metastasis. Traditional sequencing methods can only detect cell populations and then average the signals within a cell group. With the help of single cell sequencing and analysis, researchers can investigate heterogeneity between individual cells, detect rare cell types, study immune processes, and do much more. (Fig. 1).
Since then, single-cell technology has improved dramatically with the new next-generation sequencing platform. Commercially available single-cell kits such as inDrops, 10X Genomics, Drop-seq, and SMART-seq utilize two main methods to achieve single-cell resolution, droplet encapsulation, and plate-based separation. .. Single-cell applications such as transcriptome sequencing, TCR / BCR sequencing, ATAG-seq, and even spatial gene expression are increasingly on the market. The MedGenome US research group offers different levels of analysis for single-cell applications. You can leverage centralized computing resources or AWS to perform standard cell ranger analysis. Analysis results include the transcriptome sequencing gene expression matrix, TCR / BCR sequencing chronotypes, and more. Further downstream analysis, such as cell type assignment and clonal growth studies, is also provided in a sophisticated pipeline developed in-house.
In addition to these individual applications, multi-model analysis is another major trend in the single-cell space. In 2019, single-cell multi-model omics was chosen as the method of the year. This is the ability to get the most out of your samples and measure multiple data types from the same cell at the same time. Common multi-model applications include antibody-tagged CITE-seq, transcriptome sequences and TCR / BCR overlays, transcriptome sequences and ATAG-seq overlays. Antibody-tagged CITE-seq helps researchers to quantitatively understand protein-level expression as well as transcriptome expression. You can also use it to tag multiple samples together in one pool and build one library. This makes it more cost effective. By overlaying transcriptome sequencing with the TCR / BCR, researchers can study the distribution of chronotypes across different cell types and track the abundance of specific chronotypes. By overlaying transcriptome sequencing with the epigenome ATAG-seq, researchers can gain deeper insights into gene regulation mechanisms. The MedGenome US research group offers customized multi-model analysis consisting of custom pipeline development, publishable visualizations, and knowledge-based data interpretation.
More and more bioinformatics tools are being developed to meet the needs of complex single cell analysis. Tumor and normal single-cell transcriptome datasets can be used to understand CNV using tools such as InferCNV. Complex cell communications can also be characterized using tools such as CellPhoneDB that predict cell communication pairs. We believe that the help and unique advantages of single-cell technology will make research in various biological fields easier and more accurate.
- Tang et al. , Single Cell Sequencing: New developments and medical applications. Cell Biosci 9, 53 (2019). https://doi.org/10.1186/s13578-019-0314-y