Single-Cell RNA Sequencing

To understand a system, you need to understand its components. If you look at complex biological systems, like the immune system or a growing tumor, understanding the function and behavior of the different cells in these systems is a big challenge. With single-cell RNA sequencing, scientist now have a great and powerful tool to face this challenge. In contrast to traditional bulk RNA sequencing, where gene expression levels are averaged across cells, single-cell RNA sequencing allows you to look at the gene expression of thousands of individual cells. Single-cell RNA sequencing is the perfect technique for all applications, where understanding cellular diversity is essential, like studying tumor heterogeneity, cellular differentiation, or therapy resistance.

At CeGaT, we provide high throughput single-cell RNA sequencing using the state-of-the-art Chromium™ X system from 10x Genomics® in combination with the latest NGS technology. The sample processing workflow includes three main steps: (1) cell barcoding and library construction, (2) sequencing, and (3) data analysis. During cell barcoding, up to 10,000 single cells are encapsulated into tiny micro-droplets together with gel beads. These gel beads contain oligonucleotides with cell-specific barcodes and unique molecular identifiers. By labeling all mRNA molecules of a single cell with these two barcode sequences, their cellular origin is recorded, and each mRNA molecule can be uniquely quantified. The following library construction differs between our two products: While the 3’ SCR assay uses poly-(dT)-primers for reverse transcription, the SCR Flex assay is probe-based. 

After further library preparation steps, the samples are ready for sequencing. Sequencing is performed on one of our Illumina sequencers. We can adjust the sequencing output according to your needs depending on the number of input cells and the read depth required per cell. The generated sequencing data is then processed by our bioinformatic data analysis pipeline. The transcripts are quantified and assigned to the different cells. Cell clusters with similar gene expression patterns are identified, and differential gene expression analysis between cell clusters is performed. Thus, you can identify different cell populations or cell phenotypes in your sample and explore their function and behavior to fully understand your sample’s complexity.

Single-cell RNA sequencing, also known as scRNAseq, is a robust tool to detect rare cell populations in complex samples. It is well suited to determine cell type proportion shifts in time series experiments, where samples are collected over different time points. Single-cell RNA sequencing holds a great potential to discover new biomarkers and to analyze therapeutic mechanisms using differential gene expression or changes in cellular diversity on a cell-by-cell basis.