Exploring Single-Cell Sequencing

Single cell (SC) transcriptomics is a technique developed to examine the gene expression on an individual cell level. Ever since the 90s, researchers have been developing methods to look at the gene expression level of individual cells, starting from SC qPCR, and finally achieving the first well documented SC transcriptomic method observing millions of cells in 2009 (1).   

The initial step of SC transcriptomics starts off with isolating the cells from their native environment via standard techniques of either mechanical isolation or enzymatic digestion. Frequently, either through a process of fluorescence activated cell sorting (FACS) or magnetic bead separation, dead cells are removed from the cell suspension to ensure a cleaner sample.

From there, the cells need to be isolated, for which there are several techniques available. Microfluidic technologies or microwells are frequently utilised for this process. Microfluidic technologies are based upon the interactions between antigens and antibodies. With the selection of a cell-type specific antibody, the cells displaying the corresponding antigen on their cell surface can bind to the chip, immobilising them on the chip’s surface. It is then possible to specifically elute them for further downstream analysis (2). Microplates work on a similar principle, but instead of cells’ antigens binding to antibodies, microplates are loaded with barcoded suspension beads that bind with high specificity to the particular cell type they are designed to detect (3).

Once the cells are compartmentalised via a combination of the methods highlighted above, they are lysed and prepared for sequencing by being tagged with a barcode system. This allows researchers to identify from which cell population the genomic information originates. After that, the throughput sequencing is conducted, and the raw data analysed.

As oppose to traditional transcriptomic methods, SC transcriptomics is incredibly useful for unravelling heterogenous cell populations and examining cellular developmental trajectories. For example, SC transcriptomics can provide insight whether there is a shift of gene expression from one cell population to another under different types of conditions, such as disease or drug treatment. With bulk analysis, gene expression is average across the sample, cancelling out the heterogeneity occurring within the sample.

Recently, a study that used SC sequencing explored the diversity of cells within the developing human neural tube (4). One aspect of their experiments focused on comparing the gene expression of the motor neurons within their samples with the stages of differentiation of stem cell derived motor neurons in vitro. This revealed a high level of correlation between the two early time points of human neural tube development, and  early stem cell differentiation. However, later time points of the human motor neurons revealed a reduction in similarity to their stem cell equivalents. This result was predominantly associated with the asynchronous differentiation of stem cells at later time points. The SC sequencing also aided with the classification of PNS cells, as the data allowed the researchers to follow the differentiation dynamics of the neural crest cells into PNS neurons during the different developmental stages. This revealed a delay in the neurogenesis of the peripheral nervous system (PNS) in comparison to the central nervous system (CNS). This led to the conclusion that the diversification of the PNS occurs at later developmental stages than CNS.  This study has provided data of cell type classification and gene expression that shall be a crucial tool to explore the sensory and motor control systems further.

Citations:

1.        Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009 65. 2009 Apr 6;6(5):377–82.

2.        Hu P, Zhang W, Xin H, Deng G. Single Cell Isolation and Analysis. Front Cell Dev Biol. 2016 Oct 25;0(OCT):116.

3.        Yuan J, Sims PA. An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq. Sci Reports 2016 61. 2016 Sep 27;6(1):1–10.

4.        Rayon T, Maizels RJ, Barrington C, Briscoe J. Single-cell transcriptome profiling of the human developing spinal cord reveals a conserved genetic programme with human-specific features. Development. 2021 Aug 1;148(15).