10 November 2017
Single cell analysis (SCA) is the application of ‘omics technologies - genomics, proteomic and transcriptomics - to an individual cell, as opposed to whole populations of cells or tissue samples. Conventional molecular assays in research or medicine tend to work at the population level.
Single cell analysis - hot or not? View the infographic
For example, a cheek swab will allow collection of a population of cells from which DNA is extracted for analysis; consequently the DNA is an amalgamation of DNA from all the cells in the sample. In single cell analysis, special techniques are used to isolate individual cells before DNA, RNA or proteins are extracted for analysis. The material analysed is therefore specific to an individual cell.
No two human cells (even the same cell types from the same individual) are exactly alike. There are subtle differences in intrinsic cellular factors (such as which genes are active or silent at a given point in time), and responses to external factors such as environmental challenges. This variability can influence individual cell fate, which in turn can impact on disease development and progression. The classic example is cancer, where a single cell can evolve into a cancerous cell and lead to the formation of a malignant tumour, itself composed of a heterogeneous (non-identical) population of cells.
As most conventional molecular assays in research or medicine tend to be conducted on a population of cells, these methods do not have capacity to identify rare cell types or individual cell-specific biomarkers linked to disease. SCA can potentially allow:
Research is burgeoning in this area, and single cell analysis has been applied in a research context to fields such as cancer, reproductive health, immunology, neurology, drug discovery, stem cells and metagenomics. The main focus of activity is single cell sequencing, especially in relation to cancer. Although currently there are no applications ready for use in healthcare, promising areas of interest are:
Pre-implantation genetic diagnosis (PGD): Single cell genomics could widen the scope of genomic analysis undertaken in the context of IVF, allowing diagnosis of specific genetic disorders and enabling a standard approach for detection of a number of types of genomic variation.
Cancer: SCA has been used to gain a better understanding of clonal evolution and diversity in primary tumours. The focus has now shifted to applying SCA to circulating tumour cells.
If achievable, this along with examination of ctDNA could have important implications for disease monitoring and examining the impact of treatments.
Metagenomics: The ability to identify rare microbial populations that are not easily grown, or to follow microbial evolution over time to identify emergence of disease resistance is another valuable clinical application of SCA.
Despite its increasing use in research there are technical challenges which, at least for now, limit the clinical application of SCA.
Despite its increasing use in research there are technical challenges which, at least for now, limit the clinical application of SCA. Single cell analysis is a complex process requiring the careful isolation of individual cells from a larger, more heterogeneous population. In the case of single cell genomics, the next steps are extraction and amplification of DNA from the isolated cell, followed by sequencing and analysis of the material. Although the process is similar to sequencing the genome from ‘conventional’ samples, errors are more likely with SCA because:
The growing body of research deploying SCA is enabling more detailed analysis of the molecular pathways underpinning disease. However, further technical and scientific progress is needed before this technology is more routinely applied and ready for clinical purposes. In addition to addressing the challenges of isolating single cells and developing high-throughput technologies and robust workflows, there are two other areas which will significantly affect the pace of clinical development:
Computation methods to analyse raw data: This is a major challenge - SCA cannot be undertaken using conventional methods as each cell is measured once, meaning there are no replicates. Replicates of any given analysis, increase confidence in the findings by showing the result is robust. In addition, the wide variation in the amount of starting material that is analysed makes statistical interpretation difficult.
Bridging the knowledge gap: We also need better scientific knowledge in relation to single cell biology and its relationship to disease. This will help identify in which situations the use of this technique is warranted and how it can help inform the care of individuals.
Whilst SCA is currently very much in the research arena, as these technical hurdles are overcome, the more precise information it can provide is likely to have a role in enabling personalised healthcare.