Single Cell Biology: A Step Toward Precision Diagnostics
January 21st, 2016
Posted by Shane Climie, Ph.D.
The past several years have seen a dramatic increase in the ability to isolate and characterize single cells – leading to advances in diagnostics, drug discovery, stem cell biology, cancer, and many other areas of biomedical research.
These advances have arisen thanks to growing capabilities in various single cell “omics” technologies – which have enabled RNA and DNA sequencing on a genome-wide scale (the interrogation of proteins, metabolites, and many other types of molecules that provide information about cellular growth, differentiation, and the underlying molecular basis of disease).
And why has single cell technology become so attractive within the biomedical research community? Because most studies are currently hampered by sample heterogeneity.
Why pooled cell samples aren’t enough
Most biological samples – including cancers, immune cells, neurons, and most types of tissues – are made up of multiple cell types. Although the analysis of pooled cells from such samples can provide a useful profile of the original population of cells, subtle but important differences can be hidden – typically as a result of averaged signals that arise from cell to cell variability within mixtures of cells. Even within seemingly homogenous populations of cells, significant cell to cell variation can exist. This may reflect cells at different stages of the cell cycle, or many other factors that could affect the biology within a given cell.
As such, a typical human genome is really an average of cellular genomes from the individual cells that comprise the original sample – all of which are somewhat variable. Such variability may be due to genetic rearrangements within various loci (eg – HLA genes, which are important to understand for bone marrow transplants), the presence of somatic mutations, or various types of mosaicism. The development of cancer is often driven by the acquisition of somatic mutations within individual cells, which may activate oncogenes or cause cancer by other means.
Such mutations may not readily be detectable unless DNA sequencing is done – either at great depth or at the level of the single cell. Metaphorically speaking, the former can reveal the presence of a needle, but the latter can also show where that needle lies within the haystack.
Thus biological context is crucial, and therefore the ability to interrogate genes from individual cells within a tumor (or even to localize RNA or other molecules to specific locations within an individual cell), can provide important information to support diagnosis and treatment.
In particular, the isolation and characterization of circulating tumor cells (CTCs), fetal cells in maternal blood, and other rare cell types is providing a foundation for better diagnostics. The characterization of CTCs can provide information about the mechanism of carcinogenesis, cellular transformation, cell lineage, and response to therapy – all delivered in a tidy cellular package. Single cell analysis of circulating fetal cells is transforming the field of prenatal testing by providing a safe, non-invasive, and accurate way to detect chromosomal abnormalities or other mutations that may result in various disease states. In fact, the ability to better diagnose and treat cancer or to effect prenatal detection of genetic defects is even changing the way in which diagnostic tests are being developed.
The road ahead
The single-cell toolbox now includes a number of powerful technologies – including methods to isolate individual cells, methods to support DNA and RNA sequencing, expression profiling, epigenetic analysis, proteomics, and the detection of many other types of biologically important molecules. As these tools become more widely available, biomedical researchers will be able to build better and better diagnostic tests, and they will be armed with the information needed to influence the selection of drugs, monitor the progression of disease, and understand the molecular basis of disease, even as it evolves over time.
No doubt our fundamental classification of disease will change, and large disease states will undergo what business people might call, segmentation and sub-segmentation.
The coming years will likely see an acceleration in the development and use of tools for single-cell analysis. We will also see a further convergence of advanced technologies – including informatics, imaging, molecular analysis, and modelling. All of this will create opportunities to develop better products and services and is cause for optimism about our future ability to more precisely diagnose and treat disease.
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