"If you had a population of men and women and assumed everyone in that population was an average between men and women you wouldn't represent a single person within that population"
Marc Kirschner, Marrying Microfluidics and Barcoding Technology 1
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To date most work in the genomics field has been at the level of a tissue or bulk population of cells. However, though this has resulted in huge advances in our understanding of genetics and genomics, we have not been able to find out until now what is happening at the cellular level.
When studying bulk cell populations or tissues, an average is drawn by pooling the DNA or RNA from thousands to millions of cells. The results from this can be strongly biased by a few individual cells, meaning findings may not be reflective of individual cells from within that population 2.
Even cells from the same organism or tissue will have different DNA sequences due to mutations and even cells with identical genomes have fluctuations in regulatory molecules and gene expression. This can result in significant deviations between cells within a population 3. With recent advances in high-throughput sequencing technology, there is now the opportunity to obtain information about the genomic and transcriptomic profiles of single cells.
Single-cell approaches provide higher-resolution views of the genomic content of samples by reducing the complexity of the genomic signal through the physical separation of cells or chromosomes.4
Convergence of “Omics” Biology and Single-Cell Biology 3
The major benefit of single cell genomics is that we are now able to analyse very small amounts of material. This opens many doors, including the study of:
- Unculturable microogranisms 3.
- Populations of rare cells.
- Individual cells within bulk populations - which will advance our understanding heterogeneity and development 5.
For genomic analysis of single cells to be successful, various technical and analytical challenges must be overcome.
From the technical standpoint; individual cells need to be effectively isolated, minute amounts of cellular material need to be amplified into libraries and sequenced. This can lead to the accumulation of non-specific by-products and errors during amplification 6.
From the analytical standpoint; alongside the same challenges faced when analysing all sequencing data there is the additional issue that for statistical significance, many individual cells from a sample must be sequenced (hundreds to thousands). This leads to vast amounts of data, which needs storing, and leads to questions about how to combine the analysis of all these samples efficiently without losing the individual resolution. It is still difficult to distinguish between biological variability and technical noise, which can result in the loss of information about individual cells 5.
Cells passing through a microfluidic system 1
High resolution and high throughput sequencing of single cell genomes has really only been viable for the last decade, and has only been available to the 'masses' in the last 5 years. Nevertheless, it is now a hugely popular and rapidly growing field of research. On Repositive, we are already indexing over 48,000 human single cell sequencing datasets.
Methods for studying single cells
Many researchers are trying to find out how organisms can have many different cell types with the even through they all have the same DNA. Differences in gene expression is one of the main drivers of this heterogeneity, and gene expression is best studied by RNA-Seq. Therefore the application of single cell transcriptome analysis has been widely adopted and is the main way in which researchers analyse single cells 3.
The 'popularity' of single cell RNA-Seq is supported by the data on the Repositive platform, where we have over 32,000 single cell RNA-Seq datasets indexed.
Whole genome sequencing
Even though RNA-Seq is the most popular method used to analyse cells at the single cell level, the application of whole genome sequencing (WGS) to single cells is becoming ever more prevalent. On Repositive, we have over 3000 single cell WGS datasets. The reason researchers want to apply WGS to single cells, is that (contrary to what I said above) all the cells within an organism don't have identical genomic DNA.
All cells within a human derive from one cell, the zygote. This cell then divides and differentiates to become a multi-cellular organism. Therefore, all the cells within an organism start with the same DNA, but the process of division results in mutations being introduced into the DNA of cells, which results in genetic variation between cells within the same organism. WGS of single cells allows researchers to study rare genetic variants, co‑occurrence of mutations and evolutionary history of samples 3.
In this rapidly advancing field more and more different and novel sequencing methods are now being applied to single cell analysis. On Repositive, we have 170 single cell bisulfite-seq datasets and over 300 single cell whole exome sequencing datasets. As technologies for isolation, amplification and analysis in this area advance it is likely that we will see more analysis of single cells using new and exciting methods.
Fluidigm was one of the first companies to develop complex microfluidics systems, and to apply this to the single cell research. Fluidgm is considered to be the leader in single cell analysis technology and techniques, which is supported by the fact that on Repositive there are over 14,000 datasets which have been processed using Fluidigm technologies.
A high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. 1
inDrop is a droplet-based, single-cell RNA-Seq method developed by researchers from Harvard Medical School, which was then commercialised in the launch of 1CellBio Inc. This technique takes the potential of single cell RNA-Seq to another level as it enables isolation and processing of large numbers of single cells.
On Repositive we have four single cell RNA-Seq samples processed using inDrop. These are from one SRA study on the cellular population structure the human pancreas. This study was performed in collaboration with original inventors of inDrop from Harvard 7.
In this ever advancing field academica and industry alike are searching for new and more advanced technologies to isolate, amplify and analyse single cell genomic data. Some more recent technologies which have huge potential, but currently are not being widely used are DropSeq, ICell8 and CellRaft.
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Cover image from Leung et al., Genome Biol 2015, 16:55
Search: ("single cell" NOT "single cell suspension") with (assay:RNA-Seq datasource:SRA fluidigm NOT single) with indrop