The advent of single-cell genomics ushered in a new era for studying cells, leading to high-fidelity glimpses into how they maintain different functions, phenotypes, and states in health and in disease. Single-cell transcriptomics, as the name suggests, is a measurement of all the RNA transcripts expressed in a single cell. The unparalleled resolution and clarity resulting from this technology have made it possible to approach hundreds of biological questions that were previously impenetrable.
Before single-cell transcriptomics, gene expression studies were limited to bulk RNA-sequencing (RNA-seq) whereby data from all cells were aggregated indiscriminately, making it difficult to trace molecular patterns to specific cells of interest. The popular analogy for bulk RNA-seq versus single-cell RNA-seq is that the former is like a smoothie and the latter is like a fruit salad. In the smoothie of bulk RNA-seq, it is impossible to separate the individual fruits (cell types) while in the single-cell analogy, each fruit (cell) is identifiable and intact.
Consider the following practical example that depicts the differences between single-cell and bulk RNA-seq.
A researcher is interested in characterizing cellular changes in glioblastoma, a form of brain cancer in which tumors grow in glial cells rather than neurons. The brain is harvested from a mouse model for glioblastoma and is subsequently dissociated and processed for bulk RNA-seq.
The resulting data from this experiment will contain the merged profiles from all cell types. This means that the results cannot be used to make conclusions about the diseased cell type (glia) because it reflects the average gene expression patterns across all cell types in the brain (neurons, glia, and others). It is important to note that this aggregated data is likely to represent the cell type that is most abundant in the sample. On the other hand, if the researcher opts for single-cell RNA-seq instead, the data will reflect gene expression changes in every cell type present in the brain. This would not only allow for accurate results on the molecular changes in neurons versus glia and other cells in glioblastoma, but it would also provide multi-faceted and deep insight into the disease process across different cell types. Furthermore, every cell type will be represented, whether there are only 1,000 or 10,000 of them.
It is important to note that while it is possible to obtain transcriptomic data from specific cell types using cell sorting to enrich for the cell type of interest followed by bulk RNA-seq, these protocols are labor- and time-intensive and are not suitable for characterizing cell types in complex samples or new tissues of interest. This approach carries limitations due to the need for specific antibodies that can isolate the cell types, in addition to extensive optimization of sorting and other key steps. This is particularly difficult in the case of cell types that are present in low numbers.
With single-cell, it is possible to capture transcriptomes of hundreds of cell types in a given tissue in one experiment rather than carrying out the experiment hundreds of times to capture individual cell type profiles using bulk RNA-seq.
It is apparent that single-cell transcriptomics offers unique technical advantages that can expand the range and depth of feasible biological questions. Overall, single-cell RNA-seq can and has been consistently used to achieve the following major aims:
- Characterize cellular heterogeneity of a sample in vitro (organoids, patient-derived cells) or in vivo.
- Discover new cellular subtypes and characterize rare cell types across various tissues and species.
- Map out cell trajectories to identify key timepoints and processes in organ differentiation and development.
- Evaluate the impact of drugs on different cell subtypes and characterize mechanisms of action in depth.
- Identify or validate new biomarkers of pathological progression, response, or resistance to treatment.
These possibilities enable researchers and clinicians to significantly and rapidly enhance our understanding of cell composition in different organs in health and disease. In turn, this can potentially lead to the development of cell type-specific and targeted therapies that were unlikely to be identified without the ability to peer into individual cells to determine how each may have contributed to pathological processes. This is of particular significance in oncology research and tumor characterization, since tumors display a wide range of cellular heterogeneity that is associated with varying rates of response to treatment.
Our level of understanding of normal physiological processes is also crucial for our baseline knowledge across different biological systems and timepoints, from cells that are only present in development to cells that begin to die upon aging and cognitive decline. Moreover, it is now possible to identify and characterize rare cell types that were previously difficult to investigate in-depth due to low abundance and masking of their transcriptional signatures by dominant cell types in bulk RNA-sequencing. Amazingly, this has given rise to huge atlas projects whereby researchers aim to profile every cell type across human organs and at various points throughout life (from fetuses to elderly individuals).
Because it is an incredible technological feat but a relatively new one, single-cell RNA-seq remains out of the grasp of many scientists due to cost barriers. This is changing. Scipio bioscience’s Asteria kit is not only cost-effective, but also designed to be hassle-free, beginner-friendly, and equipment-free to boost accessibility and reach.