To pick apart biology in new ways, scientists can sequence the RNA from single cells. The complexity of this technique depends on the sample, because some are more difficult to work with than others. Plus, understanding the data poses an equally complex challenge. Scientists address these challenges in many ways to make the most of single-cell RNA sequencing (scRNA-seq), which has a myriad of benefits.
“You can see things that you’d otherwise miss,” says Miles Wilkinson, distinguished professor of obstetrics, gynecology and reproductive sciences at the University of California, San Diego, when asked about some of the benefits of scRNA-seq. “You can see things that you never knew occurred!”
When asked about the reasons to use scRNA-seq, Q. Lee, chief technology officer at Invent Biotechnologies, says, “The major application is to dissect the heterogeneity of a tissue or organ in different development stages.” He adds that this technique “can also be used for elucidating the pathogenicity of tumor development and plays an important role for diagnosis and targeted therapy in precision medicine.”
The good and the bad
One of scRNA-seq’s key strengths is seeing into the unknown. That ‘vision,’ though, faces some flaws, including a lack of sensitivity.
With conventional RNA-seq, a scientist typically works with bigger samples, composed of many cells. Thus, conventional RNA-seq typically provides far more sensitivity than scRNA-seq. That’s not much of a surprise when working with lots of RNA versus much less.
When applying RNA-seq to a bulk sample of cells, a scientist can “detect very low levels of gene expression, as well as very small changes in gene expression in response to what is being studied, such as treatment with chemotherapy or knockout of a gene,” Wilkinson says. “In contrast, single-cell RNA-seq typically only recognizes a small portion of the expressed genes in the genome.” He adds, “It typically cannot detect small changes in gene expression; it often requires at least two- or threefold effect for statistical significance.”
That’s not scRNA-seq’s only challenge. Other factors to consider include “cost and complexity,” says Steve Kain, director of product management, RNA-seq at Fluidigm. “It is important to look at a large number of individual cells to get statistically meaningful results.” Kain adds, “Typically, that can increase costs for library prep and sequencing compared to bulk methods.” He adds, “Such costs are minimized through the use of microfluidics approaches.”
Despite those drawbacks, the upsides of scRNA-seq make it well worth using. Imagine treating a tissue sample with a drug and then finding 100 times more RNA in a bulk sample of cells. “It is not clear whether every cell in the tissue responded equally to get that value, or if only a few cells responded at 1,000x or more,” Kain explains. scRNA-seq offers the opportunity to take a closer look at which cells are responding and by how much. Where a bulk sample provides an average of gene expression, scRNA-seq reveals the transcription status of each cell type in a tissue or organ.
Technical advances
To use scRNA-seq, a scientist needs to get single cells from a sample. Although available protocols and fluorescence-activated cell sorting (FACS) can be used with some samples, some tissues create a bigger challenge. “It is very difficult to get single cells out of certain tissue—such as brain, adipose tissue, and heart—without damaging the cells during the isolation process,” Lee says. “Another major disadvantage is that most clinical samples are frozen, and it is very difficult to isolate single intact cells from frozen tissues.” So, Invent Biotechnologies developed several kits, Lee says, that make it “easier to isolate nuclei especially from frozen tissues.” One example is the Minute™ Cell Suspension Isolation Kit from Fresh/Frozen Tissues.
Instead of using single cells, scientists can work with single nuclei. For example, one team of scientists wrote: “We confirmed a high concordance between nuclear and whole cell transcriptomes in the expression of cell type and metabolic modeling markers, but less so for a subset of genes associated with mitochondrial respiration.” To work with single nuclei, Invent Biotechnologies offers several kits, Lee says, that make it “easier to isolate nuclei from a variety of fresh/frozen tissues.” These kits from Invent Biotechnologies include: Minute Detergent-free Nuclei Isolation Kit, Minute Nuclei and Cytosol Isolation Kit for Adipose Tissues, Minute Single Nuclei Isolation Kit for Neuronal Tissues/Cells, and Minute Single Nucleus Isolation Kit for Tissue/Cells.
Scientists can also consider other approaches to cell handling. Fluidigm® microfluidics technology, for instance, provides “automatic processing of hundreds of individual cells using the C1™ single-cell mRNA sequencing workflow to more easily characterize the cellular makeup of a tissue and compare the abundance of specific cell types among samples,” says Kain. “The C1 also supports multiple multi-omic approaches to single-cell analysis, allowing researchers to obtain more information from each cell by looking at RNA and other analytes, such as protein, instead of RNA alone.”
Different methods can also be used to collect the needed cells from a sample. “Advances in laser capture microdissection, in particular, can greatly increase both the efficiency and quality of scRNA-seq,” says Kain. “New technologies, such as the Fluidigm AccuLift™ Laser Capture Microdissection System, ensure exceptionally precise targeting both for gentle single-cell capture and for efficient excision of larger areas of tissue while preserving biomolecular integrity for downstream scRNA-seq analysis.”
Image: The Fluidigm AccuLift™ Laser Capture Microdissection System can be used to capture single cells.
Some platforms also focus on high throughput. As an example, Wilkinson says that technology from 10x Genomics “handles more cells and is a fairly simple machine.”
Development and disease
Changes in RNA in single cells can tell scientists and clinicians more about diseases. At McGill University, neurosurgeon Kevin Petrecca says, “We used scRNA-seq to characterize the transcriptomes of the different types of cancer cells within a glioblastoma and within the developing human brain.” The results from such studies reveal more specific information about the development of a disease. “The advantage of this single cell–profiling approach is that it allows for a full understanding of the different cancer cell types that exist within a single tumor, and how these cancer cells are similar to normal brain cells,” Petrecca explains.
As an example, Petrecca and his colleagues applied scRNA-seq to 53,586 adult glioblastoma cells and 22,637 normal brain cells and reported: “Our analyses show that normal brain development reconciles glioblastoma development, suggests a possible origin for glioblastoma hierarchy, and helps to identify cancer stem cell-specific targets.” That information is especially useful in such an aggressive cancer.
Image: This represents the flow of cancer-cell differentiation in one patient’s cancer as progenitor cancer cells (black) evolve to oligodendrocytic (purple), astrocytic (red), and mesenchymal (green) cancer cell types. These data demonstrate the continuous nature of lineage differentiation within a cancer hierarchy. Image courtesy of Kevin Petrecca
The start and the finish
As noted, collecting and isolating single cells presents a problem in some samples. Without those cells, though, scientists cannot use scRNA-seq. Plus, those cells must be collected in good condition.
Once a scientist collects the needed cells and sequences them, the data must be analyzed. “For a laboratory with bioinformatics expertise, the current pipelines are fairly user friendly,” Wilkinson says. “But for laboratories without bioinformatics expertise, I recommend either collaborating or using a core, if available.”
In conclusion, scRNA-seq provides lots of information that is not possible to obtain by other methods, including conventional RNA-seq. However, scRNA-seq can be challenging, including dissociating single cells from the tissues of interest and performing the detailed analysis required to make sense of the data. When isolation of healthy single cells is not an option, single nuclei can be used to substitute for single cells in RNA-seq. If a lab is willing to make a strong commitment toward using this method—including making use of the many new scRNA-seq products that are available—the results can be a game changer.