November 2020 Share
Medical advancements have provided researchers with strategies that change the way in which we understand biology. Scientists are now capable of studying coded protein patterns at a genomic level. RNA-sequencing (RNA-seq) is the process of detecting and analyzing the messenger RNA (mRNA) of a cell type. The range of mRNA molecules present in an organism is also known as the transcriptome. Identifying a biological transcriptome can give a better idea of genetic expressions on DNA and how they may predispose a living thing to a certain disease.
While RNA-seq can be extremely beneficial for treatment and research purposes, it lacks a degree of specificity. This technique is often carried out with a large number of cells in each sample. While it may be practical to use as large of a population as possible, less attention is paid to the individual cells. More specialized versions of RNA-seq are available for those looking to gain more detailed information about a cell type.
The most effective research method that focuses on individual cells is called single-cell RNA-sequencing (scRNA-seq). This is a form of RNA-seq that prioritizes the mRNA sequence of a particular cell or cell type. For a long time, it was only possible to use it in specialist laboratories that dealt with wet-lab single-cell genomics. More recently, however, scRNA-seq has become an accessible endeavor for any biomedical researcher or clinician. More scRNA-seq platforms have become commercially available and bioinformatics applications are maturing to a point where labs with less resources can still join the trend of genomic sequencing.
As technology advances, medicine is moving towards a cellular and molecular era; patients can be treated on a microscopic level for diseases they might not know they have. Genomes hold the key to identifying and treating potential risks before they cause too much harm to the body. scRNA-seq gives an in-depth understanding of different cells’ genomic codes, not only in a general sense, but also on an individual basis. The analysis of one person’s immune system cells might reveal a dangerous population of overactive cells attacking the patient’s body, while another person’s cells may be totally fine. Normal RNA-seq can sometimes miss these outliers to index as many cells as possible.
There is an inverse relationship between the number of cells that can be analyzed and the depth to which they are cataloged. There are approximately 20 different published protocols for scRNA-seq that scientists can use; these methods vary slightly in methodology and the results which they produce. The cell population under examination and the target information are both important factors when choosing with protocol to use. Some are better at detecting weakly expressed genes, while others are more adept at identifying rare variant cell populations. It should be noted, however, that all published scRNA-seq protocols are effective at determining the relative number of moderately abundant transcripts within a cell.
The main reason scRNA proves to be so beneficial to medical research is due to heterogeneity. Heterogeneity refers to the genetic variations between cells, and in this case pertains to subtle expression differences of cells believed to be the same. With normal RNA-seq, large cell samples are observed to a minimal depth. While this may give a very basic understanding of a transcriptome, it doesn’t extend deep enough into the genomic code to reveal differences from mutations.
Tests of transcriptional differences have revealed rare cell populations that would normally go unnoticed when analyzing pooled cells. This helps to identify tumor cells that are malignant or immune cells that are hyper-responsive. These both blend into homogeneous groups and can cause significant problems if not properly handled. Along with cells that seem similar, scRNA-seq is also effective for examining unique ones. Individual T cells with diverse receptors, neurons, or early-stage embryo cells can be indexed better when reviewed in more detail.
The most important step in conducting scRNA-seq is the isolation of cells from the tissue of interest. This can be done a variety of ways, each providing their own benefits and drawbacks. The goal of cell separation is to maximize the number of viable, undamaged cells to move forward with sequencing. After separation, the cells are actually lysed, or ruptured at the membrane so that the RNA molecules can be captured. Researchers use poly[T]-primers attached with an anchor sequence to collect polyadenylated mRNA molecules. The added code prevents the analysis of ribosomal RNAs and non-polyadenylated mRNAs that are more difficult to sequence.
The poly[T]-primed mRNA is then processed into complementary DNA (cDNA) through reverse transcription. Reverse transcription occurs when an enzyme called reverse transcriptase binds the complementary nucleotides to an mRNA strand to model one side of a DNA strand. These small amounts of cDNA are then multiplied by a process called polymerase chain reaction (PCR) that uses enzymes to duplicate strands. The amplified cDNA are then sequenced by bioinformatics applications that prepare and align them similar to strategies used in bulk RNA-seq. These data analysis programs have developed their own field that grows rapidly to meet expanding cell sequencing demands.
With the increasing necessity of RNA-seq in the medical field, other areas of the sequencing process need to keep up. Researchers anticipate that the choice between analysis depth and volume will disappear as the technology becomes more efficient. Streamlining has already occurred in certain stages, such as cell separation. Advances in cell isolation techniques have led to buoyancy activated cell sorting (BACS). This is a method of separation method that uses microbubbles to gently lift target cells to the top of a sample for collection.
When working with extremely rare and fragile cells for scRNA-seq, it’s important to keep the membrane intact before the lysing stage — otherwise valuable mRNA could be lost. Traditional methods can be very hard on cells and damage the overall throughput of an experiment. BACS is fast, simple, cheap, and extremely careful with the target cells. Using this method decreases the project cost and in turn makes scRNA-seq research more accessible to all. Checkout Akadeum’s microbubble products today.
If you are reading this, you probably already understand or at least have a very…
Cell isolation methods and technologies use either a positive or negative cell separation approach. Here…
Cell isolation—also referred to as cell separation or cell sorting—is the process of isolating one…