Cell sorting is the process of taking cells from an organism and separating them according to their type. The cells are labelled and tagged to identify areas of interest and their effect. They are separated based on differences in cell size, morphology (shape), and surface protein expression. The resulting homologous populations of cells have important applications in research and as therapeutics.
There are three major methods used for cell sorting: single cell sorting, fluorescent activated cell sorting, and magnetic activated cell sorting. The most commonly used methods are, FACS (fluorescent activated cell sorting) and MACS (magnetic activated cell sorting). These methods are mostly used in hematology, cytogenetics and stem cell research laboratories. Due to many years of refinement and increased demand for cellular analysis however, researchers are working to develop microfluidic sorting devices that have many benefits in comparison to traditional FACS and MACS devices, but are seeing many barriers to commercialization.
Fluorescent Activated Cell Sorting, or FACS, utilizes flow cytometry to provide a fast, objective and quantitative measurement of intra- and extracellular properties, not including morphology, for sorting a heterogeneous mixture of cells. This is the most common method of currently separating cells and involves encapsulating cells into small liquid droplets selectively labeled with electric charges and sorted by an external electric field. FACS devices are fairly expensive and are typically found in only lab environments but have the ability to sort at rates around 50,000 cells/second and with high performances with some reaching purities at or above 99.9%, however, they are large and utilize high pressures that can compromise cell viability. FACS has several systems that work together to achieve successful sorting of events of interest. These include fluidics, optics, sorting systems. Utilizing fluorescence, users can identify a particular population of events within a sample containing many differently characterized cells. This technology is used extensively in hematology labs. However, researchers can use a variety of fluorescent dyes to design multi-color panels to achieve successful, simultaneous sorting of multiple, precisely defined cell-types. Diagram A shows FACS of negative cell selection (undesired group) and diagram B shows FACS of positive cell selection (desired group).
Fluorescent Dyes in cell sorting
Fluorescent dyes can act very differently. Generally, a fluorescent dye will be excited by a coherent light source (a laser) at a particular wavelength and emit light at a lower energy and longer wavelength. The most common dyes act by binding to antigens presented on cells. Common antigens targeted are clusters of differentiation (CDs). These are specific to a certain type of cell. If you can identify which CD is presented on your cells of interest, then you can stain your sample with a fluorescent dye specific to it and using FACS, sorting out only those cells. However, there are many other mechanisms by which fluorescent dyes can act.
Some dyes are able to diffuse across membranes. By taking advantage of this property of the dye, users can characterize intracellular activity as well as surface-expression of proteins. For example, in dead cells, propidium iodide (PI) can penetrate the nucleus where it binds to DNA. The fluorescent signal of PI can be used to quantify DNA content for cell cycle analysis or to identify dead cells in a sample.
Certain fluorescent dyes can be used to characterize kinetic intracellular activity rather than fixing cells in formaldehyde and losing viable cells. The table below outlines dyes that can be used to measure several parameters of cytotoxicity caused by oxidative stress.
|Dye||Parameter||Mechanism of Action||Excitation/ Emission|
|DCFH-DA||Reactive Oxygen Species
2’7’ dichlorofluorescin which reacts with ROS under radical conditions to 2’7 dichlorofluorescein (DCF)
|488 nm/525 nm|
|Rh123||Mitochondria Membrane Potential
|Sequestered by active mitochondria||488 nm/525 nm|
|Indo-1 AM||Calcium Levels||Emits at two different wavelengths depending on presence of calcium ions||350 nm/[400 nm/485 nm]|
|PI||Live/Dead||Permeates dead cells only and binds to DNA||488 nm/675 nm|
This experimental setup is just one example of the capability of flow cytometry. In FACS systems, these characterized cells can then be sorted and purified for further experiments.
Magnetic-activated cell sorting (MACS) provides a method for enriching a heterogeneous mixture of cells based upon extracellular properties, typically cell-surface proteins (antigens).This is a column based separation technique where labeled cells are passed through a magnetic column. Magnetic beads, called microbeads, are paired with a group of cells, then either placed within a magnet or exposed to a magnetic field after being incubated or shaken in a buffering solution. The cells paired with the microbeads attach themselves to the magnetic field and the non-paired cells are removed. This process can then be repeated to continue removal of the selected cells.
SEP system provides a column-free cell separation technique in which a tube of labeled cells is placed inside a magnetic field. Positively selected cells are retained in the tube while negatively selected cells are in the liquid suspension.
MACS devices are significantly cheaper than FACS devices because they do not require cells to be focused into a single stream and instead can separate cells in bulk. This method suffers from a lower target cell purity due to entrainment of non-labeled cells in target populations and highly non linear magnetic forces that causes cells far from the source to be poorly sorted. However, MACS has shown to be beneficial when used with NPC (neural progenitor cell) cultures in particular, as it is easier to manage and causes minimal damage to live cells.
NPC cultures are especially difficult to work with because live brain cells are sensitive and tend to contaminate each other. In order to get clearer results, labs need cleaner materials, meaning more pure NPC lines. A study done in 2019 (with the funding support of New York Stem Cell Foundation and the Association for Frontotemporal Degeneration) found MACS to be a cheap, simple way to yield such purity with minimal damage to the cell lines, therefore maintaining better quality cells, collecting more homogeneous NPCs, and increasing their chances of finding effective treatments for neurological disorders. They used both the MACS and FACS methods to filter out CD271- (useful markers for mesenchymal stem cells) and CD133+ (markers for cancer stem cells) to compare viability of each method.
The MACS method has also been used in assistance with reproduction (artificial insemination) and retinal transplant treatment. In the case of assisting reproduction, apoptotic sperm cells (dead or damaged cells) are separated out so more non-apoptotic sperm (non-fragmented) cells can be collected and used to increase the subject's chances of fertility. This type of treatment has shown to be more effective when done repeatedly, increasing the amount of non-apoptotic cells present during insemination.
A 2018 study done in France (with the support of multiple individuals and agencies including: the Insitut de la Vision in Paris and the Retina France Association) used rats and the MACS method to show that photoreceptors (cells in the retina which respond to light) may be transplanted to cure blindness. In this process, the microbeads were attached to the CD73 enzyme to assist in the separation of PRs (photoreceptors) from retinal organoids. When a CD73+ antigen expressed itself with RCVRN+ cells (calcium-binding proteins in the eye), it showed researchers that this combination of CD73+ and RCVRN+ could be used with post-mitotic PR precursors for repair. Although the study could not verify success in humans, they have the foundation for further research based on the success of pairing non-damaged photoreceptors with a CD73 antigen and the transplantation in rats. This success in cell separation and pairing through transplantation shows promise for a potential cure for retinal diseases including total blindness. So far, only partial vision repair has been reported.
Due to some of the problems with FACS and MACS devices, microfluidic cell sorting devices have been recently investigated and research is being conducted in an attempt to commercialize these devices for clinical use and point of care applications. Microfluidic devices overcome some of the limitations of other methods by providing a comparable target cell purity of around 99% in some cases and high cell outputs of around 48,000 cells/second. Other benefits include reduced risk of compromising the cell viability due to lowering the need for high pressure sheath flow to focus the cells, multi-pathway sorting ability with some devices allowing 5 different outlets for separation, decreased cost of microfluidic channels due to cheap manufacturing methods, lower power consumption, and smaller size footprint with some devices being the size of a credit card.
The development of small micro channels made with soft lithography techniques that utilize materials such as polydimethylsiloxane (PDMS) and epoxies offers a unique way to sort cells based on the fluid behavior in the device microchannels. These devices take advantage of the fluid behavior and the small forces within this micrometer order domain to manipulate cells in solution. Efforts have been made to develop methods of sorting rare cells from a solution that can be used in point-of-care applications, low sample volumes, and do not require expensive equipment in order to sort cells. The isolation of rare cells from a blood solution such as cancer, tumor, or stem cells still remains to be a critical technical challenge due to other methods requiring either expensive equipment that is limited to lab environments or requiring a large sample volume in order to develop a hydro dynamically focused stream of single cells. This type of sorting has also been recognized to have a more gentle approach to cells due to the small forces needed to sort them in these domains which leads to better cell viability after sorting. The different methods can be broken down into active and passive:
Active cell sorting involves the detection of targets cells through a microscope or camera followed by the automatic or manual activation of an actuation method to alter the fluid flow in the microfluidic channels. Some techniques utilize fluorescent tagging similar to what is used in FACS to identify target cells. The flow path is changed in order to direct cells of particular interest to an outlet that traps the cells for further experimentation. There exists different methods of actuation that have been shown to alter the flow characteristics and maintain cell viability after sorting and these include: piezoelectric actuation, dielectrophoresis of droplets, optical manipulation, and surface acoustic wave (SAW) deflection. These methods are usually more costly due to specialized equipment or complex components that require control for sorting and data acquisition.
This method of cell sorting uses the behavior of the fluid itself within the microchannels to alter and separate cells based on size and morphology. The fluid in a colloidal solution will be subject to a velocity profile due to the interactions of the fluid with the walls of the channel while the cells in the solution are subject to various drag and inertial forces that are dependent on the size of the cell and balance accordingly at different locations along the velocity profile. In curved microfluidic channels, vortices are formed due to the Dean force which locate different sized particles in different cross sectional locations due to the Reynolds number and curve radius of curvature.
For example, in a straight channel, larger cells in colloidal solution are found closer to the center of the microchannel than smaller cells due to the larger drag forces from the wall that pushes the cell away from the wall and the shear gradient force from the velocity profile that balances this wall drag force to set the cell into equilibrium.
While microfluidic devices are receiving a lot of attention from the university and research sectors, there lack of commercialization is due to their lack of complete integration such as needing complex control or highly specific valves or tubing and their inability to scale up to an industrial process due to PDMS dissolving gases and losing integrity over time. The lifetime of these devices due to bubble formation or cell entrapment is fairly short which affects output and purity levels after only a few cycles.
Buoyancy activated cell sorting (BACS), developed by Akadeum Life Sciences, is a separation technique in which microbubbles bind to cells through antibodies binding to the surface of cells. The targeted cells are then removed from a biological sample through flotation.
Single cell sorting
Single cell sorting provides a method for sorting a heterogeneous mixture of cells based upon intracellular and extracellular properties. Single cell methods allow us to understand cellular properties that can be sometimes obscured or non evident with populations of cells. There are several methods for sorting single cells:
The microraft array provides a rapid, cost-effective method for isolating cells, analyzing cells over time, and generating clonal populations with the unique ability to monitor all intra- and extracellular properties. This system is ideal for both adherent and non-adherent cell types.
A single cell method to observe the response to an external stimulus, in this case, cellular response to a ligand was studied using a microfluidic device with micro channel valves to trap a single cell into a chamber. A 23 valve system was used for actuation and fluorescent dye was used stimulus response imaging.
Single cell clustering methods are a series of statistical methods designed by data scientists based on intracellular properties. The whole process includes Single Cell RNA-Seq data gathering, Data preprocessing for clustering, CLustering and Evaluations of clustering. Scientists apply machine learning methods (mainly clustering analysis) on the single cell RNA-Seq data to divide the cells into different categories. All the methods are modified to resolve the problems in RNA-Seq data such as dropout of low-expression genes and ambiguous cell markers in the presence of technical biases. The most state of art methods are SC3., CIDR., Seurat and for more detailed information, please refer to the Wiki page: Single Cell RNA-Seq Clustering.
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