Single-cell analysis

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This single cell shows the process of the central dogma of biology, which are all steps researchers are interested to quantify (DNA, RNA, and Protein).

In the field of cellular biologysingle-cell analysis is the study of: genomics, transcriptomics, proteomics and metabolomics at the single cell level.[1][2] Due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells.[3] Technologies such as fluorescence-activated cell sorting (FACS) have increased the throughput of single cell sorting, and increased the development of single cell analysis techniques. The development of new technologies are increasing our ability to sequence the genome, and transcriptome, of single cells, as well as to quantify their proteome and metabolome.[4][5][6]

Single-Cell Isolation[edit]

The first step of single-cell analysis is the isolation of single cells. There are 7 methods currently used for single cell isolation: serial dilution, micromanipulation, laser capture microdissection, FACS, microfluidics, manual picking, and Raman tweezers.[7]

Manual single cell picking is a method is where cells in a suspension are viewed under a microscope, and individually picked using a micropipette.[8] Raman tweezers is a technique where  Raman spectroscopy is combined with optical tweezers, which uses a laser beam to trap, and manipulate cells.[9]

Genomics[edit]

Techniques[edit]

Single-cell genomics is heavily dependent on increasing the copies of DNA found in the cell so there is enough to be sequenced. This has led to the development of strategies for whole genome amplification (WGA). One widely adopted WGA techniques is called Degenerate Oligonucleotide–Primed Polymerase Chain Reaction (DOP-PCR). This method uses the well established DNA amplification method PCR to try and amplify the entire genome using a large set of primers. Although simple, this method has been shown to have very low genome coverage. An improvement on DOP-PCR is Multiple Displacement Amplification (MDA), which uses random primers and a high fidelity enzyme, usually Φ29 DNA polymerase, to accomplish the amplification of larger fragments and greater genome coverage than DOP-PCR. Despite these improvement MDA still has a sequence dependent bias (certain parts of the genome are amplified more than others because of their sequence). The method shown to largely avoid the bias seen in DOP-PCR and MDA is Multiple Annealing and Looping–Based Amplification Cycles (MALBAC). Bias in this system is reduced by only copying off the original DNA strand instead of making copies of copies. The main draw backs to using MALBA, is it has reduced accuracy compared to DOP-PCR and MDA due to the enzyme used to copy the DNA.[4] Once amplified using any of the above techniques, the DNA is sequenced using next-generation sequencing (NGS).

Purpose[edit]

There are two major applications to studying the genome at the single cell level. One application is to track the changes that occur in bacterial populations, where phenotypic differences are often seen. These differences are missed by bulk sequencing of a population, but can be observed in single cell sequencing.[10] The second major application is to study the genetic evolution of cancer. Since cancer cells are constantly mutating it is of great interest to see how cancers evolve at the genetic level. These patterns of mutation can be observed using single cell sequencing.[11]

Transcriptomics[edit]

Techniques[edit]

Single-cell transcriptomics uses very similar techniques as single cell genomics. The first step in quantifying the transcriptome is to convert RNA do cDNA using reverse transcriptase so that the contents of the cell can be sequenced using NGS methods as was done in genomics. Once converted, there is not enough cDNA to be sequenced so the same DNA amplification techniques discussed in single cell genomics are applied to the cDNA to make sequencing possible.[11]

Purpose[edit]

The purpose of single cell transcriptomics is to determine what genes are being expressed in each cell. The transcriptome is often used to quantify the gene expression instead of the proteome because of the difficulty currently associated with amplifying protein levels.[11]

There are three major reasons gene expression has been studied using this technique: to study gene dynamics, RNA splicing, and cell typing. Gene dynamics are usually studied to determine what changes in gene expression effect different cell characteristics. For example this type of transcriptomic analysis has often been used to study embryonic development. RNA splicing studies are focused on understanding the regulation of different transcript isoforms. Single cell transcriptomics has also been used for cell typing, where the genes expressed in a cell are used to identify types of cells. The main goal in cell typing is to find a way to determine the identity of cells that don't have known genetic markers.[11]

Proteomics[edit]

Techniques[edit]

There are two major approaches to single-cell proteomics: antibody based methods, and mass spectroscopy based methods.

Antibody Based Methods[edit]

The antibody based methods use designed antibodies to bind to proteins of interest. These antibodies can be bound to fluorescent molecules such as quantum dots of different colors. Since different colored quantum dots are attached to different antibodies it is possible to identify multiple different proteins in a single cell. This method also allows the quantum dots to be washed off of the antibodies without damaging the sample, making it possible to do multiple rounds of protein quantification using this method on the same sample.[12]

Another antibody based method converts protein levels to DNA levels. The conversion to DNA makes it possible to amplify protein levels and use NGS to quantify proteins. To do this, two antibodies are designed for each protein needed to be quantified. The two antibodies are then modified to have single stranded DNA connected to them that are complimentary. When the two antibodies bind to a protein the complimentary strands will anneal and produce a double stranded piece of DNA that can then be amplified using PCR. Each pair of antibodies designed for one protein is tagged with a different DNA sequence. The DNA amplified from PCR can then be sequenced, and the protein levels quantified.[13]

Mass Spectroscopy Based Methods[edit]

In mass spectroscopy based proteomics there are three major steps needed for peptide identification: sample preparation, separation of peptides, and identification of peptides.[14][15][16] Multiple methods exist to isolate the peptides for analysis. These include using filter aided sample preparation, the use of magnetic beads, or using a series of reagents and centrifuging steps.[17][14][16]  The separation of differently sized proteins can be accomplished by using capillary electrophoresis (CE) or liquid chromatograph (LC) (using liquid chromatography with Mass spectroscopy is also known as LC-MS).[14][15][16] This step gives order to the peptides before quantification using tandem mass-spectroscopy (MS/MS). The major difference between quantification methods is some use labels on the peptides such as tandem mass tags (TMT) or dimethyl labels which are used to identify which cell a certain protein came from (proteins coming from each cell have a different label) while others use not labels (quantify cells individually). The mass spectroscopy data is then analyzed by running data through databases that convert the information about peptides identified to quantification of protein levels.[14][15][16][18] These methods are very similar to those used to quantify the proteome of bulk cells, with modifications to accommodate the very small sample volume.

Purpose[edit]

The purpose of studying the proteome is to better understand the activity of cells at the single cells level. Since proteins are responsible for determining how the cell acts, understanding the proteome of single cell gives the best understanding of how a cell operates, and how gene expression changes in a cell due to different environmental stimuli. Although transcriptomics has the same purpose as proteomics it is not as accurate at determining gene expression in cells as it does not take into account post-transcriptional regulation.[5] Transcriptomics is still important as studying the difference between RNA levels and protein levels could give insight on which genes are post-transcriptionally regulated.

Metabolomics[edit]

Techniques[edit]

There are four major methods used to quantify the metabolome of single cells, they are: Fluorescence-Based Detection, Fluorescence Biosensors, FRET Biosensors, and Mass Spectroscopy. The first three methods listed use fluorescence microscopy to detect molecules in a cell. Usually these assays use small fluorescent tags attached to molecules of interest, however this has been shown be too invasive for single cell metabolomics, and alters the activity of the metabolites. The current solution to this problem is to use fluorescent proteins which will act as metabolite detectors, fluorescing when ever they bind to a metabolite of interest.[19]

Mass Spectroscopy is becoming the most frequently used method for single cell metabolomics. Its advantages are that there is no need to develop fluorescent proteins for all molecules of interest, and is capable of detecting metabolites in the femtomole range. Similar to the methods discussed in proteomics, there has also been success in combining mass spectroscopy with separation techniques such las capillary electrophoresis to quantify metabolites. This method is also capable of detecting metabolites present in femtomole concentrations.[19]

Purpose[edit]

The purpose of single cell metabolomics is to gain a better understanding at the molecular level of major biological topics such as: cancer, stem cells, aging, as well as the development of drug resistance. In general the focus of metabolomics is mostly on understanding how cells deal with environmental stresses at the molecular level, and to give a more dynamic understanding of cellular functions.[19]

See also[edit]

References[edit]

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  2. ^ Habibi, Iman; Cheong, Raymond; Lipniacki, Tomasz; Levchenko, Andre; Emamian, Effat S.; Abdi, Ali (2017-04-05). "Computation and measurement of cell decision making errors using single cell data". PLOS Computational Biology. 13 (4): e1005436. ISSN 1553-7358. PMID 28379950. doi:10.1371/journal.pcbi.1005436. 
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