In cell biology, single-cell analysis and subcellular analysis refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cell–cell interactions at the level of an individual cell, as opposed to more conventional methods which study bulk populations of many cells.
The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell within a bulk population of cells under the influence of a particular condition using optical or electron microscopy. Due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing the biochemical processes and features of a single cell makes it possible to discover mechanisms which are too subtle or infrequent to be detectable when studying a bulk population of cells; in conventional multi-cell analysis, this variability is usually masked by the average behavior of the larger population. Technologies such as fluorescence-activated cell sorting allow the precise isolation of selected single cells from complex samples, while high-throughput single-cell partitioning technologies enable the simultaneous molecular analysis of hundreds or thousands of individual unsorted cells; this is particularly useful for the analysis of variations in gene expression between genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes.
The development of new technologies is increasing scientists' ability to analyze the genome and transcriptome of single cells, and to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled the quantification of thousands of proteins across hundreds of single cells, making possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.
Single-cell isolation Many single-cell analysis techniques require the isolation of individual cells. Methods currently used for single-cell isolation include: dielectrophoretic digital sorting, enzymatic digestion, fluorescence-activated cell sorter, hydrodynamic traps, laser capture microdissection, manual picking, microfluidics, inkjet printing, micromanipulation, serial dilution, and Raman tweezers.
Manual single-cell picking is a method where cells in suspension are viewed under a microscope and individually picked using a micropipette. The Raman tweezers technique combines Raman spectroscopy with optical tweezers, using a laser beam to trap and manipulate cells.
The dielectrophoretic digital sorting method utilizes a semiconductor-controlled array of electrodes in a microfluidic chip to trap single cells in dielectrophoretic (DEP) cages. Cell identification is ensured by the combination of fluorescent markers with image observation. Precision delivery is ensured by the semiconductor-controlled motion of DEP cages in the flow cell.
Inkjet printing combines microfluidics with MEMS on a CMOS chip to provide individual control over a large number of print nozzles, using the same technology as home Inkjet printing. Inkjet printing allows for the adjustment of shear force to the sample ejection, greatly improving cell survivability. This approach, when combined with optical inspection and AI-driven image recognition, not only guarantees single-cell dispensing into the well plate or other medium but also can qualify the cell sample for quality of sample, rejecting defective cells, debris, and fragments.
The development of hydrodynamic-based microfluidic biochips has been increasing over the years. In this technique, the cells or particles are trapped in a particular region for single-cell analysis, usually without application of any external force fields such as optical, electrical, magnetic, or acoustic. There is a need to explore the insights of SCA in the cell's natural state and development of these techniques is highly essential for that study. Researchers have highlighted the vast potential field that needs to be explored to develop biochip devices to suit market/researcher demands. Hydrodynamic microfluidics facilitates the development of passive lab-on-chip applications.
Hydrodynamic traps allow for the isolation of an individual cell in a "trap" at a single given time by passive microfluidic transport. The number of isolated cells can be manipulated based on the number of traps in the system.
The Laser Capture Microdissection technique utilizes a laser to dissect and separate individual cells, or sections, from tissue samples of interest. The methods involve the observation of a cell under a microscope, so that a section for analysis can be identified and labeled so that the laser can cut the cell. Then, the cell can be extracted for analysis.
Microfluidics allows for the isolation of individual cells for further analyses. The following principles outline the various microfluidic processes for single-cell separation: droplet-in-oil-based isolation, pneumatic membrane valving, and hydrodynamic cell traps. Droplet-in-oil-based microfluidics uses oil-filled channels to hold separated aqueous droplets. This allows the single cell to be contained and isolated from inside the oil-based channels. Pneumatic membrane valves manipulate air pressure to isolate individual cells by membrane deflection. The manipulation of the pressure source allows the opening or closing of channels in a microfluidic network. Typically, the system requires an operator and is limited in throughput.
Genomics Techniques Single-cell genomics is heavily dependent on increasing the copies of DNA found in the cell so that there is enough statistical power for accurate sequencing. This has led to the development of strategies for whole genome amplification (WGA). Currently, WGA strategies can be grouped into three categories: Controlled priming and PCR amplification: Adapter-Linker PCR WGA Random priming and PCR amplification: DOP-PCR, MALBAC Random priming and isothermal amplification: MDA
The Adapter-Linker PCR WGA is reported in many comparative studies to be the best-performing technique for diploid single-cell mutation analysis, thanks to its very low Allelic Dropout effect, and for copy number variation profiling due to its low noise, both with aCGH and with NGS low Pass Sequencing. This method is only applicable to human cells, both fixed and unfixed.
One widely adopted WGA technique 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 improvements MDA still has a sequence-dependent bias (certain parts of the genome are amplified more than others because of their sequence, causing some parts to be overrepresented in the resulting genomic dataset). The method shown to largely avoid the biases 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 drawback to using MALBAC is that it has reduced accuracy compared to DOP-PCR and MDA due to the enzyme used to copy the DNA.
Once amplified using any of the above techniques, the DNA can be sequenced using Sanger sequencing or next-generation sequencing (NGS).
Purpose 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 easily missed by bulk sequencing of a population, but can be observed in single-cell sequencing. The second major application is to study the genetic evolution of cancer. Since cancer cells are constantly mutating it is of great interest to researchers to see how cancers evolve at the level of individual cells. These patterns of somatic mutations and copy number aberration can be observed using single-cell sequencing.
Transcriptomics Techniques Single-cell transcriptomics uses sequencing techniques similar to single-cell genomics or direct detection using fluorescence in situ hybridization. The first step in quantifying the transcriptome is to convert RNA to 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. Alternatively, fluorescent compounds attached to RNA hybridization probes are used to identify specific sequences and sequential application of different RNA probes will build up a comprehensive transcriptome.
Purpose The purpose of single-cell transcriptomics is to determine what genes are being expressed in each individual cell. The transcriptome is often used to quantify gene expression instead of the proteome because of the difficulty currently associated with amplifying protein levels sufficiently to make them convenient to study.
There are three major reasons gene expression has been studied using this technique: to study gene dynamics, RNA splicing, and for cell typing. Gene dynamics are usually studied to determine what changes in gene expression affect 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 and classify different types of cells. The main goal in cell typing is to find a way to determine the identity of cells that do not express known genetic markers.
RNA expression can serve as a proxy for protein abundance. However, protein abundance is governed by the complex interplay between RNA expression and post-transcriptional processes. While more challenging technically, translation can be monitored by ribosome profiling in single cells.
Proteomics Techniques There are three major approaches to single-cell proteomics: antibody-based methods, fluorescent protein-based methods, and mass spectroscopy-based methods.
Antibody–based methods The antibody based methods use designed antibodies to bind to proteins of interest, allowing the relative abundance of multiple individual targets to be identified by one of several different techniques.