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

In cell biology, single-cell analysis and subcellular analysis^[1] 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.^[2]^[3]^[4]

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.^[5] 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.^[6] Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high-throughput single-cell partitioning technologies^[7]^[8]^[9] 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,^[10] and to quantify their proteome and metabolome.^[11]^[12]^[13] Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells.^[14]^[15] Recent advances have enabled the quantification of thousands of proteins across hundreds of single cells,^[16] making possible new types of analysis.^[17]^[18] 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.^[19]

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, FACS, hydrodynamic traps, laser capture microdissection, manual picking, microfluidics, Inkjet Printing (IJP), 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.^[20]^[21] The Raman tweezers technique combines Raman spectroscopy with optical tweezers, using a laser beam to trap and manipulate cells.^[22]

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^[23] 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. IJP 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.^[24]

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,^[25]^[26]^[27] and for copy number variation profiling due to its low noise, both with aCGH and with NGS low Pass Sequencing.^[28]^[29] 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.^[11]

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.^[30] 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.^[2]

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.^[2] 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.^[31]^[32]

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.^[2]

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.^[2]

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.^[33]

Proteomics

Techniques

There are three major approaches to single-cell proteomics: antibody-based methods, fluorescent protein-based methods, and mass spectroscopy-based methods.^[34]^[18]

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.

Imaging: Antibodies can be bound to fluorescent molecules such as quantum dots or tagged with organic fluorophores for detection by fluorescence microscopy. Since different colored quantum dots or unique fluorophores are attached to each antibody it is possible to identify multiple different proteins in a single cell. Quantum dots can 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.^[35] For the methods based on organic fluorophores, the fluorescent tags are attached by a reversible linkage such as a DNA-hybrid (that can be melted/dissociated under low-salt conditions)^[36] or chemically inactivated,^[37] allowing multiple cycles of analysis, with 3-5 targets quantified per cycle. These approaches have been used for quantifying protein abundance in patient biopsy samples (e.g. cancer) to map variable protein expression in tissues and/or tumors,^[37] and to measure changes in protein expression and cell signaling in response to cancer treatment.^[36]

Mass Cytometry: rare metal isotopes, not normally found in cells or tissues, can be attached to the individual antibodies and detected by mass spectrometry for simultaneous and sensitive identification of proteins.^[38] These techniques can be highly multiplexed for simultaneous quantification of many targets (panels of up to 38 markers) in single cells.^[39]

Antibody-DNA quantification: another antibody-based method converts protein levels to DNA levels.^[34] The conversion to DNA makes it possible to amplify protein levels and use NGS to quantify proteins. In one such approach, two antibodies are selected for each protein needed to be quantified. The two antibodies are then modified to have single stranded DNA connected to them that are complementary. When the two antibodies bind to a protein the complementary strands will anneal and produce a double stranded segment 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.^[40]

Mass spectrometry–based methods

In mass spectroscopy-based proteomics there are three major steps needed for peptide identification: sample preparation, separation of peptides, and identification of peptides. Several groups have focused on oocytes or very early cleavage-stage cells since these cells are unusually large and provide enough material for analysis.^[41]^[42]^[43]^[44] Another approach, single cell proteomics by mass spectrometry (SCoPE-MS) has quantified thousands of proteins in mammalian cells with typical cell sizes (diameter of 10-15 μm) by combining carrier-cells and single-cell barcoding.^[45]^[46] The second generation, SCoPE2,^[47]^[48] increased the throughput by automated and miniaturized sample preparation;^[49] It also improved quantitative reliability and proteome coverage by data-driven optimization of LC-MS/MS^[50] and peptide identification.^[51] The sensitivity and consistency of these methods have been further improved by prioritization,^[52] and massively parallel sample preparation in nanoliter size droplets.^[53] Another direction for single-cell protein analysis is based on a scalable framework of multiplexed data-independent acquisition (plexDIA) enables time saving by parallel analysis of both peptide ions and protein samples, thereby realizing multiplicative gains in throughput.^[54]^[55]^[56]

The separation of differently sized proteins can be accomplished by using capillary electrophoresis (CE) or liquid chromatography (LC) (using liquid chromatography with mass spectroscopy is also known as LC-MS).^[42]^[43]^[44]^[45] 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 do not use labels but rather quantify cells individually. The mass spectroscopy data is then analyzed by running data through databases that count the peptides identified to quantify protein levels.^[42]^[43]^[44]^[45]^[57] These methods are very similar to those used to quantify the proteome of bulk cells, with modifications to accommodate the very small sample volume.^[58]

Ionization techniques used in mass spectrometry-based single-cell analysis

A huge variety of ionization techniques can be used to analyze single cells. The choice of ionization method is crucial for analyte detection. It can be decisive which type of compounds are ionizable and in which state they appear, e.g., charge and possible fragmentation of the ions.^[59] A few examples of ionization are mentioned in the paragraphs below.

Nano-DESI

One of the possible ways to measure the content of single cells is nano-DESI (nanospray desorption electrospray ionization). Unlike desorption electrospray ionization, which is a desorption technique, nano-DESI is a liquid extraction technique that enables the sampling of small surfaces, therefore suitable for single-cell analysis. In nano-DESI, two fused silica capillaries are set up in a V-shaped form, closing an angle of approx. 85 degrees. The two capillaries are touching therefore a liquid bridge can be formed between them and enable the sampling of surfaces as small as a single cell. The primary capillary delivers the solvent to the sample surface where the extraction happens and the secondary capillary directs the solvent with extracted molecules to the MS inlet. Nano-DESI mass spectrometry (MS) enables sensitive molecular profiling and quantification of endogenous species as small as a few hundred fmol-s in single cells in a higher throughput manner. Lanekoff et al. identified 14 amino acids, 6 metabolites, and several lipid molecules from single cheek cells using nano-DESI MS.^[60]

LAESI

In Laser ablation electrospray ionization (LAESI), a laser is used to ablate the surface of the sample and the emitted molecules are ionized in the gas phase by charged droplets from electrospray. Similar to DESI the ionization happens in ambient conditions. Anderton et al. used this ionization technique coupled to a Fourier transform mass spectrometer to analyze 200 single cells of Allium cepa (red onion) with high spatial resolution.^[61]

SIMS

Secondary-ion mass spectrometry (SIMS) is a technique similar to DESI, but while DESI is an ambient ionization technique, SIMS happens in vacuum. The solid sample surface is bombarded by a highly focused beam of primary ions. As they hit the surface, molecules are emitted from the surface and ionized. The choice of primary ions determines the size of the beam and also the extent of ionization and fragmentation.^[62] Pareek et al. performed metabolomics to trace how purines are synthesized within purinosomes and used isotope labeling and SIMS imaging to directly observe hotspots of metabolic activity within frozen HeLa cells.^[63]

MALDI

In matrix-assisted laser desorption and ionization (MALDI), the sample is incorporated in a chemical matrix that is capable of absorbing energy from a laser. Similar to SIMS, ionization happens in vacuum. Laser irradiation ablates the matrix material from the surface and results in charged gas phase matrix particles, with the analyte molecules ionized from this charged chemical matrix. Liu et al. used MALDI-MS to detect eight phospholipids from single A549 cells.^[64] MALDI MS imaging can be used for spatial metabolomics and single-cell analysis.^[65]^[66]

Purpose

The purpose of studying the proteome is to better understand the activity of proteins at the single-cell level. Since proteins are responsible for determining how the cell acts, understanding the proteome of single cells 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 (not all messenger RNA transcripts are actually translated into proteins).^[12] Transcriptomics is still important, of course, as studying the difference between RNA levels and protein levels can give insight regarding which genes are post-transcriptionally regulated.

Metabolomics

Techniques

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.^[67]

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.^[15] Similar to the methods discussed in proteomics, there has also been success in combining mass spectroscopy with separation techniques such as capillary electrophoresis to quantify metabolites. This method is also capable of detecting metabolites present in femtomole concentrations.^[67] Another method utilizing capillary microsampling combined with mass spectrometry with ion mobility separation has been demonstrated to enhance the molecular coverage and ion separation for single cell metabolomics.^[21]^[68] Researchers are trying to develop a technique that can fulfil what current techniques are lacking: high throughput, higher sensitivity for metabolites that have a lower abundance or that have low ionization efficiencies, good replicability and that allow quantification of metabolites.^[69]

Purpose

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.^[67]

Reconstructing developmental trajectories

Single-cell transcriptomic assays have allowed reconstruction development trajectories. Branching of these trajectories describes cell differentiation. Various methods have been developed for reconstructing branching developmental trajectories from single-cell transcriptomic data.^[70]^[71]^[72]^[73]^[74] They use various advanced mathematical concepts from optimal transportation^[72] to principal graphs.^[73] Some software libraries for reconstruction and visualization of lineage differentiation trajectories are freely available online.^[75]

Cell–cell interaction

Cell–cell interactions are characterized by stable and transient interactions.

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^ Pinello Lab. Single-Cell Trajectory Reconstruction Exploration and Mapping

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-{{short description|Testbg biochemical processes and reactions in an individual cell}}
+{{short description|Study of biochemical processes in an individual cell}}
+{{cs1 config|name-list-style=vanc|display-authors=6}}
 [[File:Proteinsynthesis.png|thumb|This single cell shows the process of the [[central dogma of molecular biology]], which are all steps researchers are interested to quantify (DNA, RNA, and Protein).]]
-In the field of&nbsp;[[cellular biology]],&nbsp;'''single-cell analysis and subcellular analysis'''<ref>{{Cite journal |last=Siuzdak |first=Gary |date=September 2023 |title=Subcellular quantitative imaging of metabolites at the organelle level |url=https://www.nature.com/articles/s42255-023-00882-z |journal=Nature Metabolism |language=en |volume=5 |issue=9 |pages=1446–1448 |doi=10.1038/s42255-023-00882-z |issn=2522-5812}}</ref> is the study of [[genomics]], [[Transcriptome|transcriptomics]], [[proteomics]], [[metabolomics]] and [[cell–cell interaction]]s at the single cell level.<ref name=":0">{{cite journal | vauthors = Wang D, Bodovitz S | title = Single cell analysis: the new frontier in 'omics' | journal = Trends in Biotechnology | volume = 28 | issue = 6 | pages = 281–90 | date = June 2010 | pmid = 20434785 | pmc = 2876223 | doi = 10.1016/j.tibtech.2010.03.002 }}</ref><ref>{{cite journal | vauthors = Habibi I, Cheong R, Lipniacki T, Levchenko A, Emamian ES, Abdi A | title = Computation and measurement of cell decision making errors using single cell data | journal = PLOS Computational Biology | volume = 13 | issue = 4 | pages = e1005436 | date = April 2017 | pmid = 28379950 | pmc = 5397092 | doi = 10.1371/journal.pcbi.1005436 | bibcode = 2017PLSCB..13E5436H | doi-access = free }}</ref><ref>{{cite journal | vauthors = Merouane A, Rey-Villamizar N, Lu Y, Liadi I, Romain G, Lu J, Singh H, Cooper LJ, Varadarajan N, Roysam B | display-authors = 6 | title = Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING) | journal = Bioinformatics | volume = 31 | issue = 19 | pages = 3189–97 | date = October 2015 | pmid = 26059718 | pmc = 4693004 | doi = 10.1093/bioinformatics/btv355 }}</ref>  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 in a bulk population of cells at a particular condition using optical or electronic microscope.<ref>{{cite journal | vauthors = Loo J, Ho H, Kong S, Wang T, Ho Y | title = Technological Advances in Multiscale Analysis of Single Cells in Biomedicine | journal =  Advanced Biosystems| volume = 3 | issue = 11 | pages = e1900138 | date = September 2019 | pmid = 32648696 | doi = 10.1002/adbi.201900138| s2cid = 203101696 }}</ref> To date, 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.<ref>{{cite journal | vauthors = Altschuler SJ, Wu LF | title = Cellular heterogeneity: do differences make a difference? | journal = Cell | volume = 141 | issue = 4 | pages = 559–63 | date = May 2010 | pmid = 20478246 | pmc = 2918286 | doi = 10.1016/j.cell.2010.04.033 }}</ref> Technologies such as [[Flow cytometry|fluorescence-activated cell sorting]] (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies,<ref>{{cite journal | vauthors = Hu P, Zhang W, Xin H, Deng G | title = Single Cell Isolation and Analysis | journal = Frontiers in Cell and Developmental Biology | volume = 4 | pages = 116 | date = 2016-10-25 | pmid = 27826548 | pmc = 5078503 | doi = 10.3389/fcell.2016.00116 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Mora-Castilla S, To C, Vaezeslami S, Morey R, Srinivasan S, Dumdie JN, Cook-Andersen H, Jenkins J, Laurent LC | display-authors = 6 | title = Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing | journal = Journal of Laboratory Automation | volume = 21 | issue = 4 | pages = 557–67 | date = August 2016 | pmid = 26891732 | pmc = 4948133 | doi = 10.1177/2211068216630741 }}</ref><ref>{{cite journal | vauthors = Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH | display-authors = 6 | title = Massively parallel digital transcriptional profiling of single cells | journal = Nature Communications | volume = 8 | pages = 14049 | date = January 2017 | pmid = 28091601 | pmc = 5241818 | doi = 10.1038/ncomms14049 | bibcode = 2017NatCo...814049Z }}</ref> enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells,<ref>{{cite journal | vauthors = Mercatelli D, Balboni N, Palma A, Aleo E, Sanna PP, Perini G, Giorgi FM | title = Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines | journal = Biomolecules | volume = 11 | issue = 2 | date = January 2021 | page = 177 | pmid = 33525507 | doi = 10.3390/biom11020177 | pmc = 7912277 | doi-access = free }}</ref> as well as to quantify their proteome and [[metabolome]].<ref name=":6">{{cite journal | vauthors = Huang L, Ma F, Chapman A, Lu S, Xie XS | s2cid = 12987987 | title = Single-Cell Whole-Genome Amplification and Sequencing: Methodology and Applications | journal = Annual Review of Genomics and Human Genetics | volume = 16 | issue = 1 | pages = 79–102 | year = 2015 | pmid = 26077818 | doi = 10.1146/annurev-genom-090413-025352 | doi-access = free }}</ref><ref name=":4">{{cite journal | vauthors = Wu AR, Wang J, Streets AM, Huang Y | s2cid = 40069109 | title = Single-Cell Transcriptional Analysis | journal = Annual Review of Analytical Chemistry | volume = 10 | issue = 1 | pages = 439–462 | date = June 2017 | pmid = 28301747 | doi = 10.1146/annurev-anchem-061516-045228 }}</ref><ref>{{cite journal | vauthors = Tsioris K, Torres AJ, Douce TB, Love JC | title = A new toolbox for assessing single cells | journal = Annual Review of Chemical and Biomolecular Engineering | volume = 5 | pages = 455–77 | year = 2014 | pmid = 24910919 | pmc = 4309009 | doi = 10.1146/annurev-chembioeng-060713-035958 }}</ref> Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells.<ref name=":17">{{cite journal | vauthors = Comi TJ, Do TD, Rubakhin SS, Sweedler JV | title = Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry | journal = Journal of the American Chemical Society | volume = 139 | issue = 11 | pages = 3920–3929 | date = March 2017 | pmid = 28135079 | pmc = 5364434 | doi = 10.1021/jacs.6b12822 }}</ref><ref name=":10">{{cite journal | vauthors = Zhang L, Vertes A | s2cid = 4928231 | title = Single-Cell Mass Spectrometry Approaches to Explore Cellular Heterogeneity | journal = Angewandte Chemie | volume = 57 | issue = 17 | pages = 4466–4477 | date = April 2018 | pmid = 29218763 | doi = 10.1002/anie.201709719 | doi-access = free }}</ref> Recent advances have enabled quantifying thousands of protein across hundreds of single cells,<ref>{{cite journal | vauthors = Slavov N | s2cid = 219966629 | title = Single-cell protein analysis by mass spectrometry | journal = Current Opinion in Chemical Biology | volume = 60 | pages = 1–9 | date = June 2020 | pmid = 32599342 | doi = 10.1016/j.cbpa.2020.04.018|issn=1367-5931   | pmc = 7767890 | arxiv = 2004.02069 }}</ref> and thus make possible new types of analysis.<ref>{{cite journal | vauthors = Specht H, Slavov N | title = Transformative Opportunities for Single-Cell Proteomics | journal = Journal of Proteome Research | volume = 17 | issue = 8 | pages = 2565–2571 | date = August 2018 | pmid = 29945450 | pmc = 6089608 | doi = 10.1021/acs.jproteome.8b00257 }}</ref><ref name="Unpicking the proteome in single ce">{{cite journal | vauthors = Slavov N | title = Unpicking the proteome in single cells | journal = Science | volume = 367 | issue = 6477 | pages = 512–513 | date = January 2020 | pmid = 32001644 | pmc = 7029782 | doi = 10.1126/science.aaz6695 | bibcode = 2020Sci...367..512S }}</ref> 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.<ref>{{cite journal | vauthors = Lee JH | title = De Novo Gene Expression Reconstruction in Space | journal = Trends in Molecular Medicine | volume = 23 | issue = 7 | pages = 583–593 | date = July 2017 | pmid = 28571832 | pmc = 5514424 | doi = 10.1016/j.molmed.2017.05.004 }}</ref>
+In [[cell biology]],&nbsp;'''single-cell analysis''' and '''subcellular analysis'''<ref>{{cite journal | vauthors = Siuzdak G | title = Subcellular quantitative imaging of metabolites at the organelle level | journal = Nature Metabolism | volume = 5 | issue = 9 | pages = 1446–1448 | date = September 2023 | pmid = 37679555 | doi = 10.1038/s42255-023-00882-z | s2cid = 261607846 }}</ref> refer to the study of [[genomics]], [[transcriptomics]], [[proteomics]], [[metabolomics]], and [[cell–cell interaction]]s at the level of an individual cell, as opposed to more conventional methods which study bulk populations of many cells.<ref name=":0">{{cite journal | vauthors = Wang D, Bodovitz S | title = Single cell analysis: the new frontier in 'omics' | journal = Trends in Biotechnology | volume = 28 | issue = 6 | pages = 281–90 | date = June 2010 | pmid = 20434785 | pmc = 2876223 | doi = 10.1016/j.tibtech.2010.03.002 }}</ref><ref>{{cite journal | vauthors = Habibi I, Cheong R, Lipniacki T, Levchenko A, Emamian ES, Abdi A | title = Computation and measurement of cell decision making errors using single cell data | journal = PLOS Computational Biology | volume = 13 | issue = 4 | pages = e1005436 | date = April 2017 | pmid = 28379950 | pmc = 5397092 | doi = 10.1371/journal.pcbi.1005436 | bibcode = 2017PLSCB..13E5436H | doi-access = free }}</ref><ref>{{cite journal | vauthors = Merouane A, Rey-Villamizar N, Lu Y, Liadi I, Romain G, Lu J, Singh H, Cooper LJ, Varadarajan N, Roysam B | title = Automated profiling of individual cell-cell interactions from high-throughput time-lapse imaging microscopy in nanowell grids (TIMING) | journal = Bioinformatics | volume = 31 | issue = 19 | pages = 3189–97 | date = October 2015 | pmid = 26059718 | pmc = 4693004 | doi = 10.1093/bioinformatics/btv355 }}</ref>
+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.<ref>{{cite journal | vauthors = Loo J, Ho H, Kong S, Wang T, Ho Y | title = Technological Advances in Multiscale Analysis of Single Cells in Biomedicine | journal =  Advanced Biosystems| volume = 3 | issue = 11 | pages = e1900138 | date = September 2019 | pmid = 32648696 | doi = 10.1002/adbi.201900138| s2cid = 203101696 }}</ref> 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.<ref>{{cite journal | vauthors = Altschuler SJ, Wu LF | title = Cellular heterogeneity: do differences make a difference? | journal = Cell | volume = 141 | issue = 4 | pages = 559–63 | date = May 2010 | pmid = 20478246 | pmc = 2918286 | doi = 10.1016/j.cell.2010.04.033 }}</ref> Technologies such as [[Flow cytometry|fluorescence-activated cell sorting]] (FACS) allow the precise isolation of selected single cells from complex samples, while high-throughput single-cell partitioning technologies<ref>{{cite journal | vauthors = Hu P, Zhang W, Xin H, Deng G | title = Single Cell Isolation and Analysis | journal = Frontiers in Cell and Developmental Biology | volume = 4 | pages = 116 | date = 2016-10-25 | pmid = 27826548 | pmc = 5078503 | doi = 10.3389/fcell.2016.00116 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Mora-Castilla S, To C, Vaezeslami S, Morey R, Srinivasan S, Dumdie JN, Cook-Andersen H, Jenkins J, Laurent LC | title = Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing | journal = Journal of Laboratory Automation | volume = 21 | issue = 4 | pages = 557–67 | date = August 2016 | pmid = 26891732 | pmc = 4948133 | doi = 10.1177/2211068216630741 }}</ref><ref>{{cite journal | vauthors = Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson CM, Bharadwaj R, Wong A, Ness KD, Beppu LW, Deeg HJ, McFarland C, Loeb KR, Valente WJ, Ericson NG, Stevens EA, Radich JP, Mikkelsen TS, Hindson BJ, Bielas JH | title = Massively parallel digital transcriptional profiling of single cells | journal = Nature Communications | volume = 8 | pages = 14049 | date = January 2017 | pmid = 28091601 | pmc = 5241818 | doi = 10.1038/ncomms14049 | bibcode = 2017NatCo...814049Z }}</ref> 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,<ref>{{cite journal | vauthors = Mercatelli D, Balboni N, Palma A, Aleo E, Sanna PP, Perini G, Giorgi FM | title = Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines | journal = Biomolecules | volume = 11 | issue = 2 | date = January 2021 | page = 177 | pmid = 33525507 | doi = 10.3390/biom11020177 | pmc = 7912277 | doi-access = free }}</ref> and to quantify their proteome and [[metabolome]].<ref name=":6">{{cite journal | vauthors = Huang L, Ma F, Chapman A, Lu S, Xie XS | s2cid = 12987987 | title = Single-Cell Whole-Genome Amplification and Sequencing: Methodology and Applications | journal = Annual Review of Genomics and Human Genetics | volume = 16 | issue = 1 | pages = 79–102 | year = 2015 | pmid = 26077818 | doi = 10.1146/annurev-genom-090413-025352 | doi-access = free }}</ref><ref name=":4">{{cite journal | vauthors = Wu AR, Wang J, Streets AM, Huang Y | s2cid = 40069109 | title = Single-Cell Transcriptional Analysis | journal = Annual Review of Analytical Chemistry | volume = 10 | issue = 1 | pages = 439–462 | date = June 2017 | pmid = 28301747 | doi = 10.1146/annurev-anchem-061516-045228 }}</ref><ref>{{cite journal | vauthors = Tsioris K, Torres AJ, Douce TB, Love JC | title = A new toolbox for assessing single cells | journal = Annual Review of Chemical and Biomolecular Engineering | volume = 5 | pages = 455–77 | year = 2014 | pmid = 24910919 | pmc = 4309009 | doi = 10.1146/annurev-chembioeng-060713-035958 }}</ref> [[Mass spectrometry]] techniques have become important analytical tools for proteomic and metabolomic analysis of single cells.<ref name=":17">{{cite journal | vauthors = Comi TJ, Do TD, Rubakhin SS, Sweedler JV | title = Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry | journal = Journal of the American Chemical Society | volume = 139 | issue = 11 | pages = 3920–3929 | date = March 2017 | pmid = 28135079 | pmc = 5364434 | doi = 10.1021/jacs.6b12822 }}</ref><ref name=":10">{{cite journal | vauthors = Zhang L, Vertes A | s2cid = 4928231 | title = Single-Cell Mass Spectrometry Approaches to Explore Cellular Heterogeneity | journal = Angewandte Chemie | volume = 57 | issue = 17 | pages = 4466–4477 | date = April 2018 | pmid = 29218763 | doi = 10.1002/anie.201709719 | doi-access = free }}</ref> Recent advances have enabled the quantification of thousands of proteins across hundreds of single cells,<ref>{{cite journal | vauthors = Slavov N | s2cid = 219966629 | title = Single-cell protein analysis by mass spectrometry | journal = Current Opinion in Chemical Biology | volume = 60 | pages = 1–9 | date = June 2020 | pmid = 32599342 | doi = 10.1016/j.cbpa.2020.04.018|issn=1367-5931   | pmc = 7767890 | arxiv = 2004.02069 }}</ref> making possible new types of analysis.<ref>{{cite journal | vauthors = Specht H, Slavov N | title = Transformative Opportunities for Single-Cell Proteomics | journal = Journal of Proteome Research | volume = 17 | issue = 8 | pages = 2565–2571 | date = August 2018 | pmid = 29945450 | pmc = 6089608 | doi = 10.1021/acs.jproteome.8b00257 }}</ref><ref name="Unpicking the proteome in single ce">{{cite journal | vauthors = Slavov N | title = Unpicking the proteome in single cells | journal = Science | volume = 367 | issue = 6477 | pages = 512–513 | date = January 2020 | pmid = 32001644 | pmc = 7029782 | doi = 10.1126/science.aaz6695 | bibcode = 2020Sci...367..512S }}</ref> ''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.<ref>{{cite journal | vauthors = Lee JH | title = De Novo Gene Expression Reconstruction in Space | journal = Trends in Molecular Medicine | volume = 23 | issue = 7 | pages = 583–593 | date = July 2017 | pmid = 28571832 | pmc = 5514424 | doi = 10.1016/j.molmed.2017.05.004 }}</ref>
 == 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|FACS]], hydrodynamic traps, [[laser capture microdissection]], manual picking, [[Microfluidic whole genome haplotyping|microfluidics]], [[Micromanipulator|micromanipulation]], [[serial dilution]], and Raman tweezers.
+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|FACS]], hydrodynamic traps, [[laser capture microdissection]], manual picking, [[Microfluidic whole genome haplotyping|microfluidics]], Inkjet Printing (IJP), [[Micromanipulator|micromanipulation]], [[serial dilution]], and Raman tweezers.
-Manual single cell picking is a method where cells in a suspension are viewed under a microscope, and individually picked using a [[Pipette|micropipette]].<ref>{{cite journal | vauthors = Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P | title = Technologies for Single-Cell Isolation | journal = International Journal of Molecular Sciences | volume = 16 | issue = 8 | pages = 16897–919 | date = July 2015 | pmid = 26213926 | pmc = 4581176 | doi = 10.3390/ijms160816897 | doi-access = free }}</ref><ref name=":12">{{cite journal | vauthors = Zhang L, Vertes A | title = Energy Charge, Redox State, and Metabolite Turnover in Single Human Hepatocytes Revealed by Capillary Microsampling Mass Spectrometry | journal = Analytical Chemistry | volume = 87 | issue = 20 | pages = 10397–405 | date = October 2015 | pmid = 26398405 | doi = 10.1021/acs.analchem.5b02502 | doi-access = free }}</ref> Raman tweezers is a technique where &nbsp;[[Raman spectroscopy]] is combined with [[optical tweezers]], which uses a laser beam to trap, and manipulate cells.<ref>{{cite book | vauthors = Faria EC, Gardner P | title = Single-Cell Analysis | chapter = Analysis of single eukaryotic cells using Raman Tweezers | volume = 853 | pages = 151–67 | date = January 2012 | pmid = 22323146 | doi = 10.1007/978-1-61779-567-1_12 | publisher = Humana Press | isbn = 978-1-61779-566-4 | series = Methods in Molecular Biology | veditors = Lindström S, Andersson-Svahn H }}</ref>
+Manual single-cell picking is a method where cells in suspension are viewed under a microscope and individually picked using a [[Pipette|micropipette]].<ref>{{cite journal | vauthors = Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P | title = Technologies for Single-Cell Isolation | journal = International Journal of Molecular Sciences | volume = 16 | issue = 8 | pages = 16897–919 | date = July 2015 | pmid = 26213926 | pmc = 4581176 | doi = 10.3390/ijms160816897 | doi-access = free }}</ref><ref name=":12">{{cite journal | vauthors = Zhang L, Vertes A | title = Energy Charge, Redox State, and Metabolite Turnover in Single Human Hepatocytes Revealed by Capillary Microsampling Mass Spectrometry | journal = Analytical Chemistry | volume = 87 | issue = 20 | pages = 10397–405 | date = October 2015 | pmid = 26398405 | doi = 10.1021/acs.analchem.5b02502 | doi-access = free }}</ref> The Raman tweezers technique combines [[Raman spectroscopy]] with [[optical tweezers]], using a laser beam to trap and manipulate cells.<ref>{{cite book | vauthors = Faria EC, Gardner P | title = Single-Cell Analysis | chapter = Analysis of Single Eukaryotic Cells Using Raman Tweezers | volume = 853 | pages = 151–67 | date = January 2012 | pmid = 22323146 | doi = 10.1007/978-1-61779-567-1_12 | publisher = Humana Press | isbn = 978-1-61779-566-4 | series = Methods in Molecular Biology | veditors = Lindström S, Andersson-Svahn H }}</ref>
-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.
+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]]<ref>{{Cite web |title=MMS |url=https://fluidmems.com/scp4000 |access-date=2024-05-15 |website=MMS |language=en-US}}</ref> 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. IJP 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 (SCA) usually without any application of 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. A latest review gives an account of the recent advances in this field, along with their mechanisms, methods and applications.<ref>{{cite journal| vauthors = Narayanamurthy V, Nagarajan S, Samsuri F, Sridhar TM |date=2017-06-30|title=Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications|url=https://pubs.rsc.org/is/content/articlelanding/2017/ay/c7ay00656j|journal=Analytical Methods|language=en|volume=9|issue=25|doi=10.1039/C7AY00656J|issn=1759-9679|pages=3751–3772}}</ref>
+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.<ref>{{cite journal| vauthors = Narayanamurthy V, Nagarajan S, Samsuri F, Sridhar TM |date=2017-06-30|title=Microfluidic hydrodynamic trapping for single cell analysis: mechanisms, methods and applications|url=https://pubs.rsc.org/is/content/articlelanding/2017/ay/c7ay00656j|journal=Analytical Methods|language=en|volume=9|issue=25|doi=10.1039/C7AY00656J|issn=1759-9679|pages=3751–3772}}</ref>
-=== Associated Technologies ===
-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.
 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.
@@ Line 19: / Line 23: @@
 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.
-Manual single cell picking is a method where cells in a suspension are viewed under a microscope and individually picked using a [[Pipette|micropipette]].
-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 the inside the oil based channels. Pneumatic membrane valves use the manipulation of 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.
-The technique Raman tweezers combines the use [[Raman spectroscopy]] and [[optical tweezers]], which use a laser beam to trap and manipulate cells.
-The development of hydrodynamic-based microfluidic biochips has been increasing over the years. In this technique, the cells are trapped in a particular region for single cell analysis (SCA). This usually occurs without any application of 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 need to develop biochip devices to suit market and researcher demands. Hydrodynamic microfluidics facilitate the development of passive lab-on-chip applications.
 == Genomics ==
 === Techniques ===
-[[Single cell sequencing|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). Currently WGA strategies can be grouped into three categories:
+[[Single-cell sequencing|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
+* Controlled priming and PCR amplification: Adapter-Linker PCR WGA
-* Random priming and PCR Amplification: DOP-PCR, MALBAC
+* 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 best performing for diploid single cell mutation analysis, thanks to its very low Allelic Dropout effect,<ref>{{cite journal | vauthors = Babayan A, Alawi M, Gormley M, Müller V, Wikman H, McMullin RP, Smirnov DA, Li W, Geffken M, Pantel K, Joosse SA | display-authors = 6 | title = Comparative study of whole genome amplification and next generation sequencing performance of single cancer cells | journal = Oncotarget | volume = 8 | issue = 34 | pages = 56066–56080 | date = August 2017 | pmid = 28915574 | pmc = 5593545 | doi = 10.18632/oncotarget.10701 }}</ref><ref>{{cite journal | vauthors = Binder V, Bartenhagen C, Okpanyi V, Gombert M, Moehlendick B, Behrens B, Klein HU, Rieder H, Ida Krell PF, Dugas M, Stoecklein NH, Borkhardt A | display-authors = 6 | title = A new workflow for whole-genome sequencing of single human cells | journal = Human Mutation | volume = 35 | issue = 10 | pages = 1260–70 | date = October 2014 | pmid = 25066732 | doi = 10.1002/humu.22625 | s2cid = 27392899 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Borgström E, Paterlini M, Mold JE, Frisen J, Lundeberg J | title = Comparison of whole genome amplification techniques for human single cell exome sequencing | journal = PLOS ONE | volume = 12 | issue = 2 | pages = e0171566 | year = 2017 | pmid = 28207771 | pmc = 5313163 | doi = 10.1371/journal.pone.0171566 | bibcode = 2017PLoSO..1271566B | doi-access = free }}</ref> and for copy number variation profiling due to its low noise, both with aCGH and with NGS low Pass Sequencing.<ref>{{cite journal | vauthors = Normand E, Qdaisat S, Bi W, Shaw C, Van den Veyver I, Beaudet A, Breman A | title = Comparison of three whole genome amplification methods for detection of genomic aberrations in single cells | journal = Prenatal Diagnosis | volume = 36 | issue = 9 | pages = 823–30 | date = September 2016 | pmid = 27368744 | doi = 10.1002/pd.4866 | s2cid = 5537482 }}</ref><ref>{{cite journal | vauthors = Vander Plaetsen AS, Deleye L, Cornelis S, Tilleman L, Van Nieuwerburgh F, Deforce D | title = STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods | journal = Scientific Reports | volume = 7 | issue = 1 | pages = 17189 | date = December 2017 | pmid = 29215049 | pmc = 5719346 | doi = 10.1038/s41598-017-17525-5 | bibcode = 2017NatSR...717189V }}</ref> This method is only applicable to human cells, both fixed and unfixed.
+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,<ref>{{cite journal | vauthors = Babayan A, Alawi M, Gormley M, Müller V, Wikman H, McMullin RP, Smirnov DA, Li W, Geffken M, Pantel K, Joosse SA | title = Comparative study of whole genome amplification and next generation sequencing performance of single cancer cells | journal = Oncotarget | volume = 8 | issue = 34 | pages = 56066–56080 | date = August 2017 | pmid = 28915574 | pmc = 5593545 | doi = 10.18632/oncotarget.10701 }}</ref><ref>{{cite journal | vauthors = Binder V, Bartenhagen C, Okpanyi V, Gombert M, Moehlendick B, Behrens B, Klein HU, Rieder H, Ida Krell PF, Dugas M, Stoecklein NH, Borkhardt A | title = A new workflow for whole-genome sequencing of single human cells | journal = Human Mutation | volume = 35 | issue = 10 | pages = 1260–70 | date = October 2014 | pmid = 25066732 | doi = 10.1002/humu.22625 | s2cid = 27392899 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Borgström E, Paterlini M, Mold JE, Frisen J, Lundeberg J | title = Comparison of whole genome amplification techniques for human single cell exome sequencing | journal = PLOS ONE | volume = 12 | issue = 2 | pages = e0171566 | year = 2017 | pmid = 28207771 | pmc = 5313163 | doi = 10.1371/journal.pone.0171566 | bibcode = 2017PLoSO..1271566B | doi-access = free }}</ref> and for [[copy number variation]] profiling due to its low noise, both with aCGH and with NGS low Pass Sequencing.<ref>{{cite journal | vauthors = Normand E, Qdaisat S, Bi W, Shaw C, Van den Veyver I, Beaudet A, Breman A | title = Comparison of three whole genome amplification methods for detection of genomic aberrations in single cells | journal = Prenatal Diagnosis | volume = 36 | issue = 9 | pages = 823–30 | date = September 2016 | pmid = 27368744 | doi = 10.1002/pd.4866 | s2cid = 5537482 }}</ref><ref>{{cite journal | vauthors = Vander Plaetsen AS, Deleye L, Cornelis S, Tilleman L, Van Nieuwerburgh F, Deforce D | title = STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods | journal = Scientific Reports | volume = 7 | issue = 1 | pages = 17189 | date = December 2017 | pmid = 29215049 | pmc = 5719346 | doi = 10.1038/s41598-017-17525-5 | bibcode = 2017NatSR...717189V }}</ref> This method is only applicable to human cells, both fixed and unfixed.
-One widely adopted WGA techniques is called degenerate oligonucleotide–primed polymerase chain reaction (DOP-PCR). This method uses the well established DNA amplification method [[Polymerase chain reaction|PCR]] to try and amplify the entire genome using a large set of [[Primer (molecular biology)|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&nbsp;[[enzyme]], usually&nbsp;[[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 [[MALBAC|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.<ref name=":6" /> Once amplified using any of the above techniques, the DNA can be sequenced using Sanger or [[DNA sequencing|next-generation sequencing]] (NGS).
+One widely adopted WGA technique is called degenerate oligonucleotide–primed polymerase chain reaction (DOP-PCR). This method uses the well established DNA amplification method [[Polymerase chain reaction|PCR]] to try and amplify the entire genome using a large set of [[Primer (molecular biology)|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&nbsp;[[enzyme]], usually&nbsp;[[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 [[MALBAC|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.<ref name=":6" />
+Once amplified using any of the above techniques, the DNA can be sequenced using [[Sanger sequencing]] or [[DNA sequencing|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 missed by bulk sequencing of a population, but can be observed in single cell sequencing.<ref>{{cite journal | vauthors = Kalisky T, Quake SR | s2cid = 5601612 | title = Single-cell genomics | journal = Nature Methods | volume = 8 | issue = 4 | pages = 311–4 | date = April 2011 | pmid = 21451520 | doi = 10.1038/nmeth0411-311 }}</ref> 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 somatic mutations and copy number aberration can be observed using single cell sequencing.<ref name=":0" />
+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.<ref>{{cite journal | vauthors = Kalisky T, Quake SR | s2cid = 5601612 | title = Single-cell genomics | journal = Nature Methods | volume = 8 | issue = 4 | pages = 311–4 | date = April 2011 | pmid = 21451520 | doi = 10.1038/nmeth0411-311 }}</ref> 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.<ref name=":0" />
 == 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 [[Complementary DNA|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.<ref name=":0" /> Alternately, 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.<ref>{{cite journal | vauthors = Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L | title = Single-cell in situ RNA profiling by sequential hybridization | journal = Nature Methods | volume = 11 | issue = 4 | pages = 360–1 | date = April 2014 | pmid = 24681720 | pmc = 4085791 | doi = 10.1038/nmeth.2892 }}</ref><ref>{{cite journal | vauthors = Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X | title = RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells | journal = Science | volume = 348 | issue = 6233 | pages = aaa6090 | date = April 2015 | pmid = 25858977 | pmc = 4662681 | doi = 10.1126/science.aaa6090 | url = https://dash.harvard.edu/bitstream/handle/1/17467198/CHEN-DISSERTATION-2015.pdf?sequence=1 }}</ref>
+[[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 [[Complementary DNA|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.<ref name=":0" /> Alternatively, fluorescent compounds attached to RNA [[hybridization probe]]s are used to identify specific sequences and sequential application of different RNA probes will build up a comprehensive transcriptome.<ref>{{cite journal | vauthors = Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L | title = Single-cell in situ RNA profiling by sequential hybridization | journal = Nature Methods | volume = 11 | issue = 4 | pages = 360–1 | date = April 2014 | pmid = 24681720 | pmc = 4085791 | doi = 10.1038/nmeth.2892 }}</ref><ref>{{cite journal | vauthors = Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X | title = RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells | journal = Science | volume = 348 | issue = 6233 | pages = aaa6090 | date = April 2015 | pmid = 25858977 | pmc = 4662681 | doi = 10.1126/science.aaa6090 | url = https://dash.harvard.edu/bitstream/handle/1/17467198/CHEN-DISSERTATION-2015.pdf?sequence=1 }}</ref>
 === Purpose ===
-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.<ref name=":0" />
+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.<ref name=":0" />
-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 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 [[Alternative splicing|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 marker]]s.<ref name=":0" />
+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 [[Alternative splicing|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 marker]]s.<ref name=":0" />
-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.<ref name="pmid37344592">{{cite journal| author=Ozadam H, Tonn T, Han CM, Segura A, Hoskins I, Rao S | display-authors=etal| title=Single-cell quantification of ribosome occupancy in early mouse development. | journal=Nature | year= 2023 | volume= 618 | issue= 7967 | pages= 1057–1064 | pmid=37344592 | doi=10.1038/s41586-023-06228-9 | pmc=10307641 }}</ref>
+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.<ref name="pmid37344592">{{cite journal | vauthors = Ozadam H, Tonn T, Han CM, Segura A, Hoskins I, Rao S, Ghatpande V, Tran D, Catoe D, Salit M, Cenik C | title = Single-cell quantification of ribosome occupancy in early mouse development | journal = Nature | volume = 618 | issue = 7967 | pages = 1057–1064 | date = June 2023 | pmid = 37344592 | pmc = 10307641 | doi = 10.1038/s41586-023-06228-9 | bibcode = 2023Natur.618.1057O }}</ref>
 == Proteomics ==
 === Techniques ===
-There are three major approaches to single-cell proteomics: antibody based methods, fluorescent protein based methods, and mass-spectroscopy based methods.<ref name=":7">{{cite journal | vauthors = Levy E, Slavov N | title = Single cell protein analysis for systems biology | journal = Essays in Biochemistry | volume = 62 | issue = 4 | pages = 595–605 | date = October 2018 | pmid = 30072488 | pmc = 6204083 | doi = 10.1042/EBC20180014 }}</ref><ref name="Unpicking the proteome in single ce"/>
+There are three major approaches to single-cell proteomics: antibody-based methods, fluorescent protein-based methods, and mass spectroscopy-based methods.<ref name=":7">{{cite journal | vauthors = Levy E, Slavov N | title = Single cell protein analysis for systems biology | journal = Essays in Biochemistry | volume = 62 | issue = 4 | pages = 595–605 | date = October 2018 | pmid = 30072488 | pmc = 6204083 | doi = 10.1042/EBC20180014 }}</ref><ref name="Unpicking the proteome in single ce"/>
 ==== 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.
-''Imaging:'' Antibodies can be bound to fluorescent molecules such as [[quantum dot]]s or tagged with organic [[fluorophore]]s for detection by [[Fluorescence microscope|fluorescence microscopy]]. Since different colored quantum dots or unique fluorophores are attached to each antibody it is possible to identify multiple different proteins in a single cell. Quantum dots can 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.<ref>{{cite journal | vauthors = Zrazhevskiy P, True LD, Gao X | title = Multicolor multicycle molecular profiling with quantum dots for single-cell analysis | journal = Nature Protocols | volume = 8 | issue = 10 | pages = 1852–69 | date = October 2013 | pmid = 24008381 | pmc = 4108347 | doi = 10.1038/nprot.2013.112 }}</ref>  For the methods based on organic fluorophores, the fluorescent tags are attached by a reversible linkage such as a DNA-hybrid (that can be melted/dissociated under low-salt conditions)<ref name=":8">{{cite journal | vauthors = Giedt RJ, Pathania D, Carlson JC, McFarland PJ, Del Castillo AF, Juric D, Weissleder R | title = Single-cell barcode analysis provides a rapid readout of cellular signaling pathways in clinical specimens | journal = Nature Communications | volume = 9 | issue = 1 | pages = 4550 | date = October 2018 | pmid = 30382095 | pmc = 6208406 | doi = 10.1038/s41467-018-07002-6 | bibcode = 2018NatCo...9.4550G }}</ref> or chemically inactivated,<ref name=":9">{{cite journal | vauthors = Lin JR, Izar B, Wang S, Yapp C, Mei S, Shah PM, Santagata S, Sorger PK | display-authors = 6 | title = Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes | journal = eLife | volume = 7 | pages = e31657 | date = July 2018 | pmid = 29993362 | pmc = 6075866 | doi = 10.7554/eLife.31657 | veditors = Chakraborty AK, Raj A, Marr C, Horváth P | doi-access = free }}</ref> allowing multiple cycles of analysis, with 3-5 targets quantified per cycle. These approaches have been used for quantifying protein abundance in patient biopsy samples (e.g. cancer) to map variable protein expression in tissues and/or tumors,<ref name=":9" /> and to measure changes in protein expression and cell signaling in response to cancer treatment.<ref name=":8" />
+''Imaging:'' Antibodies can be bound to fluorescent molecules such as [[quantum dot]]s or tagged with organic [[fluorophore]]s for detection by [[Fluorescence microscope|fluorescence microscopy]]. Since different colored quantum dots or unique fluorophores are attached to each antibody it is possible to identify multiple different proteins in a single cell. Quantum dots can 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.<ref>{{cite journal | vauthors = Zrazhevskiy P, True LD, Gao X | title = Multicolor multicycle molecular profiling with quantum dots for single-cell analysis | journal = Nature Protocols | volume = 8 | issue = 10 | pages = 1852–69 | date = October 2013 | pmid = 24008381 | pmc = 4108347 | doi = 10.1038/nprot.2013.112 }}</ref>  For the methods based on organic fluorophores, the fluorescent tags are attached by a reversible linkage such as a DNA-hybrid (that can be melted/dissociated under low-salt conditions)<ref name=":8">{{cite journal | vauthors = Giedt RJ, Pathania D, Carlson JC, McFarland PJ, Del Castillo AF, Juric D, Weissleder R | title = Single-cell barcode analysis provides a rapid readout of cellular signaling pathways in clinical specimens | journal = Nature Communications | volume = 9 | issue = 1 | pages = 4550 | date = October 2018 | pmid = 30382095 | pmc = 6208406 | doi = 10.1038/s41467-018-07002-6 | bibcode = 2018NatCo...9.4550G }}</ref> or chemically inactivated,<ref name=":9">{{cite journal | vauthors = Lin JR, Izar B, Wang S, Yapp C, Mei S, Shah PM, Santagata S, Sorger PK | title = Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes | journal = eLife | volume = 7 | pages = e31657 | date = July 2018 | pmid = 29993362 | pmc = 6075866 | doi = 10.7554/eLife.31657 | veditors = Chakraborty AK, Raj A, Marr C, Horváth P | doi-access = free }}</ref> allowing multiple cycles of analysis, with 3-5 targets quantified per cycle. These approaches have been used for quantifying protein abundance in patient biopsy samples (e.g. cancer) to map variable protein expression in tissues and/or tumors,<ref name=":9" /> and to measure changes in protein expression and cell signaling in response to cancer treatment.<ref name=":8" />
-[[Mass cytometry|''Mass Cytometry'']]'':'' rare metal isotopes, not normally found in cells or tissues, can be attached to the individual antibodies and detected by [[mass spectrometry]] for simultaneous and sensitive identification of proteins.<ref>{{cite journal | vauthors = Nair N, Mei HE, Chen SY, Hale M, Nolan GP, Maecker HT, Genovese M, Fathman CG, Whiting CC | display-authors = 6 | title = Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy | journal = Arthritis Research & Therapy | volume = 17 | pages = 127 | date = May 2015 | issue = 1 | pmid = 25981462 | pmc = 4436107 | doi = 10.1186/s13075-015-0644-z | doi-access = free }}</ref> These techniques can be highly multiplexed for simultaneous quantification of many targets (panels of up to 38 markers) in single cells.<ref>{{cite journal | vauthors = Spitzer MH, Nolan GP | title = Mass Cytometry: Single Cells, Many Features | language = English | journal = Cell | volume = 165 | issue = 4 | pages = 780–91 | date = May 2016 | pmid = 27153492 | pmc = 4860251 | doi = 10.1016/j.cell.2016.04.019 | url = }}</ref>
+[[Mass cytometry|''Mass Cytometry'']]'':'' rare metal isotopes, not normally found in cells or tissues, can be attached to the individual antibodies and detected by [[mass spectrometry]] for simultaneous and sensitive identification of proteins.<ref>{{cite journal | vauthors = Nair N, Mei HE, Chen SY, Hale M, Nolan GP, Maecker HT, Genovese M, Fathman CG, Whiting CC | title = Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy | journal = Arthritis Research & Therapy | volume = 17 | pages = 127 | date = May 2015 | issue = 1 | pmid = 25981462 | pmc = 4436107 | doi = 10.1186/s13075-015-0644-z | doi-access = free }}</ref> These techniques can be highly multiplexed for simultaneous quantification of many targets (panels of up to 38 markers) in single cells.<ref>{{cite journal | vauthors = Spitzer MH, Nolan GP | title = Mass Cytometry: Single Cells, Many Features | language = English | journal = Cell | volume = 165 | issue = 4 | pages = 780–91 | date = May 2016 | pmid = 27153492 | pmc = 4860251 | doi = 10.1016/j.cell.2016.04.019 | url = }}</ref>
 Antibody-DNA quantification: another antibody-based method converts protein levels to DNA levels.<ref name=":7" /> The conversion to DNA makes it possible to amplify protein levels and use NGS to quantify proteins. In one such approach, two antibodies are selected for each protein needed to be quantified. The two antibodies are then modified to have single stranded DNA connected to them that are complementary. When the two antibodies bind to a protein the complementary strands will anneal and produce a double stranded segment 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.<ref>{{cite journal | vauthors = Gong H, Holcomb I, Ooi A, Wang X, Majonis D, Unger MA, Ramakrishnan R | title = Simple Method To Prepare Oligonucleotide-Conjugated Antibodies and Its Application in Multiplex Protein Detection in Single Cells | journal = Bioconjugate Chemistry | volume = 27 | issue = 1 | pages = 217–25 | date = January 2016 | pmid = 26689321 | doi = 10.1021/acs.bioconjchem.5b00613 | doi-access = free }}</ref>
-==== Mass spectroscopy–based methods ====
+==== Mass spectrometry–based methods ====
-In mass spectroscopy based proteomics there are three major steps needed for peptide identification: sample preparation, separation of peptides, and identification of peptides. Several groups have focused on oocytes or very early cleavage-stage cells since these cells are unusually large and provide enough material for analysis.<ref name=":1">{{cite journal | vauthors = Lombard-Banek C, Reddy S, Moody SA, Nemes P | title = Label-free Quantification of Proteins in Single Embryonic Cells with Neural Fate in the Cleavage-Stage Frog (Xenopus laevis) Embryo using Capillary Electrophoresis Electrospray Ionization High-Resolution Mass Spectrometry (CE-ESI-HRMS) | journal = Molecular & Cellular Proteomics | volume = 15 | issue = 8 | pages = 2756–2768 | date = August 2016 | pmid = 27317400 | pmc = 4974349 | doi = 10.1074/mcp.M115.057760 }}</ref><ref name=":2">{{cite journal | vauthors = Sun L, Dubiak KM, Peuchen EH, Zhang Z, Zhu G, Huber PW, Dovichi NJ | title = Single Cell Proteomics Using Frog (Xenopus laevis) Blastomeres Isolated from Early Stage Embryos, Which Form a Geometric Progression in Protein Content | journal = Analytical Chemistry | volume = 88 | issue = 13 | pages = 6653–6657 | date = July 2016 | pmid = 27314579 | pmc = 4940028 | doi = 10.1021/acs.analchem.6b01921 }}</ref><ref name=":3">{{cite journal | vauthors = Virant-Klun I, Leicht S, Hughes C, Krijgsveld J | title = Identification of Maturation-Specific Proteins by Single-Cell Proteomics of Human Oocytes | journal = Molecular & Cellular Proteomics | volume = 15 | issue = 8 | pages = 2616–2627 | date = August 2016 | pmid = 27215607 | pmc = 4974340 | doi = 10.1074/mcp.M115.056887 }}</ref> Another approach, single cell proteomics by mass spectrometry (SCoPE-MS) has quantified thousands of proteins in mammalian cells with typical cell sizes (diameter of 10-15 μm) by combining carrier-cells and single-cell barcoding.<ref name="SCoPE-MS">{{cite bioRxiv | vauthors = Budnik B, Levy E, Slavov N |date=2017-03-15|title=Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation|biorxiv=10.1101/102681}}</ref><ref>{{cite journal | vauthors = Budnik B, Levy E, Harmange G, Slavov N | title = SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation | language = En | journal = Genome Biology | volume = 19 | issue = 1 | pages = 161 | date = October 2018 | pmid = 30343672 | pmc = 6196420 | doi = 10.1186/s13059-018-1547-5 | doi-access = free }}</ref> The second generation, SCoPE2,<ref>{{cite journal| vauthors = Specht H, Emmott E, Koller T, Slavov N |date=2019-07-09|title=High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity|journal=bioRxiv|doi=10.1101/665307|doi-access=free}}</ref><ref>{{cite journal | vauthors = Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N | display-authors = 6 | title = Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2 | journal = Genome Biology | volume = 22 | issue = 1 | pages = 50 | date = January 2021 | pmid = 33504367 | pmc = 7839219 | doi = 10.1186/s13059-021-02267-5 | doi-access = free }}</ref> increased the throughput by automated and miniaturized sample preparation;<ref name="Specht_2018">{{Cite journal |vauthors=Specht H, Harmange G, Perlman DH, Emmott E, Niziolek Z, Budnik B, Slavov N |date=2018-08-25 |title=Automated sample preparation for high-throughput single-cell proteomics |journal=bioRxiv |language=en |pages=399774 |doi=10.1101/399774 |doi-access=free}}</ref> It also improved quantitative reliability and proteome coverage by data-driven optimization of LC-MS/MS<ref>{{cite journal | vauthors = Huffman RG, Chen A, Specht H, Slavov N | title = DO-MS: Data-Driven Optimization of Mass Spectrometry Methods | journal = Journal of Proteome Research | volume = 18 | issue = 6 | pages = 2493–2500 | date = June 2019 | pmid = 31081635 | pmc = 6737531 | doi = 10.1021/acs.jproteome.9b00039 }}</ref> and peptide identification.<ref>{{cite journal | vauthors = Chen AT, Franks A, Slavov N | title = DART-ID increases single-cell proteome coverage | journal = PLOS Computational Biology | volume = 15 | issue = 7 | pages = e1007082 | date = July 2019 | pmid = 31260443 | pmc = 6625733 | doi = 10.1371/journal.pcbi.1007082 | veditors = Cox J | bibcode = 2019PLSCB..15E7082C | doi-access = free }}</ref> The sensitivity and consistency of these methods have been further improved by prioritization,<ref>{{Cite journal | vauthors = Huffman RG, Leduc A, Wichmann C, di Gioia M, Borriello F, Specht H, Derks J, Khan S, Emmott E, Petelski AA, Perlman DH, Cox J, Zanoni I, Slavov N | display-authors = 6 |date=2022-03-18 |title=Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages | journal = bioRxiv |language=en |pages=2022.03.16.484655 |doi=10.1101/2022.03.16.484655| s2cid = 247599981 }}</ref> and massively parallel sample preparation in nanoliter size droplets.<ref>{{cite journal | vauthors = Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N | title = Exploring functional protein covariation across single cells using nPOP | journal = Genome Biology | volume = 23 | issue = 1 | pages = 261 | date = December 2022 | pmid = 36527135 | pmc = 9756690 | doi = 10.1186/s13059-022-02817-5 | doi-access = free }}</ref> Another direction for single-cell protein analysis is based on a scalable framework of multiplexed data-independent acquisition (plexDIA) enables time saving by parallel analysis of both peptide ions and protein samples, thereby realizing multiplicative gains in throughput.<ref>{{cite journal | vauthors =  | title = Framework for multiplicative scaling of single-cell proteomics | journal = Nature Biotechnology | pages = 23–24 | date = July 2022 | volume = 41 | issue = 1 | pmid = 35851377 | doi = 10.1038/s41587-022-01411-1 | s2cid = 250642572 }}</ref><ref>{{cite journal | vauthors = Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N | display-authors = 6 | title = Increasing the throughput of sensitive proteomics by plexDIA | journal = Nature Biotechnology | pages = 50–59 | date = July 2022 | volume = 41 | issue = 1 | pmid = 35835881 | doi = 10.1038/s41587-022-01389-w | pmc = 9839897 }}</ref><ref>{{Cite journal | vauthors = Derks J, Slavov N |date=2022-11-05 |title=Strategies for increasing the depth and throughput of protein analysis by plexDIA | journal = bioRxiv |language=en |pages=2022.11.05.515287 |doi=10.1101/2022.11.05.515287|s2cid=253399025 }}</ref>
+In mass spectroscopy-based proteomics there are three major steps needed for peptide identification: sample preparation, separation of peptides, and identification of peptides. Several groups have focused on oocytes or very early cleavage-stage cells since these cells are unusually large and provide enough material for analysis.<ref>{{cite journal | vauthors = Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJ, Vermeulen M | title = Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs | journal = Nucleic Acids Research | volume = 42 | issue = 15 | pages = 9880–9891 | date = September 2014 | pmid = 25056316 | pmc = 4150773 | doi = 10.1093/nar/gku661 }}</ref><ref name=":1">{{cite journal | vauthors = Lombard-Banek C, Reddy S, Moody SA, Nemes P | title = Label-free Quantification of Proteins in Single Embryonic Cells with Neural Fate in the Cleavage-Stage Frog (Xenopus laevis) Embryo using Capillary Electrophoresis Electrospray Ionization High-Resolution Mass Spectrometry (CE-ESI-HRMS) | journal = Molecular & Cellular Proteomics | volume = 15 | issue = 8 | pages = 2756–2768 | date = August 2016 | pmid = 27317400 | pmc = 4974349 | doi = 10.1074/mcp.M115.057760 | doi-access = free }}</ref><ref name=":2">{{cite journal | vauthors = Sun L, Dubiak KM, Peuchen EH, Zhang Z, Zhu G, Huber PW, Dovichi NJ | title = Single Cell Proteomics Using Frog (Xenopus laevis) Blastomeres Isolated from Early Stage Embryos, Which Form a Geometric Progression in Protein Content | journal = Analytical Chemistry | volume = 88 | issue = 13 | pages = 6653–6657 | date = July 2016 | pmid = 27314579 | pmc = 4940028 | doi = 10.1021/acs.analchem.6b01921 }}</ref><ref name=":3">{{cite journal | vauthors = Virant-Klun I, Leicht S, Hughes C, Krijgsveld J | title = Identification of Maturation-Specific Proteins by Single-Cell Proteomics of Human Oocytes | journal = Molecular & Cellular Proteomics | volume = 15 | issue = 8 | pages = 2616–2627 | date = August 2016 | pmid = 27215607 | pmc = 4974340 | doi = 10.1074/mcp.M115.056887 | doi-access = free }}</ref> Another approach, single cell proteomics by mass spectrometry (SCoPE-MS) has quantified thousands of proteins in mammalian cells with typical cell sizes (diameter of 10-15 μm) by combining carrier-cells and single-cell barcoding.<ref name="SCoPE-MS">{{cite bioRxiv | vauthors = Budnik B, Levy E, Slavov N |date=2017-03-15|title=Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation|biorxiv=10.1101/102681}}</ref><ref>{{cite journal | vauthors = Budnik B, Levy E, Harmange G, Slavov N | title = SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation | language = En | journal = Genome Biology | volume = 19 | issue = 1 | pages = 161 | date = October 2018 | pmid = 30343672 | pmc = 6196420 | doi = 10.1186/s13059-018-1547-5 | doi-access = free }}</ref> The second generation, SCoPE2,<ref>{{cite journal| vauthors = Specht H, Emmott E, Koller T, Slavov N |date=2019-07-09|title=High-throughput single-cell proteomics quantifies the emergence of macrophage heterogeneity|journal=bioRxiv|doi=10.1101/665307|doi-access=free}}</ref><ref>{{cite journal | vauthors = Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N | title = Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2 | journal = Genome Biology | volume = 22 | issue = 1 | pages = 50 | date = January 2021 | pmid = 33504367 | pmc = 7839219 | doi = 10.1186/s13059-021-02267-5 | doi-access = free }}</ref> increased the throughput by automated and miniaturized sample preparation;<ref name="Specht_2018">{{Cite journal |vauthors=Specht H, Harmange G, Perlman DH, Emmott E, Niziolek Z, Budnik B, Slavov N |date=2018-08-25 |title=Automated sample preparation for high-throughput single-cell proteomics |journal=bioRxiv |language=en |pages=399774 |doi=10.1101/399774 |doi-access=free}}</ref> It also improved quantitative reliability and proteome coverage by data-driven optimization of LC-MS/MS<ref>{{cite journal | vauthors = Huffman RG, Chen A, Specht H, Slavov N | title = DO-MS: Data-Driven Optimization of Mass Spectrometry Methods | journal = Journal of Proteome Research | volume = 18 | issue = 6 | pages = 2493–2500 | date = June 2019 | pmid = 31081635 | pmc = 6737531 | doi = 10.1021/acs.jproteome.9b00039 }}</ref> and peptide identification.<ref>{{cite journal | vauthors = Chen AT, Franks A, Slavov N | title = DART-ID increases single-cell proteome coverage | journal = PLOS Computational Biology | volume = 15 | issue = 7 | pages = e1007082 | date = July 2019 | pmid = 31260443 | pmc = 6625733 | doi = 10.1371/journal.pcbi.1007082 | veditors = Cox J | bibcode = 2019PLSCB..15E7082C | doi-access = free }}</ref> The sensitivity and consistency of these methods have been further improved by prioritization,<ref>{{Cite journal | vauthors = Huffman RG, Leduc A, Wichmann C, di Gioia M, Borriello F, Specht H, Derks J, Khan S, Emmott E, Petelski AA, Perlman DH, Cox J, Zanoni I, Slavov N |date=2022-03-18 |title=Prioritized single-cell proteomics reveals molecular and functional polarization across primary macrophages | journal = bioRxiv |language=en |pages=2022.03.16.484655 |doi=10.1101/2022.03.16.484655| s2cid = 247599981 }}</ref> and massively parallel sample preparation in nanoliter size droplets.<ref>{{cite journal | vauthors = Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N | title = Exploring functional protein covariation across single cells using nPOP | journal = Genome Biology | volume = 23 | issue = 1 | pages = 261 | date = December 2022 | pmid = 36527135 | pmc = 9756690 | doi = 10.1186/s13059-022-02817-5 | doi-access = free }}</ref> Another direction for single-cell protein analysis is based on a scalable framework of multiplexed data-independent acquisition (plexDIA) enables time saving by parallel analysis of both peptide ions and protein samples, thereby realizing multiplicative gains in throughput.<ref>{{cite journal | vauthors =  | title = Framework for multiplicative scaling of single-cell proteomics | journal = Nature Biotechnology | pages = 23–24 | date = July 2022 | volume = 41 | issue = 1 | pmid = 35851377 | doi = 10.1038/s41587-022-01411-1 | s2cid = 250642572 }}</ref><ref>{{cite journal | vauthors = Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N | title = Increasing the throughput of sensitive proteomics by plexDIA | journal = Nature Biotechnology | pages = 50–59 | date = July 2022 | volume = 41 | issue = 1 | pmid = 35835881 | doi = 10.1038/s41587-022-01389-w | pmc = 9839897 }}</ref><ref>{{Cite journal | vauthors = Derks J, Slavov N |date=2022-11-05 |title=Strategies for increasing the depth and throughput of protein analysis by plexDIA | journal = bioRxiv |language=en |pages=2022.11.05.515287 |doi=10.1101/2022.11.05.515287|s2cid=253399025 }}</ref>
-The separation of differently sized proteins can be accomplished by using [[capillary electrophoresis]] (CE)  or [[Chromatography|liquid chromatography]] (LC) (using [[Liquid chromatography–mass spectrometry|liquid chromatography with mass spectroscopy]] is also known as LC-MS).<ref name=":1" /><ref name=":2" /><ref name=":3" /><ref name="SCoPE-MS" /> This step gives order to the peptides before quantification using [[Tandem mass spectrometry|tandem mass-spectroscopy]] (MS/MS). The major difference between quantification methods is some use labels on the peptides such as [[tandem mass tag]]s (TMT) or [[Isotopic labeling|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.<ref name=":1" /><ref name=":2" /><ref name=":3" /><ref name="SCoPE-MS" /><ref>{{cite journal | vauthors = Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJ, Vermeulen M | title = Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs | journal = Nucleic Acids Research | volume = 42 | issue = 15 | pages = 9880–91 | date = September 2014 | pmid = 25056316 | pmc = 4150773 | doi = 10.1093/nar/gku661 }}</ref> These methods are very similar to those used to [[Proteomics|quantify the proteome of bulk cells]], with modifications to accommodate the very small sample volume.<ref name="Proteomicsnews">{{Cite news |date=2017-03-09 |title=SCoPE-MS -- We can finally do single cell proteomics!!! |language=en-US |work=News in Proteomics Research |url=http://proteomicsnews.blogspot.com/2017/04/scope-ms-we-can-finally-do-single-cell.html |access-date=2017-06-28}}</ref>
+The separation of differently sized proteins can be accomplished by using [[capillary electrophoresis]] (CE) or [[Chromatography|liquid chromatography]] (LC) (using [[Liquid chromatography–mass spectrometry|liquid chromatography with mass spectroscopy]] is also known as LC-MS).<ref name=":1" /><ref name=":2" /><ref name=":3" /><ref name="SCoPE-MS" /> This step gives order to the peptides before quantification using [[Tandem mass spectrometry|tandem mass-spectroscopy]] (MS/MS). The major difference between quantification methods is some use labels on the peptides such as [[tandem mass tag]]s (TMT) or [[Isotopic labeling|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 do not use labels but rather quantify cells individually. The mass spectroscopy data is then analyzed by running data through databases that count the peptides identified to quantify protein levels.<ref name=":1" /><ref name=":2" /><ref name=":3" /><ref name="SCoPE-MS" /><ref>{{cite journal | vauthors = Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJ, Vermeulen M | title = Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs | journal = Nucleic Acids Research | volume = 42 | issue = 15 | pages = 9880–91 | date = September 2014 | pmid = 25056316 | pmc = 4150773 | doi = 10.1093/nar/gku661 }}</ref> These methods are very similar to those used to [[Proteomics|quantify the proteome of bulk cells]], with modifications to accommodate the very small sample volume.<ref name="Proteomicsnews">{{Cite news |date=2017-03-09 |title=SCoPE-MS -- We can finally do single cell proteomics!!! |language=en-US |work=News in Proteomics Research |url=http://proteomicsnews.blogspot.com/2017/04/scope-ms-we-can-finally-do-single-cell.html |access-date=2017-06-28}}</ref>
 ===== Ionization techniques used in mass spectrometry-based single-cell analysis =====
@@ Line 79: / Line 77: @@
 ====== LAESI ======
-In [[Laser ablation electrospray ionization]] (LAESI) a laser is used to ablate the surface of the sample and the emitted molecules are ionized in the gas phase by charged droplets from electrospray. Similar to DESI the ionization happens in '''ambient conditions'''. Anderton ''et al''. used this ionization technique coupled to a Fourier transform mass spectrometer to analyzed 200 single cells of ''Allium cepa'' (red onion) in high spatial resolution.<ref>{{cite journal | vauthors = Taylor MJ, Liyu A, Vertes A, Anderton CR | title = Ambient Single-Cell Analysis and Native Tissue Imaging Using Laser-Ablation Electrospray Ionization Mass Spectrometry with Increased Spatial Resolution | journal = Journal of the American Society for Mass Spectrometry | volume = 32 | issue = 9 | pages = 2490–2494 | date = September 2021 | pmid = 34374553 | doi = 10.1021/jasms.1c00149 | osti = 1824325 | s2cid = 236968123 }}</ref>
+In [[Laser ablation electrospray ionization]] (LAESI), a laser is used to ablate the surface of the sample and the emitted molecules are ionized in the gas phase by charged droplets from electrospray. Similar to DESI the ionization happens in ambient conditions. Anderton ''et al''. used this ionization technique coupled to a Fourier transform mass spectrometer to analyze 200 single cells of ''Allium cepa'' (red onion) with high spatial resolution.<ref>{{cite journal | vauthors = Taylor MJ, Liyu A, Vertes A, Anderton CR | title = Ambient Single-Cell Analysis and Native Tissue Imaging Using Laser-Ablation Electrospray Ionization Mass Spectrometry with Increased Spatial Resolution | journal = Journal of the American Society for Mass Spectrometry | volume = 32 | issue = 9 | pages = 2490–2494 | date = September 2021 | pmid = 34374553 | doi = 10.1021/jasms.1c00149 | osti = 1824325 | s2cid = 236968123 }}</ref>
 ====== SIMS ======
-[[Secondary ion mass spectrometry]] (SIMS) is a technique similar to DESI, but while DESI is an ambient ionization technique, SIMS happens in [[vacuum]]. The solid sample surface is bombarded by a highly focused beam of '''primary ions'''. As they hit the surface, molecules are emitted from the surface and ionized. The choice of primary ions determines the size of the beam and also the extent of ionization and fragmentation.<ref>{{cite journal | vauthors = Lanekoff I, Sharma VV, Marques C | title = Single-cell metabolomics: where are we and where are we going? | journal = Current Opinion in Biotechnology | volume = 75 | pages = 102693 | date = June 2022 | pmid = 35151979 | doi = 10.1016/j.copbio.2022.102693 | s2cid = 246773056 | doi-access = free }}</ref> Pareek ''et al.'' performed metabolomics to trace how purines are synthesized within [[purinosome]]s and used isotope labeling and SIMS imaging to directly observe hotspots of metabolic activity within frozen HeLa cells.<ref>{{cite journal | vauthors = Pareek V, Tian H, Winograd N, Benkovic SJ | title = Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells | journal = Science | volume = 368 | issue = 6488 | pages = 283–290 | date = April 2020 | pmid = 32299949 | pmc = 7494208 | doi = 10.1126/science.aaz6465 | bibcode = 2020Sci...368..283P }}</ref>
+[[Secondary-ion mass spectrometry]] (SIMS) is a technique similar to DESI, but while DESI is an ambient ionization technique, SIMS happens in [[vacuum]]. The solid sample surface is bombarded by a highly focused beam of primary ions. As they hit the surface, molecules are emitted from the surface and ionized. The choice of primary ions determines the size of the beam and also the extent of ionization and fragmentation.<ref>{{cite journal | vauthors = Lanekoff I, Sharma VV, Marques C | title = Single-cell metabolomics: where are we and where are we going? | journal = Current Opinion in Biotechnology | volume = 75 | pages = 102693 | date = June 2022 | pmid = 35151979 | doi = 10.1016/j.copbio.2022.102693 | s2cid = 246773056 | doi-access = free }}</ref> Pareek ''et al.'' performed metabolomics to trace how purines are synthesized within [[purinosome]]s and used isotope labeling and SIMS imaging to directly observe hotspots of metabolic activity within frozen HeLa cells.<ref>{{cite journal | vauthors = Pareek V, Tian H, Winograd N, Benkovic SJ | title = Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells | journal = Science | volume = 368 | issue = 6488 | pages = 283–290 | date = April 2020 | pmid = 32299949 | pmc = 7494208 | doi = 10.1126/science.aaz6465 | bibcode = 2020Sci...368..283P }}</ref>
 ====== MALDI ======
-In [[Matrix-assisted laser desorption/ionization|matrix-assisted laser desorption]] and ionization (MALDI), the sample is incorporated in a '''chemical matrix''' that is capable of absorbing energy from a laser. Similar to SIMS, ionization happens in vacuum. Laser irradiation ablates the matrix material from the surface and results in charged gas phase matrix particles, the analyte molecules are ionized from this charged chemical matrix. Liu ''et al.'' used MALDI-MS to detect eight phospholipids from single A549 cells.<ref>{{cite journal | vauthors = Xie W, Gao D, Jin F, Jiang Y, Liu H | title = Study of Phospholipids in Single Cells Using an Integrated Microfluidic Device Combined with Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry | journal = Analytical Chemistry | volume = 87 | issue = 14 | pages = 7052–7059 | date = July 2015 | pmid = 26110742 | doi = 10.1021/acs.analchem.5b00010 }}</ref> MALDI MS imaging can be used for spatial metabolomics and single cell analysis.<ref>{{Cite journal |last=Bourceau |first=Patric |last2=Geier |first2=Benedikt |last3=Suerdieck |first3=Vincent |last4=Bien |first4=Tanja |last5=Soltwisch |first5=Jens |last6=Dreisewerd |first6=Klaus |last7=Liebeke |first7=Manuel |date=2023-09-06 |title=Visualization of metabolites and microbes at high spatial resolution using MALDI mass spectrometry imaging and in situ fluorescence labeling |url=https://www.nature.com/articles/s41596-023-00864-1 |journal=Nature Protocols |language=en |pages=1–30 |doi=10.1038/s41596-023-00864-1 |issn=1750-2799}}</ref><ref>{{Cite journal |last=Rappez |first=Luca |last2=Stadler |first2=Mira |last3=Triana |first3=Sergio |last4=Gathungu |first4=Rose Muthoni |last5=Ovchinnikova |first5=Katja |last6=Phapale |first6=Prasad |last7=Heikenwalder |first7=Mathias |last8=Alexandrov |first8=Theodore |date=July 2021 |title=SpaceM reveals metabolic states of single cells |url=https://www.nature.com/articles/s41592-021-01198-0 |journal=Nature Methods |language=en |volume=18 |issue=7 |pages=799–805 |doi=10.1038/s41592-021-01198-0 |issn=1548-7105|pmc=7611214 }}</ref>
+In [[Matrix-assisted laser desorption/ionization|matrix-assisted laser desorption and ionization]] (MALDI), the sample is incorporated in a chemical matrix that is capable of absorbing energy from a laser. Similar to SIMS, ionization happens in vacuum. Laser irradiation ablates the matrix material from the surface and results in charged gas phase matrix particles, with the analyte molecules ionized from this charged chemical matrix. Liu ''et al.'' used MALDI-MS to detect eight phospholipids from single [[A549 cell]]s.<ref>{{cite journal | vauthors = Xie W, Gao D, Jin F, Jiang Y, Liu H | title = Study of Phospholipids in Single Cells Using an Integrated Microfluidic Device Combined with Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry | journal = Analytical Chemistry | volume = 87 | issue = 14 | pages = 7052–7059 | date = July 2015 | pmid = 26110742 | doi = 10.1021/acs.analchem.5b00010 }}</ref> MALDI MS imaging can be used for spatial metabolomics and single-cell analysis.<ref>{{cite journal | vauthors = Bourceau P, Geier B, Suerdieck V, Bien T, Soltwisch J, Dreisewerd K, Liebeke M | title = Visualization of metabolites and microbes at high spatial resolution using MALDI mass spectrometry imaging and in situ fluorescence labeling | journal = Nature Protocols | volume = 18 | issue = 10 | pages = 3050–3079 | date = October 2023 | pmid = 37674095 | doi = 10.1038/s41596-023-00864-1 | s2cid = 261580460 }}</ref><ref>{{cite journal | vauthors = Rappez L, Stadler M, Triana S, Gathungu RM, Ovchinnikova K, Phapale P, Heikenwalder M, Alexandrov T | title = SpaceM reveals metabolic states of single cells | journal = Nature Methods | volume = 18 | issue = 7 | pages = 799–805 | date = July 2021 | pmid = 34226721 | pmc = 7611214 | doi = 10.1038/s41592-021-01198-0 }}</ref>
 === Purpose ===
-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]].<ref name=":4" /> Transcriptomics is still important as studying the difference between RNA levels and protein levels could give insight on which genes are post-transcriptionally regulated.
+The purpose of studying the proteome is to better understand the activity of proteins at the single-cell level. Since proteins are responsible for determining how the cell acts, understanding the proteome of single cells 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]] (not all messenger RNA transcripts are actually translated into proteins).<ref name=":4" /> Transcriptomics is still important, of course, as studying the difference between RNA levels and protein levels can give insight regarding which genes are post-transcriptionally regulated.
 == Metabolomics ==
 === Techniques ===
-There are four major methods used to quantify the metabolome of single cells, they are: fluorescence–based detection, fluorescence biosensors, [[Förster resonance energy transfer|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.<ref name=":5">{{cite journal | vauthors = Zenobi R | s2cid = 21381091 | title = Single-cell metabolomics: analytical and biological perspectives | journal = Science | volume = 342 | issue = 6163 | pages = 1243259 | date = December 2013 | pmid = 24311695 | doi = 10.1126/science.1243259 }}</ref>
+There are four major methods used to quantify the metabolome of single cells; they are: fluorescence–based detection, fluorescence biosensors, [[Förster resonance energy transfer|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.<ref name=":5">{{cite journal | vauthors = Zenobi R | s2cid = 21381091 | title = Single-cell metabolomics: analytical and biological perspectives | journal = Science | volume = 342 | issue = 6163 | pages = 1243259 | date = December 2013 | pmid = 24311695 | doi = 10.1126/science.1243259 }}</ref>
-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 [[Molar concentration|femtomole]] range.<ref name=":10" /> Similar to the methods discussed in proteomics, there has also been success in combining mass spectroscopy with separation techniques such as capillary electrophoresis to quantify metabolites. This method is also capable of detecting metabolites present in femtomole concentrations.<ref name=":5" /> Another method utilizing capillary microsampling combined with mass spectrometry with ion mobility separation has been demonstrated to enhance the molecular coverage and ion separation for single cell metabolomics.<ref name=":12" /><ref name=":11">{{cite journal | vauthors = Zhang L, Foreman DP, Grant PA, Shrestha B, Moody SA, Villiers F, Kwak JM, Vertes A | display-authors = 6 | title = In situ metabolic analysis of single plant cells by capillary microsampling and electrospray ionization mass spectrometry with ion mobility separation | journal = The Analyst | volume = 139 | issue = 20 | pages = 5079–85 | date = October 2014 | pmid = 25109271 | doi = 10.1039/C4AN01018C | url = http://pubs.rsc.org/-/content/articlehtml/2014/an/c4an01018c | bibcode = 2014Ana...139.5079Z }}</ref>  Researchers are trying to develop a technique that can fulfil what current techniques are lacking: high throughput, higher sensitivity for metabolites that have a lower abundance or that have low ionization efficiencies, good replicability and that allow quantification of metabolites.<ref>{{cite journal | vauthors = Duncan KD, Fyrestam J, Lanekoff I | title = Advances in mass spectrometry based single-cell metabolomics | journal = The Analyst | volume = 144 | issue = 3 | pages = 782–793 | date = January 2019 | pmid = 30426983 | doi = 10.1039/C8AN01581C | doi-access = free | bibcode = 2019Ana...144..782D }}</ref>
+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 [[Molar concentration|femtomole]] range.<ref name=":10" /> Similar to the methods discussed in proteomics, there has also been success in combining mass spectroscopy with separation techniques such as capillary electrophoresis to quantify metabolites. This method is also capable of detecting metabolites present in femtomole concentrations.<ref name=":5" /> Another method utilizing capillary microsampling combined with mass spectrometry with ion mobility separation has been demonstrated to enhance the molecular coverage and ion separation for single cell metabolomics.<ref name=":12" /><ref name=":11">{{cite journal | vauthors = Zhang L, Foreman DP, Grant PA, Shrestha B, Moody SA, Villiers F, Kwak JM, Vertes A | title = In situ metabolic analysis of single plant cells by capillary microsampling and electrospray ionization mass spectrometry with ion mobility separation | journal = The Analyst | volume = 139 | issue = 20 | pages = 5079–85 | date = October 2014 | pmid = 25109271 | doi = 10.1039/C4AN01018C | url = http://pubs.rsc.org/-/content/articlehtml/2014/an/c4an01018c | bibcode = 2014Ana...139.5079Z }}</ref>  Researchers are trying to develop a technique that can fulfil what current techniques are lacking: high throughput, higher sensitivity for metabolites that have a lower abundance or that have low ionization efficiencies, good replicability and that allow quantification of metabolites.<ref>{{cite journal | vauthors = Duncan KD, Fyrestam J, Lanekoff I | title = Advances in mass spectrometry based single-cell metabolomics | journal = The Analyst | volume = 144 | issue = 3 | pages = 782–793 | date = January 2019 | pmid = 30426983 | doi = 10.1039/C8AN01581C | doi-access = free | bibcode = 2019Ana...144..782D }}</ref>
 === Purpose ===
@@ Line 101: / Line 98: @@
 == Reconstructing developmental trajectories ==
-Single-cell transcriptomic assays have allowed reconstruction development trajectories. Branching of these trajectories describes cell differentiation. Various methods have been developed for reconstructing branching developmental trajectories from single-cell transcriptomic data.<ref>{{cite journal | vauthors = Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ | s2cid = 3594049 | title = Diffusion pseudotime robustly reconstructs lineage branching | journal = Nature Methods | volume = 13 | issue = 10 | pages = 845–8 | date = October 2016 | pmid = 27571553 | doi = 10.1038/nmeth.3971 | url = http://edoc.mdc-berlin.de/19027/1/19027oa.pdf }}</ref><ref>Setty M, et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol.&nbsp;34, 637–645 (2016).</ref><ref name = Schiebinger2019>{{cite journal | vauthors = Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V, Solomon A, Gould J, Liu S, Lin S, Berube P, Lee L, Chen J, Brumbaugh J, Rigollet P, Hochedlinger K, Jaenisch R, Regev A, Lander ES | display-authors = 6 | title = Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming | journal = Cell | volume = 176 | issue = 4 | pages = 928–943.e22 | date = February 2019 | pmid = 30712874 | pmc = 6402800 | doi = 10.1016/j.cell.2019.01.006 }}</ref><ref name = Chen2019>{{cite journal | vauthors = Chen H, Albergante L, Hsu JY, Lareau CA, Lo Bosco G, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan GC, Pinello L | display-authors = 6 | title = Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM | journal = Nature Communications | volume = 10 | issue = 1 | pages = 1903 | date = April 2019 | pmid = 31015418 | pmc = 6478907 | doi = 10.1038/s41467-019-09670-4 | bibcode = 2019NatCo..10.1903C | doi-access = free }}</ref><ref name= Pandey2022>{{cite journal | vauthors = Pandey K, Zafar H | title = Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET | journal = Nucleic Acids Research | volume = 50 | issue = 15 | pages = e86 | date = August 2022 | pmid = 35639499 | doi = 10.1093/nar/gkac412 | pmc = 9410915 }}</ref> They use various advanced mathematical concepts from [[Transportation theory (mathematics)|optimal transportation]]<ref name = Schiebinger2019 /> to principal graphs.<ref name = Chen2019 /> Some software libraries for reconstruction and visualization of lineage differentiation trajectories are freely available online.<ref>Pinello Lab. [http://stream.pinellolab.org/ Single-Cell Trajectory Reconstruction Exploration and Mapping]</ref>
+Single-cell transcriptomic assays have allowed reconstruction development trajectories. Branching of these trajectories describes cell differentiation. Various methods have been developed for reconstructing branching developmental trajectories from single-cell transcriptomic data.<ref>{{cite journal | vauthors = Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ | s2cid = 3594049 | title = Diffusion pseudotime robustly reconstructs lineage branching | journal = Nature Methods | volume = 13 | issue = 10 | pages = 845–8 | date = October 2016 | pmid = 27571553 | doi = 10.1038/nmeth.3971 | url = http://edoc.mdc-berlin.de/19027/1/19027oa.pdf }}</ref><ref>Setty M, et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol.&nbsp;34, 637–645 (2016).</ref><ref name = Schiebinger2019>{{cite journal | vauthors = Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V, Solomon A, Gould J, Liu S, Lin S, Berube P, Lee L, Chen J, Brumbaugh J, Rigollet P, Hochedlinger K, Jaenisch R, Regev A, Lander ES | title = Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming | journal = Cell | volume = 176 | issue = 4 | pages = 928–943.e22 | date = February 2019 | pmid = 30712874 | pmc = 6402800 | doi = 10.1016/j.cell.2019.01.006 }}</ref><ref name = Chen2019>{{cite journal | vauthors = Chen H, Albergante L, Hsu JY, Lareau CA, Lo Bosco G, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan GC, Pinello L | title = Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM | journal = Nature Communications | volume = 10 | issue = 1 | pages = 1903 | date = April 2019 | pmid = 31015418 | pmc = 6478907 | doi = 10.1038/s41467-019-09670-4 | bibcode = 2019NatCo..10.1903C | doi-access = free }}</ref><ref name= Pandey2022>{{cite journal | vauthors = Pandey K, Zafar H | title = Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET | journal = Nucleic Acids Research | volume = 50 | issue = 15 | pages = e86 | date = August 2022 | pmid = 35639499 | doi = 10.1093/nar/gkac412 | pmc = 9410915 }}</ref> They use various advanced mathematical concepts from [[Transportation theory (mathematics)|optimal transportation]]<ref name = Schiebinger2019 /> to principal graphs.<ref name = Chen2019 /> Some software libraries for reconstruction and visualization of lineage differentiation trajectories are freely available online.<ref>Pinello Lab. [http://stream.pinellolab.org/ Single-Cell Trajectory Reconstruction Exploration and Mapping]</ref>
 == Cell–cell interaction ==
@@ Line 118: / Line 115: @@
 {{refbegin}}
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+* {{cite journal | vauthors = Luo C, Liu H, Xie F, Armand EJ, Siletti K, Bakken TE, Fang R, Doyle WI, Stuart T, Hodge RD, Hu L, Wang BA, Zhang Z, Preissl S, Lee DS, Zhou J, Niu SY, Castanon R, Bartlett A, Rivkin A, Wang X, Lucero J, Nery JR, Davis DA, Mash DC, Satija R, Dixon JR, Linnarsson S, Lein E, Behrens MM, Ren B, Mukamel EA, Ecker JR | title = Single nucleus multi-omics identifies human cortical cell regulatory genome diversity | journal = Cell Genomics | volume = 2 | issue = 3 | date = March 2022 | page = 100107 | pmid = 35419551 | doi = 10.1016/j.xgen.2022.100107 | pmc = 9004682 }}
 * {{cite journal | vauthors = Descamps L, Le Roy D, Deman AL | title = Microfluidic-Based Technologies for CTC Isolation: A Review of 10 Years of Intense Efforts towards Liquid Biopsy | journal = International Journal of Molecular Sciences | volume = 23 | issue = 4 | date = February 2022 | page = 1981 | pmid = 35216097 | doi = 10.3390/ijms23041981 | pmc = 8875744 | doi-access = free }}
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Single-cell analysis: Difference between revisions

Latest revision as of 18:06, 12 November 2024

Single-cell isolation

Genomics

Techniques

Purpose

Transcriptomics

Techniques

Purpose

Proteomics

Techniques

Antibody–based methods

Mass spectrometry–based methods

Ionization techniques used in mass spectrometry-based single-cell analysis

Nano-DESI

LAESI

SIMS

MALDI

Purpose

Metabolomics

Techniques

Purpose

Reconstructing developmental trajectories

Cell–cell interaction

See also

References

Further reading