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Mass spectrometry's sensitivity to detect different ions allows measurements of upwards of 50 targets per cell while avoiding issues with spectral overlap seen when using fluorescent probes.<ref>{{cite journal | vauthors = Spitzer MH, Nolan GP | title = Mass Cytometry: Single Cells, Many Features | journal = Cell | volume = 165 | issue = 4 | pages = 780–91 | date = May 2016 | pmid = 27153492 | pmc = 4860251 | doi = 10.1016/j.cell.2016.04.019 }}</ref><ref name=":5">{{cite journal | vauthors = Gadalla R, Noamani B, MacLeod BL, Dickson RJ, Guo M, Xu W, Lukhele S, Elsaesser HJ, Razak AR, Hirano N, McGaha TL, Wang B, Butler M, Guidos CJ, Ohashi PS, Siu LL, Brooks DG | display-authors = 6 | title = Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials | language = English | journal = Frontiers in Oncology | volume = 9 | pages = 415 | date = 2019 | pmid = 31165047 | pmc = 6534060 | doi = 10.3389/fonc.2019.00415 }}</ref> However, this sensitivity also means trace heavy metal contamination is a concern.<ref name=":1">{{cite journal | vauthors = Olsen LR, Leipold MD, Pedersen CB, Maecker HT | title = The anatomy of single cell mass cytometry data | journal = Cytometry. Part A | volume = 95 | issue = 2 | pages = 156–172 | date = February 2019 | pmid = 30277658 | doi = 10.1002/cyto.a.23621 }}</ref> Using large numbers of probes creates new problems in analyzing the high dimensional data generated.<ref name=":2">{{cite journal | vauthors = Palit S, Heuser C, de Almeida GP, Theis FJ, Zielinski CE | title = Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology | language = English | journal = Frontiers in Immunology | volume = 10 | pages = 1515 | date = 2019 | pmid = 31354705 | pmc = 6634245 | doi = 10.3389/fimmu.2019.01515 }}</ref>
Mass spectrometry's sensitivity to detect different ions allows measurements of upwards of 50 targets per cell while avoiding issues with spectral overlap seen when using fluorescent probes.<ref>{{cite journal | vauthors = Spitzer MH, Nolan GP | title = Mass Cytometry: Single Cells, Many Features | journal = Cell | volume = 165 | issue = 4 | pages = 780–91 | date = May 2016 | pmid = 27153492 | pmc = 4860251 | doi = 10.1016/j.cell.2016.04.019 }}</ref><ref name=":5">{{cite journal | vauthors = Gadalla R, Noamani B, MacLeod BL, Dickson RJ, Guo M, Xu W, Lukhele S, Elsaesser HJ, Razak AR, Hirano N, McGaha TL, Wang B, Butler M, Guidos CJ, Ohashi PS, Siu LL, Brooks DG | display-authors = 6 | title = Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials | language = English | journal = Frontiers in Oncology | volume = 9 | pages = 415 | date = 2019 | pmid = 31165047 | pmc = 6534060 | doi = 10.3389/fonc.2019.00415 }}</ref> However, this sensitivity also means trace heavy metal contamination is a concern.<ref name=":1">{{cite journal | vauthors = Olsen LR, Leipold MD, Pedersen CB, Maecker HT | title = The anatomy of single cell mass cytometry data | journal = Cytometry. Part A | volume = 95 | issue = 2 | pages = 156–172 | date = February 2019 | pmid = 30277658 | doi = 10.1002/cyto.a.23621 }}</ref> Using large numbers of probes creates new problems in analyzing the high dimensional data generated.<ref name=":2">{{cite journal | vauthors = Palit S, Heuser C, de Almeida GP, Theis FJ, Zielinski CE | title = Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology | language = English | journal = Frontiers in Immunology | volume = 10 | pages = 1515 | date = 2019 | pmid = 31354705 | pmc = 6634245 | doi = 10.3389/fimmu.2019.01515 }}</ref>
[[File:Mass Spectrometry Principle.png|thumb|490x490px|Mass spectrometry principle]]
[[File:Mass Spectrometry Principle.png|thumb|490x490px|CyTOF WorflowMajor steps of a CyTOF procedure]]


== History ==
== History ==
Line 26: Line 26:
== Applications ==
== Applications ==
{{unsourced|section}}
{{unsourced|section}}
CyTOF provides important information at a single cell level about protein expression, immunophenotype, and functional characterization. It is a valuable tool in immunology, where the large number of parameters has helped to elucidate the workings of this complex system<ref name=":4">{{Cite journal|last=Bjornson|first=Zach B|last2=Nolan|first2=Garry P|last3=Fantl|first3=Wendy J|date=2013-08-01|title=Single-cell mass cytometry for analysis of immune system functional states|url=https://www.sciencedirect.com/science/article/pii/S0952791513001088|journal=Current Opinion in Immunology|series=Host pathogens / Immune senescence|language=en|volume=25|issue=4|pages=484–494|doi=10.1016/j.coi.2013.07.004|issn=0952-7915|pmc=PMC3835664|pmid=23999316}}</ref><ref name=":6" />. For example, natural killer cells have diverse properties affected by numerous markers in various combinations, which could not be analyzed with ease prior to this technology. Simultaneously measuring many biomarkers makes it possible to identify over 30 distinct immunophenotype subsets within one complex group of cells. This can help to more fully characterize immune function, infectious disease, and cancers, and understand cells response to therapy.
CyTOF provides important information at a single cell level about protein expression, immunophenotype, and functional characterization. It is a valuable tool in immunology, where the large number of parameters has helped to elucidate the workings of this complex system<ref name=":4">{{Cite journal|last=Bjornson|first=Zach B|last2=Nolan|first2=Garry P|last3=Fantl|first3=Wendy J|date=2013-08-01|title=Single-cell mass cytometry for analysis of immune system functional states|url=https://www.sciencedirect.com/science/article/pii/S0952791513001088|journal=Current Opinion in Immunology|series=Host pathogens / Immune senescence|language=en|volume=25|issue=4|pages=484–494|doi=10.1016/j.coi.2013.07.004|issn=0952-7915|pmc=PMC3835664|pmid=23999316}}</ref><ref name=":6" />. For example, natural killer cells have diverse properties affected by numerous markers in various combinations, which could not be analyzed with ease prior to this technology<ref>{{Citation|last=Leipold|first=Michael D.|title=Multiparameter Phenotyping of Human PBMCs Using Mass Cytometry|date=2015|url=https://doi.org/10.1007/978-1-4939-2963-4_7|work=Immunosenescence: Methods and Protocols|pages=81–95|editor-last=Shaw|editor-first=Albert C.|series=Methods in Molecular Biology|place=New York, NY|publisher=Springer|language=en|doi=10.1007/978-1-4939-2963-4_7|isbn=978-1-4939-2963-4|pmc=PMC4748856|pmid=26420710|access-date=2021-02-25|last2=Newell|first2=Evan W.|last3=Maecker|first3=Holden T.}}</ref>. Simultaneously measuring many biomarkers makes it possible to identify over 30 distinct immunophenotype subsets within one complex group of cells<ref name=":4" />. This can help to more fully characterize immune function, infectious disease, and cancers, and understand cells response to therapy.


== Advantages and disadvantages ==
== Advantages and disadvantages ==

Revision as of 22:09, 25 February 2021

Cytometry by time of flight, or CyTOF, is an application of mass cytometry used to quantify labeled targets on the surface and interior of single cells. CyTOF allows the quantification of multiple cellular components simultaneously using an ICP-MS detector.

CyTOF takes advantage of immunolabeling to quantify proteins, carbohydrates or lipids in a cell. Targets are selected to answer a specific research question and are labeled with lanthanide metal tagged antibodies. Labeled cells are nebulized and mixed with heated argon gas to dry the cell containing particles. The sample-gas mixture is focused and ignited with an argon plasma torch.  This breaks the cells into their individual atoms and creates an ion cloud. Abundant low weight ions generated from environmental air and biological molecules are removed using a quadrupole mass analyzer. The remaining heavy ions from the antibody tags are quantified by Time-of-flight mass spectrometry.[1] Ion abundances correlate  with the amount of target per cell and can be used to infer cellular qualities.[2]

Mass spectrometry's sensitivity to detect different ions allows measurements of upwards of 50 targets per cell while avoiding issues with spectral overlap seen when using fluorescent probes.[3][4] However, this sensitivity also means trace heavy metal contamination is a concern.[5] Using large numbers of probes creates new problems in analyzing the high dimensional data generated.[6]

CyTOF WorflowMajor steps of a CyTOF procedure

History

In 1994 Tsutomu Nomizu and colleagues at Nagoya University performed the first mass spectrometry experiments of single cells. Nomizu realized that single cells could be nebulized, dried,  and ignited in plasma to generate clouds of ions which could be detected by emission spectrometry.[7] In this type of experiment elements such as calcium within the cell could be quantified. Inspired by Flow cytometry, in 2007 Scott D. Tanner built upon this ICP-MS with the first multiplexed assay using lanthanide metals to label DNA and cell surface markers.[8] In 2008 Tanner described the tandem attachment of a flow cytometer to an ICP-MS machine as well as new antibody tags that would allow massively multiplexed analysis of cell markers.[9] By further optimizing the detection speed and sensitivity of this flow coupled ICP-MS machine they built the first CyTOF machine.[1]

The CyTOF machine was originally owned by the Canadian company DVS Sciences but is now the exclusive product of Fluidigm after their acquisition in 2014 of DVS sciences. There have been 3 iterations of the CyTOF apparatus named CyTOF, CyTOF2 and Helios™.[5] The successive improvements were largely in increased detection range and software parameters with the Helios machine able to detect from metals ranging from yttium-89 to bismuth-209 and throughput and analyze 2000 events per minute.

Workflow

The Lanthanide group of elements are used for tagging antibodies, as the background in biological samples is very low [10]. When choosing the appropriate isotope for the biomarker, low expression biomarkers should be paired with an isotope that has high signal intensity[11]. If a less pure isotope must be used, it should be paired with a low expression biomarker, to minimize any non specific binding or background.

Isotope polymers are constructed using diethylenetriaminepentaacetic acid (DTPA) chelator to bind ions together[11]. The polymer terminates with a thiol or a maleimide that links it to reduced disulfides in the Fc region of the antibody.[12] Four to five polymers are bound to an antibody, resulting in about 100 isotope atoms per antibody [12]. Tagged antibodies may be in solution, conjugated to beads, or surface immobilized. The cell staining follows the same procedures as in fluorescent staining for flow cytometry.

To distinguish between live and dead cells, cells can be probed with rhodium, an intercalator which can only penetrate dead cells. Then all cells are fixed and stained with iridium, which penetrates all cells, to be able to visualize which are alive[12].

This is kind of about the machine:

The cell introduction method of the mass cytometer is an aerosol splitter injection[11]. The cells are then captured in a stream of argon gas, then transported to the plasma where they are vaporized, atomized, and ionized. The cell is now a cloud of ions, the cell passes into the ion optics center

Applications

CyTOF provides important information at a single cell level about protein expression, immunophenotype, and functional characterization. It is a valuable tool in immunology, where the large number of parameters has helped to elucidate the workings of this complex system[13][11]. For example, natural killer cells have diverse properties affected by numerous markers in various combinations, which could not be analyzed with ease prior to this technology[14]. Simultaneously measuring many biomarkers makes it possible to identify over 30 distinct immunophenotype subsets within one complex group of cells[13]. This can help to more fully characterize immune function, infectious disease, and cancers, and understand cells response to therapy.

Advantages and disadvantages

The major advantage of CYTOF is the ability to investigate a larger number of parameters per panel than other cytometry methods. This allows a greater understanding of complex and heterogeneous cell populations, without the need for many complex and overlapping panels. Panels can include up to 45 antibodies, as opposed to the 10 that can be done in flow cytometry but require great expertise to design [13]. More antibodies per panel saves on time, allows understanding of a larger picture, and requires fewer numbers of cells per experiment, which is particularly advantageous when samples are limited such as with tumour studies[4].

The use of the heavy metal isotopes also lowers background when compared to using fluorescent antibodies. Some cell types such as myeloid cells, have high rates of autofluorescence, that creates a lot of background noise in cytometry[4]. However, the rare heavy metal isotopes used are not present in biological systems, therefore there is very little or no background seen, and overall sensitivity is increased[4]. The detection overlap between the different heavy metals is also very low compared to the overlap seen in fluorescent cytometry, which makes it much simpler to design a panel of many markers. Fluorescent dyes are subject to photobleaching, requiring the entire process to happen within a few hours after staining. Metal tagged antibodies however are viable for up to 2 weeks without losing signal, adding more flexibility to experiments. The stained samples can also be cryopreserved, which may be particularly useful for clinical trials when samples are collected over a longer period of time[4].

Costs of CyTOF are high, as the metal tagged antibodies and antibody conjunction kits are expensive.  A major downside of CyTOF is that acquisition flow rate is quite slow compared to flow cytometry, by almost an order of magnitude[11][4]. Because heavy metals are common in laboratory reagents, avoiding contamination during sample preparation is very important.

Data analysis

Ions are accelerated  through the spectrometer in pulses. The electron cloud generated from a single cell is typically 10-150 pulses. The output of a Helios™ run is a binary  integrated mass data (IMD) file which contains electron intensities measured from the ions for each mass channel. The continuous pulses must be resolved into individual cell events corresponding to the ion cloud generated from one cell. Each bin of between 10-150 pulses that passes the user set lower convolution threshold, is considered a cell event by the Helios™ software.[1][5] The lower convolution threshold is the minimal ion count that must be reached across all ion channels to be considered a cell event. The value for this parameter  increases with the number of ions being measured and thus more counts are required to define a cell event when more labels are used.[5]  

For data analysis the IMD file is converted into the flow cytometry standard (FCS) format. This file contains the total ion counts for each channel for every cell arranged in a matrix and is the same file generated during flow cytometry.[5] Data analysis is performed on the FCS file. Manual gating of this data can be performed as is done for flow cytometry and most of the tools available for flow cytometry analysis have been ported to CyTOF(See flow cytometry bioinformatics).[6] CyTOF data is typically high dimensional. To delineate relationships between cell populations dimensionality reduction algorithms are often used. Several multidimensional analysis clustering algorithms are common. Popular tools include tSNE,  FlowSOM, and the diffusion pseudo time (DPT).[6] The downstream analysis methods depend on the research goals.

References

  1. ^ a b c Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R, Lou X, et al. (August 2009). "Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry". Analytical Chemistry. 81 (16): 6813–22. doi:10.1021/ac901049w. PMID 19601617.
  2. ^ Wang W, Su B, Pang L, Qiao L, Feng Y, Ouyang Y, et al. (June 2020). "High-dimensional immune profiling by mass cytometry revealed immunosuppression and dysfunction of immunity in COVID-19 patients". Cellular & Molecular Immunology. 17 (6): 650–652. doi:10.1038/s41423-020-0447-2. PMC 7186533. PMID 32346099.
  3. ^ Spitzer MH, Nolan GP (May 2016). "Mass Cytometry: Single Cells, Many Features". Cell. 165 (4): 780–91. doi:10.1016/j.cell.2016.04.019. PMC 4860251. PMID 27153492.
  4. ^ a b c d e f Gadalla R, Noamani B, MacLeod BL, Dickson RJ, Guo M, Xu W, et al. (2019). "Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials". Frontiers in Oncology. 9: 415. doi:10.3389/fonc.2019.00415. PMC 6534060. PMID 31165047.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  5. ^ a b c d e Olsen LR, Leipold MD, Pedersen CB, Maecker HT (February 2019). "The anatomy of single cell mass cytometry data". Cytometry. Part A. 95 (2): 156–172. doi:10.1002/cyto.a.23621. PMID 30277658.
  6. ^ a b c Palit S, Heuser C, de Almeida GP, Theis FJ, Zielinski CE (2019). "Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology". Frontiers in Immunology. 10: 1515. doi:10.3389/fimmu.2019.01515. PMC 6634245. PMID 31354705.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  7. ^ Nomizu T, Kaneco S, Tanaka T, Ito D, Kawaguchi H, Vallee BT (1994-10-01). "Determination of Calcium Content in Individual Biological Cells by Inductively Coupled Plasma Atomic Emission Spectrometry". Analytical Chemistry. 66 (19): 3000–3004. doi:10.1021/ac00091a004.
  8. ^ Tanner SD, Ornatsky O, Bandura DR, Baranov VI (March 2007). "Multiplex bio-assay with inductively coupled plasma mass spectrometry: Towards a massively multivariate single-cell technology". Spectrochimica Acta Part B: Atomic Spectroscopy. 62 (3): 188–195. doi:10.1016/j.sab.2007.01.008.
  9. ^ Tanner SD, Bandura DR, Ornatsky O, Baranov VI, Nitz M, Winnik MA (2008-01-01). "Flow cytometer with mass spectrometer detection for massively multiplexed single-cell biomarker assay". Pure and Applied Chemistry. 80 (12): 2627–2641. doi:10.1351/pac200880122627.
  10. ^ Ornatsky OI, Kinach R, Bandura DR, Lou X, Tanner SD, Baranov VI, et al. (2008-03-28). "Development of analytical methods for multiplex bio-assay with inductively coupled plasma mass spectrometry". Journal of Analytical Atomic Spectrometry. 23 (4): 463–469. doi:10.1039/B710510J. PMC 2600572. PMID 19122859.
  11. ^ a b c d e Chattopadhyay, Pratip K.; Roederer, Mario (2012-07-01). "Cytometry: Today's technology and tomorrow's horizons". Methods. Flow Cytometry and Cell Sorting: the Next Generation. 57 (3): 251–258. doi:10.1016/j.ymeth.2012.02.009. ISSN 1046-2023. PMC 3374038. PMID 22391486.{{cite journal}}: CS1 maint: PMC format (link)
  12. ^ a b c Tanner SD, Baranov VI, Ornatsky OI, Bandura DR, George TC (May 2013). "An introduction to mass cytometry: fundamentals and applications". Cancer Immunology, Immunotherapy. 62 (5): 955–65. doi:10.1007/s00262-013-1416-8. PMID 23564178.
  13. ^ a b c Bjornson, Zach B; Nolan, Garry P; Fantl, Wendy J (2013-08-01). "Single-cell mass cytometry for analysis of immune system functional states". Current Opinion in Immunology. Host pathogens / Immune senescence. 25 (4): 484–494. doi:10.1016/j.coi.2013.07.004. ISSN 0952-7915. PMC 3835664. PMID 23999316.{{cite journal}}: CS1 maint: PMC format (link)
  14. ^ Leipold, Michael D.; Newell, Evan W.; Maecker, Holden T. (2015), Shaw, Albert C. (ed.), "Multiparameter Phenotyping of Human PBMCs Using Mass Cytometry", Immunosenescence: Methods and Protocols, Methods in Molecular Biology, New York, NY: Springer, pp. 81–95, doi:10.1007/978-1-4939-2963-4_7, ISBN 978-1-4939-2963-4, PMC 4748856, PMID 26420710, retrieved 2021-02-25{{citation}}: CS1 maint: PMC format (link)
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