Mass cytometry

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CyTOF mass cytometer. Mass cytometer.jpg
CyTOF mass cytometer.

Mass cytometry is a mass spectrometry technique based on inductively coupled plasma mass spectrometry and time of flight mass spectrometry used for the determination of the properties of cells (cytometry). [1] [2] In this approach, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argon plasma, which ionizes the metal-conjugated antibodies. The metal signals are then analyzed by a time-of-flight mass spectrometer. The approach overcomes limitations of spectral overlap in flow cytometry by utilizing discrete isotopes as a reporter system instead of traditional fluorophores which have broad emission spectra. [3]

Contents

Commercialization

Tagging technology and instrument development occurred at the University of Toronto and DVS Sciences, Inc. [1] [4] CyTOF (cytometry by time of flight) was initially commercialized by DVS Sciences in 2009. In 2014, Fluidigm acquired DVS Sciences [5] to become a reference company in single cell technology. [6] In 2022 Fluidigm received a capitol infusion and changed its name to Standard BioTools. [7] The CyTOF, CyTOF2, Helios (CyTOF3) and CyTOF XT [8] (4th generation) have been commercialized up to now. Fluidigm sells a variety of commonly used metal-antibody conjugates, and an antibody conjugation kit.

Imaging Mass Cytometry (IMC)

Imaging mass cytometry (IMC) is a relatively new imaging technique, emerged from previously available CyTOF technology (cytometry by time of flight), that combines mass spectrometry with UV laser ablation to generate pseudo images of tissue samples. [9] [10] This approach adds spatial resolution to the data, which enables simultaneous analysis of multiple cell markers at subcellular resolution and their spatial distribution in tissue sections. [9] [11] The IMC approach, in the same way as CyTOF , relies on detection of metal-tagged antibodies using time-of-flight mass spectrometry, allowing for quantification of up to 40 markers simultaneously. [12] [13]

Data analysis

CyTOF mass cytometry data is recorded in tables that list, for each cell, the signal detected per channel, which is proportional to the number of antibodies tagged with the corresponding channel's isotope bound to that cell. These data are formatted as FCS files, which are compatible with traditional flow cytometry software. Due to the high-dimensional nature of mass cytometry data, novel data analysis tools have been developed as well. [14]

Imaging Mass Cytometry data analysis has its specificity due to different nature of data obtained. In terms of data analysis, both IMC and CyTOF generate large datasets with high dimensionality that require specialized computational methods for analysis. However, data generated by IMC can be more challenging to analyze due to additional data complexity and need for specific tools and pipelines specific for digital image analysis, whereas the data generated by CyTOF is generally analyzed using conventional flow cytometry software. A comprehensive overview of IMC data analysis techniques has been given by Milosevic in. [15]

Advantages and disadvantages

Advantages include minimal overlap in metal signals meaning the instrument is theoretically capable of detecting 100 parameters per cell, entire cell signaling networks can be inferred organically without reliance on prior knowledge, and one well-constructed experiment produces large amounts of data. [3]

Disadvantages, in the case of CyTOF, include the practical flow rate is around 500 cells per second versus several thousand in flow cytometry and current reagents available limit cytometer use to around 50 parameters per cell. Additionally, mass cytometry is a destructive method and cells cannot be sorted for further analysis. In the case of IMC, the resolution of the data is relatively low (1μm2/pixel), the technique is as well destructive, acquiring of the data is also very slow, and it requires specialized expensive equipment and expertise.

Applications

Mass cytometry has research applications in medical fields including immunology, hematology, and oncology. It has been used in studies of hematopoiesis, [16] cell cycle, [17] cytokine expression, and differential signaling responses.

MC has been used in various research fields, such as cancer biology, immunology, and neuroscience, to provide a more comprehensive understanding of tissue architecture and cellular interactions. [18] [19] [20] [21] [22] [23]

Related Research Articles

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<span class="mw-page-title-main">Ion-mobility spectrometry–mass spectrometry</span>

Ion mobility spectrometry–mass spectrometry (IMS-MS) is an analytical chemistry method that separates gas phase ions based on their interaction with a collision gas and their masses. In the first step, the ions are separated according to their mobility through a buffer gas on a millisecond timescale using an ion mobility spectrometer. The separated ions are then introduced into a mass analyzer in a second step where their mass-to-charge ratios can be determined on a microsecond timescale. The effective separation of analytes achieved with this method makes it widely applicable in the analysis of complex samples such as in proteomics and metabolomics.

<span class="mw-page-title-main">CyTOF</span>

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<span class="mw-page-title-main">Dmitry Bandura</span> Canadian Scientist, Co-inventor of Mass Cytometry technology

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<span class="mw-page-title-main">Vladimir Baranov</span>

Vladimir Baranov is a Soviet born Canadian scientist and one of the original co-inventors of Mass cytometry technology...

<span class="mw-page-title-main">Scott D. Tanner</span> Canadian scientist

Scott Tanner is a Canadian scientist, inventor, and entrepreneur. His areas of expertise include mass spectroscopy, especially inductively coupled plasma mass spectrometry (ICP-MS), and mass cytometry.

<span class="mw-page-title-main">Olga Ornatsky</span> Canadian Scientist

Olga Ornatsky is a Soviet born, Canadian scientist. Ornatsky co-founded DVS Sciences in 2004 along with Dmitry Bandura, Vladimir Baranov and Scott D. Tanner.

References

  1. 1 2 Dmitry Bandura; Vladimir Baranov; Olga Ornatsky; Scott D. Tanner; Kinach R; Lou X; Pavlov S; Vorobiev S; Dick JE; Antonov A (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): 6813–6822. doi:10.1021/ac901049w. PMID   19601617.
  2. ↑ Di Palma, Serena; Bodenmiller, Bernd (2015). "Unraveling cell populations in tumors by single-cell mass cytometry". Current Opinion in Biotechnology. 31: 122–129. doi:10.1016/j.copbio.2014.07.004. ISSN   0958-1669. PMID   25123841.
  3. 1 2 Spitzer, Matthew H.; Nolan, Garry P. (2016). "Mass Cytometry: Single Cells, Many Features". Cell. 165 (4): 780–791. doi:10.1016/j.cell.2016.04.019. ISSN   0092-8674. PMC   4860251 . PMID   27153492.
  4. ↑ Ornatsky, O; Bandura D; Baranov V; Nitz M; Winnick MA; Tanner S (30 September 2010). "Highly multiparametric analysis by mass cytometry". Journal of Immunological Methods. 361 (1–2): 1–20. doi:10.1016/j.jim.2010.07.002. PMID   20655312.
  5. ↑ "Fluidigm | Press Releases | FLUIDIGM TO ACQUIRE DVS SCIENCES". www.fluidigm.com. Retrieved 2015-11-11.
  6. ↑ "An Open Letter to Customers of Fluidigm and DVS Sciences, Inc" (PDF). Fluidigm and DVS Sciences, Inc. 13 February 2014. Retrieved 4 July 2014.
  7. ↑ "Standard BioTools| Press Releases | Fluigigm Capital Infusion". www.globenewswire.com. Retrieved 2022-01-22.
  8. ↑ "CyTOF XT". StandardBio. 2021-05-21. Retrieved 8 December 2022.
  9. 1 2 Giesen, Charlotte; Wang, Hao A O; Schapiro, Denis; Zivanovic, Nevena; Jacobs, Andrea; Hattendorf, Bodo; Schüffler, Peter J; Grolimund, Daniel; Buhmann, Joachim M; Brandt, Simone; Varga, Zsuzsanna; Wild, Peter J; Günther, Detlef; Bodenmiller, Bernd (2014). "Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry". Nature Methods. 11 (4): 417–422. doi:10.1038/nmeth.2869. ISSN   1548-7091. PMID   24584193. S2CID   205422590.
  10. ↑ Chang, Qing; Ornatsky, Olga I.; Siddiqui, Iram; Loboda, Alexander; Baranov, Vladimir I.; Hedley, David W. (2017). "Imaging Mass Cytometry". Cytometry Part A. 91 (2): 160–169. doi: 10.1002/cyto.a.23053 . PMID   28160444. S2CID   29523347.
  11. ↑ Schapiro, Denis; Jackson, Hartland W; Raghuraman, Swetha; Fischer, Jana R; Zanotelli, Vito R T; Schulz, Daniel; Giesen, Charlotte; Catena, Raúl; Varga, Zsuzsanna; Bodenmiller, Bernd (2017). "histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data". Nature Methods. 14 (9): 873–876. doi:10.1038/nmeth.4391. ISSN   1548-7091. PMC   5617107 . PMID   28783155.
  12. ↑ Ijsselsteijn, Marieke E.; van der Breggen, Ruud; Farina Sarasqueta, Arantza; Koning, Frits; de Miranda, Noel F. C. C. (2019-10-29). "A 40-Marker Panel for High Dimensional Characterization of Cancer Immune Microenvironments by Imaging Mass Cytometry". Frontiers in Immunology. 10: 2534. doi: 10.3389/fimmu.2019.02534 . ISSN   1664-3224. PMC   6830340 . PMID   31736961.
  13. ↑ Han, Guojun; Chen, Shih-Yu; Gonzalez, Veronica D.; Zunder, Eli R.; Fantl, Wendy J.; Nolan, Garry P. (2017). "Atomic mass tag of bismuth-209 for increasing the immunoassay multiplexing capacity of mass cytometry". Cytometry Part A. 91 (12): 1150–1163. doi:10.1002/cyto.a.23283. ISSN   1552-4922. PMC   5802970 . PMID   29205767. S2CID   205731719.
  14. ↑ Krishnaswamy, Smita; Spitzer, Matthew; Mingueneau, Michael; Bendall, Sean; Litvin, Oren; Stone, Erica; Pe'er, Dana; Nolan, Garry (28 Nov 2014). "Conditional density-based analysis of T cell signaling in single-cell data". Science. 346 (6213): 1250689. doi:10.1126/science.1250689. PMC   4334155 . PMID   25342659.
  15. ↑ Milosevic, Vladan (2023). "Different approaches to Imaging Mass Cytometry data analysis". Bioinformatics Advances. 3: vbad046. doi:10.1093/bioadv/vbad046. ISSN   2635-0041. PMC   10115470 . PMID   37092034.
  16. ↑ Bendall SC, Simonds EF, Qiu P, Amir El-ad D, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe'er D, Tanner SD, Nolan GP (2011). "Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum". Science. 332 (6030): 687–96. Bibcode:2011Sci...332..687B. doi:10.1126/science.1198704. PMC   3273988 . PMID   21551058.
  17. ↑ Behbehani, Gregory K.; Bendall, Sean C.; Clutter, Matthew R.; Fantl, Wendy J.; Nolan, Garry P. (2012-07-01). "Single-cell mass cytometry adapted to measurements of the cell cycle". Cytometry Part A. 81 (7): 552–566. doi:10.1002/cyto.a.22075. ISSN   1552-4930. PMC   3667754 . PMID   22693166.
  18. ↑ Damond, Nicolas; Engler, Stefanie; Zanotelli, Vito R.T.; Schapiro, Denis; Wasserfall, Clive H.; Kusmartseva, Irina; Nick, Harry S.; Thorel, Fabrizio; Herrera, Pedro L.; Atkinson, Mark A.; Bodenmiller, Bernd (2019). "A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry". Cell Metabolism. 29 (3): 755–768.e5. doi:10.1016/j.cmet.2018.11.014. PMC   6821395 . PMID   30713109.
  19. ↑ Ramaglia, Valeria; Sheikh-Mohamed, Salma; Legg, Karen; Park, Calvin; Rojas, Olga L; Zandee, Stephanie; Fu, Fred; Ornatsky, Olga; Swanson, Eric C; Pitt, David; Prat, Alexandre; McKee, Trevor D; Gommerman, Jennifer L (2019-08-01). "Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry". eLife. 8: e48051. doi: 10.7554/eLife.48051 . ISSN   2050-084X. PMC   6707785 . PMID   31368890.
  20. ↑ Jackson, Hartland W.; Fischer, Jana R.; Zanotelli, Vito R. T.; Ali, H. Raza; Mechera, Robert; Soysal, Savas D.; Moch, Holger; Muenst, Simone; Varga, Zsuzsanna; Weber, Walter P.; Bodenmiller, Bernd (2020-02-27). "The single-cell pathology landscape of breast cancer". Nature. 578 (7796): 615–620. Bibcode:2020Natur.578..615J. doi:10.1038/s41586-019-1876-x. ISSN   0028-0836. PMID   31959985. S2CID   256820400.
  21. ↑ Moldoveanu, Dan; Ramsay, LeeAnn; Lajoie, Mathieu; Anderson-Trocme, Luke; Lingrand, Marine; Berry, Diana; Perus, Lucas J.M.; Wei, Yuhong; Moraes, Cleber; Alkallas, Rached; Rajkumar, Shivshankari; Zuo, Dongmei; Dankner, Matthew; Xu, Eric Hongbo; Bertos, Nicholas R. (2022). "Spatially mapping the immune landscape of melanoma using imaging mass cytometry". Science Immunology. 7 (70): eabi5072. doi:10.1126/sciimmunol.abi5072. ISSN   2470-9468. PMID   35363543. S2CID   247865180.
  22. ↑ Hoch, Tobias; Schulz, Daniel; Eling, Nils; Gómez, Julia Martínez; Levesque, Mitchell P.; Bodenmiller, Bernd (2022). "Multiplexed imaging mass cytometry of the chemokine milieus in melanoma characterizes features of the response to immunotherapy". Science Immunology. 7 (70): eabk1692. doi:10.1126/sciimmunol.abk1692. ISSN   2470-9468. PMID   35363540. S2CID   247865735.
  23. ↑ Liu, Zehan; Xun, Jing; Liu, Shuangqing; Wang, Botao; Zhang, Aimin; Zhang, Lanqiu; Wang, Ximo; Zhang, Qi (2022). "Imaging mass cytometry: High-dimensional and single-cell perspectives on the microenvironment of solid tumours". Progress in Biophysics and Molecular Biology. 175: 140–146. doi:10.1016/j.pbiomolbio.2022.10.003. PMID   36252872. S2CID   252961111.
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