By Haoyu Li
Flow Cytometry (FC) is a technique used to examine the chemical and physical properties of a population of cells. A suspension of cells is focused to an ideal flow rate of one cell a time through a laser beam. The laser is scattered in relation to the size and complexity of the cell. This allows identification and classification of the cell population. Often, stains (fluorochrome conjugated antibodies) are also used to study the expression of various markers on the cell surface membrane. The fluorochromes transit into an excited state when in contact with the laser and then emits light of signature wave lengths that are picked up by a detector.
FC has emerged as a key tool to profile multiple parameters of the immune system, including vital functional and exhaustion markers associated with the quality of the immune response (Irish and Doxie, 2014). However, FC is limited by the number of parameters that can be analyzed at one time (generally 12 per staining panel) because the emission spectra of some fluorochromes overlap significantly. This means that stains must be broken up into groups with redundancy of many of the cell lineage markers in different stains. As a result, FC requires large sample sizes for coverage of diverse immune subsets. This is particularly detrimental for tumor biopsies where sample sizes are often limiting, and the broad array of FC staining panels often cannot be performed. Further, when only a few markers can be analyzed in a single sample, researchers must design panels using a priori knowledge of marker expression patterns to characterize cells of interest. If unusual marker expression patterns are encountered, as is often the case in disease states, follow-up studies that require time-consuming design and optimization of new panels must be performed, assuming more patient sample is available.
Mass Cytometry, or Cytometry by Time Of Flight (CyTOF) offers a different approach to the question. Spitzer and Nolan (2016) summarizes the process in a concise and clear fashion: Cell samples are first incubated with a cocktail of antibodies that have been previously conjugated with heavy metal isotopes via a polymer chain of chelating groups. These antibodies bind targets of interest within the sample, enabling the attached isotopes to serve as reporters for expression levels of the targets. Cells are then passed into a nebulizer, which prepares the cells for complete ionization in the mass spectrometer. Upon entry to the mass spectrometer, an argon plasma breaks all of the chemical bonds to atomize the cells, and also ionizes the free atoms produced along the way. The resulting ion cloud is enriched for heavy metal ions using a quadrupole which traps common biological elements. The heavy metal ions are then separated in a time of flight mass spectrometer by their mass-to-charge ratio and detected as an electrical signal at the terminal gate. Ion counts for each isotope directly resembles the expression of their corresponding targets, because each target molecule is bound by one antibody molecule, which has one metal isotope atom conjugated to it.
The heavy metal ions are not normally present in biological specimens and produces very distinctive peaks when detected, which allows far more markers to be measured in a single tube using CyTOF. Consequently, fewer cells are required per experiment than would be needed for traditional FC, which would require multiple tubes (with different antibody panels) to cover the same number of markers. By incorporating many parameters into single stains, CyTOF enables acquisition of large amounts of immunologic data from limited sample sizes to better understand biological systems, response to therapy and signatures of disease. Examples include characterization of intra- and inter-tumor leukemia heterogeneity that correlates with clinical outcomes(Levine et al, 2015) as well as dissections of T and NK cell subtypes with high resolution (Horowitz et al, 2013; Wong et al, 2015), antiviral T cell responses (Sen et al, 2014; Huang et al, 2017; Simoni et al, 2018), and immune cell signatures linked to recovery from surgery (Gaudillière et al, 2014). Thus, CyTOF has enormous potential to discover disease associated immunologic changes in cancer, identify functional changes to guide subsequent therapy and ultimately predict therapeutic outcomes.
However, this is not to say that CyTOF is now the go to option when it comes to single cell profiling. Samples are atomized and ionized in the spectrometer and any action to recover the living cells for further analysis remains infeasible (Spitzer and Nolan, 2016). Sensitivity of CyTOF falls short in comparison to more quantum-efficient fluorochromes (e.g. phycoerythrin), making it less suitable for measuring molecules at low expression levels. In contrast to the several thousand cells per second flow rate of a traditional FC, it is limited to 500 per second in CyTOF, not to mention that CyTOF instruments are often more expensive than FC instruments. Moreover, most important are the limitations shared by FC and CyTOF. Some mediators of cellular behavior, such as many small-molecule metabolites, are difficult or impossible to measure by CyTOF or FC because there is no easy technical approach that maintains small molecules and a binding agent associated with the cell (Spitzer and Nolan, 2016).
In conclusion, both FC and CyTOF have their advantages and limitations. It is a choice between casting a broad net to capture more information at a particular level with more parameters in CyTOF; or target fewer markers to reveal relevant interactions with a greater resolution in FC. Such a dilemma is often what is faced by researchers when addressing a biological question.
References
Gaudillière B. et al (2014). Clinical recovery from surgery correlates with single-cell immune signatures. Science Translational Medicine, 6(255), pp. 255ra131.
Horowitz A. et al (2013). Genetic and Environmental Determinants of Human NK Cell Diversity Revealed by Mass Cytometry. Science Translational Medicine, 5(208), pp. 208ra145.
Huang A. et al (2017). T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature, 545, pp. 60-65.
Irish J. and Doxie D. (2014). High-Dimensional Single-Cell Cancer Biology. In: Fienberg H., Nolan G. (eds) High-Dimensional Single Cell Analysis. Current Topics in Microbiology and Immunology, vol 377. Springer, Berlin, Heidelberg.
Levine J. et al (2015). Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 162(1), pp. 184-197.
Sen N. at al (2014). Single-Cell Mass Cytometry Analysis of Human Tonsil T Cell Remodeling by Varicella Zoster Virus. Cell Reports, 8(2), pp. 633-645.
Simoni Y. et al (2018). Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature, 557, pp. 575-579.
Spitzer M. and Nolan G. (2016). Mass Cytometry: Single Cells, Many Features. Cell, 165(4), pp. 780-791.
Wong M. et al (2015). Mapping the Diversity of Follicular Helper T Cells in Human Blood and Tonsils Using High-Dimensional Mass Cytometry Analysis. Cell Reports, 11(11), pp. 1822-1833.
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