viSNE plot of mass cytometry derived cell populations. Image Source: “viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nature Biotech.” 2013, ttp://

Cell analysis technologies within immunology are advancing at a rapid rate and are rendering deeper data across higher volumes of cells thanks to the ongoing mainstreaming of mass cytometric analysis. In comparison to older cell analysis methods, mass cytometry allows for vastly more detailed phenotyping of cells in exchange for much lower throughput and much higher cost. 1 Mature cell analysis technologies such as flow cytometry will continue to be used alongside mass cytometry as cheaper and higher throughput adjuncts until they are eventually supplanted. Once mass cytometry completes its maturation, it will become the dominant method of immune cell analysis.

Mass cytometry is currently performed by only a few specialized instruments which must be operated by skilled personnel.2 Preparing for mass cytometric experiments also involves a substantial resource commitment as researchers must frequently generate their own reagents instead of purchasing them ready-to-use like in other cell analysis methods.3 This resource commitment will decrease over time as the scope of commercially offered reagents increases in response to demand. If the refinement of flow cytometry technologies to the point of near-automation are any indication, mass cytometry will only become easier and cheaper to perform as time goes by, meaning that the number of users and quantity of data will continue to increase. Users who are accustomed to performing cell analysis with popular systems like flow cytometry will not be able to make sense of data gathered using mass cytometry without investing in a software platform that enables information management and collaborative experimentation and analysis.   

Cellular Time of Flight (CyTOF)

Mass cytometry operates via the time of flight principle that is used in mass spectrometry in conjunction with the antibody-tagging of other immunological analysis methods. This makes it well suited to be adapted to existing protocols.4 Unlike in other analysis methods, antibodies intended for mass cytometric analyses are tagged with metal isotopes before use in phenotyping. After being tagged with metal, the antibodies can then be stained onto cultured cells, much like in flow cytometric analysis, though much slower. After staining, the cells are ready to be analysed and are put into the mass cytometric analyzer.

Within the mass cytometry system, cells are nebulized into a plasma torch, destroying all material except for the metals that were attached to the antibodies used to stain. The hot cloud of metal particles is then sprayed into a detection chamber, which measures the time of flight of each particle, allowing for software to determine which particle corresponds to which metal and thus which antibody.5 For each individual cell that is analysed in a mass cytometric system, researchers can examine the frequency of anything they can target with an antibody, just like in mainstream analyzer technology.

Unlike in other cell analysis systems, mass cytometry’s data output has a few unique characteristics:

  • No interference during detection like in fluorescent cell analyzers
  • No day-to-day variability caused by discrepancies in detection apparatus setup or operator error
  • Drastically reduced background
  • Cell populations with low levels of expression can be detected as easily as populations with high levels of expression
  • All data for each cell is detected agnostically, allowing for multiple populations of interest to be investigated without compromising rigor during experimental setup
  • Number of separate detection channels is only limited by the ability to create different metal isotopes, with greater than 30 channels being standard
  • Metal intercalators establish whether detected particles contained a cell, replacing imprecise non-fluorescent light scatter based size-measurement

These differences result in vast amounts of high quality cell expression data. Researchers have struggled to find a way to standardize analysis of mass cytometry data, turning to information technology for aid.6 Though it’s possible to analyze mass cytometry data using system intended for flow cytometry, performing a total analysis of every parameter against every other parameter is no longer viable due to the sheer number of parameters that can be examined.7 Instead, researchers pick one of several different algorithms to create treelike visualizations of cell populations based off of their expression.8

The resulting trees offer an extremely granular view of cell expression across many micropopulations. Given the number of parameters that mass cytometry is capable of investigating, detecting hundreds of micropopulations from a single sample is to be expected. Clusters of micropopulations can then be compared with each other in order to derive subtle insights. Previous cell analysis methods produced data depicting a few dozen cell populations at most. Mass cytometry can very easily generate more data than can be analyzed by one person, even with the help of sorting algorithms.

Data Management Meets Cellular Analysis

Mass cytometry will continue to take off and thus managing and analyzing the resulting data will be ever more important. There are a number of different levels of data management and analysis for mass cytometry data which contribute to their complexity and difficulty of processing:

  • Raw data sets directly from the instrument
  • Normalized data sets
  • Data set of one experiment
  • Bulk normalized detection data for one sample within an experiment
  • Schematic cell population visualization of bulk data
  • Cell-population tree visualization of bulk data or of large populations identified via the schematic visualization
  • Comparison of branches of the tree visualization
  • Comparisons between expression levels within groups of micropopulations within the same branch
  • Examination of single cells within each micropopulation, if the experimental setup permits
  • Generation of bivariate dot plots for each previous comparison  

The sheer number of data comparisons and insights that a single mass cytometry experiment can produce begs for a robust collaboration suite that can also support group analysis and experiment planning. An informatics platform with the following characteristics will be well suited for use with mass cytometry:

  • Tracking and planning generation of metal-labeled antibodies required for experimentation
  • Tracking inventory of previously purchased or produced metal-labeled antibodies as well as other necessary logistics
  • Tracking cell cultures to be used in experimentation
  • Managing mass cytometry experimental pipelines
  • Managing booking of the mass cytometers across many users
  • Comparison between data produced by differing users
  • Intelligent sorting and categorization of data in order for users to quickly identify the algorithm or analysis method used to derive conclusions of prior experiments performed by others
  • Collaborative data viewing and analysis
  • Integration with other information technology solutions which are used to analyze mass cytometry data
  • Integrative data analysis which allows users to plan experiments based off of results generated by another user

Without a system for collaboration, data management and analysis, it’s impossible to unlock the wealth of insights that mass cytometry data sets hold. Many researchers continue to struggle with information technology solutions that aren’t able to accommodate mass cytometry data or group analysis and their numbers will only increase as the technology matures. There is currently an informatics system which can meet the needs of mass cytometry users everywhere, however.

BIOVIA’s Collaborative Science Solutions is the collaboration, data management and data analysis platform that can truly unlock the potential of mass cytometry. Using Collaborative Science Solutions, you can break up your mass cytometry data sets and distribute them to the team to analyze expediently and leave no cell population unexamined. Contact us today to find out how BIOVIA can help you get the most out of the latest technology and move science forward.

  1.  “A deep profiler’s guide to cytometry.” 2012,
  2. “Analyzing the phenotypic and functional complexity of lymphocytes using CyTOF (cytometry by time-of-flight).“ 2012,
  4. “Mass Cytometry: Technique for Real Time Single Cell Multitarget Immunoassay Based on Inductively Coupled Plasma Time-of-Flight Mass Spectrometry.” 2009,
  6. “viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.” 2013,