The CyTOF Effect: The Future Of Flow Cytometry (Mass Cytometry)

CyTOF (Cytometry by Time Of Flight), or Mass Cytometry, is a relatively new scientific method. It allows more parameters to be measured through the use of heavy metal isotopes, and maximises the use of smaller cell samples. It has led to some significant changes in how we analyse cytometric data, and flow cytometry analysis software has evolved as a result.

What is CyTOF/Mass Cytometry

The process of CyTOF differs quite a lot from traditional flow cytometry.

Traditional flow cytometry uses the reaction between staining on fluorochrome conjugated antibodies and the laser to pick up wavelengths which can be used to measure a number of parameters e.g. functional and exhaustion markers.

Mass cytometry on the other hand, involves incubating the cell samples with heavy metal isotopes, which bind. Then the sample is put through a nebulizer before the mass spectrometer. Here, the cells and free atoms are ionized, making an ion cloud where elements are trapped with a quadrupole. The heavy metal ions are separated in a CyTOF mass spectrometer by mass-to-charge ratio.

Why did we need Mass Cytometry/CyTOF?

The need for CyTOF (mass cytometry), came from the limitations of flow cytometry.

The numbers of parameters that can be measured in traditional flow cytometry methods are low, due to spectral overlap of the fluorochromes. This meant that sample sizes of cells needed to be larger, which could prove difficult in certain biopsies.

Mass cytometry offers the opportunity for increased speed, sensitivity, and resolution.

Because of the metal ions used to enable a multitude of markers to be measured, less cells are needed. More immunologic data can be gathered, from less cells. Therefore, it is more effective and efficient at helping researchers and scientists to discover earlier signs of disease and a more detailed biological response to therapy and treatment.

Challenges in Designing Mass Cytometry Software

The influx of information that mass cytometry has brought to the field, has brought with it challenges in interpreting that data. To visualise the multiple dimensions of data proves tricky, and reducing dimensionality loses multiple properties of the cells.

There have been multiple new algorithms created, specifically for mass cytometry. These attempt to reduce the loss of information from multiple dimensions.

Flow Analysis Software providers have raced to keep up with these changes, and provide products specifically designed to help with this type of data.

New types of plots, visualisation, and reporting features have accompanied new plugins for the new algorithms.

Incyt247: Mass Cytometry Analysis Software

At Applied Cytometry, we have curated incyt247 for your mass cytometry software requirements. The platform can be as complex or easy to use as you like, as you can add extra options and features depending on your own needs.

It uses a drag and drop interface, making everything very visual and simple to move around. We offer multi-variate plots and heatmaps, as well as bi-variate plots, to enable you to customise your visualisation to what you need, when you need it.

In terms of statistics, we offer clustering and dimension reduction, as well as differential statistics and meta annotation. We offer integration for single cell RNAseq and any other molecular readouts you might need to analyse.

You can use R plugins, but we also offer python plugins and a central exchange to enable your analysis to be modified from a range of different sources.

Reports can be customised and made online, and exported to a PDF within seconds.

Find out more and get in touch to try it here.

Conclusion

Through this article, we have discussed CyTOF / Mass Cytometry, where it came from, why it’s useful, and the influence on the scientific analysis software industry.

Mass cytometry, like any scientific method, should not be hailed as the last step on the journey of cytometric improvement.

There are limitations, such as having to know and define your targets prior to starting. You have to know what you’re looking for, and only measure that specifically.

Now, we have to appreciate and enjoy what Mass Cytometry has given to us, whilst continually searching for the next step for knowledge and discovery.

AI in flow cytometry research