M6  Cytometry by time‐of‐flight (CyTOF)

Course Goals 

The course will comprise theory and practice of mass-cytometry applied to assessing the immune response in human and animal models. Cytometry by time‐of‐flight (CyTOF), or mass cytometry, allows the simultaneous detection of multiple phenotypic markers at the single cell level by making use of antibodies and specific probes conjugated with pure heavy metal isotopes. Labelled cells are then detected by atomic mass spectrometry. Whereas most conventional flow cytometry can be used to measure fluorescence in a maximum of 18 channels, with considerable spectral overlap requiring ad hoc mathematical processing, CyTOF technology makes it possible to measure more than 50 different parameters (in theory 130), with minimal spectral overlap. The course will describe the technology in details, and will present methods for characterizing surface markers and intracellular markers (cytokines and degranulation markers), allowing the characterization of activation, differentiation and functionality of lymphoid and myeloid cells. The training will be particular focused on new methods for unsupervised analysis of high dimensional data generated by flow and mass cytometry.


Application Deadline:  

15 December 2019

Course Dates:

4-5 March 2020 (1,5 days) 

/Q3 2021 


Dr. Anne-Sophie Gallouet


Course content

DAY-1 (1:30 am to 5:30 pm)

1. Introduction to mass cytometry

2. Optimization of protocols for lymphoid and myeloid cell staining

3. Barcoding of multi-samples acquisition

4. Acquisition and data storage


DAY-2 (8:30 am to 5:30)

5. QC and data normalization

6. Principles of bioinformatics for mass and flow cytometry

7. Unsupervised data analysis (SPADE, ViSnE)

8. Comparing phenotypes of different origins (CytoCompare method)

9. Statistical tools for data analysis

Learning Outcomes 

To be introduced to mass cytometry from the basic theory to data analysis and biological interpretation of the results.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N° 730964.

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