Best bioinformatics software for mass-cytometry analysis

Conventional flow cytometry is limited by the number of fluorochromes and cell markers that can be targeted at once. Overcoming this limitation, mass-cytometry combines mass-spectrometry and flow cytometry by using metal-conjugated antibodies to label cellular proteins and extend the number of markers to be targeted.

 

A combination of spectrometry and cytometry

 

In mass-cytometry, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argonplasma, 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.

 

Due to its numerous advantages (minimal overlap, amount of data generated), mass-cytometry has become a leading technology in immunology, cancer, developmental biology and other areas of research that focus on heterogeneous cell populations. To help you choose between all available tools, we asked OMICtools members to choose for their favorite mass-cytometry analysis tools. Here is the top 3 of this survey.

 

1. CellCycleTRACER
2. Cytofkit
3. ImmunoClust

First position for CellCycleTRACER

 

You were 70% to choose CellCycleTRACER as your favorite mass-cytometry analysis tool.

 

CellCycleTRACER is a supervised machine-learning algorithm that classifies and sorts single-cell mass cytometry data according to their cell cycle, which allows to correct for cell-cycle-state and cell-volume heterogeneity.

The algorithm is implemented as a simple and intuitive web application and can be applied to any mass cytometry dataset. Its application requires that four channels be dedicated to the cell-cycle markers p-HH3, p-RB, cyclin B1, and IdU. The developing team is currently bringing it to the cloud, where scientists throughout the world will be able to upload and analyze their datasets for free. The web application allows different experiment visualization, such as cell cycle prediction and cell cycle trajectories. It comes with an online manual and is accessible freely after registration here.

 

Second place for Cytofkit

 

47% of OMICtools users chose Cytofkit as their favorite mass-cytometry analysis tool.

 

This software is a Bioconductor package which integrates bioinformatics methods and novel algorithms to offer a comprehensive pipeline of tools for mass-cytometry analysis. In a nutshell, it allows data pre-processing, data visualization, automatic identification of cell subsets and inference of the relatedness between cell subsets.

 

For less tech-savvy users, it also provides a graphical user interface and a shiny application for interactive visualization of cell populations. It is freely available from the Bioconductor website at this link.

 

Third place for ImmunoClust

 

Chosen by 47% of voters, ImmunoClust is an automated analysis pipeline for mass cytometry data, which consists of two parts. First, cell events of each sample are grouped into individual clusters. Subsequently, a classification algorithm assorts these cell event clusters into populations comparable between different samples. The clustering of cell events is designed for datasets with large event counts in high dimensions as a global unsupervised method, sensitive to identify rare cell types even when next to large populations.

 

ImmunoClust is implemented as an R-package and is provided as source code from Bioconductor.

 

References

Rapsomaniki et al. (2018). CellCycleTRACER accounts for cell cycle and volume in mass cytometry data. Nature Communications.

Sörensen et al. (2015). ImmunoClust—An Automated Analysis Pipeline for the Identification of Immunophenotypic Signatures in High-Dimensional Cytometric Datasets. Cytometry.

Chen et al. (2016). Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline. PLOS Computational Biology.