Immune monitoring using mass cytometry and related high-dimensional imaging approaches

Abstract

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

Key points

  • Immune monitoring of human cells using systems immunology approaches has the potential to produce new insights into pathological processes and therapeutic opportunities for rheumatic disease research.

  • Proteomic approaches that use elemental (heavy metal) reporter ions, such as mass cytometry and high-dimensional imaging techniques, might be of value for the study of a wide variety of clinical samples.

  • Mass cytometry enables in-depth analysis of the phenotype and functional state of immune cells at the single-cell level.

  • High-dimensional imaging techniques use concepts analogous to mass cytometry to image cells in their histological context, providing spatial and cell–cell interaction information.

  • A combination of these technologies with data-driven analytical approaches can give predictive insights into disease mechanisms for rheumatic diseases.

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Fig. 1: Analysis of single-cell suspensions by mass cytometry.
Fig. 2: High-dimensional imaging analysis of tissue sections.
Fig. 3: Conducting large-scale immune-monitoring studies using mass cytometry.

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Acknowledgements

The work of F.J.H. is supported by the EMBO organization (EMBO Long-Term Fellowship ALTF 1141-2017), the Novartis Foundation for Medical-Biological Research (16C148) and the Swiss National Science Foundation (SNF Early Postdoc Mobility P2ZHP3-171741). The work of S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09), the NIH (1DP2OD022550-01, 1R01AG056287-01, 1R01AG057915-01, 1-R00-GM104148-01, 1U24CA224309-01, 5U19AI116484-02 and U19 AI104209) and a Translational Research Award from the Stanford Cancer Institute.

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The authors contributed equally to all aspects of the article.

Correspondence to Sean C. Bendall.

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Competing interests

S.C.B. declares that he is an inventor of multiplexed ion beam imaging technology and a scientific founder of IONpath Inc., the company that commercialized this technology. F.J.H. declares no competing interests.

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Peer review information

Nature Reviews Rheumatology thanks J. Lederer, P. Brodin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Inductively coupled plasma

A type of plasma in which the energy is supplied through electromagnetic induction (changes in magnetic fields).

Time-of-flight

The time taken by a particle to travel through a medium; measuring the time-of-flight of ions in an electric field can be used to infer the ions’ mass-to-charge ratio and therefore its identity.

Cell barcoding

A method of labelling cells with a sample-specific signature that can be used to subsequently pool cells from several samples for downstream staining and processing.

Rastering

A pattern of scanning in which an area is scanned (for example, with an ion beam) in lines from side to side, starting at the top.

Image segmentation

The process of identifying and partitioning an image into meaningful objects (such as cells) in order to facilitate their downstream analysis.

Deep learning

A type of machine learning in which artificial neural networks with multiple layers of adjustable nodes are used to learn how to perform specific tasks from large amounts of data.

Classifier

An algorithm that has been trained to predict the class of data points.

Artificial neural networks

A type of machine learning framework inspired by the biological structure of the brain, in which (potentially many) layers of interconnected nodes transmit information to each other and apply transformations to perform classification or prediction tasks.

Minimum spanning tree

In a graph consisting of points (nodes) connected through edges, the minimum spanning tree represents the subset of the graph that connects all nodes with the minimum total edge weight, usually representing the length of the edge.

Force-directed layouts

Graphical renderings that assign spring-like (attractive and repulsive) forces between the edges and nodes of a graph to position them in 2D space.

Clustering

Grouping a set of points that are similar to each other.

Self-organizing maps

A type of unsupervised clustering and dimensionality reduction approach that preserves the topological information of the input data.

Representation learning

The automated process of transforming raw data into useful features that are subsequently used in other machine learning applications.

Simpson’s diversity index

A measurement of diversity that takes into account the number of different groups present in a dataset, as well as their relative abundance.

Autoencoders

A type of artificial neural network that aims to learn a lower-dimensional data representation from which the original input can be reconstructed as closely as possible.

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Hartmann, F.J., Bendall, S.C. Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat Rev Rheumatol 16, 87–99 (2020). https://doi.org/10.1038/s41584-019-0338-z

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