High-dimensional analysis of the murine myeloid cell system

Becher, Burkhard; Schlitzer, Andreas; Chen, Jinmiao; Mair, Florian; Sumatoh, Hermi; Teng, Karen Wei Weng; Low, Donovan; Ruedl, Christiane; Riccardi-Castagnoli, Paola; Poidinger, Michael; Greter, Melanie; Ginhoux, Florent; Newell, Evan W.

doi:10.1038/ni.3006

Cited by 331 publications

(339 citation statements)

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“…Cell fate mapping and in vivo imaging technologies have enabled real-time, dynamic and in situ assessment of the activity and function of immune system. [176][177][178] Future investigations will provide essential insights into the regulatory mechanisms of innate immunity and inflammation and outline potential clues for the development of effective therapeutic approaches of inflammatory diseases.…”

Section: Discussionmentioning

confidence: 99%

Cellular and molecular regulation of innate inflammatory responses

2016

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Section: Discussionmentioning

confidence: 99%

Cellular and molecular regulation of innate inflammatory responses

2016

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“…2). It has been firmly established that the GM-CSFR is essential for the genesis of alveolar macrophages, and is also involved in the development of some tissue-resident DC subsets and eosinophils [35,54,55]. While full analysis of this 14-parameter dataset using manual gating would require at least 91 bivariate plots, a meaningful analysis using prior knowledge still employs 15-30 plots, during which the focus of the analysis is usually biased by previous experiences of the researcher.…”

Section: End Of Gating -A Practical Examplementioning

confidence: 99%

“…Also, the density-peak detection algorithms mentioned above (ACCENSE and DensVM) [34,35] do not take the entire dimensionality of a given dataset into account. To do so, a novel algorithm was recently developed and termed "PhenoGraph" [45].…”

Section: Phenograph -Clustering In High-dimensional Spacementioning

confidence: 99%

“…This can be accomplished either by subsequent manual gating on the t-SNE map or by using automated clustering algorithms. Prominent examples of such clustering methods are ACCENSE (automatic classification of cellular expression by nonlinear stochastic embedding) [34] or DensVM [35], both employing a density-peak detection approach on the 2D t-SNE map. Additionally, the stand-alone ACCENSE application also provides the alternative option of using a k-means clustering method.…”

Section: T-sne -Visualizing the Similarity Landscapementioning

confidence: 99%

“…Additionally, the stand-alone ACCENSE application also provides the alternative option of using a k-means clustering method. The usability of t-SNE to identify immune subpopulations has been demonstrated recently for both flow cytometry and mass cytometry data [32,35,36]. Various implementations of t-SNE exist.…”

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The end of gating? An introduction to automated analysis of high dimensional cytometry data

et al. 2015

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Ever since its invention half a century ago, flow cytometry has been a major tool for single-cell analysis, fueling advances in our understanding of a variety of complex cellular systems, in particular the immune system. The last decade has witnessed significant technical improvements in available cytometry platforms, such that more than 20 parameters can be analyzed on a single-cell level by fluorescence-based flow cytometry. The advent of mass cytometry has pushed this limit up to, currently, 50 parameters. However, traditional analysis approaches for the resulting high-dimensional datasets, such as gating on bivariate dot plots, have proven to be inefficient. Although a variety of novel computational analysis approaches to interpret these datasets are already available, they have not yet made it into the mainstream and remain largely unknown to many immunologists. Therefore, this review aims at providing a practical overview of novel analysis techniques for high-dimensional cytometry data including SPADE, t-SNE, Wanderlust, Citrus, and PhenoGraph, and how these applications can be used advantageously not only for the most complex datasets, but also for standard 14-parameter cytometry datasets. Keywords: Citrus CyTOF Data analysis Flow cytometry Mass cytometry PSM PhenoGraph SPADE t-SNE WanderlustYear 2015 marked the 50-year anniversary of the publication of the first cell sorter, a device that was able to separate cells in suspension based on their difference in volume [1], as well as the first cytometry-based cell analyzer [2]. Shortly after that, the first sorter that could discriminate cells based on fluorescence was developed [3,4], and this seminal work marked the advent of flow cytometry as a widely used, single-cell analysis technique driving the identification of all major immune cell subsets known today [3,5] (for an overview, see Fig. 1, top). These days, many laboratories are equipped with flow cytometers capable of detecting 10-20 parameters [6], revealing an ever-increasing diversity within established cellular subsets, such as CD4 + T-helper cells or professional APCs. Quite recently, an alternative cytometry-based technique has been developed that relies on antibodies labeled with heavyCorrespondence: Prof. Burkhard Becher e-mail: becher@immunology.uzh.ch metal isotopes instead of fluorophores, detecting the resulting signals using a time-of-flight detector as is done in atomic mass spectrometers [7][8][9]. This method has been termed "mass cytometry" and became commercially known as the "CyTOF" (cytometry by time-of-flight), allowing the theoretical detection of more than 100 parameters per cell.While both fluorescence-based flow cytometry as well as mass cytometry provide a technological platform to interrogate the immune system at a previously unprecedented level (for a comparison of the two techniques see [10]), scientific progress depends on our ability to analyze and comprehend the resulting data in a meaningful way. Historically, flow cytometric data were-and still is-analyzed using a se...

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Principle of Mass Cytometry

Korin

Ghorbanihaghjo

2018

Encyclopedia of Analytical Chemistry

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Mass cytometry is an evolving technology combining flow cytometry methodology with inductively coupled plasma mass spectrometry (ICP‐MS). It overcomes flow cytometry's inherent limitation, by conjugated metal isotopes as reporter tags to antibodies instead of fluorophores. Its great advantage lies in its capacity to analyze more than 45 parameters simultaneously, at a single‐cell level, with very low signal spillover between adjacent masses. Mass cytometry high parameterization offers unparalleled characterization of single cells within heterogeneous populations, in a system‐wide view manner. Accordingly, it has been applied for a deeper understanding of specific cell subsets and network signaling. These new phenotypic characterization capabilities have provided a significant advancement in our ability to resolve biological questions. The high dimensionality of the data necessitates the development of new analytical tools, using data reduction and clustering of cells, in order to visualize and interpret the data. In this article, we will discuss the main principles of mass cytometry, its applications, main advancements, and the tools available for analysis.

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High-dimensional analysis of the murine myeloid cell system

Cited by 331 publications

References 44 publications

Cellular and molecular regulation of innate inflammatory responses

Cellular and molecular regulation of innate inflammatory responses

The end of gating? An introduction to automated analysis of high dimensional cytometry data

Principle of Mass Cytometry

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