Individual reports suggest that the central nervous system (CNS) contains multiple immune cell types with diverse roles in tissue homeostasis, immune defense, and neurological diseases. It has been challenging to map leukocytes across the entire brain, and in particular in pathology, where phenotypic changes and influx of blood-derived cells prevent a clear distinction between reactive leukocyte populations. Here, we applied high-dimensional single-cell mass and fluorescence cytometry, in parallel with genetic fate mapping systems, to identify, locate, and characterize multiple distinct immune populations within the mammalian CNS. Using this approach, we revealed that microglia, several subsets of border-associated macrophages and dendritic cells coexist in the CNS at steady state and exhibit disease-specific transformations in the immune microenvironment during aging and in models of Alzheimer's disease and multiple sclerosis. Together, these data and the described framework provide a resource for the study of disease mechanisms, potential biomarkers, and therapeutic targets in CNS disease.

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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...

Cellular metabolism regulates immune cell activation, differentiation and effector functions but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. Here, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using a high-dimensional antibody-based approach. We employed mass cytometry (CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naïve and memory CD8 + T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with imaging mass spectrometry (MIBI-TOF), we uncovered the spatial organization of metabolic programs, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

The central nervous system (CNS) is under close surveillance by immune cells, which mediate tissue homeostasis, protection, and repair. Conversely, in neuroinflammation, dysregulated leukocyte invasion into the CNS leads to immunopathology and neurological disability. To invade the brain parenchyma, autoimmune encephalitogenic T helper (TH) cells must encounter their cognate antigens (Ags) presented via local Ag-presenting cells (APCs). The precise identity of the APC that samples, processes, and presents CNS-derived Ags to autoaggressive T cells is unknown. Here, we used a combination of high-dimensional single-cell mapping and conditional MHC class II ablation across all CNS APCs to systematically interrogate each population for its ability to reactivate encephalitogenic THcells in vivo. We found a population of conventional dendritic cells, but not border-associated macrophages or microglia, to be essential for licensing T cells to initiate neuroinflammation.

Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in MS patients, characterized by the expression of GM-CSF and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 (VLA4) in peripheral blood, was also enriched in the central nervous system of RRMS patients. In independent validation cohorts, we confirmed that this cell population is increased in MS patients compared to other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.

Hartmann et al. show that, in narcolepsy, T cells exhibit a proinflammatory signature characterized by increased production of TNF, IL-2, and B cell–supporting cytokines.

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