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Publicly Available Published by De Gruyter August 9, 2019

CNS myeloid cell heterogeneity at the single-cell level

Chotima Böttcher

Chotima Böttcher is senior scientist of Laboratory of Molecular Psychiatry at the Charité – Universitätsmedizin Berlin, Germany. Dr. Böttcher obtained her PhD at Institute of Pharmacy, Faculty of Natural Sciences at Martin-Luther-University Halle-Wittenberg, Halle/Saale, Germany in 2005. In the first year of her postdocteral fellow, she continued her study on biosynthesis of mammalian morphine at Martin-Luther-University Halle-Wittenberg and later at Donald Danforth Plant Science Center, MO, USA. In 2006, she moved to the group of Dr. Priller (Laboratory of Molecular Psychiatry) at the Charité – Universitätsmedizin Berlin and has started her new research field –systems immunology in neuroscience, with particular emphasis on myeloid cells including monocytes and brain microglia/macrophages.

Currently, her research aims to identify cellular complexity and heterogeneity of the myeloid compartment of the human central nervous system (CNS) and to further investigate how these signatures alter during neurodegeneration/neuroinflammation. In the Laboratory of Molecular Psychiatry, high-dimensional single-cell immune profiling technique such as mass cytometry (cytometry by time-of-flight, CyTOF) has been established for an investigation of phenotypic profiles of circulating myeloid cells in the peripheral blood and the cerebrospinal fluid, as well as for immune phenotyping of tissue-resident macrophages such as the CNS microglia and macrophages.

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, Roman Sankowski

Roman Sankowski studied Medicine at Philpps-University of Marburg. During his PhD at the Feinstein Institute for Medical Research he studied the effect of neuroinflammation on spatial learning. Since 2016, he is Neuropathology resident at Freiburg University Medical Center. His research focuses are brain myeloid cells in health and disease.

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, Josef Priller

Prof. Dr. med. Josef Priller

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and Marco Prinz

Marco Prinz is Professor of Neuropathology and Chair of the Institute of Neuropathology at the University of Freiburg, Germany. Dr. Prinz obtained his MD at the Charitè, Humboldt-University Berlin in 1997. He performed his residency in Neuropathology at the University Hospital Zurich, Switzerland and studied there the role of the peripheral and CNS-restricted immune system for the pathogenesis of neurodegenerative diseases such as prion diseases. He was recruited to the University of Freiburg, Germany, in 2008 and was promoted to the rank of Full Professor and Chair of the Institute of Neuropathology.

Dr. Prinz laboratory studies the mechanisms that regulate the development and function of the mononuclear phagocyte lineage in the central nervous system including microglia, perivascular and meningeal macrophages. His laboratory has made seminal discoveries in CNS macrophage biology revealing their embryonic origin and their local maintenance in situ.

Currently, his research group aims to understand myeloid cell biology in the CNS during health and disease and studies the impact of the immune system on the pathogenesis of neurological disorders such as neurodegenerative diseases, ultimately aimed at recognizing novel therapeutic strategies and targets to treat these central nervous system diseases.

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From the journal Neuroforum

Abstract

The cellular composition of the central nervous system (CNS) is highly complex and dynamic. Regulation of this complexity is increasingly recognized to be spatially and temporally dependent during development, homeostasis and disease. Context-dependent cellular heterogeneity was shown for neuroectodermal cells as well as the myeloid compartment of the CNS. The brain myeloid compartment comprises microglia and other CNS-associated macrophages. These are brain-resident cells with critical roles in brain development, maintenance, and immune responses during states of disease. Profiling of CNS myeloid cell heterogeneity has been greatly facilitated in the past years by development of high-throughput technologies for single-cell analysis. This review summarizes current insights into heterogeneity of the CNS myeloid cell population determined by single-cell RNA sequencing and mass cytometry. The results offer invaluable insights into CNS biology and will facilitate the development of therapies for neurodegenerative and neuroinflammatory pathologies.

Zusammenfassung

Die zelluläre Zusammensetzung des zentralen Nervensystems (ZNS) ist hochkomplex und dynamisch. Sie unterliegt einer räumlichen und zeitlichen Regulation im Rahmen der Hirnentwicklung, Homöostase und bei Erkrankungen. Kontext-abhängige zelluläre Heterogenität wurde sowohl für neuroektodermale, als auch für myeloische ZNS-Zellen gezeigt. Das myeloische ZNS-Kompartment besteht aus Mikroglia und anderen ZNS-Makrophagen. Dabei handelt es sich um ortsständige Zellen, die wichtige Aufgaben während der Hirnentwicklung und -instandhaltung sowie bei Immunantworten im Rahmen von Hirnerkrankungen übernehmen. Die Analyse der Heterogenität myeloischer ZNS-Zellen wurde in den vergangenen Jahren von der Entwicklung neuartiger Hochdurchsatz-Methoden begünstigt. Diese Übersichtsarbeit beschreibt neueste Erkenntnisse über die Heterogenität myeloischer Zellen im ZNS, die mittels Einzelzell-RNA-Sequenzierung und Massenzytometrie gewonnen wurden. Diese Erkenntnisse vertiefen nicht nur den Kenntnisstand über die Biologie des ZNS, sondern werden auch zur Therapieentwicklung im Bereich der Neurodegeneration und Neuroinflammation beitragen.

1 Introduction

The myeloid compartment of the central nervous system (CNS) is increasingly recognized as a key player in CNS homeostasis and pathology. In the brain, this compartment comprises a diverse population of tissue-resident macrophages including parenchymal microglia and non-parenchymal macrophages that reside in perivascular (Virchow-Robin) spaces, choroid plexus, and meningeal compartments (Prinz and Priller, 2014; Prinz et al., 2017). Parenchymal microglia and most of the CNS-associated macrophages (CAMs, these are meningeal, perivascular and choroid plexus macrophages) originate from prenatal yolk sac- or fetal liver-derived myeloid precursors that enter the CNS during early embryonic development (Ginhoux et al., 2010; Goldmann et al., 2016; Kierdorf et al., 2013; Mizutani et al., 2012; Schulz et al., 2012). Development of both parenchymal microglia and CNS macrophages relies largely on similar transcription factors such as PU.1 (Goldmann et al., 2016). CNS myeloid cells differentially regulate immune responses at the boundaries and in the parenchyma of both healthy and diseased brain, and are involving in the maintenance of CNS integrity and function (Goldmann et al., 2016; Parkhurst et al., 2013; Sierra et al., 2010). They sense danger signals at brain interfaces such as the blood-brain barrier (BBB; perivascular macrophages) or the blood-cerebrospinal fluid barrier (choroid plexus macrophages). Furthermore, spatial heterogeneity and functional differences of the resident brain macrophages, especially microglia, were detected on the basis of transcriptomic bulk system analysis in the murine brain (Grabert et al., 2016).

During CNS pathology, cells from the periphery can enter the CNS, differentiate into brain parenchymal and/or non-parenchymal macrophages, and integrate into the existing CNS myeloid compartment (Böttcher et al., 2013; Mildner et al., 2007; Priller et al., 2001). These hematogenous brain macrophages share some phenotypic signatures with resident CNS myeloid cells. In the past, it was technically challenging to distinguish these infiltrating cells from their CNS counterparts and models such as bone marrow chimerism were used (Mildner et al., 2007; Priller et al., 2001). Nowadays, comprehensive approaches for cell profiling at the single-cell level have been developed to unravel the transcriptomic, phenotypic and functional complexity of the CNS myeloid compartment. Recently, single-cell RNA sequencing (scRNA-Seq) and Cytometry by Time-of-Flight (CyTOF) or mass cytometry have been used by us and other groups to profile the heterogeneity of the murine and human CNS myeloid compartments in health and disease. scRNA-Seq examines single-cell transcriptomes by sequencing the mRNA of thousands of genes, while CyTOF measures dozens of proteins in individual cells (Figure 1). As both methods offer complementary information, side-by-side application of both offers the prospect of identifying disease-associated myeloid populations that can potentially be therapeutically targeted.

In this review, we report an overview of CNS myeloid heterogeneity determined by single-cell RNA sequencing, as well as single-cell multiplexed mass cytometry.

2 Single-cell RNA-sequencing

Recent years have witnessed pioneering studies mapping the murine and human brain tissues with single-cell RNA-sequencing (scRNA-Seq). This technique offers an efficient way to distinguish cell types based on their transcriptomes. Thus, it serves a long-unmet need to examine heterogeneity of cell populations, i. e. differential gene expression due to functional specialization or pathological perturbations. Facilitated by declining sequencing costs, ambitious consortium approaches are currently performed using scRNA-Seq to construct a single-cell atlas of the human body (Regev et al., 2017). The goal is to eventually understand healthy and diseased human tissues at an unprecedented level. Notably, atlases of a whole mouse and other model organisms have already been achieved (Han et al., 2018; The Tabula Muris Consortium et al., 2018). Early scRNA-Seq studies of the brain have applied exploratory approaches by analyzing all cell types of a given brain region, e. g. somatosensory cortex and hippocampus (Zeisel et al., 2015). Initially, the cells analysed numbered in the hundreds. Within a few years, technological advances increased the scale up to over 500,000 cells from all nervous tissues of the mouse (Zeisel et al., 2018). The resulting high-dimensional datasets offered even more information than was sought by a given study. Due to this richness of information, it has become common for studies with scRNA-Seq data to release online data viewers to facilitate independent exploration of data (Han et al., 2018; The Tabula Muris Consortium et al., 2018; Zeisel et al., 2015) (Table 1).

Fig. 1: Comparative schematic representation between single-cell RNA sequencing and CyTOF analysis. Isolated single cells including perivascular macrophages (pv mФ), microglia from different brain regions (coded accordingly by colour; differently colour-coded rectangles represent different regions of interest), infiltrating immune cells and macrophages (mФ) can be analyzed in parallel by scRNA-Seq and mass cytometry (CyTOF). General workflows of scRNA-Seq and CyTOF are shown on the left and right sides, respectively. scRNA-Seq consists of either unbiased acquisition of all cells or cell type enrichment using FACS (different cell types are represented by coloured dots). Cell types are differentiated in silico based on transcriptomic similarities and visualized using dimension reduction techniques, such as t-Distributed Stochastic Neighbor Embedding (t-SNE) that facilitate human-readable visualization of complex data in two dimensions (t-SNE1 and t-SNE2). Note that, as t-SNE places similar cells in close proximity to each other, dots of the same color now form distinct clouds representing cell populations. Finally, a number of downstream analyses enables the researcher to conduct fine-grained examinations beyond cell type distinction, such as the evaluation of gradual transcriptional changes across ‘pseudo-time’ that represent differentiation trajectories or different stages of disease etc. (here, with a heatmap of gene clusters across pseudo-time). For CyTOF analysis, samples are generally barcoded, pooled and stained with panels of selected antibodies. Stained cells are then acquired into CyTOF via a nebulizer. A fine spray of droplets are then completely atomized and ionized in an inductively coupled plasma (ICP). The resulting ion cloud is then measured in a Time-of-flight (TOF) chamber. The data are then compensated. The panel of two-dimension plots are shown as a symbol representing how compensation matrix was calculated, thereby signal intensity of each metal-channel is plotted against the other metals. Compensated data are then processed (debarcoding and data pooling) prior to algorithm-based data analysis. Advantages (text in blue) and disadvantages (text in red) of both techniques are summarized.
Fig. 1:

Comparative schematic representation between single-cell RNA sequencing and CyTOF analysis. Isolated single cells including perivascular macrophages (pv mФ), microglia from different brain regions (coded accordingly by colour; differently colour-coded rectangles represent different regions of interest), infiltrating immune cells and macrophages (mФ) can be analyzed in parallel by scRNA-Seq and mass cytometry (CyTOF). General workflows of scRNA-Seq and CyTOF are shown on the left and right sides, respectively. scRNA-Seq consists of either unbiased acquisition of all cells or cell type enrichment using FACS (different cell types are represented by coloured dots). Cell types are differentiated in silico based on transcriptomic similarities and visualized using dimension reduction techniques, such as t-Distributed Stochastic Neighbor Embedding (t-SNE) that facilitate human-readable visualization of complex data in two dimensions (t-SNE1 and t-SNE2). Note that, as t-SNE places similar cells in close proximity to each other, dots of the same color now form distinct clouds representing cell populations. Finally, a number of downstream analyses enables the researcher to conduct fine-grained examinations beyond cell type distinction, such as the evaluation of gradual transcriptional changes across ‘pseudo-time’ that represent differentiation trajectories or different stages of disease etc. (here, with a heatmap of gene clusters across pseudo-time). For CyTOF analysis, samples are generally barcoded, pooled and stained with panels of selected antibodies. Stained cells are then acquired into CyTOF via a nebulizer. A fine spray of droplets are then completely atomized and ionized in an inductively coupled plasma (ICP). The resulting ion cloud is then measured in a Time-of-flight (TOF) chamber. The data are then compensated. The panel of two-dimension plots are shown as a symbol representing how compensation matrix was calculated, thereby signal intensity of each metal-channel is plotted against the other metals. Compensated data are then processed (debarcoding and data pooling) prior to algorithm-based data analysis. Advantages (text in blue) and disadvantages (text in red) of both techniques are summarized.

The brain myeloid field has benefitted from the availability of scRNA-Seq data from mouse and human brains and the concomitant sharing of data. A number of exploratory studies included data on microglia and other CNS-associated macrophages (CAMs) (Campbell et al., 2017; Darmanis et al., 2017, 2015; Gokce et al., 2016; Hochgerner et al., 2018; La Manno et al., 2016; Macosko et al., 2015; Moffitt et al., 2018; Tepe et al., 2018; Tirosh et al., 2016; Vanlandewijck et al., 2018; Venteicher et al., 2017; Zeisel et al., 2018, 2015; Zhong et al., 2018; Zywitza et al., 2018; Campbell et al., 2017; Darmanis et al., 2017, 2015; Gokce et al., 2016; Hochgerner et al., 2018; La Manno et al., 2016; Macosko et al., 2015; Moffitt et al., 2018; Tepe et al., 2018; Tirosh et al., 2016; Vanlandewijck et al., 2018; Venteicher et al., 2017; Zeisel et al., 2018, 2015; Zhong et al., 2018; Zywitza et al., 2018; Jordão et al., 2019; Masuda et al., 2019). While the aforementioned studies conducted scRNA-Seq on fresh tissues, RNA sequencing of nuclei extracted from frozen tissues has enabled the study of immune and non-immune cells of archived cryopreserved tissues (Habib et al., 2017; Jäkel et al., 2019; Renthal et al., 2018). The main shortcoming of exploratory studies is their limited ability to resolve heterogeneity of CNS myeloid cells due to the low numbers of these cells. This shortcoming was addressed through scRNA-Seq of FACS-sorted microglia and CAMs under homeostatic and pathological conditions in developing and adult humans and mice. Microglia and CAMs were shown to be derived from the yolk sac or fetal liver during embryonic development and to show lasting transcriptional changes in the adult animals that were influenced by the microbiome and maternal infections (Goldmann et al., 2016; Li et al., 2019; Matcovitch-Natan et al., 2016; Thion et al., 2018; Erny et al., 2015). In adult mice, microglia and CAMs show pronounced heterogeneity in healthy animals and drastically change during demyelination, characterized by increasing expression levels of Apoe, Axl, Igf1, Lyz2, Itgax, Gpnmb and Apoc1 and strongly down-regulated microglial markers such as TMEM119 and P2RY12 (Hammond et al., 2019; Jordão et al., 2019; Masuda et al., 2019; Li et al., 2019). Increased transcription of Apoe and reduced expression of microglial genes was confirmed in brain tissue from patients with multiple sclerosis (Masuda et al., 2019). Neurodegeneration-associated microglia were found to display a largely consistent proinflammatory phenotype that is distinct from demyelination-associated microglia (Keren-Shaul et al., 2017; Masuda et al., 2019; Mathys et al., 2017; Tay et al., 2018).

Tab. 1:

Overview of open-access single-cell sequencing data viewers.

Resource URL

Cell types

Method

http://brainrnaseq.org/

Mouse and human microglia

Bulk RNA-Seq and Smart-Seq2

http://research-pub.gene.com/BrainMyeloidLandscape/

Mouse brain CD45+ sorted cells

Bulk RNA-Seq

http://celltypes.brain-map.org/rnaseq

Different datasets

Single-cell and single-nucleus RNA-Seq

https://portals.broadinstitute.org/single_cell

Different datasets

Single-cell and single-nucleus RNA-Seq

http://betsholtzlab.org/VascularSingleCells/database.html

Mouse brain and lung cells

Smart-Seq2

http://www.gbmseq.org/

Human brain tumors

Smart-Seq2

http://linnarssonlab.org/data/

Different datasets

Fluidigm C1 & 10x Genomics

http://www.microgliasinglecell.com/

Mouse microglia

10x Genomics

https://tabula-muris.ds.czbiohub.org/

Different mouse tissues

Smart-Seq2 & 10x Genomics

http://bis.zju.edu.cn/MCA

Different mouse tissues

Microwell-Seq

Major insights into the astonishing heterogeneity of CNS myeloid cells during development, as well as in healthy and disease states were recently gleaned using scRNA-Seq. These findings paved the way for a deeper understanding of the human brain immune system with a potential for future diagnostic use.

3 Revealing myeloid heterogeneity by mass cytometry

Unlike scRNA-Seq, elucidating total proteomics in a single cell has remained challenging. A high-dimensional single-cell phenotypic analysis, which is widely used for cellular profiling in neurosciences, is CyTOF. CyTOF combines metal isotope-labelling technology, flow cytometric analysis with time-of-flight mass spectrometry to identify and quantify cells. Using CyTOF technology, cellular targets are labelled with metal-conjugated antibodies and detected and quantified by time-of-flight mass spectrometry. Taking advantage of the low signal overlap between metal isotopes, CyTOF allows simultaneous cell identification and quantification on the basis of more than 45 marker targets on a single cell. Data obtained from CyTOF measurements are processed and analysed in an unsupervised manner using algorithm-based data analysis. The combination of a comprehensive array of protein markers and unsupervised data analysis provides a powerful strategy for cell identification and quantification in a complex system like the CNS.

Utilizing this technique, key transcriptomic signatures of mouse (Galatro et al., 2017) and human microglia (Gosselin et al., 2017) were confirmed at the protein level in individual cells. We found the microglial phenotypic signature to be distinct from peripheral myeloid cells isolated from the peripheral blood or cerebrospinal fluid (Böttcher et al., 2019). Compared to peripheral cells, microglia expressed higher levels of P2Y12, TMEM119, CD64, TGF-β1, CCR5, CD32, CD172a, CD91, HLA-DR, CD11c, CX3CR1, CD115 and TREM2, whereas expression of CD44, CCR2, CD45, CD206, CD163, CD274, CD14 and CD16 was lower in the microglial population (Table 2). These results agree with previous microglial profiling studies using multi-parameter mass cytometry (more than 35 markers). The resulting high-dimensional data demonstrated that murine microglia are heterogenous and show distinct phenotypes from circulating monocytes and other tissue-resident macrophages (Becher et al., 2014; Korin et al., 2017; Ajami et al., 2018). On the basis of multi-parameter characterization and computational unbiased data analysis, we could clearly distinguish parenchymal microglia from perivascular macrophages, which highly expressed CD45, CD206 and CD163. Interestingly, we detected the expression of EMR1 (F4/80) on microglia. In humans, this protein was demonstrated to be highly specific to eosinophils and was suggested to be absent from monocytes, macrophages and dendritic cells (Hamann et al., 2007). Similar to the previous results obtained from transcriptomic profiling of the mouse brain (Grabert et al., 2016), massive single-cell immune profiling by CyTOF revealed regional heterogeneity of human microglial phenotypes between the subventricular zone, thalamus, cerebellum, as well as temporal and frontal lobes of the human brain. We detected higher expression of markers involved in microglial activation (these were CD68, CD86, CD45, CX3CR1, CD11c, CD64, EMR1 and HLA-DR) in microglia from the subventricular zone and thalamus, compared with that from cerebellum, temporal and frontal lobes (Table 2). The human microglial subset, which was predominantly found in the frontal and temporal cortex, expressed the mannose receptor, CD206, a marker of M2-polarized macrophages, whereas microglia found in other regions were negative for this marker. Whether this microglial heterogeneity implies a region-specific function and/or a region-dependent vulnerability of microglia in different neurodegenerative disorders remains to be investigated.

Tab. 2:

Overview of CNS myeloid cell phenotypes.

CNS cell types

High expressed markers

Low/negative expressed markers

Microglia (MG)

P2Y12, TMEM119, CD64,

TGF-β1, CCR5, CD32,

CD172a, CD91, HLA-DR,

CD11c, CX3CR1, CD115,

EMR1, TREM2

CD44, CCR2, CD45, CD206,

CD163, CD274, CD14, CD11b,

CD16, CD33

SVZ- and THA-MG

CD68, CD86, CD45,

CX3CR1, CD11c, CD64,

EMR1,HLA-DR

FL- and TL-MG

CD206

perivascular macrophage

CD45, CD206, CD163,

CD14, HLA-DR, CD68,

CD33, EMR1, CD64,

CD11c, CD11b, IRF8,

TGF-β1, CD115

P2Y12, TMEM119, CX3CR1,

CD16

infiltrating myeloid cells

CD44, CCR2, CD45,

CD206, CD163, CD274,

CD14, CD16, CD32,

CD172a, CD18

P2Y12, TMEM119, TREM2,

EMR1

SVZ = Subventricular zone; THA = Thalamus; FL = Frontal lobe; TL = Temporal lobeSummarized from Böttcher et al., 2019.

4 Conclusion

Using unsupervised high-dimensional scRNA-seq and CyTOF analyses, CNS myeloid cell heterogeneity is currently being unravelled at the single-cell level in both mouse and human brains. However, open questions including the spatial characteristics of and the dynamic interactions between different myeloid cell subsets and the surrounding neuronal and non-neuronal cells remain to be answered. Imaging mass cytometry (IMC) is a technology that couples mass cytometry to immunohistochemical methods with high-resolution laser ablation (Keren et al., 2018). This technology enables high-dimensional cell profiling of a wide spectrum of cell types on tissue sections using the same principle as mass cytometry. Likewise, spatial transcriptomics approaches investigate cellular transcriptomics in situ (Lein et al., 2017). To this end, a recent study utilized tissue clearing to achieve three-dimensional single-cell spatial transcriptomics (Wang et al., 2018). As both IMC and spatial transcriptomics can be applied to fresh as well as archived tissues, they hold great promise to uncover spatial phenotypic and functional heterogeneity of the CNS myeloid compartment in health and disease.

Since scRNA-seq and CyTOF record complementary information, the combination of both methods appears promising. This has already shown promise for dendritic cells (See et al., 2017), and also for healthy and glioma-associated microglia (own unpublished data).

The findings on CNS myeloid heterogeneity may imply microglial region-dependent vulnerability and regionally differential involvement in neurological and psychiatric diseases. Better understanding of heterogeneity-associated functional differences of CNS myeloid cells, as well as the mechanisms involved in heterogeneity may have clinical implications for diagnosis and treatment of CNS disorders.

List of abbreviations used in this text

CNS

central nervous system

CAMs

CNS-associated macrophages

BBB

blood-brain barrier

scRNA-Seq

single-cell RNA sequencing

CyTOF

cytometry by time-of-flight

IMC

imaging mass cytometry

About the authors

Dr.  Chotima Böttcher

Chotima Böttcher is senior scientist of Laboratory of Molecular Psychiatry at the Charité – Universitätsmedizin Berlin, Germany. Dr. Böttcher obtained her PhD at Institute of Pharmacy, Faculty of Natural Sciences at Martin-Luther-University Halle-Wittenberg, Halle/Saale, Germany in 2005. In the first year of her postdocteral fellow, she continued her study on biosynthesis of mammalian morphine at Martin-Luther-University Halle-Wittenberg and later at Donald Danforth Plant Science Center, MO, USA. In 2006, she moved to the group of Dr. Priller (Laboratory of Molecular Psychiatry) at the Charité – Universitätsmedizin Berlin and has started her new research field –systems immunology in neuroscience, with particular emphasis on myeloid cells including monocytes and brain microglia/macrophages.

Currently, her research aims to identify cellular complexity and heterogeneity of the myeloid compartment of the human central nervous system (CNS) and to further investigate how these signatures alter during neurodegeneration/neuroinflammation. In the Laboratory of Molecular Psychiatry, high-dimensional single-cell immune profiling technique such as mass cytometry (cytometry by time-of-flight, CyTOF) has been established for an investigation of phenotypic profiles of circulating myeloid cells in the peripheral blood and the cerebrospinal fluid, as well as for immune phenotyping of tissue-resident macrophages such as the CNS microglia and macrophages.

Dr.  Roman Sankowski

Roman Sankowski studied Medicine at Philpps-University of Marburg. During his PhD at the Feinstein Institute for Medical Research he studied the effect of neuroinflammation on spatial learning. Since 2016, he is Neuropathology resident at Freiburg University Medical Center. His research focuses are brain myeloid cells in health and disease.

Prof. Dr. med. Josef Priller

Prof. Dr. med. Josef Priller

Prof. Dr. med. Marco Prinz

Marco Prinz is Professor of Neuropathology and Chair of the Institute of Neuropathology at the University of Freiburg, Germany. Dr. Prinz obtained his MD at the Charitè, Humboldt-University Berlin in 1997. He performed his residency in Neuropathology at the University Hospital Zurich, Switzerland and studied there the role of the peripheral and CNS-restricted immune system for the pathogenesis of neurodegenerative diseases such as prion diseases. He was recruited to the University of Freiburg, Germany, in 2008 and was promoted to the rank of Full Professor and Chair of the Institute of Neuropathology.

Dr. Prinz laboratory studies the mechanisms that regulate the development and function of the mononuclear phagocyte lineage in the central nervous system including microglia, perivascular and meningeal macrophages. His laboratory has made seminal discoveries in CNS macrophage biology revealing their embryonic origin and their local maintenance in situ.

Currently, his research group aims to understand myeloid cell biology in the CNS during health and disease and studies the impact of the immune system on the pathogenesis of neurological disorders such as neurodegenerative diseases, ultimately aimed at recognizing novel therapeutic strategies and targets to treat these central nervous system diseases.

References

Ajami, B., Samusik, N., Wieghofer, P., Ho, P.P., Crotti, A., Bjornson, Z., Prinz, M., Fantl, W.J., Nolan, G.P., Steinman, L. (2018). Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models. Nat Neurosci. 21, 541–551. https://doi.org/10.1038/s41593-018-0100-x.10.1038/s41593-018-0100-xSearch in Google Scholar

Becher, B., Schlitzer, A., Chen, J., Mair, F., Sumatoh, H.R., Teng, K.W.W., Low, D., Ruedl, C., Riccardi-Castagnoli, P., Poidinger, M., Greter, M., Ginhoux, F., Newell, E.W. (2014). High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189. https://doi.org/10.1038/ni.300610.1038/ni.3006Search in Google Scholar

Böttcher, C., Fernández-Klett, F., Gladow, N., Rolfes, S., Priller, J. (2013). Targeting Myeloid Cells to the Brain Using Non-Myeloablative Conditioning. PLoS ONE 8, e80260. https://doi.org/10.1371/journal.pone.008026010.1371/journal.pone.0080260Search in Google Scholar

Böttcher, C., Schlickeiser, S., Sneeboer, M.A.M., Kunkel, D., Knop, A., Paza, E., Fidzinski, P., Kraus, L., Snijders, G.J.L., Kahn, R.S., Schulz, A.R., Mei, H.E., NBB-Psy, Hol, E.M., Siegmund, B., Glauben, R., Spruth, E.J., de Witte, L.D., Priller, J. (2019). Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90. https://doi.org/10.1038/s41593-018-0290-210.1038/s41593-018-0290-2Search in Google Scholar

Campbell, J.N., Macosko, E.Z., Fenselau, H., Pers, T.H., Lyubetskaya, A., Tenen, D., Goldman, M., Verstegen, A.M.J., Resch, J.M., McCarroll, S. A., Rosen, E.D., Lowell, B.B., Tsai, L.T. (2017). A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496. https://doi.org/10.1038/nn.449510.1038/nn.4495Search in Google Scholar

Darmanis, S., Sloan, S. A., Croote, D., Mignardi, M., Chernikova, S., Samghababi, P., Zhang, Y., Neff, N., Kowarsky, M., Caneda, C., Li, G., Chang, S. D., Connolly, I.D., Li, Y., Barres, B.A., Gephart, M.H., Quake, S.R. (2017). Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma. Cell Rep. 21, 1399–1410. https://doi.org/10.1016/j.celrep.2017.10.03010.1016/j.celrep.2017.10.030Search in Google Scholar

Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L.M., Hayden Gephart, M.G., Barres, B.A., Quake, S.R. (2015). A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. 112, 7285–7290. https://doi.org/10.1073/pnas.150712511210.1073/pnas.1507125112Search in Google Scholar

Erny, D., Hrabě de Angelis, A.L., Jaitin, D., Wieghofer, P., Staszewski, O., David, E., Keren-Shaul, H., Mahlakoiv, T., Jakobshagen, K., Buch, T., Schwierzeck, V., Utermöhlen, O., Chun, E., Garrett, W.S., McCoy, K.D., Diefenbach, A., Staeheli, P., Stecher, B., Amit, I., Prinz, M. (2015). Host microbiota constantly control maturation and function of microglia in the CNS. Nat. Neurosci. 18:965–977. https://doi.org/10.1038/nn.4030.10.1038/nn.4030Search in Google Scholar

Galatro, T.F., Holtman, I.R., Lerario, A.M., Vainchtein, I.D., Brouwer, N., Sola, P.R., Veras, M.M., Pereira, T.F., Leite, R.E.P., Möller, T., Wes, P.D., Sogayar, M.C., Laman, J.D., den Dunnen, W., Pasqualucci, C.A., Oba-Shinjo, S.M., Boddeke, E.W.G.M., Marie, S.K.N., Eggen, B.J.L. (2017). Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. 20, 1162–1171. https://doi.org/10.1038/nn.459710.1038/nn.4597Search in Google Scholar

Ginhoux, F., Greter, M., Leboeuf, M., Nandi, S., See, P., Gokhan, S., Mehler, M.F., Conway, S.J., Ng, L.G., Stanley, E.R., Samokhvalov, I.M., Merad, M. (2010). Fate Mapping Analysis Reveals That Adult Microglia Derive from Primitive Macrophages. Science 330, 841–845. https://doi.org/10.1126/science.119463710.1126/science.1194637Search in Google Scholar

Gokce, O., Stanley, G.M., Treutlein, B., Neff, N.F., Camp, J.G., Malenka, R.C., Rothwell, P. E., Fuccillo, M.V., Südhof, T.C., Quake, S.R. (2016). Cellular Taxonomy of the Mouse Striatum as Revealed by Single-Cell RNA-Seq. Cell Rep. 16, 1126–1137. https://doi.org/10.1016/j.celrep.2016.06.05910.1016/j.celrep.2016.06.059Search in Google Scholar

Goldmann, T., Wieghofer, P., Jordão, M.J.C., Prutek, F., Hagemeyer, N., Frenzel, K., Amann, L., Staszewski, O., Kierdorf, K., Krueger, M., Locatelli, G., Hochgerner, H., Zeiser, R., Epelman, S., Geissmann, F., Priller, J., Rossi, F.M.V., Bechmann, I., Kerschensteiner, M., Linnarsson, S., Jung, S., Prinz, M. (2016). Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805. https://doi.org/10.1038/ni.342310.1038/ni.3423Search in Google Scholar

Gosselin, D., Skola, D., Coufal, N.G., Holtman, I.R., Schlachetzki, J.C.M., Sajti, E., Jaeger, B.N., O’Connor, C., Fitzpatrick, C., Pasillas, M.P., Pena, M., Adair, A., Gonda, D.D., Levy, M.L., Ransohoff, R.M., Gage, F.H., Glass, C.K. (2017). An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222. https://doi.org/10.1126/science.aal322210.1126/science.aal3222Search in Google Scholar

Grabert, K., Michoel, T., Karavolos, M.H., Clohisey, S., Baillie, J.K., Stevens, M.P., Freeman, T.C., Summers, K.M., McColl, B.W. (2016). Microglial brain region−dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516. https://doi.org/10.1038/nn.422210.1038/nn.4222Search in Google Scholar

Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S.R., Aguet, F., Gelfand, E., Ardlie, K., Weitz, D.A., Rozenblatt-Rosen, O., Zhang, F., Regev, A. (2017). Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958. https://doi.org/10.1038/nmeth.440710.1038/nmeth.4407Search in Google Scholar

Hamann, J., Koning, N., Pouwels, W., Ulfman, L.H., van Eijk, M., Stacey, M., Lin, H.-H., Gordon, S., Kwakkenbos, M.J. (2007). EMR1, the human homolog of F4/80, is an eosinophil-specific receptor. Eur. J. Immunol. 37, 2797–2802. https://doi.org/10.1002/eji.20073755310.1002/eji.200737553Search in Google Scholar

Hammond, T.R., Dufort, C., Dissing-Olesen, L., Giera, S., Young, A., Wysoker, A., Walker, A.J., Gergits, F., Segel, M., Nemesh, J., Marsh, S.E., Saunders, A., Macosko, E., Ginhoux, F., Chen, J., Franklin, R.J.M., Piao, X., McCarroll, S. A., Stevens, B. (2019). Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity 50, 253–271.e6. https://doi.org/10.1016/j.immuni.2018.11.00410.1016/j.immuni.2018.11.004Search in Google Scholar

Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., Huang, D., Xu, Y., Huang, W., Jiang, M., Jiang, X., Mao, J., Chen, Y., Lu, C., Xie, J., Fang, Q., Wang, Y., Yue, R., Li, T., Huang, H., Orkin, S.H., Yuan, G.-C., Chen, M., Guo, G. (2018). Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17. https://doi.org/10.1016/j.cell.2018.02.00110.1016/j.cell.2018.02.001Search in Google Scholar

Hochgerner, H., Zeisel, A., Lönnerberg, P., Linnarsson, S. (2018). Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299. https://doi.org/10.1038/s41593-017-0056-210.1038/s41593-017-0056-2Search in Google Scholar

Jäkel, S., Agirre, E., Mendanha Falcão, A., van Bruggen, D., Lee, K.W., Knuesel, I., Malhotra, D., ffrench-Constant, C., Williams, A., Castelo-Branco, G. (2019). Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547. https://doi.org/10.1038/s41586-019-0903-210.1038/s41586-019-0903-2Search in Google Scholar

Jordão, M.J.C., Sankowski, R., Brendecke, S.M., Sagar, Locatelli, G., Tai, Y.-H., Tay, T.L., Schramm, E., Armbruster, S., Hagemeyer, N., Groß, O., Mai, D., Çiçek, Ö., Falk, T., Kerschensteiner, M., Grün, D., Prinz, M. (2019). Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554. https://doi.org/10.1126/science.aat755410.1126/science.aat7554Search in Google Scholar

Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S.-R., Kurian, A., Van Valen, D., West, R., Bendall, S.C., Angelo, M. (2018). A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell 174, 1373–1387.e19. https://doi.org/10.1016/j.cell.2018.08.03910.1016/j.cell.2018.08.039Search in Google Scholar

Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T.K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., Itzkovitz, S., Colonna, M., Schwartz, M., Amit, I. (2017). A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell 169, 1276–1290.e17. https://doi.org/10.1016/j.cell.2017.05.01810.1016/j.cell.2017.05.018Search in Google Scholar

Kierdorf, K., Erny, D., Goldmann, T., Sander, V., Schulz, C., Perdiguero, E. G., Wieghofer, P., Heinrich, A., Riemke, P., Hölscher, C., Müller, D.N., Luckow, B., Brocker, T., Debowski, K., Fritz, G., Opdenakker, G., Diefenbach, A., Biber, K., Heikenwalder, M., Geissmann, F., Rosenbauer, F., Prinz, M. (2013). Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280. https://doi.org/10.1038/nn.331810.1038/nn.3318Search in Google Scholar

Korin, B., Ben-Shaanan, T.L., Schiller, M., Dubovik, T., Azulay-Debby, H., Boshnak, N.T., Koren, T., Rolls, A. (2017). High-dimensional, single-cell characterization of the brain’s immune compartment. Nat. Neurosci. 20, 1300–1309. https://doi.org/10.1038/nn.461010.1038/nn.4610Search in Google Scholar

La Manno, G., Gyllborg, D., Codeluppi, S., Nishimura, K., Salto, C., Zeisel, A., Borm, L.E., Stott, S.R.W., Toledo, E.M., Villaescusa, J.C., Lönnerberg, P., Ryge, J., Barker, R.A., Arenas, E., Linnarsson, S. (2016). Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells. Cell 167, 566–580.e19. https://doi.org/10.1016/j.cell.2016.09.02710.1016/j.cell.2016.09.027Search in Google Scholar

Lein, E., Borm, L.E., Linnarsson, S. (2017). The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69. https://doi.org/10.1126/science.aan682710.1126/science.aan6827Search in Google Scholar

Li, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N.F., Okamoto, J., Gulati, G., Bennett, M.L., Sun, L.O., Clarke, L.E., Marschallinger, J., Yu, G., Quake, S.R., Wyss-Coray, T., Barres, B.A. (2019). Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing. Neuron 101, 207–223.e10. https://doi.org/10.1016/j.neuron.2018.12.00610.1016/j.neuron.2018.12.006Search in Google Scholar

Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., Trombetta, J.J., Weitz, D.A., Sanes, J.R., Shalek, A.K., Regev, A., McCarroll, S. A. (2015). Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202–1214. https://doi.org/10.1016/j.cell.2015.05.00210.1016/j.cell.2015.05.002Search in Google Scholar

Masuda, T., Sankowski, R., Staszewski, O., Böttcher, C., Amann, L., Scheiwe, C., Nessler, S., Kunz, P., van Loo, G., Coenen, V. A., Reinacher, P.C., Michel, A., Sure, U., Gold, R., Priller, J., Stadelmann, C., Prinz, M. (2019). Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature. https://doi.org/10.1038/s41586-019-0924-x10.1038/s41586-019-0924-xSearch in Google Scholar

Matcovitch-Natan, O., Winter, D.R., Giladi, A., Vargas Aguilar, S., Spinrad, A., Sarrazin, S., Ben-Yehuda, H., David, E., Zelada Gonzalez, F., Perrin, P., Keren-Shaul, H., Gury, M., Lara-Astaiso, D., Thaiss, C.A., Cohen, M., Bahar Halpern, K., Baruch, K., Deczkowska, A., Lorenzo-Vivas, E., Itzkovitz, S., Elinav, E., Sieweke, M.H., Schwartz, M., Amit, I. (2016). Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670–aad8670. https://doi.org/10.1126/science.aad867010.1126/science.aad8670Search in Google Scholar

Mathys, H., Adaikkan, C., Gao, F., Young, J.Z., Manet, E., Hemberg, M., De Jager, P.L., Ransohoff, R.M., Regev, A., Tsai, L.-H. (2017). Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution. Cell Rep. 21, 366–380. https://doi.org/10.1016/j.celrep.2017.09.03910.1016/j.celrep.2017.09.039Search in Google Scholar

Mildner, A., Schmidt, H., Nitsche, M., Merkler, D., Hanisch, U.-K. Mack, M., Heikenwalder, M., Brück, W., Priller, J., Prinz, M., (2007). Microglia in the adult brain arise from Ly-6ChiCCR2+ monocytes only under defined host conditions. Nat. Neurosci. 10, 1544–1553. https://doi.org/10.1038/nn201510.1038/nn2015Search in Google Scholar

Mizutani, M., Pino, P.A., Saederup, N., Charo, I.F., Ransohoff, R.M., Cardona, A.E. (2012). The Fractalkine Receptor but Not CCR2 Is Present on Microglia from Embryonic Development throughout Adulthood. J. Immunol. 188, 29–36. https://doi.org/10.4049/jimmunol.110042110.4049/jimmunol.1100421Search in Google Scholar

Moffitt, J.R., Bambah-Mukku, D., Eichhorn, S.W., Vaughn, E., Shekhar, K., Perez, J.D., Rubinstein, N.D., Hao, J., Regev, A., Dulac, C., Zhuang, X. (2018). Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324. https://doi.org/10.1126/science.aau532410.1126/science.aau5324Search in Google Scholar

Parkhurst, C.N., Yang, G., Ninan, I., Savas, J.N., Yates, J.R., Lafaille, J.J., Hempstead, B.L., Littman, D.R., Gan, W.-B. (2013). Microglia Promote Learning-Dependent Synapse Formation through Brain-Derived Neurotrophic Factor. Cell 155, 1596–1609. https://doi.org/10.1016/j.cell.2013.11.03010.1016/j.cell.2013.11.030Search in Google Scholar

Priller, J., Flügel, A., Wehner, T., Boentert, M., Haas, C.A., Prinz, M., Fernández-Klett, F., Prass, K., Bechmann, I., de Boer, B.A., Frotscher, M., Kreutzberg, G.W., Persons, D.A., Dirnagl, U. (2001). Targeting gene-modified hematopoietic cells to the central nervous system: use of green fluorescent protein uncovers microglial engraftment. Nat. Med. 7, 1356–1361. https://doi.org/10.1038/nm1201-135610.1038/nm1201-1356Search in Google Scholar

Prinz, M., Erny, D., Hagemeyer, N. (2017). Ontogeny and homeostasis of CNS myeloid cells. Nat. Immunol. 18, 385–392. https://doi.org/10.1038/ni.3703.10.1038/ni.3703Search in Google Scholar

Prinz, M., Priller, J. (2014). Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat. Rev. Neurosci. 15, 300–312. https://doi.org/10.1038/nrn372210.1038/nrn3722Search in Google Scholar

Regev, A., Teichmann, S. A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., Clevers, H., Deplancke, B., Dunham, I., Eberwine, J., Eils, R., Enard, W., Farmer, A., Fugger, L., Göttgens, B., Hacohen, N., Haniffa, M., Hemberg, M., Kim, S., Klenerman, P., Kriegstein, A., Lein, E., Linnarsson, S., Lundberg, E., Lundeberg, J., Majumder, P., Marioni, J.C., Merad, M., Mhlanga, M., Nawijn, M., Netea, M., Nolan, G., Pe’er, D., Phillipakis, A., Ponting, C.P., Quake, S., Reik, W., Rozenblatt-Rosen, O., Sanes, J., Satija, R., Schumacher, T.N., Shalek, A., Shapiro, E., Sharma, P., Shin, J.W., Stegle, O., Stratton, M., Stubbington, M.J.T., Theis, F.J., Uhlen, M., van Oudenaarden, A., Wagner, A., Watt, F., Weissman, J., Wold, B., Xavier, R., Yosef, N. (2017). Human Cell Atlas Meeting Participants. The Human Cell Atlas. eLife 6. https://doi.org/10.7554/eLife.2704110.7554/eLife.27041Search in Google Scholar

Renthal, W., Boxer, L.D., Hrvatin, S., Li, E., Silberfeld, A., Nagy, M.A., Griffith, E.C., Vierbuchen, T., Greenberg, M.E. (2018). Characterization of human mosaic Rett syndrome brain tissue by single-nucleus RNA sequencing. Nat. Neurosci. 21, 1670–1679. https://doi.org/10.1038/s41593-018-0270-610.1038/s41593-018-0270-6Search in Google Scholar

Schulz, C., Perdiguero, E. G., Chorro, L., Szabo-Rogers, H., Cagnard, N., Kierdorf, K., Prinz, M., Wu, B., Jacobsen, S.E.W., Pollard, J.W., Frampton, J., Liu, K.J., Geissmann, F. (2012). A Lineage of Myeloid Cells Independent of Myb and Hematopoietic Stem Cells. Science 336, 86–90. https://doi.org/10.1126/science.121917910.1126/science.1219179Search in Google Scholar

See, P., Dutertre, C.-A., Chen, J., Günther, P., McGovern, N., Irac, S.E., Gunawan, M., Beyer, M., Händler, K., Duan, K., Sumatoh, H.R.B., Ruffin, N., Jouve, M., Gea-Mallorquí, E., Hennekam, R.C.M., Lim, T., Yip, C.C., Wen, M., Malleret, B., Low, I., Shadan, N.B., Fen, C.F.S., Tay, A., Lum, J., Zolezzi, F., Larbi, A., Poidinger, M., Chan, J.K.Y., Chen, Q., Rénia, L., Haniffa, M., Benaroch, P., Schlitzer, A., Schultze, J.L., Newell, E.W., Ginhoux, F. (2017). Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009. https://doi.org/10.1126/science.aag300910.1126/science.aag3009Search in Google Scholar

Sierra, A., Encinas, J.M., Deudero, J.J.P., Chancey, J.H., Enikolopov, G., Overstreet-Wadiche, L.S., Tsirka, S.E., Maletic-Savatic, M. (2010). Microglia Shape Adult Hippocampal Neurogenesis through Apoptosis-Coupled Phagocytosis. Cell Stem Cell 7, 483–495. https://doi.org/10.1016/j.stem.2010.08.01410.1016/j.stem.2010.08.014Search in Google Scholar

Tay, T.L., Sagar, Dautzenberg, J., Grün, D., Prinz, M. (2018). Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration. Acta Neuropathol. Commun. 6. https://doi.org/10.1186/s40478-018-0584-310.1186/s40478-018-0584-3Search in Google Scholar

Tepe, B., Hill, M.C., Pekarek, B.T., Hunt, P.J., Martin, T.J., Martin, J.F., Arenkiel, B.R. (2018). Single-Cell RNA-Seq of Mouse Olfactory Bulb Reveals Cellular Heterogeneity and Activity-Dependent Molecular Census of Adult-Born Neurons. Cell Rep. 25, 2689–2703.e3. https://doi.org/10.1016/j.celrep.2018.11.03410.1016/j.celrep.2018.11.034Search in Google Scholar

The Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and processing, Library preparation and sequencing, Computational data analysis, Cell type annotation, Writing group, Supplemental text writing group, Principal investigators, 2018. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372. https://doi.org/10.1038/s41586-018-0590-410.1038/s41586-018-0590-4Search in Google Scholar

Thion, M.S., Low, D., Silvin, A., Chen, J., Grisel, P., Schulte-Schrepping, J., Blecher, R., Ulas, T., Squarzoni, P., Hoeffel, G., Coulpier, F., Siopi, E., David, F.S., Scholz, C., Shihui, F., Lum, J., Amoyo, A.A., Larbi, A., Poidinger, M., Buttgereit, A., Lledo, P.-M., Greter, M., Chan, J.K.Y., Amit, I., Beyer, M., Schultze, J.L., Schlitzer, A., Pettersson, S., Ginhoux, F., Garel, S. (2018). Microbiome Influences Prenatal and Adult Microglia in a Sex-Specific Manner. Cell 172, 500–516.e16. https://doi.org/10.1016/j.cell.2017.11.04210.1016/j.cell.2017.11.042Search in Google Scholar

Tirosh, I., Venteicher, A.S., Hebert, C., Escalante, L.E., Patel, A.P., Yizhak, K., Fisher, J.M., Rodman, C., Mount, C., Filbin, M.G., Neftel, C., Desai, N., Nyman, J., Izar, B., Luo, C.C., Francis, J.M., Patel, A.A., Onozato, M.L., Riggi, N., Livak, K.J., Gennert, D., Satija, R., Nahed, B.V., Curry, W.T., Martuza, R.L., Mylvaganam, R., Iafrate, A.J., Frosch, M.P., Golub, T.R., Rivera, M.N., Getz, G., Rozenblatt-Rosen, O., Cahill, D.P., Monje, M., Bernstein, B.E., Louis, D.N., Regev, A., Suvà, M.L. (2016). Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313. https://doi.org/10.1038/nature2012310.1038/nature20123Search in Google Scholar

Vanlandewijck, M., He, L., Mäe, M.A., Andrae, J., Ando, K., Del Gaudio, F., Nahar, K., Lebouvier, T., Laviña, B., Gouveia, L., Sun, Y., Raschperger, E., Räsänen, M., Zarb, Y., Mochizuki, N., Keller, A., Lendahl, U., Betsholtz, C. (2018). A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480. https://doi.org/10.1038/nature2573910.1038/nature25739Search in Google Scholar

Venteicher, A.S., Tirosh, I., Hebert, C., Yizhak, K., Neftel, C., Filbin, M.G., Hovestadt, V., Escalante, L.E., Shaw, M.L., Rodman, C., Gillespie, S.M., Dionne, D., Luo, C.C., Ravichandran, H., Mylvaganam, R., Mount, C., Onozato, M.L., Nahed, B.V., Wakimoto, H., Curry, W.T., Iafrate, A.J., Rivera, M.N., Frosch, M.P., Golub, T.R., Brastianos, P.K., Getz, G., Patel, A.P., Monje, M., Cahill, D.P., Rozenblatt-Rosen, O., Louis, D.N., Bernstein, B.E., Regev, A., Suvà, M.L. (2017). Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478. https://doi.org/10.1126/science.aai847810.1126/science.aai8478Search in Google Scholar

Wang, X., Allen, W.E., Wright, M.A., Sylwestrak, E.L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., Nolan, G.P., Bava, F.-A., Deisseroth, K. (2018). Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691. https://doi.org/10.1126/science.aat569110.1126/science.aat5691Search in Google Scholar

Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L.E., La Manno, G., Codeluppi, S., Furlan, A., Lee, K., Skene, N., Harris, K.D., Hjerling-Leffler, J., Arenas, E., Ernfors, P., Marklund, U., Linnarsson, S. (2018). Molecular Architecture of the Mouse Nervous System. Cell 174, 999–1014.e22. https://doi.org/10.1016/j.cell.2018.06.02110.1016/j.cell.2018.06.021Search in Google Scholar

Zeisel, A., Munoz-Manchado, A.B., Codeluppi, S., Lonnerberg, P., La Manno, G., Jureus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., Rolny, C., Castelo-Branco, G., Hjerling-Leffler, J., Linnarsson, S. (2015). Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142. https://doi.org/10.1126/science.aaa193410.1126/science.aaa1934Search in Google Scholar

Zhong, S., Zhang, S., Fan, X., Wu, Q., Yan, L., Dong, J., Zhang, H., Li, L., Sun, L., Pan, N., Xu, X., Tang, F., Zhang, J., Qiao, J., Wang, X. (2018). A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528. https://doi.org/10.1038/nature2598010.1038/nature25980Search in Google Scholar

Zywitza, V., Misios, A., Bunatyan, L., Willnow, T.E., Rajewsky, N. (2018). Single-Cell Transcriptomics Characterizes Cell Types in the Subventricular Zone and Uncovers Molecular Defects Impairing Adult Neurogenesis. Cell Rep. 25, 2457–2469.e8. https://doi.org/10.1016/j.celrep.2018.11.00310.1016/j.celrep.2018.11.003Search in Google Scholar

Published Online: 2019-08-09
Published in Print: 2019-08-07

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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