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A core transcriptional signature of human microglia: derivation and utility in describing region-dependent alterations associated with Alzheimer\'s disease

Anirudh Patir1, Barbara Shih1, Barry W. McColl1, Tom C. Freeman1 
26 Apr 2018-bioRxiv (Cold Spring Harbor Laboratory)-pp 308908

TL;DR: A core transcriptional signature of human microglia with 249 genes was derived and found conserved across brain regions, encompassing the CNS, and the utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease.

AbstractGrowing recognition of the pivotal role microglia play in neurodegenerative and neuroinflammatory disorders has accentuated the need to better characterize their function in health and disease. Studies in mouse, have applied transcriptome-wide profiling of microglia to reveal key features of microglial ontogeny, functional profile and phenotypic diversity. Whilst similar in many ways, human microglia exhibit clear differences to their mouse counterparts, underlining the need to develop a better understanding of the human microglial profile. On examining published microglia gene signatures, little consistency was observed between studies. Hence, we set out to define a conserved microglia signature of the human central nervous system (CNS), through a comprehensive meta-analysis of existing transcriptomic resources. Nine datasets derived from cells and tissue, isolated from different regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified. Comparison of individual microglia clusters showed 249 genes highly conserved between them. This core gene signature included all known markers and improves upon published microglial signatures. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer’s disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression. We believe this resource and the analyses described here, will support further investigations in the contribution of human microglia towards the CNS in health and disease. Table of Contents Main points Published microglial transcriptional signatures in mouse and human show poor consensus. A core transcriptional signature of human microglia with 249 genes was derived and found conserved across brain regions, encompassing the CNS. The signature revealed region-dependent microglial alterations in Alzheimer’s, highlighting susceptible CNS regions and the involvement of TYROBP signaling.

Summary (3 min read)

Introduction

  • Microglia are the most abundant myeloid cell type in the central nervous system (CNS), accounting for approximately 5-20% of the brain parenchyma depending on region (Lawson, Perry, Dri, & Gordon, 1990; Mittelbronn, Dietz, Schluesener, & Meyermann, 2001) .
  • These cells are phenotypically plastic and exhibit a wide spectrum of activity influenced by local and systemic factors (Cunningham, 2013; Perry & Holmes, 2014) .
  • As the primary immune sentinels of the CNS, microglia migrate towards lesions and sites of infection, where they attain an activated state that reflects their inflammatory environment (Leong & Ling, 1992) .
  • Recent transcriptomic studies have sought to characterize the human microglial transcriptomic signature from the CNS of non-neuropathologic individuals using data derived from either cells or tissue isolated from different brain regions (Darmanis et al., 2015; Galatro et al., 2017; Hawrylycz et al., 2012; Oldham et al., 2008) .

Comparison of published microglial signatures

  • Ten publications that defined microglial signatures, four in human and six in mouse, were identified (Table 1 ).
  • To compare across studies, genes from each signature were converted to a common identifier i.e. HGNC (Povey et al., 2001) or MGI (Shaw, 2009) for human and mouse, respectively, using the online tool g:Profiler (Reimand et al., 2016) .
  • CC-BY-NC-ND 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was not this version posted April 26, 2018.

Transcriptomics data acquisition and pre-processing

  • Tissue and cell transcriptomic datasets derived from the CNS were acquired for the derivation of the human microglial signature.
  • The ABA data, generated on the Agilent microarray platform, consisted of 3,702 tissue samples taken from six individuals with up to 411 unique anatomical regions of the brain.
  • Data quality was assessed using the ArrayQualityMetrics package (Kauffmann, Gentleman, & Huber, 2008) in Bioconductor, and samples failing more than one of three metrics (between arrays comparison, array intensity distribution and variance mean difference) were removed.
  • GTEX and ABA preprocessed RNA-Seq data was downloaded directly from the GTEx portal .
  • CC-BY-NC-ND 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Gene annotation through coexpression networks analysis

  • To define a core microglial gene signature, the tissue and cell transcriptomics datasets described above were analyzed using the coexpression network analysis tool Graphia Professional.
  • For each dataset, Pearson correlations (r) were calculated between all genes to produce a gene-to-gene correlation matrix.
  • From this matrix, a gene coexpression network (GCN) was generated, where nodes represented genes and genes correlating greater than a defined threshold were connected by edges.
  • All Pearson threshold values used for individual datasets were above r ≥ 0.7 and thereby graphs included only correlations that were highly unlikely to occur by chance .
  • For each dataset, the threshold for correlations was further adjusted to achieve a single microglial cluster containing the three canonical marker genes for microglia, CX3CR1, AIF1 and CSF1R (Elmore et al., 2014; Mittelbronn et al., 2001) .

Validation of the core human microglial signature

  • Various lines of evidence were investigated to validate the conserved nature of the derived human microglial signature.
  • The copyright holder for this preprint (which was not this version posted April 26, 2018.
  • BioRxiv preprint mouse, for comparable regions (Lawson et al., 1990), also known as doi.
  • Signature genes were then compared with other published mouse and human microglial signatures.
  • A GCN constructed (r ≥ 0.7) from the GTEx RNA-Seq dataset revealed five gene clusters enriched in Galatro et al. signature genes, representing various region-specific expression profiles (Table S5 ).

Heterogeneity of existing microglial signatures from human and mouse

  • To examine the human microglia gene signature, previous signatures from human brain tissue or cells were compared .
  • Four such studies varied considerably in the number of genes they defined, ranging from 21 to 1,236 genes.
  • Of the 1,464 unique genes identified in all these studies, only a fraction (15%, 214 genes) were present in two or more signatures, with only 10 genes reported by all four publications.
  • To verify that these results were not purely attributed by the individual variation in humans, the six publications reporting mouse-microglial signatures were also compared.
  • Altogether these listed 690 genes (ranging from 47 to 433 genes) with 300 orthologues common to studies in human.

Derivation of a conserved core human microglial signature

  • Observing the variability across published studies, the authors set out to define a human microglia gene signature from human tissue and cell data using a GCN . CC-BY-NC-ND 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • This method exploits the inherent variability amongst samples due to variation in sampling, donors and cellular diversity across different CNS regions.
  • For constructing GCNs, genes are represented by nodes, and connected by an edge based on the similarity between their expression profiles, as quantified by the Pearson correlation coefficient .
  • The final high confidence microglia gene signature was defined by 249 genes, which were present in three or more dataset-derived clusters, so as to avoid biases towards individual datasets.

Validation and description of the core human microglial signature

  • To validate the microglial signature genes, various lines of evidence were examined.
  • CC-BY-NC-ND 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was not this version posted April 26, 2018.
  • A majority of the core signature genes (64%, 142 genes) were identified in two or more human studies, whilst 99 genes overlapped solely with the Galatro et al. signature.
  • To further validate the specificity of the current microglial signature, the coexpression of these genes was compared with that of the Galatro et al. signature (1,236 genes), which included the majority of genes in other signatures.

Discussion

  • CC-BY-NC-ND 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was not this version posted April 26, 2018.
  • The authors initial investigations demonstrated that published microglia gene signatures vary considerably in their size and composition relative to one another.
  • When comparing Alzheimer's to age-matched controls, a similar trend towards increased expression levels of signature genes was also observed.

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A core transcriptional signature of human microglia: derivation and 1
utility in describing region-dependent alterations associated with 2
Alzheimer’s disease 3
Anirudh Patir
1
, Barbara Shih
1
, Barry W. McColl
1,2
and Tom C. Freeman
1†
4
5
1. The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, 6
Scotland, UK EH25 9RG. 7
2. UK Dementia Research Institute at The University of Edinburgh, Edinburgh 8
Medical School, The Chancellor's Building, 49 Little France Crescent, 9
Edinburgh, EH16 4TJ, UKUK. 10
11
Corresponding author 12
13
Running title: A functional profile of human microglia. 14
15
Acknowledgments 16
T.C.F. and B.W.M. are funded by an Institute Strategic Programme Grant funding 17
from the Biotechnology and Biological Sciences Research Council [BB/J004227/1]. 18
B.W.M. receives funding from the UK Dementia Research Institute and Medical 19
Research Council [MR/L003384/1]. B.S. is supported by Experimental Medicine 20
Challenge Grant funding from the Medical Research Council [MR/M003833/1]. 21
22
Conflict of Interest Statement 23
The authors have no competing financial interests. 24
25
Word Count: 5781 26
27
.CC-BY-NC-ND 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted April 26, 2018. ; https://doi.org/10.1101/308908doi: bioRxiv preprint

Abstract 28
Growing recognition of the pivotal role microglia play in neurodegenerative and 29
neuroinflammatory disorders has accentuated the need to better characterize their 30
function in health and disease. Studies in mouse, have applied transcriptome-wide 31
profiling of microglia to reveal key features of microglial ontogeny, functional profile 32
and phenotypic diversity. Whilst similar in many ways, human microglia exhibit clear 33
differences to their mouse counterparts, underlining the need to develop a better 34
understanding of the human microglial profile. On examining published microglia 35
gene signatures, little consistency was observed between studies. Hence, we set out 36
to define a conserved microglia signature of the human central nervous system 37
(CNS), through a comprehensive meta-analysis of existing transcriptomic resources. 38
Nine datasets derived from cells and tissue, isolated from different regions of the 39
CNS across numerous donors, were subjected independently to an unbiased 40
correlation network analysis. From each dataset, a list of coexpressing genes 41
corresponding to microglia was identified. Comparison of individual microglia clusters 42
showed 249 genes highly conserved between them. This core gene signature 43
included all known markers and improves upon published microglial signatures. The 44
utility of this signature was demonstrated by its use in detecting qualitative and 45
quantitative region-specific alterations in aging and Alzheimer’s disease. These 46
analyses highlighted the reactive response of microglia in vulnerable brain regions 47
such as the entorhinal cortex and hippocampus, additionally implicating pathways 48
associated with disease progression. We believe this resource and the analyses 49
described here, will support further investigations in the contribution of human 50
microglia towards the CNS in health and disease. 51
52
53
54
55
56
57
58
.CC-BY-NC-ND 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted April 26, 2018. ; https://doi.org/10.1101/308908doi: bioRxiv preprint

Keywords 59
Microglia, transcriptome, neurodegenerative disease, aging, Alzheimer’s. 60
Table of Contents: Main points 61
Published microglial transcriptional signatures in mouse and human show 62
poor consensus. 63
A core transcriptional signature of human microglia with 249 genes was 64
derived and found conserved across brain regions, encompassing the CNS. 65
The signature revealed region-dependent microglial alterations in Alzheimer’s, 66
highlighting susceptible CNS regions and the involvement of TYROBP 67
signaling. 68
69
.CC-BY-NC-ND 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted April 26, 2018. ; https://doi.org/10.1101/308908doi: bioRxiv preprint

Introduction 70
Microglia are the most abundant myeloid cell type in the central nervous system 71
(CNS), accounting for approximately 5-20% of the brain parenchyma depending on 72
region (Lawson, Perry, Dri, & Gordon, 1990; Mittelbronn, Dietz, Schluesener, & 73
Meyermann, 2001). These cells are phenotypically plastic and exhibit a wide 74
spectrum of activity influenced by local and systemic factors (Cunningham, 2013; 75
Perry & Holmes, 2014). Through development into adulthood, microglia influence the 76
proliferation and differentiation of surrounding cells while regulating processes such 77
as myelination, synaptic organization and synaptic signaling (Colonna & Butovsky, 78
2017; Hoshiko, Arnoux, Avignone, Yamamoto, & Audinat, 2012; Paolicelli et al., 79
2011; Prinz & Priller, 2014). As the primary immune sentinels of the CNS, microglia 80
migrate towards lesions and sites of infection, where they attain an activated state 81
that reflects their inflammatory environment (Leong & Ling, 1992). In these states, 82
they can support tissue remodeling and phagocytose cellular debris, toxic protein 83
aggregates and microbes (Colonna & Butovsky, 2017; Li & Barres, 2017). During 84
neuroinflammation these cells coordinate an immune response by releasing 85
cytokines, chemoattractants and presenting antigens, thereby communicating with 86
other immune cells locally and recruited from the circulation (Hanisch & Kettenmann, 87
2007; Hickey & Kimura, 1988; Scholz & Woolf, 2007). 88
In common with mononuclear phagocyte populations throughout the body, recent 89
studies have begun to reveal the diversity of microglial phenotypes in health, aging 90
and disease states, as well as their unique molecular identity in relation to other CNS 91
resident cells and non-parenchymal macrophages (Durafourt et al., 2012; Hanisch, 92
2013; Li & Barres, 2017; McCarthy; Salter & Stevens, 2017). The application of 93
transcriptomic methods has been integral to these advances by enabling an 94
unbiased and panoramic perspective of the functional profile of microglia. In addition 95
to an improved understanding of the variety of context-dependent microglial 96
phenotypes, other key benefits have arisen from these studies, notably the 97
development of new tools to label, isolate and manipulate microglia (Bennett et al., 98
2016; Butovsky et al., 2014; Hickman et al., 2013; Satoh et al., 2016). Although most 99
studies have been conducted in mice, a considerable body of data is now emerging 100
from human post-mortem and biopsy tissue (Darmanis et al., 2015; Galatro et al., 101
2017; Gosselin et al., 2017; Olah et al., 2018; Y. Zhang et al., 2016). Whilst there are 102
.CC-BY-NC-ND 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted April 26, 2018. ; https://doi.org/10.1101/308908doi: bioRxiv preprint

many conserved features between rodent and human microglia, the importance of 103
further refining our understanding specifically of human microglia is underscored by 104
important differences that have been observed between them (Butovsky et al., 2014; 105
Galatro et al., 2017; Miller, Horvath, & Geschwind, 2010). 106
Recent transcriptomic studies have sought to characterize the human microglial 107
transcriptomic signature from the CNS of non-neuropathologic individuals using data 108
derived from either cells or tissue isolated from different brain regions (Darmanis et 109
al., 2015; Galatro et al., 2017; Hawrylycz et al., 2012; Oldham et al., 2008). These 110
analyses have been crucial in expanding our knowledge of their functional biology, 111
however, our preliminary analyses found there to be little inter-study agreement 112
across the published microglia gene signatures. Such inconsistency may have arisen 113
due to technical differences in tissue sampling, brain areas analyzed, differences in 114
patient characteristics and biological variance including the heterogeneity of different 115
microglia populations (Grabert et al., 2016; Lai, Dhami, Dibal, & Todd, 2011; Lawson 116
et al., 1990; Vincenti et al., 2016; Yokokura et al., 2011). This highlighted a need to 117
derive a refined human microglial signature that would enable a more precise 118
characterization of these cells in the healthy and diseased human brain. We 119
therefore set out to define the core transcriptional signature of human microglia, i.e. 120
shared by all microglial populations of the human CNS. To achieve this, we have 121
performed an extensive meta-analysis of nine human cell and tissue transcriptomics 122
datasets derived from numerous brain regions and donors. Secondly, we have used 123
this signature to investigate region-dependent changes, while highlighting the 124
influence of microglial numbers and activation in human tissue transcriptomics for 125
Alzheimer’s and aging. 126
Methods 127
Comparison of published microglial signatures 128
Ten publications that defined microglial signatures, four in human and six in mouse, 129
were identified (Table 1). To compare across studies, genes from each signature 130
were converted to a common identifier i.e. HGNC (Povey et al., 2001) or MGI (Shaw, 131
2009) for human and mouse, respectively, using the online tool g:Profiler (Reimand 132
et al., 2016). Subsequently, the tool was also used for interspecies comparison 133
based on the MGI homology database, identifying human orthologues of mouse 134
.CC-BY-NC-ND 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted April 26, 2018. ; https://doi.org/10.1101/308908doi: bioRxiv preprint

Figures (1)
Citations
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Abstract: DAP12 is a signaling adaptor containing an immunoreceptor tyrosine-based activation motif (ITAM) that pairs with receptors on myeloid cells and natural killer cells. We examine here the responses of mice lacking DAP12 to stimulation through Toll-like receptors (TLRs). Unexpectedly, DAP12-deficient macrophages produced higher concentrations of inflammatory cytokines in response to a variety of pathogenic stimuli. Additionally, macrophages deficient in spleen tyrosine kinase (Syk), which signals downstream of DAP12, showed a phenotype identical to that of DAP12-deficient macrophages. DAP12-deficient mice were more susceptible to endotoxic shock and had enhanced resistance to infection by the intracellular bacterium Listeria monocytogenes. These data suggest that one or more DAP12-pairing receptors negatively regulate signaling through TLRs.

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The authors believe this resource and the analyses 49 described here, will support further investigations in the contribution of human 50 microglia towards the CNS in health and disease.