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ARTICLE
Received 29 Jul 2016 |Accepted 2 Mar 2017 |Published 2 May 2017
Neurons and neuronal activity control gene
expression in astrocytes to regulate their
development and metabolism
Philip Hasel1, Owen Dando1,2,3,*, Zoeb Jiwaji1,2,*, Paul Baxter1, Alison C. Todd1, Samuel Heron4,No
´ra M. Ma
´rkus1,
Jamie McQueen1, David W. Hampton2, Megan Torvell2, Sachin S. Tiwari2, Sean McKay1, Abel Eraso-Pichot5,
Antonio Zorzano6,7,8, Roser Masgrau5, Elena Galea5,9, Siddharthan Chandran2,3, David J.A. Wyllie1,
T. Ian Simpson4& Giles E. Hardingham1,10
The influence that neurons exert on astrocytic function is poorly understood. To investigate
this, we first developed a system combining cortical neurons and astrocytes from closely
related species, followed by RNA-seq and in silico species separation. This approach uncovers
a wide programme of neuron-induced astrocytic gene expression, involving Notch signalling,
which drives and maintains astrocytic maturity and neurotransmitter uptake function, is
conserved in human development, and is disrupted by neurodegeneration. Separately,
hundreds of astrocytic genes are acutely regulated by synaptic activity via mechanisms
involving cAMP/PKA-dependent CREB activation. This includes the coordinated activity-
dependent upregulation of major astrocytic components of the astrocyte–neuron lactate
shuttle, leading to a CREB-dependent increase in astrocytic glucose metabolism and elevated
lactate export. Moreover, the groups of astrocytic genes induced by neurons or neuronal
activity both show age-dependent decline in humans. Thus, neurons and neuronal activity
regulate the astrocytic transcriptome with the potential to shape astrocyte–neuron metabolic
cooperation.
DOI: 10.1038/ncomms15132 OPEN
1Deanery of Biomedical Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh EH8 9XD, UK. 2MRC Centre for Regenerative Medicine,
University of Edinburgh, Edinburgh EH16 4SB, UK. 3Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine,
National Centre for Biological Sciences, Bangalore 560065, India. 4School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK. 5Institut de
Neurocie
`ncies and Departament de Bioquı
´mica i Biologia Molecular, Unitat de Bioquı
´mica de Medicina, Edifici M, Universitat Auto
`noma de Barcelona,
Bellaterra, Barcelona 08193, Spain. 6Institute for Research in Biomedicine, Barcelona 08028, Spain. 7Department of Biochemistry and Molecular Biology,
University of Barcelona, Barcelona 08028, Spain. 8Centro de Investigacio
´n Biome
´dica en Red de Diabetes y Enfermedades Metabo
´licas Asociadas
(CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid 28029, Spain. 9Institucio
´Catalana De Recerca I Estudis Avanc¸ats (ICREA), Passeig Lluı
´s
Companys 23, Barcelona, Catalonia, 08010, Spain. 10 UK Dementia Research Institute at The University of Edinburgh, Edinburgh Medical School, 47 Little
France Crescent, Edinburgh EH16 4TJ, UK. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed
to G.E.H. (email: Giles.Hardingham@ed.ac.uk).
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 1
Signalling between neurons triggers programmes of gene
expression that mediate specific functions in development
and maturity. Signalling between neurons and glia is also
implicated in diverse processes, however, insight into the scope
and mechanism by which different cell types in the neuro-glial
unit influence each other is lacking. A notable example is neuron-
to-astrocyte signalling: neurons induce the classic stellate
morphology in astrocytes, resembling their appearance in vivo1.
However, knowledge of the transcriptional changes that
accompany this transformation are restricted to three
astrocytic genes (Slc1a2,Slc1a3 and Gja1 (refs 2,3)) induced by
neurons. Similarly, despite astrocytes playing a critical role in the
uptake of synaptically released neurotransmitters, whether
synaptic activity controls astrocytic gene expression is unclear.
This contrasts with the wealth of knowledge concerning the
mechanisms and the roles of activity-dependent gene expression
in neurons4–7.
Understanding non-cell-autonomously regulated gene expres-
sion poses challenges associated with the physical separation of
cell types before transcriptome analysis by methods including
RNA-seq. Immunopanning or FACS are powerful approaches,
but are subject to off-target cell type contamination, and cause
aberrant induction of transcriptional stress responses, or the loss
of material from delicate subcellular regions8–10. Theoretically, if
RNA-seq reads could be unambiguously attributed to one cell
type or another, physical sorting could be avoided. We
investigated the possibility of achieving this by deriving the
distinct cell types from different species11. We chose rat and
mouse, two species that we reasoned may be closely enough
related to enable questions of non-cell-autonomous signalling to
be answered. We used this system as a starting point to
investigate how and to what extent neurons and neuronal
activity control astrocytic gene expression, as well as the
underlying mechanisms and functional consequences.
Results
Unsorting rat and mouse RNA-seq reads in silico. We first
established the feasibility of our species-specific sorting (SSS)
RNA-seq workflow. In the vast majority of genes, the majority of
mouse RNA-seq paired-end reads can be unambiguously attributed
to mouse (as opposed to rat), using both a simulated set of
reads (Supplementary Fig. 1a; Supplementary Data 1) as well as
a ‘real-world’ RNA-seq data set of mouse cortical neurons
(Supplementary Fig. 1b; Supplementary Data 2). Also, compared to
normal read mapping, the SSS workflow does not substantially
affect differential gene expression analysis (DGE) when performed
on a single-species (mouse) RNA-seq data-set taken from neurons
treated±bicuculline þ4-aminopyridine (BiC/4-AP) to induce
synaptic activity via network disinhibition12:weobservedatight
correlation in fold-induction, comparing normal and SSS
approaches (Supplementary Fig. 1c; Supplementary Data 3).
There was also little change in the adjusted Pvalues: of the 4,632
genes altered by BiC/4-AP treatment, only 33 were rendered
insignificant by the SSS approach (Supplementary Fig. 1d;
Supplementary Data 4). Large Pvalue deviations were more
likely to occur in the small number of genes that had lost a lot of
reads due to the SSS approach. However, the tiny number of genes
affected leads us to conclude that the SSS workflow does not
substantially impact on DGE analysis.
Neurons transform astrocytic transcriptome and function.To
profile the influence of neurons on the astrocytic transcriptome,
we cultured primary mouse cortical astrocytes, onto which we
co-cultured primary rat cortical neurons. The mouse cortical
astrocytes are 496% GFAP positive13, which we confirmed with
another marker (Aldh1l1, 99.4±0.2% positive (n¼4), Suppleme-
ntary Fig. 2a). The astrocytes were o0.1% Neuro-Chrom þ
neurons and o0.1% Iba þmicroglia.
Compared to parallel astrocytic mono-cultures, co-cultured
astrocytes developed the expected stellate morphology over 9 days
(Fig. 1a,b). At this point, RNA was extracted (3 biological
replicates), RNA-seq performed and SSS employed to identify the
mouse (that is, astrocyte) reads. DGE analysis revealed wide-
spread up- and down-regulation of astrocytic gene expression due
to the presence of neurons (Fig. 1c; Supplementary Data 5),
including previously identified genes (Slc1a2,Slc1a3 and Gja1
(refs 2,3)). Four hundred and ten genes were induced more than
twofold and 353 genes repressed more than twofold (Adjusted P
value o0.05, Supplementary Data 5).
A notable cluster of upregulated genes is involved in the
uptake and metabolism of neurotransmitters, including
GABA (Slc6a1,Slc6a11 and Abat), NAAG (Slc17a5), biogenic
amine neurotransmitters (Slc29a2 and Maob) and glutamate
(Slc1a2,Slc1a3,Glul and Glud1; Supplementary Data 6).
Focussing on glutamate uptake, we confirmed that these
transcriptional changes are reflected at the functional level, by
measuring the electrogenic currents of the glutamate transporters
EAAT1/GLAST (Slc1a3), and EAAT2/GLT-1 (Slc1a2), using
whole-cell voltage clamp recordings. Astrocytic EAAT currents14
were increased around 10-fold by neuronal co-culture (Fig. 1d,e),
while astrocytic resting membrane potential and passive memb-
rane conductance were unaffected (Supplementary Fig. 2b,c)15,16.
In addition, many cytoskeletal and extracellular matrix genes
were altered (Supplementary Data 5), consistent with the
profound changes in astrocytic morphology. Astrocyte-specific
marker and endfoot component Aqp4 was also induced, but other
markers, S100b,Aldh1l1 and Gfap were unaffected (Fig. 1c). Thus,
neuron-induced changes to the morphology of astrocytes are
associated with marked changes to their transcriptome and
functional properties.
To assess the advantages of this approach over imperfect
physical separation approaches, we firstly simulated a sorting
process that achieved 95% purity, by ‘contaminating’ the mouse
astrocytic mRNA with mouse cortical neuronal mRNA at a ratio
of 95:5, by number of cells collected, and performed RNA-seq.
The effects were substantial: 863 genes were expressed more than
two-fold higher in the 95% sample, compared to the pure
astrocytic sample (Supplementary Fig. 3a, Supplementary Data 7),
of which 216 were expressed 410-fold higher. Second, we
investigated the induction of transcriptional stress responses to
physical sorting methods9. Both simple trypsinization,
immunopanning and FACS protocols applied to a
homogeneous population of cultured astrocytes induced
multiple immediate early genes (Supplementary Fig. 3b–d).
These data show key advantages of in silico read separation in
avoiding DGE artefacts introduced by physical sorting.
Neurons induce an in vivo-like transcriptome in astrocytes.
While neuronal co-culture promotes a more in vivo-like
morphology and functional profile in astrocytes, we wanted
to determine whether the observed changes in the astrocytic
transcriptome were consistent with a more in vivo-like shift.
We compared our data to a microarray study that characterized
differences in gene expression between mono-cultures of astro-
cytes in vitro, and astrocytes acutely sorted from the cortex
in vivo17. The group of 695 genes elevated in vivo more
than twofold and meeting expression level cut-off criteria
(see Methods) in the data of Cahoy et al. were significantly
induced by neuronal co-culture, compared to mono-culture in our
data set (Fig. 2a; Supplementary Data 8). The group of 654 genes
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
2NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications
repressed in vivo more than twofold in the data set of Cahoy et al.
were significantly repressed by neuronal co-culture in our data set
(Fig. 2b; Supplementary Data 9). These analyses suggest that the
mature in vivo gene expression profile of astrocytes may be at least
in part due to neuron–astrocyte communication, since it can be
mimicked in vitro by neuronal co-culture.
AstrMus
mono-culture
AstrocyteMus
mono-culture
AstrMus- NeurRat
co-culture
GFP +ve astrocyte
Neuro-ChromTM
GFAP
Neuro-ChromTM
AstrocyteMus- NeuronRat
co-culture
AstrMus mono-culture
AstrMus mono-culture
AstrMus mono-culture
P<0.0001 *
***
**
****
0.15
Perimeter/area (a.u.)
0.10
0.05
0.00
*
L-Asp L-Asp
L-Asp L-Asp TFB-TBOA
TFB-TBOA
Glu-T current (pA)
212 Glul
Slc1a3
Slc1a2
Aqp4
Glud1
Gfap
Aldh1l1
S100b
212
211
211
210
210
29
29
28
28
27
27
26
26
25
25
>2-fold and P_adj<0.05
>1.3-,≤2-fold and P_adj<0.05
24
24
23
23
22
22
Astrocytic expression in mono-culture (FPKM)
Astrocytic expression in co-culture (FPKM)
38,056,262
Genes expressed >0.5 FPKM
13,380,921
12.499.9
763
2,116
13,16313,122
21
21
20
20
2–1
2–1
2–2
2–2
2–3
2–3
Avg no. of reads mapped to
mouse genome per sample (n=3)
% of mapped reads attributed to
the mouse genome
Differentially expressed genes
(≥2-fold difference)
Differentially expressed genes
(≥1.3-fold difference)
30
35
25
20
15
10
5
0
Mono- Co-
Pre 10987654321
Days after neuronal co-culture
AstrMus mono-culture
AstrMus - NeurRat co-culture
AstrMus - NeurRat co-culture
AstrMus - NeurRat co-culture
AstrMus - NeurRat co-culture
a
cde
b
Figure 1 | Mixed-species RNA-seq uncovers neuronally induced astrocytic gene expression. (a) The mixed-species co-culture system recapitulates
characteristic neuron-induced changes to astrocyte morphology. Upper: example picture of a GFP-transfected mouse cortical astrocyte cultured for10days
with (right) or without (left) rat cortical neurons. Neurons are identified by immunofluorescent staining with pan-neuronal antibody cocktail Neuro-Chrom.
Lower: Staining of mouse astrocyte monoculture (left) and mixed-species co-culture (right) for astrocyte-specific gene Gfap (plus Neuro-Chrom). Scale bar,
20 mm. (b) Perimeter:area ratio of GFP þastrocytes described in aat the indicated times (days in vitro). 6–16 cells were analysed for each culture type, for
each day. *Po0.05 compared to astrocytic monoculture on that day (two-way analysis of variance (ANOVA), plus Holm-Sidak’s post hoc test). Example
outlines are shown on the right. Scale bar, 20mm. (c) Neurons promote widespread gene expression changes in astrocytes. RNA from mixed-species mouse
astrocyte/rat neuron co-cultures (n¼3 biological replicates) was subjected to RNA-seq, followed by SSS workflow to identify reads that were unambiguously
mouse (that is, astrocytic) in origin. The same workflow was applied to mouse astrocytic mono-cultures. Expression of genes (FPKM) in astrocytes in the
presence or absence of neurons is plotted for all genes expressed 40.5 FPKM average across mono- and co-cultures. Red and black crosses indicate the
astrocytic genes induced or repressed by neurons more than twofold or 41.3, r2-fold respectively (DESeq2 P_adjo0.05). See Supplementary Data 5. The
table summarizes read depth, species sorting and number of differentially expressed genes. (d) Neurons promote increased glutamate transporter capacity in
astrocytes. Mouse astrocytes cultured with or without neurons (as in a–c) were subject to whole-cell voltage-clamp recording of glutamate transporter
currents, measured upon application of 200 mM aspartate (see Methods). Currents were inhibited by the glutamate transporter blocker TFB-TBOA (20mM).
*Po0.05 (unpaired t-test, n¼13 (mono), 22 (co-culture)). (e) Example traces. Scale bar, 5 s, 10 pA. All error bars represent s.e.m.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 3
Neuron-induced changes to human astrocytes. To determine
the extent to which the transcriptional programmes induced
by neurons are relevant to human astrocytes, we created
co-cultures in which rat neurons were overlaid onto primary
human fetal astrocytes. The presence of neurons transformed
human astrocytes from a simple polygonal shape to a complex
4
3
Astrocytic expression in co-culture
(Log2 fold change versus mono-culture)
Astrocytic expression in co-culture
(Log2 fold change versus mono-culture)
2
1
1
0
–1
–2
–3
0.3
AstrHum
mono-culture
AstrHum-NeurRat
co-culture
1.00
*
AstrHum mono-culture
AstrHum- NeurRat co-culture
0.75
0.55
0.25
0.00
25
–6
–4
–2
0
2
4
4
P=0.003
P=0.002
P=4.2×10–8
P=2.5×10–6
2
0
–2
–4
–6
–8
–10
6
8
10
50 75 100
Astroc
y
tic
g
enes induced >2-fold b
y
neurons Astrocytic genes repressed >2-fold by neurons
125 150 175 200 225 250 25 50 75 100 125
SLC1A2
SLC1A3
AQP4
0.2
0.1
0.0
Mono- Co-
Perimeter/area (a.u.)
Human astrocyte expression
(Log2 fold-change post-natal versus fetal)
Human astrocyte expression
(Log2 fold-change post-natal versus fetal)
Astrocytic expression
relative to co-culture
100 200 300 400 500 600
0
–1
100
Astrocytic genes elevated in vivo compared to
in vitro mono-culture (Cahoy et al.)
Astrocytic genes repressed in vivo compared to
in vitro mono-culture (Cahoy et al.)
200 300 400 500 600
a
c
d
gf
e
b
***
Figure 2 | Linking neuron-induced astrocytic gene expression to mouse and human development. (a,b) Neurons are sufficient to drive the astrocytic
transcriptome towards an in vivo profile. Gene fold-change caused by neuronal co-culture (as analysed in Fig. 1c) is shown for those genes identified by
Cahoy et al.17 in a microarray screen as being either elevated (a) or lowered (b) in astrocytes in vivo, compared to in vitro mono-culture17. Genes expressed
40.5 FKPM in either mono- or co-culture are shown. In some cases gene names are updated from those quoted in Cahoy et al.17. Within the group of
genes elevated in vivo (a, 695 genes induced more than twofold) the influence of neurons on astrocytic expression is shown, ranked according to fold-
change. P¼2.5E 6 (paired t-test comparing FPKM of these 695 genes in mono-culture versus co-culture). Within the group of genes repressed in vivo
(b, 654 genes repressed more than twofold) the influence of neurons on astrocytic expression is shown, ranked according to fold-change. P¼4.2E 8
(paired t-test comparing FPKM of these 654 genes in mono-culture versus co-culture). All the data are available in Supplementary Data 8 and 9.
(c,d) Primary human astrocytes are morphologically transformed by co-culture with neurons. Experiment performed as per Fig. 1a,b except that instead of
mouse astrocytes, primary human fetal astrocytes were used. *Po0.05 (unpaired t-test, n¼6 (mono-), 6 (co-culture)). Scale bar, 50 mm. (e) Human
astrocytic gene expression in mono- and co-culture measured using species-specific qPCR primers. *Po0.05 (unpaired t-test, n¼5). (f,g) The neuronal
influence on expression of astrocytic genes aligns significantly with the developmental trajectory of those genes in the human brain. Astrocytic genes
induced more than twofold (f) or repressed more than twofold (g) (Fig. 1c) were cross-referenced to the data in Zhang et al.18, and the fold change in
expression in human astrocytes (post-natal versus fetal) from this study calculated. Only genes meeting an expression threshold of 40.5 FPKM in both
studies are shown. (f)*P¼0.002 (paired t-test comparing average FPKM of these genes in post-natal versus fetal). (g)*P¼0.003 (paired t-test
comparing FPKM of these genes in post-natal versus fetal). Data are available in Supplementary Data 17 and 18. All error bars represent s.e.m.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
4NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications
stellate morphology (Fig. 2c,d). Moreover, neuron-induced
increases in astrocytic SLC1A2,SLC1A3 and AQP4 expression
were observed (Fig. 2e), suggesting conservation of neuron-to-
astrocyte signalling, and that human astrocytes also receive
maturation cues from neurons.
We next investigated the extent to which the astrocytic genes
we identified as being neuronally regulated (NR) are differentially
expressed in astrocytes in human development. We compared
our data to a recent RNA-seq analysis of fetal and post-natal
astrocytes extracted from human tissue from individuals of
a variety of ages18. The group of genes we identified as
being induced more than twofold in astrocytes by neurons was
significantly upregulated in post-natal astrocytes (age 8–63 years)
compared to fetal astrocytes (18–19 weeks gestation), (Fig. 2f,
P¼0.002 paired t-test, Supplementary Data 17). In a similar vein,
the group of genes we identified as being repressed more than
twofold in astrocytes by neurons was significantly downregulated
in post-natal astrocytes (age 8–63 years) compared to fetal
astrocytes (18–19 weeks gestation; Fig. 2g; P¼0.003, paired
t-test; Supplementary Data 18). Thus, the neuronal influence
on expression of astrocytic genes (up or down) aligns
significantly with the developmental trajectory of those genes in
the human brain.
Neuron-induced astrocytic genes are deregulated ex vivo. While
neuron-derived signals are sufficient to drive the astrocytic
transcriptome towards a more in vivo-like profile in co-culture
(Fig. 2a,b), we wanted to determine whether similar signals are
responsible for maintaining this profile in vivo. We investigated
whether astrocytic expression of neuronally induced genes
declines if astrocytes are removed from their normal in vivo
environment. Astrocytes were isolated from the mouse cortex
by immunopanning at P7, when the astrocytic transcriptome is
near maturity19,20. We measured expression of a panel of
neuron-induced astrocytic genes both immediately post-
isolation and again after 4 days of ex vivo maintenance in
isolation from neurons, and observed a marked decline in
expression (Fig. 3a). Moreover, subsequent addition of neurons
reversed this loss of expression (Fig. 3b), suggesting that in vivo,
a neuronally derived signal is responsible for maintaining
key aspects of the astrocytic transcriptome.
As an additional control to rule out that any astrocytic gene
induction in our co-culture system was due to species differences
of the neurons and astrocytes, we analysed expression of the NR
astrocytic genes shown in Fig. 3a,b whose induction could be
tracked in a single species co-culture by virtue of their expression
being 410-fold lower in neurons than astrocytes (Hes5,Dio2,
Slco1c1,Glul and Cldn10). This group of genes studied was indeed
significantly elevated in the single species co-culture, compared to
astrocytic mono-culture (Supplementary Fig. 4a). We also
confirmed that the functional impact of neurons in inducing
mouse astrocytic glutamate transporter capacity is observed in
a single species mouse neuron/astrocyte co-culture (Suppleme-
ntary Fig. 4b) consistent with our observations based on the
mixed-species preparation (Fig. 1d).
Neuron-induced astrocytic genes are repressed by tauopathy.
The reversibility of neuron-induced influences on astrocytic gene
expression raises the possibility that neurodegeneration could
lead to a decline in NR astrocytic gene expression. We employed
the Thy1-P301S neurodegenerative tauopathy model21,22,
focussing on the spinal cord, where neurodegeneration is most
abundant22, and confirmed the presence of phosphorylated tau
(Fig. 3c) and neurodegeneration (Fig. 3d). We studied the
expression of those genes we identified as being strongly
up-regulated by neurons (Z5-fold) whose expression in a
mixed cell population could be reasonably attributed to
astrocytes (astrocytic expression Z10-fold higher than in
neurons, oligodendrocytes, microglia or endothelial cells, based
on the data within Zhang et al.23, nine genes in total). We also
confirmed that as a group they were significantly induced by
neurons in spinal astrocytes (Supplementary Fig. 4c) and
that spinal astrocytes underwent a similar neuron-induced
morphological transformation (Supplementary Fig. 4d).
The expression of these genes in wild type versus P301S mice
was normalized to astrocytic marker Aldh1l1 (refs 17,18) whose
expression is not influenced by neuronal contact (Fig. 1c).
Aldh1l1 also has stable expression in vivo following inflammatory
or ischaemic injury24, and is unaltered in the P301S versus
wild-type spinal cord at the mRNA or protein level (Suppleme-
ntary Fig. 4e,f,g). The group of nine genes was significantly
downregulated in the 20-week-old P301S mouse (Fig. 3e;
Po0.0001, two-way analysis of variance). Thus, neurodege-
neration triggered by a neuron-specific transgene leads to
a decline in the astrocytic expression of NR genes in vivo.
Notch contributes to neuron-induced astrocytic maturation.
We next investigated what neuron-derived signal(s) may be
responsible for the induction and maintenance of these genes in
astrocytes, since signals involved in the developmental matura-
tion of astrocytes are not well understood1.
We noted that expression of Notch target genes Hes5 and Hey2
were (i) induced by rat neurons in mouse astrocytes
(Supplementary Data 5), (ii) repressed when astrocytes were
removed from their in vivo environment (Fig. 3a) and
(iii) induced again when neurons were overlaid onto ex vivo
astrocytes (Fig. 3b). Hes5 was also downregulated in vivo in the
P301S mouse (Fig. 3e) and induced in mouse astrocytes by
mouse neurons (Supplementary Fig. 4a). These observations
suggested to us a potential role for neuron-derived Notch
signalling. Analysis of expression levels of Notch and
Notch ligands in neurons and astrocytes revealed that neurons
(but not astrocytes) strongly express Notch ligands, particularly
Jag2 and Dlk2, while astrocytes strongly express receptors Notch1
and Notch2 (Fig. 4a). To investigate neuron-to-astrocyte Notch
signalling, astrocytes were transfected with a Notch reporter
(CBF1-luciferase) containing binding sites for the transcription
factor CBF1, which is converted from repressor to activator
upon binding the intracellular domain of Notch25. Astrocytic
CBF1-luciferase activity was strongly induced in the presence of
neurons, compared to mono-culture (Fig. 4b). Moreover, the
neuron-dependent induction of CBF1-luciferase activity (and of
Hes5 and Hey2) was blocked by inhibiting endogenous Notch
signalling by treatment with the Notch pathway inhibitor DAPT,
which prevents cleavage of Notch by g-secretase (Fig. 4b,c). The
neuron-dependent induction of a panel of other neuronally
induced astrocytic genes was also reduced or blocked by DAPT,
including the glutamate transporter Slc1a2 (Fig. 4c).
DAPT treatment was also found to inhibit the neuron-induced
increase in astrocytic glutamate transporter EAAT activity
(Fig. 4e,f). Interestingly, DAPT did not influence the neuron-
induced transformation of astrocytic morphology (Suppleme-
ntary Fig. 5a), suggesting that neuronal-induced functional and
morphological changes can be uncoupled and are mediated by
different mechanisms. Finally, to test whether canonical Notch
signalling is sufficient to boost astrocytic glutamate uptake
capacity, we expressed a constitutively active form of CBF1
(CBF1-VP16 (ref. 26; Fig. 4g)) in neuron-free astrocyte mono-
cultures, and found that this did indeed increase glutamate
transporter currents (Fig. 4h). These experiments show that
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 5
Notch signalling is necessary and sufficient to drive glutamate
uptake capacity in astrocytes.
We hypothesized that many of the genes whose induction is
repressed by DAPT are likely to be a secondary response to Notch
signalling, as opposed to direct Notch targets. We expressed
a strong activator of Notch signalling (intracellular domain of
Notch1: Notch1-IC) in mono-cultured astrocytes and analysed
gene expression 48 h post-transfection. We observed that, while
Hes5 and Hey2 were induced, the other genes were not
(Supplementary Fig. 5b), suggesting that they may be a delayed
downstream consequence of Notch signalling.
In addition, we observed that rat neurons can also induce Hes5
and Hey2 in human astrocytes (Fig. 4d), in addition to inducing
the glutamate transporters Slc1a2 and Slc1a3 (Fig. 2e), suggesting
that the ability of astrocytes to receive neuron-derived
Notch-dependent maturation signals is evolutionarily conserved.
Moreover, we confirmed that this neuronally-derived signal is not
sensitive to the neuronal species employed. We generated
‘reversed’ mixed-species co-cultures comprised of rat cortical
astrocytes in the presence of mouse cortical neurons, and
observed a similar neuron-induced astrocytic expression of
Hes5,Hey2,Slc1a2 and Slc1a3 (Supplementary Fig. 5c) that we
observed with rat neurons co-cultured with either mouse or
human astrocytes. This strong inter-species cross-compatibility is
consistent with the high amino acid conservation between
rodents and humans for both Notch and Notch ligand interaction
domains (495%).
Another issue we addressed is the possibility that small
numbers of non-neuronal cells in the rat neuronal preparation
were important for inducing the changes observed in mouse
astrocytic gene expression. In the co-culture system, the rat
neuronal preparation is co-cultured in zero serum to limit
proliferation of non-neuronal cells. To limit this further, we
performed an additional experiment with an adapted protocol
such that the rat neuronal preparation is added to the astrocytes
in the continuous presence of the anti-mitotic agent AraC. Under
these conditions, 499% of the rat neuronal preparation is Neuro-
Chrom þneurons, o0.01% Aldh1l1 þastrocytes, o0.01%
Iba1 þmicroglia, after 9 days in culture (Supplementary
Fig. 5d). The only caveat to these experiments is that the
astrocytes do not appear as healthy in AraC and there is a small
level of cell death. Nevertheless, the group of putative Notch-
dependent NR astrocytic genes studied in Fig. 4c are still induced
(Supplementary Fig. 5e), consistent with the notion that neurons
are the key mediators of these changes, although a minor
contribution of other cell types cannot be completely ruled out.
Collectively, these data point to neuron-to-astrocyte juxtacrine
Notch signalling as an important inducer and maintainer of
astrocytic functional maturation, in addition to its known role in
astrocytic fate specification1,27.
100
40
30
20
*
*
*
***
*
*
AstrMus ex vivo
AstrMus ex vivo- NeurRat co-culture
15
10
5
0
200
100
0
–20
–40
–60
–80
300
*
*
*
**
*
***
WT
P301S P301S
WT
ChAT
DAPI 200
100
% change in expression
P301S versus WT (normalized to Aldh1|1)
0
ChAT–+ve MNs per mm2
WT
P301S
Cldn10
Hes5
Slc6a11
Slc1a2
Grm3
Fmo1
Dio2
Rik
Cyp4f15
GFAP
P<0.0001
Hes5
Hey2
Slc1a2
Slc1a3
Dio2
Slco1c1
Glul
Cldn10
Adora2b
ab
e
cd
Expression 4 days post-sort
Relative mRNA expression
Expression level
immediately post-sort
80
60
40
20
0
Hes5
Hey2
Slc1a2
Slc1a3
Dio2
Slco1c1
Glul
Cldn10
Adora2b
AT8 α-pi-Tau
Figure 3 | Neuron-induced astrocytic gene expression is reversible and disrupted in neurodegenerative disease. (a) Mouse astrocytes were isolated by
immunopanning (using an anti-GLAST antibody) at P7. Expression of the indicated neuron-induced astrocytic genes was measured both immediately
post-isolation and after 4 days of ex vivo maintenance (neuron-free). *Po0.05 unpaired t-test, (n¼5). (b) Mouse astrocytes isolated as in a, and
maintained for 22 days in culture, with (black) or without (grey) the last 11 days being in the presence of rat neurons. *Po0.05 unpaired t-test, (n¼7).
(c) Example micrographs showing phospho-tau immunoreactivity in spinal cord sections of the Thy1-P301S transgenic mouse (at week 20). Scale bar,
500 mm. (d) (Left) Example micrographs showing loss of ChAT-positive motor neurons in spinal cord sections of the Thy1-P301S transgenic mouse at week
20. Scale bar, 50 mm. (Right) Quantitation of loss of ChAT-positive motor neurons in spinal cord sections of the Thy1-P301S transgenic mouse. *Po0.05
unpaired t-test, (n¼5 animals of both genotypes). (e) Expression of the indicated astrocyte-specific, neuron-induced genes in the spinal cord of week 20
wild type (WT) and P301S mice, normalized to astrocyte-specific gene Aldh1l1. Rik: 2900052N01Rik.Po0.0001, two-way analysis of variance, Pvalue
indicates main effect of genotype (n¼6 animals of both genotypes). All error bars represent s.e.m.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
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Synaptic activity directs astrocytic gene expression. We next
determined whether astrocytic gene expression is also subject to
acute control by synaptic activity. To alter neuronal activity
within the mixed-species co-culture system we incubated the cells
overnight in the voltage-gated Na þchannel blocker tetrodotoxin
(TTX), to block action potential (AP) firing. We then washed out
the TTX in the presence of the GABA
A
(g-aminobutyric acid)
receptor antagonist bicuculline (BiC), to trigger excitatory
synaptic activity associated with bursts of synchronous action
potential firing (Fig. 5a). We performed RNA-seq on
RNA extracted 16 h post-washout, using the SSS workflow
to identify mouse (astrocytic) reads, and compared these to
a control co-culture treated the same way except that TTX
remained in the medium throughout. DGE analysis revealed that
351 astrocytic genes were significantly induced 41.3-fold by
neuronal TTX-sensitive synaptic activity (57 genes more than
twofold), while 154 genes were repressed 41.3-fold (4 genes
more than two-fold; Fig. 5b; Supplementary Data 10). We
confirmed that BiC did not induce astrocytic gene expression in
the absence of neuronal activity (Fig. 5c). Since this network
activity is glutamatergic in nature, we tested the influence of
prolonging synaptic glutamate levels with the glutamate uptake
inhibitor TBOA. Indeed, TBOA resulted in slightly prolonged AP
bursts (Supplementary Fig. 6a), prolonged exposure of astrocytes
to glutamate (assayed using iGluSnFR, Fig. 5d) and triggered
stronger activity-dependent induction of astrocytic genes
(Fig. 5e,f; Supplementary Data 11). Thus, using mixed-species
RNA-seq we have identified a novel type of synapse-to-nucleus
signalling; one that targets astrocytic genes rather than neuronal
ones. We refer to these genes as astrocytic activity-response genes
(AAR genes), to distinguish them from the NR genes identified in
Fig. 1c. Of note, it is unlikely that glutamate itself is the key
messenger sensed by astrocytes resulting in signal transduction to
de novo gene expression: exogenous glutamate application was
unable to induce a panel of AAR genes (Supplementary Fig. 6b).
In contrast, exogenous application of ATP, a molecule whose
26
4100
(=+DAPT)
=+DAPT)(
Relative mouse (astrocytic) mRNA
****** ** ** ******
80
60
40
20
1.00
35 1.0
0.8
0.6
0.4
0.2
0.0 0
5
10
15
20 *
*
*
**
Con
CBF1-VP16
Con
CBF1-VP16
30
25
20
15
10
5
0
0.75
0.50
0.25
Astrocytic expression
relative to co-culture
Glu-T current (pA)
Glu-T current (pA)
Astrocytic CBF1-Luc activity
0.05
0
Hes5
Hey2
HES5
HEY2
Slc1a2
Dio2
Glul
Cldn10
RIbp1
Adora2b
3
2
1
0
Con DAPT
Con
Con
L-Asp
L-Asp
DAPT
DAPT
Astrocytic CBF1-Luc activity
(normalized to renilla control)
Notch genes AstrMus mono-culture
AstrMus- NeurRat co-culture
AstrMus mono-culture
AstrMus- NeurRat co-culture
AstrHum mono-culture
AstrHum- NeurRat co-culture AstrMus- NeurRat co-culture
Notch ligand genes
×DIk2
×Jag2
×DII1
×DII3
×DII4
×DIk1
×Jag1
Notch1
Notch2
Notch3
Notch4
26
25
25
24
24
23
23
22
22
21
21
20
20
2–1
2–1
Astrocytic expression (FPKM)
Neuronal expression (FPKM)
2–2
2–2
2–3
2–3
2–4
a
def
gh
bc
Figure 4 | Notch signalling contributes to neuron-induced astrocytic gene expression and functional maturation. (a) Mean expression levels (n¼3) of
Notch and Notch ligand genes are compared between pure mouse astrocyte cultures and pure mouse neuronal cultures, derived from RNA-seq data and
calculated as FPKM. (b) Neurons induce astrocytic Notch/CBF1-dependent gene expression. Mouse astrocytes were transfected with a CBF1-luciferase
reporter (plus renilla control) before rat neurons were overlaid (or not) for 9 days in the presence or absence of the g-secretase inhibitor DAPT (10 mM,
Tocris). Firefly luciferase activity was normalized to Renilla transfection control. *Po0.05 (two-way analysis of variance (ANOVA) plus Sidak’s post hoc test
(n¼3)). (c) Mouse astrocyte mono-cultures and mouse astrocyte/rat-neuron co-cultures were treated with DAPT, and gene expression analysed at DIV9,
normalized to housekeeping gene H1f0, using mouse species-specific PCR primers. *Po0.05 (two-way ANOVA, plus Holm-Sidak’s multiple comparisons
test, n¼3). (d) Neuron-induced expression of Notch target genes analysed in human primary astrocytes. *Po0.05 (unpaired t-test, n¼5). (e,f) DAPT
treatment inhibits the neuron-induced increase in glutamate transporter capacity in astrocytes in response to 200 mM L-Aspartate. *Po0.05 (unpaired
t-test, n¼22 Con, n¼8 DAPT). (f) Example traces. Scale bar, 5 s, 10pA. (g) Verification that CBF1-VP16 induces Notch/CBF1-dependent gene expression,
compared to control plasmid (b-globin). Neuron-free astrocyte mono-cultures were used for this experiment. *Po0.05 (t-test). (h) CBF1-dependent gene
expression is sufficient to induce glutamate transporter currents in astrocytes. Astrocyte mono-cultures were transfected as indicated (plus eGFP marker)
and glutamate transporter currents in response to 200 mML-Aspartate application measured 9 days later. *Po0.05 (unpaired t-test, n¼8Con,n¼10
CBF1-VP16). All error bars represent s.e.m.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 7
210
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
210
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
210
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
210
29
28
27
26
25
24
23
22
21
20
2–1
2–2
2–3
210
29
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20
2–1
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2–1
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210
29
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25
24
23
22
21
20
2–1
2–2
2–3
>2-fold and P_adj<0.05
>1.3-, ≤2-fold and P_adj<0.05
>2-fold in Fig. 5b
>1.3-, ≤2-fold in Fig. 5b
>2-fold and P_adj<0.05
>1.3-, ≤2-fold and P_adj<0.05
>2-fold in Fig. 5e
>1.3-, ≤2-fold in Fig. 5e
Active neurons (BiC)
Active neurons (BiC)
Inactive neurons (TTX)
Inactive neurons (TTX)
Inactive neurons (BiC+TTX)
Inactive neurons (BiC+TTX) Inactive neurons (BiC+TTX)
Inactive neurons (BiC+TTX)
Astrocytic gene expression in
AstrMus- NeurRat co-culture (FPKM)
Astrocytic gene expression in
AstrMus- NeurRat co-culture (FPKM)
Astrocytic gene expression in
AstrMus- NeurRat co-culture (FPKM)
in presence of TBOA
Astrocytic gene expression in
AstrMus- NeurRat co-culture (FPKM)
in presence of TBOA
AstrocyteMus -NeuronRat co-culture
BiC
35,867,367
25.8
13,836
61
505
BiC+TTX
42,982,822
31.2
13,798
AstrocyteMus -NeuronRat co-culture (+TBOA)
BiC
35,691,176
26.4
13,770
204
1,369
BiC+TTX
39,727,147
29.3
13,792
Avg no. of reads mapped to
mouse genome per sample (n=4)
% of mapped reads attributed to
the mouse genome
Genes expressed >0.5 FPKM
Differentially expressed genes
(≥2-fold difference)
Differentially expressed genes
(≥1.3-fold difference)
Avg no. of reads mapped to
mouse genome per sample (n=4)
% of mapped reads attributed to
the mouse genome
Genes expressed >0.5 FPKM
Differentially expressed genes
(≥2-fold difference)
Differentially expressed genes
(≥1.3-fold difference)
10
0
–10
–20
–30
–40
–50
–60
P=0.047
Expression 12 h post MK-801
injection (relative to veh. control)
Rorb
PPP1r3c
PPP1r3g
Dio2
Tmem100
Alpl
Fluorescence (a.u.)
1.0
0.8
0.6
0.4
0.2
0.0
iGluSnFR (astro)
GCaMP2 (neur)
BiC
TBOA
TTX
20 s
TTX BiC
TTX BiC
a
d
g
ef
bc
Figure 5 | Synaptic activity regulates astrocytic gene expression. (a) Mixed-species astrocyte–neuron co-cultures were transferred into TTX-containing
medium for 22h, after which it was washed out in the presence of BiC. Whole-cell current-clamp (upper) and voltage-clamp (lower) recording of the resultant
burst activity. Scale bar, 15mV, 30s (upper); 50pA, 30 s (lower). (b) Mixed-species co-cultures were treated as in a, or a control that was ‘washed’ but
remained in TTX (BiC þTTX), and RNA extracted at 16 h post-wash. SSS RNA-seq identified mouse (that is, astrocyte) reads. Expression of genes (FPKM) in
astrocytes±neuronal synaptic activity is plotted for the genes expressed 40.5 FPKM averaged over the conditions. Red and black crosses indicate the
astrocytic genes changed by neurons more than twofold or 41.3, r2-fold respectively (DESeq2 P_adjo0.05, n¼4 biological replicates). See Supplementary
Data 10. (c) To confirm that BiC was not having a direct effect on astrocytic gene expression independent of AP firing, gene expression in the (BiCþTTX)
condition was compared to a condition where the neurons remained in TTX. The crosses indicate those genes significantly changed in b.(d) Example trace
depicting an astrocyte–neuron co-culture in which astrocytes had been transfected with iGluSnFR68 before neuronal co-culture, after which neurons were
transfected with GCaMP2. GCaMP2-positive neurons and iGluSnFR-positive astrocytes were imaged concurrently in the presence where indicated of BiC
(50 mM), TBOA (50 mM) and TTX. (e) Impairing glutamate re-uptake enhances activity-dependent astrocytic gene regulation. Experiment performed exactly
as in bexcept co-cultures were treated with TBOA (50mM) at the time of BiC addition (n¼4). See Supplementary Data 11. (f)ToconfirmthatBiCþTBOA
were not having a direct effect on astrocytic gene expression, expression in the (BiC þTBOA þTTX) condition was compared to a further condition where the
neurons remained in TTX. The crosses indicate those genes significantly changed in e.(g) P7 mice were treated with MK-801 (0.5 mg kg 1)orPBSand
cortical expression of the indicated astrocyte-specific, activity-regulated genes analysed 12 h post-injection. *Po0.05 (2-way analysis of variance, Con versus
MK-801; n¼8 con, 8 MK), Pvalue indicates the main effect of drug treatment versus PBS. Error bars represent s.e.m.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
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metabolite adenosine can induce the de novo astrocytic gene
expression (of Ppp1r3c (PTG)) in astrocytes via A2B receptors28
was able to induce some of these genes (Supplementary Fig. 6b).
To further validate the mixed-species approach we wanted to
rule out the possibility that co-culturing neurons and astrocytes of
different species results in erroneous effects of synaptic activity on
astrocytic gene expression. We focused on the genes whose
activity-dependent induction we could track in astrocytes in
a single-species co-culture, that is, genes highly enriched in
astrocytes over neurons. Within the 56 genes induced more
than twofold by BiC in astrocytes, we selected the 15 genes that
fulfilled this enrichment criterion (expressed 410-fold higher in
a mixed neuron–astrocyte culture compared to neurons alone,
Supplementary Data 12) and studied their regulation in a single
species (mouse) neuron–astrocyte co-culture treated±BiC/4-AP
to increase synaptic activity levels. All 15 genes were significantly
induced by BiC/4-AP treatment, while their expression in an
astrocyte-free neuronal mono-culture treated±BiC/4-AP was
extremely low (Supplementary Fig. 6d), strongly suggestive of an
astrocytic locus for their induction in the co-cultures. In contrast,
neuronally expressed genes were induced in neuronal mono-
culture as well as neuron–astrocyte co-culture (Supplementary
Fig. 6e). Thus, with this subset of genes, there is no evidence that
having a different neuronal species results in erroneous effects of
synaptic activity on astrocytic gene expression.
We next investigated whether neuronal activity can control
astrocytic gene expression in vivo. We treated P7 mice with a
mildly sedating dose of the NMDA (N-methyl-D-aspartate)
receptor antagonist MK-801 (refs 12,29), and analysed
expression of genes whose expression could be attributed to
astrocytes. We looked at those genes (six) induced more than
twofold by BiC that are expressed 410-fold higher in astrocytes
than neurons, microglia or oligodendrocytes, according to a
recent study23, and robustly expressed in the cortical samples
from control animals (at least 10% of the Aldh1l1 level). MK-801
administration significantly repressed expression of these genes
(Fig. 5g). Note that none of these genes are regulated directly by
glutamate (in the presence or absence of MK-801; Supplementary
Fig. 6b) meaning that MK-801 is unlikely to be having direct
effects on astrocytic gene expression. Thus, neuronal activity may
influence astrocytic gene expression in vivo.
Synaptic activity induces astrocytic transcription via CREB.
Comparison of the induced NR and AAR genes revealed a modest
overlap. Only 17 of the NR-genes induced more than twofold
were also within the 56 genes induced more than twofold by BiC-
induced neuronal activity, suggestive of distinct mechanisms of
gene induction. We performed a promoter analysis of the genes
induced more than twofold by both BiC and BiC þTBOA to look
for enrichment in transcription factor consensus matrices. The
highest enrichment was for members of the cAMP response
element binding protein (CREB) family (Fig. 6a,b; Supplementary
Data 13), a group of transcription factors responsive to both
Ca2þand cAMP/PKA signalling, and important mediators of
activity-dependent gene expression in neurons4,6,7,30,31.
We performed simultaneous GCaMP2-based Ca2þimaging of
neurons and astrocytes, which revealed an increase in astrocytic
Ca2þtransients upon BiC treatment, enhanced with TBOA
application (Fig. 6c,d). Moreover, imaging astrocytic PKA activity
with the FRET probe AKAR4 (ref. 32) also revealed an
activity-dependent induction (Fig. 6e,f). Thus, second messenger
pathways central to CREB activation are induced in astrocytes
by synaptic activity. Moreover, synaptic activity induced
astrocytic CREB-dependent gene expression (Fig. 6g, assayed
using a CRE-luciferase reporter transfected into the astrocytes
before neuronal co-culture). This induction was inhibited by
co-expression of the PKA inhibitor protein PKI (gene name
Pkia)33, implicating the cAMP–PKA pathway. We also observed
that elevating cAMP levels by treatment with the adenylate
cyclase activator forskolin was sufficient to induce a panel of AAR
genes (Ppp1r3c,Slco1c1,Dio2,Sod3 and Scg2), but (as expected)
had no effect on members of the NR gene set, Glul and Cldn10.
These observations were qualitatively similar in both co- (Fig. 6h)
and mono-cultured astrocytes (Supplementary Fig. 6c). Thus,
while neuronal co-culture is required for many functional
changes to occur in astrocytes, the signalling machinery
required to induce many AAR genes is intrinsic to astrocytes.
It was not possible to pharmacologically inhibit PKA to assess
its role in activity-dependent astrocytic gene expression because
the inhibitors tend to interfere with neuronal activity itself.
Instead, we investigated the ability of portions of the promoters of
the AAR genes Scg2 and Dio2 to confer activity inducibility on a
luciferase reporter in astrocytes. Neuronal activity induced both
Scg2-Luc and Dio2-Luc in astrocytes, which was inhibited by both
co-expression of PKI and by ICER, a dominant negative member
of the CREB superfamily (Fig. 6i,j). Collectively, these data
implicate the cAMP–PKA–CREB pathway as an important
mediator of activity-dependent gene expression in astrocytes.
Neuronal activity boosts astrocytic metabolic capacity. Given
the number of AAR genes identified, the functional consequences
are likely to be diverse. We focussed on one pathway: a large
cluster of AAR genes centred on astrocytes’ role in utilizing
glucose to supply oxidizable substrates to neurons via the
astrocyte–neuron lactate shuttle34. Remarkably, every sequential
enzymatic and transport step on the pathway from glucose
uptake, glycolysis, pyruvate-to-lactate conversion and lactate
export included genes which were induced in astrocytes by
synaptic activity (Fig. 7a; Supplementary Data 14). In contrast,
components involved in mitochondrial import of pyruvate
(pyruvate carriers) and NADH (the malate–aspartate shuttle),
were not upregulated, suggesting that the reason for increased
glycolytic capacity is to enable a boost in lactate export.
To investigate this, we used the pyruvate and lactate FRET
probes Pyronic and Laconic35. By monitoring the rate of build-up
of pyruvate or lactate upon acute blockade of lactate export using
the monocarboxylic acid transporter (MCT) blocker AR-C155858
(ref.35),wegainedameasureofpyruvateproductionorlactate
export rates. To ensure no acute effects of activity could confound
the result, all experiments took place in TTX. We observed that in
astrocytes co-cultured with neurons that had been previously active
(BiC-treated for 16h), the rate of pyruvate and lactate build-up
upon MCT blockade was far faster than when the neurons were
silent (TTX-treated; Fig. 7b–e). This indicates that neuronal activity
has a long-lasting effect on the rate of astrocytic lactate export,
consistent with the transcriptional changes observed.
Increased lactate export is likely to require increased glucose
uptake and metabolism. To determine this we used the glucose
FRET probe FLII12Pglu-700md6 (ref. 36), and used the method of
measuring the rate of decline of the glucose FRET signal upon
acute blockade of the glucose transporter with cytochalasin B35.We
observed that prior activity resulted in an increased rate of decline
of the glucose signal upon cytochalasin B treatment by Btwo-fold,
indicating that neuronal activity boosts glucose metabolism in
astrocytes, consistent with elevated lactate export (Fig. 7f,g).
We next investigated the link between activity-dependent,
CREB-mediated astrocytic gene expression (Fig. 6) and activity-
induced increase in astrocytic metabolism. Sixteen of the 18 genes
induced in the metabolic pathway highlighted in Fig. 7a have
CRE full or half sites in the 5 to 1 kb region relative to
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8
a
b
gh ij
cd
e
f
3.00 4
3
2
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0
TTX
BiC
BiC+TBOA
TTX
BiC
BiC+TBOA
TTX
Ppp1r3c
SIco1c1
AAR-genes NR-genes
Dio2
Sod3
Scg2
Glul
Cldn10
Astrocytic CRE-dependent
gene expression
Astrocytic Dio2 promoter activity
Astrocytic Scg2 promoter activity
Fold-induction by Fsk
BiC
BiC+TBOA
TTX
BiC
BiC+TBOA
TTX
BiC
BiC+TBOA
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Change in AKAR4
YFP/CFP ratio
Change in AKAR4
YFP/CFP ratio
400 600
Time (s)
400 600 800
0 200
TTX BiC TBOA
TTX BiC TBOA
**
*
*
*
****
*
*
*
*
*
*
*
*
*
*
400
Astrocytic [Ca2+]
Astrocytic AKAR4
imaging
Control
Control Control
PKI PKI
ICER ICER
PKI
Neuronal [Ca2+]
Astrocytic global Ca2+
transients (per minute)
600 800
Time (s)
6
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–2
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6
100 3
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1.0
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+
Bits
from Mat Base 9.3 (c) Genomatix
2
1
–2 2
123
468
Z-score for enrichment relative to
promoter background
Z-score for enrichment relative to
promoter background
Astrocytic genes >2-fold (BiC+TBOA)
Astrocytic genes >2-fold (BiC+TBOA)
Fold-enrichment above the
promoter background (Z>2)
Fold-enrichment above the
promoter background (Z>2)
Astrocytic genes >2-fold (BiC)
Astrocytic genes >2-fold (BiC)
Figure 6 | Astrocytic cAMP/PKA-dependent CREB activation contributes to activity-dependent astrocytic gene induction. (a,b) Promoters of astrocytic
activity-responsive genes (induced more than twofold) were analysed within Genomatix for enrichment in TRANSFAC Library matrices, with the Z-score
for enrichment calculated. Matrices enriched (Z42) in the (overlapping) set of genes induced more than twofold by BiC and by BiC þTBOA are highlighted
in a, and the degree of enrichment is shown in b, with the CREB family matrix highlighted (Supplementary Data 13). (c,d) Influence of neuronal activity
induced by BiC (±TBOA) on astrocytic Ca2þsignals was measured. Left shows example traces of neuronal and astrocytic GCaMP2 imaging. Right shows
influence of activity on astrocytic Ca2þtransient frequency. *Po0.05 (one-way analysis of variance (ANOVA) plus Tukey’s post hoc test, n¼5, 32 cells
total). (e,f) Influence of neuronal activity induced by BiC (±TBOA) on astrocytic PKA activity (PKA FRET probe AKAR4 transfected into astrocytes before
neuronal co-culture. (e) shows an example experiment showing the mean (±s.e.m.) of several cells within a single field of view. (f) shows the quantitation.
*Po0.05 (one-way ANOVA plus Tukey’s post hoc test, n¼5, 15 cells). (g) Neuronal activity-induced PKA-dependent CRE-mediated gene expression in
astrocytes. Mouse astrocytes were transfected with a CRE-luciferase reporter (plus renilla control, þglobin control or PKI vectors) before rat neurons were
overlaid for 9 days. Neuronal activity was induced by BiC (±TBOA) as described in Fig. 5a. Firefly luciferase activity was normalized to renilla transfection
control. *Po0.05 (two-way ANOVA plus Sidak’s post hoc test, n¼3). (h) Forskolin treatment is sufficient to induce astrocytic activity-response (AAR)
genes. Mixed rat neuron/mouse astrocytes were treated with forskolin (Fsk, 10 mM, 4 h) and gene expression measured by qPCR using mouse-specific
primers. *Po0.05 (two-way ANOVA plus Sidak’s post hoc test, n¼3). (i,j) Activity-dependent induction of astrocytic Dio2 and Scg2 promoter activity is
mediated by PKA and CREB. Mouse astrocytes were transfected with a Dio2 (i)orScg2 (j) -luciferase reporter (plus renilla control, þcontrol, PKI, or ICER
vectors) before rat neurons were overlaid for 9 days. Neuronal activity was induced as described in Fig. 5a. Firefly luciferase activity was normalizedto
renilla control. *Po0.05 (two-way ANOVA plus Sidak’s post hoc test, n¼3). All error bars represent s.e.m.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
10 NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications
the transcription start site (CREB Target Gene Database
http://natural.salk.edu/CREB37). We investigated the effect of
expressing the dominant negative CREB family member ICER on
activity-dependent astrocytic glucose metabolism. While
astrocytic ICER expression had no effect on glucose metabolism
rates in astrocytes co-cultured with silent neurons, ICER strongly
inhibited the activity-dependent induction of astrocytic glucose
metabolism (Fig. 7f,g). These data demonstrate that neuronal
activity, via the activation of astrocytic CREB-mediated gene
expression, boosts astrocytic glucose metabolic flux. Moreover,
this is a pathway conserved in evolution: an identical experiment
performed using human astrocytes also revealed a CREB-
Glucose
Glucose
Cyt-B
Cyt-B
AR-C155858
AR-C155858
Extracellular
ab
c
e
f
g
j
i
h
d
Extracellular
Neuronal uptake
TCA cycle
Got2
Got1
Eno1
Pgk1
Aldoa
Pfkp
Pfkfb1
Hk1
Hk2
Hk3
Pfkfb2
Pfkfb3
Pfkfb4
Pfkl
Pfkm
Gpi1
Gapdh
Pkm
Ldhd
Slc16a1
Slc16a2
Slc16a3
Slc16a4
Slc16a6
Ldhb
Ldha
Pgam5
Pgam2
Pgam1
Slc2a13
Slc2a12
Slc2a8
Slc2a3
Slc2a1
Aldoc
Tpi1
Eno2
Eno3
Eno4
Mdh2
Mdh1b
Mdh1
Mpc2
Pdha1
Pdhb
Pdhx
Dld
Dlat
Mpc1
Glucose
Glucose-6P
Fructose-6P
Fructose-2,6BP
Fructose-1,6BP
Glyceraldehyde 3P Dihydroxyacetone-P
1,3BP-Glycerate
3P-Glycerate
2P-Glycerate
P-enolpyruvate
Aspartate
Astrocyte
0.3
100
80
60
40
20
00100
Normalized ratio
100
80
60
40
20
0
Normalized ratio
200 300 400 Time (s)
0 100 200 300 400
Time (s)
100
80
60
40
20
0
Normalized ratio
0 100 200 300 Time (s)
100
80
60
40
20
0
Normalized ratio
100
80
60
40
20
0
Normalized ratio
100
80
60
40
20
0
Normalized ratio
0
0
100
350 400 450 500 550
200 300
Time (s)
Time (s)
0 350 400 450 500 550
Time (s)
Globin Control
P=0.0007 P=0.004 P<0.0001
ICER
**
*
0.2
0.1
0.0
0.6
0.4
0.2
0.0
0.0
0.5
1.0
1.5
2
11
20
0
–20
–40
–60
–80
Aldoa
Pfkl
Pfkm
Ldha
Pgam1
Pfkfb3
Tpi1
Gpi
Gapdh
Pkm2
Pgk1
Eno1
Eno2
Slc2a1
00
–1
–2
–1
–2
–3
TTX
Rate of change of Pyronic
FRET upon AR-C addition
Rate of change of Laconic
FRET upon AR-C addition
Rate of change of FLII12Pglu-700μδ6
FRET (glucose probe) upon Cyt-B addition
TTX
AR-C155858
BiC
TTX
TTX
TTX
BiC
TTX
25
Astrocytic mRNAs identified as being induced
>2-fold by neurons (this study)
Log2 Fold-change in human astrocytes
(>40 versus <40): Zhang et al (2016)
Log2 Fold-change in human astrocytes
(>40 versus <40): Zhang et al (2016)
% change in human astrocytes
(>40 versus <40): Zhang et al (2016)
Astrocytic mRNAs identified as being induced
>2-fold by neuronal activity (this study) Activity-induced astrocytic
g
l
y
col
y
tic
g
enes
50 75 100 125 150 5 10152025
BiC
BiC
BiC
BiC
*
Cytosol
Mitochondrion
Aspartate
Oxalocetate Oxalocetate
Malate Malate
Pyruvate
Pyruvate
Acetyl-CoA
Lactate
Lactate
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 11
dependent induction of glucose metabolism in response to
neuronal activity (Supplementary Fig. 7a). In addition, as well
as CREB being necessary for activity-dependent increases in
astrocytic glycolytic metabolism, it is also sufficient: astrocytes
transfected with constitutively active CREB-VP16 exhibit elevated
levels of glucose metabolism and lactate export (Supplementary
Fig. 7b,c). Moreover, we observed that CREB-VP16 expression
caused an increase in glycolysis (Supplementary Fig. 7d) that
was not accompanied by changes in mitochondrial oxygen
consumption rate (Supplementary Fig. 7e,f). This supports
a model whereby the increase in astrocytic glucose metabolism
serves to increase lactate export, rather than reflecting a strong
increase in the astrocytes’ own energy requirements.
Given the importance of gene regulation controlled by neurons
and neuronal activity in astrocytic metabolic function, we mined
an existing data set from Zhang et al.18 to determine any
age-related changes in NR or AAR genes in humans by
comparing the 6 young samples (age 8–35 years) with the
6 older samples (age 47–63 years). We observed a significant
decline in expression of both induced NR genes and AAR genes
in astrocytes from older individuals (Fig. 7h; Supplementary
Data 15; Fig. 7i; Supplementary Data 16). Within the group of
activity-induced components of the astrocyte–neuron lactate
shuttle there was also a significant reduction in astrocytes from
the older cohort (Fig. 7j; Supplementary Data 19). Thus,
astrocytic genes boosted by neurons and neuronal activity
appear to decline with age, raising the possibility that their
ability to support key homeostatic functions may also decline.
Discussion
Studies into the signals and pathways that direct astrocytic fate
specification from neural stem cells have revealed roles for CNTF
or LIF-induced JAK/STAT signalling, BMP-SMAD signalling and
Notch signalling1. In contrast, the mechanisms involved in
astrocytic developmental maturation were not well understood1.
Here we have not only uncovered the transcriptional changes that
are associated with this transformation, but identified neuron-
derived Notch signalling as a key mediator. This pathway is
necessary and sufficient to promote one of the key functional
roles of a mature astrocyte: glutamate uptake. Of note, expression
of transporters involved in other neurotransmitters: GABA
(Slc6a1 and Slc6a11), NAAG (Slc17a5) and biogenic amines
(Slc29a2) are also induced, as are genes responsible for
neurotransmitter metabolism: Glul and Glud1 (for glutamate),
Abat (for GABA) and Maob (for biogenic amines). Thus,
neurotransmitter uptake and metabolism capacity in astrocytes
may be induced in a coordinated manner by neurons. Another
implication from this study is that neuron-derived signals may be
required for the ongoing maintenance of astrocytic phenotype
in vivo, raising the possibility that loss of neuron–astrocyte
contacts in neurodegenerative disease has a knock-on effect on
astrocytic phenotype, which could in turn amplify the
pathological cascade.
Another finding of this study is the influence of neuronal
activity on astrocytic metabolism. In astrocytes, energy-requiring
processes of glutamate uptake and glutamine synthesis, increased
in times of synaptic activity, trigger acute increases in astrocytic
glucose uptake and utilization by the laws of mass action38. Much
of this glucose utilization in astrocytes is non-oxidative: pyruvate
is converted to lactate and exported by astrocytes where it can be
taken up and used as a substrate in neurons, a pathway known as
the astrocyte–neuron lactate shuttle (ANLS)38. Astrocytes are
major contributors to brain glucose uptake, and represent the
dominant route for uptake during periods of strong synaptic
activity39. Moreover, astrocytic glucose uptake is of key
importance to neurons, as evidenced by the severe neurological
phenotype of mice haplo-insufficient for the astrocyte-specific
glucose transporter Glut1 (refs 40–42). During execution of
spatial memory tasks, extracellular levels of glucose decline, while
lactate rises, in the hippocampus, with blockade of astrocytic
glycogenolysis and neuronal lactate uptake leading to memory
impairment, supportive of a key role for the ANLS in neuronal
function43, consistent with observations that astrocytic lactate
export through Mct1/4 plays a key role in long-term memory44.
Our study shows that synaptic activity triggers the transcriptional
upregulation of astrocytic genes at every step of the ANLS,
inducing glucose metabolism and lactate export. This may
represent a homeostatic tuning of astrocytic metabolic flux to
reflect the needs of nearby active neurons45. The observation that
activity-dependent astrocytic ANLS genes decline with age in
humans (Fig. 7j) raises the possibility that ageing astrocytes may
be less equipped to metabolically support neurons, potentially
leading to cognitive decline. Whether ageing astrocytes express
activity-induced genes at lower levels (Fig. 7i) because they
receive fewer neuronal signals, or are impaired in signal
transduction properties, awaits investigation.
Moreover, future studies will likely uncover further functional
changes to astrocytes in response to neuronal activity, since other
notable gene clusters induced include those involved in thryroid
hormone signalling (Slco1c1,Dio2 and Slc7a5)46 and extracellular
antioxidant capacity (Sod3 and Grx3). The latter may be
important given the vulnerability of neurons to oxidative
stress47–49 and the importance of non-cell-autonomous
astrocytic antioxidant support13,50,51. Of note, genes such as
Dio2,Slco1c1 and Sod3 are very weakly expressed in neurons, and
Figure 7 | Neuronal activity controls astrocytic metabolic flux via CREB activation. (a) Schematic depicting astrocytic ANLS, mitochondrial NADH
shuttling and pyruvate uptake genes (expressed 40.5 FPKM). Red indicates upregulated (P
adj
o0.05) by BiC-induced synaptic activity. The schematic was
created using WikiPathways70.(b–e) Prior neuronal activity boosts steady-state rate of astrocytic lactate export. Astrocytes were transfected with FRET
probes for pyruvate (Pyronic, b,c) or lactate (Laconic, d,e) before rat neuronal co-culture for 9 days. The co-cultures were transferred into TTX for 22 h,
before washout ( þBiC) to induce activity for 16h, while others were ’washed’ in TTX-containing medium as a control. Before and throughout the imaging
experiment, the co-cultures were re-treated with TTX to avoid any influence of ongoing neuronal activity. Pyronic and Laconic FRET ratios were measured
before and after MCT blockade (AR-C155858, 1 mM). *Po0.05; n¼30 TTX, 23 BiC (b), n¼13, TTX, 12 BiC (d). Example traces are shown in c(Pyronic) and
e(Laconic). (f,g) Astrocytes were transfected with the glucose FRET probe FLII12Pglu-700md6 plus either a ICER or control vector (b-globin) before rat
neuronal co-culture for 9 days. Neuronal activity was induced as in b–e. Glucose probe FRET ratios were measured before and after glucose transporter
blockade (cytochalasin-B, 20 mM) *Po0.05, two-way ANOVA plus Sidak’s post hoc test (n¼33 TTX, 26 BiC (Con), n¼10, TTX, 13 BiC (ICER)). (h) Neuron-
induced astrocytic genes (more than twofold) were cross-referenced to data in Zhang et al.18, taking genes expressed 40.5 FPKM, and the fold change in
expression in human astrocytes in the individuals o40 (n¼6) and 440 (n¼6) calculated. P¼0.0007 (age effect, paired t-test). (i) Activity-induced
astrocytic genes (more than twofold) were cross-referenced to data in Zhang et al (2016), taking genes expressed 40.5 FPKM, and the fold-change in
expression in human astrocytes in the individuals o40 (n¼6) and 440 (n¼6) calculated. P¼0.004 (age effect, paired t-test). (j) Activity-induced ANLS
genes (a) were cross-referenced to data in Zhang et al.18, taking genes expressed 40.5 FPKM, and the % change in expression in human astrocytes in the
individuals o40 (n¼6) and 440 (n¼6) calculated. Po0.0001 (age-group effect, two-way ANOVA). All error bars represent s.e.m.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
12 NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications
not altered by neuronal activity. Thus, this new type of ‘synapse-
to-nucleus’ signalling likely has different effects on the neuro-glial
unit than inter-neuronal signalling leading to gene expression
changes.
Methods
Tissue cultures and stimulations.Astrocytes and neurons were cultured
from E17.5 CD1 mouse and E20.5 Sprague Dawley rat embryos as previously
described52,53. Cortices or spinal cords were dissected, enzymatically digested with
papain and mechanically dissociated using a 5 ml pipette. Mouse cortical and spinal
cord astrocytes were obtained by growing cells at low density in DMEM containing
10% fetal bovine serum and were passaged twice, using Trypsin (both Life
Technologies), before co-culturing them with neurons in a 24-well plate. These
astrocytes are 499% GFAP positive and o0.1% NeuN positive12. For mixed–
species co-cultures, rat neurons were plated on top of a confluent layer of DIV14
mouse astrocytes and both astrocyte monocultures and astrocyte–neuron
co-cultures were subsequently kept in Neurobasal-A medium containing B27
(both Life Technologies), but devoid of serum. Note that the rat neuronal cells
plated do contain a small number of non-neuronal cells (principally astrocytes),
but since the focus of the study is the mouse astrocytic transcriptome, this was
deemed acceptable. For Supplementary Fig. S5e, rat neuronal cells were plated in
the continuous presence of the anti-mitotic AraC. Where used, pure neuronal
cultures were generated as described54 from E17.5 CD1 mouse embryos, with the
anti-mitotic AraC added on the day of plating which restricts astrocyte numbers to
o0.1%13,54,55. All cultures were used 8–10 days post neuron plate down. Cells were
transferred into media containing 10% minimum essential media (MEM, Life
Technologies) and 90% salt–glucose–glycine (SGG) medium containing 114 mM
NaCl, 0.219% NaHCO
3
, 5.292 mM KCl, 1 mM MgCl
2
, 2 mM CaCl
2
,10mM
HEPES, 1 mM Glycine, 30 mM Glucose, 0.5mM sodium pyruvate, 0.1% Phenol
Red; osmolarity 325 mOsm/l either overnight or for 4 h before stimulations. To
investigate the effect of synaptic activity on astrocytic gene expression, co-cultures
were transferred into TTX-containing medium on DIV8 for 22 h to inhibit
neuronal action potential firing. Subsequently, cells were washed with medium free
of TTX and then kept in medium either containing Bicuculline (BiC, 50 mM,
Tocris) to induce network bursting, or BiC and glutamate transporter inhibitor
TBOA (DL-threo-b-Benzyloxyaspartic acid, 50 mM, Tocris), to increase the half-
time of glutamate uptake, for 16 h, after which RNA was collected. Control
conditions were washed with TTX and left in medium containing either BiC or
BiC/TBOA in the presence of TTX, or TTX alone. For experiments investigating
the role of Notch signalling in astrocytic maturation, mouse astrocyte
monocultures and mouse astrocyte/rat-neuron co-cultures were treated with the
g-secretase inhibitor DAPT (10 mM, Tocris) on DIV0 of neuron plate down. Fresh
DAPT was added twice more and RNA collected on DIV9.
Species-specific sorting of mixed species RNA-seq reads.To generate
RNA-seq data, barcoded RNA-seq libraries were prepared by Edinburgh
Genomics using the Illumina TruSeq stranded mRNA-seq kit, according to the
manufacturer’s protocol (Illumina). The libraries were pooled and sequenced to 50
base paired-end on an Illumina HiSeq 2500 in high output mode (v4 chemistry).
For single-species RNA-seq experiments sequencing was performed to a depth of
B50 million paired-end reads per sample, whereas for mixed-species RNA-seq
a greater depth of B150 million paired-end reads per sample was done.
Given a set of RNA-seq reads that may have derived from transcripts of both
mouse and rat, we implemented a sorting procedure (called Sargasso: Sargasso
Assigns Reads to Genomes According to Species-Specific Origin) to assign reads to
their true species of origin. Essentially, Sargasso is a Python tool to disambiguate
mixed-species RNA-seq reads according to their species of origin. Given a set of
RNA-seq samples containing RNA-seq data originating from two different species,
mapped, disambiguated reads are written to per-sample and -species-specific
output BAM files. Sargasso is freely available (http://doi.org/10.5281/
zenodo.206619) and is described briefly below.
In the balance between precision and recall, our initial strategy is conservative,
in that it aims foremost to minimize the number of reads allocated to the
incorrect species; but we simultaneously seek to maximize the number of reads
that can be unambiguously assigned to the correct species. The sorting criteria
detailed below were chosen to effect this goal. In this species-specific sorting (SSS)
procedure, reads (or read pairs in the case of paired-end reads) are first mapped
to the genomes of each species with version 2.4.0i of the STAR RNA-seq aligner56.
At this stage multi-mapping alignments are allowed (–outFilterMultimapNmax
10000), but only those with an alignment score equal to the maximum
(–outFilterMultimapScoreRange 0). Subsequently, for each RNA-seq read
(or read pair) the alignments of that read to each genome are compared. If the
read has alignments to the mouse genome, but no alignments to the rat genome
exist, the read is provisionally assigned to the mouse; note, however, the further
requirements given below for a read to be finally allocated to this genome.
Similarly, if alignments to the rat genome exist, but there are no alignments to the
mouse genome, the read is provisionally assigned to the rat. If alignments to both
genomes exist, these alignments are examined in more detail. First, any read which
aligns multiple times to either genome is discarded, since in this initial conservative
strategy their exclusion removes a potential source of ambiguity. Next, the number
of mismatched bases between the mapped read and each genome is examined. If
the number of mismatches is smaller for one species, the read is provisionally
allocated to that genome. If the number of mismatches for each species is equal,
a further check is made on the structure of the alignments; a successful alignment is
required to span the full length of the read (without any clipping of bases), and, if
the alignment spans an intron, at least 5 bases are required to align to the exons on
either side of the boundary. If these criteria are satisfied for the alignment to one
genome, but not to the other, then the read is provisionally assigned to the first
genome. If the criteria are satisfied for the alignments to both genomes, the read is
rejected as ambiguous; it cannot be assigned with confidence to one species rather
than the other. The read is also rejected if the structural criteria are not satisfied for
the alignments to either genome.
At this stage, a read has either been provisionally allocated to one species, or has
been rejected. In the former case, a final set of checks are made on the provisional
alignment. In this conservative strategy, these are that there should be no
mismatches between the mapped read and the genome, and that the structural
criteria outlined above are satisfied (if this has not already been confirmed). The
outcome of this procedure is that all reads successfully assigned to one species or
the other have a single, full-length alignment to that species’ genome, with no
mismatched bases. Subsequently, for each sample, per-gene read counts were
summarized using featureCounts version 1.4.6-p2 (ref. 57). Relative expression
levels of genes are expressed as fragments per million reads per kilobase of message
(FPKM). Within the SSS workflow, only reads that are unambiguously attributed to
a particular species are used as the denominator in the FPKM calculation. The
value for the length of message for a particular gene refers to the maximum
transcript length. Where gene length data are given, this refers to the number of
nucleotides contained within the union of all exons of all transcripts of the gene,
including 50and 30UTRs. Differential expression analysis on data sets was
performed using DESeq2 (R package version 1.10.0)58, with a significance
threshold set at a Benjamini–Hochberg-adjusted Pvalue o0.05, calculated within
DESeq2).
For assessing the theoretical feasibility of SSS of mixed-species RNA-seq reads,
we selected those genes in the mouse transcriptome, which were classified as
protein coding in Ensembl version 82, but excluded predicted genes. Transcript
sequences for all transcripts of these genes were created with the tool rsem-prepare-
reference from the RSEM (RNA-Seq by Expectation-Maximization) software
package59. For the analysis in Supplementary Fig. S1a, we created all possible
theoretical 50 nucleotide paired-end reads, with insert size 150 nucleotide,
obtainable from those transcripts (such read parameters were chosen as typical of
the real RNA-seq data used in this study). This set of all possible 50 nucleotide
paired-end read sequences were then mapped to the mouse and rat genomes using
STAR, and the mapped reads assigned unambiguously to each species via the SSS
procedure described above.
Before arriving at a final DGE data sets, we carried out an additional control by
performing RNA-seq on a single-species rat co-culture of neurons and astrocytes,
and determining whether the SSS workflow resulted in any rat reads being
incorrectly called as mouse. This is important to rule out, since it could lead to
erroneous DGE results. Performing this control revealed that for about 0.5% of rat
genes 45% of their RNA-seq reads were incorrectly called as mouse, primarily due
to imperfect annotation of the rat genome. We took a conservative approach and
discarded any genes for which we estimated 410% of mouse reads within the
mixed-species co-culture could be due to incorrectly called rat reads. This resulted
in 268 out of the 16,629 genes expressed 40.5 FPKM in mouse astrocytes in the
mixed-species co-culture being excluded from our analysis in Fig. 1c.
Transfections and luciferase assays and plasmids.Astrocytes and astrocyte–
neuron co-cultures in a 24-well plate were transfected using Lipofectamine 2000
(Life Technologies) at 0.65 mg of DNA per well and 2.33 ml Lipofectamine 2000 per
well, as described previously60. Cells were transferred into SGG(90%)/MEM(10%)
containing insulin–transferrin–selenium (Life Technologies) and transfected for
either 45 min (astrocyte monocultures) or 3 h (astrocyte–neuro n co-cultures). To
obtain astrocyte-specific transfection in co-cultures, the transfection of the
astrocytes monoculture was performed 24 h before the addition of neurons to
create the co-culture. This approach was taken when performing luciferase reporter
assays, where Firefly luciferase-based reporter gene constructs (CBF1-Luc,
CRE-Luc, Scg2-Luc and Dio2-Luc) were transfected along with a renilla expression
vector (pTK-RL). Luciferase assays were performed using the Dual Glo assay kit
(Promega) with Firefly luciferase-based reporter gene activity normalized to
the renilla control (pTK-RL plasmid) in all cases. Plasmids encoding ICER61,
CBF1-VP16 (ref. 62), GCaMP2 (ref. 63), FLII12Pglu-700md6 (ref. 36), Pyronic64
and Laconic65 have been described previously.
Electrophysiological recordings.Recording were performed as described66,67.
Coverslips containing cortical neurons and astrocytes were transferred to
a recording chamber perfused (at a flow rate of 3–5 ml min 1) with an external
recording solution composed of (in mM): 150 NaCl, 2.8 KCl, 10 HEPES, 2 CaCl
2
,
1 MgCl
2
, and 10 glucose, pH 7.3 (320–330 mOsm). Patch-pipettes were made from
thick-walled borosilicate glass (Harvard Apparatus, Kent, UK), and when filled
with the internal recording solution had tip resistances of 4–8 MO. A K-gluconate-
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 13
based internal solution was used for patching neurons composed of (in mM):
K-gluconate 141, NaCl 2.5, HEPES 10, and EGTA 11; pH 7.3 with KOH.
A KCl-based internal was used for patching astrocytes composed of (in mM):
KCl 130, glucose 4, HEPES 10, EGTA 0.1, CaCl
2
0.025, and sucrose 20; pH 7.2 with
KOH. Astrocytes were voltage-clamped at 80 mV and any cells with a resting
membrane potential 460 mV upon break-in were discarded. To determine the
maximal induced EAAT transport current, 200 mML-Aspartate was bath applied
followed by the addition of the high affinity EAAT inhibitor TFB-TBOA (20 mM)
to ensure that any L-Aspartate-induced current was mediated by the transporter.
AP5 (100 mM) was included in the external solution of all astrocyte recordings to
block the activation of NMDARs by L-Aspartate. To determine the passive
properties of astrocytes, cells were voltage-clamped from 110 to 0 mV in
10 mV steps and the steady-state current recorded. Neurons were voltage clamped
at 60 mV and recordings were rejected if the holding current was 4100 pA or
if the series resistance drifted by 420% of its initial value (o30 MO). No current
was injected for passive current-clamp recordings and liquid-junction potential was
not corrected for in any recording. Recordings were at room temperature
(21±2°C) using a Multiclamp 200B amplifier (Molecular Devices, Union City,
CA). Recordings were filtered at 5 kHz and digitized online at 20 kHz via
a BNC-2090A/PCI-6251 DAQ board interface (National Instruments, Austin,
TX, USA) and analysed using WinEDR 3.6 software (Dr John Dempster,
University of Strathclyde, Glasgow, UK).
Analysis of P301S tauopathy mouse model.All procedures were performed in
compliance with the UK Animals (Scientific Procedures) Act 1986 and institutional
regulations, and approved by University of Edinburgh Local Ethical Review Board.
The P301S mice were terminated at 5–6 months of age because of increasing
hindlimb disability, in line with United Kingdom Home Office animal license
regulations requiring humane killing. For genotyping, genomic DNA was isolated
from ear tissue according to the Wizard SV Genomic DNA Purification System
before quantitative PCR (qPCR) using Taqman techniques. Primers were hMAPT
and mTERT (Life Technologies).
For fresh tissue for qPCR, C57BL/6 and P301S mice were sacrificed at 20 weeks
of age. Sample size was estimated based on the variance of degeneration reported
previously22. Mice were killed in a CO
2
chamber before careful cervical dislocation.
Cervical spinal cord (c5–c7) was removed and immediately frozen on dry ice and
stored at 80 °C. To prepare tissue for IHC, mice were sacrificed at 20 weeks of
age by a lethal intraperitoneal injection of 0.3 ml per 100 g body weight of sodium
pentobarbital (euthetal). They were transcardially perfused with 30–50 ml of
1% PBS, then 50–100 ml 4% paraformaldehyde. Cervical spinal cord (c5–c7) was
removed and post fixed in 4% paraformaldehyde overnight before cryoprotection
in 25% sucrose solution. Spinal cords were frozen and cut on a cryostat (Leica) into
16 mm sections. Sections were mounted onto superfrost slides (VWR) and stored at
80 °C. All immunohistochemical analyses of spinal cord sections were
performed blind.
GCaMP2 and iGluSnFR imaging.Astrocyte–neuron co-cultures were grown on
glass coverslips (VWR) and imaged 9–10 days post neuron plate down. Cells were
moved into an imaging chamber perfused with aCSF at 3–5 ml min 1and
containing (in mM): NaCl (150), KCl (3), HEPES (10), glycine (0.1), CaCl
2
(2),
MgCl
2
(1) and glucose (10) at pH 7.4. Imaging was performed at 37 °C on a Leica
AF6000 LX using a DFC350 FX digital camera. To investigate the effect of synaptic
activity on astrocytic glutamate exposure, astrocytes expressing iGluSnFR68 and
GCaMP2/mCherry-expressing neurons were transferred into TTX the night before
imaging. On the day of imaging, cells were transferred into aCSF containing TTX.
GCaMP2-positive neurons and iGluSnFR-positive astrocytes were brought into one
field of view at 20 and subsequently washed with TTX-free aCSF containing BiC
to induce neuronal network bursting. To investigate the effect of glutamate uptake
inhibition on astrocytic glutamate exposure, TBOA was applied to the cells.
GCaMP2 and iGluSnFR were imaged using a standard GFP filter set at 1 Hz.
To monitor astrocyte development, images of GFP þastrocytes in mono- and
co-culture were taken every day between DIV0 and DIV10 after the addition of
neurons using a standard GFP filter set at 40. The cell outline was manually
traced using Photoshop and the perimeter and area quantified using ImageJ. The
ratio of perimeter:area was calculated to serve as a measure for morphological
complexity.
FRET probe imaging.Mouse astrocytes were transfected with FRET probes for
glucose (FLII12Pglu-700uDelta6), pyruvate (Pyronic) or lactate (Laconic) 24 h
before co-culture with primary rat neurons. Cells were placed in tetrodotoxin
(TTX) 100 nM at DIV9 following co-culture, and either kept in TTX, or washed
with TTX-free medium containing bicuculline (50 mM) 24 h before imaging. All
imaging was performed at 37 degrees Celsius in continuous perfusion with aCSF
(composition: 150 mM NaCl, 3 mM KCl, 10 mM HEPES buffer, 0.1 mM
glycine, 2 mM CaCl2, 1 mM MgCl2, and 10 mM glucose, pH 7.4). TTX (100 nM)
was added to aCSF perfused in all conditions to exclude acute effects of altered
neuronal activity on astrocyte metabolism at the time of imaging. Images were
captured using a DFC350 FX digital camera as part of a Leica AF6000 LX imaging
system. Images were acquired every 10 s. All probes were imaged with a standard
FRET CFP/YFP filter wheel, with excitation of CFP and measurements taken
within the CFP and YFP emission spectra. For glucose measurement, the YFP/CFP
ratio of FLII12Pglu-700uDelta6 FRET probe was used to determine intracellular
glucose concentrations for individual astrocytes. Levels were measured at baseline
in aCSF with TTX, before the addition of the glucose-uptake inhibitor cytochalasin
B (20 mM) after 60 s, resulting in a reduction in intracellular glucose levels
corresponding to the rate of glucose consumption. For pyruvate and lactate
measurements, the CFP/YFP ratio of the Laconic and Pyronic FRET probes were
used to determine intracellular lactate and pyruvate levels respectively. Levels were
measured at baseline in aCSF, before the addition of the MCT inhibitor
AR-C155858 (1 mM) resulting in an increase in concentration corresponding to
lactate or pyruvate production. All FRET ratios were normalized by subtracting
baseline and expressed as percentage of maximum. A linear least-squares fitting
routine was used to determine the line of best fit and slopes calculated for the
portion of the curve corresponding to the rate of consumption or production of
substrate.
Seahorse bioanalyser.Cortical astrocytes from rats were seeded in XF 24-well
cell culture microplates and infected with MOI 25 of Ad5-VP16-CREB, a serotype
5 adenovirus harbouring a constitutively active CREB or Ad5-Null, as a control.
After 18–24 h of viral expression, extracellular acidification rate (ECAR), proton
production rate (PPR) and oxygen consumption rate (OCR) were measured using a
Seahorse Bioscience XF-24 instrument (Seahorse Bioscience, North Billerica, MA).
The instrument was calibrated following manufacturer’s instructions. Cells were
incubated with XF Assay Medium (Agilent) supplemented with 2 mM L-glutamine,
5.5 mM glucose and 1 mM sodium pyruvate for 30 min at 37 °C without CO
2
to
allow temperature and pH to reach equilibrium before the first measurement.
Four measurements were done to measure each condition: baseline levels,
1 ug/ml of oligomycin (complex V inhibitor), 2 mM FCCP (mitocondrial
uncoupler) and 0.4 mM rotenone (complex I inhibitor) plus 1 mM antimycin A
(complex III inhibitor). All values were normalized to protein content of each well
(measured by BCA assay) and expressed relative to basal level of control virus
infected cells.
RNA extraction RT-PCR and qPCR.Total RNA extraction from astrocyte
mono-cultures and astrocyte–neuron co-cultures was performed using the High
Pure RNA Isolation Kit (Roche) and cDNA was subsequently created using the
Transcriptor First Strand cDNA Synthesis Kit (Roche) using the following
programme: 10 min at 25°C, 30 min at 55 °C and 5 min at 85 °C. qPCRs were run on
a Stratagene Mx3000P QPCR System (Agilent Technologies) using SYBR Green
MasterRox (Roche) with 6 ng of cDNA per well of a 96-well plate, using the following
programme: 10 min at 95 °C, 40 cycles of 30 s at 95 °C, 40 s at 60 °Cand30sat72°C,
with a subsequent cycle of 1 min at 95 °C and 30 s at 55 °C ramping up to 95 °Cover
30 s (to measure the dissociation curve). Species-specific mouse primers were used
(Table 1). Primers were designed to only pick up mouse transcripts in a mouse
astrocyte/rat neuron co-culture, and were validated by running qPCRs with cDNA
derived from either pure mouse or pure rat cultures, and discarded if they picked up
any rat transcripts. H1f0 encodes a histone subunit and was used as loading control.
Additional mouse primers were used without requiring species-specificity (Table 2).
Human-specific primers were also used (Table 3).
Immunohistochemistry.For cell culture IHC, established protocols were
employed69. Briefly, cells were fixed in 4% formaldehyde for 20 min at room
temperature, washed with PBS and permeabilized with the detergent NP40
(Life Technologies). Cells were subsequently incubated in either rabbit anti-GFP
(1:750, Life Technologies) or mouse anti-GFAP (1:400, Sigma) overnight at 4 °C.
The next day, cells were washed with PBS and incubated with the appropriate
secondary antibody at room temperature for 2 h. To stain neurons, cells were
incubated with the pan neuronal marker Neuro-Chrom, directly conjugated to Cy3
(Merck Millipore). Cells were then mounted using the mounting medium
Vectashield (Vector Labs).
For spinal cord IHC, fixed spinal sections from 20-week-old C57BL/6 (n¼5)
and P301S (n¼5) mice were stained for AT8, GFAP, Aldh1l1 and ChAT. Spinal
cord slides were allowed to defrost and air dry before two 5 minute washes in
1% PBS (137 mM NaCl; 2.7 mM KCl;10 mM Na
2
HPO
4
; 1.8 mM KH2PO4;
pH ¼7.4 adjusted with HCL), on a shaker. Sections were blocked and
permeabilized with 3% normal goat serum or 3% normal horse serum (S-1000 or
S-2000, Vector Laboratories) and 0.2% Triton-X (X-100, Sigma) in PBS (B300 ml
per slide) for 1 h. They were incubated with primary antibodies with 1% blocking
serum in 0.2% Tx-PBS over-night. Three 10 min washes in 1% PBS on a shaker
were followed by 2 h incubation with secondary antibodies, as required, with 1%
NGS, Bis-benzamide (1:4000, B1155, Sigma Aldrich) in 1% PBS. Slides were
washed in 1% PBS (10 min) and TNS (Tris non-saline solution, 50 mM Tris,
pH 7.4 with Nitric acid) twice for 15 min, before mounting in fluorosave
reagent (345789, Millipore). Primary antibodies used were mouse monoclonal
anti-phospho-tau (AT8, 1:1000, Autogen Bioclear); rabbit polyclonal anti-GFAP
(1:1000, Sigma C9205); polyclonal goat anti-ChAt (1:200, Millipore AB144P),
rabbit polyclonal anti-Aldh1l1 (1:1,000, Abcam, ab190298). Secondary antibodies
were donkey anti-mouse Alexa 555 (1:1,000 Invitrogen); Donkey anti-goat Alexa
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132
14 NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications
Fluor 488 (1:1000, A11055 Life Technologies), Goat anti-mouse Alexa Fluor
568 (1:1,000, A11004 Life Technologies) and donkey anti-rabbit Alexa 488
(1:500, Jackson ImmunoResearch).
For image analysis, Tau (AT8) was imaged using a Zeiss Axiovision microscope,
(20 objective with exposure time 800 ms) and Axiovision 4.8 software via a digital
camera and stitched together using AxioVision software panorama tool. For imaging
of C57BL/6 spinal cord exposure was increased to enable stitching. The imaging of
GFAP and ChAT was carried out on a Zeiss LSM710 laser scanning confocal
microscope. Images were taken at 20 magnification. Quantitative analysis for
motor neurons using ChAt stain was performed on the ventral horn of six spinal
sections from each animal. The analysis was carried out at 63 magnification under
152 mm152 mm grid. Independent sample t-test was performed for statistical
significance at a¼0.05. Aldh1l1 images were taken with a DFC350 FX digital camera
on a Leica AF6000 LX microscope at 20 magnification.
Statistical analysis.Statistical testing of the RNA-seq data is described in that
section. Other testing involved a two-tailed paired Student’s t-test, or a one- or two-
way analysis of variance followed by Sidak’s post hoc test, as indicated in the
legends. For t-tests, variance was generally found to be similar, abrogating the need
for Welsh’s Correction. Sample sizes were calculated using standard power cal-
culations, requiring an effect size of 30% at 80% power. Throughout the manu-
script, independent biological replicates are defined as independently performed
experiments on material derived from different animals.
Data availability.The RNA-seq read sorting procedure (Sargasso: Sargasso
Assigns Reads to Genomes According to Species-Specific Origin) is a Python tool to
disambiguate mixed-species RNA-seq reads according to their species of origin.
Given a set of RNA-seq samples containing RNA-seq data originating from two
Table 1 | Species-specific mouse qPCR primers.
Gene Fwd Rev
H1f0 50-GTTTGTCTTCCAAGACTTTCTT-3050-CTTTGCCCCTTTAGACAATGGG-30
Dio2 50-CCCTTCTGAGCGAATTGATCCA-3050-CACATCGTAAGTATGTATCTGGG-30
Slc1a2 50-TATCATCTCCAGTTTAATCAC-3050-TTCATTCAACATGGAGATGACC-30
Hes5 50-TGCAGAGTTGTCATTTGGGG-3050-AACGGGCCCTGAAGAAAGT-30
Hey2 50-GAATGTAACGTAGCACAAGATCAG-3050-AGGTCTTTCGACTTAATTTCCC-30
Glul 50-GAGATTGACATTTCCACTGTTGG-3050-ATCCATCAGGTGACGCGGTGAG-30
Cldn10 50-CCCACACTTCAAGCCATGAGAT-3050-GGAAGGAGCCCAGAGCGTT-30
Rlbp1 50-CGTGGAAGGCAGAGTTAAAGGC-3050-CAAGGATCACATCCAAGATGGG-30
Adora2b 50-ACTGGCCGATCCTCACTGTGAA-3050-GAATCAATTCAAGCTGCCACCA-30
Slco1c1 50-GATCCAGACCCTTGCGAACAT-3050-GATATCCGACTGTAAAGGATGG-30
Ppp1r3c 50-CAGATGTGGACTGTGTCTACA-3050-ATCCTCCCATTAGCGTGATAA-30
Slc6a11 50-CTATGATGCCCCTCTCTCCAC-3050-CTGTCACAAGACTCTCCACG-30
Grm3 50-CATGTTGTTTGCCATTGATGAA-3050-ATGCTCTGACAAACTCCAGTGAC-30
Fmo1 50-TCGTCTTTGCGACTGGATATACT-3050-GCTTGATGAGGCCAATCACA-30
Cyp4f15 50-CACACAGTGACTCCCTGCAC-3050-TCTGGGCCAAAGGATGCT-30
2900052N01Rik 50-ACAATCCAAATCTACCCACGAG-3050-TGGGATTGTAGATTGTGCTGTC-30
Slc1a3 50-CAAGACACTGACACGCAAGGAC-3050-CTTAACATCTTCCTTGGTGAGGC-30
Rorb 50-AGCATAGATTCCGGTCAGC-3050-GAGTTCTTCCATGGTGTACTGAC-30
Ppp1r3g 50-GATGCCTGATCCTCTCTTG-3050-ACTGATCACTCGGCCAG-30
Tmem100 50-GCCCACACTGCTCTAACTC-3050-CACTCCCTAAACGTTTAACAGG-30
Alp/ 50-GGCAATGAGGTCACATCC-3050-CTGGTGGCATCTCGTTATC-30
Fwd, forward; qPCR, quantitative PCR; Rev, reverse.
Table 2 | Non species-specific mouse qPCR primers.
Gene Fwd Rev
Aldh1l1 50-CATCCAGACCTTCCGATACTTC 50-ACAATACCACAGACCCCAAC-30
Fmo1 50-CATCTGCCAAAACCAACTCTG 50-TGGCGGTGGTAATGTAGTTG-30
Grm3 50-CCAAGCTCTGTGATGCAATG 50-CCGTCTCCGTAAGTGTCAAAC-30
2900052N01Rik 50-ACAATCCAAATCTACCCACGAG 50-TGGGATTGTAGATTGTGCTGTC-30
Gfap 50-GCAAAAGCACCAAAGAAGGGGA 50-ACATGGTTCAGTCCCTTAGAGG-30
Cyp4f15 50-CCCCAGTAAGCATGAGGATG 50-CAAACATGAAGGTGTCAGCC-30
Slc6a11 50-CTATGATGCCCCTCTCTCCAC 50-CTGTCACAAGACTCTCCACG-30
Fwd, forward; qPCR, quantitative PCR; Rev, reverse.
Table 3 | Species-specific human qPCR primers.
Gene Fwd Rev
SLC1A2 50-TTATTTATGTTCGGTTTGCCT-3050-CTAGGACGATGAGATGATGACT-30
SLC1A3 50-ACCTGCCCTCTGTTTCC-3050-ATGAATAATCCCACTCCTGG-30
AQP4 50-GAGAGTCGTCACACCAGTG-3050-TCCCAGCCAGGAAGTAACTA-30
RPL13A 50-CCACTACCGGAAGAAGAAACAG-3050-CAGGGCAACAATGGAGG-30
HES5 50-TCTTCTGCCAAGTGTCTGAC-3050-CCGGCACTACAAATATCATAGA-30
HEY2 50-TGAGAGAGTCGTGTTTCGTAAG-3050-CAACTTGAAAATTATTTTCAGCAG-30
Fwd, forward; qPCR, quantitative PCR; Rev, reverse.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 15
different species, mapped, disambiguated reads are written to per-sample and
-species specific output BAM files. Sargasso is freely available (http://doi.org/
10.5281/zenodo.206619). All the RNA-seq data that support the findings of this
study have been deposited in the European Bioinformatics Institute depository
(accession code: E-MTAB-5514). All the other data are available from the
corresponding author upon reasonable request.
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Acknowledgements
We acknowledge the wealth of informative gene expression data published by the
laboratory of Ben Barres, which formed part of our meta-analyses in this study. We thank
Paul Skehel and David Price for comments on the manuscript. We are very grateful to
Paulo Sassone-Corsi, Richard Maurer, Ronald Evans, Wolf Frommer and L Felipe Barros
for plasmids. RNA-seq raw reads were generated by Edinburgh Genomics, The Uni-
versity of Edinburgh. Edinburgh Genomics is partly supported through core grants from
NERC (R8/H10/56), MRC (MR/K001744/1) and BBSRC (BB/J004243/1). This work is
also supported by the Medical Research Council, the Wellcome Trust, the Biotechnology
and Biological Research Council, the NPlast European Commission Initial Training
Network, and Biogen through a Edinburgh University-Biogen Collaborative Research
Initiative.
Author contributions
G.E.H. conceived the project and wrote the manuscript. D.J.A.W., S.C., A.Z., R.M., E.G.
and G.E.H. directed the research. P.H., N.M.M., Z.J., A.T., P.B., J.M., D.H., S.M., S.S.T.,
A.E.-P., D.H., M.T. performed the wet lab experiments. O.D., S.H. and T.I.S. developed
and tested the SSS workflow.
Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing interests: The authors declare no competing financial interests.
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How to cite this article: Hasel, P. et al. Neurons and neuronal activity control gene
expression in astrocytes to regulate their development and metabolism. Nat. Commun.
8, 15132 doi: 10.1038/ncomms15132 (2017).
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rThe Author(s) 2017
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ARTICLE
NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 17
