Synapses are points of contact where electrochemical signals are transferred between neurons in a given circuit (Petzoldt and Sigrist, 2014; Südhof, 2018). Synapse development occurs in several molecularly and functionally defined stages, which include initiation, maturation, and pruning steps prior to stabilization and establishment of mature circuits. In the rodent cortex, this occurs over the period of the first postnatal month, initiating at around postnatal day (P) 7, peaking at P14, and stabilizing towards P28 (Blue and Parnavelas, 1983a; Blue and Parnavelas, 1983b; Farhy-Tselnicker and Allen, 2018; Li et al., 2010). Synaptic deficits, for example, caused by mutations in synapse-related genes, are associated with developmental disorders such as autism spectrum disorder and epilepsy (Lepeta et al., 2016). Therefore, understanding how synaptic development is regulated will provide important insights into how circuits form and function in health and are altered in disease.

The majority of synapses in the mammalian cortex are contacted by astrocytes, a type of glia and key regulators of circuit development and function (Allen, 2013; Batool et al., 2019; Bernardinelli et al., 2014a; Genoud et al., 2006). Astrocytes express many genes encoding proteins that regulate distinct stages of synapse formation and maturation (Baldwin and Eroglu, 2017). For example, thrombospondin family members induce formation of structurally normal but functionally silent synapses (Christopherson et al., 2005; Eroglu et al., 2009). Glypicans induce the formation of active synapses by recruiting GLUA1 to the postsynaptic side (Allen et al., 2012; Farhy-Tselnicker et al., 2017) and chordin-like 1 induces synapse maturation by recruiting GLUA2 to the postsynaptic side (Blanco-Suarez et al., 2018). These and other signals have been identified using in vitro cell culture approaches and analyzed individually across ages and brain regions in vivo. Yet, a systematic analysis of their expression patterns in a complete circuit in vivo, which would reveal distinct regulatory roles for specific synaptic connections, has not been attempted.

The mouse visual circuit is a well-characterized and powerful model to investigate the role of astrocytes in regulating different stages of synaptogenesis. Visual information perceived by the retina is relayed to the visual cortex (VC) via the neurons of the thalamic lateral geniculate nucleus (LGN). Before eye opening (from birth to ~P12 in mice), spontaneous retinal activity evokes correlated cortical responses (Gribizis et al., 2019; Hanganu et al., 2006) that are important for the correct establishment of thalamocortical synapses (Cang et al., 2005). Eye opening marks a step towards synapse maturation in the VC, with the appearance of visually evoked neuronal responses across the retinal-LGN-VC circuit (Espinosa and Stryker, 2012; Hooks and Chen, 2006; Hooks and Chen, 2020). Perturbing this process by methods of visual deprivation, such as dark rearing, delays both synaptic (Albanese et al., 1983; Desai et al., 2002; Freire, 1978; Funahashi et al., 2013; Hsu et al., 2018; Ishikawa et al., 2014; Ko et al., 2014; Majdan and Shatz, 2006; Tropea et al., 2006) and astrocyte maturation (Müller, 1990; Stogsdill et al., 2017).

The VC’s glutamatergic neurons are arranged in spatially defined layers, with distinct transcriptomic, functional, and connectivity profiles (Bannister, 2005; Douglas and Martin, 2004). For example, neurons in layers 1 and 4 receive input from the LGN, while layer 5 neurons, the main output to subcortical regions, send their dendrites to layer 1, where they receive input from both local and subcortical projections. Recent work has shown that cortical astrocytes are also spatially arranged in diverse populations (Batiuk et al., 2020; Bayraktar et al., 2020; John Lin et al., 2017; Lanjakornsiripan et al., 2018), in line with evidence from other brain regions showing astrocyte heterogeneity (Chaboub and Deneen, 2012; Chai et al., 2017; Khakh and Deneen, 2019; Oberheim et al., 2012; Rusnakova et al., 2013; Schitine et al., 2015). However, whether this astrocyte diversity is reflected in their regulation of synapses across the distinct layers of the VC is unknown. To understand how the diverse astrocytic signals act together to regulate formation of a complete circuit, it is important to determine both temporal and spatial expression patterns of astrocytic synapse-regulating genes in vivo as well as identify the mechanisms that control their level of expression and the subsequent effect on synapse formation.

Here, we use RNA sequencing and in situ hybridization to obtain the developmental transcriptome of astrocytes in the mouse VC in vivo. We find that astrocyte synapse-regulating genes display differential temporal and spatial expression patterns, which correspond to stages of synapse initiation and maturation. Furthermore, we find that developmental regulation of these genes, namely glypican 4 and chordin-like 1, depends on thalamocortical neuronal activity, with additional regulation by astrocyte IP3R2-dependent Ca²⁺ activity. Manipulating either neuronal or astrocytic activity leads to shifts in synaptic development and maturation. Finally, single-nucleus RNA sequencing analysis reveals diverse populations of astrocytes in the developing VC and identifies novel groups of genes that are regulated by neuronal and astrocyte activity. These findings demonstrate how astrocyte expression of synapse-regulating genes is controlled during development, and how synapse maturation is dependent on neuron-astrocyte communication. These data further provide an important resource for future studies of astrocyte development and astrocyte regulation of synapse formation.

To address key questions regarding astrocyte regulation of synapse development, this study has three major goals. First, to characterize the spatio-temporal profile of astrocyte development during distinct stages of cortical synaptogenesis at the transcriptomic level. Second, to investigate the mechanisms that influence spatio-temporal expression levels of select astrocytic synapse-regulating genes, focusing on neuronal and astrocyte activity. Third, to determine the global dependence of the astrocyte transcriptome on neuronal and astrocyte activity using an unbiased RNA sequencing approach. All analyses were performed in the mouse VC to enable comparison of these metrics within a defined circuit, providing a blueprint for future analysis of the functional roles of astrocytes in synaptic development.

Developmental profiling of the astrocyte transcriptome in the postnatal VC

Astrocytes appear in the cortex at birth, and migrate, proliferate, and mature throughout the first postnatal month (Ge et al., 2012), coincidently with the stages of synapse development. To determine the transcriptomic profile of astrocytes at these stages (P7, P14, P28, and adult, P120), we used the astrocyte Ribotag mouse model to isolate mRNA for bulk RNA sequencing (B6N.129-Rpl22^tm1.1Psam/J crossed to B6.Cg-Tg(Gfap-cre)73.12Mvs/J – labeled Astrocyte-RiboTag) (Figure 1, Figure 1—figure supplement 1, Figure 1—source data 1; Boisvert et al., 2018; Chai et al., 2017). In this mouse line, cre recombinase drives the expression of an HA-tagged ribosomal subunit, enabling purification of cell-type-specific ribosomes and associated mRNA for analysis by RNA sequencing (Figure 1A and B). We found a significant enrichment in astrocytic genes over other cell types in the mRNA isolated by HA immunopurification (IP; labeled Astro) compared to total VC mRNA (input; IN; Figure 1—figure supplement 1A). Furthermore, immunostaining analysis of VC sections at P28 shows a high overlap between the HA ribosome tag and the astrocyte marker S100β, but not with other cell-type markers (Figure 1—figure supplement 1B-D), with more than 95% of S100β-positive cells also positive for the HA tag, suggesting high astrocyte coverage of our mRNA isolation, consistent with our previous analysis in the adult (Boisvert et al., 2018). The complete RNA sequencing dataset is available in a searchable format online (http://igc1.salk.edu:3838/astrocyte_transcriptome/).

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Temporal profiling of astrocyte transcriptome in the postnatal visual cortex (VC).

(A) Complete list of genes (expression levels shown as FPKM) at each developmental stage as indicated. Expression levels for each sample, as well as average, are shown, as well as pairwise analysis and false discovery rate (FDR) values. (B) Complete list of Gene Ontology (GO) terms (Biological Process) identified for astrocyte-enriched genes at each developmental time point as indicated.

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To assess if there are broad changes in the transcriptomic profiles of astrocytes across development, we performed principal component analysis (PCA; Figure 1C). This showed that P7 and P14 astrocytes form distinct clusters, while P28 and P120 astrocytes cluster together. To investigate this further, we analyzed the number of differentially expressed genes (DEGs; fragments per kilobase of exon per million mapped fragments [FPKM] > 1; false discovery rate [FDR] < 0.05) between each age group. DEGs are classified into total genes (all changes detected), genes that are expressed by astrocytes (IP/input > 0.75), and genes that are enriched in astrocytes (IP/input > 3; Figure 1D, for definitions, see also Boisvert et al., 2018). The largest number of DEGs is between P7 and P28 (~6000 total genes), and smallest numbers between P14 and P28 (~1000 total genes), and P28 and P120 (~2000 total genes). Analysis of astrocyte-enriched genes (IP/input > 3) showed that ~60% of all astrocyte-enriched genes are significantly changed from P7 to P14, while only ~20% are changing between P28 and P120 (Figure 1E). This shows that most changes in astrocyte gene expression are occurring between the first and second postnatal weeks, a time of transition from synapse formation to synapse maturation, and in the VC, from spontaneous to visually evoked neuronal activity.

To determine the different astrocyte functions at each age, we focused on the astrocyte-enriched gene lists (~1000 total genes FPKM >1, astro IP/input > 3). To identify common genes expressed at all ages, as well as astrocyte-enriched genes that are most highly expressed at each age, we performed Gene Ontology (GO) analysis, focusing on Biological Process (BP) terms (Figure 1F–H, Figure 1—figure supplement 1F and G). This showed that genes common to all ages are enriched in GO terms related to cholesterol processing and serine synthesis, confirming the previously established important role of astrocytes in regulating brain cholesterol (Orth and Bellosta, 2012; Figure 1G). Analysis of GO terms unique to each age showed that at P7 astrocyte genes are enriched in GO terms related to cortical development, while at P14 astrocyte genes are enriched in GO terms related to Wnt and BMP signaling pathways. Conversely, adult astrocytes (P120) are enriched in terms related to regulation of extracellular matrix assembly and contact inhibition (Figure 1H). In all we found both the number of genes and the GO terms common to all ages constitute about 50% of all terms identified for each age, while genes and GO terms unique to each age consist less than 10% of all terms (Figure 1—figure supplement 1F and G). These results demonstrate that astrocytes perform their core functions across all developmental stages similarly, with several age-specific functions that turn on and off depending on the developmental stage and the environment in the VC.

An important analysis to be performed with this dataset is determining the temporal expression changes of known astrocyte genes, for example, to identify potential astrocyte markers that are either global or age specific. We found that Apoe and Cst3 are the most highly expressed genes in astrocytes at all ages (peak FPKM ~10,000; Figure 1F, Figure 1—source data 1), while amongst highly expressed astrocyte genes (FPKM >100) Lars2 is the most astrocyte-enriched gene at all ages (astro IP/ input ~40; Figure 1—figure supplement 1E). These genes are highly expressed in adult astrocytes across multiple brain regions (Morel et al., 2017), thus can be potentially utilized to mark or target astrocytes globally. The expression of another well-known astrocytic gene Aldh1l1 (Morel et al., 2017; Figure 1I) is stable across all ages, making it an optimal marker for astrocytes during both development and in the adult (Srinivasan et al., 2016). On the other hand, S100b expression is upregulated later in development, making it more suitable to mark adult astrocytes (Figure 1I). For genes that encode proteins important for astrocyte function, we found that the metabotropic glutamate receptor Grm5 (mGluR5) is most highly expressed at P7 and then declines with maturation, confirming previous reports (Catania et al., 1994; Sun et al., 2013), while the glutamate transporter Slc1a2 (Glt1) and the connexins (Gja1, Gjb6) are significantly upregulated from P14 onwards (Figure 1I).

Astrocytic synapse-regulating genes show differential spatio-temporal expression patterns

Our goal is to characterize how astrocytes regulate synapses, so we next used the astrocyte Ribotag dataset to investigate the developmental expression changes of key synapse-regulating genes (Figure 1J). These include astrocyte-secreted thrombospondins (Thbs), which induce silent synapse formation. The family members expressed by astrocytes in the CNS show divergent expression, with Thbs1 being significantly higher at P7 than later ages, whereas Thbs4 is significantly higher at P120 than P7 (Figure 1J). This temporal expression profile fits with previous studies that have demonstrated important roles for Thbs1 in initial synapse formation at P7 (Christopherson et al., 2005), and suggested roles for Thbs4 in the adult brain (Benner et al., 2013). Similarly, glypican (Gpc) family members have a divergent expression. Though redundant in culture for their ability to regulate immature synapse formation (Allen et al., 2012), Gpc4 and Gpc6 show different temporal expression in vivo. While Gpc4 is most highly expressed at P7 and gradually declines with maturation, Gpc6 peaks at P14–P28. Gpc5, a glypican family member with yet unknown function, has low expression at P7, and is significantly increased at all later ages. Astrocyte-secreted chordin-like 1 (Chrdl1) regulates synapse maturation and its expression peaks at P14, confirming previous analysis (Blanco-Suarez et al., 2018; Figure 1J). These temporal expression profiles are not limited to factors that promote synapse formation. Astrocyte phagocytic receptors involved in synapse elimination, Megf10 and Mertk (Chung et al., 2013), significantly increase in expression between P7 and P14, coincident with the initiation of synapse elimination.

Our RNA sequencing analysis demonstrates that in the developing VC synapse-regulating genes show differential temporal expression patterns. However, whether these levels are equal across astrocytes in all cortical layers throughout development is unknown. During the first postnatal weeks, when astrocytes regulate synapse development, they are still migrating and dividing (Ge et al., 2012). Yet, how developing astrocytes populate each of the cortical layers, and their ratio relative to other cortical cells across development has not been quantified. To address this, we utilized the well-established mouse line where astrocytes express GFP under the Aldh1l1 promoter (Cahoy et al., 2008; Dougherty et al., 2010; John Lin et al., 2017; Stogsdill et al., 2017; Tien et al., 2012). Immunostaining of brain sections from Aldh1l1-GFP mice at P7 and P28 with antibodies against known astrocyte markers ALDH1L1, S100β, and SOX9 showed high overlap between GFP and marker-positive cells, suggesting the majority of astrocytes in the VC express GFP in this mouse line (Figure 2—figure supplement 1A–C), further validating its usage. We quantified astrocyte numbers within each of the six neuronal layers at the developmental time points, which correspond to astrocyte and synapse development throughout the first postnatal month (P1, P4, P7, P14, P28; Figure 2A–C). At birth (P1), very few astrocytes are present, comprising 0.5–2% of the total cell number in the VC (represented as GFP-positive cells as a percentage of all cells marked by the nuclear dye DAPI), with a significantly higher percentage of astrocytes in deeper layers than upper layers (Figure 2C and D, Figure 2—source data 1A). The astrocyte percentage increases with development in all cortical layers, peaking at P21–P28. At this time, astrocytes are ~10% of total cell number in layers (L) 2–6, and ~50% in L1 (Figure 2C and D, Figure 2—source data 1A). As the brain develops, the distance between cells grows to accommodate the increase in cell size and complexity, as evident by a significant decrease in DAPI-positive nuclei per mm² of VC that occurs from P1 to P14–P28 (Figure 2—figure supplement 1D, Figure 2—source data 1A). Despite this decrease in total cell density, the proportion of astrocytes remains constant in all cortical layers and across ages, with the exception of L1–2/3, where it is significantly lower at P1 (Figure 2C and E). This stability in astrocyte density is likely explained by new astrocytes still being generated in the weeks after birth (Ge et al., 2012).

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Astrocytic synapse-regulating genes show differential spatio-temporal expression patterns.

(A) Full statistical analysis of astrocyte number changes during development. Each comparison (e.g., astrocyte number/area, comparison between ages within each layer) is labeled accordingly. (B) Full statistical analysis of developmental changes in mRNA expression of selected synapse-regulating genes by single-molecule fluorescent in situ hybridization (smFISH). Averages and analysis calculated for N = 3, that is, per mouse. (C) Full statistical analysis of developmental changes in mRNA expression of selected synapse-regulating genes by smFISH. Averages and analysis calculated for n = ~50–350, that is, total number of astrocytes per group (across the three mice).

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Having established the developmental profile of astrocytes, we next determined if there are layer-specific developmental changes in mRNA expression of synapse-regulating genes by performing single-molecule fluorescent in situ hybridization (smFISH; RNAscope) on brain sections of Aldh1l1-GFP mice. We probed for seven genes that regulate distinct aspects of synaptogenesis: active synapse-regulating – glypicans (Gpc) 4, 5, 6; synapse maturation regulating – chordin-like 1 (Chrdl1); and silent synapse-regulating – thrombospondins (Thbs1, 2, 4) (Figure 2F–M, Figure 2—figure supplement 2, Figure 2—source data 1B and C). Expression of each gene (represented as total area of thresholded signal in µm²; labeled as thresh area) was analyzed within the territory of GFP-positive astrocytes in each cortical layer at similar time points as above: P4, P7, P14, and P28, when most alterations in astrocyte transcriptome (Figure 1) and synapse development occur (Farhy-Tselnicker and Allen, 2018), and excluding P1, when the numbers of astrocytes in the cortex are very low. A negative control probe was used to determine the minimal signal threshold of detection (Figure 2—figure supplement 2H and I).

We first analyzed glypicans, factors that promote active synapse formation. Bulk RNAseq (Ribotag) showed that Gpc4 expression by astrocytes is highest at P7 and gradually declines with maturation (Figure 1J, Figure 1—source data 1A). Layer-specific analysis, however, shows that these differences are driven by astrocytes in L1: Gpc4 expression decreases between P7 and P14 only in L1 astrocytes, staying stable across development in all other layers (thresh area [μm²] L1: P4 3.1 ± 0.5; P7 4.1 ± 0.1; P14 1.7 ± 0.2; P28 1.1 ± 0.3; Figure 2F and G, Figure 2—source data 1B and C). Gpc6, on the other hand, peaks at P14–P28 in bulk sequencing. Layer-specific analysis showed that this increase occurs in astrocytes between P7 and P14 in L2–5, with significant upregulation in L5, and remaining high at P28 (thresh area [μm²] L5: P4 2.5 ± 0.4; P7 2.5 ± 0.4; P14 4.4 ± 0.4; P28 3.1 ± 0.4; Figure 2H and I, Figure 2—source data 1B and C). Gpc5 is strongly upregulated at P14 and remains high at P28 in astrocytes in all layers, matching the bulk RNAseq data (thresh area [μm²] L1: P4 0.5 ± 0.1; P7 1.3 ± 0.2; P14 5.9 ± 0.7; P28 5.3 ± 1.1; Figure 2J and K, Figure 2—source data 1B and C).

The expression of the synapse maturation factor Chrdl1 peaks at P14 in the bulk RNAseq data (Figure 1J, Figure 1—source data 1A). Spatial analysis revealed that this increase from P7 to P14 is layer-specific, with the largest increase in Chrdl1 occurring in upper layer astrocytes with significant upregulation in L2/3 (thresh area [μm²] L2/3: P4 4.1 ± 0.2; P7 4.1 ± 1; P14 9.9 ± 0.6; P28 7.6 ± 1; Figure 2L and M, Figure 2—source data 1B and C). Further, at its peak of expression, Chrdl1 is highest in L1–4 astrocytes compared to L5–6, demonstrating a heterogeneous expression across layers, as previously reported (Bayraktar et al., 2020; Blanco-Suarez et al., 2018). Finally, we analyzed astrocyte thrombospondins, factors that induce silent synapse formation (Figure 2—figure supplement 2B-G). Thbs mRNA levels in the VC are much lower at their peak expression than glypicans or Chrdl1, consistent with our bulk RNAseq results (Figure 1J, Figure 1—source data 1A), and previous studies showing low thrombospondin expression in the resting state, and an upregulation by learning or injury (Nagai et al., 2019; Tyzack et al., 2014). Nevertheless, we observed an increase in Thbs2 expression in L4–5 at P28 (thresh area [μm²] L5: P4 0.7 ± 0.1; P7 0.7 ± 0.19; P14 1 ± 0.1; P28 1.8 ± 0.3; Figure 2—figure supplement 2D and E, Figure 2—source data 1B); and an increase in Thbs4 at P28 in all layers (thresh area [μm²] L1: P4 0.1 ± 0.01; P7 0.1 ± 0.1; P14 0.3 ± 0.1; P28 1.9 ± 0.5; Figure 2—figure supplement 2F and G, Figure 2—source data 1).

The seven astrocyte synapse-regulating genes we analyzed using smFISH all regulate formation and function of excitatory glutamatergic synapses. In the case of Gpc4, Gpc6, and Chrdl1, they regulate glutamatergic synapse development by primarily affecting localization of AMPA glutamate receptor (AMPAR) subunits (Allen et al., 2012; Blanco-Suarez et al., 2018). Previous studies in the rat somatosensory cortex have identified developmental alterations in AMPAR subunit expression (Brill and Huguenard, 2008; Kumar et al., 2002); however, their spatio-temporal expression patterns in the developing mouse VC, and whether these correlate with the expression of astrocytic genes that regulate them, have not been systematically analyzed (Figure 3A and B). To detect postsynaptic AMPA glutamate receptors, we stained for GLUA1 and GLUA2 subunits (Figure 3C–F, Figure 3—source data 1). To detect the corresponding glutamatergic presynaptic terminals, we stained for VGLUT1 to identify local cortico-cortical connections (Figure 3G and H, Figure 3—figure supplement 1A, Figure 3—source data 1) and VGLUT2 to identify thalamocortical connections (Figure 3I and J, Figure 3—figure supplement 1B, Figure 3—source data 1; Fremeau et al., 2001). As expected based on the literature, GLUA1 levels peak at earlier time points than GLUA2 (Figure 3C and D, Figure 3—source data 1). GLUA2 immunoreactivity significantly increases from P7 to P14 in L1–5, and then remains stable to P28 (Figure 3E and F, Figure 3—source data 1). At all ages, the levels of GLUA1 and GLUA2 are significantly higher in L1 than all other layers, consistent with L1 being rich in synaptic connections (Figure 3—source data 1; Douglas and Martin, 2004). VGLUT1 immunoreactivity greatly increases between P7 and P14 in all cortical layers and remains stable at later ages (Figure 3G and H, Figure 3—figure supplement 1A, Figure 3—source data 1). VGLUT2 levels steadily increase from P1 to P14, and then remain stable. VGLUT2 immunoreactivity is significantly higher in L1 and L4 than other layers at all ages, consistent with these being thalamic innervation zones and previous findings in rat cortex (Figure 3I and J, Figure 3—figure supplement 1B, Figure 3—source data 1; Boulland et al., 2004; Conti et al., 2005; Fremeau et al., 2001; Lopez-Bendito and Molnar, 2003). Taken together, these data show that synaptic proteins show complex spatio-temporal expression patterns, with multiple significant changes occurring between P7 and P14, as also observed for astrocyte gene expression.

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Spatio-temporal profiling of synaptic protein levels.

Full statistical analysis of synaptic protein VGLUT1, VGLUT2, GLUA1, GLUA2 changes during development. Each comparison (e.g., comparison between ages within each layer) is labeled accordingly. In each table, statistical comparison between ages within each layer (top), as well as between layers at each age (bottom), are shown.

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In summary, the transcriptomic analysis reveals significant changes in astrocyte gene expression across development, with the most prominent changes occurring between P7 and later ages, a time between synapse initiation and maturation. We have further determined the spatio-temporal expression profile of key astrocyte synapse-regulating factors, identifying divergent developmental and layer-specific expression patterns within the same families of genes. These findings strongly suggest that astrocyte expression of synapse-regulating genes is closely tied to the developmental stage of the cortex, which features both changes in neuronal and astrocyte activities across development. In addition to revealing important information about the developmental changes in expression of synapse-regulating genes, these transcriptomic data can be further utilized to inform further studies on astrocyte development. The complete dataset and GO term list are available in Figure 1—source data 1.

Thalamic glutamate release tunes astrocyte expression of synapse-regulating genes

Having found broad differences in astrocyte expression of synapse-regulating genes across cortical layers, we next asked what are the possible physiological mechanisms that regulate these layer-specific alterations between P7 and P14 in the developing VC in vivo. Our experiments using cultured astrocytes and neurons show that GPC4 protein secretion from astrocytes is significantly reduced in the presence of neurons (Figure 4—figure supplement 1A, Figure 4—figure supplement 1—source data 1) or when astrocytes are incubated with neurotransmitters including glutamate, adenosine, or ATP (Figure 4—figure supplement 1B, Figure 4—figure supplement 1—source data 1). Others have reported a similar effect on Gpc4 mRNA expression (Hasel et al., 2017), suggesting that neuronal activity can influence expression and release of synapse-regulating factors from astrocytes. Indeed, significant alterations in the activity patterns of both glutamatergic and GABAergic neurons occurs in the VC at around P14 (Espinosa and Stryker, 2012). Since the developmental expression changes in Gpc4 and Chrdl1 occurred mostly in the upper cortical layers innervated by thalamic neurons, we hypothesized that levels of these genes may be regulated by changes in the activity of thalamic neurons that occur upon eye opening at ~P12, when there is a transition from spontaneous to visually evoked activity in the retina that is relayed via the thalamus to the VC.

To investigate this, we generated mice where the release of glutamate from thalamocortical terminals in the VC is perturbed by knocking out the vesicular glutamate transporter VGlut2 (Slc17a6) from neurons in the dLGN. Knockout of VGlut2 has been previously shown to abolish presynaptic release of glutamate in VGlut2-expressing neurons, in full or conditional knockout mouse models (Wallén-Mackenzie et al., 2010). We crossed Slc17a6^f/f mice (Slc17a6^tm1Lowl/J; labeled as VGlut2 WT) to an RORα cre line (Rora^tm1(cre)Ddmo; Slc17a6^f/f;cre labeled as VGlut2 cKO), where cre recombinase is expressed in thalamic neurons including the dLGN (Figure 4A; Chou et al., 2013; Farhy-Tselnicker et al., 2017). Immunostaining experiments showed a significant decrease in VGLUT2 signal in the VC of VGlut2 cKO mice compared to WT at P14 (Figure 4B). Notably, VGlut2 cKO did not strongly affect the levels of VGLUT1, which marks cortico-cortical connections (Figure 4C; Wallén-Mackenzie et al., 2010), though a small but significant increase in VGLUT1 immunoreactivity was observed in L4, a major target of thalamocortical projections. This suggests that the normal upregulation in VGLUT1 that occurs at P14 across all other cortical layers (Figure 3) is either intrinsic to the cortical neurons and/or regulated by mechanisms other than dLGN-VC-evoked activity.

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Neuronal activity regulates astrocytic expression of synapse-regulating genes.

(A) Full statistical analysis of mRNA expression differences between WT and KO in VGlut2 cKO model. Averages and analysis calculated for N = 5, i.e. per mouse. (B) Full statistical analysis of mRNA expression differences between WT and KO in VGlut2 cKO model. Averages and analysis calculated for n = ~200–400, that is, total number of astrocytes per group (across five mice). All comparisons are between WT and KO within each layer.

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A lack of VGLUT2 immunoreactivity in the VC could also result from an absence of thalamic axons innervating their target regions. To test whether that is the case, we crossed RORα cre and VGlut2 cKO mice with a tdTomato reporter line (B6.C-Gt(ROSA)26Sor^{tm14(CAG-tdTomato)Hze/J}; labeled as tdTomato) to visualize dLGN axons (Figure 4A). All cre+ tdTomato+ groups (VGlut2 WT (Slc17a6^+/+), VGlut2 cHet (Slc17a6^f/+), and VGlut2 cKO (Slc17a6^f/f)) showed a comparable number and volume of tdTomato-labeled projections in L1 and L4 of the VC (Figure 4—figure supplement 2A and B). Analysis of VGLUT2 puncta colocalized with tdTomato-positive axons showed a significant decrease in number in VGlut2 cHet and VGlut2 cKO compared to WT (Figure 4—figure supplement 2A, C). These results show that in the VGlut2 cKO mice thalamic axons are present at their target layers in the VC but lack VGLUT2, as has been shown in studies performing similar manipulations, suggesting that they are functionally silent (Li et al., 2013; Zechel et al., 2016).

Does the lack of thalamocortical glutamate release influence the expression of synapse-regulating genes in astrocytes? To address this, we performed smFISH probing for Gpc4, Gpc6, or Chrdl1, along with a probe for the glutamate transporter Slc1a3 (Glast) (Ullensvang et al., 1997) to label astrocytes (Figure 4D–I, Figure 4—figure supplement 2D–J, Figure 4—source data 1). The number of cortical astrocytes marked by Slc1a3, and the expression level of Slc1a3 mRNA, is not affected by VGlut2 cKO in any cortical layer at P14, showing that gross astrocyte development proceeds normally in the absence of thalamic input (Figure 4—figure supplement 2D and E). During normal development, Gpc4 expression significantly decreases between P7 and P14 specifically in L1 astrocytes (Figure 2F and G). In VGlut2 cKO mice, this change no longer occurs (Figure 4D and E). Gpc4 expression is significantly increased in VGlut2 cKO compared to WT at P14 specifically in L1 astrocytes and unchanged in all other layers (thresh area [μm²]: L1 WT 1.3 ± 0.1; cKO 2.3 ± 0.2; Figure 4D and E, Figure 4—source data 1). Gpc6 expression is normally increasing in astrocytes in deep layers between P7 and P14 (Figure 2H and I). In the absence of thalamic glutamate release, Gpc6 is lower in astrocytes in layers 4 and 5 than in the WT at P14, showing that the normal developmental upregulation has been blocked (thresh area [μm²]: L4 WT 2.7 ± 0.4; cKO 1.5 ± 0.1; Figure 4F and G, Figure 4—source data 1). Similarly, Chrdl1 expression normally increases in upper layer astrocytes between P7 and P14 (Figure 2L and M); however, it is significantly decreased in VGlut2 cKO compared to WT specifically in astrocytes in upper layers (1–4), and not affected in deep layers at P14 (thresh area [μm²]: L1 WT 6 ± 0.4; cKO 4.6 ± 0.5; Figure 4H and I, Figure 4—source data 1). To determine if these alterations are due to the loss of thalamic neuron glutamate release at a specific developmental time (e.g., eye opening at ~P12), we analyzed the expression of Gpc4, Gpc6, and Chrdl1 at P7 (prior to eye opening). We found no difference in expression of any of these genes (Figure 4—figure supplement 2H–J), nor any difference in the number of cortical astrocytes marked by Slc1a3, or the expression level of Slc1a3 mRNA (Figure 4—figure supplement 2F and G). These results show that during VC development from P7 to P14 glutamate release from thalamic neurons regulates the developmental expression of astrocyte Gpc4, Gpc6, and Chrdl1 in a layer-specific manner.

Are there any consequences of decreased glutamate release and subsequent altered expression of astrocyte synapse-regulating genes on synaptic development? To address this, we performed immunostaining for pre- and postsynaptic proteins in VGlut2 cKO mice and their WT controls in L1 of VC at P14. We first analyzed cortico-cortical synapses marked by VGLUT1 and found that, as in the low-resolution characterization (Figure 4C), there is no change in VGLUT1 puncta in the absence of VGLUT2 (Figure 4J, K, N and O). In the case of GLUA1 containing AMPARs, which are regulated by Gpc4, we found a significant increase in the number of GLUA1 puncta and their colocalization with VGLUT1 in the VGlut2 cKO, correlating with the observed increase in Gpc4 (GLUA1 FC 1.24 ± 0.04; Figure 4J and L; Coloc FC 1.38 ± 0.07; Figure 4J and M). For GLUA2 containing AMPARs, which are regulated by Chrdl1, we found a significant decrease in both the number of GLUA2 puncta and the number of colocalized GLUA2-VGLUT1 puncta in VGlut2 cKO mice compared to WT, correlating with the observed decrease in Chrdl1 (GLUA2 FC 0.77 ± 0.04; Figure 4N and P; Coloc FC 0.75 ± 0.1; Figure 4N and Q). We asked if similar effects are also present at thalamocortical synapses. Since VGLUT2 is absent in cKO mice, we used Slc17a6^{f/f; RORαcre;tdTomato} (labeled as VGlut2 cKO; tdTomato) to label thalamic axons (Figure 4A, Figure 4—figure supplement 2A) and identified presynaptic active zones within tdTomato axons by immunostaining for the presynaptic marker Bassoon (Figure 4—figure supplement 2K and O). We found no difference in the number of Bassoon puncta colocalized with tdTomato between the WT and cKO mice, a further indication that synapses form in the absence of VGLUT2 (Figure 4—figure supplement 2K, L, O, P), and fitting with findings from mice that globally lack presynaptic release (Verhage et al., 2000). As is the case for cortico-cortical synapses, we found an increase in GLUA1 and colocalization of GLUA1 with Bassoon and tdTomato (GLUA1 FC 1.21 ± 0.09; Figure 4—figure supplement 2K and M; Coloc FC 1.36 ± 0.23; Figure 4—figure supplement 2K and N), and a decrease in total GLUA2 and GLUA2-Bassoon synapses (GLUA2 FC 0.70 ± 0.08; Figure 4—figure supplement 2O and Q; Coloc FC 0.65 ± 0.13; Figure 4—figure supplement 2O and R).

These results show that synaptic GLUA1 and GLUA2 levels are altered in VGlut2 cKO VC in the direction which follows the change in astrocytic expression of Gpc4 (which recruits GLUA1) and Chrdl1 (which recruits GLUA2) in L1. These correlated changes in astrocyte genes and synaptic proteins suggest a disruption in synapse maturation in the VC at P14 in the absence of thalamocortical glutamate release that may be mediated by astrocytes.

Astrocytic calcium signaling tunes expression of synapse-regulating genes

Since we observed that changes in thalamic glutamate release influence the expression of astrocyte synapse-regulating genes, we next asked how perturbing the astrocyte response to neurotransmitters affects the expression of Gpc4, Gpc6, and Chrdl1. Astrocytes express many neurotransmitter receptors, in particular GPCRs, and respond to neurotransmitters with increased intracellular calcium (Kofuji and Araque, 2021; Porter and McCarthy, 1997). In the case of somal increases in calcium, which have the potential to regulate expression of activity-regulated genes, most of this increase is mediated by the release of calcium from intracellular stores via IP3R2 (Itpr2) (Srinivasan et al., 2015). We therefore asked if blunting astrocyte calcium signaling by removing store-mediated calcium release using Itpr2 KO mice (Itpr2^tm1.1Chen; labeled Ip3r2 KO; Figure 5A and B) has an impact on the expression of synapse-regulating genes.

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Astrocyte calcium activity regulates astrocytic expression of synapse-regulating genes.

(A) Full statistical analysis of mRNA expression differences between WT and KO in Ip3r2 KO model. Averages and analysis calculated for N = 5, i.e, per mouse. (B) Full statistical analysis of mRNA expression differences between WT and KO in Ip3r2 KO model. Averages and analysis calculated for n = ~200–400, that is, total number of astrocytes per group (across five mice). All comparisons are between WT and KO within each layer.

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Figure 5—source data 2

Astrocyte calcium activity regulates astrocytic expression of synapse-regulating genes.

Full uncropped western blot representative image showing IP3R2 protein levels are reduced in Ip3r2 KO mice compared to WT. Left panel shows IP3R2, right panel shows β3 tubulin used as loading control. Red arrows indicate IP3R2 signal at ~300 kDa; β3 tubulin signal at ~50 kDa.

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To determine this, we performed smFISH on the VC of P14 Ip3r2 KO and WT mice, marking astrocytes with a probe against Slc1a3 along with Gpc4, Gpc6, or Chrdl1. At P14, knocking out Ip3r2 does not affect the number of astrocytes or the expression levels of Slc1a3, showing astrocytes develop grossly normally when store-mediated calcium release is diminished (Figure 5—figure supplement 1A and B, Figure 5—source data 1, Figure 5—source data 2). However, loss of IP3R2 does affect expression of synapse-regulating genes. In the case of Gpc4, the mRNA level is reduced in astrocytes in all layers, with a significant decrease occurring in L1, 4, and 6 (thresh area [μm²]: L1 WT 1.5 ± 0.1; KO 1.1 ± 0.1; Figure 5C and D, Figure 5—source data 1). For Gpc6, there is no difference in the mRNA level between Ip3r2 KO and WT in astrocytes in any layer (Figure 5E and F, Figure 5—source data 1). Chrdl1 expression is increased in astrocytes in all layers, with a significant increase occurring in L1, 2/3, and 5 (thresh area [μm²]: L1 WT 7.3 ± 1.1; KO 10.6 ± 1; Figure 5G and H, Figure 5—source data 1). To ask if these alterations are present throughout development, we performed the same analysis at P7. As with P14, at P7 there is no change in astrocyte number or Slc1a3 mRNA signal, showing astrocytes develop grossly normally (Figure 5—figure supplement 1C and D). In the case of Gpc4 and Chrdl1, there is no difference in expression between Ip3r2 KO and WT at P7 (Figure 5—figure supplement 1E and G), whereas for Gpc6 there is a significant increase in the Ip3r2 KO restricted to L4 (Figure 5—figure supplement 1F). Therefore, in contrast to the layer-specific alterations in gene expression in the VGlut2 cKO mice, removing IP3R2 impacts astrocytes in all layers and does not strictly follow the developmental trajectory. This suggests a broad requirement for astrocyte calcium signaling in all astrocytes to maintain the correct level of gene expression, and that the signals to do this come from multiple sources and are not restricted to thalamic inputs.

Are there any consequences of diminished astrocyte calcium signaling and altered expression of synapse-regulating genes on synaptic development? As with the VGlut2 cKO, we addressed this by performing immunostaining for presynaptic terminals (VGLUT1 or VGLUT2) and postsynaptic AMPAR subunits (GLUA1 or GLUA2) in Ip3r2 KO mice and WT controls in L1 of VC at P14. We found that the numbers of both cortico-cortical presynaptic terminals marked by VGLUT1, and thalamocortical terminals marked by VGLUT2, are significantly decreased in Ip3r2 KO mice compared to WT, demonstrating a global deficit in synapse formation in the absence of astrocyte calcium signaling (VGLUT1 from GLUA1: FC 0.86 ± 0.04; Figure 5I and J; VGLUT1 from GLUA2: FC 0.87 ± 0.05; Figure 5M and N. VGLUT2 from GLUA1: FC 0.89 ± 0.05; Figure 5—figure supplement 1H and I; VGLUT2 from GLUA2: FC 0.80 ± 0.05; Figure 5—figure supplement 1L and M). For GLUA1 containing AMPARs, which are regulated by Gpc4, we found a significant decrease in the total number of puncta and their colocalization with VGLUT1 and VGLUT2 in the Ip3r2 KO, correlating with the observed decrease in Gpc4 mRNA (VGLUT1-GLUA1: GLUA1 FC 0.84 ± 0.03; Figure 5I and K; Coloc FC 0.70 ± 0.03; Figure 5I and L; VGLUT2-GLUA1: GLUA1 FC 0.83 ± 0.03; Figure 5—figure supplement 1H and J; Coloc FC 0.74 ± 0.03; Figure 5—figure supplement 1H and K). For GLUA2 containing AMPARs, which are regulated by Chrdl1, we found a significant increase in their number, correlating with the observed increase in Chrdl1 (VGLUT1-GLUA2: GLUA2 FC 1.22 ± 0.07; Figure 5M and O; VGLUT2-GLUA2: GLUA2 FC 1.14 ± 0.03; Figure 5—figure supplement 1L and N). The number of colocalized presynaptic puncta and GLUA2 is, however, unchanged, likely due to the opposing decrease in presynaptic puncta and increase in GLUA2 (VGLUT1-GLUA2 Coloc FC 1.09 ± 0.1; Figure 5M and P; VGLUT2-GLUA2 Coloc FC 1.00 ± 0.08; Figure 5—figure supplement 1L and O).

These results show that GLUA1 and GLUA2 levels are altered in Ip3r2 KO VC in the direction which follows the change in astrocytic expression of Gpc4 (which recruits GLUA1) and Chrdl1 (which recruits GLUA2). This strongly suggests that both astrocytes and neurons play an important role in regulating the expression of synapse-regulating genes, and subsequently AMPAR subunit protein levels, and the final expression levels arise from the complex interaction between these two cell types.

Unbiased determination of astrocyte transcriptomic diversity and activity-regulated genes in the developing VC

Having found that multiple synapse-regulating genes in astrocytes show layer-specific enrichment, and that these patterns are regulated by neuronal and astrocyte activity, we next asked if these findings are specific to synapse development, or if other astrocyte genes show a similar pattern. To address this using an unbiased approach, we performed single-nucleus RNA sequencing of glial cells isolated from the P14 VC of wild type, VGlut2 cKO, and Ip3r2 KO mice. To isolate the glial cell populations, we immunostained VC nuclei in suspension with an antibody against the neuronal marker NeuN and performed FACS to select the NeuN-negative population (Figure 6A). We used the Chromium 10X system to isolate individual glial nuclei and performed RNA sequencing to quantify mRNA levels (Figure 6A). This identified 22,781 cells in the VGlut2 condition (cKO and WT) (Figure 6B, Figure 6—figure supplement 1A) and 21,240 cells in the Ip3r2 condition (KO and WT) (Figure 6—figure supplement 1B and C). Initial clustering analysis determined 17 distinct cell populations in both models, with the majority of cells detected clustered within the main glial cell types: astrocytes, microglia, and oligodendrocyte lineage cells (Figure 6B, Figure 6—figure supplement 1A–C). Just two clusters enriched for neuronal markers are present (Hernandez et al., 2019), showing that the NeuN depletion had been successful. This dataset is available in a searchable format online (https://cells.ucsc.edu/?ds=mouse-astro-dev; Speir et al., 2021 ).

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Figure 6—source data 1

Unbiased determination of astrocyte layer-enriched genes and global astrocyte gene expression changes following silencing of neuronal or astrocyte activity.

(A) Complete list of differentially expressed genes (DEGs) identified in pairwise analysis between astrocyte layer groups for VGlut2 WT dataset. (B) Complete list of DEGs identified in pairwise analysis between astrocyte layer groups for Ip3r2 WT dataset. (C) Complete list of Gene Ontology (GO) terms (Biological Process) identified for astrocyte layer group-enriched genes for the VGlut2 WT dataset. (D) Complete list of DEGs between WT and KO for each model, VGlut2 cKO and Ip3r2 KO. Common DEGs to both models marked with ‘yes’ in separate column. (E) Complete list of genes common to KO/WT DEGs and layer group-enriched DEGs identified for the WT. (F) Complete list of genes common to KO/WT DEGs and developmentally regulated genes (P14/P7 DEGs) identified in bulk RNAseq. (G) Complete list of GO terms (Biological Process) identified for DEGs between WT and KO (VGlut2, Ip3r2), as well as terms identified for DEGs common to both models. Common GO terms to both models marked with ‘yes’ in separate column. The term GO:0023052 ‘signaling’ appears in both up- and downregulated DEGs in the Ip3r2 KO model. The term with lower false discovery rate (FDR) (upregulated DEGs) is plotted in Figure 6—figure supplement 3C.

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In this study, we demonstrate how astrocytes and synapses develop together in the postnatal mouse VC, signaling to one another to ensure correct development (Figure 7). In particular, we show that:

• expression of select synapse-regulating genes (Gpc4, Gpc6, and Chrdl1) is differentially regulated during development at both temporal and spatial levels

• astrocyte transcriptome changes during development are correlated to expression changes in some synaptic proteins

• expression of astrocyte synapse-regulating genes is affected by changes in thalamic neuronal activity and astrocyte calcium activity

• astrocytes form heterogeneous populations in the developing cortex, based on their spatial location

• neuronal and astrocyte activity regulates multiple non-overlapping genetic programs in astrocytes, suggesting effects beyond synapse regulation.

Figure 7

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All data supporting the results of this study can be found in the following locations: Processed RNA sequencing data presented in Figure 1 is available in Figure 1- Source Data 1. Data presented in graphs in Figures 2-5 is available in Figure 2-5 Source Data files. Processed single nucleus RNA sequencing data presented in Figure 6 is available in Figure 6-Source Data 1. The RNA sequencing data has been deposited at GEO. Ribotag data is available at GSE161398 and glial snRNAseq at GSE163775.

Processed RNA sequencing data is available in a searchable format at the following web locations: ribotag astrocyte developmental dataset: http://igc1.salk.edu:3838/astrocyte_transcriptome/; single nucleus glial cell sequencing: http://mouse-astro-dev.cells.ucsc.edu/.

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Author details

1. Isabella Farhy-Tselnicker

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Present address

   Department of Biology, Texas A&M University, College Station, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing – original draft, Supervision, Visualization, Writing – review and editing

   For correspondence

   ifarhy@bio.tamu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3733-7120

2. Matthew M Boisvert

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Present address

   Jungers Center for Neuroscience Research, Department of Neurology, Oregon Health and Science University, Portland, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Hanqing Liu

   1. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States
   2. Division of Biological Sciences, University of California San Diego, La Jolla, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

4. Cari Dowling

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Galina A Erikson

   Razavi Newman Integrative Genomics and Bioinformatics Core, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Elena Blanco-Suarez

   Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Present address

   Department of Neuroscience, Thomas Jefferson University Hospital for Neuroscience, Philadelphia, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2131-6376

7. Chen Farhy

   Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6160-3479

8. Maxim N Shokhirev

   Razavi Newman Integrative Genomics and Bioinformatics Core, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Formal analysis, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8379-8657

9. Joseph R Ecker

   1. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States
   2. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5799-5895

10. Nicola J Allen

    Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing – original draft

    For correspondence

    nallen@salk.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7542-5930

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