DOI: 10.5061/dryad.83bk3jb0j
title: Network-level encoding of local neurotransmitters in cortical astrocytes

description: Astrocytes, the most abundant non-neuronal cell type in the mammalian
 brain, are crucial circuit components that respond to and modulate
 neuronal activity via calcium (Ca2+) signaling. Astrocyte Ca2+ activity is
 highly heterogeneous and occurs across multiple spatiotemporal scales—from
 fast, subcellular activity to slow, synchronized activity across connected
 astrocyte networks—to influence many processes. However, the inputs that
 drive astrocyte network dynamics remain unclear. Here we used ex
 vivo and in vivo two-photon astrocyte imaging while mimicking neuronal
 neurotransmitter inputs at multiple spatiotemporal scales. We find that
 brief, subcellular inputs of GABA and glutamate lead to widespread,
 long-lasting astrocyte Ca2+ responses beyond an individual stimulated
 cell. Further, we find that a key subset of Ca2+ activity—propagative
 activity—differentiates astrocyte network responses to these two major
 neurotransmitters, and may influence responses to future inputs. Together,
 our results demonstrate that local, transient neurotransmitter inputs are
 encoded by broad cortical astrocyte networks over a minutes-long
 time-course, contributing to accumulating evidence that significant
 astrocyte-neuron communication occurs across slow, network-level
 spatiotemporal scales. These findings will enable future studies
 investigating the link between specific astrocyte Ca2+ activity and
 specific functional outputs, which could build a consistent framework for
 astrocytic modulation of neuronal activity.
description: <strong>Animals</strong>
 Experiments were carried out using young adult mice, in
 accordance with protocols approved by the University of California, San
 Francisco Institutional Animal Care and Use Committee (IACUC). Animals
 were housed in a 12:12 light-dark cycle with food and water provided
 <em>ad libitum</em>. Animal housing rooms were kept at 68–74
 degrees Fahrenheit and 30–70% humidity. Male and female mice were used
 whenever available. Transgenic mice used in this study were
 Cx43<sup>fl/fl</sup> mice<sup>56</sup> from the Bhattacharya Lab (UCSF, USA) and EAAT2-tdT mice<sup>57</sup> from the Yang Lab (Tufts University, USA). For <em>in vivo</em> imaging, all experiments were performed at the same time each day.  <strong>Surgical procedures</strong> For viral expression for <em>ex vivo</em> experiments, neonatal Swiss Webster or C57Bl/6 (P0–3) mice were anesthetized on ice for 3 min before injecting viral vectors (<em>AAV5.GfaABC<sub>1</sub>D.GCaMP6f.SV40</em> [Addgene, 52925-AAV5], <em>AAV9.hGfap.pinkFlamindo</em>, <em>pENN.AAV9.Gfap.iGluSnFr.WPRE.SV40 </em>[Addgene, 98930-AAV9], or <em>AAV5.GFAP(0.7).RFP.T2A.iCre </em>[Vector Biolabs, 1133]). Pups were placed on a digital stereotax and coordinates were zeroed at lambda. Four injection sites in a 2 × 2 grid pattern over V1 were chosen. Injection sites were 0.8–0.9 mm and 1.6–1.8 mm lateral, and 0 and 0.8–0.9 mm rostral. At each injection site, 30–120 nl of virus were injected at a rate of 3 nl/s at two depths (0.1 mm and 0.2 mm ventral/below pia) using a microsyringe pump (UMP-3, World Precision Instruments). For viral expression for the <em>in vivo</em> experiments, adult C57BL/6 mice (2–4 months at the time of surgery) were administered dexamethasone (5mg/kg, s.c.) &gt;1 hour before surgery, and anesthetized using 1.5% isoflurane (Patterson Veterinary Supply, 78908115). After hair removal and three alternating swabs of 70% ethanol (Thermo Fisher Scientific, 04-355-720) and Betadine (Thermo Fisher Scientific, NC9850318), a custom-made titanium headplate was attached to the skull using cyanoacrylate glue and C&B Metabond (Parkell, S380). A 3mm craniotomy was made over the right visual cortex. Virus was injected at two sites in right visual cortex at coordinates centered on +2.4mm and +2.7mm medial/lateral, +0.35mm and +0.65mm anterior/posterior and -0.3mm dorsal/ventral from lambda. 300nL of <em>AAV5.GfaABC<sub>1</sub>D.GCaMP6f.SV40</em> (Addgene, 52925-AAV5) was injected at each site through a glass pipette and microsyringe pump (UMP-3, World Precision Instruments). After allowing at least ten minutes for viral diffusion, the pipette was slowly withdrawn and a glass cranial window implanted using a standard protocol. <strong><em>Ex vivo </em></strong><strong>two-photon (2P) imaging and uncaging</strong> Coronal, acute V1 slices (400-µm thick) from P28–32 (bath-application) and P27–42 (uncaging) mice were cut with a vibratome (VT 1200, Leica) in ice-cold slicing solution containing (in mM) 27 NaHCO<sub>3</sub>, 1.5 NaH<sub>2</sub>PO<sub>4</sub>, 222 sucrose, 2.6 KCl, 2 MgSO<sub>4</sub>, 2 CaCl<sub>2</sub>. Slices were transferred to pre-heated, continuously aerated (95% O<sub>2</sub>/5% CO<sub>2</sub>) standard artificial cerebrospinal fluid (ACSF) containing (in mM) 123 NaCl, 26 NaHCO<sub>3</sub>, 1 NaH<sub>2</sub>PO<sub>4</sub>, 10 dextrose, 3 KCl, 2 MgSO<sub>4</sub>, 2 CaCl<sub>2</sub>. Younger mice were sliced in the same solutions for GCaMP bath application of LY379268 and Baclofen (P20–25), Pink Flamindo (P20–22), and GluSnFR (P14–17). Slices were kept at room temperature until imaging. Bath-application experiments were performed at room temperature and 2P uncaging experiments were performed at 29°C using an in-line heater (TC-324B and SH-27B, Warner Instruments). To block neuronal action potentials during all slice imaging experiments, except for GluSnFr recordings, TTX (1 µM) was added to the ACSF &gt; 10 min before imaging and remained in the circulating bath for the duration of the experiments. Images were acquired on an upright microscope (Bruker Ultima IV) equipped with two Ti:Sa lasers (MaiTai, SpectraPhysics). Laser beam intensities were modulated using two independent Pockels cells (Conoptics) and images were acquired by scanning with linear galvanometers. Images were acquired with a 16×, 0.8 N.A. (Nikon) or a 40×, 0.8 N.A. (Nikon) water-immersion objective via photomultiplier tubes (Hamamatsu) using PrairieView (Bruker) software. For GCaMP imaging, 980 nm excitation and a 515/30 emission filter were used. For RFP imaging, 980 nm excitation and a 605/15 emission filter were used. For Pink Flamindo and Alexa Fluor 594 imaging, 1040 nm excitation and a 605/15 emission filter were used. Images were acquired at 1.42 Hz frame rate, 512 × 512 pixels and 0.64–1.61 µm/px resolution. For GluSnFR imaging only, images were acquired at 6.21 Hz frame rate, 200 × 200 pixels and 0.64 µm/px resolution, with 980 nm excitation and a 515/30 emission filter. For bath-application experiments, a 5-min baseline was recorded to monitor spontaneous activity, after which receptor agonists were added along with a fluorescent dye (Alexa Fluor 594 Hydrazide) to assess the time at which drugs reached the imaging field/recording chamber (except for Pink Flamindo due to spectral overlap). An ACSF washout period (&gt; 10 min), followed by a TTX incubation period (&gt;10min), occurred between trials when imaging the same slice sequentially for bath-application of different receptor agonists or uncaging of different RuBi-subtypes. To account for any changes resulting from prior agonist exposure or uncaging, we alternated the order of agonists between concentrations or RuBi-subtypes between slices. For simultaneous 2P imaging and uncaging, a second Ti-Sa laser beam was tuned to 800 nm and controlled using an independent set of linear galvanometers from those used for scanning. Laser beam intensity was modulated using an independent Pockels cell (Conoptics) to achieve a power measurement of ~2–8 mW at the slice. The beam paths for imaging and uncaging were combined after the linear galvanometers using an 855-longpass dichroic mirror (T855lpxr, Chroma). The uncaging laser was calibrated each experimental day by burning spots into a fluorescent slide. RuBi- compounds (300 µM) and TTX (1 µM) were added to the ACSF &gt;10 minutes before imaging each slice. Fields of view (FOV) were chosen based on the location of GCaMP expression, which was often biased to/brighter in deeper cortical layers (distance of FOV from pia: 615 ± 196µm [mean ± SD, n = 121 FOV]). Prior to imaging at each FOV, a 60-s period was recorded to identify potential uncaging sites. Areas of GCaMP expression that exhibited moderate levels of spontaneous Ca<sup>2+</sup> activity were chosen as uncaging sites. For FOVs with sequential GABA/glutamate uncaging, a continuous 5-min recording was used to monitor activity in each FOV. For FOVs with three sequential rounds of glutamate uncaging, a continuous 12.5-min recording was used to monitor activity in each FOV. Each recording began with a 2.5-min baseline period and at the 2.5-min mark, neurotransmitter was uncaged with 10 x 100 ms pulses, 100 ms apart. Sequential recordings of GABA/glutamate uncaging within the same FOV were separated by &gt; 20min. Rounds of sequential glutamate uncaging were separated by ≥ 25min. Voltage from the uncaging laser Pockels cell was recorded to mark the time of uncaging pulses. Because RuBi-GABA and RuBi-glutamate are light-sensitive, care was taken to ensure experiments were carried out in minimal light. The computer screen and red-shifted headlamp were covered with two layers of red filter paper (Roscolux #27 filter, Rosco) and all indicator lights on equipment were covered.  <strong><em>In vivo </em></strong><strong>2P imaging</strong> <em>In vivo</em> 2P imaging was performed on the same microscope as <em>ex vivo </em>imaging, via a Nikon 16x, 0.8 N.A. water-dipping objective with a 2x-optical zoom (frame rate: 1.7Hz, FOV: 412µm<sup>2</sup>, resolution: 512x512 pixels). Animals were given &gt; 1 week after surgery for recovery and viral expression. They were then habituated on a custom-made circular running wheel over at least two days, and for a cumulative time of at least 2.5 hours, before recording. After habituation, mice were head-fixed on the wheel and movements were recorded by monitoring deflections of colored tabs on the edge of the wheel using an optoswitch (Newark, HOA1877-003). To compute wheel speed, a detected break in the optoswitch circuit was determined when the absolute value of the derivative of the raw voltage trace was at least 2 standard deviations above the mean. For recordings with little movement (std &lt; 0.1), this threshold generated false positives, so a set threshold of 0.1 was used. The number of breaks in the optoswitch circuit per second was then calculated, and using the circumference and number of evenly spaced colored tabs at the edge of the wheel, the wheel speed was determined and used for all subsequent analyses using speed. Movement periods were defined by wheel speed ≥ 10 cm/s and movement bouts that were separated by ≤ 2 s were considered one event. To ensure that movement related dynamics were not included in stationary analysis, data was excluded from &lt; 10 s around identified movement periods. GCaMP was imaged with 950nm excitation light and a 515/30 emission filter. Recordings lasted 30 minutes. <strong><em>Ex vivo</em></strong><strong> pharmacology</strong> The following concentrations of each pharmacological reagent were used for experiments as indicated in the text: Tetrodotoxin Citrate (TTX, 1 µM, Hello Bio); Carbenoxelone disodium (CBX, 50 µM, Tocris Bioscience); R(+)-Baclofen hydrochloride (5–100 µM, Sigma-Aldrich); (1S,3R)-ACPD (t-ACPD, 5–100 µM, Tocris); LY 379268 disodium salt (100 µM, Tocris); Alexa Fluor 594 Hydrazide (0.1–2 µM, ThermoFisher Scientific); RuBi GABA trimethylphosphine (RuBi-GABA-Pme<sub>3</sub>, 300 µM, Tocris); RuBi-Glutamate (300 µM, Tocris); CGP 55845 hydrocholoride (10 µM, Tocris); and LY 341495 (10 µM, Tocris).  <strong>Immunohistochemistry and image quantification</strong> After recording, slices from 2P imaging experiments were immersed in 4% PFA for 30 min and switched to 30% sucrose for one day at 4°C before being embedded in OCT and stored at -80°C. Slices were re-sectioned coronally at 40 µm on a cryostat and then stored in cryoprotectant at -20°C until staining. For immunohistochemistry, sections were washed three times in 1X PBS for 5 min and permeabilized for 30 min with 0.01% Triton-X in 1X PBS. Sections were next blocked with 10% NGS (Abcam) for 1 h and incubated overnight with primary antibodies at 4°C in 2% NGS. The next day, they were washed three times in 1X PBS before incubating with secondary antibodies for 2 h at room temperature. Sections were washed three times in 1X PBS for 5 min before being mounted on slides with Fluoromount-G (SouthernBiotech). To validate reduction of Connexin 43 (Cx43) protein in astrocytes transduced with AAVs to express GCaMP-GFP and Cre-RFP, primary antibodies for α-connexin-43 (1:1500, rabbit, Sigma-Aldrich), α-GFP (1:3000, chicken, Abcam), and α-mCherry (1:2000, rat, Thermo Fisher Scientific) in 2% NGS were used. Secondary antibodies include α-rabbit Alexa Fluor 405, α-chicken Alexa Fluor 488, and α-rat Alexa Fluor 555 (all Thermo Fisher Scientific), which were all used at 1:1000 dilution. 60x multi-channel z-stack images were acquired on a CSU-W1 Spinning Disk Confocal (Nikon) using MicroManager from V1 in which AAVs were injected. To quantify loss of Cx43 in RFP<sup>+</sup> and RFP<sup>-</sup> astrocytes, Fiji (ImageJ) was used. Through batch processing, cell maps were created through a semi-automated pipeline to segment astrocytes, with <em>post hoc </em>ROI adjustments for vasculature artifacts. Multi-channel z-stacks were split into 405, 488, and 555 channels, and unstacked into sequential 8-bit z-plane images. For each z-plane, RFP<sup>+</sup> and RFP<sup>-</sup> astrocytes were detected using a Gaussian blur (sigma = 3), thresholding using the Phansalkar method (radius = 1000), and applying ImageJ’s “Analyze Particles” (size &gt; 175 µm<sup>2</sup>, circularity = 0–0.60) to outline ROIs using the wand tool. Corresponding Cx43 images were binarized and the Fiji plugin SynQuant<sup>58</sup> was used to detect Cx43 puncta number within each RFP<sup>+</sup> and RFP<sup>-</sup> astrocyte in a z-plane’s cell map. Puncta counts were normalized to astrocyte area, and the normalized count from each z-stack was averaged for each slice. <strong>2P image and data analysis</strong> Individual-astrocyte cell maps for time series images were created in Fiji using the following process: For each FOV, an 8-bit z-projection of the time series was created. The z-projection was binarized using the ‘Auto Local Threshold’ feature, using the Niblack method and a radius of 30 or 75, for 16× and 40× images, respectively. Cell maps were drawn on binarized images using a combination of the Lasso and Blow Tool and freehand drawing tool in Fiji, and verified on the z-projected image. Cell maps were also verified against a static indicator of astrocyte morphology when available (EAAT2-tdT<sup>+ </sup>mice for bath-application of LY379268 and Baclofen; GFAP(0.7)-RFP-T2A-iCre in Cx43<sup>floxed</sup> mice). To load cell masks into AQuA, regions were saved to the ROI manager and filled in with a color. The regions were projected onto a black image the same size as the original (512 × 512 pixels). The overlay of regions was flattened, converted to an 8-bit image, and saved as a tiff. For the 12.5-min recordings with sequential rounds uncaging glutamate, drift of the slice in X and Y was corrected post-hoc using moco<sup>59</sup>. <strong><em>AQuA:</em></strong> GCaMP and GluSnFR 2P image sequences were analyzed using AQuA<sup>8</sup> and custom MATLAB (MATLAB R2018b) and Python (v3.8.18) code. Signal detection thresholds were adjusted for each video to account for differences in noise levels after manually checking for accurate AQuA detection. Cell maps were loaded into AQuA to define cells consistently over multiple time-series featuring the same FOV. For all bath-application experiments, the direction of pia was marked as anterior. For 2P uncaging experiments, the uncaging site was marked as a 3 × 3-pixel landmark. <strong><em>Bath-application event-based analysis:</em></strong> For Baclofen and t-ACPD Ca<sup>2+</sup> imaging experiments, <em>Event count per frame</em> was quantified by counting all AQuA-detected events, new or ongoing, in each frame (Fig. 1c). <em>Percent of field active</em> values were calculated by recording the number of active pixels in each frame, as determined by the frame-by-frame footprints of AQuA-detected events. These values were normalized by total number of active pixels in the recording and multiplied by 100. For the <em>Percent of field active</em> dose-response curve (Fig. 1e), the percent of field active values from all frames within the chosen timepoints were averaged by slice. <em>Event propagation</em> was calculated by summing the growing propagation from all cardinal directions, using the AQuA feature <em>propGrowOverall</em>. For dose-response curves for discrete event features (area, duration and propagation [Fig. 1f–h]), all detected Ca<sup>2+</sup> events within the chosen timepoints were averaged by slice. The frame the agonist entered the recording chamber was estimated using fluorescence from Alexa Fluor 594 (0.1–2 µM) added to the ACSF reservoir along with agonist. The frame agonist entered the recording chamber was estimated using the maximal curvature method on frames 1–600 of the raw Alexa Fluor 594 fluorescence trace. The <em>maximum curvature method</em><sup>60</sup> defines the onset fluorescence changes as the point of maximum curvature during the rising phase of the signal. To identify this point, traces were fit using a modified Boltzmann’s sigmoidal equation: where <em>a</em> is the difference between the minimum and the maximum fluorescence, <em>b</em> is the inflection point, <em>c</em> is the baseline fluorescence and <em>d</em> is the slope, using a nonlinear least squares algorithm (Levenberg-Marquardt) in MATLAB (Mathworks). Next, the frames of maximum curvature were calculated by setting the fourth derivative of the fitted curve equal to zero and solving for its three solutions. The earliest frame identified out of these three solutions was recorded as the onset frame. <strong><em>Bath-application ROI-based</em></strong><strong> <em>analysis: </em></strong>Pink Flamindo and GCaMP imaging experiments were analyzed using ROI-based approaches in Fiji. To identify responding cells in Pink Flamindo experiments (Extended Data Fig. 1j), sigmoidal curves were fit to ΔF/F traces using the modified Boltzmann’s sigmoidal equation detailed above. Cells were defined as “responding” if the difference between the minimum and maximum values of the fit curve (<em>a</em> in the Boltzmann’s sigmoidal equation) &gt; baseline noise (3 SD of baseline fluorescence). Responding cells were defined as “increasing” if  and decreasing if   To identify fluctuations in Pink Flamindo and GCaMP fluorescence (Extended Data Fig. 1k), peaks were detected from ΔF/F traces from individual cells. Peaks were counted if they were 3 SD above the mean baseline fluorescence, had a minimum peak width of 5 frames and a minimum distance of 10 frames between detected peaks. The baseline period was defined as all frames before the frame of agonist entry. For GCaMP, all astrocytes exhibiting ≥ 1 AQuA-detected event during the 10-min recording were run through peak finding. For Pink Flamindo, all detected astrocytes were run through peak finding. For GCaMP experiments, the frame agonist entered the recording chamber was estimated using the fluorescence from Alexa Fluor 594 (0.1–2 µM) added to the ACSF reservoir along with agonist. Time of agonist entry in the recording chamber was estimated by identifying the first frame Alexa Fluor 594 fluorescence reached ³ 3 SD above baseline mean (frames 1–300); only frames &gt; 375 were considered for evaluation of exceeding the threshold. For Pink Flamindo experiments, dye was not added with agonist to avoid spectral overlap. Time of agonist entry in the recording chamber was estimated by adding 90 frames (the average number of frames for ACSF to travel from the reservoir to the recording chamber) to the frame agonist was added to the reservoir of ACSF <strong><em>2P uncaging event-based analysis:</em></strong> Individual astrocytes were excluded from analyses (Fig. 2–4, Extended Data Fig. 2–7) if the baseline event rate changed significantly. Changes in baseline event rate for each cell were determined by performing Poisson regression of events in 1-s bins during the period from 90–10s pre-uncaging. Cells with a regression coefficient with p &lt; 0.1 at baseline and with &gt; 5 AQuA-detected events throughout the recording were excluded from all analyses, except for Extended Data Fig. 7d RuBi-glutamate uncaging control. ∆F/F values in raster plots (Fig. 2h and 3c) were calculated using the AQuA output <em>dffMatFilter(:,:,2)</em>, the ∆F/F traces from events after removing the contributions from other events in the same location. Cells (Fig. 2h) or local astrocyte networks (Fig. 3c) were sorted based on the onset time of a response following uncaging. A response was defined as the first post-stim peak ≥ threshold (mean baseline DF/F + 3SD), with thresholds calculated by cell or local network using 90–0s before uncaging. For Fig. 3c, the z-score of each local network was calculated on the mean DF/F from AQuA-detected events in the network, using a baseline period of 90–0s before uncaging. For the Sholl-like analysis (Fig. 3h), events were sorted into 50µm bands radiating out from the uncaging site based on the minimum distance between an event and the uncaging site at event onset (using the AQuA output <em>ftsFilter.region.landmarkDist.distPerFrame</em>). 50µm-wide bands began 25mm and end 175µm from uncaging, as events &lt;25mm from the uncaging site likely occur within the stimulated astrocyte and &gt;175mm from the uncaging site can be outside the FOV; see Extended Data Fig. 3i. 90–0s before and 0–150s after uncaging used to calculate change in event number/30s/band. In order to categorize events as propagative or static (Fig. 4d–m and Extended Data Fig. 5b–j, 6 and 7c), the total propagation distance of each event was computed by summing the growing propagation from all cardinal directions, using the AQuA feature <em>propGrowOverall</em>. Events were categorized as propagative if the total propagation distance &gt; 1µm. <strong>Statistics and reproducibility for representative micrographs and spatial heatmaps</strong> Representative micrographs were chosen from experiments repeated with similar results from the following n. Fig. 1b: n = 4 slices, 4 mice; Fig. 2c: n = 72 trials, 12 recordings, 4 slices, 2 mice; Fig. 2f–g: n = 28 astrocytes, 7 slices, 4 mice (note the heterogeneity displayed in Fig. 2h for individual astrocyte responses to NT); Fig. 3b: n = 28 FOV, 7 slices, 4 mice; Fig. 4a: n = 28 FOV, 7 slices, 4 mice; Fig. 4c: n = 15 recordings, 5 mice; Extended Data Fig. 1i: n = 8 slices, 3 mice; Extended Data Fig. 3b: n = 91 FOV, 16 slices, 8 mice; Extended Data Fig. 5a: n = 28 FOV, 7 slices, 4 mice.<strong> </strong> <strong>Statistics for Fig. 1–3 and associated Extended Data Figures</strong> All statistical tests used and exact n values can be found for each figure in the corresponding figure legend. Adjustments for multiple comparisons using Bonferroni-Holm correction were implemented using fwer_holmbonf<sup>61</sup>. Significance levels defined as the following: ns: <em>p </em>≥ 0.05, *: <em>p </em>&lt; 0.05, **: <em>p </em>&lt; 0.01, ***: p &lt; 0.001. <strong><em>Permutation testing: </em></strong>Statistical significance for time-series (t-series) data was computed using permutation testing with custom-written code in MATLAB. 10,000 permutations were run and one- or two- sided <em>p</em>-values for each time point were calculated. <em>P</em>-values were corrected for multiple comparisons using the Benjamini-Yekutieli procedure (implemented using ref. <sup>62</sup>) with a False Discovery Rate (FDR) ≤ 0.05. Data were shuffled/permuted in the following way: To test change in event number/cell (Fig. 1c, Extended Data Fig. 2b and 3g,h), events were shuffled independently for each active cell (≥ 1 AQuA-detected event) in each t-series. For each active cell, events were randomly placed in time bins spanning the duration of the recording (time bins = 60s [Fig. 1c] and 30s [Extended Data Fig. 2b and 3g,h]) and the change in number of events/time bin was calculated as for the experimental data. Permuted changes in event number/cell were averaged across active cells in each t-series and across all t-series to obtain the permuted mean for one round of permutation testing. To test change in event number/ band (Fig. 3h), permutation tests were run separately for each band and events were shuffled independently for each t-series. For each t-series, events from the tested band were randomly placed in 30 s time bins spanning the duration of the recording, and the change in event number/30 s was calculated as for the experimental data. Permuted changes in event number/30 s were averaged across all t-series to obtain the permuted mean for one round of permutation testing. To test magnitude of change in experimental data versus permuted data, two-sided <em>p</em>-values were calculated as: (# of times |permuted change| ≥ |experimental change|) +1 # of permutations + 1 For testing increases in ∆F/F (Extended Data Fig. 1d), frames were shuffled independently for each t-series. For each t-series, the average ∆F/F/frame from active regions (≥ 1 AQuA-detected event in either condition [baclofen or t-ACPD]) was calculated, the frame order was shuffled, and the mean ∆F/F/30s was calculated. Permuted mean ∆F/F was averaged across all t-series to obtain the permuted mean for one round of permutation testing. To test magnitude of increases in experimental data versus permuted data, one-sided <em>p</em>-values were calculated as: (# of times the permuted mean ≥ the experimental mean) +1 # of permutations + 1 <strong>Statistics for Fig. 3i­– l, Fig. 4, and associated Extended Data Figures</strong> <strong><em>2P uncaging grid-based ROI analysis: </em></strong>Grid-based regions of interest (ROIs) were determined by dividing the 300 x 300 µm imaging field into a uniform 20 x 20 µm grid (Fig. 3i–l). Each identified Ca<sup>2+</sup> event was assigned to the ROI in which the centroid of its spatial footprint was located. ROIs with any baseline events were identified as ROIs with ≥1 events in the baseline window 60–0s before uncaging. “Active” ROIs for each NT were identified as ROIs with a ≥ 50% increase in event rate in the window 0–120s after uncaging for that NT, as compared with the rate during the baseline window. Active ROIs were a subset of ROIs with baseline events, as the relative increase in event rate is not defined when there are no baseline events, which results in division by 0. The distance from the uncaging site to each active ROI was determined using the Euclidean distance between the uncaging site, at (0, 0), and the center of each grid ROI (Fig. 3j). The fraction of overlap (<em>i.e.</em>, Jaccard index) <em>O<sub>i</sub></em> between active ROIs for GABA and glutamate were determined for the <em>i</em>th field of view by where <em>A</em><sub>GABA,<em>i</em></sub><em> </em>and <em>A</em><sub>glutamate,<em>i</em></sub> are the sets of active ROIs for GABA and glutamate, respectively. The overall fraction of overlap <em>O</em> between active ROIs for GABA and glutamate was computed as the mean of the individual <em>O<sub>i  </sub></em>(Fig. 3l). To determine if the observed fraction of overlap was expected due to chance, a distribution of <em>N</em> = 10,000 surrogate fractions of overlap was computed. The <em>k</em>th surrogate value,  was computed as above, but replacing, for each NT, the set of active ROIs <em>A</em><sub>NT,<em>i</em></sub><em> </em>with a new set,   , which was chosen as a random subset of size |<em>A</em><sub>NT,<em>i</em></sub>| of the set of ROIs with any baseline events for that NT. The <em>p</em>-value for this comparison was estimated<sup>63</sup> as <strong><em>Propagation probability (Fig. 4b):</em></strong> Each Ca<sup>2+</sup> event was identified as “growing in the depth axis” if the frontier of that event’s spatial footprint extended over time either toward the pia or away from the pia, as determined by the posterior and anterior component of the <em>propGrowOverall</em> metric computed <em>via</em> segmentation by AQuA<sup>8</sup>. The probability of events growing in the depth axis was computed separately for recordings of GABA and glutamate uncaging within each examined time window. Probabilities were estimated for the baseline window of 60–0s before uncaging, as well as in nonoverlapping 30s bins ranging from 0–150s post-uncaging, by computing the fraction of events that were identified as growing in the depth axis among all events from all recordings within the relevant time window. The change in the probability of events growing in the depth axis was then estimated for each bin as the difference between the fraction of events growing in the depth axis for that bin versus for the baseline period. To empirically determine the distribution of each of these estimators, we performed this same procedure for estimating the probability of events growing in the depth axis for each NT and time bin on surrogate data generated by hierarchically bootstrapping Ca<sup>2+</sup> event data, where the hierarchy was sampled cells within sampled recordings (i.e., all events for an individual cell-recording always remained together); this procedure was repeated 10,000 times for each bin. Standard errors were computed as the standard deviation of these empirical distributions. To determine the probability of observing effects this large under a null hypothesis of no effect of time on the probability of events growing in the depth axis, we computed the distribution of the estimator under an imposed condition in which the overall temporal structure of astrocyte Ca<sup>2+</sup> events was disrupted. To do this, we performed the same procedure as above for estimating the probability of events growing in the depth axis for each bin, but on surrogate data generated by circularly shifting the timing of each individual cell’s Ca<sup>2+</sup> events from 90s before to 150s after uncaging by its own independent, uniform random shift between 0s and 240s; this procedure was repeated <em>N</em> = 10,000 times for each bin. As it was unknown whether event propagation would increase or decrease post-uncaging, two-sided <em>p</em>-values were estimated<sup>63</sup> as where <em>X</em> denotes the actual observed value of the estimator, and each  is the value of the estimator computed from the <em>k</em>th shifted dataset. These <em>p</em>-values were adjusted across tested time bins and NTs using the Benjamini-Hochberg procedure to obtain <em>q</em>-values, as implemented in statsmodels 0.12.2 (ref <sup>64</sup>). <strong><em>Event feature changes (Extended Data Fig. 4a,b):</em></strong> Each Ca<sup>2+</sup> event is assigned several metrics by AQuA-segmentation<sup>8</sup>, including size (<em>area</em>, <em>perimeter</em>, <em>circMetric</em> [circularity, based on area and perimeter]), amplitude (<em>dffMax</em>), and dynamics (<em>rise19</em> [rise time], <em>fall91</em> [fall time], <em>decayTau</em> [decay time constant], <em>width11</em> [duration]). For each non-propagation metric, the mean metric value among events was computed separately for recordings of GABA and glutamate uncaging for the baseline window 60–0s before uncaging, as well as in nonoverlapping 30s bins from 0–150s post-uncaging. For each bin, the ratio of that bin’s mean metric value to the baseline mean metric value was computed. AQuA metrics also capture information about events’ directional propagation. Each Ca<sup>2+</sup> event was identified as “growing” or “shrinking” in each cardinal direction if the frontier of that event’s spatial footprint extended or receded, respectively, over time in that direction, as determined by the components of the <em>propGrowOverall</em> and <em>propShrinkOverall</em> metrics. For each propagation metric, the change in the probability of events growing or shrinking in each axis was computed separately for recordings of GABA and glutamate uncaging within each examined time window, as above in <em>Propagation probability</em>, but using the “growing” or “shrinking” identifiers for each cardinal direction. To empirically determine the distribution of each of these estimators (<em>i.e.</em>, binned post/baseline ratio for non-propagation metrics, binned change in growing/shrinking probability for propagation metrics), we performed the same procedures for computing each metric’s relevant estimators for each NT and time bin outlined above on 10,000 surrogate datasets generated by hierarchically bootstrapping Ca<sup>2+</sup> event data, as described in <em>Propagation probability</em>. Standard errors were computed as the standard deviation of these empirical distributions. To determine the probability of observing effects this large under a null hypothesis of no effect of time on the probability of events growing in the depth axis, we computed the distribution of each estimator under 10,000 realizations of an imposed condition in which the overall temporal structure of astrocyte Ca<sup>2+</sup> events was disrupted by randomly circularly shifting each cell’s Ca<sup>2+</sup> events, as described in <em>Propagation probability</em>. As it was unknown whether event propagation would increase or decrease post-uncaging, two-sided <em>p</em>-values were estimated using equation (2) above<sup>63</sup>. These <em>p</em>-values were adjusted across tested time bins and NTs using the Benjamini-Hochberg procedure to obtain <em>q</em>-values, as implemented in statsmodels 0.12.2 (ref. <sup>64</sup>). <strong><em>Comparison of </em></strong><strong>in vivo <em>and </em>ex vivo <em>event propagation (Fig. 4d):</em></strong> Events were categorized as propagative or static, as outlined above in the <em>2P uncaging event-based analysis </em>section. The fraction of propagative events observed <em>in vivo </em>and <em>ex vivo </em>was calculated using baseline events. Ca<sup>2+</sup> events in <em>in vivo</em> recordings were labeled as “baseline events” if they occurred during periods when the mouse was stationary, as outlined above in the <em>in vivo 2P imaging</em> section. Ca<sup>2+</sup> events in <em>ex vivo</em> recording were labeled as “baseline events” if they occurred in neighboring astrocytes (<em>i.e. </em>cells not directly stimulated by NT) during the 60–0s before NT uncaging. To determine the distribution of the two median propagative event fractions empirically, we computed the medians of 10,000 bootstrapped samples of the per-recording fractions for each setting. Standard errors for each statistic were determined from the standard deviations of these empirical distributions. <strong><em>Computing rate changes for propagative and static events (Fig. 4f, j and Extended Data Fig. 6b–c):</em></strong> The overall rates of propagative and static events for neighboring astrocytes were computed separately for recordings of GABA and glutamate uncaging. For each event class (<em>i.e.</em>, propagative and static events), for each recording, the event rate was computed in each time window as the total number of events from all neighboring cells in that recording in the given time window divided by the duration of that time window. These recording-level rates were computed for the baseline window of 60–0s before uncaging and in nonoverlapping 30s bins ranging from 0–150s post-uncaging. For each recording, the relative rate of propagative and static events was computed for each time bin as the ratio of the event rate for the given event class in that time bin divided by the corresponding event rate in the baseline window. For each time bin, the overall relative rate was estimated as the median of the per-recording relative rates in that time bin. To determine the distribution of each of these relative rate estimators empirically, we performed this same procedure for estimating relative event rates on surrogate data generated by hierarchically bootstrapping Ca<sup>2+</sup> event data 10,000 times for each bin (as above in <em>Propagation probability</em>). Standard errors were computed as the standard deviation of these empirical distributions. To determine the probability of observing effects this large under a null hypothesis of no effect of time post-uncaging on the rate of astrocyte Ca<sup>2+</sup> events, we computed the distribution of the relative rate estimators under an imposed condition in which the overall temporal structure of astrocyte Ca<sup>2+</sup> events was disrupted <em>via</em> a random circular shift of the events in each cell, as above in Fig. 4b; this procedure was repeated <em>N</em> = 10,000 times for each bin. Motivated by results in bath application experiments above demonstrating robust aggregate astrocyte Ca<sup>2+</sup> activity increases in response to agonism of glutamate receptors (Fig. 1h), one-sided <em>p</em>-values were estimated from these permuted datasets, as in equation (1) above. These <em>p</em>-values were adjusted across tested time bins and NTs using the Benjamini-Hochberg procedure to obtain <em>q</em>-values, as implemented in statsmodels 0.12.2 (ref. <sup>64</sup>). <strong><em>Determining responding cells based on static and propagative events (Fig. 4h,k and Extended Data Fig. 6e–f):</em></strong> The overall rates of propagative and static events were computed for each neighboring astrocyte, with paired measurements made for recordings of GABA and glutamate uncaging. For each neighboring astrocyte, for each event class (<em>i.e.</em> propagative and static events), the event rate was computed in each time window as the total number of events from that cell in the given time window divided by window’s duration (baseline window: 60–0s before uncaging, response window: 0–120s after NT-uncaging; Extended Data Fig. 5c). Relative event rates were calculated as for Fig. 4f, j and Extended Data Fig. 6b–c above. Cell-recording combinations with zero events of a given type in the baseline window were excluded for computation of relative rates of propagative (GABA: 36 cell-recordings [26.7% of total]; glutamate: 37 [32.2%]) and static (GABA: 0; glutamate: 0) events, as the relative rate would require a division by zero and be undefined in those cases. Astrocytes were identified as “responders” with a particular event type (<em>i.e.</em>, static or propagative) to GABA or glutamate if their relative rate of that type of event was ≥ 1.5 for the corresponding NT uncaging recording (Extended Data Fig. 5d). The fraction of astrocytes that were responders was computed for each individual recording, as well as the overall fraction of responders averaged across all recordings for each NT. To determine the distribution of these overall responder fractions, we performed this same procedure for estimating relative event rates on surrogate data generated by hierarchically bootstrapping Ca<sup>2+</sup> event data 10,000 times (as above in <em>Propagation probability</em>). Standard errors were computed as the standard deviation of these empirical distributions. To determine whether there were significant differences between the overall responder fractions for GABA and glutamate, we computed the distribution of the difference between these two fractions under an imposed condition in which there was no systematic difference between GABA and glutamate. To do this, we performed the same procedure as above for estimating the difference between the overall responder fractions for “GABA” and “glutamate”, but on surrogate data generated by, for each cell, swapping the labels for “GABA” and “glutamate” responses from that in the experimental data with probability 1/2; this procedure was repeated 10,000 times. As it was unknown <em>a priori </em>whether GABA or glutamate would have a higher fraction of responder cells, a two-sided <em>p</em>-value was estimated as in equation (2) above. <strong><em>Decoding NT identity from propagative event responses (Fig. 4i):</em></strong> To quantify the extent to which the observed difference in propagative event responses to uncaged glutamate and GABA enabled reliable identification of NT identity on a trial-by-trial basis, we built a simple classifier that took as input a single value, the relative change in propagative event rate across a FOV in the window 0–120s post-uncaging relative to the window 60–0s pre-uncaging, and classified that FOV as responding to glutamate if the value was ≥ a set threshold, and GABA if the value was &lt; the threshold. To evaluate this classifier’s performance, we built a receiver operating characteristic (ROC) curve by varying the classification threshold across the entire domain of the feature, and at each value of the threshold, computing the empirical true positive rate and false negative rate of the classifier. With the threshold fixed in the ROC analysis, the classifier did not have any remaining free parameters, so did not need to be trained on data and was therefore not a function of any of the data, obviating the need for cross-validation. We computed the area under the ROC curve (AUC) using the trapezoidal rule. To determine the distribution of the observed AUC statistic, we performed this same analysis on 10,000 surrogate datasets generated by bootstrapping (<em>i.e.</em>, resampling FOVs with replacement). To determine whether the observed AUC statistic was above 0.5 (indicating completely non-informative decoding) to a degree greater than expected by chance alone, we performed this same analysis on 10,000 surrogate datasets generated by permuting the NT labels. <strong><em>Determining correlations between GABA and glutamate responses (Fig. 4l):</em></strong> To determine whether individual cells’ responses to GABA and glutamate—as determined in 4h above—were correlated, we computed the Spearman  between the binary paired responses to GABA and glutamate across cells which could be assessed in both conditions (<em>i.e.</em>, had &gt; 0 propagating baseline Ca<sup>2+</sup> events in both recordings) using SciPy 1.6.2 <sup>65</sup>. To determine the probability of observing a correlation at least this large under a null hypothesis of independence between cells’ responses for GABA and glutamate, we computed Spearman  on surrogate data in which the identities of the cells’ responses to GABA and glutamate were independently permuted; this procedure was repeated 10,000 times. To maintain the ability to identify correlation or anticorrelation, we estimated a two-sided <em>p</em>-value from these surrogate values, as in equation (2). To complement this analysis, we computed the fraction of overlap (<em>i.e.</em>, Jaccard index) between the sets  and  of cells that were responders to GABA and glutamate, respectively: This statistic is larger when the fraction of overlap between responders for the two neurotransmitters is larger. To determine the probability of observing an overlap at least this large under a null hypothesis of independent responses for GABA and glutamate, we computed this same statistic, but on 10,000 permuted surrogate datasets, as above. To determine significant overlap, we estimated a one-sided <em>p</em>-value from these surrogate values, as in equation (1). <strong><em>Segregating responding cells based on baseline propagation (Fig. 4m): </em></strong>For each neighboring astrocyte with propagative events during the baseline period of 60–0s pre-uncaging, we computed the fraction of baseline events that were propagative (# propagative baseline events / # all baseline events). Separately for GABA and glutamate, we used the propagative fraction across all given astrocytes to define the threshold fraction of baseline propagative activity, <em>f</em><sub>50</sub>, as the 50<sup>th</sup> percentile of all observed values; cells with fractions &lt; <em>f</em><sub>50</sub> were said to have “low fraction of propagative events at baseline”, while cells with fractions ≥ <em>f</em><sub>50</sub> were said to have “high fraction of propagative events at baseline” (Extended Data Fig. 5e, top). The fraction of astrocytes that were responders with propagative events to GABA or glutamate were separately estimated from amongst those astrocytes that had low baseline propagation and those that had high baseline propagation, as described above in <em>Determining responding cells based on static and propagative events</em>. Due to the low number of cells in each split group for individual FOVs, the overall average was estimated by pooling all neighboring astrocytes in each group across FOVs. Similarly for each neighboring astrocyte with baseline propagative events, we computed the rate of all events within the baseline period. Separately for GABA and glutamate, we used the baseline event rate across all neighboring astrocytes to define the threshold baseline event rate, <em>r</em><sub>50</sub>, as the 50<sup>th</sup> percentile of all observed values; cells with baseline rates &lt; <em>r</em><sub>50</sub> were said to have “low overall baseline event rates”, while cells with fractions ≥ <em>r</em><sub>50</sub> were said to have “high overall baseline event rates” (Extended Data Fig. 5e, bottom). The fraction of astrocytes that were responders with propagative events to GABA or glutamate were separately estimated from amongst those astrocytes that had low overall baseline event rates and those that had high overall baseline event rates, as above. To determine the distribution of these responder fractions (amongst astrocytes with low and high fraction of propagative events at baseline, or amongst astrocytes with low and high overall baseline event rates), we performed the same procedure for estimating these fractions on surrogate data generated by hierarchically bootstrapping Ca<sup>2+</sup> event data 10,000 times (as above in <em>Propagation probability</em>). Standard errors were computed as the standard deviation of these empirical distributions. For each NT, we next sought to determine whether there were significant differences between the fraction of astrocytes that were responders with propagative events amongst cells within the two groupings (<em>i.e.</em>, low vs. high fraction of propagative events at baseline; low vs. high overall baseline event rate). Separately for GABA and glutamate, for each group comparison, we computed the difference between the two responder fractions, as well as the distribution of this difference under an imposed condition in which there was no systematic difference in uncaging response between astrocytes in the two groups. To do this, we performed the same procedure as above for estimating responder fractions in the specified groups (<em>e.g.</em>, “low fraction of propagative events at baseline” and “high fraction of propagative events at baseline”) as well as the difference between the two, but on surrogate data generated by permuting the group labels; this procedure was repeated 10,000 times. As it was unknown <em>a priori </em>which group in either comparison—low or high baseline propagation, or low or high overall baseline event rate—would have a higher fraction of responder cells, a two-sided <em>p</em>-value was estimated from these surrogate values, as in equation (2). <strong><em>Simulations to validate characteristics of responder fraction estimates (Extended Data Fig. 5k)</em></strong>: Stratifying propagative event responses by the fraction of propagative events in the baseline may induce regression to the mean (RTM) effects, resulting in a bias toward higher observed responsiveness in the low fraction of propagative events at baseline group as compared to the high fraction group. In general, observed effects in differences of repeated measurements stratified by baseline values can arise from a combination of RTM effects and real effects—with the strength of the contribution from RTM depending on the dependency structure and measurement error characteristics in the data—complicating attribution of the observed total effect. To contextualize the observed effect sizes relative to the distribution of effects produced from a pure RTM process, we performed the same procedure as above for estimating responder fractions in the low and high fraction of propagative events at baseline groups, but using surrogate data generated using a random point process model. This model produced simulated event data structured in the same way as the observed dataset: for each cell, the model generated two independent homogeneous Poisson processes, one corresponding to static events and the other corresponding to propagative events. During the simulated baseline period, from 60s to 0s pre-”uncaging”, the rates of these two processes in each cell were set to the observed rate of the corresponding type of event during the veridical baseline period. During the simulated post-”uncaging” period, from 0s to 120s, the rates of these two processes in each cell were determined by multiplying that cell’s baseline rate for the corresponding event type by a response ratio, which was chosen from the empirical distribution of observed post-/pre-uncaging event ratios from amongst all neighboring cells for the given event type. In this way, the simulation modeled the overall response characteristics for propagative events, but in a way that was decoupled from the propagative event fraction in the baseline period. This simulation procedure was repeated 10,000 times, resulting in a distribution of low-high response fraction differences observed in surrogate data structured in the same way as either the GABA or glutamate uncaging datasets, but with no explicit dependence of cells’ propagative event responses on the baseline propagative event fraction. To summarize the observed effect relative to the effects seen in these simulations, we calculated the fraction of simulations with low-high differences larger than the observed effect. Additional References:   56.   Wiencken-Barger, A. E., Djukic, B., Casper, K. B. & McCarthy, K. D. A role for Connexin43 during neurodevelopment. Glia 55, 675–686 (2007). 57.   Yang, Y. et al. Molecular comparison of GLT1+ and ALDH1L1+ astrocytes in vivo in astroglial reporter mice. Glia 59, 200–207 (2011). 58.   Wang, Y. et al. SynQuant: an automatic tool to quantify synapses from microscopy images. Bioinformatics 36, 1599–1606 (2020). 59.   Dubbs, A., Guevara, J. & Yuste, R. moco: Fast Motion Correction for Calcium Imaging. Front. Neuroinformatics 10, 6 (2016). 60.   Fedchyshyn, M. J. & Wang, L.-Y. Activity-dependent changes in temporal components of neurotransmission at the juvenile mouse calyx of Held synapse. J Physiol (Lond) 581, 581–602 (2007). 61.   Martínez-Cagigal, V. Multiple Testing Toolbox, 1.1.0. MATLAB Central File Exchange https://www.mathworks.com/matlabcentral/fileexchange/70604-multiple-testing-toolbox (2021). 62.   Groppe, D. fdr_bh, 2.3.0.0. MATLAB Central File Exchange https://www.mathworks.com/matlabcentral/fileexchange/27418-fdr_bh (2010). 63.   Phipson, B. & Smyth, G. K. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 9, Article39 (2010). 64.   Seabold, S. & Perktold, J. Statsmodels: Econometric and Statistical Modeling with Python. In Proceedings of the 9th Python in Science Conference 92–96 (SciPy, 2010). Doi:10.25080/Majora-92bf1922-011. 65.   Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). 66.   Kingston, A. E. et al. LY341495 is a nanomolar potent and selective antagonist of group II metabotropic glutamate receptors. Neuropharmacology 37, 1–12 (1998).
description: # Network-level encoding of local neurotransmitters in cortical astrocytes
 [https://doi.org/10.5061/dryad.83bk3jb0j](https://doi.org/10.5061/dryad.83bk3jb0j) ## Description of the data and file structure **Datasets** **AQuA Cyto-GCaMP6f receptor agonist bath application (Fig. 1 and associated extended data figures)** 16 slices, 4 mice. 400µm thick, acute, coronal V1 slices were prepared from mice injected with GfaABC1D-cyto-GCaMP6f. Simultaneous 2P imaging of astrocyte Ca2+ activity and bath application of receptor agonists in the presence of TTX (1 µM), to block neuronal action potentials. Two recordings (10 min, 853 frames each at 1.42Hz) were acquired for each slice, GABA and glutamate receptor agonists were applied sequentially (Baclofen and t-ACPD, respectively), separated by an inter-imaging interval of &gt;20 min, including an ACSF washout period of &gt; 10min. Four concentrations of agonists were tested (5–100 µM), and the order of agonists was alternated between concentrations. Alexa Fluor 594 added along with receptor agonists to assess the time at which drugs reached the imaging field/recording chamber. Dataset name: ‘AQuA_CytoGCaMP_ReceptorAgonistBathApp_Fig1.mat’ ·       The following variables are included in the MATLAB workspace (~7.12GB, load time ~8min): o   mydata: a structured array with a field for each agonist. Each row of a field contains a structure with all data for a single recording. Corresponding rows in each field correspond to the same field-of-view recorded in response to each agonist (ie. Row 3 of mydata contains data acquired 20210622 from slice 3 in response to sequential stimulation with 25µM of t-ACPD [field 2] and then Baclofen [field 1]). Information for a single recording includes: §  Alexa594: Alexa594 fluorescence traces to track the time agonist enters the imaging field/recording chamber ·       Raw fluorescence trace: column 1 frame number, column 2 fluorescence value (‘RawTraceXY’) §  file: File name for the recording. Information following the final ‘\’ formatted as follows ·       date in YYYYMMDD format (ie. ‘20210622’) ·       fluorescent indicator (ie. ‘Cyto’) ·       age and sex (ie. ‘p30m’) ·       slice number (ie. ‘S3’) ·       Agonist concentration and name (ie. ‘25µMBaclofen’) ·       T-series number that each recording was acquired (ie. ‘006’, this can be used to figure out the order agonists were applied for each FOV) §  res: the relevant AQuA detection results for the recording. Descriptions of variables can be found in the AQuA documentation (see “Details on output files” under “Getting started” at [https://github.com/yu-lab-vt/AQuA](https://github.com/yu-lab-vt/AQuA)) §  Concentration of agonist applied, identified from the file name §  Fluorescence traces from FIJI for each identified cell/region ·       RawTrace: rows are frames, columns are regions o   DatesToExclude: any mice or recordings to exclude (due to viral overexpression) o   experiment_type: ‘BathApp’, to note bath application of agonists o   indicator: ‘CytoGCaMP’ o   pulse_x_idx: listing the indices of recordings for each agonist concentration (excluding the excluded recordings) o   pulse_x_idx_ALLMice: listing the indices of recordings for each agonist concentration (including the excluded recordings) o   TreatmentVals: vector listing the concentrations of agonist tested **AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX (Fig. 2–3 and associated extended data figures)** 400µm thick, acute, coronal V1 slices were prepared from mice injected with GfaABC1D-cyto-GCaMP6f. Simultaneous 2P imaging of astrocyte Ca2+ activity and 2P uncaging of RuBi-GABA or RuBi-glutamate (300 µM) at a subcellular region of an individual astrocyte in the presence of TTX (1 µM), to block neuronal action potentials. Two recordings (5 min, 427 frames each at 1.42Hz) were acquired for each field-of-view (FOV), in which GABA and glutamate were uncaged sequentially, separated by an inter-imaging interval of &gt;20 min, including an ACSF washout period of &gt; 10min. The uncaging site was consistent between GABA and glutamate uncaging in the same FOV and was a region showing moderate Ca2+ activity during a baseline recording taken prior to uncaging recordings. The order of neurotransmitter uncaging was alternated between slices and between experimental days. Voltage from the uncaging laser Pockels cell was recorded to mark the time of the uncaging pulse (recorded at 1,000Hz). Dataset name: ‘AQuA_CytoGCaMP_2PUncaging_WTCx43CBX_Fig2_3.mat’ ·       The following variables are included in the MATLAB workspace (~11.1GB, load time ~30min): o   experiment_type: ‘uncaging’ o   indicator: ‘CytoGCaMP’ o   mydata: a structured array containing three separate datasets. The datasets are noted at the end of each fieldname (ie. ‘RuBiGABA_WT’ refers to the wild-type dataset during GABA uncaging). Each row of a field contains a structure with all data for a single recording. Corresponding rows in each field of the same dataset correspond to the same field-of-view (FOV) recorded in response to both GABA and glutamate uncaging (ie. Row 3 of ‘mydata’ with fieldnames ending in ‘WT’ contains data acquired 20200810 from slice 1, FOV 3 in response to sequential uncaging of glutamate, then GABA). Corresponding rows in fields from different datasets (ie. a fieldname ending in ‘WT’ and fieldname ending in ‘CBX’) are not paired and refer to different FOVs from different mice. The following datasets are included: §  Wild-type (fieldname: ‘RuBiGABA_WT’ or ‘RuBiGlu_WT’)  ·       n = 28 FOV, 7 slices, 4 mice §  Connexin43 floxed (fieldname: ‘RuBiGABA_Cx43’ or ‘RuBiGlu_Cx43’) ·       n = 63 FOV, 16 slices, 8 mice ·       These were slices from Cx43fl/fl or Cx43fl/+ mice injected with GFAP-RFP-cre, in addition to cyto-GCaMP6f. This resulted in mosaic expression of RFP-cre in V1 astrocytes. RFP-cre+ astrocytes showed reduced Cx43 expression in both genotypes (fl/fl and fl/+), as confirmed by IHC. As such, data from both genotypes were pooled together ·       RFP-cre+ astrocytes were targeted for uncaging, regardless of the cre expression in neighboring astrocytes §  Carbenoxolone (fieldname: ‘RuBiGABA_CBX’ or ‘RuBiGlu_CBX’) ·       n = 30 FOV, 8 slices, 4 mice ·       These were wild-type slices, with the pharmacological gap-junctional blocker, Carbenoxolone (50µM), applied along with TTX.  Information for a single recording within ‘mydata’ includes: ·       file: File name for the recording. Information following the final ‘\’ formatted as follows o   date in YYYYMMDD format (ie. ‘20200810’) o   fluorescent indicator (ie. ‘Cyto’) o   age and sex (ie. ‘p31m’) o   slice number (ie. ‘S1’) o   Neurotransmitter and FOV within the slice (ie. ‘RuBiGABA3’) o   Number of uncaging pulses (ie. ‘10stim’ refers to 10-100ms pulses spaced apart by 100ms) o   T-series number that each recording was acquired (ie. ‘011’, this can be used to figure out the order NT was uncaged for each FOV) ·       res: the relevant AQuA detection results for the recording. Descriptions of variables can be found in the AQuA documentation (see “Details on output files” under “Getting started” at [https://github.com/yu-lab-vt/AQuA](https://github.com/yu-lab-vt/AQuA)) o   Information about which astrocyte included the uncaging site (drawn as a 3x3 px landmark during AQuA event detection) can be found within: ‘res.ftsFilter.region.cell.incluLmk’. ·       UncLaserVoltageRecording: the voltage recording of the uncaging laser Pockels cell to indicate the time that uncaging occurred. The uncaging laser pulse occurred halfway through the recording and was composed of 10-100ms pulses, separated by intervals of 100ms.   o   Column 1: time from recording start (ms) o   Column 2: Pockels cell voltage (V) o   ramping_cells: a structed array with a field for each dataset and neurotransmitter, corresponding to the fields in ‘mydata’ (the fields ‘GABA’ and ‘Glu’ refer to the WT dataset). Each field includes a table with information about baseline ramping activity for each identified astrocyte for all FOVs. o   Each row is an astrocyte in which ‘filename’ corresponds to a filename listed in ‘mydata’. o   ‘cell_recording’ lists local astrocyte indices for astrocytes included in that specific FOV/ recording. o   Astrocytes with a p-value ≤ 0.1 (p) and &gt; 5 AQuA-detected events (‘n_events’) were excluded from all analyses due to ‘ramping’ (aka significant increase or decrease in event frequency during the baseline). \** ** **AQuA GluSnFr 2P uncaging RuBi-glutamate (Fig. 2 and Extended Data Fig. 7)** 400µm thick, acute, coronal V1 slices were prepared from mice injected with Gfap-iGluSnFR-WPRE-SV40. Simultaneous 2P imaging of extracellular glutamate activity and 2P uncaging of RuBi-glutamate (300 µM) at subcellular regions of an individual astrocyte. Each recording was 70s (416­–435 frames acquired at 5.94–6.21Hz). Six sites within the FOV were targeted for sequential uncaging, with each site separated by 10s. The uncaging laser stimulation at each site consisted of 10-100ms pulses separated by 100ms. Voltage from the uncaging laser Pockels cell was recorded to mark the time of the 6 uncaging pulses (recorded at 1,000Hz). The power of the uncaging laser was modulated across the three included datasets (2–8mW at the sample), resulting in different amounts/spread of released neurotransmitter. Dataset name: ‘AQuA_GluSnFR_2PGluUncaging.mat’ (2GB, ~1min load time) The following variables are included in the MATLAB workspace: o   experiment_type: ‘uncaging’ o   indicator: ‘GluSnFR’ o   MultiUncagingReps = 1 (indicating that there were multiple uncaging trains within a single recording) o   mydata: a structured array containing three separate datasets. The datasets are noted at the end of each fieldname. Each row of a field contains a structure with all data for a single recording. The following datasets are included: §  Single round glutamate uncaging (fieldname: ‘RuBiGlu_10_orig’)  ·       n = 12 recordings, 4 slices, 2 mice ·       Uncaging laser power 70 A.U. on software (~8mW at the sample) §  Multi-round glutamate uncaging (fieldname: ‘RuBiGlu_10_70AU’)  ·       n = 11 recordings, 2 slices, 1 mouse ·       Uncaging laser power 70 A.U. on software (~8mW at the sample) o   Laser re-alignment between single round and multi-round uncaging datasets led to small difference in amount of neurotransmitter released §  RuBi-glutamate uncaging control (fieldname: ‘RuBiGlu_10_25AU’)  ·       n = 11 recordings, 2 slices, 1 mouse ·       Uncaging laser power 25 A.U. on software (~2mW at the sample), a stimulation that did not result in detectable release of neurotransmitter Information for a single recording within ‘mydata’ includes: ·       file: File name for the recording. Information following the final ‘\’ formatted as follows o   date in YYYYMMDD format (ie. ‘20230608’) o   fluorescent indicator (ie. ‘GluSnFR’) o   age and sex (ie. ‘p15m’) o   slice number (ie. ‘S1’) o   Laser Power (ie ‘70AU’, only included in some file names) o   Neurotransmitter and FOV within the slice (ie. ‘RuBiGlu3’) o   Number of uncaging pulses (ie. ‘10_100’ refers to 10-100ms pulses spaced apart by 100ms) o   T-series number that each recording was acquired (ie. ‘011’) ·       res: the relevant AQuA detection results for the recording. Descriptions of variables can be found in the AQuA documentation (see “Details on output files” under “Getting started” at [https://github.com/yu-lab-vt/AQuA](https://github.com/yu-lab-vt/AQuA)) o   The 6 uncaging sites were drawn as 3x3 px landmarks during AQuA event detection o   All events resulting from uncaging (during time of uncaging pulses) were selected as “favorites” during AQuA event detection ·       UncLaserVoltageRecording: the voltage recording of the uncaging laser Pockels cell to indicate the times uncaging occurred.   o   Column 1: time from recording start (ms) o   Column 2: Pockels cell voltage (V) \** ** **Other datasets (extended data figures)** **FIJI (and AQuA) Cyto-GCaMP6f bath application of receptor agonists baclofen and LY379268 (Extended Data Fig. 1k)** ·       9 slices, 3 mice. Dataset collected in the same manner described above in “AQuA Cyto-GCaMP6f receptor agonist bath application”, apart from two differences. 1. The agonist applied to activate glutamate receptors was LY379268 (100µM) instead of t-ACPD. The agonist applied to activate GABA receptors remained the same (baclofen 100µM). 2. Agonists were applied in the presence of TTX (1µM), to block action potentials, and additionally carbenoxolone (CBX, 100µM), to block gap junctions.       Dataset name: ‘FIJIAQuA_CytoGCaMP_BathAppBacLY_ExtFig1k.mat’ The following variables are included in the MATLAB workspace (~7.7GB): o   mydata: a structured array with a field for each agonist. Each row of a field contains a structure with all data for a single recording, as described in “AQuA Cyto-GCaMP6f receptor agonist bath application”. Information for a single recording includes: §  Alexa594: Alexa594 fluorescence traces to track the time agonist enters the imaging field/recording chamber ·       Raw fluorescence trace: column 1 frame number, column 2 fluorescence value (‘RawTraceXY’) ·       Fluorescence trace with light artifact removed (‘LAremoved’) ·       FirstFrameAboveBL: identified as the frame agonist enters imaging field (the first frame fluorescence from ‘LAremoved’ ≥ baseline mean + 3std; baseline period = frames 1:300). This rounded value is listed as the ‘uncagingframe’  §  Imaging parameters pulled from res.opts including the total number of frames and the temporal resolution (‘SecPerFrame’) §  file, res, raw fluorescence traces from FIJI and concentration, as described in “AQuA Cyto-GCaMP6f receptor agonist bath application”. o   experiment_type: ‘BathApp’, to note bath application of agonists o   indicator: ‘CytoGCaMP’ o   method_AgonistEntry: ‘Threshold’ to identify the method used to identify the frame of agonist entry into the imaging chamber (‘uncagingframe’, described for FirstFrameAboveBL)   **FIJI pinkFlamindo receptor agonist bath application (Extended Data Fig. 1j–k)** ·       8 slices, 3 mice. 400µm thick, acute, coronal V1 slices were prepared from mice injected with Gfap-pinkFlamindo. Simultaneous 2P imaging of astrocyte cAMP activity and bath application of receptor agonists (100µM) in the presence of TTX (1 µM), to block neuronal action potentials, and CBX (50 µM), to block gap junctions. Alexa Fluor 594 was not added along with receptor agonist to avoid spectral overlap. The time at which drugs reached the imaging field/recording chamber was estimated to be 90 frames (the average time for ACSF to travel from the reservoir to the recording chamber) following the frame agonist was added to the reservoir of ACSF. Two recordings (10 min, 853 frames each at 1.42Hz) were acquired for each slice, GABA and glutamate receptor agonists were applied sequentially, separated by an inter-imaging interval of &gt;20 min, including an ACSF washout period of &gt; 10min. Fluorescence traces from identified cells in each FOV were measured in FIJI, along with fluorescence from two backgrounds regions within the FOV (regions without labeled astrocytes or outside of the slice). Background fluorescence from the two regions was averaged in each frame and subtracted from the fluorescence traces of each identified cell.       Dataset name: ‘FIJI_pinkFlamindo_ReceptorAgonistBathApp_ExtFig1.mat’ o   The following variables are included in the MATLAB workspace (~2MB): §  mydata: a structured array with a field for each agonist. Each row of a field contains a structure with all data for a single recording. Corresponding rows in each field correspond to the same field-of-view recorded in response to each agonist. Information for a single recording includes: ·       RawFluorescence: fluorescence traces from FIJI; rows are frames, columns are identified cells ·       RawMinusBackground: fluorescence traces from FIJI with mean background fluorescence subtracted; same format as ‘RawFluorescence’ ·       SecPerFrame: temporal resolution for image acquisition §  experiment_type: ‘BathApp’, to note bath application of agonists §  indicator: ‘pinkFlamindo’ §  EndLightArtifact: the frame in RawFluorescence that the light artifact end (light artifact associated with headlamp used when adding agonist into ACSF reservoir) \** ** **AQuA Cyto-GCaMP6f 2P uncaging WT, Receptor Antagonist and Laser Uncaging Control (Extended Data Fig. 2–3)** Data were collected as described above for AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX. The MATLAB workspace includes the same variables described above for AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX. Briefly, astrocyte Ca2+ activity was recorded during 2P uncaging of GABA or glutamate. Two types of controls were performed in separate slices. First, astrocyte Ca2+ activity was recorded during 2P uncaging of GABA or glutamate in the presence of GABABR or mGluR3 antagonists. Second, astrocyte Ca2+ activity was recorded during the uncaging laser stimulation, in the absences of caged compounds, to test the effects of laser stimulation alone. The WT dataset included here is the same dataset included above in AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX, in which the same FOVs were recorded in response to sequential GABA and glutamate uncaging. The other three datasets included, NT uncaging in the presence of receptor antagonists or with the uncaging laser in the absence of RuBis, are not paired (i.e. are not always from the same FOVs, slices and mice). All datasets included come from wild-type slices. The MATLAB workspace is 8.34 GB and takes ~10 min to load. Dataset name: ‘AQuA_CytoGCaMP_2PUncaging_WTRecAntaLaserCtrl_ExtDataFig2_3.mat’ The following datasets are included within the variable ‘mydata’: ·       Wild-type (fieldname: ‘RuBiGABA_WT’ or ‘RuBiGlu_WT’)  o   n = 28 FOV, 7 slices, 4 mice ·       GABA uncaging in the presence of a GABABR antagonist, CGP55845 (10µM; fieldname: ‘RuBiGABA_CGP’) o   n = 32 FOV, 8 slices, 5 mice ·       Glutamate uncaging in the presence of an mGluR3 antagonists, LY341495 (10µM; fieldname: ‘RuBiGlu_LY’) o   n = 28 FOV, 7 slices, 4 mice ·       Uncaging laser stimulation in the absence of RuBis (fieldname: ‘NoRuBi_LaserUncagingControl’) o   n = 48 FOV, 9 slices, 3 mice \** ** **AQuA Cyto-GCaMP6f 2P multi-round glutamate uncaging (Extended Data Fig. 7)** 400µm thick, acute, coronal V1 slices were prepared from mice injected with GfaABC1D-cyto-GCaMP6f. Simultaneous 2P imaging of astrocyte Ca2+ activity and 2P uncaging of RuBi-glutamate (300 µM) at a subcellular region of an individual astrocyte in the presence of TTX (1 µM), to block neuronal action potentials. Three rounds of glutamate uncaging (12.5 min, 1066 frames each at 1.42Hz) were acquired for each field-of-view (FOV). Each round of glutamate uncaging was separated by an inter-imaging interval of &gt;25 min. The uncaging site was consistent across rounds and was a region showing moderate Ca2+ activity during a baseline recording taken prior to uncaging recordings. Voltage from the uncaging laser Pockels cell was recorded to mark the time of the uncaging pulse (recorded at 1,000Hz). The uncaging laser was pulsed at the 2.5 min mark. Dataset name: ‘AQuA_CytoGCaMP_2PMultiRoundGluUncaging_70AU_ExtDataFig7.mat’ The following variables are included in the MATLAB workspace (2.43GB, load time ~2min): o   experiment_type: ‘uncaging’ o   indicator: ‘CytoGCaMP’ o   mydata: a structured array containing a field for each round of glutamate uncaging. The round number is noted at the end of the fieldname (ie. ‘RuBiGlu_R3’ refers to the third round of glutamate uncaging). Each row of a field contains a structure with all data for a single recording. Corresponding rows in each field correspond to the same field-of-view (FOV) recorded across each round of glutamate uncaging (ie. Row 3 of ‘mydata’ contains data acquired 20230626 from slice 2, FOV 2 in response to 3 rounds of glutamate uncaging). §  n = 23 FOV, 9 slices, 5 mice Information/variables for a single recording within ‘mydata’: file, res and UncLaserVoltageRecording as described above for AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX o   ramping_cells: as described above for AQuA Cyto-GCaMP6f 2P uncaging WT, Cx43 and CBX o   RecordingsToExclude: an mx1 cell array containing the file names for m recordings to exclude. Two recordings were excluded from this dataset due to drift of the slice in z (change in focal plane) over the course of the 12.5min recording.  \** ** **AQuA Cyto-GCaMP6f 2P multi-round glutamate uncaging control (Extended Data Fig. 7)** Data were collected as described above for AQuA Cyto-GCaMP6f 2P multi-round glutamate uncaging, with the exception of the uncaging laser power. In this control dataset, the power of the uncaging laser was set to 25 A.U. (~2mW at the sample), a power which did not lead to detectable release of glutamate. This is in contrast to all other 2P uncaging datasets described above, in which the uncaging laser was set to 70 A.U. (~8mW at the sample). Apart from the difference in uncaging laser power, all other parameters for this control dataset were the same as described above for AQuA Cyto-GCaMP6f 2P multi-round glutamate uncaging, including the concentration of RuBi-glutamate (300 µM).   Dataset name: ‘AQuA_CytoGCaMP_2PMultiRoundGluUncagingCtrl_25AU_ExtDataFig7.mat’ (1.31 GB, load time ~1min).  Variables included in the MATLAB workspace are the same as described above for AQuA Cyto-GCaMP6f 2P multi-round glutamate uncaging. o   mydata includes recordings from n = 20 FOV, 8 slices, 5 mice o   RecordingsToExclude is not included in the workspace, as no recordings were excluded for drift in z \** ** **Cx43 immunohistochemistry in Cx43fl/fl and Cx43fl/+ slices from primary visual cortex (Extended Data Fig. 3c)** n = 91 FOVs, 16 slices, 8 mice. 40µm slices were re-sectioned from 400µm thick acute slices fixed in 4% PFA and embedded in OCT following 2P imaging experiments. Viral expression of GfaABC1D-cyto-GCaMP6f and GFAP-RFP-cre resulted in mosaic expression of cyto-GCaMP6f and RFP-cre in astrocytes within V1. Immunohistochemistry was carried out using primary antibodies for α-connexin-43 (to label Cx43 protein), α-GFP (to label GCaMP-expressing astrocytes) and α-mCherry (to label RFP-cre+ astrocytes). 60x multi-channel z-stack images were acquired on a confocal microscope with a step-size of 0.25–0.26µm. For each slice date/mouse, the following fluorescent signals correspond to the following confocal channels: | **Slice date** | **Genotype** | **Ch 1** | **Ch 2** | **Ch 3** | | :------------- | :----------- | :------- | :------- | :------- | | 20210211 | Cx43 fl/+ | Cx43 | GFP | RFP | | 20210212 | Cx43 fl/+ | Cx43 | GFP | RFP | | 20210323 | Cx43 fl/+ | GFP | RFP | Cx43 | | 20210324 | Cx43 fl/+ | GFP | RFP | Cx43 | | 20210706 | Cx43 fl/+ | Cx43 | GFP | RFP | | 20210707 | Cx43 fl/fl | Cx43 | GFP | RFP | | 20210708 | Cx43 fl/+ | Cx43 | GFP | RFP | | 20210709 | Cx43 fl/fl | Cx43 | GFP | RFP |  Dataset name: ‘Cx43_IHC_ExtDataFig3c’, as a compressed zip folder (23.4GB).   **Ribosomal-mRNA expression in visual cortex astrocytes (Extended Data Fig. 1a and 3a) from the Farhy-Tselnicker et. al. publicly available dataset (NCBI Gene Expression Omnibus, GSE161398)** ·       Excel spreadsheet from database: GSE161398_FPKM_MasterTable_Development. See [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161398](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161398) for experimental details.   **Derived datasets** **Tables of event characteristics (Figs. 3, 4; Extended Data Figs. 4, 5, 6, and 7)** These intermediate files were produced from the datasets above using the conversion script ‘python/conversion/aqua_export.m’ in the associated code repository at [accompanying Zenodo DOI]; the intermediate converted files generated and used as part of the analysis are provided for convenience. See documentation in the repository for details on generating these from the raw data. Dataset name: ‘events.zip’ (62MB) **Tables of neighboring cell ramping effects (Figs. 2–4; Extended Data Figs. 2–7)** These intermediate files were produced from the datasets above using the auxiliary notebook ‘python/net_astro-ramping.ipynb’ in the associated code repository at [accompanying Zenodo DOI]; the intermediate files generated and used as part of the analysis are provided for convenience. See documentation in the repository for details on generating these from the raw data. Dataset name: ‘ramping.zip’ (151kB) Sharing/Access information Links to other publicly accessible locations of the data: * [https://github.com/poskanzerlab/astro-network-encoding/releases/tag/v1.0](https://github.com/poskanzerlab/astro-network-encoding/releases/tag/v1.0) ## Code/Software See above.