Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Technical Report
  • Published:

Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology

Nature Neuroscience volume 22, pages 1936–1944 (2019)Cite this article

Subjects

Abstract

Recent work examining astrocytic physiology centers on fluorescence imaging, due to development of sensitive fluorescent indicators and observation of spatiotemporally complex calcium activity. However, the field remains hindered in characterizing these dynamics, both within single cells and at the population level, because of the insufficiency of current region-of-interest-based approaches to describe activity that is often spatially unfixed, size-varying and propagative. Here we present an analytical framework that releases astrocyte biologists from region-of-interest-based tools. The Astrocyte Quantitative Analysis (AQuA) software takes an event-based perspective to model and accurately quantify complex calcium and neurotransmitter activity in fluorescence imaging datasets. We apply AQuA to a range of ex vivo and in vivo imaging data and use physiologically relevant parameters to comprehensively describe the data. Since AQuA is data-driven and based on machine learning principles, it can be applied across model organisms, fluorescent indicators, experimental modes, and imaging resolutions and speeds, enabling researchers to elucidate fundamental neural physiology.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: AQuA-based event detection.
Fig. 2: Performance comparison among image analysis methods.
Fig. 3: AQuA features capture heterogeneities among single astrocytes.
Fig. 4: AQuA resolves astrocytic Ca2+ propagation directionality across scales.
Fig. 5: AQuA-based detection of extracellular dynamics via astrocytic and neuronal expression of genetically encoded neurotransmitter sensors.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The code has been released under GNU General Public License v.3.0 and is available at https://github.com/yu-lab-vt/AQuA.

References

  1. Srinivasan, R. et al. New transgenic mouse lines for selectively targeting astrocytes and studying calcium signals in astrocyte processes in situ and in vivo. Neuron 92, 1181–1195 (2016).

    Article  CAS  Google Scholar 

  2. Marvin, J. S. et al. An optimized fluorescent probe for visualizing glutamate neurotransmission. Nat. Methods 10, 162–170 (2013).

    Article  CAS  Google Scholar 

  3. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  CAS  Google Scholar 

  4. Srinivasan, R. et al. Ca(2+) signaling in astrocytes from Ip3r2(-/-) mice in brain slices and during startle responses in vivo. Nat. Neurosci. 18, 708–717 (2015).

    Article  CAS  Google Scholar 

  5. Paukert, M. et al. Norepinephrine controls astroglial responsiveness to local circuit activity. Neuron 82, 1263–1270 (2014).

    Article  CAS  Google Scholar 

  6. Haustein, M. D. et al. Conditions and constraints for astrocyte calcium signaling in the hippocampal mossy fiber pathway. Neuron 82, 413–429 (2014).

    Article  CAS  Google Scholar 

  7. Ding, F. et al. α1-Adrenergic receptors mediate coordinated Ca2+ signaling of cortical astrocytes in awake, behaving mice. Cell Calcium 54, 387–394 (2013).

    Article  CAS  Google Scholar 

  8. Poskanzer, K. E. & Yuste, R. Astrocytes regulate cortical state switching in vivo. Proc. Natl Acad. Sci. USA 113, E2675–E2684 (2016).

  9. Ma, Z., Stork, T., Bergles, D. E. & Freeman, M. R. Neuromodulators signal through astrocytes to alter neural circuit activity and behaviour. Nature 539, 428–432 (2016).

    Article  CAS  Google Scholar 

  10. Poskanzer, K. E. & Yuste, R. Astrocytic regulation of cortical UP states. Proc. Natl Acad. Sci. USA 108, 18453–18458 (2011).

    Article  CAS  Google Scholar 

  11. Looger, L. L. A genetically encoded fluorescent sensor for in vivo imaging of GABA. Nat. Methods 16, 763–770 (2019).

  12. Feng, J. et al. A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102, 745–761 (2019).

    Article  Google Scholar 

  13. Lobas, M. A. et al. A genetically encoded single-wavelength sensor for imaging cytosolic and cell surface ATP. Nat. Commun. 10, 711 (2019).

    Article  Google Scholar 

  14. Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360, eaat4422 (2018).

    Article  Google Scholar 

  15. Agarwal, A. et al. Transient opening of the mitochondrial permeability transition pore induces microdomain calcium transients in astrocyte processes. Neuron 93, 587–605.e7 (2017).

    Article  CAS  Google Scholar 

  16. Armbruster, M., Hanson, E. & Dulla, C. G. Glutamate clearance is locally modulated by presynaptic neuronal activity in the cerebral cortex. J. Neurosci. 36, 10404–10415 (2016).

    Article  CAS  Google Scholar 

  17. Bindocci, E. et al. Three-dimensional Ca(2+) imaging advances understanding of astrocyte biology. Science 356, eaai8185 (2017).

    Article  Google Scholar 

  18. Wu, Y.-W. et al. Spatiotemporal calcium dynamics in single astrocytes and its modulation by neuronal activity. Cell Calcium 55, 119–129 (2014).

    Article  CAS  Google Scholar 

  19. Wang, Y. et al. Graphical time warping for joint alignment of multiple curves. Adv. Neural Inf. Process. Syst. 29, 3648–3656 (2016).

    Google Scholar 

  20. Giovannucci, A. et al. CaImAn: an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019).

    Article  Google Scholar 

  21. Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2016).

  22. Poskanzer, K. E. & Molofsky, A. V. Dynamism of an astrocyte in vivo: perspectives on identity and function. Annu. Rev. Physiol. 80, 143–157 (2018).

    Article  CAS  Google Scholar 

  23. Shigetomi, E. et al. Imaging calcium microdomains within entire astrocyte territories and endfeet with GCaMPs expressed using adeno-associated viruses. J. Gen. Physiol. 141, 633–647 (2013).

    Article  CAS  Google Scholar 

  24. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  25. Stobart, J. L. et al. Cortical circuit activity evokes rapid astrocyte calcium signals on a similar timescale to neurons. Neuron 98, 726–735.e4 (2018).

    Article  CAS  Google Scholar 

  26. Kanemaru, K. et al. In vivo visualization of subtle, transient, and local activity of astrocytes using an ultrasensitive Ca(2+) indicator. Cell Rep. 8, 311–318 (2014).

    Article  CAS  Google Scholar 

  27. Nimmerjahn, A., Mukamel, E. A. & Schnitzer, M. J. Motor behavior activates Bergmann glial networks. Neuron 62, 400–412 (2009).

    Article  CAS  Google Scholar 

  28. Parsons, M. P. et al. Real-time imaging of glutamate clearance reveals normal striatal uptake in Huntington disease mouse models. Nat. Commun. 7, 11251 (2016).

    Article  CAS  Google Scholar 

  29. Jiang, R., Diaz-Castro, B., Looger, L. L. & Khakh, B. S. Dysfunctional calcium and glutamate signaling in striatal astrocytes from Huntington’s disease model mice. J. Neurosci. 36, 3453–3470 (2016).

    Article  CAS  Google Scholar 

  30. Hefendehl, J. K. et al. Mapping synaptic glutamate transporter dysfunction in vivo to regions surrounding Aβ plaques by iGluSnFR two-photon imaging. Nat. Commun. 7, 13441 (2016).

    Article  CAS  Google Scholar 

  31. Marvin, J. S. et al. Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR. Nat. Methods 15, 936–939 (2018).

    Article  CAS  Google Scholar 

  32. Fino, E. et al. RuBi-glutamate: two-photon and visible-light photoactivation of neurons and dendritic spines. Front. Neural Circuits 3, 2 (2009).

    Article  Google Scholar 

  33. Baraban, M., Koudelka, S. & Lyons, D. A. Ca2+ activity signatures of myelin sheath formation and growth in vivo. Nat. Neurosci. 19, 190 (2017).

    Google Scholar 

  34. Krasnow, A. M., Ford, M. C., Valdivia, L. E., Wilson, S. W. & Attwell, D. Regulation of developing myelin sheath elongation by oligodendrocyte calcium transients in vivo. Nat. Neurosci. 93, 9887 (2017).

    Google Scholar 

  35. Higley, M. J. & Sabatini, B. L. Calcium signaling in dendritic spines. Cold Spring Harb. Perspect. Biol. 4, a005686–a005686 (2012).

    Article  Google Scholar 

  36. Keller, P. J. & Ahrens, M. B. Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy. Neuron 85, 462–483 (2015).

    Article  CAS  Google Scholar 

  37. Goldey, G. J. et al. Removable cranial windows for long-term imaging in awake mice. Nat. Protoc. 9, 2515–2538 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge members of the Poskanzer and Yu laboratories for discussions and comments on the manuscript. We thank G. Chin and S. Yokoyama for technical assistance. K.E.P. is supported by the National Institutes of Health (grant nos. R01NS099254 and R21DA048497), the National Science Foundation (grant no. 1604544), the Brain Research Foundation (Frank/Fay Seed Grant), the E. Matilda Ziegler Foundation for the Blind and the Quantitative Biological Institute at UCSF (Bold & Basic grant). G.Y. is supported by the National Institutes of Health (grant no. R01MH110504) and an NSF grant (no. 1750931).

Author information

Author notes

  1. These authors contributed equally: Yizhi Wang, Nicole V. DelRosso.

Authors and Affiliations

  1. Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA

    Yizhi Wang, Xuelong Mi & Guoqiang Yu

  2. Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Nicole V. DelRosso, Silvia Pittolo & Kira E. Poskanzer

  3. Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Trisha V. Vaidyanathan, Michelle K. Cahill, Michael E. Reitman & Kira E. Poskanzer

  4. Kavli Institute for Fundamental Neuroscience, San Francisco, CA, USA

    Kira E. Poskanzer

Authors
  1. Yizhi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicole V. DelRosso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trisha V. Vaidyanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michelle K. Cahill
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael E. Reitman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Silvia Pittolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xuelong Mi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guoqiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kira E. Poskanzer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.E.P. and G.Y. conceived and designed the study. Y.W. and G.Y. designed and implemented the AQuA algorithm, software and simulations. N.V.D. analyzed all imaging data and provided critical conceptual input to software design. T.V.V. carried out GluSnFR experiments. M.K.C. performed ex vivo Ca2+, GABASnFR and GluSnFR experiments. M.E.R. performed in vivo Ca2+ imaging experiments. S.P. performed GRAB-NE experiments. X.M. implemented the Java version of the software, and built and tested the 3D prototype. K.E.P. supervised the experimental team. G.Y. supervised the computational team. Y.W., N.V.D., K.E.P. and G.Y. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Guoqiang Yu or Kira E. Poskanzer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Eftychios Pnevmatikakis, Mirko Santello and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Eight steps in the AQuA algorithm.

The eight steps can be grouped into four modules indicated by the brackets below panels. The last three modules are further illustrated in Supplementary Fig. 2.

Supplementary Figure 2 Schematic illustration of three major modules in AQuA algorithm.

Curves and regions taken from a real data set. (a) AQuA flowchart, with three gray bars below indicating where the three major modules are located with respect to the AQuA flowchart. (b) Detect and cluster peaks: Curves in the detect peaks by curve panel are associated with the location labeled by the red diamond in the seed location panel. One curve may have multiple peaks, which are detected one-by-one. Once a peak is detected at a seed location, the peak is spatially extended to include its neighboring pixels as in the grow to all pixels with signals panel. Clustering of peaks starts from the peak with the earliest onset time and includes its spatially adjacent peaks based on the two inclusion rules shown in the grow to all pixels with signals panel. Two peaks at one location are never clustered into one group. Once the greedy search strategy cannot find more peaks to include, it stops and one peak group is formed. To find another peak group, the greedy search restarts from the first onset in the remaining peaks. The process is repeated until no peaks remain. (c) Propagation estimation and super-event detection: This module is applied to each peak group. The five colored curves are the dynamics of the five exemplar pixels with corresponding colors. The dashed curve is the representative or reference curve. In the graphical time warping model panel, red arrows indicate how the reference curve can be warped to represent the curve at each location. The graphical time warping model incorporates the information that nearby locations should have more similar curves than distant locations. A double-headed arrow between two functions informs the model that these curves should be warped similarly to the reference curve. As a comparison, if there is no double-headed arrow between curves, dissimilar warping functions are allowed. Once the warping function is calculated by the graphical time warping model, onset time is computed for each pixel, resulting in an onset time map. Note discontinuity of onset time examples at black triangles. These pixels are removed to obtain the final super-event, which may contain multiple events and are subject to the next operation. (d) Propagation source and event detection: Local propagation sources are obtained by finding local minima on the onset time map. According to the rules described in Methods, some local sources will be combined/merged, resulting in global propagation sources. Briefly, if the path between two local sources does not have to go through a location with a late onset time, these two local sources are combined. Then, each global propagation source leads to an event. Each event is obtained by growing each global source to include its neighboring pixels. In the event detection from sources panel, solid dots are pixels already assigned to an event, white dots are unexplored pixels, and grey dots are explored but await a later decision to be assigned to an event.

Supplementary Figure 3 Limitations of thresholding-based analysis.

(a) Top row: Six example frames of ex vivo Ca2+ data smoothed with a Gaussian kernel of σ=0.5 after square root transformation. Second row: Baseline for each pixel is estimated with a 20-frame time window, noise level is estimated as σ_noise, and baseline is subtracted from raw data to obtain ΔF. Third row: Standard threshold is set at 3σ_noise. Many individual events are erroneously detected as one very long and large spatiotemporal component, for reasons graphically explained in (b). Fourth row: A high threshold (10σ_noise) leads to loss of many true events, and many detected events are incomplete. Each color indicates an event. Fifth row: AQuA-detection avoids the pitfalls in threshold-setting and identifies each individual event. (b) Two events are incorrectly connected after thresholding (incorrect events in yellow in each sub-panel). Intensity color bar on right, with red indicating the threshold, refers to all panels. Top: Between multiple events in the same location, even though the intensity drops a lot, not all pixels will fall below the threshold. Each event is shown with a gray bounding box. The super-voxel step in AQuA solves this problem by finding a time window for each seed, and spatially extending the windows. Middle: Neighboring events are initiated at distinct times, but are spatiotemporally connected at a later time. If two regions have very different onset times, AQuA will treat them as different events in the super-event detection step. Bottom: Two events can be separated when they appear, but meet after propagating. In the event detection step, AQuA distinguishes these events based on the single-source rule.

Supplementary Figure 4 AQuA detects ground truth events across three types of simulated data.

Color represents event count for each pixel (note colors bars have different scales in each dataset). Red borders show ROIs detected by ROI-based methods. (AQuA does not detect ROIs.) (a) Pure ROI. (b) Size change odds of 5, indicating size changes 20–500% of ROI. (c) Location change ratio of 1. Average distance to the center of the ROI is 100% the ROI diameter. (d) Mixed propagation with 10 frames.

Supplementary Figure 5 Event counts under different SNRs.

Impact of SNR change when size change ratio is 3. The color shows the count of events on each individual pixel, and all plots share the same scale. The red lines are the boundaries of detected ROIs. (a) Ground truth event count and the color bar for all plots. (b) The event count for all methods under four different SNRs.

Supplementary Figure 6 Peer method performance on growing and moving propagation types.

Schematic (top) and results (bottom) of performance of five image-analysis methods (AQuA, GECI-quant, CaSCaDe, CaImAn, and Suite2P) on simulated datasets with (a) growing propagation and (b) moving propagation. Change of the propagation frame number is shown in the bottom left panel, and varying SNR in the bottom right, with center point indicating mean. The bars on each curve indicate the 95% confidence interval calculated from 10 independent replications of simulation, where each simulation contains hundreds of events. When the number of propagation frames (not the event duration) is 0, the simulation is under pure ROI assumptions. IoU (intersection over union) measures the overlap between detected and ground-truth events. An IoU of 1 is the best performance achievable by any method, meaning that all detected events are ground-truth and all ground-truth events are detected.

Supplementary Figure 7 AQuA features enable detailed Ca2+ activity plots.

(a) Spatiotemporal plot of Ca2+ activity from a five minute video (the first minute of which is shown in Fig. 3b). Each event is represented by a polygon proportional to the area of the event as it changes over its lifetime, and color-coded by propagation direction. (b) Example time series illustrating how propagation direction is determined (left). A propagation direction score is calculated for each event by multiplying the Euclidian distance between the event pixels’ proximity to the soma at each frame by each pixel’s intensity. The overall score is the summation of this weighted pixel intensity distance over the lifetime of the event. Therefore, if more pixels with higher intensity move toward the soma it will be classified as such (top). While some events appear in the plot as moving toward the soma, they are actually calculated as moving away from the soma (middle) since we are only displaying the minimum event proximity to the soma in the spatiotemporal plot, but calculate each pixel’s proximity to the soma when generating a propagation score. Additionally, pixel intensity is first thresholded at 0.3 dF/F. Therefore, events that move toward or away from the soma yet have pixel intensities below the threshold (bottom) appear to have a propagation direction when plotted, yet have a zero propagation direction score when calculated. Scale bar = 20μm. (c) Additional events plotted for each propagation direction category to demonstrate range of detected/plotted events. Scale is not equivalent to events shown in b, but is equivalent within entire group shown here.

Supplementary Figure 8 Distribution of Ca2+ event features.

(a) Left: total number of Ca2+ events that are dynamic (gray, propagation direction score > 0) and static (blue, propagation direction score = 0) within the 2D imaging plane, ***p < 0.001, n=5 slices, 11 cells, chi-square test for independence; mean ± s.e.m. Middle: distribution of Ca2+ event area for dynamic and static events, ***p < 0.001, one-tailed Wilcoxon rank sum test. Right: distribution of Ca2+ event duration for dynamic and static events, ***p < 0.001, one-tailed Wilcoxon rank sum test (right). (b) Distribution, average area, and average duration of events propagating toward soma (pink), away from soma (purple), and static events (blue) compared to starting distance from soma (top row), ending distance from soma (middle row), and minimum distance from soma (bottom row). Bin widths calculated by Freedman-Diaconis’s rule.

Supplementary Figure 9 Cluster analysis on features generated from three spatial footprint methods.

(a) Heatmap of z-scores for eight AQuA features (x-axis) describing each event. White lines demarcate events and features from individual cells. For entire figure, n=5 slices, 11 cells. (b–c) Top: heatmaps of z-scores for three features describing the Ca2+ activity at each ROI (b) or tile (c) location. ROIs detected using average projection image with a 5μm square filter applied (for ROIs) or 5x5μm tiles, based on fluorescence intensity and size. Ca2+ events with signals > 0.03 dF/F and two times the noise of each individual trace were selected. Pixels within each ROI or tile were averaged and dF/F was calculated by dividing each value by the mean values from the previous 25 seconds. (d–f) t-SNE visualizations of each cell’s Ca2+ activity using features calculated using AQuA (d), ROIs (e), and tiles (f). High dimensional data (a–c) are reduced are displayed in two dimensions. Points that are clustered closer together can be interpreted as having more similar Ca2+ activity features. k-means clustering boundary denoted as dashed line. (g–h) t-SNE plots using only subsets of AQuA-calculated features from (a) and (d). (g) t-SNE plot of only the features specific to AQuA and not shared with ROI or tile analysis. (h) Plot using only AQuA-extracted features that correspond to those in ROI- or tile-based analyses. (i) Comparison of difference between two clusters generated from the t-SNE analysis followed by k-means clustering. Note increased separation using AQuA-specific features compared to others when plotting mean ± s.e.m. (One-tailed paired t-test, ***p<0.001).

Supplementary Figure 10 Defining in vivo burst and inter-burst events.

(a) Population Ca2+ events represented as two temporal traces: percentage of imaging field active (top) and number of AQuA event onsets (middle). Burst periods (pink) are defined from the top trace as periods when Ca2+ activity exceeds 1% of the active field of view (red dashed line, top), and exceeds 10% of the maximum number of event onsets (red dashed line, middle). Burst periods correlate with wheel velocity of the treadmill (bottom). (b) Burst onset is defined as the first frame in which 10% of the peak is exceeded and burst offset is defined as the last frame exceeding 10% of the peak. (c) The relationship between all interburst events’ total propagation distance and size, similar to the burst events plotted in Fig 4c. (n=6 mice).

Supplementary Figure 11 Comparison of AQuA and Caltracer for event detection of astrocytic GluSnFR signals.

(a) Applied to the same data sets, AQuA detects 157 events, while Caltracer8, using a rising faces algorithm, detects 76 events with manually defined single-cell ROIs. (n=3 slices) (b) ROI example (left) and temporal trace with detected events marked by black circle using Caltracer software. Scale bar = 50μm. (c) AQuA-detected events from the same cell as in (b), and corresponding temporal traces (black dot, specific events shown above each trace). Scale bar = 10μm.

Supplementary Figure 12 AQuA performance on simulated 3D datasets.

(a) The 3D model used for performance-testing the 3D AQuA extension was extracted from real Ca2+ imaging data. (b) Example of simulated data in which event size varies; each column represents a different event radius. (c) Examples of simulated events with slow (top row, growth rate 2) and fast (bottom row, growth rate 10) growing propagation rates. (d–e) Results of AQuA performance on simulated datasets with respect to varying SNR under independently varying event (d) size and (e) growing propagation rates. IoU (intersection over union) measures the overlap between detected and ground-truth events. An IoU of 1 is the best performance achievable, indicating that all detected events are ground-truth and all ground-truth events are detected. Mean is plotted, and error bars indicate the 95% confidence interval calculated from 5 independent replications of simulation, where each simulation contains 10–20 events.

Supplementary information

Supplementary Figs. 1–12 and Supplementary Tables 1–5.

Reporting Summary

Supplementary Video 1

Ex vivo astrocytic GCaMP in a single cell with AQuA-detected events overlaid on the same data at right. Frame rate is 1.1 Hz. Scale bar: 50 μm. Representative video from a total of five slices and 11 cells.

Supplementary Video 2

In vivo GCaMP activity in a population of astrocytes with AQuA-detected events overlaid on the same data at right. Frame rate is 2 Hz. Scale bar: 50 μm. Representative video from a total of six mice.

Supplementary Video 3

Ex vivo neuronally expressed GluSnFR with AQuA-detected events overlaid on the same data at right. Frame rate is 1.7 Hz. Scale bar: 10 μm. Representative video from a total of three slices with neuronal GluSnFR expression.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., DelRosso, N.V., Vaidyanathan, T.V. et al. Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology. Nat Neurosci 22, 1936–1944 (2019). https://doi.org/10.1038/s41593-019-0492-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-019-0492-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing