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Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology
Open accessAbstract
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.
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2D and 3D Ca2+ imaging generates kinetic profiles and maps for single astrocytes and 2D or 3D networks.
“An example of functional data is 2D and 3D Ca2+ imaging that generates kinetic profiles and maps for single astrocytes and 2D or 3D networks (Fig. 1c)119,120.”
From Reactive astrocyte nomenclature, definitions, and future directions (2021) · cites this paper as [120]
The Results section on single-cell physiological heterogeneities describes extracting time courses and mapping Ca2+ activity in individual astrocytes, while the Introduction describes analysis across cellular and population-wide scales. But the Discussion says AQuA was optimized for 2D data and that its 3D version was only a prototype tested on simulated data; this paper does not establish the claimed 3D network mapping. (LLM, conf=0.90)
Experimental — accuracy not yet measuredArtificial intelligence can identify patterns of Ca2+ signaling in astrocytes.
“Artificial intelligence can identify patterns of Ca2+ signaling in astrocytes55,120.”
From Reactive astrocyte nomenclature, definitions, and future directions (2021) · cites this paper as [120]
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References 38 · Cited by 194
Research works printed but not linked: 30
- Marvin, J. S. et al. An optimized fluorescent probe for visualizing glutamate neurotransmission. Nat. Methods 10, 162–170 (2013). · DOI
- 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). · DOI
- Paukert, M. et al. Norepinephrine controls astroglial responsiveness to local circuit activity. Neuron 82, 1263–1270 (2014). · DOI · Paper page
- Haustein, M. D. et al. Conditions and constraints for astrocyte calcium signaling in the hippocampal mossy fiber pathway. Neuron 82, 413–429 (2014). · DOI
- Ding, F. et al. α1-Adrenergic receptors mediate coordinated Ca2+ signaling of cortical astrocytes in awake, behaving mice. Cell Calcium 54, 387–394 (2013). · DOI
- Poskanzer, K. E. & Yuste, R. Astrocytes regulate cortical state switching in vivo. Proc. Natl Acad. Sci. USA 113, E2675–E2684 (2016). · DOI · Paper page
- 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). · DOI
- Poskanzer, K. E. & Yuste, R. Astrocytic regulation of cortical UP states. Proc. Natl Acad. Sci. USA 108, 18453–18458 (2011). · DOI
- Feng, J. et al. A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102, 745–761 (2019). · DOI · Paper page
- Lobas, M. A. et al. A genetically encoded single-wavelength sensor for imaging cytosolic and cell surface ATP. Nat. Commun. 10, 711 (2019). · DOI
- Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360, eaat4422 (2018). · DOI
- Bindocci, E. et al. Three-dimensional Ca(2+) imaging advances understanding of astrocyte biology. Science 356, eaai8185 (2017). · DOI · Paper page
- Wu, Y.-W. et al. Spatiotemporal calcium dynamics in single astrocytes and its modulation by neuronal activity. Cell Calcium 55, 119–129 (2014). · DOI
- Wang, Y. et al. Graphical time warping for joint alignment of multiple curves. Adv. Neural Inf. Process. Syst. 29, 3648–3656 (2016).
- Giovannucci, A. et al. CaImAn: an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019). · DOI
- Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2016). · DOI
- Poskanzer, K. E. & Molofsky, A. V. Dynamism of an astrocyte in vivo: perspectives on identity and function. Annu. Rev. Physiol. 80, 143–157 (2018). · DOI
- 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). · DOI
- van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
- 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). · DOI
- Nimmerjahn, A., Mukamel, E. A. & Schnitzer, M. J. Motor behavior activates Bergmann glial networks. Neuron 62, 400–412 (2009). · DOI
- 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). · DOI
- 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). · DOI
- Marvin, J. S. et al. Stability, affinity, and chromatic variants of the glutamate sensor iGluSnFR. Nat. Methods 15, 936–939 (2018). · DOI
- Fino, E. et al. RuBi-glutamate: two-photon and visible-light photoactivation of neurons and dendritic spines. Front. Neural Circuits 3, 2 (2009). · DOI
- Baraban, M., Koudelka, S. & Lyons, D. A. Ca2+ activity signatures of myelin sheath formation and growth in vivo. Nat. Neurosci. 19, 190 (2017).
- 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).
- Higley, M. J. & Sabatini, B. L. Calcium signaling in dendritic spines. Cold Spring Harb. Perspect. Biol. 4, a005686–a005686 (2012). · DOI
- 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). · DOI
- Goldey, G. J. et al. Removable cranial windows for long-term imaging in awake mice. Nat. Protoc. 9, 2515–2538 (2014). · DOI
Metadata
- Authors
- Wang, Y., DelRosso, N. V., Vaidyanathan, T. V., Cahill, M. K., Reitman, M. E., Pittolo, S., Mi, X., Yu, G., & Poskanzer, K. E.
- Affiliations
- Virginia Tech (US)
- University of California, San Francisco (US)
- Corresponding author
- Yu, Guoqiang
- Poskanzer, Kira E.
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2019-09-30
- Volume
- 22
- Issue
- 11
- Pages
- 1936-1944
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC6858541
- OpenAlex
- W2975543744
- Cited by
- 194
- References
- 38
- Citations by year
- 2026: 25 · 2025: 31 · 2024: 30 · 2023: 31 · 2022: 31 · 2021: 27 · 2020: 18 · 2019: 1
- FWCI
- 9.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation (1650113, 1750931, 1604544)
- U.S. Department of Health & Human Services | National Institutes of Health (R01MH110504, R21DA048497)
- Brain Research Foundation
- E. Matilda Ziegler Foundation for the Blind
- UCSF QBI
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- astrocytes, calcium activity, neurotransmitter release, fluorescence dynamics, event-based modeling, machine learning
- MeSH terms
- Adenoviridae, Algorithms, Animals, Animals, Newborn, Astrocytes, Calcium, Genetic Vectors, Image Processing, Computer-Assisted, Software, Transfection, Visual Cortex, Neurotransmitter Agents, Mice, Optical Imaging
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