| Literature DB >> 35211642 |
Kelsea A Gorzo1, Grant R Gordon1.
Abstract
Astrocytes integrate information from neurons and the microvasculature to coordinate brain activity and metabolism. Using a variety of calcium-dependent cellular mechanisms, these cells impact numerous aspects of neurophysiology in health and disease. Astrocyte calcium signaling is highly diverse, with complex spatiotemporal features. Here, we review astrocyte calcium dynamics and the optical imaging tools used to measure and analyze these events. We briefly cover historical calcium measurements, followed by our current understanding of how calcium transients relate to the structure of astrocytes. We then explore newer photonics tools including super-resolution techniques and genetically encoded calcium indicators targeted to specific cellular compartments and how these have been applied to astrocyte biology. Finally, we provide a brief overview of analysis software used to accurately quantify the data and ultimately aid in our interpretation of the various functions of astrocyte calcium transients.Entities:
Keywords: analysis; astrocyte; calcium; genetically encoded fluorescent calcium indicator; stimulation emission depletion; two-photon
Year: 2022 PMID: 35211642 PMCID: PMC8857908 DOI: 10.1117/1.NPh.9.2.021907
Source DB: PubMed Journal: Neurophotonics ISSN: 2329-423X Impact factor: 3.593
Fig. 1A comparison of small molecule calcium indicators with GECIs. A visual representation of the area of detected calcium signals inside the astrocyte is shown along with representative traces and general properties for various common indicators. A description of the advantages and disadvantages for small molecule indicators versus GECIs is also provided.
Fig. 2A schematic comparison of the neuro–glio–vascular interface as observed under diffraction-limited versus diffraction-limit exceeded microscopy for live-cell imaging. Diffraction limited tools (two-photon, visible confocal) are well suited to in vivo and brain slice preparations; however, only large astrocyte processes near synapses are resolved. Diffraction-limit exceeded microscopy (STED, 2P-STED, etc.) in acute slices or culture allow for specific compartments or organelles in addition to the loop-like/spongiform astrocyte arbor to be visualized.
A comparison of the most current image analysis toolboxes catered to astrocyte calcium imaging data. The primary reference for each toolbox is provided along with the platform on which the toolbox is run. The strategy of analysis the toolbox preforms is also indicated (ROI versus event-based) with intended use scenarios and the key outputs generated.
| Toolbox | Primary reference | Platform | ROI based | Event based | Intended uses | Key outputs |
|---|---|---|---|---|---|---|
| GECIquant | Srinivasan et al. (2015) | ImageJ and MATLAB | ✓ | User friendly, ROI-based detection using thresholding | Intensity versus time traces for each ROI | |
| Best suited to examine larger/localized | An additional script can be used to extract ROI features (amplitude/frequency) | |||||
| CaSCaDe | Agarwal et al. (2017) | MATLAB | ✓ | ROI-based machine learning detection of active regions | Spatial map of detected ROIs | |
| Best suited to examine | Frequency and amplitude of ROIs | |||||
| AQuA | Wang et al. (2019) | MATLAB or ImageJ | ✓ | Flexible platform, event-based machine learning detection of individual | Frequency, amplitude, area, and duration of | |
| Best suited to examine dynamic properties of distinct | Propagation direction, rise/decay time, and proximity to a user-identified “landmark” | |||||
| Begonia | Bjørnstad et al. (2021) | MATLAB | ✓ | ✓ | Automatic, event, and ROI-based detection plus data management toolbox | Frequency, amplitude, area, and duration of |
| Best suited to examine | ||||||
| Astral | Dzyubenko et al. (2021) | Apache airflow | ✓ | Event-based detection optimized for multiple cell scenarios | Frequency, amplitude, area, and duration of | |
| Best suited to examine | Evaluates intracellular propagation of | |||||
| CHIPs | Barrett et al. (2018) | MATLAB | ✓ | ✓ | Extensible toolbox integrating preprocessing and several analysis methods | |
| Best suited to examine a dataset using multiple approaches simultaneously | E.g., can simultaneously analyze cell volume or diameter of vasculature |