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

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.

Figure 1.. AQuA-based event detection.

(a) Individual representative frames from 5-min ex vivo astrocytic GCaMP imaging experiment (top, Video 1) with AQuA-detected events shown below. Each color represents individual event and is chosen at random. Right column shows the average GCaMP fluorescence (top) and all AQuA-detected events (bottom) from the entire video. Note that contrast differs between rows to highlight events. Similar event detection results are observed in all 48 ex vivo, 46 in vivo GCaMP data sets and 14 GluSnFr data sets used in this report. (b) Flowchart of AQuA algorithm. Raw data is visualized as a stack of images across time with grey level indicating signal intensity. In the detect peaks panel, five peaks are detected and highlighted by solid diamonds, each color denoting one peak. Based on the single-cycle rule and spatial adjacency of the apexes (solid dots) of each peak, peaks are clustered into spatially disconnected groups. Based on smoothness, propagation patterns are estimated for each peak group. By applying the single-source rule, two events are detected for peak group 1. Three total events are detected. (c) Feature extraction. Based on the event-detection results, AQuA outputs four sets of features relevant to astrocytic activity: 1) propagation-related (path, direction, and speed); 2) source of events, indicating where an event is initiated; 3) features related to the event footprint, including area and shape. Event 2 is plotted here; 4) features derived from the dF/F dynamics.

Figure 2.. Performance comparison among image-analysis methods.

(a–c) Schematic (top) and results (bottom) of five image analysis methods (AQuA, GECI-quant, CaSCaDe, CaImAn, and Suite2P) on simulated datasets, independently changing event size (a), location (b), and propagation duration (c). In results, independent parameter change is shown in the left panel, and varying SNR in the right. For each, the smallest value of the independent parameter corresponds to a simulation under pure ROI assumptions. The larger the values, the greater the violation of the ROI assumptions. IoU (intersection over union) measures the overlap between detected and ground-truth events. An IoU=1 is the best achievable performance, meaning that all detected events are ground-truth and all ground-truth events are detected. For all graphs, mean value is plotted, and error bars indicate the 95% confidence interval calculated from 10 independent replications of simulation, where each simulation contains hundreds of events.

Figure 3.. AQuA features capture heterogeneities among single astrocytes.

(a) Representative GCaMP6f ex vivo image (left) with AQuA events overlaid from 1 min of a 5 min video. Soma marked with black s. (Video 1). Right: Representative image sequence for each propagation direction class (blue = static, pink = toward soma, purple = away from soma. Soma direction marked with s and white arrow. Data from a total of 11 cells from 5 slices. (b) Spatiotemporal plot of Ca²⁺ activity from 1 min of video. Each event is represented by a polygon that is proportional to its area as it changes over its lifetime. (c) Distribution of dynamic and static events as a function of minimum distance from soma. All bin widths calculated by Freedman-Diaconis’s rule. (d) Left: Propagative event size versus starting distance from soma, segregated by propagation direction. Dashed gray line denotes half the distance between the soma and the cell border. Right: Average event area for those that start <50% (top) and >50% (bottom) from the soma, (one-tailed paired t-test, *p=0.0107). Graphs in d and e display mean ± s.e.m. (e) Left: Event duration versus starting distance from soma. Right: Average event duration for those that start <50% (top) and >50% (bottom) from the soma (one-tailed paired t-test, *p=0.0269). (f) Two event-based measurements of frequency are schematized: events with activity overlapping in time (left) and in space (right;). Left: one example event (orange) co-occurs with six other events (white) within 10s. Right: event colors indicate event number/min (0.2–4) at each location. Median (red) and interquartile range (gray) from cells in each cluster in Supp. Fig. 9 (one-tailed Wilcoxon rank sum, ***p<0.001). (g) Centroid distances between cells from two clusters determined by t-SNE plots of Ca²⁺ activity using features calculated from ROIs and 5×5μm tiles (top), (bottom, one-tailed paired t-test, ***p=9.37e-8(tiles), 2.11e-10(ROIs)).

Figure 4.. AQuA resolves astrocytic Ca²⁺ propagation directionality across scales.

(a) Representative in vivo GCaMP6f images during a burst period (top) and inter-burst period (bottom) with overlaid AQuA-detected events. (b) Population Ca²⁺ events represented as percentage of the imaging field active as a function of time. Burst periods (pink) are defined by Ca²⁺ activity >1% of the field of view and >10% of the maximum number of event onsets. (c) In vivo Ca²⁺ events propagate with specific directionality. Top: representative propagative event from the burst in panel a. The propagation direction (change of centroid relative to its original location) for each frame is overlaid on the event (right). Bottom: Total propagation distance versus event size for all events within bursts (n=6 mice, 66 bursts, 14,967 events for all data in this figure). (d) Event propagation direction from all events over the entire field in the burst in e. Arrow length indicates propagation distance. (e) To test consistency of subregional directionality during bursts, sixteen 96×96μm tiles are overlaid on images. (f) Top: All events within highlighted tile in d (red square) for five burst periods, color-coded by propagation direction (top). Bottom: Event propagation direction distributions (P=posterior; A=anterior; M=medial; L=lateral). (g) Cumulative distribution of percentage of bursts with events (within individual tiles/regions) propagating in the same direction in actual (solid) and simulated (dashed) data (one-tailed Wilcoxon rank sum, ***p=3.77e-15) (h) Two representative maps of population burst propagation direction with each event color-coded by onset time relative to the beginning of the burst, demonstrating variability of burst size. (i) Burst propagation direction calculated from onset maps in h (n=66 bursts). Event locations from the first 20% of the frames after burst onset are averaged together to determine burst origin. Event locations from 20% of the last frames after burst onset are averaged together and the difference between this and the origin determines burst propagation distance. Red arrow denotes average of all bursts.

Figure 5.. AQuA-based detection of extracellular dynamics via astrocytic and neuronal expression of genetically encoded neurotransmitter sensors.

(a) Representative images of ex vivo slices with expression of astrocytic (left) or neuronal (right) GluSnFR. Color indicates detected events. Those with dynamic shape are shown in magenta, and static events in cyan. (b) Examples of timecourse of astrocytic (left, top) and neuronal (right, top) glutamate events. Scale bar = 10μm. Raster plot of astrocytic (left, bottom) and neuronal (right, bottom) glutamate event area. (c) Size dynamics (area increase [left] and decrease [middle] per frame) and shape (circularity index, right) of glutamate events when GluSnFR is expressed on astrocytes (red) or neurons (blue). (n=3 slices for each cell type, comparison using two-tailed t-test with mean displayed as center, p=0.414(left), 0.0297(middle), 4.26e-6(right)) (d) Left: Single astrocyte expressing GluSnFR, with AQuA-detected events (colors) with ~100Hz frame rate imaging and 25–150ms uncaging of RuBi-glutamate. Uncaging locations marked with white circles. Right: Percent correct events detected by AQuA, as a function of laser uncaging pulse duration. (n=5 cells, mean ± s.e.m.) (e) Example of three AQuA-detected events at single timepoint (97s) after addition of 300μM GABA to slice with astrocytes expressing GABASnFR (left). Right: Detected events before and after addition of 300μM GABA (gray bar) to circulating bath. Events are plotted to display spatial position in imaging field (y-axis), event area (height), and gradually increasing amplitude (color) over time. (n=1 slice) (f) Left: two detected events in cortical slice expressing GRAB-NE in neurons after addition of 10μM NE. Right: Events plotted to display spatial position (y-axis), event area (height), and amplitude (color) dynamics over the course of the experiment. In (e) and (f), average xy position at each timepoint is calculated using the following equation: (((xLoc-1)*frameSize) + yLoc)/frameSize. (n=1 slice)