SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

doi:10.21203/rs.3.rs-2616951/v1

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Research Article

SIGAA – SIGnaling Automated Analysis: a new tool for Ca²⁺ signaling quantification using ratiometric Ca²⁺ dyes

https://doi.org/10.21203/rs.3.rs-2616951/v1

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Journal Publication

published 04 Nov, 2023

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Astrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte-astrocyte and astrocyte-neuron interactions occur through cytoplasmic Ca²⁺ levels changes, that are assessed to determine cell function and response (i.e. drug testing). Evaluation of changes in intracellular Ca²⁺ levels is mostly centered on fluorescence imaging approaches, performed through video recording of cells incubated with Ca²⁺-sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) comprising a single cell, it is possible to attain conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA – SIGnaling Automated Analysis, for astrocyte ROI-based fluorescent imaging, which is tailored for two wavelengths dyes by using two inputs of Ca²⁺ signaling recorded frames/videos and outputs a set of features relevant to the experiment’s conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. SIGAA then extracts intracellular Ca²⁺ evolution functions for all identified ROIs, detects function transients, and estimates a set of features for each signal, which are very similar to the ones obtained by the traditional methods and software used so far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate.

Astrocyte

central nervous system

calcium (Ca2+) ion

automation

image processing

video drift correction

template matching

automatic detection

morphological reconstruction

The calcium (Ca²⁺) ion is a ubiquitous second messenger that impacts nearly all aspects of cellular functioning, by binding and modulating thousands of proteins in the cell. This ion has a wide range of physiological functions such as muscle contraction, neurogenesis, oxidative stress, the release of neurotransmitters and gliotransmitters from neurons and astrocytes [1]. Ca²⁺ functions are achieved through the Ca²⁺-mediated regulation of enzymatic activity, ion pumps or channels, and other components of the cytoskeleton [2]. A universal mechanism for Ca²⁺ signaling is the release of this ion from the intracellular stores [3], such as the endoplasmic reticulum (ER) and the mitochondria. This mechanism is usually triggered by activation of a G-protein coupled receptor, primarily the Gq/11 subtype which led to the activation of phospholipase C (PLC) cleaving the phosphatidylinositol 4,5 bisphosphate (PIP2) into 1,4,5- inositol triphosphate (IP3) and diacylglycerol (DAG). IP3 then binds to the IP3 receptor (IP3R) in the ER allowing the Ca²⁺ to diffuse from the ER to the cytosol [4]. Ca²⁺ entering through plasma membrane located channels, also contributes to rises in intracellular Ca²⁺, whereas Ca²⁺ exit through transporters and pumps present at the ER or cytoplasm membrane allows restoring of basal levels of free Ca²⁺ at the cytoplasm, which in a healthy cell are at the low nanomolar range.

Astrocytes are a glial cell type in the central nervous system (CNS) that play an important role in brain homeostasis by maintaining the extracellular ionic environment, through the control of extracellular K⁺ levels, the levels of neurotransmitters in the synapses, and the ”feeding” of neurons through the astrocyte-neuron lactate shuttle system [5]. Moreover, astrocytes can modulate the vascular function and neuronal synaptic transmission and plasticity by sensing and responding to environmental signals, through increases in the intracellular concentration of Ca²⁺ ([Ca²⁺]_i) with specific spatial and temporal properties that can be used as a readout of astrocytic function [6]. Thus, although astrocytes do not have the ability to trigger action potentials, they are considered excitable cells, through variations in [Ca²⁺]_I [7], which leads to the release of gliotransmitters that can modulate synaptic function, generating a bi-directional neuronastrocyte communication within the synapses[1, 8]. Such properties make astrocytes not only able to modulate synaptic transmission but essential for it[9, 10]. The study of Ca²⁺ signaling in astrocytes sheds light on cell physiology and its response to certain conditions (i.e. drug testing). However, astrocytes display a complex range of Ca²⁺ signaling within the cell itself and/or across astrocytic networks. Decrypting the meaning of these signals has presented to be a major challenge and new tools are needed [2].

Studies on Ca²⁺ signaling are mostly centered on fluorescence imaging approaches, by using Ca²⁺ sensitive indicators, and quantifying concentration shifts over a time period at a visually based delimitation of a region of interest (ROI), usually, comprising a single cell [11]. Alternatives to the visually based ROI definition have been proposed [12, 13]. Such methods move away from a single-cell intracellular Ca²⁺ signal approach to a Ca²⁺ wave-based study, aiming for inter-astrocyte connection and causality. Indeed, ROI-based analysis is still the prevalent method while studying Ca²⁺ imaging in astrocytes. Astrocytic Ca²⁺ signalling studies using ROIs are, in many cases, performed with minimal to no computer assistance which is time-consuming and prone to inter-subject variability and bias, such as video drift. Therefore, it is important to develop novel computational approaches to automate and accelerate the study of Ca²⁺ signalling with minimal human error.

Here, we describe a new tool named SIGAA - SIGnaling Automated Analysis - that consists of a MATLAB script that performs an drift correction of the recorded Ca²⁺ imaging videos with a template matching algorithm followed by astrocyte identification using ROIs through morphological reconstruction techniques. Even though some tools are already developed, many of them if not all are tailored for single wavelength dyes [14–16] and to the best of our knowledge this is the first tool of Ca²⁺ imaging with a radiometric dye in mind. In this study, only FURA-2- acetoxymethyl ester (FURA 2-AM) was used but any other raciometric dyes can be integrated by this toolbox. SIGAA generates intracellular Ca²⁺ evolution functions for all the identified ROIs, detects occurence of the Ca²⁺ transients, the Ca²⁺ transients amplitude, as well as a set of features calculated for each signal, such as slope, duration, rise and decay.

The Ca²⁺ imaging videos were recorded from primary cultures of astrocytes incubated with the ratiometric Ca²⁺ fluorophore, FURA-2 AM, to measure the variation of [Ca²⁺]_i over time. FURA-2-AM [17] is a high-affinity Ca²⁺ selective fluorescent indicator that is rapidly metabolized, by cytoplasmic esterase, to the active dye FURA-2 (Kd = 145 nM), which excitation spectrum depends on [Ca²⁺]_i since FURA-2 has an emission peak at 505 nm and changes its excitation peak from 340 nm (when it is bound to Ca²⁺) to 380 nm (when it is free) in response to Ca²⁺ binding. The intracellular Ca²⁺ evolution signals were generated from two videos that were obtained while performing the Ca²⁺ imaging recording, and each video was captured for a single wavelength corresponding to the 340 nm or the 380 nm wavelength excitation, respectively. Therefore, the 340/380 nm ratio gives a representation of the evolution of intracellular Ca²⁺. The Ca²⁺ imaging videos were obtained using specific software, the MetaFluor® Fluorescence Ratio Imaging Software, that is not prepared to cope with artificial cell movement that occurs due to human handling of the laboratory setup throughout drug administration to the cells during the experimental procedure. To overcome this issue the correction of cell position is essential for the precise Ca²⁺ concentration function extraction, which by itself presents a time-consuming task and error-prone procedure.

With the analytic tool SIGAA, we are proposing a method to accelerate ROI-based fluorescent imaging analysis, allowing for quicker results extraction and conclusions over astrocyte communication characteristics. By using as input the laboratory recorded experiment videos for the 340 nm and 380 nm wavelengths, SIGAA is able of automating video drift correction, minimize artificial cell movement, automate the identification of the astrocytes, and generate the respective Ca²⁺ ratio functions allowing to systematize the extraction of the relevant parameters to characterize astrocyte function. This is achieved with a set of template matching algorithms for drift correction together with morphological reconstruction for cell identification and segmentation.

In this paper, we describe our results as following. We started Section 2 by describing the procedure for the acquisition of astrocyte ROI-based Ca²⁺ fluorescent imaging, and the consequent correction of cell movement due to human handling, as one of the main motivations for SIGAA system implementation. In Section 3, the proposed system implementation is presented, as well as the corresponding methods used to overcome the concern with video drift correction and to achieve astrocyte cell automatic identification. In Section 4, results and discussion are presented, including generated function comparisons for SIGAA generation vs. previously used manual procedure as well as runtime performance benchmarks. Finally, Section 5 concludes the paper and also gives directions for future work.

The software is freely available at https://github.com/ISEL-DEETC/SIGAA, where detailed documents and example applications can be found.

2.1. Ca²⁺ Imaging Set up

Primary astocyte cultures were prepared as previously described [18]. The cells were positioned on an inverted epifluorescent optics microscope (Axiovert 135TV, Zeiss, Germany) and image pairs obtained every 250 ms by exciting the preparations at 340 and 380 nm were recorded to obtain the 340/380 ratio images. Excitation wavelengths were changed through a high-speed wavelength switcher, Lambda DG-4 (Sutter Instrument, Novato, CA), and the emission wavelength was set to 510 nm. Image data were recorded with a cooled CCD camera (Photometrics CoolSNAP fx) and processed using the software MetaFluor (Universal Imaging, West Chester, PA). Throughout all experiments, cells were kept at 36ºC with 5% CO₂ and visualized with a 40x oil-immersion objective (Zeiss).

2.2. Astrocyte ROI based fluorescent ratio imaging

The resulting 340 and 380 nm recorded images are used as input to a specific imaging software (MetaFluor) that calculates the 340/380 ratio and is able of generating functions of Ca²⁺ concentration evolution over time, based on the images brightness levels variation. From the acquired sequence of images, before SIGAA, there was a manual selection of a set of ROIs from the cells with interest to study. From these ROIs, is possible to extract the resulting Ca²⁺ functions which are then studied to allow for conclusions over specific cell characteristics, being the baseline, the number of transients, the transient slopes, and amplitudes the main Ca²⁺ features usually evaluated. The main issue with the previously described procedure is the induction of minimal vibration in the laboratory setup that will result in an artificial frame-to-frame movement over the experiment recorded frames/videos. Such movement is unavoidable since is caused by human handling of the laboratory setup and is mostly raised during drug incubations. Thus, this minimal vibration of the system results in a position displacement between the manually defined ROI (that is always in the same place) and the astrocyte that is being studied (that will change location), during function generating and running on the software that processes the acquired images. This will lead to a considerable error in the output Ca²⁺ functions. Consequently, to overcome this issue, the function extraction procedure must be stopped on each displacement, to redefine the new ROI over the new cell position, and then resume. For optimal function this manual adjusting has to be performed on every frame change, over multiple cells, resulting in an iterative process that will represent the major time spent for the study, with no added value to the final results and prone to errors.

Moreover, once all Ca²⁺ functions are generated, these are often studied with the extraction of relevant parameters, with little computer assistance, which is also a timeconsuming step that is prone to errors.

2.3. Proposed system and methods

The system that we are now proposing (Fig. 1) follows a three-function block structure. The first block of SIGAA is responsible for computing the 340/380 ratio values, using both input experimental frames/videos, and for performing automatic video drift correction from the input recorded videos of an experiment. This block is achieved with a two-step template matching algorithm using normalized cross-correlation. The second block takes the videos with drift correction from block one and applies morphological reconstruction techniques to automatically detect and segment detectable astrocytes that were collected as defined ROIs. The third block is responsible for processing Ca²⁺ functions previously generated and extracting a set of numeric parameters, such as function baseline identification, duration of the transient, and slopes, which are relevant for cellular experimental conclusions.

2.4 Block 1: Ratio values and input video drift correction

SIGAA’s first block considers the laboratory recorded 340 nm and 380 nm videos of astrocyte response to a specific test substance as input and is responsible for computing 340/380 ratio values and performing video drift correction.

Since Ca²⁺ intracellular levels are represented by the 340/380 ratio values, the first block starts by dividing 340 nm frames by 380 nm frames, which will be used by the second block to generate Ca²⁺ functions. For the second part of the first block, the video drift correction will be made since the input videos, due to artificial movement previously described, inherently pose two obstacles:

1) repeated vibrations to the camera focus throughout the recording procedure can move some astrocytes out of the same video frames. Such cells are not valid for the study, as it is not possible to extract a valid function for these cases, and thus, should not be considered for Ca²⁺ function extraction;

2) other astrocytes are shown in the visual field during the whole duration of the recorded videos, but still, less significant vibrations can displace the cells away from the initial ROI, compromising brightness value extraction inside the ROI for the cell Ca²⁺ function extraction.

The SIGAA system tackles both issues with a two-step template matching algorithm, based on normalized cross-correlation. Template matching [19] is a high-level machine vision method that distinguishes the parts on an image that match a predefined layout. Cross-correlation represents, by definition, one of such methods as a measure of similarity between two different signals (images in this case), by sliding the first shorter template signal over the tested signal [20], [21]. The resulting coefficients will define a measure of the probability of the template signal to be found on a specific position on the second signal. Consider in Eq. 1 the cross-correlation coefficients $r\left(k\right)$ for signals $x\left(i\right)$ and $y\left(i\right)$with $i=\text{0,1},2\dots N-1$, at position $k$:

$$r\left(k\right)=\frac{\sum _{i}\left[\left(x\left(i\right)-mx\right)\left(y\left(i-k\right)-my\right)\right]}{\sqrt{{\varSigma }_{i}{\left(x\left(i\right)-mx\right)}^{2}}\sqrt{{\varSigma }_{i}{\left(y\left(i-k\right)-my\right)}^{2}}} , \left(1\right)$$

where $mx$ and $my$ are the corresponding means for signals $x\left(i\right)$ and $y\left(i\right)$. Function normalization is used meaning that $-1\le r\left(k\right)\le 1$. For $r\left(k\right)=1$, cross-correlation represents the maximum similarity between the template and the tested signal. For $r\left(k\right)=0$, shows no similarities between the two signals. Negative values express symmetric similarity (negative image). Thus, the first step of the implemented algorithm determines the relevant window for the region that only contains ROIs which are present during the whole video’s duration. The second step uses this resulting window to perform video drift correction and output a new video with minimized interframe cell movement.

Since 340 nm and 380 nm videos result from one recording of a laboratory experiment over two different wavelengths, both videos will have the same artificial movement offset values, meaning that video drift correction will only be generated for one wavelength of the two (in SIGAA’s approach only for the 380 nm version) and the resulting offset values are then applied to the other wavelength video.

Thus, SIGAA first block uses the first frame of the video as a template versus each frame using normalized cross-correlation as in Eq. 1. This step will output a smaller image that will represent the significant window of the experimental acquired video; hence, this image will contain only the astrocytes that do not move out of the video focus during laboratory recording. Once the significant window is found, it will become the new template image for the second step of Eq. 1, again, versus the entire video. This second run will output interframe movement offset values that are then applied to both 340 nm and 380 nm videos to produce a new set of frames that, when aggregated back in order, result in new drift correction versions of the laboratory videos for the relevant window.

2.5. Block 2: Laboratory video analysis and function extraction

The second block is responsible for analysing the output drift correction videos from block one and generating Ca²⁺ functions for further analysis on block three. Thus, Block 2 detect and segment every visible astrocyte, with no human interaction. These segmented astrocytes are the ROI for brightness probing and function extraction. To achieve this segmentation, the system applies morphological reconstruction for the first frame of the input drift correction video.

Morphologic reconstruction [21–23] is applied between two images and a structuring element, which defines connectivity during the reconstruction process. It allows building an image from small components removing non-desired features while maintaining the initial shape of the present objects. The marker image is the starting point for the transformation. The mask image limits the transformation inside its contours. The basic reconstruction procedure$R\left(F\right)$, starting on marker image $F\left(k\right)$, with $\text{k}=1$ is described on Eq. 2:

$$R\left(F\right)= \left(F\left(k\right)\oplus B\right)\cap G, \left(2\right)$$

where $k$ represents the number of dilation necessary of F with $B$, the structuring element until the reconstruction matches $G$’s limits. Dilation is a basic morphological operation that expands F borders based on the structuring element $B$. SIGAA applies a speed-up version of the previous iterative algorithm, proposed and fully detailed in Vincent, 1993, to detect and segment detectable astrocytes on a given image.

Astrocyte morphology can be lost after ROI definition process, cell detection, and segmentation, therefore morphologic reconstruction cannot be performed using the ratio values between the two input experimental videos. This means that cell detection and segmentation can only be performed for either on 340 nm or 380 nm input video. Like video drift correction in block one, detection and segmentation need to be performed only on one of the input videos, since cell position is the same for both. In our case, Block 2 uses 380 nm drift correction video to perform astrocyte detection and segmentation.

Considering that astrocytes’ nuclei are mainly of rounded shape and because the accuracy of the reconstruction procedure depends on the similarity between the object shapes and the structuring element [22], [24] SIGAA uses an almost-circular structuring element for ROI reconstruction procedure. During system execution, after cell automated identification and segmentation, these ROIs are presented to the system operator for manual correction (if needed) and/or manual addition of new unidentified ROI by the system. This is the only (eventual) human interaction during runtime and helps avoid false positives for automatic cell detection. Once all automatic (and manual) defined ROI are approved by human interaction, SIGAA will run defined regions through the stabilized obtained 340/380 ratio video and extract Ca²⁺ evolution functions over time based on the mean pixel brightness value attained (Fig. 2). In this particular case, researchers studied the Cannabinoid type 1 receptor (CB1R)-mediated Ca²⁺ transients in astrocytes by incubating these cells with a specific CB1R agonist, Arachidonyl-2'-chloroethylamide (ACEA) [26]. To this end, the baseline was established in the first five minutes of the experiment and at 1200 seconds (20 minutes) ACEA was added and Ca²⁺ transients were detected. However, the time limits of the second segment can be edited directly in the MATLAB script. To confirm cell viability, at 2400 seconds (40 minutes), ionomycin salt (2 µM) was added to the medium, in which viable cells had a considerable increase in their intracellular Ca²⁺ [27]. These functions will constitute the block two’s output.

2.6. Block 3: Feature extraction

The third block takes as input the previously generated Ca²⁺ functions (second block output), and extracts a set of numeric features that are relevant for the specific experiment’s conclusions on astrocyte Ca²⁺ signaling characteristics: function baseline; the number of transients; the transient amplitude, and slopes; the transients rise/decay time; and inter-transient duration. All of these are extracted according to the defined features established previously by the user such as the basal end time estimation, minimum and maximum transient duration (Table 1).

However, before attempting to detect transients and extract features, it is important to understand the structure of the Ca²⁺ functions. Ca²⁺ concentration is measured by the ion Ca²⁺ ratio bound to fluorescent markers (340 nm) with the measure of the same markers that did not bound to Ca²⁺ (380 nm). Generated functions are divisible in three different segments (Fig. 2): a first segment for which Ca²⁺ concentration is roughly constant when the astrocyte is at resting state; a second segment caused by the drug administration phase which leads to substantial basal Ca²⁺ level variation and transients occurrence, with a stable increase corresponding to the function baseline; and the third segment with an accentuated increase of Ca²⁺ concentration, caused by the ionomycin salt. This last segment is not featured in the subsequent data analysis and can be cut off by incorporating it into the second segment. Feature extraction performed by SIGAA’s Block 3 focuses on the second segment of the Ca²⁺ functions. The time boundaries of these segments are manually defined by the research team since they depend on the substance administration signature.

In the event of an increasing baseline, baseline functions are estimated by Lagrange polynomial interpolation [28], for the input Ca²⁺ functions over the first and second segments. The polynomial order is defined empirically and is manually adjustable if needed. This baseline function is then subtracted to the Ca²⁺ function (Fig. 3), allowing to better distinguish function transients after substance administration. This feature can be switch on or off according to the user (Table 1).

After baseline subtraction, SIGAA’s third block can apply noise reduction. If the user feels that the use of filter interferes with signal in a substantial manner, they can choose to switch off this part of the block (Table 1). The noise reduction is performed by using a moving average filter described by Eq. 3:

$$r\left[n\right]=\frac{1}{M+1}\sum _{m=-M/2}^{M/2}s\left[n-m\right], \left(3\right)$$

where $r\left[n\right]$ are the output signal and $s\left[n\right]$ the input signal (in this case, Ca²⁺ function subtracted by the baseline function) and $M=4$ is the filter order.

Function transients represent the astrocyte’s response to the administrated substance during laboratory testing and are distinguishable as higher amplitude peaks on the Ca²⁺ function signal, after baseline subtraction and noise reduction steps. Transients are very important for the Ca²⁺ signaling study of cells, hence, SIGAA’s final output is a set of numerical features based on these function peaks. Transient identification is automated by the system’s third block using Eq. 4:

$$th=\stackrel{-}{r}+marge*{\sigma }_{r}, \left(4\right)$$

as a sequence of adjacent function points with a value above a threshold ($th$), defined as the function’s mean ($\stackrel{-}{r}$) estimated during the second segment, added by a factor $marge$ of its standard deviation (${\sigma }_{r}$) for the same segment.

Once all cells (Fig. 4), and subsequently all transients are identified, the system will estimate a set of numeric features to allow for experiment conclusions, being these features outputted as SIGAA’s result in an Excel spreadsheet (.xls format). There are 7 spreadsheets with Ca²⁺ transient information: (1) transient amplitude: amplitude of each transient for the selected cells (Fig. 4); (2) non-zero amplitudes: transients amplitudes of all responding cells; (3) transient slopes: Rise and decay slopes of each transient; (4) duration of the transient rise and decay: Rise and decay duration of each transient; (5) time between transients: time between consecutive transients; (6) transient starting time: starting time of each transient, and; (7) transient duration: duration of each transient. Beyond this, an additional sheet is included with the 5 minutes slope of the Ca²⁺ function per cell through the whole experiment. As an exemple the transient amplitude spreadsheet is shown in the Fig. 4 (bottom panel), where columns 1 to n corresponds to the n selected astrocytes. The first row corresponds to the transient frequency of occurrence, and the remaining rows correspond to the transients amplitudes, respectively. Moreover, the final column of the amplitude spreadsheet contains system setup parameters, namely: (1) marge, (2) on/off baseline compensation, (3) start time, (4) minimum transient duration, (5) maximum transient duration and (6) baseline estimation polynomial order. In the other spreadsheets, similarly to the first one, each column corresponds to the identified astrocytes, and the parameters of each astrocyte are presented in the following rows.

Table 1

Description of the script set up parameters
Set up Parameters	Description
Marge	Marge value to detect transient (Eq. 4)
On/Off baseline compensation	1 - Use baseline compensation before transient detection. 0 – do not use baseline compensation before transient detection.
Basal end time statistic estimation	Mean and standard deviation estimation (Eq. 4) is estimated between 0 and this parameter that represents the basal condition (in seconds).
Minimum transient duration	Minimum transient duration (in seconds) to detect transients.
Maximum transient duration	Maximum transient duration (in seconds) to detect transients.
On/Off noise reduction	use noise reduction (Eq. 3) before transient detection. 0 – do not use noise reduction before transient detection.
Baseline estimation polynomial order	Order of the baseline polynomial estimation.

Overall, Ca²⁺ function extraction can be fully automated in a few minutes, for multiple cells. As an exemple, in Fig. 4 is represented an output spreadsheet containing the transient amplitudes. In this experiment, 10 astrocytes were studied, and the first raw display the transient frequency of occurrence. Two transients were identified for the first and sixth cell, one transient for the third cell, and four transients for the fifth cell, while the second and fourth cells had no function transients identified, which can suggest no cell reaction to the possible drug tested in these cells.

In this section, we discuss the obtained results while using SIGAA, to assist in astrocyte ROI-based fluorescent imaging recorded videos. The following tests were performed on a setup computer, with 6 CPU core running at 3.2 GHz, 8 Gb RAM, and a 4 GB RAM GPU.

3.1 Automated cell detection

SIGAA automates cell detection using morphologic reconstruction techniques from the experimental recorded frames/video. This means that, ideally, by using SIGAA the researcher no longer needs to manually define ROI for every detectable cell.

As previously specified, the similarity between the detectable object and the structuring element is important for correct segmentation. These parameters were defined empirically, for this test, and are fully adjustable, as system setup. Correct object segmentation is a complex task, due to the low contrast and resolution of the images. Morphologic reconstruction works well in removing image noise and segmenting objects correctly, however, cell shape variability may hamper this task. Since astrocytes can be round-shaped, hexagonal, or elongated shape cells, morphological reconstruction may be harder to achieve. Nevertheless, the structuring element for the reconstruction is of utmost importance and should be adapted for better cell segmentation. Segmented astrocytes are validated by the system operator who can manually ignore one cell, or more, from the study. New cells can also be added to the study via manual ROI definition at this stage. Figure 4 shows 10 automatically detected cells, using SIGAA, and considering an almostcircular shape structuring element with 0.85 circular shape threshold and a 7-pixel radius. The system generated Ca²⁺ functions for all these cells with no need for manual ROI adjustment on every cell movement that occurred during the video. Additionally in Fig. 5 we shows the Ca²⁺ function generated using both the toolbox SIGAA (Cell 1, blue) with drift correction and ROI identification and the previous manual procedure (MetaFluor®, Cell2, red), including manual drift correction and manual ROI identification, and a similar profile can be observed, thus demonstrating the reliability of SIGAA.

3.2 Function Form

The following test verifies if SIGAA can generate the same function form for a single cell when compared to the manually produced using MetaFluor®. Figure 5 shows two functions extracted through the manually produced using MetaFluor® (red line) and the SIGAA system (blue line). When compared, both the manual and the automated functions follow generically the same pattern. The slight differences observed arise from the differences in the ROI shape obtained by ROI hand-made drawn or by the SIGAA’s almostcircular automat drawn. Around the 1200 second time mark, a difference in function form is visible as an artificial function transient on the manual generated function (red line), created by a human error by adjusting the ROI to the new cell’s position, between video frames. SIGAA does not produces this error by performing automatic video drift correction prior to function extraction.

3.3 Time Performance Benchmarks

In order to obtain benchmark runtime execution of automated Ca²⁺ function extraction performance two different experiments videos were analysed (Fig. 6A and 6B). The extraction is performed cell by cell.

With SIGAA, the 340/380 estimation ratio takes 36.36 seconds and 42.47 seconds (Fig. 6C) for the first and second video experiments, respectively. Results show that with SIGAA, video drift correction takes less than five minutes (Fig. 6C), when compared to what would be an hour-long procedure if manual ROI cell adjustment was accomplished, as per the research team’s feedback. Furthermore, this procedure is only performed once by the system for every new experiment execution with SIGAA, even if other cells are studied on a second run. This procedure takes 241.96 seconds and 259.73 seconds (Fig. 6C) for the first and second video experiments respectively.

The results for the runtime of Ca²⁺ function extraction indicate that (Fig. 6D), for each cell, it will take around 7 seconds for the first video and 8 seconds for the second video, being this value depending on the duration of each video. This procedure takes, for the 7 cells, 38.61 seconds and 48.38 seconds for the first and second video experiments respectively (Fig. 6C).

In this work, we propose a MATLAB tool for speedup analysis for ROI-based fluorescent ratio imaging, named SIGAA – SIGnaling Automated Analysis. SIGAA is able to perform automatic laboratory recorded video drift correction followed by cell identification (ROI) and intracellular Ca²⁺ concentration evolution functions extraction and analysis. The proposed method is based on a template matching algorithm for video drift correction, using zero normalized cross-correlation, followed by morphologic reconstruction operations for astrocyte (ROI) identification and segmentation, and signal processing tools for functional analysis.

Benchmark tests show that function form is preserved by SIGAA’s signal extraction, after automated drift correction and astrocyte identification, when compared with the previously used manual motion correction and function extraction procedure. SIGAA allows automating hour-long procedures, with no added value to the study’s conclusions, to a few minutes, as benchmark tests show.

Since SIGAA performs pixel-brightness analysis to define ROI, it is not restricted to Ca²⁺ signaling testing in astrocytes alone and it can also be used with other cell types such as neurons or microglia [28, 29]. Indeed, this script was already used to unveil the dual influence of adenosine receptors in neuronal cultures Ca²⁺ levels [31], and to evaluate the Ca²⁺ concentration changes observed in astrocytes prepared from ALS SOD1 mice [32]. SIGAA can also be used for automated quantification of transient signals obtained with other fluorescent indicators, not being restricted to FURA-2 AM or Ca²⁺-sensitive fluorescent dyes such as Indo-1 or FURA-2AM.

In summary, we herein describe in detail and validate an automated method to be used whenever transient light emission-based signals are to be analysed by living cells. Further improvements on SIGAA tool should focus on a new system block implementation, able to perform cell classification, based on Ca²⁺ signaling patterns observed. Such classification will allow a better understanding of cell characteristics and, in the case of astrocytes, which likely differ in Ca²⁺ signaling properties across astrocytic and neuronal networks, will shed further insight into astrocyte function and their relations with different neuronal populations. Such implementation should be possible via deep learning methods if a reasonable dataset is available and labeled.

Ethical Approval

Sprague-Dawley rats pups were obtained from Charles River (Barcelona, Spain). The European Union guidelines (2010/63/EU) and Portuguese law were respected in all procedures regarding the protection of animals for scientific purposes, being the number of animals and their suffering minimized. This study was approved by the “iMM’s Institutional Animal Welfare Body – ORBEA-iMM and the National competent authority – DGAV (Direção Geral de Alimentação e Veterinária).”

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Authors' contributions

R.F.L. developed the script, prepared the figures, and wrote the main manuscript. J.G.R. performed the calcium experiments, designed the script, prepared the figures, and wrote the main manuscript. A.M.S. wrote the main manuscript. C.M. developed the script and wrote the main manuscript. S.H.V. designed the calcium experiments, designed the script, and wrote the main manuscript. All authors reviewed the manuscript.

Funding

This work was supported by project funding from Fundação para a Ciência e para a Tecnologia (FCT) AMS (PTDC/MED-FAR/4834/2021). This project has received funding from the International Society for Neurochemistry (ISN) career grant and the H2020-WIDESPREAD 05-2017-Twinning (EpiEpinet) under grant agreement No. 952455. JGR (PD/BD/150342/2019) is a FCT PhD grant recipient.

Availability of data and materials

The software is freely available at https://github.com/ISEL-DEETC/SIGAA, where detailed documents and example applications can be found.

G. Perea, M. Navarrete, and A. Araque, “Tripartite synapses: astrocytes process and control synaptic information,” Trends in Neurosciences, vol. 32, no. 8. pp. 421–431, Aug. 2009, doi: 10.1016/j.tins.2009.05.001.
A. Semyanov, C. Henneberger, and A. Agarwal, “Making sense of astrocytic calcium signals — from acquisition to interpretation,” Nat. Rev. Neurosci. 2020 2110, vol. 21, no. 10, pp. 551–564, Sep. 2020, doi: 10.1038/s41583-020-0361-8.
D. Lipscombe, D. V. Madison, M. Poenie, H. Reuter, R. W. Tsien, and R. Y. Tsien, “Imaging of cytosolic Ca2+ transients arising from Ca2+ stores and Ca2+ channels in sympathetic neurons,” Neuron, vol. 1, no. 5, pp. 355–365, 1988, doi: 10.1016/0896-6273(88)90185-7.
M. J. Berridge, “Calcium signalling and cell proliferation,” BioEssays, vol. 17, no. 6, pp. 491–500, Jun. 1995, doi: 10.1002/BIES.950170605.
A. Armada-Moreira et al., “Going the Extra (Synaptic) Mile: Excitotoxicity as the Road Toward Neurodegenerative Diseases,” Front. Cell. Neurosci., vol. 14, p. 90, Apr. 2020, doi: 10.3389/FNCEL.2020.00090.
B. S. Khakh and K. D. McCarthy, “Astrocyte calcium signaling: from observations to functions and the challenges therein,” Cold Spring Harb. Perspect. Biol., vol. 7, no. 4, 2015, doi: 10.1101/CSHPERSPECT.A020404.
S. Guerra-Gomes, N. Sousa, L. Pinto, and J. F. Oliveira, “Functional Roles of Astrocyte Calcium Elevations: From Synapses to Behavior,” Front. Cell. Neurosci., vol. 11, p. 427, Jan. 2018, doi: 10.3389/fncel.2017.00427.
G. Perea and A. Araque, “Glial calcium signaling and neuron–glia communication,” Cell Calcium, vol. 38, no. 3–4, pp. 375–382, Sep. 2005, doi: 10.1016/J.CECA.2005.06.015.
N. J. Allen and B. A. Barres, “Neuroscience: Glia - more than just brain glue,” Nature, vol. 457, no. 7230. Nature, pp. 675–677, Feb. 05, 2009, doi: 10.1038/457675a.
A. Araque, G. Carmignoto, P. G. Haydon, S. H. R. Oliet, R. Robitaille, and A. Volterra, “Gliotransmitters Travel in Time and Space,” Neuron, vol. 81, no. 4, pp. 728–739, Feb. 2014, doi: 10.1016/J.NEURON.2014.02.007.
Y. Yamada and K. Mikoshiba, “‘Quantitative comparison of novel GCaMP-type genetically encoded Ca2+ indicators in mammalian neurons,’” Front. Cell. Neurosci., vol. 6, no. SEPTEMBER, p. 41, 2012, doi: 10.3389/fncel.2012.00041.
Y. Wang et al., “An event-based paradigm for analyzing fluorescent astrocyte activity uncovers novel single-cell and population-level physiology,” bioRxiv. bioRxiv, p. 504217, Dec. 21, 2018, doi: 10.1101/504217.
M. Pires, F. Raischel, S. H. Vaz, A. Cruz-Silva, A. M. Sebastião, and P. G. Lind, “Modeling the functional network of primary intercellular Ca2+ wave propagation in astrocytes and its application to study drug effects,” J. Theor. Biol., vol. 356, pp. 201–212, Sep. 2014, doi: 10.1016/j.jtbi.2014.04.024.
A. Agarwal et al., “Transient Opening of the Mitochondrial Permeability Transition Pore Induces Microdomain Calcium Transients in Astrocyte Processes,” Neuron, vol. 93, no. 3, pp. 587-605.e7, Feb. 2017, doi: 10.1016/J.NEURON.2016.12.034.
R. Srinivasan et al., “Ca2+ signaling in astrocytes from Ip3r2−/− mice in brain slices and during startle responses in vivo,” Nat. Neurosci. 2015 185, vol. 18, no. 5, pp. 708–717, Apr. 2015, doi: 10.1038/nn.4001.
Y. Wang et al., “Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data,” Front. Neuroinform., vol. 11, p. 48, Jul. 2017, doi: 10.3389/fninf.2017.00048.
J. N. MacLean and R. Yuste, “Imaging Action Potentials with Calcium Indicators,” Cold Spring Harb. Protoc., vol. 2009, no. 11, p. pdb.prot5316, Nov. 2009, doi: 10.1101/PDB.PROT5316.
P. F. Jacob, S. H. Vaz, J. A. Ribeiro, and A. M. Sebastião, “P2Y1 receptor inhibits GABA transport through a calcium signalling-dependent mechanism in rat cortical astrocytes,” Glia, vol. 62, no. 8, pp. 1211–1226, 2014, doi: 10.1002/glia.22673.
N. Jayanthi and I. Sreedevi, “Comparison of image matching techniques,” Int. J. LATEST TRENDS Eng. Technol., vol. 7, no. 3, 2016, doi: 10.21172/1.73.552.
K. Kapinchev, A. Bradu, F. Barnes, and A. Podoleanu, “GPU implementation of cross-correlation for image generation in real time,” 2015, doi: 10.1109/ICSPCS.2015.7391783.
O. C. Ibe, Fundamentals of Applied Probability and Random Processes: Second Edition. Elsevier Inc., 2014.
J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” in IEEE Transactions on Geoscience and Remote Sensing, Mar. 2005, vol. 43, no. 3, pp. 480–491, doi: 10.1109/TGRS.2004.842478.
B. Zhang, H. Zhu, A. Song, and W. Zhang, “A fast morphological reconstruction algorithm for MRI brain image simplification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 5938 LNCS, pp. 542–549, doi: 10.1007/978-3-642-11842-5_76.
P. Soille and M. Pesaresi, “Advances in mathematical morphology applied to geoscience and remote sensing,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 2042–2055, Sep. 2002, doi: 10.1109/TGRS.2002.804618.
L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and EEcient Algorithms,” 1993.
M. Navarrete and A. Araque, “Endocannabinoids Mediate Neuron-Astrocyte Communication,” Neuron, vol. 57, no. 6, pp. 883–893, Mar. 2008, doi: 10.1016/j.neuron.2008.01.029.
R. Montagne et al., “Necrosis- and apoptosis-related Met cleavages have divergent functional consequences,” Cell Death Dis., vol. 6, no. 5, pp. e1769–e1769, May 2015, doi: 10.1038/cddis.2015.132.
William and J. Taylor, “Method of lagrangian curvilinear interpolation,” 1945.
T. Möller, “Calcium signaling in microglial cells,” GLIA, vol. 40, no. 2. Glia, pp. 184–194, Nov. 01, 2002, doi: 10.1002/glia.10152.
M. Brini, T. Calì, D. Ottolini, and E. Carafoli, “Neuronal calcium signaling: Function and dysfunction,” Cellular and Molecular Life Sciences, vol. 71, no. 15. Birkhauser Verlag AG, pp. 2787–2814, 2014, doi: 10.1007/s00018-013-1550-7.
R. S. Martins et al., “Caffeine has a dual influence on NMDA receptor–mediated glutamatergic transmission at the hippocampus,” Purinergic Signal., vol. 16, no. 4, pp. 503–518, Dec. 2020, doi: 10.1007/s11302-020-09724-z.
M. Barbosa et al., “Recovery of Depleted miR-146a in ALS Cortical Astrocytes Reverts Cell Aberrancies and Prevents Paracrine Pathogenicity on Microglia and Motor Neurons,” Front. Cell Dev. Biol., vol. 9, Apr. 2021, doi: 10.3389/FCELL.2021.634355.

No competing interests reported.

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You are reading this latest preprint version

SIGAA – SIGnaling Automated Analysis: a new tool for Ca²⁺ signaling quantification using ratiometric Ca²⁺ dyes

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Experimental Setup And Data Extraction

2.1. Ca²⁺ Imaging Set up

2.2. Astrocyte ROI based fluorescent ratio imaging

2.3. Proposed system and methods

2.4 Block 1: Ratio values and input video drift correction

2.5. Block 2: Laboratory video analysis and function extraction

2.6. Block 3: Feature extraction

3. Results And Discussion

3.1 Automated cell detection

3.2 Function Form

3.3 Time Performance Benchmarks

4. Conclusions

5. Declarations

6. References

Additional Declarations

Status:

Journal Publication

Version 1

SIGAA – SIGnaling Automated Analysis: a new tool for Ca2+ signaling quantification using ratiometric Ca2+ dyes

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Experimental Setup And Data Extraction

2.1. Ca2+ Imaging Set up

2.2. Astrocyte ROI based fluorescent ratio imaging

2.3. Proposed system and methods

2.4 Block 1: Ratio values and input video drift correction

2.5. Block 2: Laboratory video analysis and function extraction

2.6. Block 3: Feature extraction

3. Results And Discussion

3.1 Automated cell detection

3.2 Function Form

3.3 Time Performance Benchmarks

4. Conclusions

5. Declarations

6. References

Additional Declarations

Status:

Journal Publication

Version 1

SIGAA – SIGnaling Automated Analysis: a new tool for Ca²⁺ signaling quantification using ratiometric Ca²⁺ dyes

2.1. Ca²⁺ Imaging Set up