Genetically encoded Ca2+ indicators (GECIs) have radically changed how in situ Ca2+ imaging is performed. To maximize data recovery from such recordings, appropriate analysis of Ca2+ signals is required. The protocols in this paper facilitate the quantification of Ca2+ signals recorded in situ using spatiotemporal mapping and particle-based analysis.
Ca2+ imaging of isolated cells or specific types of cells within intact tissues often reveals complex patterns of Ca2+ signaling. This activity requires careful and in-depth analyses and quantification to capture as much information about the underlying events as possible. Spatial, temporal and intensity parameters intrinsic to Ca2+ signals such as frequency, duration, propagation, velocity and amplitude may provide some biological information required for intracellular signalling. High-resolution Ca2+ imaging typically results in the acquisition of large data files that are time consuming to process in terms of translating the imaging information into quantifiable data, and this process can be susceptible to human error and bias. Analysis of Ca2+ signals from cells in situ typically relies on simple intensity measurements from arbitrarily selected regions of interest (ROI) within a field of view (FOV). This approach ignores much of the important signaling information contained in the FOV. Thus, in order to maximize recovery of information from such high-resolution recordings obtained with Ca2+dyes or optogenetic Ca2+ imaging, appropriate spatial and temporal analysis of the Ca2+ signals is required. The protocols outlined in this paper will describe how a high volume of data can be obtained from Ca2+ imaging recordings to facilitate more complete analysis and quantification of Ca2+ signals recorded from cells using a combination of spatiotemporal map (STM)-based analysis and particle-based analysis. The protocols also describe how different patterns of Ca2+ signaling observed in different cell populations in situ can be analyzed appropriately. For illustration, the method will examine Ca2+ signaling in a specialized population of cells in the small intestine, interstitial cells of Cajal (ICC), using GECIs.
Ca2+ is a ubiquitous intracellular messenger which controls a wide range of cellular processes, such as muscle contraction1,2, metabolism3, cell proliferation3,4,5, stimulation of neurotransmitter release at nerve terminals6,7, and activation of transcription factors in the nucleus.7 Intracellular Ca2+ signals often take the form of transient elevations in cytosolic Ca2+, and these can be spontaneous or arise from agonist stimulation depending on the cell type8. Spatial, temporal and intensity parameters intrinsic to Ca2+ signals such as frequency, duration, propagation, velocity, and amplitude can provide the biological information required for intracellular signalling5,7,9. Cytoplasmic Ca2+ signals can result from the influx of Ca2+ from the extracellular space or via Ca2+ release from the endoplasmic reticulum (ER) via Ca2+ release channels such as ryanodine receptors (RyRs) and inositol-tri-phosphate receptors (IP3Rs)10. RyRs and IP3Rs may both contribute to the generation of Ca2+ signals and the concerted opening of these channels, which combined with various Ca2+ influx mechanisms can result in a myriad of Ca2+ signalling patterns that are shaped by the numbers and open probability of Ca2+ influx channels, the expression profile of Ca2+ release channels, the proximity between Ca2+ influx and release channels, and expression and distribution of Ca2+ reuptake and extrusion proteins. Ca2+ signals may take the form of uniform, long lasting, high intensity global oscillations that may last for several seconds or even minutes, propagating intracellular and intercellular Ca2+ waves that may cross intracellular distances over 100 µm10,11,12,13,14,15,16, or more brief, spatially localized events such as Ca2+ sparks and Ca2+ puffs that occur on tens of millisecond timescales and spread less than 5 µm17,18,19,20.
Fluorescent microscopy has been used widely to monitor Ca2+ signalling in isolated and cultured cells and in intact tissues. Traditionally, these experiments involved the use of fluorescent Ca2+ indicators, ratiometric and non-ratiometric dyes such as Fura2, Fluo3/4 or Rhod2, among others 21,22,23. These indicators were designed to be permeable to cell membranes and then become trapped in cells by the cleavage of an ester group via endogenous esterases. Binding of Ca2+ to the high affinity indicators caused changes in fluorescence when the cells and tissues were illuminated by appropriate wavelengths of light. The use of cell permeable Ca2+ indicators greatly enhanced our understanding of Ca2+ signaling in living cells and permitted spatial resolution and quantification of these signals that was not possible by assaying Ca2+ signals through other means, such as electrophysiology. However, traditional Ca2+ indicators have several limitations, such as photobleaching that occurs over extended recording periods24. While newer Ca2+ indicator dyes such as Cal520/590 have greatly improved signal to noise ratios and the ability to detect local Ca2+ signals25, issues with photobleaching can still remain a concern for some investigators26,27,28. Precipitous photobleaching also restricts the magnification, rate of image acquisition, and resolution that can be used for recordings, as increased objective power and higher rates of image acquisition require increased excitation light intensity that increases photobleaching.
These limitations of traditional Ca2+ indicator dyes are exacerbated when recording Ca2+ signals in situ, for example when recording intracellular Ca2+ signals from intact tissues. Due to the problems above, visualization of Ca2+ signals in situ using cell permeable Ca2+ indicators has been limited to low power magnification and reduced rates of image capture, constraining the ability of investigators to record and quantify temporally or spatially restricted subcellular Ca2+ signals. Thus, it has been difficult to capture, analyze, and appreciate the spatial and temporal complexity of Ca2+ signals, which can be important in the generation of desired biological responses, as outlined above. Analysis of Ca2+ signals from cells in situ typically relies on simple intensity measurements from selected regions of interest (ROI) within a field of view (FOV). The arbitrary choice of the number, size and position of ROIs, dependent on the whim of the researcher, can severely bias the results obtained. As well as inherent bias with ROI analysis, this approach ignores much of the important signaling information contained in the FOV, as dynamic Ca2+ events within an arbitrarily chosen ROI are selected for analysis. Furthermore, analysis of ROIs fails to provide information about the spatial characteristics of the Ca2+ signals observed. For example, it may not be possible to distinguish between a rise in Ca2+ resulting from a propagating Ca2+ wave and a highly localized Ca2+ release event from tabulations of Ca2+ signals within an ROI.
The advent of genetically encoded Ca2+ indicators (GECIs) has radically changed how Ca2+ imaging can be performed in situ29,30,31,32,33. There are several advantages to using GECIs over dyes. The most important perhaps is that expression of GECI can be performed in a cell specific manner, which reduces unwanted background contamination from cells not of interest. Another advantage of GECIs over traditional Ca2+ indicators is that photobleaching is reduced (as fluorescence and consequentially photobleaching only occurs when cells are active), as compared to dye-loaded specimens, particularly at high magnification and high rates of image capture34. Thus, imaging with GECIs, such as the GCaMP series of optogenetic sensors, affords investigators the ability to record brief, localized sub-cellular Ca2+ signals in situ and investigate Ca2+ signaling in cells within their native environments that have not been possible previously. To maximize recovery of information from such high-resolution recordings, appropriate spatial and temporal analysis of the Ca2+ signals is required. It should be noted that while GECIs can offer some clear advantages, recent studies have revealed that Ca2+ imaging can be successfully performed from large populations of different neurochemical classes of neurons simultaneously using conventional Ca2+ indicators that are not genetically encoded into the animal35. This approach used post hoc immunohistochemistry to reveal multiple different classes of neurons firing at high frequency in synchronized bursts, and avoided the potential that genetic modifications to the animal may have interfered with the physiological behavior the investigator seeks to understand35,36.
The protocols outlined in this paper facilitate more complete analysis and quantification of Ca2+ signals recorded from cells in situ using a combination of spatiotemporal map (STM)-based analysis and particle-based analysis. The protocols also describe how different patterns of Ca2+ signaling observed in different cell populations in situ can be analyzed appropriately. For illustration, the method will examine Ca2+ signalling in a specialized population of cells in the small intestine, interstitial cells of Cajal (ICC). ICC are specialized cells in the gastrointestinal (GI) tract that exhibit dynamic intracellular Ca2+ signaling, as visualized using mice expressing GCaMPs37,38,39,40,41,42. Ca2+ transients in ICC are linked to activation of Ca2+-activated Cl– channels (encoded by Ano1) that are important in regulating the excitability of intestinal smooth muscle cells (SMCs)43,44,45. Thus, the study of Ca2+ signaling in ICC is fundamental to understanding intestinal motility. The murine small intestine offers an excellent example for this demonstration, as there are two classes of ICC that are anatomically separated and can be visualized independently: i) ICC are located in the area between the circular and longitudinal smooth muscle layers, surrounding the myenteric plexus (ICC-MY). These cells serve as pacemaker cells and generate the electrical activity known as slow waves46,47,48,49; ii) ICC are also located amongst a plexus rich in the terminals of motor neurons (deep muscular plexus, thus ICC-DMP). These cells serve as mediators of responses to enteric motor neurotransmission37,39,40,50. ICC-MY and ICC-DMP are morphologically distinct, and their Ca2+ signaling behaviors differ radically to accomplish their specific tasks. ICC-MY are stellate in shape and form a network of interconnected cells via gap junctions51,52. Ca2+ signals in ICC-MY manifest as brief and spatially localized Ca2+ release events occurring at multiple sites asynchronously through the ICC-MY network as visualized within a FOV (imaged with a 60X objective)38. These asynchronous signals are organized temporally into 1 second clusters that, when tabulated together, amount to a net 1 s cellular rise in Ca2+. These signals propagate cell-to-cell within the ICC network and therefore organize Ca2+ signaling, generated from sub-cellular sites, into a tissue wide Ca2+ wave. Temporal clustering and summation of Ca2+ signals in ICC-MY has been termed Ca2+ transient clusters (CTCs)38. CTCs occur rhythmically (e.g. quite similar durations and similar periods between CTCs) 30 times per minute in the mouse. Conversely, ICC-DMP are spindle shaped cells, some with secondary processes, that distribute between SMCs and varicose nerve processes and do not independently form a network51,52. ICC-DMP form gap junctions with SMCs, however, and function within this greater syncytium, known as the SIP syncytium53. Ca2+ signals occur at multiple sites along the lengths of cells, but these transients are not entrained or temporally clustered, as observed in ICC-MY37. Ca2+ signals in ICC-DMP occur in a stochastic manner, with variable intensities, durations and spatial characteristics. The protocols below, using the example of Ca2+ signaling in ICC-MY and ICC-DMP, describe techniques to analyze complex signaling in specific types of cells in situ. We utilized the inducible Cre-Lox p system to express GCaMP6f exclusively in ICC, after inducing activation of Cre-Recombinase (Cre) driven by an ICC specific promoter (Kit).
All animals used and the protocols carried out in this study were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All procedures were approved by the Institutional Animal Use and Care Committee at the University of Nevada, Reno.
1. Generation of KitGCaMP6f Mice
2. Preparation of Tissues for Ca2+ Imaging
3. Analysis of Stochastic Ca2+ Signals in ICC-DMP Using Spatio-Temporal Mapping (STM)
4. Quantification of CTCs in ICC-MY Using Particle Based Analysis
Using Kit-Cre-GCaMP6F mice (Figure 1), dynamic Ca2+ signaling behaviors of ICC in the gastrointestinal tract can be imaged in situ. With confocal microscopy, high-resolution images of specific populations of ICC can be acquired without contaminating signals from other populations of ICC within the same tissue but in anatomically distinct planes of focus (Figure 2A)37,39,40,41. It is possible to record brief (<100 ms), localized Ca2+ events that were not possible with membrane permeable Ca2+ indicators. Spatio-temporal mapping with Volumetry or ImageJ software can be used to generate STMs of all Ca2+ events within cells in situ. Using this approach, Ca2+ events in an entire FOV can be visualized and mapped (Figure 2B,C), rather than just recording the limited activity of a single ROI. These methods can be extended to each cell within a given FOV, ensuring representative data collection from all cells and providing quantitative information about relative amplitudes, transient durations, rate of rise and fall of transients, etc. (Figure 2E,F). STM analysis, as opposed to ROI-based intensity plots, also provide the ability to monitor and record spatial characteristics of Ca2+ signaling, such as spatial spread and propagation velocity, as shown in Figure 2G. This information can be amassed to provide a rather complete view of Ca2+ signaling behaviors in cells in their native environments (Figure 2H).
PTCL analysis can be used to quantify more complex Ca2+ signaling behaviors, such as those occurring within interconnected cellular networks. An example of this application is provided by the analysis performed on ICC-MY (Figure 3A). Usually in such complex preparations, background noise and signal to noise can be an issue. However, using Volumetry software to apply differential and smoothing filters on movies of Ca2+ activity and then applying noise threshold protocols to filter out noise (Figure 3B-D) background noise can be removed from complex recordings of dynamic activity. Using PTCL analysis such as those shown in Figure 3E-G, quantitative information about Ca2+ signaling can be calculated by measuring the PTCL area, PTCL count and PTCL size which indicate spatial ranges of activation of Ca2+ signals in a FOV. These data can be compiled as shown in Figure 3H and analyzed statistically, as appropriate. Figure 4 illustrates how PTCL analysis allows in depth quantification of sub-cellular Ca2+ signaling by examining the location and firing probabilities of Ca2+ firing sites. By allocating PTCLs into different FLAGs based on their temporal characteristics, initiating PTCLs can be accurately mapped as shown in Figure 4C-E and a wealth of hard data acquired on the number of initiation sites (referred to as 'domains'), the size of the site in pixels and µm2, probability of each initiation site firing either once or multiple times during each CTC (given as a%), the average duration and size of PTCLs occurring at the initiation site, the number of CTC cycles (as defined in step 4.23) and the% of firing sites that fired during each CTC cycle. These techniques allow a high level of data mining and quantification of in situ Ca2+ signals occurring within an intact cellular network that are not possible with ROI-based analyses.
Figure 1: Generation of KitGCaMP6f mice. Schematic diagram of how Ai95 (RCL-GCaMP6f)-D (GCaMP6f mice) were crossed with c-Kit+/Cre-ERT2 (Kit-Cre mice) to generate Kit-Cre-GCaMP6f mice. These mice are injected with tamoxifen at ages of 6-8 weeks to induce Cre Recombinase and subsequent GCaMP6f expression exclusively in ICC. Please click here to view a larger version of this figure.
Figure 2: Analysis of stochastic Ca2+ signals in ICC-DMP using spatio-temporal mapping (STM). (A) Representative image of several ICC-DMP from the small intestine of a Kit-Cre-GCaMP6F mouse in situ. A green ROI indicates the size and orientation of ROI to draw around a single ICC-DMP within the FOV to create an STM in Volumetry. (B) STM of Ca2+ activity in the ICC-DMP highlighted in panel A after it has been properly calibrated for amplitude, space and time. (C) The same STM shown in panel B after it has been color coded with a lookup table (QUBPallete). (D) Expanded image of ICC-DMP Ca2+ transients displayed on a color coded STM, indicating where to draw a line through a Ca2+ event across its time (x) axis to create a plot profile of its activity in ImageJ. (E) Plot profile of the Ca2+ event highlighted in panel D, indicating where lines shown be drawn to accurately measure the amplitude and duration of the event. (F) Plot profile of the Ca2+ event highlighted in panel D, indicating where lines shown be drawn to accurately measure the rate of rise and rate of fall of the event. (G) Expanded image of ICC-DMP Ca2+ transients displayed on a color coded STM, indicating where to draw a line through a Ca2+ event across its space (y) axis to accurately measure its spatial spread. (H) Representative histograms of pooled data from ICC-DMP illustrating how to graphically display the amplitude, duration, and spatial spread values acquired from following the above steps. Please click here to view a larger version of this figure.
Figure 3: Quantification of CTCs in ICC-MY using particle based analysis. (A) Representative image of an ICC-MY network from the small intestine of a Kit-Cre-GCaMP6F mouse in situ. (B) Image taken from the recording shown in panel A after it has undergone a differential filter of Δt = ±66–70 ms and a Gaussian filter of 1.5 x 1.5 µm, StdDev 1.0. (C) Image taken from the video in B after thresholding was completed with PTCLs above threshold shown in red. (D) Traces of PTCL count and mean PTCL size in a thresholding protocol to eliminate noise in the movie shown in panel B. PTCLs were created using a flood-fill algorithm that marked the structure of all adjoining pixels that had intensities above threshold, Ca2+ transient PTCLs were larger than noise PTCLs. The threshold at which large numbers of small sized noise PTCLs emerged and began to reduce the mean size of PTCLs can be used as a common threshold for all recordings. (E) Representative image from the coordinate-based Ca2+ PTCL file created from the thresholded recording in C. (F) Representative image taken from the PTCL file of E after a screening criteria of >6 µm2 (diameter ~2 µm or smaller) was applied; PTCLs above this limit are flagged (FLAG 1) as light purple particles and considered valid PTCLs. (G) Heat map showing the total PTCLs (FLAG 1) for the entire recording of the video shown in panel F, with total PTCLs summated with colors representing occurrence throughout the recording (warm colors indicate increased occurrence at that location). (H) Representative traces of PTCL area (blue) and PTCL count (red) derived from the PTCL file created in panel A-G. Representative histograms of pooled data from several experiments are shown below the traces. Please click here to view a larger version of this figure.
Figure 4: Analysis of Ca2+ firing sites in ICC-MY using particle based analysis. (A) Representative image taken from the PTCL file of Figure 3E after FLAG 1 PTCLS are further refined into FLAG 3, the flag status for Ca2+ firing sites. FLAG 3 PTCLs are displayed as lime green (only those PTCLs that did not overlap with any particles in the previous frame but overlap with particles in the next 70 ms were considered firing sites). (B) Heat map showing the total PTCLs (FLAG 3) for the entire recording of the video shown in panel A, with total PTCLs summed with colors representing occurrence throughout the recording (warm colors indicate increased occurrence at that location). (C) Representative map of Ca2+ firing sites shown in panel B, with each different firing site allocated a different identifying color. (D) Representative traces of PTCL area (blue) and PTCL count (red) derived from the PTCL file created in Figure 3A–G. (E) An occurrence map of the activity of individual firing sites. Each firing site within the FOV is displayed as a colored block in its own 'lane' against time. (F) Representative histograms of pooled data from several experiments are shown illustrating values accumulating for Ca2+ firing site firing probability / CTC and the number of Ca2+ firing sites in a FOV. Please click here to view a larger version of this figure.
Ca2+ imaging of specific types of cells within intact tissues or within networks of cells often reveals complex patterns of Ca2+ transients. This activity requires careful and in-depth analyses and quantification to capture as much information about the underlying events and kinetics of these events as possible. STM and PTCL analysis provide an opportunity to maximize the amount of quantitative data yielded from recordings of this type.
The narrow, spindle shaped morphology of ICC-DMP make them well suited to STM analysis derived from the STMs outlined above. However, this analysis is not well suited to ICC-MY that are stellate shaped and connected in a network (Figure 3A). Furthermore, the Ca2+ signaling patterns in ICC-MY are more complex, manifesting as propagating CTCs from multiple sites of origin throughout the ICC-MY network. Thus, in order to quantify the activity occurring in the entire ICC-MY network within a FOV, particle (PTCL) analysis was implemented using custom made software (Volumetry, version G8d, GWH, operable on Mac OS, contact Grant Hennig grant.hennig@med.uvm.edu regarding inquires for Volumetry access and use).
STM analysis allows all Ca2+ events within single cells and within all of the cells in a FOV to be analyzed critically across a range of spatial and temporal parameters. The protocol described illustrates how these techniques can be applied to ICC-DMP of the mouse small intestine. By fully quantifying Ca2+ signaling in ICC-DMP, as shown in Figure 2B-G, Ca2+ signaling patterns have been characterized in detail37. These analyses have been applied to recordings where ICC-DMP undergo interventions to finely quantify the effects of blocking or stimulating Ca2+ release / Ca2+ influx / neurotransmission pathways37,39,40,41. These techniques can be easily applied to other intact tissue preparations. For example, STM analysis as described here has been utilized to identify new mechanistic pathways involved in the generation of intracellular Ca2+ waves recorded in urethral smooth muscle in situ57.
The preparation of STMs in Volumetry requires some caution, as the function in Volumetry that creates the STM from the drawn ROI (Figure 2A) is an average value of intensity. Thus, the amplitude of Ca2+ signals could potentially be diluted if the ROI is drawn wider or longer than the Ca2+ event or cell of interest. Thus, users should be careful to draw ROIs that are as tightly fitting as possible to the Ca2+ signals or particular cell that they are analyzing in order to alleviate this issue. Similarly, creating STMS using single pixel linescans in ImageJ means that accurate mapping of Ca2+ events is subject to the proximity of the Ca2+ signal to the drawn line. Such concerns are minor in thin spindle shaped cells such as ICC-DMP, however other cell types with a more stellate or round morphology may make this type of analysis inappropriate to map all Ca2+ signals accurately. When preparing STMs for analysis, regardless of whether they were made in Volumetry or with ImageJ linescans, there are a few areas to be highlighted for troubleshooting purposes. It is important to change the image quality to 32-bit before performing any calibration on the STMs. Failure to do so, or doing so after calibrating for F/F0 can lead to inconsistent measurements across experiments. Always check the image quality of the STM, which is stated in the top area of the white border of the STM itself when opened with ImageJ. Another potential area of inconsistency is selecting the F0 value when calibrating for amplitude. It is vital that for selecting the region for F0, that it covers an area of the cell that is uniform and in focus. For this reason, areas of the cell that have an unstable basal fluorescence or that change due to movement or other artefacts are not ideal and rigorous motion stabilization protocols should be employed in these cases.
Within in situ or cultured preparations containing interconnected cellular networks, such as ICC-MY in the small intestine, PTCL analysis provides a streamlined technique to quantify complex, subcellular Ca2+ events occurring in the network. Moreover, it also allows all Ca2+ events in the network within a given FOV to be analyzed, rather than using arbitrary ROIs, which only provide information on frequency and intensity within the ROI. An advantage of the PTCL analysis described here is that by applying differential and Gaussian smoothing filters to recordings, a large amount of noise can be removed from movies that may contain contaminating light from cells not of interest or due to non-dynamic bright spots or inclusions. It is important to note that the amount of differentiation applied to recordings will depend largely on the acquisition rate used by the experimenter. Differentiating movies as described in the protocol provides a means of applying a filter to the movie to remove high frequency noise from recordings. Applying a differentiation value of '2' when acquiring at 33FPS works well to remove background noise while maintaining good signal to noise (if the value is too low, noise will be picked up but signal to noise will be compromised if the value is too high). The differentiation value applied should be increased with faster acquisition rates, for example at 100 FPS, a differentiation value of '7' gives approximately the same signal to noise ratio as a value of '2' to a 33FPS recording. Experimenters will need to optimize these settings accordingly for their preparations and recording conditions.
The thresholding protocol described in Figure 3D allows a consistent thresholding procedure to be applied to different recordings made on different systems with different acquisition software. This flexibility allows data from multiple investigators working on different systems to compile their recordings into the same datasets. By using the FLAG system in Volumetry, PTCL analysis allows the visualization and quantification of individual Ca2+ firing sites within a network in detail. Information can be gathered on the number of initiation sites, the size of the site in pixels and µm2, and the average duration of PTCLs occurring at that site. This PTCL analysis allowed the first characterization of CTC activity in the small intestine at a sub-cellular level, and, using the different FLAGs in Volumetry software, PTCLs at both the network and individual firing site level were quantified in intact tissue preparations from Kit-Cre-GCaMP3 mice38. From these initial observations, this analysis has been further utilized to study novel Ca2+ influx pathways in GI ICC-MY such as store-operated-Ca2+-entry42 and the role of mitochondrial Ca2+ signalling on GI pacemaking41. Much like STM analysis described above, PTCL analysis can be easily adapted to different intact preparations other than that described in this protocol. For example, a recent study used PTCL analysis to study novel rhythmic Ca2+ events occurring in the intact cellular networks of the lamina propria of the rat urinary bladder58,59 and thus could be easily applied to other complex, intact cellular systems such as neuronal systems. While this paper focused on Ca2+ imaging in intact tissues with GECIs, these analysis techniques can also be run on isolated cells and tissues loaded with traditional Ca2+ indicator dyes. The STM based analysis has been used to successfully quantify localized Ca2+ signals and Ca2+ waves from spindle shaped interstitial cells and smooth muscle cells from a variety of preparations11,60,61,62,63. Furthermore, the PTCL analysis routines described here have also been applied to in situ network preparations visualized with Cal 52058,59. However, these studies also retain the disadvantages of such dye loading protocols such as ambiguous cell identification and problems with signal to noise.
The examples illustrated above demonstrate that both STM and PTCL analysis are highly malleable techniques that can be used to quantify complex Ca2+ signaling in a diverse range of intact tissue preparations. The approaches offer many benefits over traditional ROI based intensity plots that have been routinely used previously and should provide investigators with more valuable quantitative information on Ca2+ signaling than could be previously achieved.
The authors have nothing to disclose.
Funding was provided by the NIDDK, via P01 DK41315.
Image J software | NIH | 1.5a | Image J software |
Volumetry | GWH | Volumetry 8Gd | Custom made analysis software |
Isoflurane | Baxter, Deerfield, IL, USA | NDC 10019-360-60 | |
Tamoxifen | Sigma | T5648 | |
GCaMP6F mice | Jackson Laboratory | Ai95 (RCL-GCaMP6f)-D | |
Kit-Cre mice | Gifted From Dr. Dieter Saur | c-Kit+/Cre-ERT2 | |
Ethanol | Pharmco-Aaper | SDA 2B-6 |