Name: imaging calcium dynamics in subpopulations of mouse pancreatic islet cells
Uploaded: 2019-11-26T12:00:00
Description: Watch this Scientific Journal Video about Imaging Calcium Dynamics in Subpopulations of Mouse Pancreatic Islet Cells at JoVE.com

AH was a recipient of a Diabetes UK PhD Studentship, EV was supported by the OXION-Wellcome Trust Training Programme, AIT held an Oxford Biomedical Research Council postdoctoral fellowship.

All methods described here were developed in accordance with the United Kingdom Animals (Scientific Procedures) Act (1986) and the University of Oxford ethical guidelines.
1. Isolate mouse pancreatic islets
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	<li>Prepare the culture medium and the isolation solution.
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		<li>Make up the culture medium: RMPI1640 (see Table of Materials), supplemented with 10% of fetal calf serum, 100 units/mL penicillin, 100 &#956;g/mL streptomycin. Dispense the medium into two 60 mm ...

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There are three stages in the protocol that are critical for overall success. Successful injection of the Liberase enzyme into the bile duct determines not just the quantitative success of the isolation procedure but also impacts the quality of the isolated islets. Uninflated pancreata may result in lack of some important metabolic responses in the isolated islets. Secondly, the loading of the dye/the expression of the sensor defines the signal-to-noise ratio of the time-lapse recording. Signals are absent or attenuated ...

The purpose of the method is to image real-time changes in cytosolic calcium concentration ([Ca2+]cyt) in minor subpopulations of pancreatic islet cells. This allows uncovering the mechanisms governing hormone secretion in these cells, revealing details about the cross-talk between different cell types and, potentially, introducing a populational dimension into the larger picture of islet signaling.
Islets consist of several cell types. Besides the more well-known insulin-secreting &#946;-cells, there are at least two subpopulations that are also critical in regulating blood glucose3. &#945;-Cells (that make up around 17% of islet cells) secrete glucagon when blood glucose gets too low, which signals for release of glucose into the bloodstream from depots in the liver. Excessive glucagon levels (hyperglucagonemia) and impaired control of glucagon-release accompany (and, technically, can contribute to) the prediabetic condition of impaired insulin sensitivity4. &#948;-Cells (around 2%) secrete somatostatin in response to glucose elevation. This ubiquitous peptide hormone is likely to be present at high concentrations in the vicinity of &#945;- and &#946;-cells within islets, which has a strong Gi receptor-mediated attenuating effect on both glucagon and insulin secretion.
&#945;-Cells and &#948;-cells share a large part of the glucose-sensing machinery with their close lineage relatives, &#946;-cells. All three cells types are equipped with ATP-sensitive K+ channels, elaborate metabolic sensors5 that control the plasma membrane potential of these excitable cells. At the same time, secretion of insulin, somatostatin and glucagon is regulated differently by glucose. Imaging of Ca2+ dynamics in the two minor subpopulations of islet cells can therefore provide an insight into the cross-talk between blood glucose and islet secretory output.
Early attempts of monitoring the excitability of &#945;- and &#948;-cells using patch-clamp electrophysiology were soon followed by imaging of Ca2+ in single &#945;- and &#948;-cells. The identity of cells in these experiments was verified via a posteriori staining with anti-glucagon or anti-somatostatin antibodies. These efforts were frequently hampered by the finding that islet cells behave very differently within the islet and as single cells. Although &#946;-cells may appear to be the main benefactors of the islet arrangement (due to their overwhelming majority that underlies their strong electrical coupling), the main discrepancy was, surprisingly, found in &#945;-cells. Within the intact islet, these cells are constantly and persistently activated at low glucose, which is only true for around 7% of single dispersed &#945;-cells6. Reporting the activity of &#945;- and &#948;-cells within intact islets is therefore believed to represent a closer approximation of in vivo conditions.
In general, there are two ways of reporting Ca2+ dynamics specifically from the &#945;-cell or &#948;-cell subpopulations: (i) expressing a genetically encoded Ca2+ sensor via a tissue-specific promoter or (ii) using marker compounds. The more elegant former approach adds the substantial advantage of true 3D imaging and hence studying of cell distribution within the islet. It cannot however be applied for intact human islet material. Another potential concern is the &#39;leakiness&#39; of the promoter, particularly when the &#946;-/&#945;-cell transdifferentiation or &#945;-cell response to high glucose is in place. The latter approach can be used with freshly isolated tissue including human samples or cultured islets. The data, however, is collected solely from the peripheral layer of islet cells, as delivering the dye/marker molecule in deeper layers without altering the islet architecture is challenging. An unexpected advantage of the latter approach is the compatibility with wide-field imaging mode, which allows scaling up the experiments to simultaneous imaging of tens or hundreds of islets (i.e., thousands to tens of thousands of cells).
Calcium is imaged in vivo using genetically encoded GCaMP7 (or pericam8) family sensors, which are variants of circularly permutated green fluorescent protein (GFP) fused to the calcium-binding protein calmodulin and its target sequence, M13 fragment of myosin light chain kinase7,9. GCaMPs have superb signal-to-noise ratios in the range of nanomolar Ca2+ concentrations and a high 2-photon cross-section, which makes them an ideal choice for in vivo work10,11. The challenging aspect of using recombinant sensors is their delivery into the cells. Heterologous expression requires using a viral vector and multi-hour ex vivo culturing, which frequently raises concerns regarding potential de-differentiation or deterioration of cell functions. Although mouse models pre-engineered to express GCaMP address this problem, they add new challenges by increasing the lead time substantially and limiting the work to a non-human model. Very high sensitivity to changes of intracellular pH is another adverse side of protein-based sensors12, which is, however, less of a problem for sensing oscillatory signals, such as Ca2+.
The advantage of trappable dyes (such as green fluorescent Fluo4) is that they can be loaded into freshly isolated tissue within around an hour. Predictably, trappable dyes have lower signal-to-noise ratios and (much) lower photostability than their recombinant counterparts. We cannot confirm13 the reports of toxicity of the trappable dyes14, however, dye overloading is a frequent problem.
Red recombinant Ca2+ sensors based on circular permutation have been evolving rapidly since 201115, and most recent developments present a strong competition to GCaMPs16 for tissue imaging, given higher depth of penetration of red light. Commercially available red trappable dyes can be used reliably for single-cell imaging but, on the tissue level, cannot compete well with the green analogs.
There is seemingly very little choice of imaging technology for experiments in tissue where out-of-focus light becomes a critical problem. The confocal system provides acceptable single-cell resolution by cancellation of the out-of-focus light with any objective on the NA above 0.3 (for the case of GCaMP6) or 0.8 (trappable dye). In a technical sense, a conventional confocal microscope can be used for simultaneous imaging of [Ca2+]cyt from hundreds (GCaMP) or tens of islets (trappable dye). The only realistic alternative to confocal mode in case of 3D expression of the sensor in tissue is perhaps light-sheet microscopy.
Things are slightly different for the case when the sensor is expressed in the peripheral layer of cells within the islet tissue. For bright recombinant sensors that have a vivid intracellular expression pattern, using a wide-field imaging mode with a low-NA objective may provide sufficient quality and reward the researcher with a substantial increase in the field of view area and hence the throughput. A wide-field system provides poorer spatial resolution, as the out-of-focus light is not cancelled; therefore, imaging tissue with high-NA (low depth of field) objectives is less informative, as the single-cell signal is vastly contaminated by neighboring cells. The contamination is much smaller for low-NA (high depth of field) objectives.
There are tasks, however, for which high throughput and/or sampling rate become a critical advantage. &#945;- and &#948;-cells exhibit substantial heterogeneity, which creates a demand for high sample sizes to reveal the contribution of the subpopulations. Wide-field imaging is fast and more sensitive, with an industrial-scale large field-of-view system imaging hundreds (GCaMP) or tens (Fluo4) of islets at the same signal-to-noise ratio as the confocal experiments on ten or a single islet, respectively. This difference in throughput makes the wide-field system advantageous for populational imaging with a single-cell resolution, which can be especially critical for small subpopulations such as the &#948;-cell one. Likewise, attempts to reconstruct electrical activity from Ca2+ spiking17 would benefit from the higher sampling rate provided by a wide-field imaging mode. At the same time, several &#34;niche&#34; problems like the activity of pancreatic &#945;-cells upon stimulation of the dominating &#946;-cell subpopulation, require the use of a confocal system. A factor that influences the decision towards confocal mode is the presence of substantial contaminating signal from the &#946;-cell subpopulation.
Although using hormone-specific antibody staining to verify the identity of the cells after the imaging experiments is still an option, minor cell subpopulations can be identified using functional marker compounds, such as adrenaline and ghrelin that were shown to selectively stimulate Ca2+ dynamics in &#945;-18 and &#948;-cells19,20, respectively.
The analysis of time-lapse imaging data aims to provide information beyond trivial pharmacology, such as populational heterogeneity, correlation and interaction of different signals. Conventionally, imaging data is analyzed as intensity vs. time and normalized to the initial fluorescence (F/F0). Baseline correction is frequently needed, due to the bleaching of the fluorophore signal or contamination by changes in autofluorescence or pH (typically induced by millimolar levels of glucose12). Ca2+ data can be analyzed in many different ways, but three main trends are to measure changes in the spike frequency, the plateau fraction, or area under the curve, computed vs. time. We found the latter approach advantageous, especially in application to heavily undersampled confocal data. The advantage of the pAUC metric is its sensitivity to both changes in signal frequency and amplitude, whereas computing the frequency requires a substantial number of oscillations21, which is hard to attain using conventional imaging. The limiting factor of pAUC analysis is its high sensitivity to baseline changes.

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		<tr height="26">
			<td height="26" style="height:26px;width:216px;">40x/1.3 objective</td>
			<td style="width:277px;"></td>
			<td style="width:119px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="26">
			<td height="26" style="height:26px;width:216px;">Axiovert 200 microscope</td>
			<td style="width:277px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">emission</td>
			<td style="width:277px;"></td>
			<td style="width:119px;"></td>
			<td></td>
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			<td height="27" style="height:27px;width:216px;">Excitation</td>
			<td style="width:277px;"></td>
			<td style="width:119px;"></td>
			<td></td>
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		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Fetal bovine serum</td>
			<td style="width:277px;">Sigma-&#1040;ldrich</td>
			<td style="width:119px;">F7524-500ML</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Fluo4</td>
			<td style="width:277px;">ThermoFisher (Life Technologies)</td>
			<td style="width:119px;">&#160; F14201</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">GCaMP6f, in (human type 5) adenoviral vector</td>
			<td style="width:277px;">Vector Biolabs</td>
			<td style="width:119px;">1910</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Hanks&#39; solution&#160;</td>
			<td style="width:277px;">ThermoFisher (GibCo, Life Technologies)</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Liberase</td>
			<td style="width:277px;">Sigma-&#1040;ldrich</td>
			<td style="width:119px;">5401020001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">penicillin/streptomycin</td>
			<td style="width:277px;">ThermoFisher (GibCo, Life Technologies)</td>
			<td style="width:119px;">15140122</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RPMI medium</td>
			<td style="width:277px;">ThermoFisher (GibCo, Life Technologies)</td>
			<td style="width:119px;">61870044</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Zeiss LSM510-META confocal system</td>
			<td style="width:277px;">Carl Zeiss</td>
			<td style="width:119px;"></td>
			<td></td>
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imaging calcium dynamics in subpopulations of mouse pancreatic islet cells

Pancreatic islet hormones regulate blood glucose homeostasis. Changes in blood glucose induce oscillations of cytosolic calcium in pancreatic islet cells that trigger secretion of three main hormones: insulin (from &#946;-cells), glucagon (&#945;-cells) and somatostatin (&#948;-cells). &#946;-Cells, which make up the majority of islet cells and are electrically coupled to each other, respond to the glucose stimulus as one single entity. The excitability of the minor subpopulations, &#945;-cells and &#948;-cells (making up around 20% (30%) and 4% (10%) of the total rodent1 (human2) islet cell numbers, respectively) is less predictable and is therefore of special interest.
Calcium sensors are delivered into the peripheral layer of cells within the isolated islet. The islet or a group of islets is then immobilized and imaged using a fluorescence microscope. The choice of the imaging mode is between higher throughput (wide-field) and better spatial resolution (confocal). Conventionally, laser scanning confocal microscopy is used for imaging tissue, as it provides the best separation of the signal between the neighboring cells. A wide-field system can be utilized too, if the contaminating signal from the dominating population of &#946;-cells is minimized.
Once calcium dynamics in response to specific stimuli have been recorded, data are expressed in numerical form as fluorescence intensity vs. time, normalized to the initial fluorescence and baseline-corrected, to remove the effects linked to bleaching of the fluorophore. Changes in the spike frequency or partial area under the curve (pAUC) are computed vs. time, to quantify the observed effects. pAUC is more sensitive and quite robust whereas spiking frequency provides more information on the mechanism of calcium increase.
Minor cell subpopulations can be identified using functional responses to marker compounds, such as adrenaline and ghrelin, that induce changes in cytosolic calcium in a specific populations of islet cells.

Islets load fairly well with the trappable dyes (Figure 1A), unless the lipid composition of the membrane has been affected (e.g., by chronic exposure to fatty acids). The human adenovirus type 5 (Ad5) vector also targets all islet cells (Figure 1B). Problems may arise when more than one recombinant sensor is being expressed in the same cell. Furthermore, islets are typically very well immobilized using the technology described ...

Watch this Scientific Journal Video about Imaging Calcium Dynamics in Subpopulations of Mouse Pancreatic Islet Cells at JoVE.com

Imaging Calcium Dynamics in Subpopulations of Mouse Pancreatic Islet Cells

Here, we present a protocol for imaging and quantifying calcium dynamics in heterogeneous cell populations, such as pancreatic islet cells. Fluorescent reporters are delivered into the peripheral layer of cells within the islet, which is then immobilized and imaged, and per-cell analysis of the dynamics of fluorescence intensity is performed.

Pancreatic islet hormones regulate blood glucose homeostasis. Changes in blood glucose induce oscillations of cytosolic calcium in pancreatic islet cells ...

The protocol allows rapid isolated function of small subpopulations of cells within pancreatic islets of Langerhans. The advantages of this technique include the real-time nature and high statistical power, and hence high reproducibility, of the acquired data. To immobilize the islets for imaging, assemble an imaging chamber for an inverted microscope and place a glass cover slip inside the chamber.Make sure that the glass chamber interface is watertight and confirm that the cover slip is within the reach of the microscope objective. Next, cut 20 by 20 millimeter squares of fine and coarse mesh and use 45 to 50 micrometer pieces of thick sticky tape to create two spacer walls on a piece of fine mesh. Immerse the coarse mesh and a weight in a 35 millimeter Petri dish of imaging solution, and place the fine mesh under a dissecting microscope.Turn the fine mesh with the spacer walls upside down and the spacers facing upwards. And use a p20 pipette to transfer several islets between the two spacers. Using watchmakers'forceps, place the mesh upside down inside the imaging chamber of the inverted microscope so that the spacers face downward to sit directly on the chamber cover slip, trapping the islets between the spacers and the mesh in the middle of the cover slip.Take care that the mesh is well hydrated without containing excessive volumes of solution which would provoke the lateral motion of the sample. Then place the coarse mesh and the weight on top of the fine mesh in the chamber, and add imaging solution to the chamber. Once the islets have been immobilized, select the imaging mode and objective on the inverted microscope and place the chamber with the islets on the temperature-controlled stage of the microscope.After setting the perfusion, position the inflow lower than the outflow within the chamber and set the outflow flux to be greater than the inflow flux. Ensure that the outflow has minimal contact with the solution so that it removes the solution in multiple sequential small droplets, avoiding long intervals of continuous solution removal. Next, initiate the perfusion with imaging solution containing three millimolar glucose, and select the light path and filters for imaging green fluorophores.Then run live imaging to set up the imaging parameters and adjust the view to capture the islets of interest. To optimize the signal-to-noise ratio of the image, adjust the excitation light intensity, the exposure time, and the binning, ensuring that the settings allow a distinct visualization of each cell within the islets at the minimal possible light intensity and exposure. Then image the islets at 0.1 to five hertz, checking the quality of the acquired data as it is captured.When the alpha cell activity is detectable, use an online chart of the signal dynamics within the acquisition software as possible and add adrenaline or glutamate into the bath solution for two to five minutes. A rapid jump in calcium followed by a slowdown or cancellation of the alpha cell oscillations will be observed. Next, add ghrelin, which has been recently reported to activate delta cells selectively.A rapid reversible increase in calcium will be observed in a small subpopulation of islet cells. Then add 10 millimolar glucose. A coordinated oscillatory response in the beta cell subpopulation will be observed.Note also the responses of cells that were previously activated by adrenaline or glutamate and ghrelin. After saving the images, import the data into an appropriate data analysis software program and normalize the raw fluorescence intensity data to the initial value of fluorescence. If the cell to cell variability fluorescence intensity data set is still substantial, define a control region for the range of time during which the control solution was applied.If the control region has a clear non-oscillatory signal, assume that the fluorescence intensity returned to baseline after each application of the control solution. To correct the time lapse data for each cell, split the data into segments separated by the points at which the control solution was added and apply linear correction to each segment. If the control range has clear oscillations or additional factors are present, use a spike detection algorithm.To measure the frequency of calcium spikes and the response to the addition of agonists and antagonists, split the recording into equal time intervals, count the spikes within the interval, and normalize to the interval duration to compute the time course of the partial frequency. Alternatively, set the threshold and compute the plateau fraction for each of the intervals. The fraction indicates the percentage of time within the interval that the cell spent in the excited state.Or compute the partial area under the curve for each of the intervals. Unless the lipid composition of the membrane has been affected, islets load fairly well with tropical dyes. The human adenovirus type 5 vector also successfully targets islet cells.Calcium spiking in alpha cells can be readily detected at low glucose levels, and there is a high cell-by-cell correlation between the activity at low glucose and the response to adrenaline and glutamate. Ghrelin activates some adrenaline responsive cells at low glucose but has no effect on calcium dynamics in most of the cells that are activated by low glucose. When analyzed in terms of partial frequency, adrenaline or ghrelin-stimulated cells display a substantial increase under the all-or-nothing conditions, although the overall changes between basal spiking and the adrenaline effect are subtle.In contrast, the partial area under the curve is sensitive to the changes introduced by adrenaline in all of the cells even when the basal activity is high. Make sure all the meshes are in contact with imaging solution and take care to position the tissue reasonably densely to avoid air bubbles. The method can be expanded to include the artificial intelligence for differentiating between the cell types.The pharmacological markers can be replaced by the pattern recognition technology, thereby reducing the recording time.

Research

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Biology

Murine Pancreatic Islet Isolation

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